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How Much Do We Rely on Weather Forecasting? | The Truth About Weather Predictions · How To Protect The Ocean

• 1:00:00
  How often do you rely on the weather?
• 1:00:02
  I mean, let's be honest, like we look at
• 1:00:03
  our local weather person and they provide
• 1:00:06
  the weather for us on the news.
• 1:00:07
  Or we look at our app on our phone and we
• 1:00:10
  look at the app and we say,
• 1:00:11
  "Oh, it's supposed to rain today at three
• 1:00:13
  o'clock or four o'clock."
• 1:00:14
  Or, "It's supposed to snow if you're in
• 1:00:16
  the place where I live in Ontario."
• 1:00:18
  We rely on that, but it's not always
• 1:00:20
  right because it depends on models and
• 1:00:22
  models are not always right,
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  but they have their level of accuracy and
• 1:00:26
  weather patterns change.
• 1:00:28
  We have to understand the weather. We
• 1:00:29
  have to understand how our weather
• 1:00:31
  patterns around the world affect all of
• 1:00:33
  us from just a regular person like myself
• 1:00:36
  who goes out and walks the dogs that
• 1:00:37
  doesn't want to walk the dog in the rain.
• 1:00:39
  Even the dogs always
• 1:00:40
  want to walk in the rain.
• 1:00:41
  I have to know what the
• 1:00:42
  weather is going to be like.
• 1:00:42
  Or if a farmer knows what the weather is
• 1:00:45
  going to be like coming up over the next
• 1:00:46
  two weeks or a month or what those
• 1:00:48
  patterns are going to be like,
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  they need to know what it's going to be
• 1:00:51
  like for their crops this year.
• 1:00:53
  A lot of people rely on weather. A lot of
• 1:00:55
  people rely on understanding the climate
• 1:00:57
  and the people who understand the
• 1:00:59
  climate, meteorologists and be able to
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  communicate that towards us
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  are really, really important.
• 1:01:04
  And that's going to be our focus on
• 1:01:06
  today's episode of How to
• 1:01:06
  Protect the Ocean podcast.
• 1:01:07
  We have Dave Jones, who's joining us
• 1:01:09
  today to talk about the importance of
• 1:01:12
  data that feeds in to what we know about
• 1:01:15
  our climate, what we know about the
• 1:01:17
  weather and how our climate is changing
• 1:01:18
  and how that can protect not only
• 1:01:21
  citizens around the world,
• 1:01:22
  but especially citizens in the US. We're
• 1:01:24
  talking about cuts to the National
• 1:01:25
  Weather Service. We're talking about cuts
• 1:01:27
  to know what we're talking about cuts to
• 1:01:29
  NASA and how that may or may not impact
• 1:01:31
  the world today as we see it for US
• 1:01:33
  citizens and the world.
• 1:01:35
  I can't wait for you to
• 1:01:36
  see the show. So let's start.
• 1:01:37
  Hey, everybody. Welcome back to another
• 1:01:41
  exciting episode of the How to Protect
• 1:01:43
  the Ocean podcast. I'm your host, Andrew
• 1:01:44
  Lourin. This is the podcast where you
• 1:01:45
  find out what's happening with the ocean,
• 1:01:46
  how you can speak up for the ocean and
• 1:01:48
  what you do to live for a
• 1:01:50
  better ocean by taking action.
• 1:01:52
  And we're going to learn all about
• 1:01:53
  science today. We're going to learn about
• 1:01:54
  the climate. We're going to learn about
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  the atmosphere. We learn about the
• 1:01:57
  oceans. We're going to learn about the
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  data that provides us with the
• 1:02:00
  understanding of knowing so much more
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  about the oceans that we did in 1900s.
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  And we didn't have satellites. We didn't
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  have buoy systems. We didn't have
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  drifters all these different
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  instrumentations that provide us with
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  data to go to the people, the
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  meteorologists, the oceanographers, the
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  scientists who understand and interpret
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  that information to provide it to us in a
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  form that we can understand and that we
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  can make better decisions around where we
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  live, how we're going to live, where
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  we're going to go that day, where we're
• 1:02:28
  going to go that hour.
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  And of course, there are some times when
• 1:02:31
  it's not right and the weather changes.
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  But for the most part, we get to
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  understand, especially before we had all
• 1:02:37
  this data, we're talking about data in
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  climate in the atmosphere because it's so
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  important because there are cuts being
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  made in the US where a lot of this data
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  is funded and especially on an
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  international level that could affect US
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  citizen safety in terms of predicting
• 1:02:51
  hurricanes and other natural disasters
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  and wind patterns that would affect them
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  not only in money to clean up these
• 1:02:57
  natural disasters, but actual safety.
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  So we want to understand what's happening
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  all around that. And Dave Jones is
• 1:03:05
  joining me today. He is the founder and
• 1:03:09
  the CEO of Storm Center Communications
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  and Geolocate, which is also a company
• 1:03:14
  that he owns. And he's on
• 1:03:16
  YouTube as Storm Center Inc.
• 1:03:19
  this is a one and I'll put the link to in
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  the show notes that if you want to take a
• 1:03:23
  look and he goes on every once in a while
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  to provide data for you with the public
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  on YouTube and he's done it through
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  natural disasters like hurricanes that we
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  had in the fall of 2024.
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  He'll do it again. This year, he'll talk
• 1:03:35
  about wind patterns to talk about weather
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  patterns. Not only just in his area where
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  he lives but also around the world.
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  all different types of oceans,
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  all different types of geographies.
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  It's amazing.
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  And I can't wait for
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  you to hear this interview
• 1:03:49
  with Dave Jones.
• 1:03:50
  Here's the interview talking about
• 1:03:51
  the National Weather Service,
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  as well as the importance of data
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  when predicting climate and the weather.
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  Enjoy the interview and
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  I'll talk to you after.
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  Hey Dave, welcome back to the
• 1:04:00
  How to Protect the Ocean podcast.
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  Are you ready to talk about the weather?
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  Oh, Andrew, I'm always
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  ready to talk about the weather.
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  I eat, sleep, and drink weather.
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  I love it.
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  I love it.
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  Look, I am so excited
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  that you're on the podcast.
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  Cause we have a lot of questions to ask
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  and to get answered.
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  Right? There is a lot of cuts that
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  happened over the last month or so.
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  People are worried about their weather.
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  Are they going to get their weather data?
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  Dave, you provide the weather on a pretty
• 1:04:26
  much daily basis to a lot
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  of different organizations,
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  a lot of different, the public, you know,
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  you're on YouTube and so forth.
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  So I thought it would be good to have you
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  on to help answer these questions,
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  help put people's worries at bay or maybe
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  just be like, no,
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  there's a reason to be worried.
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  Hopefully it's not the case, but we got
• 1:04:44
  to talk about it and I'm
• 1:04:45
  so excited to do so today.
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  But before we do, Dave, why don't you
• 1:04:48
  just let, just remind people of who you
• 1:04:51
  are and what you do.
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  Oh, sure, Andrew.
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  Yeah, I'm Dave Jones.
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  I'm the CEO and founder of
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  Storm Center Communications.
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  We've been around since 2001.
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  We've done a lot of
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  different things over the years.
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  But what we do now is focus on data
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  sharing and collaboration.
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  We invented a technology called
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  GeoCollaborate, which actually can access
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  trusted data sources from many different
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  platforms and pull them together in a
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  collaborative GIS environment.
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  So what all that means is that we can
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  connect a bunch of people together in a
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  web browser and we can then move their
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  maps around and display
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  trusted data on their maps,
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  not screen sharing, but actually
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  geospatial data sharing and delivering
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  data right in front of people onto their
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  systems so they can start to use it and
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  understand how that data can be used to
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  make their operations more efficient, to
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  give them better situational awareness,
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  understand impacts of changing climate or
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  increased sea levels because of sea level
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  rise and things like that.
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  So I do that.
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  And also, like you said, the YouTube
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  page, I update my YouTube channel for the
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  All Hazards Consortium, which is a
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  nonprofit organization that brings
• 1:06:09
  together government organizations like
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  state emergency management professionals,
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  private sector liaisons within those
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  state emergency management agencies,
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  federal emergency management.
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  And also, more importantly, the private
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  sector owner and operators like from the
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  communications, transportation, medical
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  supply, logistics, energy sectors.
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  So the utility trucks that have to
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  respond to disasters, we provide them
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  with weather briefings, especially when
• 1:06:43
  the weather gets exciting and impactful.
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  So they can just when they're on their
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  way to respond, they can be watching it
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  on their phones or on their tablets.
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  And it kind of brings everybody together
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  in a collaborative common operating
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  picture, if you will.
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  That's amazing. Such an important service
• 1:07:01
  that you provide. Can you talk a little
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  bit more about some of the talk about
• 1:07:05
  specific clientele? But who would want
• 1:07:07
  this type of weather service?
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  Because obviously, you know, when people
• 1:07:10
  look at the weather, they go to their
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  apps and they say, OK, this is what the
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  weather is going to be like.
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  And then they mistrust a little bit and
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  they say, hey, no, that's actually not
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  going to be the weather.
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  You know, if you're on the coast, if you
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  don't like the weather in five minutes,
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  it'll change and this and that.
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  But like you are providing like real time
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  up to the minute data.
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  And by the way, when you mention GIS,
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  that's right down my alleyway.
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  I'm a GIS analyst at heart.
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  So this and looking at, you know, our GIS
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  server and all that kind of stuff that we
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  can geek out about that.
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  But, you know, like what what who like
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  who wants this type of data, this this
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  like real time type of data?
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  So, you know, when we talk about the all
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  hazards consortium, there's a bunch of
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  different organizations involved.
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  And typically, private sector companies
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  don't like to share information, you
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  know, because they feel like it might get
• 1:07:59
  out to competitors and things like that.
• 1:08:01
  You know, for example, if a storm comes
• 1:08:03
  through and and, you know, 10 cell phone
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  cell towers get knocked over, you know,
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  companies are a little reticent to say,
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  yeah, we just lost service in these in
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  this 200 square mile area.
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  Because then the other companies might
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  say, hey, well, we're putting in
• 1:08:19
  temporary ones. We got
• 1:08:20
  service, you know, switch to us.
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  So what we typically do is help to
• 1:08:25
  provide situational awareness for the
• 1:08:28
  general weather that's going on and the
• 1:08:31
  impacts that are happening.
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  So we've worked for several decades with
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  NOAA and the weather service and NASA.
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  And so we can identify data that might
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  help them in their planning.
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  For example, these
• 1:08:47
  highways are flooded currently.
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  We can see it in satellite imagery.
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  We can now look at the projected flood
• 1:08:56
  inundation levels and see when those
• 1:08:59
  roads might be open so those utility
• 1:09:02
  trucks can get into those neighborhoods,
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  start to assess damage and start to
• 1:09:07
  restore power, restored quicker.
• 1:09:11
  There's a really interesting one.
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  One of the partners that we serve is a
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  very large provider of prescription
• 1:09:19
  medications to around the
• 1:09:22
  country and around the world.
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  So CVS, Walgreens, you know, all these
• 1:09:26
  pharmacies, they get their prescription
• 1:09:28
  drugs from this company.
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  They're a logistics and transportation
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  company specializing in
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  medical supplies and drugs.
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  They have warehouses all around the
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  country where they where they keep these
• 1:09:41
  and then they can
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  distribute them quickly.
• 1:09:44
  But if a wildfire develops and the
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  wildfire smoke gets into the warehouse
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  through their air system and then they go
• 1:09:57
  to deliver these prescription
• 1:09:58
  medications, the receiving organization,
• 1:10:02
  whether it's a hospital or CVS or
• 1:10:04
  Walgreens or wherever,
• 1:10:06
  can refuse to take delivery of those
• 1:10:09
  medications if they smell smoke.
• 1:10:12
  Interesting.
• 1:10:13
  So that puts all those prescription drugs
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  at risk for having to be thrown away,
• 1:10:21
  millions and billions of
• 1:10:23
  dollars worth of drugs.
• 1:10:25
  So they really never had the ability to
• 1:10:28
  identify where wildfire smoke is, where
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  it's heading and where it might impact
• 1:10:33
  some of their facilities.
• 1:10:34
  So, you know, it's things like that that
• 1:10:37
  really make a
• 1:10:38
  difference for these companies.
• 1:10:39
  Now, when you first started thinking
• 1:10:42
  about the service and putting it together
• 1:10:44
  with your colleagues, did you ever
• 1:10:46
  anticipate the amount of people that
• 1:10:49
  would benefit from having the service?
• 1:10:52
  Like you probably have some idea of
• 1:10:54
  certain people and certain companies that
• 1:10:56
  would benefit. But that
• 1:10:58
  sounds like a very unique case.
• 1:11:00
  Like, but I'm sure that it comes up more
• 1:11:02
  often than not, but probably something
• 1:11:04
  you didn't know right off the bat.
• 1:11:06
  You might have known. I didn't know that.
• 1:11:07
  I feel that's that's something that's
• 1:11:09
  really that becomes really interesting
• 1:11:10
  when you talk about this type of stuff
• 1:11:12
  and how many businesses are impacted.
• 1:11:14
  But did you expect to help this many
• 1:11:16
  types of businesses and clients?
• 1:11:20
  Well, what's really interesting, Andrew,
• 1:11:22
  is that when you start to talk and you
• 1:11:24
  can get people together, I'm on a telecom
• 1:11:26
  with these groups together every Friday.
• 1:11:31
  And so, you know, and I've been on them
• 1:11:34
  for nine years. And so you build up this
• 1:11:37
  level of trust where they've now all
• 1:11:41
  elected all these, you know, not elected,
• 1:11:44
  but the all-hazard consortium has asked
• 1:11:48
  me to chair a company.
• 1:11:49
  A committee called the Weather Impacts
• 1:11:52
  Committee for all these industries. And
• 1:11:56
  so what that really does is it allows us
• 1:11:59
  to pull these groups together and and
• 1:12:02
  say, OK, guys, let's let's talk about
• 1:12:04
  hurricanes right now.
• 1:12:06
  What is what are the important indicators
• 1:12:09
  you need to know for you to make
• 1:12:12
  decisions? I mean, for utilities, for
• 1:12:16
  example, you know, if they have electric vehicles, you know, they're going to be able to get a lot of money.
• 1:12:18
  Electric substations that are close to
• 1:12:22
  the beach or an ocean or a bay or rivers
• 1:12:26
  and they see forecasted heights of storm
• 1:12:29
  surge or flooding heights from rainfall.
• 1:12:33
  And they can get that as early as
• 1:12:35
  possible. They can move to mitigate
• 1:12:37
  potential damage to that substation.
• 1:12:41
  And I think that when when we first
• 1:12:43
  started this out, we knew that weather
• 1:12:46
  information could be a very valuable
• 1:12:48
  asset and that people probably weren't
• 1:12:50
  using it to the best of their ability.
• 1:12:54
  And the thing is, it's it's complex. You
• 1:12:57
  know, it's it's complicated. It's it's
• 1:12:59
  it's not a weather app.
• 1:13:02
  It's a you know, you you look at your
• 1:13:05
  phone and you see these deterministic
• 1:13:06
  forecasts that are, you know, 10 days out
• 1:13:09
  and it's not possible. It's not possible.
• 1:13:13
  You mean, Dave, wait a minute. Hold on a
• 1:13:15
  second. This is breaking news. You
• 1:13:16
  telling me in like 10 days from now, it's
• 1:13:19
  not going to be minus 15 here in Canada
• 1:13:21
  because that's what it says. And I don't
• 1:13:23
  want to see that. I
• 1:13:24
  don't want to believe that.
• 1:13:26
  I know. I got just a quick funny story
• 1:13:28
  when I I used to do the weather for NBC
• 1:13:30
  in Washington, D.C. and I see four there
• 1:13:32
  for 10 years through the 90s. And this is
• 1:13:35
  a January day. I got done with the
• 1:13:38
  weather and I had a call in the newsroom.
• 1:13:40
  He said, hey, Dave, you got a call online
• 1:13:42
  one. OK, pick up the call. And and the
• 1:13:45
  person says, Dave, we watch you all the
• 1:13:48
  time. You and your the whole weather team
• 1:13:51
  is the most accurate. I
• 1:13:53
  said, well, thanks very much.
• 1:13:54
  I appreciate that. She says, can you tell
• 1:13:56
  me what weather you have scheduled in
• 1:13:59
  July for my daughter's wedding? Because
• 1:14:03
  we need to know if we need to get a
• 1:14:05
  canopy or not. I love how they like they
• 1:14:10
  think that you have this ability to be
• 1:14:12
  like, you know what? I think that day is
• 1:14:14
  going to be really nice for you. I'm
• 1:14:15
  going to make sure of it.
• 1:14:17
  Well, climatology kicked off in my mind
• 1:14:20
  and I said, hazy, hot and humid with the
• 1:14:22
  chance of an afternoon thunderstorm.
• 1:14:24
  Exactly. Exactly. You can pretty much
• 1:14:28
  give those predictors and stuff. But
• 1:14:30
  yeah, I think it's you know, but I mean,
• 1:14:32
  it's so true, though, especially with
• 1:14:34
  local news. And I still think this
• 1:14:35
  happens to this day with with all the
• 1:14:37
  different apps that we have for and 24
• 1:14:39
  hour news and stuff. But news, like
• 1:14:42
  weather people, like the weatherman, the
• 1:14:44
  weather woman and and and
• 1:14:45
  all folks that do the weather.
• 1:14:47
  They are a trusted source of feel like
• 1:14:50
  they have, you know, you yourself have a
• 1:14:52
  great personality. You have this ability
• 1:14:54
  to communicate with people and all the
• 1:14:56
  weather people that we see, depending on
• 1:14:58
  where they are in the world, they have
• 1:15:00
  this ability to
• 1:15:01
  communicate people and gain trust.
• 1:15:03
  Now, some people lose that trust when the
• 1:15:05
  weather doesn't get right and they blame
• 1:15:06
  it on the weather person. But for the
• 1:15:08
  most part, like, you know, I remember
• 1:15:10
  when I was a kid, you know, when the
• 1:15:11
  weather person came on, my parents like,
• 1:15:13
  no, we need to know the weather.
• 1:15:15
  Like, we need to know that forecast. And
• 1:15:18
  it's so interesting to see, like, you got
• 1:15:22
  like your your your your entire like
• 1:15:25
  field. You have to be really great
• 1:15:28
  science communicators
• 1:15:29
  to be able to do this.
• 1:15:30
  You're taking complex information just
• 1:15:32
  from a weather perspective for the
• 1:15:34
  general public, not what obviously you're
• 1:15:36
  doing in geo
• 1:15:36
  collaborate and Storm Center.
• 1:15:39
  But you have to be able to take that
• 1:15:41
  complex information and put it into a
• 1:15:43
  form where it's like, you're going to
• 1:15:44
  like the weather today or you're not
• 1:15:46
  going to like the weather.
• 1:15:48
  There's always somebody. There's always
• 1:15:50
  somebody that's going to say, oh, we got
• 1:15:52
  a beautiful day in store. And then you
• 1:15:54
  get a call and they say, I'm a farmer. We
• 1:15:56
  need rain. It's not a beautiful day.
• 1:15:58
  You know, exactly. Exactly. Right. But
• 1:16:01
  how do you how do you develop that? Is
• 1:16:03
  that happening during schooling? Or is
• 1:16:05
  that just like a talent that people are
• 1:16:07
  attracted to when they go in this field?
• 1:16:11
  Well, I mean, first of all, there's a
• 1:16:12
  couple of questions that you asked there
• 1:16:13
  and they're all really good. And they're
• 1:16:15
  they're they're ones that
• 1:16:17
  are important questions.
• 1:16:19
  First, you know, most meteorologists
• 1:16:21
  don't go into broadcasting. Right. So,
• 1:16:25
  you know, it's one percent or less of
• 1:16:28
  meteorologists that graduate are actually
• 1:16:31
  on TV communicating the
• 1:16:34
  weather to their audience.
• 1:16:37
  But those that are on TV are the drivers
• 1:16:41
  for that newscast. And what I mean by
• 1:16:45
  that is research has showed this over and
• 1:16:48
  over again for decades that local weather
• 1:16:52
  is the number one reason
• 1:16:54
  why people watch local news.
• 1:16:57
  So they do build that relationship with
• 1:17:00
  the broadcast meteorologists, whether
• 1:17:02
  it's the morning shift or the afternoon
• 1:17:05
  or evening person or the weekend person.
• 1:17:08
  I used to go to these events in
• 1:17:11
  Washington, D.C., where they'd have the
• 1:17:13
  NBC four tent set up and, you know,
• 1:17:15
  they'd have a couple of, you know, the
• 1:17:16
  sports guy and a
• 1:17:17
  meteorologist there and an anchor.
• 1:17:19
  And and, you know, people come up to me
• 1:17:22
  and say, Dave, I wake up
• 1:17:24
  with you every morning.
• 1:17:26
  Yeah, for sure. It's so weird. Right.
• 1:17:29
  You're like, it is. It is. Or you're a
• 1:17:31
  lot taller than I thought.
• 1:17:35
  It's like, yeah, once you get out of the
• 1:17:36
  box of TV, you become a real person. But
• 1:17:40
  but the communication part is really
• 1:17:42
  important for scientists and and
• 1:17:45
  scientists tend to, you know, it's it's
• 1:17:49
  an intense field, any kind of science.
• 1:17:52
  It's very intricate. It's complex. Some
• 1:17:55
  sciences involve a lot of math and
• 1:17:58
  physics and things like that. So it's not
• 1:18:01
  something you're going to really talk
• 1:18:02
  about on TV. And other
• 1:18:04
  scientists might work at NASA.
• 1:18:06
  They might work at NOAA and doing
• 1:18:08
  research or EPA or doing air quality. And
• 1:18:12
  they're all critical jobs. Yeah. But some
• 1:18:16
  of them need a little help in
• 1:18:18
  communicating a little
• 1:18:20
  bit more effectively.
• 1:18:21
  And, you know, we've talked about this
• 1:18:24
  and are really talking about it more
• 1:18:26
  right now. And that is that communication
• 1:18:29
  is so critical to talk about how
• 1:18:32
  important things are because, you know,
• 1:18:35
  many, many TV, this little kind of a pet
• 1:18:38
  peeve of mine is that most TV
• 1:18:41
  meteorologists and
• 1:18:42
  the stations themselves.
• 1:18:44
  Yeah, they'll show satellite image on the
• 1:18:47
  local newscast and they'll call it, you
• 1:18:49
  know, you know, News six sky. I, you
• 1:18:53
  know, and, you know, the radar, you know,
• 1:18:56
  digital Doppler X.T.
• 1:18:58
  You know, like it's a car with a number
• 1:19:00
  of letters together. It just people are
• 1:19:02
  like, oh, this must be serious. This is
• 1:19:04
  good. This is accurate. I watch that. And
• 1:19:07
  but what they don't do is say this radar
• 1:19:10
  data is from the
• 1:19:12
  National Weather Service.
• 1:19:14
  And and it's important to keep going
• 1:19:16
  because we can communicate to you where
• 1:19:19
  the storms are. This satellite image is
• 1:19:22
  taken from a NOAA satellite that orbits
• 1:19:24
  twenty two thousand three hundred miles
• 1:19:26
  up in space in a geosynchronous orbit.
• 1:19:29
  What's that? Well, you can tell them
• 1:19:31
  about that and then say we need these
• 1:19:34
  satellites because we won't see these
• 1:19:37
  hurricanes coming without them.
• 1:19:39
  And so that's being done maybe less than
• 1:19:44
  point five percent of the time around the
• 1:19:47
  nation, probably in Canada as well. And
• 1:19:50
  and it's it's really important that that
• 1:19:53
  attribution happened because otherwise
• 1:19:55
  people, you know, this was said on the on
• 1:19:58
  the floor of Congress.
• 1:20:01
  Years ago, a certain congressman stood up
• 1:20:05
  and said, why do we need the National
• 1:20:08
  Weather Service when I can
• 1:20:10
  turn on the weather channel?
• 1:20:13
  You know, and and so, you know, my my
• 1:20:16
  comeback to that is, hey, gotcha. Why do
• 1:20:20
  we need cows when we can go to the store
• 1:20:22
  and buy milk? Exactly. Right.
• 1:20:26
  I mean, the level of like naivete and
• 1:20:33
  just ignorance of where data comes from.
• 1:20:38
  And I guess it's like we're just we just
• 1:20:40
  end up taking it for granted.
• 1:20:42
  And I want to take the a good chunk of
• 1:20:45
  this episode to discuss this. But before
• 1:20:47
  I do, I have to ask that going back to
• 1:20:49
  the weather, the weather people, what
• 1:20:52
  short straw do you have to draw to be a
• 1:20:56
  weather person that goes out in a
• 1:20:58
  hurricane storm and
• 1:21:00
  bring it to the people?
• 1:21:02
  Like we know it's bad. Like why do they
• 1:21:04
  subject these poor weather people,
• 1:21:07
  sometimes journalists to putting them out
• 1:21:09
  in the middle of a storm? It doesn't
• 1:21:11
  matter what type of storms. It could be
• 1:21:13
  like a hurricane. It could be like
• 1:21:15
  tornado areas. It could
• 1:21:17
  be like wildfire areas.
• 1:21:18
  Like, why do we do that? Like, what's the
• 1:21:20
  point of that? I don't know. I think
• 1:21:22
  stations might just like to bleep out bad
• 1:21:25
  words when they, you know, when the winds
• 1:21:27
  come or something. And they really want
• 1:21:29
  to see you out in the elements.
• 1:21:31
  And it's like, really? Okay, so the next
• 1:21:33
  time a palm frond knocks my head off,
• 1:21:36
  make sure you describe to everybody, you
• 1:21:39
  know, how it felt because I won't be able
• 1:21:41
  to. I think, you know, I think there's a
• 1:21:44
  there's a there's a benefit for being out
• 1:21:47
  to talk to people about
• 1:21:49
  why they're not evacuating.
• 1:21:53
  Why are you going to stay on your
• 1:21:56
  sailboat right this dock when a 15 foot
• 1:21:59
  storm surges, you know, and and you do
• 1:22:02
  get people's answers like it never hits
• 1:22:05
  here. Yeah, you know, it always veers or
• 1:22:09
  it always weakens. And then, you know,
• 1:22:12
  when you go back and all of their stuff
• 1:22:14
  is gone, you can't find
• 1:22:16
  the person to interview.
• 1:22:18
  Then all of a sudden people like, Oh my
• 1:22:20
  gosh, they know they should have left.
• 1:22:22
  Yeah, but you know, chasing storms is
• 1:22:25
  always going to be kind of a ratings
• 1:22:27
  driver. It's it's dangerous. It's
• 1:22:30
  uncertain. Even the best of them get
• 1:22:33
  caught sometimes in
• 1:22:35
  precarious situations.
• 1:22:38
  And it drives ratings. Yeah. But the
• 1:22:41
  first thing they hear when they get done
• 1:22:43
  with the storm is a whole bunch of people
• 1:22:47
  leaving messages or, you know, social
• 1:22:51
  media posts like why the heck are you
• 1:22:54
  there? Why don't you leave and all that
• 1:22:57
  stuff? So that's an ongoing thing. And
• 1:22:59
  yeah, you know, even for storm chasing
• 1:23:01
  out in out in Oklahoma. Yeah.
• 1:23:04
  I think there's there's legislation
• 1:23:06
  that's now being proposed or put together
• 1:23:09
  in Oklahoma to only allow licensed
• 1:23:13
  meteorologists to chase storms. Because
• 1:23:17
  it's getting kind of crazy out there with
• 1:23:19
  accidents and people taking chances and
• 1:23:22
  unqualified people taking people on tours
• 1:23:26
  and making money. Yeah. So, you know,
• 1:23:29
  we're starting to see some impacts.
• 1:23:31
  For sure. Well, and and I think that's
• 1:23:33
  kind of goes back into, you know, knowing
• 1:23:37
  about the data being qualified to not
• 1:23:39
  only know about the data, but also to
• 1:23:41
  discuss the data in a way that can be
• 1:23:43
  interpreted. But let's let's talk about
• 1:23:46
  these sources of data that you mentioned
• 1:23:48
  earlier, you know, knowing where this
• 1:23:50
  data comes from, from NOAA, from NASA,
• 1:23:52
  from the National Weather Service. These
• 1:23:54
  are all very important systems in the
• 1:23:57
  government that have
• 1:23:58
  allowed, not only the U.S.
• 1:24:00
  but for the rest of the world to benefit
• 1:24:03
  from this data and the partnerships that
• 1:24:05
  have come from it with other countries
• 1:24:07
  and bring it all together, being able to
• 1:24:09
  piece all this data together to get
• 1:24:11
  almost a global picture of what's
• 1:24:14
  happening, whether it be
• 1:24:15
  satellites from the U.S.
• 1:24:16
  satellites from Canada, satellites from
• 1:24:18
  Europe and so forth. It's been a benefit
• 1:24:22
  to the world to have this type of data.
• 1:24:25
  Can you talk about, you know, obviously
• 1:24:28
  this is leading into government cuts, the
• 1:24:30
  recent government cuts, but let's talk
• 1:24:31
  about why this data is so important to to
• 1:24:36
  help protect citizens of
• 1:24:38
  the U.S. as well as abroad.
• 1:24:40
  Sure. Well, I mean, let's start with
• 1:24:43
  satellites, you know, first, there's two
• 1:24:46
  different types of satellites that are up
• 1:24:48
  there orbiting the Earth. One of the
• 1:24:50
  geostationary satellites. They're the
• 1:24:52
  ones and Europe has them. Japan has them.
• 1:24:55
  India has them. And they're all orbiting
• 1:24:59
  over the same spot on the Earth as the
• 1:25:01
  Earth rotates, the satellite orbits at
• 1:25:03
  the same speed. And so orbital speed. And
• 1:25:06
  so they're looking at the
• 1:25:08
  same spot at the same time.
• 1:25:10
  You know, all the time that allows us to
• 1:25:13
  sequence the clouds, put them in a loop
• 1:25:15
  and watch what's moving towards us or
• 1:25:18
  away from us. And so these satellites are
• 1:25:21
  always monitoring the entire disk, you
• 1:25:25
  know, all the hemispheres, you know,
• 1:25:27
  northern hemisphere, southern hemisphere,
• 1:25:28
  eastern hemisphere, western.
• 1:25:30
  And and we can see seedlings coming off
• 1:25:33
  of Africa that may turn into hurricanes
• 1:25:37
  or tropical cyclones and we can follow
• 1:25:40
  them across just by watching them and
• 1:25:43
  watching their dynamics, watching how
• 1:25:45
  they're developing and all
• 1:25:46
  that stuff without satellites.
• 1:25:49
  We wouldn't know that those systems were
• 1:25:52
  out there developing and we didn't have
• 1:25:56
  satellites in 1900 and 1900 there was one
• 1:26:02
  of the biggest hurricanes to ever hit the
• 1:26:04
  United States, hit Galveston, Texas,
• 1:26:07
  killed some numbers or 6000 others or
• 1:26:10
  8000 people. They didn't see it coming
• 1:26:13
  and the storm surge was monstrous, led to
• 1:26:17
  the building of the big sea.
• 1:26:18
  The big sea wall in Galveston to protect
• 1:26:21
  part of the island. And so these are what
• 1:26:24
  we call observing systems and these
• 1:26:27
  observing systems from a geostationary
• 1:26:30
  standpoint give us a
• 1:26:32
  really big broad picture.
• 1:26:35
  There are other satellites called polar
• 1:26:37
  orbiting satellites that orbit maybe
• 1:26:39
  about 300 miles up so much lower. They
• 1:26:43
  orbit from pole to pole and the earth
• 1:26:45
  rotates underneath of them. And so they
• 1:26:47
  take strips of data.
• 1:26:50
  Those satellites are critical because
• 1:26:52
  they have different sensors on them that
• 1:26:54
  can look through the atmosphere, look at
• 1:26:57
  the ocean, take temperature data. They
• 1:27:00
  can profile the atmosphere temperatures
• 1:27:03
  at different levels of the atmosphere and
• 1:27:07
  that data is critical for going into
• 1:27:12
  numerical weather prediction models.
• 1:27:15
  As a matter of fact, 95% of the data that
• 1:27:22
  goes into initializing a numerical
• 1:27:24
  weather prediction model comes from
• 1:27:27
  satellites. So if satellites went away,
• 1:27:33
  we'd be in big trouble. Yeah, because you
• 1:27:37
  know, where we don't have a lot of
• 1:27:39
  observations is over the ocean.
• 1:27:42
  We call them data sparse areas. We have
• 1:27:45
  buoys that are out there in the ocean,
• 1:27:48
  not everywhere. We have floats called
• 1:27:51
  Argo floats. They they they're at the
• 1:27:54
  surface of the ocean and then they go
• 1:27:56
  down about 200 meters record data, they
• 1:28:00
  come back up and when they hit the
• 1:28:02
  surface, they transmit
• 1:28:03
  all those observations back.
• 1:28:07
  And that goes into numerical prediction
• 1:28:08
  models so we can have coupled ocean
• 1:28:11
  atmosphere models. There are gliders that
• 1:28:16
  go through the ocean that try to that
• 1:28:18
  that not try. They measure salinity and
• 1:28:22
  they measure temperature and currents.
• 1:28:24
  All that stuff is
• 1:28:25
  they're going up and down.
• 1:28:28
  Drifters there are Lagrangian drifters
• 1:28:31
  from University of San Diego, California,
• 1:28:36
  San Diego. These drifters can be put
• 1:28:40
  anywhere and they just drift with the
• 1:28:41
  currents but they report back wave
• 1:28:44
  heights, temperatures,
• 1:28:46
  pressures, things like that.
• 1:28:49
  So we use satellite data to estimate what
• 1:28:52
  the sea surface temperature is, but we
• 1:28:54
  can then use those drifting buoys to
• 1:28:57
  validate what those temperatures are to
• 1:29:00
  know that what the satellites are
• 1:29:02
  measuring is accurate.
• 1:29:05
  And so so that's that's satellites and
• 1:29:07
  some ocean observing systems. We also
• 1:29:10
  have land observing systems in the United
• 1:29:13
  States. They're called ASOS automated
• 1:29:16
  surface observing systems located a lot
• 1:29:19
  of airports, but also in other areas.
• 1:29:22
  States are putting in
• 1:29:23
  their own meso networks.
• 1:29:26
  And they're taking constant weather
• 1:29:34
  observations every minute or less and
• 1:29:38
  sending that data into the National
• 1:29:41
  Weather Service and those states maintain
• 1:29:43
  those sensors. They quality control, make
• 1:29:46
  sure that they're you know, the data is accurate. The Weather Service quality controls the data.
• 1:29:48
  You know, if they say, Hey, you know, is
• 1:29:53
  temperature saying 80 degrees, but every
• 1:29:55
  all the other sensors around it are
• 1:29:56
  saying 62. Why don't you go out and take
• 1:29:59
  a look at it? And so these are all around
• 1:30:04
  the world. It's not just an American
• 1:30:05
  waters like they are a number of them in
• 1:30:07
  the American waters, but this is all over
• 1:30:08
  the round because everything's connected,
• 1:30:10
  right? It's not just here's the 200
• 1:30:12
  nautical mile exclusive economic zone. We're just not going to know data outside of that. That doesn't help us.
• 1:30:15
  In terms of detecting a swell that comes
• 1:30:20
  in that may or may not affect the coastal
• 1:30:23
  area or you know, looking at a system
• 1:30:26
  that comes in from Africa during you know
• 1:30:29
  the summer and might hit there in the
• 1:30:31
  fall and knowing whether that's going to
• 1:30:33
  be a hurricane or not. These are all over
• 1:30:34
  the world. There's two partnerships with
• 1:30:36
  other with other countries and and this
• 1:30:39
  is extremely important data. You know,
• 1:30:42
  it's it's funny, I was I was watching a TV show and I know the same way we also watched it, but we're also going to practice it today.
• 1:30:44
  and I know this is fictional show,
• 1:30:45
  but it kind of demonstrated a point
• 1:30:51
  of how important data is.
• 1:30:53
  And it's a show called High Surf.
• 1:30:55
  It's like a rescue show.
• 1:30:57
  It's obviously very fictional.
• 1:30:58
  It's based in Hawaii.
• 1:30:59
  I'm about to go to Hawaii next week.
• 1:31:01
  So I decided, actually by
• 1:31:02
  the time this is published,
• 1:31:05
  I'll be in Hawaii, but
• 1:31:06
  I wanted to get ready.
• 1:31:08
  I wanted to see what it was like and
• 1:31:09
  everything like that.
• 1:31:09
  So obviously I've got a
• 1:31:11
  very different illusion
• 1:31:11
  of what it really is.
• 1:31:13
  But there was an episode where they were
• 1:31:15
  expecting like a big
• 1:31:16
  swell, like a tsunami, a
• 1:31:17
  potential tsunami warning.
• 1:31:18
  And they were looking at
• 1:31:19
  three buoys is North Shore, Oahu.
• 1:31:21
  And they were looking at three
• 1:31:22
  buoys that were very far apart
• 1:31:24
  from each other and like hours apart
• 1:31:26
  where the swell based on,
• 1:31:28
  but they knew the height of the swell
• 1:31:29
  after every time it hit
• 1:31:30
  the buoy, they knew the speed of the
• 1:31:33
  swell, how fast it was
• 1:31:33
  coming and they were be able to detect
• 1:31:35
  the change in time and
• 1:31:37
  the size and the power of it
• 1:31:39
  when it by the time it hit the
• 1:31:41
  next buoy, whether to go from a warning
• 1:31:44
  to this is a serious threat
• 1:31:46
  or to say, hey, you know what, this is
• 1:31:48
  not a warning anymore.
• 1:31:49
  We're OK.
• 1:31:51
  But the hours in between that
• 1:31:52
  to try and get that data was
• 1:31:55
  very nerve wracking.
• 1:31:55
  They didn't know what to do.
• 1:31:56
  And obviously, you know, it's a show.
• 1:31:57
  So they go into all the emotional parts,
• 1:31:59
  but it kind of took you
• 1:32:00
  through what the different parts of each
• 1:32:04
  different part, federal state
• 1:32:06
  and even local governments were taking
• 1:32:08
  and actions they were taking.
• 1:32:09
  And they were relying
• 1:32:10
  on this data to come in.
• 1:32:12
  Imagine if one, if we didn't have those
• 1:32:14
  buoys, imagine if we didn't have the
• 1:32:16
  drifters that were along that may have
• 1:32:17
  been in the water at the time that
• 1:32:18
  could tell even in between what that is
• 1:32:20
  or even verify that
• 1:32:22
  data or even satellite.
• 1:32:23
  You know, we have radar set that can tell
• 1:32:25
  wave height and whether there's
• 1:32:27
  something big coming or, you know, you
• 1:32:29
  can see a boat in the water or something
• 1:32:30
  like that. There's so many pieces of this
• 1:32:32
  data that becomes such an important role.
• 1:32:35
  But it also is like there's so much data
• 1:32:37
  coming in that you need to
• 1:32:39
  interpret it properly.
• 1:32:40
  And that requires people with, you know,
• 1:32:43
  scientific degrees in
• 1:32:44
  various science fields,
• 1:32:46
  you know, not only biology, but physics
• 1:32:47
  and chemistry and, you
• 1:32:49
  know, atmosphere and so forth
• 1:32:51
  that make the National Weather Service,
• 1:32:54
  NOAA, NASA, so, so important.
• 1:32:56
  Can you talk about some of the some of
• 1:32:57
  the roles that play into that and the
• 1:33:00
  number of people that have to play
• 1:33:02
  over that role and be able to take in all
• 1:33:04
  this data and what
• 1:33:06
  requires that specialties?
• 1:33:08
  Yeah. And let me just mention, too, since
• 1:33:10
  you were talking about tsunamis, I mean,
• 1:33:12
  there are tsunami
• 1:33:13
  buoys around the oceans.
• 1:33:15
  Right. There aren't enough. Right. And
• 1:33:17
  those tsunami buoys, some of them are far
• 1:33:20
  enough out that if an earthquake
• 1:33:21
  would happen between the buoy and the
• 1:33:24
  coast, you wouldn't detect it. Right. And
• 1:33:27
  and so and some of
• 1:33:30
  those buoys need to be fixed
• 1:33:33
  because they're not working. And so
• 1:33:35
  that's why it takes a whole cadre of
• 1:33:39
  people to monitor the data.
• 1:33:42
  And when something is not reporting, you
• 1:33:44
  get out there to fix it. And in many
• 1:33:46
  cases already, for example, like within
• 1:33:49
  NOAA, they're already understaffed.
• 1:33:52
  And so they'll say, well, we're going to
• 1:33:54
  have to take a look at that buoy, you
• 1:33:56
  know, in six months, because we're not
• 1:33:58
  going to have this ship
• 1:33:59
  that we need to take out.
• 1:34:02
  And and we'll do a couple of other buoys
• 1:34:04
  at the same time. So they're not just
• 1:34:06
  going out to look at one
• 1:34:07
  buoy and then coming back.
• 1:34:08
  That's too expensive for one buoy. So you
• 1:34:12
  really do need a whole bunch of people
• 1:34:16
  that are monitoring this.
• 1:34:17
  I mean, few people can monitor, but you
• 1:34:20
  need a whole bunch to
• 1:34:21
  go out and fix. Yeah.
• 1:34:23
  And and some of these buoys just aren't
• 1:34:25
  breaking on their own. You know, illegal
• 1:34:28
  fishing, you know, they tie
• 1:34:30
  up to some of these buoys.
• 1:34:32
  And do these catches with their nets,
• 1:34:34
  which they shouldn't be doing. Right.
• 1:34:38
  Others, you know, sometimes they're a
• 1:34:39
  haven for seals or sea lions and they
• 1:34:42
  climb up on there and, you know, they're
• 1:34:44
  like, hey, why do we have a 15 foot wave
• 1:34:46
  out there in the middle
• 1:34:47
  of a high pressure system?
• 1:34:48
  It shouldn't be happening. Go out there
• 1:34:50
  and see it. And it's like a mama seal
• 1:34:52
  that made a home there and half a ton
• 1:34:54
  seal that back and forth.
• 1:34:55
  Yeah.
• 1:34:58
  You know, all sorts of things can happen.
• 1:35:00
  But but, you know, you're right, Andrew.
• 1:35:02
  I mean, the let's just take a look for a
• 1:35:05
  second at at the weather service.
• 1:35:07
  Right. You know, the
• 1:35:08
  National Weather Service, they have.
• 1:35:11
  One hundred and twenty two forecast
• 1:35:14
  offices around the nation. So that's
• 1:35:18
  include Alaska, Hawaii, Guam and the the
• 1:35:22
  the Kona's continental United States.
• 1:35:24
  They have six regional offices that help
• 1:35:28
  to coordinate those local offices.
• 1:35:32
  They have 13 river forecast centers that
• 1:35:36
  are focused on monitoring the rainfall
• 1:35:40
  and river levels and also doing outreach
• 1:35:43
  to people to make help them understand
• 1:35:46
  how fast rivers can rise.
• 1:35:49
  I mean, you know, there are campsites all
• 1:35:50
  over the place and people have no idea.
• 1:35:53
  Oh, yeah, let's camp
• 1:35:54
  by the river over there.
• 1:35:55
  It's beautiful. But if there's a you
• 1:35:58
  know, three inch rainfall out just up
• 1:36:02
  river and they don't see any rain at all,
• 1:36:04
  they could be washed
• 1:36:05
  away in a matter of hours.
• 1:36:08
  So you need to have that kind of
• 1:36:10
  monitoring going on. They also have
• 1:36:12
  national centers, nine of them. So they
• 1:36:16
  have the the National Center for
• 1:36:19
  Environmental Prediction called NSEP.
• 1:36:20
  They do a lot of modeling and they're
• 1:36:22
  kind of the the parent center over all
• 1:36:25
  the other ones. They have the National
• 1:36:27
  Hurricane Center. They have the Weather
• 1:36:30
  Prediction Center in
• 1:36:31
  College Park, Maryland.
• 1:36:32
  Hurricane Center is in Miami. That's
• 1:36:36
  probably the most popular national center
• 1:36:40
  that the Weather Service has. They have
• 1:36:42
  the Aviation Weather Center.
• 1:36:44
  They're constantly focused on aviation
• 1:36:46
  and predicting turbulence. Clear air
• 1:36:49
  turbulence is so tough, right? They have
• 1:36:52
  the Climate Prediction Center. They're
• 1:36:54
  looking out beyond seven days and they're
• 1:36:58
  going into seasonal predictions.
• 1:37:01
  What does this spring look like? What
• 1:37:02
  does the summer look like? Helping to
• 1:37:04
  monitor and predict drought conditions,
• 1:37:07
  which are so critical for agriculture.
• 1:37:11
  They have the Storm Prediction Center in
• 1:37:15
  Norman, Oklahoma. They're focused on
• 1:37:17
  severe weather, tornado outbreaks, hail,
• 1:37:21
  hail damage, hail size, things like that.
• 1:37:25
  The Ocean Prediction Center. They focus
• 1:37:28
  on the ocean and getting products out to
• 1:37:30
  mariners so they know that big waves are
• 1:37:33
  headed their way or it's
• 1:37:34
  going to be treacherous.
• 1:37:35
  Here are advisories that we issue for the
• 1:37:38
  oceans. They have the National Water
• 1:37:40
  Center, which is down in Tuscaloosa,
• 1:37:42
  Alabama. It's a fairly new center, but
• 1:37:46
  they're working on the National Water
• 1:37:47
  Model, modeling every stream and
• 1:37:50
  tributary in the in the nation and even
• 1:37:54
  in, you know, in Puerto
• 1:37:55
  Rico and Alaska and Hawaii.
• 1:37:57
  So they can see, OK, this sensor just
• 1:38:02
  measured an inch of rain falling and so
• 1:38:04
  did these other sensors close to it.
• 1:38:06
  Yeah, we're going to model what the
• 1:38:08
  downstream impacts are going to be so we
• 1:38:10
  can warn people that there's a danger if
• 1:38:13
  that's going to be any flooding.
• 1:38:15
  And then the last but not least, National
• 1:38:18
  Center is the Space Weather Prediction
• 1:38:20
  Center that's in Boulder, Colorado.
• 1:38:25
  They're monitoring the sun and the storms
• 1:38:27
  that come off the sun and what we call
• 1:38:30
  coronal mass ejections, right?
• 1:38:33
  We've had a lot of experience with those.
• 1:38:36
  I mean, the one last May 10th boy, the
• 1:38:39
  Aurora Borealis was visible down in
• 1:38:42
  Alabama and Florida and part of the
• 1:38:44
  Bahamas for crying out loud. Yeah, that
• 1:38:47
  was that was pretty pretty awesome. So I
• 1:38:49
  mean, you know, people say, well, that's
• 1:38:52
  a lot of stuff. I tend to think
• 1:38:55
  sometimes, Andrew, that the people just
• 1:38:57
  watch too many movies
• 1:38:58
  and they think that.
• 1:39:01
  Things are operated by AI and shouldn't
• 1:39:06
  one or two people be able to do these
• 1:39:08
  jobs and it's just not possible. It's not
• 1:39:12
  possible. You know, the impacts that
• 1:39:16
  weather and climate events have had just
• 1:39:20
  on the United States alone. Yeah.
• 1:39:23
  So since 1980, the nation has had the
• 1:39:30
  United States has had 403 disasters that
• 1:39:36
  each total one billion dollars or more.
• 1:39:41
  So since 1980, right, that those
• 1:39:45
  disasters have exceeded two point nine
• 1:39:48
  trillion dollars in damage.
• 1:39:53
  The investment could have been worse. It
• 1:39:55
  could have been worse. These if they
• 1:39:57
  weren't able to get like access to data
• 1:39:59
  to understand where these storms are
• 1:40:01
  coming from, predicting the levels of the
• 1:40:03
  storms and so forth, right? Could have
• 1:40:05
  been worse. No doubt. No doubt. And and
• 1:40:08
  you know, when you think of the
• 1:40:09
  investment that's needed for the National
• 1:40:11
  Weather Service, yeah, and you boil it
• 1:40:13
  down because the Weather Service
• 1:40:15
  forecasts impact every person, right?
• 1:40:18
  All the planes that are coming in and
• 1:40:21
  bringing people in internationally,
• 1:40:23
  domestically, transportation. If you boil
• 1:40:27
  it down, it's for the price of like a Big
• 1:40:31
  Mac or a Happy Meal, you know, per
• 1:40:34
  American. It's like 10 bucks, 12 bucks
• 1:40:38
  per person per year that
• 1:40:42
  funds the operations of NOAA.
• 1:40:47
  And so when you when you turn around and
• 1:40:51
  you look at cuts and you might do cuts,
• 1:40:56
  implement cuts randomly, you could cut
• 1:41:03
  out the people who are hired to warn you
• 1:41:07
  if a tornado is coming.
• 1:41:10
  That's going to cost lives.
• 1:41:12
  Yeah.
• 1:41:13
  Yeah.
• 1:41:13
  Yeah. And that's it's so important. It
• 1:41:17
  like it. I don't understand the pushback
• 1:41:24
  on this type of data and the pushback
• 1:41:28
  that I'm talking about is over the last
• 1:41:29
  month since the Trump administration has
• 1:41:31
  taken over power and they've they're
• 1:41:34
  Donald Trump's now the president.
• 1:41:35
  Doge has become a thing. Elon Musk is
• 1:41:38
  going into a number of different offices,
• 1:41:41
  say, either either closing down the
• 1:41:43
  entire department like he did with with
• 1:41:45
  USAID, but also going into other
• 1:41:48
  departments and, you know, just doing
• 1:41:50
  mass cuts, not only to the people of the
• 1:41:53
  public service that we just talked about
• 1:41:54
  in terms of what it requires and the
• 1:41:56
  people requires and
• 1:41:57
  even being understaffed.
• 1:41:58
  But they would cut just people randomly
• 1:42:02
  and we're hearing stories by the day. You
• 1:42:05
  know, at this point, the last story I
• 1:42:07
  heard is, you know, Elon Musk sends a
• 1:42:09
  mass email to a number of people in the
• 1:42:11
  government saying, what have you done the
• 1:42:12
  last five or the last five
• 1:42:14
  things you did this week?
• 1:42:15
  If you do not email me back, consider
• 1:42:17
  yourself a resident,
• 1:42:18
  consider it a resignation.
• 1:42:18
  Not still not sure if that's true or not.
• 1:42:22
  Like, you know, there's been a lot of
• 1:42:24
  stuff that's been going around. But, you
• 1:42:25
  know, talking to fellow colleagues in in
• 1:42:29
  in NOAA at EPA, a number of different
• 1:42:33
  departments, they've they're scared.
• 1:42:36
  They're worried. They're worried for
• 1:42:37
  their colleagues. They're worried for
• 1:42:38
  themselves. So obviously there's that
• 1:42:40
  portion of it. But there is this
• 1:42:42
  fundamental attack against getting
• 1:42:47
  information from the government.
• 1:42:47
  They're worried about getting information
• 1:42:48
  from the environment about the
• 1:42:50
  environment to the point now where we've
• 1:42:53
  seen a lot of cuts that have happened on
• 1:42:55
  international programs. And when I this
• 1:42:58
  is where I really want to
• 1:42:59
  focus the rest of the episode.
• 1:43:01
  And we've been kind of building up to it
• 1:43:03
  is there have been cuts to any
• 1:43:04
  international programs for Noah, the
• 1:43:07
  national, which include the National
• 1:43:08
  Weather Service in gathering data beyond
• 1:43:12
  the borders of the US.
• 1:43:16
  So I think that's a huge issue. And I
• 1:43:17
  don't even know if if all the information
• 1:43:19
  has been divulged in terms of what is
• 1:43:21
  what part of those programs are going to
• 1:43:23
  be cut. But can you give us just a little
• 1:43:26
  bit of enlightenment of what this means
• 1:43:28
  for weather predictions,
• 1:43:30
  what this means for climate
• 1:43:31
  predictions, all the different centers
• 1:43:32
  that you mentioned? What does this mean
• 1:43:35
  going forward for the US?
• 1:43:39
  You know, Andrew, if if data flow was cut
• 1:43:45
  off between internationally, you know,
• 1:43:49
  between nations, it would seriously
• 1:43:51
  impact the ability to forecast
• 1:43:54
  accurately. I mean, you know, when I when
• 1:43:58
  I give talks, I talk about the five day
• 1:44:02
  forecast, or the seven day forecast.
• 1:44:06
  The seven day forecast today is as
• 1:44:08
  accurate as a three day
• 1:44:10
  forecast was 25 years ago.
• 1:44:13
  And so we're improving in our forecast
• 1:44:15
  accuracy. There's a couple reasons for
• 1:44:18
  that. We're having more sophisticated
• 1:44:20
  physics based models that
• 1:44:22
  are processing more data.
• 1:44:24
  We're not using all the data that's being
• 1:44:26
  collected yet. There are there are still
• 1:44:29
  what's called data assimilation efforts
• 1:44:32
  to bring in different types of data, even
• 1:44:34
  data from the private sector, if they're
• 1:44:36
  launching, you know, satellites and are
• 1:44:38
  collecting data and information.
• 1:44:41
  But what it requires is a global data set
• 1:44:45
  that you're reading into that model in
• 1:44:49
  order to sufficiently initialize it.
• 1:44:52
  Right. I mean, I don't know if you've
• 1:44:53
  heard the term garbage in garbage out. If
• 1:44:57
  you if you are putting in less data or
• 1:45:01
  non quality control data because you laid
• 1:45:04
  off people that are quality controlling.
• 1:45:07
  Yeah, that means more bad data will get
• 1:45:13
  into the model. And that means the output
• 1:45:17
  is going to be bad and get worse as time
• 1:45:20
  goes out. Right. So so
• 1:45:23
  that's a that's an issue.
• 1:45:26
  I have heard of some meetings, you know,
• 1:45:31
  being delayed or canceled
• 1:45:33
  internationally, but I haven't heard of
• 1:45:37
  anything related to stopping the data
• 1:45:40
  flow and the data sharing when it when it
• 1:45:43
  comes to forecasting. Right. Because that
• 1:45:46
  would be really impactful.
• 1:45:48
  Yeah, not not only not only for the
• 1:45:52
  average citizen that wants to make a
• 1:45:54
  decision, but I mentioned all those
• 1:45:57
  centers from the weather service, the
• 1:45:58
  aviation prediction center, right? The
• 1:46:02
  Weather Center, Aviation Weather Center.
• 1:46:04
  If you and there are meteorologists at
• 1:46:06
  all these FAA locations, which are trying
• 1:46:09
  to help, you know,
• 1:46:09
  planes navigate safely.
• 1:46:10
  There will be a there could be a lot of
• 1:46:15
  impact to aviation, maybe more flights
• 1:46:18
  canceled, more flights delayed, things
• 1:46:21
  like that. They just can't handle the
• 1:46:24
  capacity without knowing what weather is
• 1:46:28
  in front of them, how long things are
• 1:46:30
  going to last. So it would have it would
• 1:46:33
  have a serious snowball effect on not
• 1:46:38
  only the impact of people, but impact to the entire population.
• 1:46:39
  And the impact to the entire economy
• 1:46:41
  around the globe. All the economies
• 1:46:44
  around the world would be impacted. Yeah.
• 1:46:47
  And, you know, from what I've heard as
• 1:46:50
  well as and I can I don't know if this is
• 1:46:52
  true or not. There have been some jobs
• 1:46:55
  and programs cut and then they've been
• 1:46:57
  reinstated, you know, or they hire back
• 1:47:00
  to people because the government realizes
• 1:47:01
  that these are some of these programs are
• 1:47:04
  vital. Hopefully, hopefully.
• 1:47:06
  If it hasn't been cut and it doesn't get
• 1:47:09
  cut, people realize how important it is.
• 1:47:11
  I mean, we've just discussed how
• 1:47:13
  integrated knowing the data that comes in
• 1:47:16
  for predicting weather is good for
• 1:47:18
  aviation, is good for fishing fleets, is
• 1:47:19
  good for cruise liners. It's good for
• 1:47:22
  just, you know, for farmers and anybody
• 1:47:24
  who requires any type of natural input to
• 1:47:28
  their products or services.
• 1:47:29
  It is going to need this data. Like I
• 1:47:32
  really don't see the need to cut any of
• 1:47:34
  these programs because of how vital it
• 1:47:37
  is. Now, one thing you've mentioned a
• 1:47:40
  number of times in this episode is the
• 1:47:43
  fact that, you know, these
• 1:47:44
  these models are so important.
• 1:47:46
  Modeling is such an important part of the
• 1:47:50
  weather service. Now, I have been hearing
• 1:47:53
  a lot of people talk about how fraudulent
• 1:47:56
  these models are, how inaccurate these
• 1:47:59
  models are. That seems to be the driving
• 1:48:02
  narrative when it comes to whether it be
• 1:48:04
  climate models or weather
• 1:48:06
  models or anything like that.
• 1:48:09
  Obviously, they're not fraudulent. I'm
• 1:48:10
  joking here, but this is what we hear and
• 1:48:12
  you've been hearing it too. Everybody's
• 1:48:14
  been talking about from government
• 1:48:15
  officials like new government officials
• 1:48:17
  all the way down to just the the person,
• 1:48:19
  the common person on the street, just
• 1:48:21
  being like these are frauds.
• 1:48:21
  Every time I mentioned, you know, climate
• 1:48:24
  change, I did an episode about the cuts
• 1:48:26
  to no one talking about the weather
• 1:48:28
  service and everybody's saying no one
• 1:48:30
  sold out and took money.
• 1:48:32
  And NASA did the same thing to continue
• 1:48:35
  the climate fraudulent sort of things and
• 1:48:38
  all these fraudulent models. None of them
• 1:48:40
  are true. Of course, none of them provide
• 1:48:41
  evidence about how untrue they were or
• 1:48:43
  how fraudulent they were.
• 1:48:45
  But this seems to be the narrative of a
• 1:48:48
  lot of people. And I don't think it's
• 1:48:51
  shared just by one party or another. I
• 1:48:54
  think there's a lot of people who think
• 1:48:55
  that these models are fraudulent.
• 1:48:58
  Dave, you've you've discussed this a
• 1:49:00
  number of times, not only on this program
• 1:49:02
  when you came on with the wonderful Alan
• 1:49:05
  Pragler about your book, Megalodons,
• 1:49:07
  Mermaids and Climate Change.
• 1:49:08
  There is a constant battle of
• 1:49:10
  misinformation when it comes to science,
• 1:49:12
  especially when it comes to weather,
• 1:49:13
  climate change, prediction models and
• 1:49:14
  everything like that. How do you combat
• 1:49:17
  this? How do you say, no, these aren't
• 1:49:20
  fraudulent? How do you
• 1:49:20
  prove that they're not?
• 1:49:22
  Well, I think, you know, that's it's
• 1:49:24
  always an important conversation to have
• 1:49:26
  because, you know, weather and observing
• 1:49:28
  and, you know, predicting is very
• 1:49:32
  complex, right? It's a it's a high order
• 1:49:35
  magnitude of complexity and
• 1:49:38
  it's it's not for everybody.
• 1:49:40
  Um, and so what people want to hear is
• 1:49:44
  it's going to be sunny in 74 four days
• 1:49:46
  out, you know, and the predictability of
• 1:49:50
  the atmosphere is is not that accurate
• 1:49:53
  when you have to go out 5, 10, 15 days
• 1:49:57
  and try to give somebody a what we call a
• 1:50:00
  deterministic forecast.
• 1:50:02
  And that's what they're saying. They're
• 1:50:03
  determining it's going to be 74 with a
• 1:50:05
  West wind at 3 miles an hour. And then
• 1:50:08
  they remember that. So, so what happens
• 1:50:11
  with a lot of folks who, you know, and
• 1:50:14
  I've had it too, you know, people say,
• 1:50:15
  what do you do? I'm a meteorologist. I
• 1:50:18
  wish I could be right 50% of the time and
• 1:50:21
  still get paid. Right?
• 1:50:26
  So, you know, I think it's always pops
• 1:50:27
  up. Yeah. But by the way, major leaguers
• 1:50:30
  make it into the Hall of Fame with less
• 1:50:32
  than a 300 batting average. Hey, yeah,
• 1:50:34
  that's a good point. I like to make
• 1:50:36
  millions and meteorologists don't make
• 1:50:38
  millions. That's for sure. Exactly. But
• 1:50:41
  when you when you talk about forecasting
• 1:50:43
  and people not believing. Um, if if if
• 1:50:47
  they're serious about wanting to
• 1:50:50
  understand how models work and, you know,
• 1:50:54
  I have the saying that every model works.
• 1:50:55
  And that every model is wrong. Right?
• 1:50:58
  It's just how much? Yeah. How wrong are
• 1:51:02
  they and models the way I grew up and
• 1:51:05
  studied and I used to work at a as a when
• 1:51:09
  I was a student at University of
• 1:51:11
  Maryland, I would work. I had a job at
• 1:51:13
  NASA Goddard Space Flight Center. I
• 1:51:15
  worked in the global modeling and
• 1:51:16
  simulation division where we I assisted
• 1:51:20
  scientists and satellite retrievals to
• 1:51:22
  see if satellite data
• 1:51:23
  could improve models.
• 1:51:24
  Right. And and so I think that some
• 1:51:31
  people just don't understand that we need
• 1:51:34
  those observing systems to go into a
• 1:51:37
  model and what models give us is
• 1:51:40
  guidance. They give the meteorologists
• 1:51:44
  guidance. Yeah. Right. It's like, okay,
• 1:51:47
  it looks like this low is going to move
• 1:51:50
  along the south and come up and intensify
• 1:51:52
  and we might have, you know, a lot of people are going to be
• 1:51:54
  like, you know, six to ten inches of snow
• 1:51:56
  because of that and the winds are going
• 1:51:58
  to get really strong. The model and there
• 1:52:02
  are a lot of models out there. So many so
• 1:52:06
  now that they pull them together in
• 1:52:10
  what's called an ensemble. Right. An
• 1:52:13
  ensemble is a whole bunch of things
• 1:52:14
  coming together, right? Just like a
• 1:52:16
  outfit, you know, you've got a whole
• 1:52:18
  ensemble together. In modeling, an
• 1:52:21
  ensemble might have
• 1:52:22
  50 members which means
• 1:52:24
  different models, all with different
• 1:52:26
  physics, tweaks, things
• 1:52:28
  like that. Yeah. And then they average
• 1:52:31
  that out and bring out a
• 1:52:32
  new forecast. And so that is, it's
• 1:52:38
  hopeful that an ensemble
• 1:52:39
  forecast is more accurate than any
• 1:52:43
  individual model. And so
• 1:52:47
  when it comes down to people, I think
• 1:52:49
  even over the years,
• 1:52:51
  thinking that weather
• 1:52:52
  forecasting has not gotten any
• 1:52:53
  more accurate, it has gotten
• 1:52:55
  worse. It's because let's say,
• 1:52:57
  for example, let's go back to that
• 1:52:59
  question. When the lady
• 1:53:01
  called me when I was at NBC in Washington
• 1:53:04
  in the 90s and said,
• 1:53:06
  you guys are so accurate. What weather
• 1:53:08
  she said, do you have
• 1:53:09
  scheduled? Yeah, six months from now.
• 1:53:13
  Well, first, we don't
• 1:53:14
  schedule. No, we can look at
• 1:53:16
  climatology and see what it
• 1:53:18
  might be. But what they do is if like,
• 1:53:23
  for example, you're on
• 1:53:25
  the way to Hawaii and you look at the
• 1:53:29
  forecast last week. Yes,
• 1:53:31
  you say, oh, it's going to be look at
• 1:53:33
  that beautiful wind at
• 1:53:34
  two miles an hour coming off. It's going
• 1:53:36
  to be 81 degrees. That
• 1:53:38
  is awesome. And you don't look at the
• 1:53:41
  forecast again until
• 1:53:43
  you're coming in for a landing. Right?
• 1:53:46
  Right. So that might be
• 1:53:47
  seven, 10 days. And so what
• 1:53:50
  people tend to do, which you
• 1:53:52
  know, it's not a crime. It's
• 1:53:54
  right, but natural behavior is
• 1:53:56
  you anchor to that forecast. It's called
• 1:54:00
  anchoring. Yeah. And
• 1:54:02
  if you have a special event, a wedding
• 1:54:04
  next Saturday, oh, you
• 1:54:06
  know what to plan. Well, I got a plan
• 1:54:07
  what to wear. What's Oh,
• 1:54:09
  it looks like 76 and sunny
• 1:54:10
  seven days ahead of time. And
• 1:54:13
  you don't look at the forecast again
• 1:54:15
  because you think that's
• 1:54:17
  what it's going to be. Then you get there
• 1:54:19
  and it's an easterly
• 1:54:20
  wind. It's 52 degrees. There's drizzle.
• 1:54:23
  You're like, what the
• 1:54:24
  heck? Those forecasters are
• 1:54:26
  never right. My app stinks, you
• 1:54:29
  know, and so you have to keep updated on
• 1:54:33
  the weather when the
• 1:54:35
  forecast, um, you know, gets closer,
• 1:54:38
  right? When that special day gets
• 1:54:41
  closer, you need to monitor the forecast.
• 1:54:44
  Why do you have to do
• 1:54:45
  that? Because observations come in that
• 1:54:50
  detect certain things
• 1:54:52
  happening, maybe 5,000 miles
• 1:54:55
  away. Yeah. That might kick off
• 1:54:57
  the model and say, Oh, wait a minute,
• 1:55:00
  something's up. This
• 1:55:01
  might develop into a low pressure system
• 1:55:03
  down, down wind and develop
• 1:55:07
  into something that could be significant
• 1:55:09
  or impact the forecast.
• 1:55:12
  So just one little, little mentioned,
• 1:55:15
  there was a professor called named
• 1:55:18
  Edward Lorentz, who was at MIT and he
• 1:55:23
  wrote a paper called, um, could
• 1:55:27
  the flap of a butterfly's wings in Brazil
• 1:55:30
  cause a tornado in Texas?
• 1:55:35
  And that paper was all about what's
• 1:55:37
  called chaos theory. Yeah. Can one little
• 1:55:40
  disturbance grow and grow and kick off
• 1:55:45
  other things that then create
• 1:55:48
  something else that leads to a severe
• 1:55:50
  weather outbreak? And it's, you know,
• 1:55:53
  the basis of modeling really, you know,
• 1:55:56
  how can you predict chaos? That's
• 1:55:59
  why the forecast is very tough. You know,
• 1:56:02
  we're pretty good out to seven
• 1:56:03
  days, but these apps, they're all
• 1:56:06
  automated. They're using one model.
• 1:56:09
  Uh, they're giving numbers to make it
• 1:56:11
  look like they're more accurate. Uh,
• 1:56:13
  I have a daughter that tells me, Dad, why
• 1:56:17
  is it snowing? My app said it was
• 1:56:19
  going to be sunny, you know, and I'd look
• 1:56:21
  at it, look at the radar and say, oh,
• 1:56:23
  it's just a little flurry moving through.
• 1:56:24
  But you know, I guess your app didn't say
• 1:56:27
  that, you know, exactly. We, we depend on
• 1:56:30
  it so much. Right. Exactly. Right.
• 1:56:32
  You're yeah, you're right. It comes to
• 1:56:33
  you because it's just so darn convenient
• 1:56:36
  to be able to look at
• 1:56:37
  something and get an answer.
• 1:56:39
  But it doesn't mean that answer is
• 1:56:42
  correct. And so, uh, when you asked about
• 1:56:45
  how do you battle
• 1:56:46
  that misinformation, um,
• 1:56:49
  you just do it by engaging people where
• 1:56:52
  they are. We, you know, Ellen Prager and
• 1:56:55
  I, we love engaging people when they want
• 1:57:00
  to complain about the weather or the
• 1:57:03
  ocean atmosphere or whatever.
• 1:57:06
  And we say, well, what, you know, what's
• 1:57:08
  your beef? Yeah. You know, what's going
• 1:57:10
  on? And they say, well, it's just, you
• 1:57:12
  know, all this talk about climate change.
• 1:57:15
  It's just not, you know, it doesn't mean
• 1:57:17
  anything. It's all made up. So scientists
• 1:57:19
  can make a lot of money and we don't make
• 1:57:22
  a lot of money. What?
• 1:57:24
  I got into is to make a lot of money. How
• 1:57:26
  come I'm not making a lot of money here,
• 1:57:28
  Dave? What's going on?
• 1:57:31
  I know. I know. Maybe, maybe if the
• 1:57:33
  currency was, you know, slaps on the back
• 1:57:36
  or something, you know, but so, you know,
• 1:57:41
  what, what, what we do is we
• 1:57:43
  say, well, what, what have you
• 1:57:45
  noticed over the years? What's, you know,
• 1:57:47
  what do you do? Oh, I'm a farmer. I'm a
• 1:57:49
  lobsterman. I'm a fisherman. Well, have
• 1:57:52
  you noticed anything? It's like, yeah,
• 1:57:53
  there's no, the fish are all South, you
• 1:57:55
  know, or the fish are going further
• 1:57:57
  North, you know, and we have to go
• 1:57:59
  further and use more fuel to get them.
• 1:58:01
  Yeah. Oh, well, why do you think they
• 1:58:04
  might be going North? Are they being
• 1:58:06
  chased by mean fish, you know, or, um,
• 1:58:10
  maybe they're
• 1:58:10
  sensitive to temperature. Yeah.
• 1:58:13
  You know, and that temperature is
• 1:58:16
  warming. Yeah. And there's a reason that
• 1:58:19
  temperature is warming. And so you, you
• 1:58:20
  know, you can take them down a path that
• 1:58:23
  they're, they're cool with. We're not
• 1:58:26
  showing any differential equations. We're
• 1:58:28
  not showing any, you know, integrals.
• 1:58:31
  We're just saying, look, animals aren't
• 1:58:35
  dumb. They're going to go where the food
• 1:58:37
  is. And if the temperature changes and
• 1:58:40
  their food goes North,
• 1:58:42
  they're going to go north.
• 1:58:42
  Right. And it's all because the
• 1:58:44
  temperature is rising. And it's a very
• 1:58:47
  simple concept. If you break it debt,
• 1:58:52
  excuse me, break it down to that. Yeah.
• 1:58:53
  That, um, you know, we have, we have not
• 1:58:58
  had, um, any dismissive people that we've
• 1:59:04
  engaged in conversations with. Um, when
• 1:59:09
  we just talk to them about what they do
• 1:59:11
  and, and, and what have they done?
• 1:59:12
  What are they, what are they saying? And,
• 1:59:14
  you know, just, I mean, one, one last
• 1:59:17
  one, we were in Florida last week, we
• 1:59:19
  gave some talks about our book. Um, one
• 1:59:23
  was at the Harbor Branch oceanographic
• 1:59:25
  Institute. Uh, and the other one was the
• 1:59:28
  Florida oceanographic society. Uh, they
• 1:59:31
  both said they were, they were both
• 1:59:33
  record attendance, which was awesome.
• 1:59:35
  That was great hearing.
• 1:59:36
  Um, but we were sitting, you know, uh, at
• 1:59:39
  the, uh, hotel and there was a guy
• 1:59:42
  sitting there and you know, we were just
• 1:59:43
  talking to him and he was, he ended up
• 1:59:45
  these windsurfs and he windsurfs on those
• 1:59:48
  really cool new windsurf boards that
• 1:59:50
  raise up, you know, and they're like,
• 1:59:52
  look like they're hovering above the
• 1:59:54
  ground, but it's a little like, you know,
• 1:59:56
  wing underneath. Yeah. So it's very cool.
• 1:59:59
  And so, um, you know, he was just talking
• 2:00:02
  to us and he was telling us about all
• 2:00:03
  these things. What he's, he's really
• 2:00:05
  there to build a house and he's building a house.
• 2:00:06
  And he's like, oh, I got all these
• 2:00:12
  permits, you know, I'm waiting on and all
• 2:00:14
  this stuff. And you're like, Oh, are you
• 2:00:15
  building it to withstand any storms or
• 2:00:18
  anything like that? And he's like, yeah,
• 2:00:20
  you know, I'm just, you know, we're
• 2:00:23
  putting in the standard stuff and you
• 2:00:25
  know, I'm going to sell it. And you know,
• 2:00:27
  I guess make a lot of money and all that
• 2:00:28
  stuff. And we didn't, we didn't tell him
• 2:00:30
  a thing about, you know, who we were, what we were doing and all that stuff. We just had a great time.
• 2:00:31
  We understood that he wasn't really, um,
• 2:00:41
  very knowledgeable about weather or the
• 2:00:44
  oceans. And then he finally said, well,
• 2:00:47
  what do you guys do? And, um, so we said,
• 2:00:50
  well, you know, Ellen's a marine
• 2:00:52
  scientist and I'm a meteorologist. Really? You know, and, and so we ended up talking and we said, yeah, we're here to give talks about it. But we wrote a book.
• 2:00:52
  On atmosphere and oceans and climate
• 2:01:05
  change and impacts and stuff like that.
• 2:01:08
  And, and, you know, after about 45
• 2:01:10
  minutes, he's like, I got a lot of
• 2:01:14
  learning to do. You know, he said, I have
• 2:01:17
  a lot of learning to do. And he said, I
• 2:01:18
  got to tell you guys something. He said, you have all my respect. And I said, I got to tell you guys something.
• 2:01:19
  And he said, you have all my respect. He
• 2:01:22
  said, you heard me go on and on and on
• 2:01:25
  and on. And you didn't interrupt me once
• 2:01:27
  and tell me, no, you're wrong because of
• 2:01:28
  this or you're wrong because of that. You
• 2:01:30
  just engaged in a conversation. And so he
• 2:01:34
  took a picture of the book. And he said,
• 2:01:36
  I'm going to get it. I'm going to read
• 2:01:37
  it. And, you know, I hope to learn stuff
• 2:01:41
  from it. And it's a great marketing plan.
• 2:01:44
  Great. It doesn't have to be an argument.
• 2:01:46
  You know, I think you're right. And I
• 2:01:48
  think that's the the fundamental problem
• 2:01:51
  that happens when we start having these
• 2:01:53
  so-called discussions online. Yeah,
• 2:01:57
  there, you know, there's this this
• 2:01:59
  stereotype that scientists are elitists
• 2:02:02
  that scientists think they know more than
• 2:02:05
  everybody else. And they're just going to
• 2:02:06
  tell you how it is. And to be honest,
• 2:02:08
  there is some merit in that in the past,
• 2:02:11
  where, you know, scientists would come
• 2:02:12
  into a community or they would come into
• 2:02:14
  a talk and they would say, this is the way to do it.
• 2:02:15
  And I would say, this is how it is. If
• 2:02:17
  somebody came up and said, well, I don't
• 2:02:19
  believe it. And then it was like an
• 2:02:21
  argument back and forth. And I really
• 2:02:23
  like your approach, where you just engage
• 2:02:24
  in conversations with people. They are
• 2:02:26
  people. And they want to enjoy a good
• 2:02:30
  life. We all want to have a good life. We
• 2:02:32
  all want to be safe. We want to survive.
• 2:02:34
  We want to live as long as possible. We
• 2:02:36
  don't want to see our houses flooded. We
• 2:02:38
  don't want to see our houses burnt down.
• 2:02:39
  Nobody wants to see that. And I feel
• 2:02:42
  like, you know, all this stuff about
• 2:02:45
  climate and the climate change and, you
• 2:02:48
  know, sea level rise and so forth has
• 2:02:50
  become so politicized and the political
• 2:02:53
  divide has increased so much that it's
• 2:02:56
  become argumentative. And to agree, I've
• 2:02:59
  been doing that too. At times you get
• 2:03:01
  emotional when you hear a response. You
• 2:03:03
  take it personally. You're like, no, like
• 2:03:05
  dummy. This is the way it is. And if you
• 2:03:07
  respond that way, you're going to get the
• 2:03:09
  same kind of response back in terms of
• 2:03:12
  it's going to be aggressive. But if you
• 2:03:13
  just kind of have a
• 2:03:14
  conversation of like, what do you do? You
• 2:03:15
  do for a living? Oh, I want to know how
• 2:03:18
  do you notice like anything changed? Just
• 2:03:20
  like this story with, you know, like the
• 2:03:22
  fishermen like, OK, so have you noticed
• 2:03:24
  your lobster being harder to catch or
• 2:03:27
  like, what have you found out there?
• 2:03:28
  Because obviously the fishing community
• 2:03:30
  has been able to tell more than we've
• 2:03:33
  been able to do as scientists because
• 2:03:34
  they're out there all the time. The
• 2:03:35
  indigenous communities have been able to
• 2:03:37
  tell us more about what's been happening
• 2:03:39
  to their land over hundreds, if not
• 2:03:41
  thousands of years because they've been
• 2:03:44
  seeing it. And they are so in tune.
• 2:03:45
  With the environment. Yeah, we need as a
• 2:03:49
  scientific society, as you know,
• 2:03:51
  scientists, scientists to listen more and
• 2:03:53
  to engage in conversations instead of
• 2:03:56
  just dictating what it needs to be. And I
• 2:03:59
  really like that approach. And then you
• 2:04:01
  offer a resource just like your book, the
• 2:04:03
  megalodons, mermaids and climate change.
• 2:04:04
  Like, offer that resource after and be
• 2:04:06
  like, hey, if you if you want to know
• 2:04:07
  more, yeah, there you go. It's right
• 2:04:09
  there. And if you want to know more, you
• 2:04:13
  can you can find out more. There's always
• 2:04:14
  resources available.
• 2:04:15
  And scientists are always willing to talk
• 2:04:17
  as long as it's in a in a in a
• 2:04:19
  comfortable situation, a calm situation
• 2:04:21
  and nothing aggressive. Right.
• 2:04:23
  Well, and you know, it gets back to our
• 2:04:25
  conversation, too, at the beginning of
• 2:04:26
  this, where, you know, scientists are not
• 2:04:28
  the best communicators. Right. And I
• 2:04:31
  remember back in 1995, 96 timeframe when
• 2:04:35
  Dan Golden was the
• 2:04:37
  administrator of NASA at the time.
• 2:04:40
  And he was very big on scientists being
• 2:04:43
  able to communicate the work that they're
• 2:04:45
  doing so much. So he went to Goddard
• 2:04:48
  Space Flight Center and gave a talk. And
• 2:04:50
  he said to the scientists, if you can't
• 2:04:53
  describe to me in three minutes what
• 2:04:56
  you're doing and why it's important,
• 2:04:59
  you're not going to get funded next year.
• 2:05:01
  Right. And so, you know, that really
• 2:05:03
  whoa, you know, we better learn how to
• 2:05:07
  describe what we're doing.
• 2:05:08
  A lot of people know it as the elevator
• 2:05:10
  speech. Yes. Yes. You've got to give that
• 2:05:13
  elevator speech. But it is it's very
• 2:05:16
  important. But what what I think is
• 2:05:18
  really even more critical for scientists
• 2:05:21
  is to listen. Mm hmm.
• 2:05:24
  You know, scientists tend to want to get
• 2:05:29
  all of their knowledge and their PhD
• 2:05:33
  thesis out, you know, over a restaurant
• 2:05:35
  table and or over a drink. And they're
• 2:05:40
  not listening to what the person is
• 2:05:42
  saying. And they're not gauging, you
• 2:05:45
  know, the level of interest and or
• 2:05:47
  education of the person that they're talking to.
• 2:05:49
  And you should just really have a
• 2:05:52
  conversation like you're not that
• 2:05:54
  scientist, but just bring in pieces of
• 2:05:56
  that science when it's relevant, you
• 2:05:58
  know, when it means something. And and
• 2:06:02
  that seems to be, you know, make a big
• 2:06:04
  impact. And I think it gets, you know,
• 2:06:08
  the conversation gets political.
• 2:06:10
  I think many times when you start talking
• 2:06:14
  about the causes of climate change,
• 2:06:17
  right? What's causing it. And that in
• 2:06:20
  itself is still a simple physics problem.
• 2:06:24
  You know, it's you know, you could have a
• 2:06:26
  closed container and you
• 2:06:28
  could put more CO2 into it.
• 2:06:30
  And it's going to heat up when the sun
• 2:06:34
  hits it. Right. And your little
• 2:06:37
  experiment is going to happen. It's a
• 2:06:39
  physics problem. Yeah. So when you do it
• 2:06:41
  on the globe, you know, more CO2 in the
• 2:06:43
  atmosphere is causing the warmth. Yeah.
• 2:06:46
  So that that's that's not that's not a
• 2:06:50
  debatable piece of science itself. But
• 2:06:55
  then when you get all how do you stop it?
• 2:06:57
  How do you, you know, ease it? Can we ease out of it? You know, all the time.
• 2:07:00
  That kind of stuff is when, you know,
• 2:07:02
  things start to diverge. Yeah. But we're
• 2:07:05
  really all about talking about impacts of
• 2:07:10
  a changing climate and how is climate
• 2:07:14
  change? Either energizing systems to
• 2:07:19
  become more extreme or stacking the dice.
• 2:07:23
  So those events happen more frequently.
• 2:07:26
  Mm hmm. Mm hmm. You know, flooding in
• 2:07:30
  Western North Carolina from Hurricane
• 2:07:33
  Helene. Never seen anything like that
• 2:07:36
  before in their history of recorded
• 2:07:39
  history. Yeah. And they weren't prepared
• 2:07:42
  for the damage either. Right. They
• 2:07:43
  weren't prepared for it. And many people
• 2:07:45
  said, Oh, you know, we're 26 feet above
• 2:07:47
  the water. Look, it's just a stream down
• 2:07:49
  there. Why do we have to worry about
• 2:07:51
  that? And all of a sudden, the water
• 2:07:52
  comes up 30 feet. Yeah.
• 2:07:55
  So there's there's a lot of understanding
• 2:07:58
  that needs to happen between people who
• 2:08:02
  just normally have sunny weather and what
• 2:08:07
  extremes could happen. Yeah. And whether
• 2:08:11
  they want to accept that level of risk or
• 2:08:14
  not. And so really with extreme weather
• 2:08:18
  becoming more frequent.
• 2:08:21
  People just have to look at where they
• 2:08:22
  live, their neighborhoods and things like
• 2:08:24
  that and say, OK, well, let's go through
• 2:08:26
  some scenarios of what what could happen.
• 2:08:29
  Yeah. I mean, you know, we live in an
• 2:08:33
  area where it's really never flooded
• 2:08:36
  major flood before. But we bought flood
• 2:08:39
  insurance. Yeah. You know, and it's it's
• 2:08:42
  cheap and it's going to it's going to
• 2:08:44
  cover a lot of
• 2:08:45
  property damage if it happens.
• 2:08:48
  And I like that idea is just, you know,
• 2:08:50
  you can move into an area. How much risk
• 2:08:52
  are you willing to take? You know, are
• 2:08:54
  you willing to move into an area that has
• 2:08:57
  a potential to be, you know, exposed to
• 2:09:01
  wildfires or to flooding, you know, and
• 2:09:04
  may or may not have
• 2:09:05
  insurance available for it.
• 2:09:07
  Or it might be extremely expensive. Are
• 2:09:09
  you willing to risk that? If you are,
• 2:09:11
  that's your prerogative. You're allowed
• 2:09:13
  to do that. So you're right.
• 2:09:14
  You can go ahead and do that.
• 2:09:15
  Unfortunately, that there's some areas
• 2:09:18
  have higher risk than others. And if you
• 2:09:20
  want to believe the models, you want to
• 2:09:22
  believe the data, that's up to you. And
• 2:09:24
  then you can avoid that. Nobody wants to
• 2:09:26
  see anybody get get impacted by this, by
• 2:09:29
  this stuff. But it's also, you know,
• 2:09:32
  there's there are there's data out there
• 2:09:34
  to kind of show where there's less risk.
• 2:09:39
  And and that will be through insurance
• 2:09:41
  agencies. That will be like in prices.
• 2:09:43
  That'll be through NOAA and and local
• 2:09:47
  government actions of looking at
• 2:09:49
  floodplains and so forth and to look at
• 2:09:51
  what risk you're in. And it's just a
• 2:09:54
  matter of keeping it available. Now, one
• 2:09:55
  thing that you do, Dave, is you go on
• 2:09:58
  YouTube every so often on a regular on a fairly regular basis.
• 2:10:01
  And also during times where there are
• 2:10:05
  hurricanes or major storms or major wind
• 2:10:08
  patterns or major meteorological events.
• 2:10:11
  One of those was during the fall. I saw
• 2:10:13
  you a number of times, not only on
• 2:10:15
  LinkedIn, but on YouTube. Like I think it
• 2:10:17
  was just being put up everywhere where
• 2:10:20
  you were discussing how these hurricanes
• 2:10:22
  one after one were coming one after the
• 2:10:24
  other coming in and what kind of impact
• 2:10:26
  it was going to do or what is the
• 2:10:28
  predicted where it's going to go, why it
• 2:10:29
  shifted and so forth.
• 2:10:30
  Providing that data to people who need it
• 2:10:33
  or who have it. Yeah. What was the
• 2:10:36
  response like to people who watched it or
• 2:10:39
  commented that kind of stuff? Yeah, no,
• 2:10:42
  Andrew. That's a thanks for that question
• 2:10:44
  because we were we were probably just as
• 2:10:47
  surprised as as anybody else to see what
• 2:10:50
  the reactions were. And and we have been
• 2:10:52
  doing YouTube updates, but more on the
• 2:10:55
  private channel side. Right. Right. So
• 2:10:58
  just sharing the link and only on the website.
• 2:10:59
  Yeah. And that was for the all hazards
• 2:11:04
  consortium to help for the movement of
• 2:11:07
  fleet utility vehicles across the country
• 2:11:09
  to restore power and and things like
• 2:11:11
  that. But then before hurricane Helene
• 2:11:15
  formed, we opened up those briefings to
• 2:11:19
  the public. And we were I mean, we're not
• 2:11:22
  out to get views. We're out to provide a
• 2:11:25
  service to the people
• 2:11:27
  who wanted those videos.
• 2:11:28
  And we had like five hundred and twenty
• 2:11:31
  subscribers at that point in time. But
• 2:11:35
  when Helene hit and we opened it up to
• 2:11:37
  the public, people started seeing those
• 2:11:40
  videos. And I don't know if it was the
• 2:11:42
  algorithm or what. But YouTube was
• 2:11:45
  recommending the video to people who were
• 2:11:49
  searching for weather or the hurricane or
• 2:11:52
  things like that. And we went from five
• 2:11:55
  hundred and twenty subscribers to seventeen thousand subscribers.
• 2:11:57
  Yeah. That's that's unheard of. And it
• 2:12:04
  was just incredible. I mean, I'm not one
• 2:12:07
  that really dives into YouTube analytics
• 2:12:10
  and says, oh, yeah, OK. Well, you know, I
• 2:12:13
  got seven hundred views or whatever. I
• 2:12:15
  mean, my daughter called me and said,
• 2:12:16
  dad, you know, I'm sitting here watching
• 2:12:20
  your subscriptions go from ten thousand
• 2:12:22
  to twelve to fifteen to sixteen. And, you know, we had millions of subscribers. And I think that's what we had to do.
• 2:12:24
  Because I think how
• 2:12:29
  on those hurricane updates. But what was
• 2:12:33
  most exciting to us about was that, and I
• 2:12:38
  still don't understand the ratio of likes
• 2:12:40
  or dislikes or whatever, but the
• 2:12:43
  likes are always between 97 and 100
• 2:12:45
  percent. And the comments came in by the
• 2:12:49
  thousands. People saying thank you for
• 2:12:54
  helping us understand what's happening
• 2:12:59
  with the storm, why we need these
• 2:13:01
  observing systems like the buoys in the
• 2:13:04
  ocean, what the satellites
• 2:13:06
  do. People are commenting on the
• 2:13:09
  terminator line of the of the of the sun
• 2:13:12
  setting and you can see the shadow
• 2:13:13
  coming across the world. I've never seen
• 2:13:15
  that before. We could see the
• 2:13:18
  sunset signs lie like pretty much
• 2:13:20
  recorded in for you and you never know
• 2:13:23
  what to expect of what
• 2:13:24
  you're going to cover. Yes, yes. And and
• 2:13:27
  so what we really did is we
• 2:13:30
  took that and we had so many
• 2:13:32
  thank yous from people watching in Japan
• 2:13:34
  or Australia and saying
• 2:13:36
  we have family on the West
• 2:13:37
  Coast of Florida. We're telling them to
• 2:13:39
  watch this, you know, and
• 2:13:41
  it was just was like, okay,
• 2:13:44
  that was a wake up moment for us thinking
• 2:13:47
  just okay, we're just
• 2:13:48
  doing these videos to help
• 2:13:50
  all hazards and then putting it out there
• 2:13:51
  for other people who
• 2:13:52
  might be interested. Yeah.
• 2:13:54
  But but now I mean, you know, you get
• 2:13:56
  comments back like this is the best
• 2:13:57
  weather channel I've
• 2:13:59
  ever seen. Thank you. And we're not doing
• 2:14:02
  it every day. No, but
• 2:14:04
  we're doing it when you know,
• 2:14:05
  things happen. And, you know, we're,
• 2:14:07
  we're, you know, hoping to ramp it up to
• 2:14:10
  do it more frequently,
• 2:14:11
  but we do want to make sure that the
• 2:14:15
  video is educational at the same time.
• 2:14:19
  It's useful for people to
• 2:14:21
  improve their situational awareness or,
• 2:14:23
  you know, to learn why, why am I even
• 2:14:27
  able to do these videos,
• 2:14:29
  right? We we have video recording
• 2:14:32
  technology. We're using some
• 2:14:34
  right now to do the podcast.
• 2:14:36
  But how in the world can I show you a
• 2:14:39
  global satellite image
• 2:14:41
  that's updating every 10 minutes?
• 2:14:43
  You know, how can I zoom in with geo
• 2:14:46
  collaborate and show you
• 2:14:47
  what the wave heights are,
• 2:14:48
  you know, 50 miles off of Tampa? Yeah,
• 2:14:52
  you know, how is that even
• 2:14:54
  possible? And and this is why
• 2:14:56
  I think it's so important to explain.
• 2:14:59
  Yeah, the technology
• 2:15:00
  that's able to make that happen.
• 2:15:02
  And to make sure people don't understand
• 2:15:04
  that, okay, you might have
• 2:15:06
  just watched this sci fi video
• 2:15:08
  on Netflix, or this movie about spies and
• 2:15:12
  all the data that they seem
• 2:15:14
  to have and be able to process,
• 2:15:15
  which you can't do that fast right today.
• 2:15:18
  It's all Hollywood. But we
• 2:15:21
  can bring this data together
• 2:15:23
  for you for a very little bit of
• 2:15:25
  investment from the American taxpayer,
• 2:15:28
  and help you keep informed
• 2:15:32
  of what's going on not only around the
• 2:15:33
  country, but around the world. And so,
• 2:15:35
  you know, I think with with all the talk
• 2:15:39
  of these cuts and, you know, major
• 2:15:41
  realignment of the US government.
• 2:15:45
  As long as you do that, understanding
• 2:15:49
  what you're cutting, and not just on
• 2:15:53
  cutting people for the
• 2:15:55
  sake of cutting people,
• 2:15:57
  that will go a much further distance with
• 2:16:02
  the American people. And also with those
• 2:16:05
  people who are benefiting from these
• 2:16:08
  technologies. Because, you know, again,
• 2:16:12
  if if if we cut out a weather data and
• 2:16:15
  the data flow, or you just say no is
• 2:16:18
  going to collect the data and then give
• 2:16:20
  it to the private sector.
• 2:16:21
  There are different jobs and roles that
• 2:16:25
  the government has versus what a private
• 2:16:28
  sector company has. The private sector
• 2:16:32
  has to make money. So they're going to
• 2:16:35
  charge for things. And the government
• 2:16:37
  agency is a public good. It's a service.
• 2:16:42
  So, you know, a lot of people say, you
• 2:16:44
  know, give it over, give it out, give it
• 2:16:46
  out, you know, do all that stuff. But if
• 2:16:48
  they just took the time to understand
• 2:16:51
  the the significance and the billions of
• 2:16:53
  dollars that have gone into the satellite
• 2:16:55
  platforms that are operating, takes
• 2:16:57
  people sitting at those controls to make
• 2:17:00
  sure if something happens, they put it
• 2:17:02
  into safe mode, or they keep it
• 2:17:04
  operating. Yeah. So it doesn't fall out
• 2:17:06
  of the sky or do reentry.
• 2:17:10
  And so I think it's important. Yeah. And that's important. And I think the biggest concern that's going to be the data is
• 2:17:10
  that all of this data is has been decades
• 2:17:13
  and decades of the weather enterprise
• 2:17:15
  working together, listening to each
• 2:17:18
  other, and really putting forth great
• 2:17:21
  products that both the private sector can
• 2:17:23
  make money on. And the government can
• 2:17:26
  stand behind. Yeah. And and you know, the
• 2:17:29
  biggest concern I have is if the forecast
• 2:17:31
  everything goes over to
• 2:17:32
  the private sector. Yeah.
• 2:17:40
  All of these issues are going to be
• 2:17:40
  covered in water taxi, going across the
• 2:17:42
  harbor in Honolulu and a thunderstorm
• 2:17:45
  comes and a gust front tips over that
• 2:17:48
  water taxi and kills people. To live are
• 2:17:52
  you gonna sue me for giving you a
• 2:17:56
  forecast that didn't
• 2:17:57
  include that thunderstorm? Yeah.
• 2:18:00
  I think if if that ultimately is what
• 2:18:02
  happens, where the inner weather
• 2:18:04
  enterprise goes because of these changes,
• 2:18:07
  the public, the the private sector doesn't.
• 2:18:10
  organizations will quickly go out of
• 2:18:12
  business because they will
• 2:18:14
  not be able to take on that
• 2:18:16
  liability and the government has always
• 2:18:18
  been protected. Yes.
• 2:18:21
  Yeah. And a hundred percent.
• 2:18:22
  And obviously they help more people than,
• 2:18:24
  than, than the people
• 2:18:26
  get harmed. This has been a
• 2:18:28
  phenomenal chat, Dave. You know, I knew
• 2:18:30
  this was an episode that you
• 2:18:31
  and I could talk for so long
• 2:18:33
  on because it's, it's such an interesting
• 2:18:35
  topic and it's such an important aspect.
• 2:18:37
  And I really want to thank you for, for
• 2:18:39
  coming on the, on the show
• 2:18:40
  today to start talking about
• 2:18:42
  really understanding why this data is so
• 2:18:45
  important and why we really
• 2:18:47
  can't live without it and what
• 2:18:48
  the consequences could be. Uh, if this
• 2:18:52
  data starts getting cut, obviously I hope
• 2:18:54
  it doesn't, you know,
• 2:18:55
  I love what you're doing with storm
• 2:18:56
  center. Uh, I'm going to put
• 2:18:57
  links, uh, to all the, like,
• 2:18:59
  you know, the website, the YouTube
• 2:19:01
  channel so people can take a look and
• 2:19:03
  really learn. You're
• 2:19:04
  such a good teacher, uh, on, on these, on
• 2:19:07
  these episodes. I know I
• 2:19:07
  learned stuff all the time when
• 2:19:09
  I watch it, I have it on in the
• 2:19:10
  background when I'm working and I'm
• 2:19:11
  watching, uh, it's, it's really
• 2:19:13
  interesting. So, you know, I really
• 2:19:14
  appreciate what you're doing. Um,
• 2:19:16
  obviously you and Ellen with the
• 2:19:17
  book, uh, and, and I'll put the link so
• 2:19:20
  that people can get access to
• 2:19:21
  the book as well to learn more
• 2:19:22
  about the ocean and the weather and
• 2:19:24
  climate and just, you know,
• 2:19:26
  more about our planet, such an
• 2:19:27
  important role and why we need this type
• 2:19:29
  of data. So Dave, thank you so much for
• 2:19:31
  coming on the podcast.
• 2:19:32
  Love to have you back to talk more about
• 2:19:34
  weather and climate and, uh, until next
• 2:19:36
  time. Thank you so much.
• 2:19:37
  Oh, that'd be great. I mean, I look, my
• 2:19:39
  biggest fear is we go back
• 2:19:40
  to 1900 with no satellites
• 2:19:43
  and we have a land falling hurricane that
• 2:19:45
  kills 15 or 20,000 people.
• 2:19:48
  Yeah. We don't want to go back
• 2:19:49
  there and Andrew, I'd love to come on and
• 2:19:51
  talk to you about weather
• 2:19:52
  all the time. Wonderful. Thank
• 2:19:54
  you so much. Okay. Take care. Thank you,
• 2:19:57
  Dave, for joining us on
• 2:19:58
  today's episode of the how to
• 2:19:59
  project the ocean podcast. Like I said
• 2:20:01
  before, folks, you learn so
• 2:20:02
  much when you hear from Dave,
• 2:20:03
  he has such a great approach to talking
• 2:20:07
  about the ocean. It's
• 2:20:08
  magnificent. That's what that's how I'm
• 2:20:10
  going to describe. It's magnificent. He
• 2:20:11
  and his cohort and partner, Ellen
• 2:20:14
  Progger, who have a book
• 2:20:15
  called Megalodon's mermaids and climate
• 2:20:17
  change. The book is out
• 2:20:18
  now answers to your ocean and
• 2:20:20
  atmosphere questions. It's basically
• 2:20:21
  tackling misinformation and
• 2:20:23
  just general questions that
• 2:20:24
  people have on the ocean and the
• 2:20:26
  atmosphere. If you want to
• 2:20:27
  learn more about those things,
• 2:20:28
  you buy this book. I'm going to put a
• 2:20:30
  link to the show notes. I highly
• 2:20:31
  recommend it. It's just a
• 2:20:33
  great book. Non-politicized,
• 2:20:34
  just about the science. You get to have
• 2:20:36
  fun learning about our planet, learning
• 2:20:38
  about its environment,
• 2:20:39
  and learning how we can predict the
• 2:20:41
  weather and the climate as well as ocean
• 2:20:43
  patterns. It's always
• 2:20:45
  fun to be there. And Dave is such an
• 2:20:47
  interesting science communicator. He's so
• 2:20:49
  engaging. He's got such a,
• 2:20:51
  you know, an emotional tie to his voice.
• 2:20:53
  It's always positive and
• 2:20:54
  uplifting. I really enjoy it.
• 2:20:55
  And if you want to check them out on
• 2:20:56
  YouTube, I'll put the links in the show
• 2:20:58
  notes. But if you have any questions or
• 2:20:59
  comments about the episode, you can get a
• 2:21:02
  hold of me by DMING me on Instagram at
• 2:21:05
  how to protect the ocean. That's at how
• 2:21:07
  to protect the ocean. Don't forget to
• 2:21:08
  subscribe to this YouTube channel as well
• 2:21:10
  as hit that notification bell. So you get
• 2:21:12
  information about the ocean, marine
• 2:21:14
  biology, animals, Monday, Wednesdays and
• 2:21:17
  Fridays every week. I can't wait to
• 2:21:19
  provide that for you. But that's it for
• 2:21:21
  today's episode. Really appreciate Dave
• 2:21:23
  coming on the podcast. We'll definitely
• 2:21:24
  have him back to you soon.
• 2:21:25
  Talk more about the weather and more
• 2:21:27
  about data and how important it is. But I
• 2:21:29
  want to thank you for joining us on
• 2:21:31
  today's episode of the how to protect the
• 2:21:32
  ocean podcast. I'm your host, Andrew from
• 2:21:34
  the true North strong and free. Have a
• 2:21:36
  great day. We'll talk to you next time
• 2:21:37
  and happy conservation.