NOAA upgrades the U.S. global weather forecast model

https://www.noaa.gov/media-release/noaa-upgrades-us-global-weather-forecast-model

by mehrdadn • 7 years ago

215 80 7 years ago

quico• 7 years ago

The fact sheet showcases the improvements in the forecast: https://www.noaa.gov/sites/default/files/atoms/files/DOCUMEN...

steve19• 7 years ago

I was researching weather prediction not long ago. From my nieve perspective it seems that dispute all the increased gpu computational power and advances in machine learning, there have not been any great advances in weather prediction. Is this true?

Edit: Downvotes for simply asking a question. sigh.

m463• 7 years ago

I have heard the opposite - predictions have been significantly increasing in accuracy over time.

On the other hand, I suspect it might not be noticeable if the forecast you always read just says "40% chance rain, high 80, low 50". It might be more noticable if you look at the hour-by-hour forecast for a specific location and see when the rain is predicted to start and end.

jhayward2• 7 years ago

The last day of the 10-day forecast is now more accurate than the 3rd day of the 3-day forecast was when I was in college.

This covers some decades, to be sure, but it's a pretty big improvement.

metaphor• 7 years ago

Would you happen to have a citation for that? Truly interested in knowing if the diff is significant or marginal.

neuronexmachina2• 7 years ago

According to this article there have been significant improvements in weather prediction in the past few decades: https://science.sciencemag.org/content/363/6425/342

A modern 5-day forecast is as accurate as a 1-day forecast was in 1980, and useful forecasts now reach 9 to 10 days into the future (1). Predictions have improved for a wide range of hazardous weather conditions, including hurricanes, blizzards, flash floods, hail, and tornadoes, with skill emerging in predictions of seasonal conditions.
... Data from the NOAA National Hurricane Center (NHC) (13) show that forecast errors for tropical storms and hurricanes in the Atlantic basin have fallen rapidly in recent decades.

dls20163• 7 years ago

I worked as a forecaster for a bit but never made it to the research world (studied theoretical pde instead of computational)... however at the time huge gains had been made in data assimilation. One fact that has stuck with me was that ~1/3 of the computation time for the UK Met global model run was consumed by data assimilation. I don't remember statistics anymore but data assimilation schemes were a big driver of improved forecast skill.

I also recall the ECMWF had surprisingly accurate long range forecasts based on ensembles. It could predict 500mb heights out two weeks, no sweat.

Re: your comments... My guess is that a gpu isn't suited for use in an operational model due to data access patterns (and possibly not even helpful with the solver). But again, I'm not a computational pde guy. Also, perhaps machine learning would be useful but that would be post-processing or perhaps parameterizing sub-grid phenomenon. There's already a process called model output statistics (MOS) for adjusting raw fields from a weather model.

labster• 7 years ago

The physics is pretty well known at this point, and there's only so much you can gain by increasing from second to third order approximation. The errors in the initial conditions are just larger. Most of the action has been on data assimilation and better parameterizations because of that.

I've been out of the field for ten years now, but it's really nice to see improvements to the core physics to this degree.

I'm still skeptical of your supposed two week 500 heights forecast from the ECMWF model. I live near the western Pacific (i.e. the data hole) and it's really easy to find crazy model solutions after 7 days. And I'm pretty sure you weren't looking at the Southern Hemisphere.

semi-extrinsic2• 7 years ago

You are correct that for this type of pde solver, you don't have enough computation per byte of data for a GPU to be fast.

shaki-dora• 7 years ago

Forecasts have improved dramatically. A 7-day forecast is now somewhat useful when deciding to have your party indoors or outdoors, whereas in the 90s next-day forecasts could hardly compete with the naive assumption of "the weather will stay as it is".

But by mentioning machine learning, I'm guessing you are looking at a different timescale, i. e. "within the last two years". And any progress in the short term will be slow compared to what we have seen in other domains such as image recognition etc.

I'm no expert on weather forecasting, but I believe the explanation may be that forecasts have long been (among the) best financed "big data" problems out there. That means they incorporate lots and lots of domain-specific work. As a result, naive machine learning models currently still lag all the specialised work, which in turn isn't structured in a way to easily take advantage of progress in, say, GPUs.

cos2pi2• 7 years ago

For hurricane forecasts issued by the NHC, you can see the official error statistics here [0]. Note that 96 and 120-hour forecasts were so poor prior to year 2003 that they were not issued.

Note these error statistics do not represent true model error as the official track and intensity forecast -- while informed by model output -- are determined by human forecasters.

[0] https://www.nhc.noaa.gov/verification/verify5.shtml

chiph• 7 years ago

This is probably in response to the Euro weather model, which has been producing better forecasts. Especially for named storms.

tinix• 7 years ago

You might be onto something... met.no has been more accurate than the usual domestic weather forecasts for as long as I can remember. Literally, it can be hailing outside, and thundering, and NOAA says like 20% chance of rain and sunny, where met.no might say 70% chance of storms, cloudy, etc...

https://www.met.no/en/free-meteorological-data

Thlom• 7 years ago

User facing interface: https://www.yr.no/?spr=eng

exhilaration• 7 years ago

The Europeans are putting big money into weather forecasting, "The goal is to be able to provide, by 2025, reliable forecasts up to two weeks in advance."

https://www.zdnet.com/article/europes-big-weather-supercompu...

As far as I know other than this model upgrade, there are no major investment being made in American weather forecasting.

ekianjo2• 7 years ago

How far from that goal are they at the moment?

the_mitsuhiko2• 7 years ago

Might be, but ECMWF just has many more observations so it will be hard to compete.

privateSFacct• 7 years ago

I use ECMWF but never even knew what was different - def more reliable where I am for cloud / sun. Haven’t paid attention enough to see if it’s wind predictions are better

esaym• 7 years ago

Somewhat related, what are some good weather sites for storm monitoring? I've been using ventusky[0] for rain forecasts and mrms[1] for storm and hail conditions. Is there anything better?

[0]https://www.ventusky.com/

[1]https://mrms.nssl.noaa.gov/qvs/product_viewer/

ghthor• 7 years ago

The Storm prediction center[1] is always my goto. After that I check the Mesoscale Precipitation discussions[2].

[1] https://www.spc.noaa.gov/

[2] https://www.wpc.ncep.noaa.gov/metwatch/metwatch_mpd.php

timdorr• 7 years ago

I really like https://www.windy.com/

blackRust• 7 years ago

Windy.com allows you to compare models, which varies based on the region you have zoomed in to.

This is really handy as national models can be much better for short-term and higher resolution predictions.

It has the widest range of models freely available that I'm aware of, including the commercial ECMWF model.

dhimes• 7 years ago

This is also my go-to, for storms and just general forecasts for sailing.

Itsdijital• 7 years ago

https://wxdisco.com/forum/3-united-states/

The forums really light up for big storms with lots of discussion and insight.

duanem• 7 years ago

My favorite is Flowx[0]. Disclaimer: I'm bias since I'm the developer.

Globally, we have GFS (FV3) and GDPS in the free version, and DWD ICON in pro.

For north America, we have NAM 12km, NAM 3km, HRRR, RDPS, HRDPS in Pro

For Europe, we have DWD ICON EU, DWD COSMO-D2, ARPEGE, AROME and HIRLAM in Pro.

If you double-tapping of a graph, you can compare the forecasts quite easily.

Sorry there is no iOS or web version, nor ECMWF (it's too costly).

[0] https://flowx.io

microcolonel• 7 years ago

This is huge. Good work to the folks at NOAA and the American taxpayer for footing the bill.

Buttons840• 7 years ago

I'm sure it wasn't the biggest part of our "bill". :)

davidw2• 7 years ago

I just wish we had a weather radar here in central Oregon

https://cliffmass.blogspot.com/2014/11/the-other-radar-gap-e...

toomuchtodo• 7 years ago

How interested are you in getting this done? Below is a link to a phased array demonstrator (SPY-1A) that was dismantled and replaced with a newer version in 2016. Might find out where SPY-1A is sitting (the phased array may have been returned to the US Navy), and since it'll perform both weather and aircraft surveillance, might be easier to sell to stakeholders for the coverage gap.

https://www.nssl.noaa.gov/tools/radar/mpar/

Alternatively, Roberts Field appears to be a major commercial air hub in central Oregon. You might argue from a safety perspective to your Congressional representatives (perhaps in concert with local air carriers and AOPA) that the airport needs a TDWR station (cost will be ~$4MM-8MM), which could also provide NOAA with the necessary weather surveillance data. Thunderstorms aren’t common on the West Coast though, hence the lack of TDWR stations in West Coast states. If you pursue this route, you'd want to get funds for this into some sort of federal transportation bill, as part of enhancing the safety of the air transportation system.

https://en.wikipedia.org/wiki/Terminal_Doppler_Weather_Radar

davidw2• 7 years ago

I've mentioned it to our representative, Greg Walden, in the past, but he doesn't seem interested, which is a pity, because this is the kind of non-partisan stuff that they ought to be getting done for their constituents.

sorenjan• 7 years ago

Maybe you could use microwave links in the cellular network instead? There's research and development going on in that area.

https://www.smhi.se/en/services/professional-services/microw...

ahje• 7 years ago

That's cool! I can imagine that could be an excellent secondary data source for rainfall monitoring.

dgzl• 7 years ago

Having better weather insights for Smith Rock would be great. Saying hi from the a Monkey!

wavesplash• 7 years ago

As far as I understand, these are still hand designed algorithms using a tiny fraction of possible weather data. Impressive for old school methods. Would be even more awesome to see how far ML could take the state of the art.

elil17• 7 years ago

Weather data is a system where we have a really good understanding of the underlying physics but can’t do enough computation to simulate them in a way that’s detailed enough to make truly accurate predictions.

Machine learning is all about finding an unknown function that underlies known data. This is sort of the opposite issue: we know the underlying function but can’t compute it.

That said, ML models are being applied to situations where the whether data doesn’t translate directly into known physical quantities, like satellite images (see https://developmentseed.org/projects/hurricane-intensity/)

nestorD• 7 years ago

You can also use ML to learn a metamodel : a model trained on an accurate (but too costly to be run in real time) simulation.

naavis• 7 years ago

One fundamental problem we have with weather forecasts is that our input data for the starting point is fairly sparse. GFS calculates the forecast on a grid with 13 km horizontal resolution and 64 vertical layers. We don't have accurate weather information at that resolution from all over the globe, so the starting point for the forecasts is a combination of previous simulations and interpolated observational data.

So even if we had a forecast engine that would perfectly simulate everything given some start state, we wouldn't have enough input data to have an accurate start state.

lambdamore2• 7 years ago

We can compute the radiative transfer processes to infer the physical processes from the satellite images.

peterlk2• 7 years ago

If anyone is interested in doing your own thing with weather data, check out MADIS [0]. There are various levels of access, some of which require NOAA approval. But if you're serious about making weather predictions, it's a good thread to pull on. I once set up a MADIS node, and our server was shut down very quickly by Amazon for "suspicious traffic", so beware of that - there's a lot of data that gets pushed through the system. If I remember correctly, it was kind of a pain in the ass to get set up/configured, but it was pretty cool.

[0] https://madis.noaa.gov/index.shtml

Something1234• 7 years ago

Can you go in to more details about how to get this setup?

peterlk2• 7 years ago

I honestly don't remember any more. At the time, we were working with NOAA, and I remember a problem that was solved by talking to an admin at NOAA (our IP needed to be on some official whitelist or something), but that may have been for a restricted data set. We didn't end up using it for long because the client said so.

But I dug around for some information to maybe get you started.

Installation: https://madis.ncep.noaa.gov/doc/INSTALL.unix

API: https://madis.ncep.noaa.gov/madis_api.shtml

Data restrictions: https://madis.ncep.noaa.gov/madis_restrictions.shtml

Another resource that may help: https://press3.mcs.anl.gov/forest/regional-models/global-dat...

When I was working on this stuff, I found that a DFS on various government subdomains (like MADIS) was the best way to find information. It was tedious, but it worked.

It's also helpful to put on your fortran hat. For example, I once attended a Haskell meetup where someone wrote a parser to deal with parsing binary files from NOAA. I also was in a meeting (with some NOAA folks) once where I was asked if I "would prefer an ASCII file, or a binary one". This is not a world that operates on JSON or XML. Expect binary blobs with flags (bits) that change the meaning of other flags in fun and exotic ways. The binary nature of the data can help with data throughput limits, but boy is it a pain to deal with.

cos2pi2• 7 years ago

Rather than try and set up MADIS, look into something like the siphon [0] Python module.

MADIS is a bit dated, data dissemination that uses OPeNDAP [1] and ERDDAP [2] is much friendlier.

[0] https://github.com/Unidata/siphon

[1] https://opendap.github.io/documentation/

[2] https://coastwatch.pfeg.noaa.gov/erddap/information.html

Azerty9999• 7 years ago

For those interested in more background on NOAA and making money from it, I highly recommend reading The Fifth Risk[0] by Moneyball author, Michael Lewis[1]. It details how a couple private companies make a lot of money using NOAA data in interesting ways (e.g.: crop insurance (now acquired by Monsanto)). Another of those companies is AccuWeather whose CEO was appointed to head NOAA by Trump[2].

P.S.: Anyone notice that monitoring "Climate" was absent in the government announcement?

[0] https://www.amazon.com/dp/1324002646

[1] https://en.wikipedia.org/wiki/Michael_Lewis

[2] https://oceanleadership.org/trump-taps-accuweather-ceo-head-...

arthurcolle• 7 years ago

How long ago was this? Surprising that AWS would shut down your node, were you trying to run this on the free tier or something?

tdxcbkif• 7 years ago

They probably thought he was mining cryptos

neuronexmachina2• 7 years ago

This site has a technical overview of some of the models and algorithms used by the new FV3 system: https://www.gfdl.noaa.gov/fv3/

Tempest19813• 7 years ago

A while back, I found a NOAA search that showed historic chance of rain by month:

https://www.wrh.noaa.gov/images/mtr/sjc_pcpn_prob.gif

Since then, I spent hours trying to find this page again, unsuccessfully. All I have is this URL for San Jose. (Replacing "sjc" with other airport codes doesn't always work, since "mtr" is a region code?)

Anyone know where this precipitation data lives?

akie4• 7 years ago

I made this: https://www.wmo.int/cpdb/united-states-of-america - click on “climate normals”

dymk• 7 years ago

Clicking on the "SEATTLE" link,

    Missing Controller
    Error: Climate.DashboardController could not be found.

tumetab1• 7 years ago

I like the cautious approach to this stuff

The retiring version of the model will no longer be used in operations but will continue to run in parallel through September 2019 to provide model users with data access and additional time to compare performance.

koboll• 7 years ago

Man, that sidebar is one hell of an aggravating UI element.

justinph• 7 years ago

Some meteorologists are not in love with the new model. My local forecaster/weather blogger suggests that the v3 model tends to overestimate cold snaps and move storms too fast in the mid-latitudes: https://blogs.mprnews.org/updraft/2019/06/milder-with-spotty...

yufeng66• 7 years ago

what kind of hardware is the GFS model run on?

plantain• 7 years ago

And it's extremely dubious whether there is actually any improvement... https://cliffmass.blogspot.com/2019/04/us-numerical-weather-...

sebnukem2• 7 years ago

Wouldn't this kind of problem be a perfect match for machine learning? It would have a huge dataset to learn from. Why isn't it happening or what prevents AI tech from forecasting the weather?

stefanpie• 7 years ago

It is, and the research applying ML in this area is starting to ramp up. For example, last year I worked on a project using ML to identify tornado vortex signatures in Doppler weather radar scans. It also turned out that a couple other groups published similar research at the same time. I would say to expect to be much more growth with ML in meteorology hoping it will all eventuality be applied in the field.

stankypickle• 7 years ago

It is because there is an understanding, from first principles, of the dynamics that drive weather (e.g. conservation of mass, momentum and energy). The current models are build upon these principles to make predictions, and conform to expectations of how physics operates. The method that these models are based on (finite volume) is efficient and adaptable if modifications need to be made.

Using AI and ML to make predictions about weather will likely not account for the conservation principles and might lead to ridiculous results (in some sense). Creating an accurate AI/ML model of a complex and chaotic system might lead to wrong predictions under extreme circumstances (e.g. predicting the weather >5 days out for an extreme hurricane) or under conditions where some implicit assumption has changed. One can at-least attempt to grapple with these issues when using finite volume. Under AI/ML you just have to hope your model is properly trained.

lambdamore2• 7 years ago

I could recommend this paper: Schneider, Tapio, et al. "Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high‐resolution simulations." Geophysical Research Letters 44.24 (2017).

westurner• 7 years ago

Working with other scientists, Lin developed a model to represent how flowing air carries these substances. The new model divided the atmosphere into cells or boxes and used computer code based on the laws of physics to simulate how air and chemical substances move through each cell and around the globe.
The model paid close attention to conserving energy, mass and momentum in the atmosphere in each box. This precision resulted in dramatic improvements in the accuracy and realism of the atmospheric chemistry.

Global Forecast System > Future https://en.wikipedia.org/wiki/Global_Forecast_System#Future

NOAA upgrades the U.S. global weather forecast model

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