The Fog Bowls of Washington State

 The visible satellite image this morning around 10 AM is impressive, with low clouds and fog enveloping the lowlands of Puget Sound, the Willamette Valley, and the entire Columbia Basin.

The clouds are associated with cool, dense, moist air trapped within the lower elevations of regional basins.

The cold air is quite shallow.   I can demonstrate this with the temperatures at SeaTac around noon today (below).  Temperatures were in the 30s near the surface, while lower 50s were observed above roughly 2200 ft.

A strong inversion, with temperature increasing with elevation, was evident between them.

Or consider the radiosonde (ballon-based) temperatures at Salem, Oregon, within the foggy Willamette Valley (see below, temperatures in °C, 0 is freezing, 10C is 50F), 900 hPa on Y-axis is around 3200 ft).  This is from 4 AM this morning.

The right line is temperature and the left line is dewpoint.  A cool, saturated sub-freezing layer near the surface is below a very strong inversion where temperature increases by about 15C (27F) over about 1000 ft. 

These are wonderful days for hiking in the local foothills.   Consider Tiger Mountain near Issaquah.  Frosty and cloudy when you start but in the 50s with bright sun at the top (around 2500 ft).

Inversions are very stable features, meaning that they suppress vertical motions.   Because of this, they can act as a lid for low-level pollutants from fireplaces, wood stoves, and other sources of particles, resulting in declining low-level air quality.

If you would like proof, take a look at the air quality situation near the surface around noon. Orange and red colors are the worst, green is good.

Notice the excellent air quality at high elevations above the cloudy murk.

If you want to see further proof of how temperatures increased with height this morning, consider the minimum temperatures today around Bellingham and Mount Baker (see below).  

Below freezing and icy around Bellingham, but in the low 40s at higher elevations.

This kind of dry, frosty lowland pattern during the winter is associated with a ridge of high pressure aloft (see upper-level, 500 hPa map at 4 PM below).  Red indicated much higher heights than normal.

With high pressure aloft, near-surface winds are light and there is strong sinking aloft, causing temperatures aloft to warm by compression.  A lack of clouds with ridges allows the surface to cool by emitting infrared radiation from the surface.

This is our fog producer.

Make American Weather Prediction Great Again!

U.S. government weather prediction once led the world.

That is no longer true. 

NOAA/National Weather Service (NWS) global numerical weather prediction, once the world leader, is now in third or fourth place.  And very far behind the state of the science.

National Weather Service official weather forecasts are far less skillful than private sector predictions, such as Weather.com or AccuWeather. 

The U.S. has the largest and technically most proficient weather research community and spends more than any other nation or collection of nations on weather prediction, yet official U.S. government forecasts are lagging behind other nations and the potential of the technology.

The cost of such inferior forecasts is substantial, resulting in unnecessary economic damage and loss of life.

This blog will describe the problem and how we can regain leadership in a few years if only the necessary reforms and reorganization steps are made.

I should note that I have published extensively on this topic in the peer-reviewed literature, have been a member of several national committees on this subject, and have testified in Congress regarding U.S. weather prediction.

The Proof

There are many ways to demonstrate that U.S. government weather prediction is lagging.

ForecastAdvisor.com evaluated providers of weather forecasts for many cities around the U.S.  The situation in Seattle is representative (see below).  The National Weather Service (NWS Digital Forecast) greatly lags behind the leaders, with the WeatherChannel being number one.

U.S. global prediction by the National Weather Service (NOAA) lags substantially behind the world leader (the European Center), as shown below, and is not catching up (red is European Center, black is NOAA).   In fact, the U.S. is also behind the UK Met. Office and the Canadians as well.

Most people get their weather from smartphones today.   That forecast does not come from NOAA or the NWS.  For example, Apple iPhones used to provide the WeatherChannel forecast but now Apple does it itself combining forecasts from many sources.  

Machine learning approaches to weather prediction are exploding and have been shown to be more skillful than traditional global models.   The European Center has aggressively developed this technology and provides operational machine-learning forecasts.   

NOAA has no operational machine-learning forecasts and the American people are not enjoying the substantial benefits that would result from a more aggressive use of this promising technology.

Why is U.S. Government Weather Prediction Lagging?

The reasons are fairly clear:

• The U.S. numerical weather prediction effort is divided, with NOAA, the U.S. Navy, the U.S. Air Force, NASA, and the Department of Energy all funding substantial efforts.   Resources are sub-optimal at each agency and uncoordinated.  All substantially lag the European Center in forecast skill.
• The capabilities of the large U.S. weather research community are not sufficiently applied by government efforts.   This is particularly true of the university-based National Center for Atmospheric Research, which has developed an advanced global prediction model that is not used for global prediction by NOAA or other agencies.  
• NOAA/NWS has grown a large inefficient bureaucracy for the management and development of numerical weather prediction.   Responsibilities are divided and duplication is extensive.

                          Too many cooks spoil the broth

How U.S. Weather Prediction Could Rapidly Become the Best in the World

The European Center is currently the best, but there is little reason why US weather prediction could not be much better.   Quite honestly, it is fairly clear what needs to be done.

After working on this issue for years and talking to dozens of weather-prediction scientists, and being further informed by off-the-record conversations with NOAA, Navy, and Air Force weather personnel, let me describe how rapid progress could be made.

(1)   Reduce or eliminate duplication of effort.   

        U.S. resources should be concentrated in one national developmental effort.

(2) U.S. numerical weather prediction research and development should occur in one national center, outside of NOAA or any other Federal agency.

        The key development and testing efforts should not be dominated by any one agency and should be outside of the Federal government to allow flexible hiring and firing of personnel.  The U.S. weather research community, including American universities and the National Center for Atmospheric Research (NCAR/UCAR) should be key participants.  Major private sector players should also be invited to participate. 

One group or individual should be given responsibility for success in weather prediction and nothing else.  They should be removed if not successful.

(3)   Sufficient computer and research/development resources should be made available

    In the past, grossly insufficient computer resources have been available for development and for running weather prediction models in the U.S.   In contrast, the European Center possesses vastly more computer resources.  Massive computer resources have been available to the Department of Energy and other agencies for other efforts---weather prediction needs similar priority.

(4) An organized effort to comprehensively improve weather prediction models needs to be made.

    Current U.S. weather prediction models have major flaws and there is no rational, comprehensive program to perfect them

The benefits of improved weather prediction to the American economy and for saving lives/property are profound and numerous.  There are obvious steps, such as running global models at high resolution (3-km grid spacing or less), and machine-learning-based prediction, that are guaranteed to provide rapid improvement in forecast skill.    Effective organization and reduction of duplication and waste may well SAVE money.

Better forecasts at a reduced cost.  That is what is possible.

After two decades of working on this issue, I and others have become somewhat pessimistic about the U.S. government's ability to deal with the failure mode that NOAA and other agencies have found themselves in.  A recent NOAA administrator told me of his frustration in trying to make progress, with his efforts stymied by middle-level bureaucrats more interested in turf than serving the American people.

But now, with the new administration's DOGE program, headed by Elon Musk and Vivek Ramaswamy, perhaps there is a chance that the wasteful and ineffective current approaches by NOAA and other agencies will be addressed.  This situation is EXACTLY the kind of government failure mode that DOGE should take on.

Finally, let me note that the above does not represent a criticism of scientists and developers in NOAA, DOD, NASA, or other agencies.  

I know many of them personally and most are motivated and technically proficient.  The problem is ineffective and poorly structured efforts in Federal agencies, with some administrators apparently content to foster duplication and waste, with little concern for the importance of excellence in weather prediction.