But I am often asked: why should we worry that the European Center is way ahead? Why don't we simply acquire their forecasts and forget about the whole business?
Well, I believe there are powerful, compelling reasons why the U.S. should regain its status as the best in the world in operational numerical weather prediction. Let me give you a few:
1. There is no reason to expect that forecasts made by the European Center (ECMWF) and the UKMET office, the current world leaders, are the best that can be achieved. Properly using its huge resources, U.S. numerical weather prediction can be much better.
I am not saying this as a speculation. This is an area with which I have great familiarity--and there are a number of ways that we can improve upon the ECMWF and UKMET approaches, including not repeating a few of their mistakes or missed opportunities. We could produce far superior forecasts.
2. The U.S. has the largest weather research community in the world-- no nation or groups of nations is even close. Thus, we have the scientific infrastructure and expertise to be the best.
The National Center for Atmospheric Research in Boulder
Numerical weather prediction also leans on expertise in computer sciences and access to advanced computer technologies. The U.S. is far ahead in these areas.
3. Many Nations And Companies Depend on U.S. Numerical Weather Prediction and Cannot Afford the ECMWF or UKMET Forecast Products. Same with U.S. universities.
The ECMWF and UKMET office charge big bucks for access to the forecast output. Like hundreds of thousands of dollars a year for private sectors firms wishing access. Many nations and companies cannot afford to pay the high fees. In contrast, U.S. agencies have a policy of making our model forecasts available at no charge--- greatly helping poorer countries, in what can considered a form of foreign aid. The free access also helps new weather start-ups and companies who can't afford expensive European forecast products.
University research, such as at the University of Washington, depend on the free model grids from the National Weather Service for research and to develop next-generation local prediction systems. ECWMF grids...at 100,000 a year or more..are beyond our financial reach. Thus, the quality of U.S. academic research depends on the quality of NOAA/NWS models.
4. Only U.S. Numerical Weather Prediction Can Service All U.S. Needs
International centers, like the European Center, do global prediction, but they aren't interested in running high-resolution and specialty weather prediction models over the U.S. Only U.S. weather entities (mainly NOAA/National Weather Service) will do that. We need to be the best for our own good.
Virtually all weather modeling centers are moving towards or using Unified Modeling Systems, in which the same forecasting model works on all scales. So if you are going to have the best model, it will serve both global and local uses.
5. U.S. Numerical Weather Prediction Research and Operation is Spending More Money Than Any Other Nation or Groups of Nation.
I mean spending five to ten times as much as the Europe or the UK. For that price we should be the best. Unfortunately, we are currently wasting huge amounts of resource with large number of redundant efforts. That needs to change. The U.S. taxpayer is already paying to be the best, they might as well get their money's worth.
6. Global Weather and Climate Prediction are Converging
Global weather prediction and climate prediction are converging towards virtually identical modeling systems: coupled global atmosphere/ocean/crysphere (ice/snow)/land surface models. Furthermore, weather and climate systems are moving together to higher resolution. Such modeling systems are obviously most easily tested for weather and seasonal forecasts. So if the U.S. gives up leadership in the weather domain, it will inevitably do the same in the climate domain. Not good.
7. Operational Weather Prediction is a Key Testbed for Evaluating Physical Understanding of the Atmosphere.
The best way to test physical understanding of the atmosphere is to "stress test" the science by including it in operational models that are run several times each day. Thus, operational modeling can greatly foster science discovery and understanding. If the U.S. gives up global modeling to the ECMWF or others, we would inevitably weaken the scientific infrastructure of the nation.
The Bottom Line: The U.S. can and should be the leader in numerical weather prediction. Giving up such leadership inevitably leads to poorer forecasting for the nation, the undermining of the U.S. scientific infrastructure, and would be damaging to the private sector and lower-income nations dependent on U.S. forecast models.
Dear Cliff....you are aboslutely right in point 3 (and probably in the rest as well). All weather services and many private companies that I know in South America rely on GFS to initialize their LAMs (WRF most often). I thank NCEP/NWS for making freely available their GFS outputs.
ReplyDeleteRene Garreaud
Universidad de Chile
Amen, how do I get a cap?
ReplyDeleteThanks for post. Important and reasonable.
ReplyDeleteThe barometric pressure has been notably high the past couple of days in NW Bellingham. I measured a max pressure of 30.60" of Hg yesterday night. This is one of the highest readings in my record and the highest since January 2019.
ReplyDeleteHey Cliff, great article. I was wondering if you could elaborate a little bit on the "large number of redundant efforts" in your fifth point? Curious as to where my taxes go in this regard!
ReplyDeleteClif has written quite a few previous blogs about gov't inefficiency and political shenanigans. Most recently:
Deletehttps://cliffmass.blogspot.com/2020/02/us-operational-weather-prediction-is.html
Most things in the .gov have overlap and redundancy. Thank (or blame) the Cold War for that. Sometimes its a breeding ground of new perspectives and innovations. Sometimes...its just straight up waste.
ReplyDeleteUltimately, leadership is a choice and it requires those who believe in a purpose. If we are talking about civic duty and benefit for all, well, there are some headwinds right now. Our national mindset has 1980s greed minus the 1980's Cold War resolve. Its why Trump will always live in Reagan's shadow.
Keep the pressure up on this issue, Cliff! #1, #1, #1 . . .
ReplyDeleteThe hat ruined the entire article. Every thing associated with Trump, dies.
ReplyDeleteUnrelated, but...
ReplyDeleteDo you know when you think we'll get our next "warm" wave? Like last year we had one in march. I wanna know when I can be outside with t-shirt again.
You missed one of the best outcomes: by besting the quality of the competing systems (EU/UK) AND making it FREE, it would completely undercut their monopolistic pricing. They'd have to chop their fees or be out of business. :-) Go Cliff!
ReplyDeleteI love the blog, Cliff- it provides an excellent forum to exchange ideas and opinions in this important science. On the issue of European vs US NWP. I think the notion that the Euro global models (UKMET & ECMWF) are superior to the US GFS comes clearly from, at least to the more general public, a few high profile events (Hurricane Sandy being a significant one) along with frequently disseminated plots showing a long historical record of usually 500 mb anomaly correlation coefficient scores showing a continued and consistent lag of the GFS (competitive enough, but it does lag overall and especially during specific synoptic weather periods). This is not to say that the GFS doesn't outperform at times, or even for some major high impact events and hurricanes (NY/Long Island major snow event a number of years back was one). That said, how well does GFS compare in other areas other than 500 mb ACC? Sensible surface weather and precipitation? Organized convective systems? Upper air wind errors? Resolution of important mesoscale flow features? Those comparisons don't seem to get presented enough- especially for the public. Obviously, good global model predictions lead to improved mesoscale forecasts. However... outside of wasting of funding on redundancy (physics, DA, etc), is it also a matter of the US focusing less on global prediction and more on short range nowcasting & LAM mesoscale and convective scale (or even now sub-km) mesoscale modeling efforts? Do the global model errors mainly diverge after about days 3-5? How does the US do in that area of short range and higher resolution vs the Europeans? Eventually, global modeling will serve practically all scales down to probably a km, at least in adaptive meshing approaches. So.. how do you view that future evolution of NWP? Should the US and Euro work harder to merge the best approaches they have developed & lessons learned over the last few decades in both areas of NWP (global & mesoscale LAM) towards a one model for everything approach? The same with data assimilation and ensemble modeling?
ReplyDeleteIn this particular article, you will see a summary, satisfy browse this post. convert $10
ReplyDelete