How Weather Modeling Gets Better
Dr_Ish writes: Bob Henson over at Weather Underground has posted a fascinating discussion of the recent improvements made to the major weather models that are used to forecast hurricanes and the like. The post also included interesting links that explain more about the models. Quoting: "The latest version of the ECMWF model, introduced in May, has significant changes to model physics and the ways in which observations are brought into and used within the model. The overall improvements include better portrayal of clouds and precipitation, including a more accurate depiction of intense rainfall. The main effect of the model upgrade for tropical cyclones is slightly lower central pressure. During the first 3 days of a forecast, the ECMWF has tended to have a slight weak bias on tropical cyclones; the new version is closer to the mark."
The other day the forecast here was a 90 degree high and nothing but sunshine and a few clouds. In reality we never got above 75 and had a pretty intense thunderstorm.
In 2015 with all these huge expensive computers and fancy algorithms, weather forecasting is still pretty hit or miss.
For the chicks
I just wish Weather Underground hadn't been taken over by The Weather Channel and, more to the point, their Web dev team. WU was an outstanding source for quick, concise reports on current weather, weather history, and news. Apparently they still post interesting content from time to time, but it just isn't worth my while to go slogging through the "new, improved UI" to get to it.
I still do pull it up for local conditions and radar; if I'm not in a hurry, it gives me the info I need, at least when I'm on my home 30/5 TWC connection. I had the misfortune of trying to use it through a throttled Hughes satellite connection the other weekend, and I finally gave up; there's so much AJAX crap going on that it took over a minute to load even part of the local conditions page, and the radar page simply wouldn't ever load. And this is with ads disabled.
I haven't been running public-facing Web projects lately, but if I go back to it, I'll insist that we test on something other than a fast LAN connection and giant screen. The current team at WU obviously doesn't do that.
Isn't weather forecasting just solving PDEs?
Interesting to note that the 5-day forecast intensity error for 2014 is lower than 1, 2, 3, or 4 day error..
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Pardon me, but Weather Underground was (is?) a far-left terrorist organization with a respectable number of murders and robberies to its name. Some of its surviving members are teaching students these days, and have likely influenced the President, but they aren't teaching them anything about predicting weather.
In Soviet Washington the swamp drains you.
I live in Central Texas and I've had raging thunderstorms when the Weather Underground report insisted there was 0% chance of rain and the radar map showed nothing. At other times, the sky was clear when Weather Underground talked about "thunderstorm warnings" and showed red all over the radar. I don't trust the weather report at all.
Dr. Masters' article is about numerical models that are used for hurricane forecasting. There is nothing simple about hurricane forecasting.
1) The atmosphere is a chaotic system. Small errors in the initial state grow exponentially as the model is integrated forward. There aren't many in situ observations over the oceans, so the initial state of the model is generally less accurate there than over land. Forecasts produced will also have larger errors.
2) A wide range of models are discussed here ranging from global models (e.g., ECMWF, GFS, UkMet) to mesoscale models (e.g., HWRF). The global models are spectral models and don't have grid points, but the approximate resolution is around 20 km. The 2015 version of the HWRF, for comparison, is a gridded model with dx and dy of the inner nest set at 2 km (an upgrade from 3 km in past years). The HWRF also has an outer nest covering basically the entire basin and provides lateral boundary conditions for the vortex-following inner nest. There are huge differences in the models and, therefore, in the output they produce.
3) Hurricane tracks are generally driven by large-scale processes like high pressure systems over the Atlantic and Pacific and frontal systems like mid-latitude cyclones. These things are large enough that even the global models simulate them pretty well. Nonetheless, the sparseness of observations over the oceans can be a problem. Large-scale processes drive hurricane tracks.
4) Hurricane intensity is completely different. Large scale factors like moisture, sea surface temperatures, and vertical wind shear (change in wind speed and/or direction with height) do modulate hurricane intensity. However, the intensity is driven directly by the deep convection (basically thunderstorms, but with very little lightning and thunder) at the core of the storm. Although a hurricane has a huge cloud and precipitation shield, the eyewall storms are only on the order of 20 km across. The global models currently can't resolve this at all. Mesoscale models with coarser resolution like the NAM (12 km) can't resolve this. The high resolution models can resolve the eyewall convection, but additional resolution is definitely beneficial. It comes at a cost, though. Improving the horizontal resolution means adding grid points, which requires more computing power. Not only that, it requires a decrease in dt, which if too large will result in a CFL error and the model won't function. Lowering dt means even more computing power. Cutting dx and dy in half will multiply the number of gridpoints by 4 (2x2), but dt must also be cut in half. Assuming the number of vertical levels remains unchanged, it will require 8 times as much processing time and 4 times as much memory to run the model.
5) Observations are key to the models and more observations of the core of the storm will improve forecasts. But this region is also very hostile to observation systems. Manned flights are dangerous and expensive. They're also around 10,000 feet, so they have to use dropsondes to get data on what's going on below. Drones are a possibility for data collection. Assimilating radar data from these airborne platforms might help with analyzing the eyewall storms for the model's initial state. That said, getting the additional observations is difficult.
6) You still have to get the data into the model, and this isn't easy. Older schemes like a two-pass Barnes Analysis really aren't suitable for noisy data like radar observations. The trend is toward stochasic schemes like the Ensemble Kalman Filter (EnKF), which are more suited to this type of data. But work on using EnKF schemes to assimilate observations is still pretty early in development. It's used a lot in thunderstorm and tornado research to make use of mobile radar observations. But it's just now starting to get into operational use. It also requires more computing power because its an ensemble scheme, generally requiring at least 50 and preferably around 100 ensemble members.
There's a lot of good work going o
The terminology and trends are the same as when I did my thesis in numerical weather prediction 30+ years ago..... and the secret sauce, even then, was the sub-grid/harmonic scale physics for convection and clouds....
They need a model that is in a different league than this to get more accurate forecasting. To begin with, they must monitor the sun heat output in the direction of the earth to calculate the temperature impact on the earth and moon, accounting for objects inbetween. Then they need to monitor sea and earth temperature down to something like 10 m2 grid or better to get better accuracy. Then there is humidity, perspiration, percipitation and a whole lot of other factors including human-made factors. The earth core temperature of course also plays a role. There are so many factors not accounted for today, that it's no wonder forecasting is as bad as it is.
https://en.wikipedia.org/wiki/...
Too bad some idiots stole the domain from the original group back in 90's that wanted it as an archive.
They still are, a news article on "the other news site" just posted about their site (and others) serving malware ads. https://soylentnews.org/articl...
Now open source weather prediction is possible, these days. You can download the required weather initialisation files and the prediction engine. The only problem is noone has made open source for it. There is free RASP (see http://www.drjack.info and http://www.drjack.info/cgi-bin/rasp-forum.cgi and http://www.drjack.info/twiki/bin/view/RASPop/WebHome ) but it is not open and has maintenance issues. The hard part is validating and tweaking for a region.
A RASP operator (I can't even install it...).
I've been a weather geek for on about 35 years. Living in south Florida, the weather can be more than unpredictable. I've seen vast improvements due to advances in radar, satellite, and just sheer record keeping. I still love it, however, when the news says, "We don't know why we haven't had a hurricane this year", I always add, "Yet."