Okay, imagine this. Picture it playing out, maybe
even right now. You've got this powerful hurricane,
just this massive swirling thing, and it's heading
straight for the coast, densely populated coast.
Millions of people. Millions, and they're all
hanging on, you know, bracing themselves, relying
completely on those forecasts. Absolute precision
needed for, well... life -saving decisions, evacuate,
shelter, and then boom, just like that, no warning,
one of our most critical eyes in the sky, this
vital window into the storm's heart. It just
goes dark. Just switched off. Switched off. Welcome,
everyone, to Meteorology Matters. We're your
guides today, and our mission, like always, is
to take this, well, complex, sometimes really
daunting information from all sorts of places
and really distill it down, make it clear, give
you actionable insights. Yeah. The goal is to
help you become genuinely well -informed, not
just the headlines, you know, but the actual
science -shaping things. Exactly. And today,
we're digging into a really sudden, very impactful...
honestly, kind of mysterious decision came down
just this week from the United States Department
of Defense. Unexpected, is putting it mildly.
They've decided to just terminate the processing
and delivery of absolutely vital data, data streaming
from a specific set of weather satellites. We're
talking about the Special Sensor Microwave Imager
Sounder, or everyone calls it SSMIS. Yes, SSMIS,
yep. That's the one. And let's be crystal clear
here. This isn't some, you know, minor technical
tweak only specialists care about. No, no, this
is profound. It's like a a seismic shift in how
we observe these storms. Absolutely. It's a move
that's going to severely impede significantly
degrade hurricane forecasts. And not just for
this year, the twenty twenty five season that's
starting now, but. way beyond future seasons,
too. We're talking direct impacts, tangible tens
of millions of people living along those coastlines,
their safety, their economic well -being. It
relies on accurate predictions. And what's really
baffling, truly perplexing is the lack of any
immediate, clear reason. Why now? Why so abrupt?
It seems to be tangled up with Department of
Defense security concerns. But the really alarming
part, even top officials at the National Hurricane
Center were completely blindsided. No heads up,
nothing. caught totally off guard. Yeah, it's
just wild. It's unprecedented, really. And the
ripples are already spreading through the whole
weather and climate community. So over the next
little while here on Meteorology Matters, our
job is to dive deep, figure out exactly why this
data from SSMIS, why it's so uniquely vital.
Right. What are the concrete impacts going to
be? It's loss on understanding, tracking, preparing
for these massive weather events, hurricanes,
climate patterns. And critically, What's the
path forward? Because the global meteorological
community is scrambling now, almost frantically,
trying to adapt to this sudden hole in the data.
So let's unpack it. Absolutely. Let's peel it
back. Because to really get the gravity here,
you first need to appreciate the, well, the absolutely
essential role microwave satellite data plays
in forecasting hurricanes. It's intricate stuff.
And hurricanes themselves, they present this
fundamental challenge for observation. It's plagued
meteorologists for well forever decades. Yeah,
these huge storms They mostly form and get stronger
over these massive stretches of open water. Think
about the scale hundreds thousands of miles from
land. Where you just can't put ground measurements.
It's not feasible. Right. No weather stations
out in the middle of the Pacific or dotted across
the deep Atlantic. We just don't have them. Exactly.
That's the core dilemma. And yeah, we have the
Hurricane Hunter planes, those amazing brave
crews flying right into the eye. Incredible data
from them. Invaluable, but they have real practical
limits. These planes, they're only available
for about one out of every three Atlantic hurricane
forecasts. One in three. Yeah, think about that.
Two -thirds of the time, we don't get that direct
granular data from inside. And for Pacific storms,
almost never. Maybe a handful of the really extreme
cases. So most of the time, for these huge ocean
storms without those planes, we're kind of flying
blind, making critical calls with way less information.
That's absolutely, yeah. It leaves this huge
observational gap, a void that desperately needs
filling for accurate forecasts. And that's where
the satellites come in. But here's where it gets
really interesting, where the power of this specific
data, the stuff we're losing, really shines.
There's a key difference, right, between the
satellites most people are familiar with, the
ones giving those beautiful swirling cloud pictures.
Yeah, the geosynchronous ones you see on TV.
Exactly. Those versus the more specialized polar
orbiting satellites carrying these microwave
sensors. OK, so what's the difference? Those
regular visible or infrared satellites, they're
great for tracking clouds, seeing the storm top.
But they struggle. They can't really see through
the dense clouds, especially at night when visible
light is gone anyway. Ah. So they completely
miss crucial details, stuff happening inside
the storm structure, changes that can signal
dangerous rapid intensification, you just can't
see the inner workings with those conventional
tools. Right, you only see the surface, the top
layer. Precisely. And what's remarkable, what
makes microwave data indispensable, is it gives
us this unique capability. It's almost like an
MRI scan of the atmosphere. An MRI, okay, I like
that analogy. Yeah, like a doctor using an x
-ray or MRI to see through PISHU to the organs
inside. That's what microwave satellites do for
us. They let forecasters literally peer through
the obscuring clouds, through the heavy rain,
right down near the surface. So they can see
the storm's inner workings. Exactly. Where the
heaviest rain is, the actual center location,
and critically, its thermal and moisture structure.
And they get these updates every few hours. It
reveals what's happening inside, even when it's
totally hidden by clouds. That's the x -ray vision
you mentioned, seeing the internal heat, the
moisture, stuff the normal pictures miss. It's
the difference between seeing a car's paint job
and understanding how the engine works. It's
that level of insight. OK, so let's zero in on
the instrument itself, the one going dark. SSMIS
Special Sensor Microwave Imager Sounder. Break
that down for us. OK, so it's a conical scanning
passive microwave radiometer. Whoa, OK. Conical
scanning? Yeah, imagine a sensor spinning like
a lighthouse beam sweeps around, but it's looking
down, sweeping this wide cone -shaped path below
the satellite as it orbits. Got it. And passive.
Passive means it's not sending out its own signal
like radar does. It's just listening. Listening
to the natural microwave energy that the Earth's
surface and the atmosphere, including the storm,
emit naturally. Different things emit different
signals exactly water vapor rain ice particles
They all have unique microwave signatures and
this instrument picks them up across four main
frequencies 19 .3 22 .2 37 .0 and 85 .5 gigahertz
and it uses seven specific channels within those
with different polarizations to like different
channels on a radio tuned to different things
kind of yeah Think of them as different wavelengths
each tuned to see different aspects lower frequencies
are good for heavy rain 22 .2 gigahertz is super
sensitive to water vapor, and that 85 .5 gigahertz
one. That gives incredible detail on ice scattering
deep inside the storm core, really vital for
structure. And this tech isn't brand new, right?
You mentioned predecessors. All right, the first
one, the SSMI, launched way back in June 1987,
part of the Defense Meteorological Satellite
Program, DMSP. So this capability, it's been
foundational for decades. And that history, that
long record, makes this loss even more significant.
These DMST satellites with SSMIS, they orbit
in a specific way. Yeah, near constant 53 degree
angle, about 830 kilometers up. And this geometry
lets them collect consistent data over a really
wide path. about 1 ,400 kilometers across. So
each pass covers a huge area. A huge area, giving
comprehensive observations and the resolution,
how much detail we get. It's 25 kilometers for
things like total water vapor and sea surface
wind and five brightness temperature channels.
But for that crucial 85 .5 GHz channel, the one
giving the sharpest internal view, it's even
finer, 12 .5 km. Well, it aren't just numbers,
are they? That defines the quality the detail
forecasters have relied on. It's high definition
storm diagnostics. Absolutely invaluable. So
what do forecasters do with all this detailed
penetrating data beyond just cool images? Oh,
its contributions are indispensable. Huge for
accuracy. For starters, it's critical for precisely
fixing the storm center. pinpointing it exactly.
That sounds simple, but you're saying it's not.
It's notoriously difficult with just visible
or infrared, especially if the center is hidden
under a big cloud shield, which often happens.
Like finding a needle in a haystack in the dark.
Microwave lights up that needle. And getting
that position right is crucial. Because it directly
fights that. Butterfly effect we mentioned. Even
small errors, maybe just tens of miles off initially
for a weaker storm. They blow up over time. Dramatically.
That tiny initial error, if you don't correct
it with precise microwave data, it cascades into
outsize forecast errors four or five days later.
Big misses in the track forecast. But where the
storm actually goes, that initial pinpoint matters
that much. Immensely. It's like aiming that billiard
ball off by a millimeter at the start. You're
way off down the table. Without SSMIS, that initial
millimeter error is much more likely. Wow. And
it also powers some advanced tech AI stuff. Absolutely
essential for that. One of the key tools for
estimating storm intensity, especially when the
Hurricane Hunters can't fly, is called DMINT,
Deep Multispectral Intensity of Tropical Cyclones
Estimator. DMINT sounds powerful. It is. It uses
AI to analyze satellite data and predict storm
strength. and it relies heavily, almost entirely,
on the microwave data it ingests. Without SSMS,
DMINT's ability to give accurate, timely intensity
estimates severely hampered. Forecasters lose
a key intensity tool. Leaving them less certain
about the storm's true power. Exactly. And that
directly increases the risk of what James Franklin,
a retired NEC branch chief, famously called a
sunrise surprise. A sunrise surprise? Sounds
ominous. It is. It's that horrible moment seeing
the first visible satellite pictures at dawn
and realizing a storm got way stronger, way better
organized overnight, maybe intensified dramatically.
But you didn't see it happening. Because the
key structural details were hidden from the infrared
views overnight. Details only microwave data
would have shown. It's finding out too late that
the storm ramped up, giving people much less
time to prepare for something far more dangerous.
That's terrifying. And that scenario becomes
more likely now. Much more likely. That microwave
data was our safeguard against it. So the bottom
line here, the immediate impact. DMSP, these
three satellites with SSMIS, they provide roughly
half of all the microwave scans forecasters get.
Roughly half, yeah. Globally. And with the DoD
pulling the plug on processing by June 30th,
that vital data stream just goes dark. Poof.
Gone. It's like a surgeon suddenly losing half
their x -rays mid -operation, or a pilot losing
half their instruments on approach. It's that
level of challenge for hurricane forecasters
now. This isn't just an inconvenience. The very
data that helps spot those sudden terrifying
bursts is rapid intensification. When a tropical
storm explodes into a cat three or four overnight,
that early warning capability is severely compromised.
data availability sliced in half. It just greatly
increases the odds of missing these events, underestimating
storm strength, misplacing centers. It degrades
forecast accuracy, period. It's a huge blow to
hurricane preparedness, and it inherently puts
more people at risk, right? Could cost people
more money, dealing with worse forecasts? Absolutely.
More risk, potentially higher costs for everyone
involved, individuals, communities. Dealing with
degraded forecasts has real consequences. And
this isn't just about hurricanes, is it? The
impact spread wider. Much wider. This data determination
sends ripples everywhere, including critical
climate monitoring, especially for sea ice in
the polar regions. Sea ice? How does microwave
data help there? Well, SSMIS data is fundamental.
It's a core input for key data sets from the
National Snow and Ice Data Center, NSIDC, run
by the National Oceanic and Atmospheric Administration.
Things like their near real -time sea ice concentration
records, the overall sea ice index, crucial for
tracking climate change, Arctic navigation. And
those products rely on SSMIS. Heavily. And the
immediate unsettling consequence. Those specific
products just stop processing after June 30th,
2025. Users, scientists, shippers, northern communities,
they have to anticipate a data gap. Like losing
a key chapter in the climate story makes it harder
to see the whole picture. It's a major disruption
for climate science. And navigating it, well,
there's this urgent scramble to find and integrate
alternatives for products built purely on SSMIS
like sea ice concentration, OSI, 401D, and the
interim record OSI 430A users just have to switch.
find new products based on different data. Which
isn't simple, right? Requires validation, introduces
uncertainty. Exactly. It takes time, resources,
not a clean swap. But some products are okay.
You mentioned multi -sensor ones. Yeah, there's
a bit of a silver lining there. Things like Sea
Ice Edge, Type, and Drift OSI 402D, 403, 405D,
they use multiple sensors already. And another
instrument, AMSR2, is their primary source, so
they're more resilient. They'll keep working
without SSMIS. Okay, so they had a backup plan,
essentially. That's fortunate. incredibly fortunate
for those specific applications. And other products
like sea ice emissivity and L2C ice concentration,
they're either actively migrating to use AMSR2
data now, or they already offer AMSR2 versions
that users are being urged to switch to. So there's
a push to adapt. A huge push. And what's really
critical for the long -term climate picture is
things like the Sea Ice Index, OSI 420. They're
undergoing complex changes to integrate AMSR2.
The absolute goal is a continuous time series,
seamlessly building on the old SSMIS data. So
we don't lose track of the long -term trends.
Precisely. That continuity is vital. It's a massive
effort, but essential to preserve decades of
climate records. OK, so we've covered the practical
impacts, hurricanes, climate. Let's really dive
into the science now. data assimilation, why
this specific data loss hurts so badly. Right.
At the very foundation of all weather prediction
models, these incredibly complex computer programs
generating forecasts is this absolute reliance
on accurate initial conditions. Starting point.
Exactly. Precise data on the atmosphere state
right now, especially moisture. Think of it like
setting up dominoes. if those first few are off
even slightly. The whole chain falls apart unpredictably.
Perfectly put. And this is where our conventional
observations weather balloons launched mostly
over land twice a day. Surface stations, also
mostly land -based, just fall short. especially
for fast -changing storms over the ocean. They're
too sparse, too infrequent for hurricanes. Entirely
inadequate for sampling those dynamic, moisture
-rich environments over the vast oceans. And
even tiny uncertainties in that initial humidity
field, which is super hard to measure globally,
they can have a huge detrimental impact later
on in the forecast. Errors grow exponentially.
Which is where satellites become absolutely essential.
Irreplaceable, even. Totally. Unlike fixed points,
satellites sweep across the globe, filling those
massive ocean gaps, providing spatially dense,
repeatable data where we had almost nothing before.
From scattered snapshots to a continuous global
video feed. A great way to think about it. Now,
getting all this indirect satellite data, not
direct temperature or pressure, but microwave
signals into the models, that requires sophisticated
techniques. Data simulation. Specifically, things
like 3DVAR, three -dimensional variational systems.
3DVAR. Okay, break that down. Imagine the atmosphere
is a giant complex puzzle. Our models predict
how the pieces move. Traditional observations
give us some pieces, but there are huge gaps,
especially over oceans. Satellite data floods
us with new clues, often indirect clues like
microwave brightness temperatures. So 3DVAR is
how we fit those clues into the puzzle. Exactly.
It's like a super smart digital detective. It
takes all observations, balloons, satellites,
everything, and mathematically finds the single
most consistent and most probable picture of
the atmosphere right now. It optimizes the initial
conditions, gets the puzzle started as accurately
as possible. So the rest of the prediction works
better. And there are different ways to feed
SSMIS data into this. Two main ways, yeah. First
is assimilating retrieved products, or RV. RV,
what's that? It's computationally faster. You
first convert the raw satellite signals into
geophysical things like total water vapor or
surface wind using statistical algorithms. Then
you feed those into the model. Quicker, but maybe
less accurate. Potentially. Because those statistical
conversions, those retrievals, they might have
errors or biases if the statistics aren't perfect
or if other info used in the process is flawed.
It's an estimation. OK, so what's the other way?
The second method is directly assimilating the
raw measurements, the brightness temperatures,
or TB. TB raw data. Right. This is more computationally
intensive but more physically robust. You feed
the raw microwave signals directly into the model
using a complex radiative transfer model. This
simulates how microwaves actually travel through
the atmosphere. So it's based more on physics
than statistics. Exactly. Less dependent on potentially
iffy stats. Better at reducing systematic biases.
It takes more computer power, but often gives
a pure, more accurate starting point because
it uses the fundamental physics. More complex,
but potentially better results. Generally, yes.
And the math works out well enough to make it
feasible. Okay, to really hammer home why this
matters, you mentioned a case study. Hurricane
Danny. Yeah, Hurricane Danny back in July 1997.
Fascinating storm formed from a non -tropical
system intensified in the Gulf. A perfect real
-world test bed for seeing the impact of SSMI
data assimilation. So what happened in the baseline?
The forecasts without the microwave data. The
control case. The results were stark. Pretty
alarming, actually. The initial conditions from
standard systems like NOAAs, GDAs, they severely
lacked moisture over the Gulf. And the storm's
initial spin, the low level circulation, was
way too weak. Which led to? Big underestimates
of intensity. For example, the model's central
pressure for Danny was 19 .5 hectopascals higher
than observed after 48 hours. A huge miss. Plus
the model needed at least eight hours of spin
up time before it even started raining realistically.
It was starting cold. Eight hours just to get
going properly. That's a long delay. A very long
delay. But here's where the SSMI data made a
transformative difference. When they assimilated,
Either the retrieved products, RV, or the raw
brightness temps, TB. What changed? Instantly,
almost. The atmospheric moisture across most
of the Gulf dramatically increased in the model
starting point. The low level circulation got
much stronger, much more realistic. The inflow
feeding the storm improved. It gave the model
the right ingredients from the start. Exactly.
And crucially, that spin up time. It plummeted.
With SSMI data in, the model started producing
light rain within just one or two hours. Wow,
from eight hours down to one or two. That's huge
for getting timely rain forecasts out. Massive.
And the intensity forecasts. They got incredibly
better, much more accurate capturing the storm's
deepening pressure. The raw data. TB approach
got the final pressure within one hectopascal
of reality after 48 hours. The retrieved data,
RV, was 8 HPA deeper than observed, still vastly
better than the control's 19 .5 HPA error. So
both methods were dramatically better than no
data at all. A revolution, really. Absolutely.
And the visual structure in the simulations.
Night and day. With SSMI, the models produced
clear hurricane eyes, distinct eye walls, spiral
bands, which is totally missing in the control
run. Like seeing an ultrasound versus just guessing?
A perfect analogy. Forecasters could suddenly
see the storm's internal evolution much more
clearly. Now what about the computational cost?
All that raw data processing sounds expensive.
You'd think so, but the study actually showed
that while the raw TB method took about twice
the CPU time as the RV method, the total assimilation
time was still only about 3 .5 minutes. compared
to the 157 hours it took to run the 48 -hour
forecast itself. Negligible. So it's totally
affordable, computationally speaking. A tiny
investment for massive forecast improvement.
Completely affordable and highly beneficial.
It pays for itself many times over in improved
warning. But it wasn't a magic bullet for everything,
right? The study noted some limitations. True.
While intensity and structure improved dramatically,
the impact on the storm track forecast was surprisingly
small. All the simulations, even with SSMI data,
had this consistent leftward track bias. They
predicted Danny to go slightly left of where
it actually went. And that matched the official
forecasts at the time. Yeah, suggesting a deeper
issue. Probably inaccuracies in the initial steering
winds higher up in the atmosphere. Stuff that
SSMI doesn't directly measure well. Exactly.
It points to the need for other instruments,
like LiR, that can measure those steering winds
directly. You need complementary data. And even
with SSMI, the model still struggled to capture
every cloud band perfectly, or subtle things
like cirrus clouds, no single instrument sees
everything. It's always about combining perspectives.
Always. But the key takeaway from Danny is undeniable.
Assimilating that SSMI data had a strong, unequivocally
positive impact, especially on intensity and
structure. While the two methods, RV versus TB,
had differences, both were vastly superior to
having no data. It proved this microwave data
is foundational. Foundational, not just incremental.
Okay, so laying all that out, knowing its value,
what's the path forward now? We have this immediate
lasting data gap. Yeah, and it casts a long shadow
over this hurricane season and beyond. Losing
SSMIS means losing half our microwave instruments.
It dangerously increases the odds of missing
rapid intensification, underestimating intensity,
misplacing storms. Putting more people at risk,
increasing potential costs. Plain and simple.
But the community isn't just sitting back. They're
urgently searching for replacements. The main
contender right now is AMSR2 Advanced Microwave
Scanning Radiometer 2. AMSR2, does it offer improvements?
It does. Better resolution than SSMIS, so potentially
more detail. That's good. Out there is a catch.
There is. AMSR2 data is still considered prototype.
It's undergoing testing. And crucially, it hasn't
been fully intercalibrated with the older SSMIS
data yet. Meaning the measurements might not
line up perfectly. Exactly. Users might see differences,
say in CI610, comparing AMSR2 to the historical
SSMIS record. partly because its higher resolution
sees edges better. So while it's promising, it's
not a seamless swap yet. It's a work in progress.
And progress takes time we don't necessarily
have right now. What about other options? There's
the weather system follow on microwave, WSFM,
designed specifically as the successor to DMSPS
SMIS. It launched successfully in April 2024.
Fantastic. So that should fill the gap. It should.
But here's the frustrating part. The data from
WSF -M is not currently available to forecasters.
Wait, it's up there, but we can't use the data.
Correct. It's uncertain if or when access will
be permitted. Bureaucratic hurdles, security
protocols, complex reasons. But the bottom line
for forecasters, a capable satellite designed
for this exact purpose is flying blindfolded
right now. The data isn't flowing. That's incredibly
frustrating. So connecting this all back, the
broader stakes for society are huge. Undeniable.
This data loss... puts more people at risk. It
could lead to higher costs, unnecessary evacuations,
or worse, failing to evacuate when needed. The
ripples hit emergency services, insurance, supply
chains, daily lives. It really underscores how
much we rely on this infrastructure. On a slightly
brighter note, anything happening right now?
A tiny fleeting positive. Current models show
the Atlantic staying quiet through the end of
June. No tropical development expected for the
next week or so. Okay, so a small window of reprieve
as this data stream shuts down. A small window,
but just a brief calm before what could be a
very challenging, very unpredictable season,
now with fewer tools in the toolbox. So to wrap
up our Meteorology Matters discussion today,
we've looked at the sudden, really impactful
loss of critical SSMIS data. An indispensable
eye in the sky, now dark. We've detailed the
profound implications for hurricane intensity
forecasts for vital sea ice monitoring that tracks
climate change. And we've looked at the urgent
ongoing scramble to find, test, and integrate
alternatives to fill this sudden gaping hole
in our observations. And this whole situation,
it really makes you think, doesn't it? It raises
a question for you, our listener. How often do
we really stop to consider the complex, often
invisible scientific web that supports our safety,
our economy, just our daily lives? Yeah. What
other crucial unseen data sources are we just
assuming will always be there? Data that, like
SSMIS, could just vanish overnight. And what's
the real cost? of unquantifiable when these critical
systems are disrupted. Something to ponder. Definitely
something to ponder. Now, if you want to stay
right on the cutting edge of meteorology, hurricane
tracking, and keep getting clarity on vital topics
like this, you absolutely need to follow meteorologist
Rob Jones. You can find him on Instagram to search.
He's also on TikTok as TVMeteorologist. And definitely
check out his YouTube channel, search Rob Jones
Hurricane. That's where you'll also find the
Meteorology Matters podcast playlist with all
our discussions. Excellent resources. Well, thank
you for joining us on this Meteorology Matters
deep dive. We look forward to exploring more
essential knowledge with you next time.