revolutions in remote sensing greatly enhanced weather prediction from the 1950s through today

Revolutions in Remote Sensing Greatly Enhanced Weather Prediction from the 1950s Through Today

Satellite and Weather Radars Give Us a More

Comprehensive View of the Atmosphere

Before Satellites We Had a VERY Poor Knowledge of What Was Happening

Over the Oceans!• As a result, forecasts were often very poor,

particularly in coastal locations.

The 1938 Hurricane was basically unforecast

Weather Satellites Give Us Much More than Pretty Pictures

• We start with imagery in several wavelengths:– Visible– Infrared– Water vapor (wavelengths where we see the

water vapor distribution)

• Plus many new capabilities

Better than Star Trek!

Water Vapor Imagery

Looks at wavelengths where water vapor absorbs and emits infrared radiation

Each wavelengthgives us information

Cloud andWater VaporTrack WindsBased on Geostationary Weather Satellites

GOES sounder unit

Satellite Temperature and Humidity Soundings

QuickScat SatelliteBounces microwaves off the ocean surfaceCapillary waves dependent on wind speed and directon

Weather Radar Has Revolutionized Local

Forecasting

Weather Radar

CamanoIslandWeatherRadar

Radar was first used operationally in WWII by the British to track German planes

• But they found some interference by heavy precipitation!

After WWII Meteorologists Experimented with Military Radars

Hurricane Radar Image

In the late 1950’s a meteorological radar network was established.

In the late 1980s, the NWS put in a network of Doppler WeatherRadarsNEXRADWSR88D

Sound of train passing:http://www.fourmilab.ch/cship/sounds/doppler.au

Now With Two New Radars

But even with all this improving technology, some forecasts fail.

Why?

Weather Radars

• Lets us track thunderstorms and hurricanes• Can see where it is precipitating and where it

is moving.• Many other uses.

Problems with the ModelsSome forecasts fail due to inadequacies in model physics…. How the model handles precipitation, friction, and other processes.Example: too much precipitation on mountain slopes

Some forecasts fail due to poor initialization, i.e., a poor starting description of the atmosphere.

This is particularly a problem for the Pacific Northwest, because we are downstream of a relatively data poor region…the Pacific Ocean.

Pacific AnalysisAt 4 PM18 November2003

Bad Observation

Eta 48 hr SLP Forecast valid 00 UTC 3 March 1999

3 March 1999: Forecast a snowstorm … got a windstorm instead

The problem of initialization should lessen as new observation

technologies come on line and mature.

New ways of using or assimilating weather data are also being

developed.

A More Fundamental Problem

• In a real sense, the way we have been forecasting has been essentially flawed.

• The atmosphere is a chaotic system, in which small differences in the initialization…well within observational error… can have large impacts on the forecasts, particularly for longer forecasts.

• Not unlike a pinball game….

A More Fundamental Problem

• Similarly, uncertainty in our model physics also produces uncertainty in the forecasts.

• Thus, all forecasts have some uncertainty.

This is Ridiculous!

Or this…

Forecast Probabilistically

• We should be using probabilities for all our forecasts or at least providing the range of possibilities.

• There is an approach to handling this issue that is being explored by the forecasting community…ensemble forecasts

Ensemble Prediction

• Instead of making one forecast…make many…each with a slightly different initialization

• Possible to do now with the vastly greater computation resources that are available.

The Thanksgiving Forecast 200142h forecast (valid Thu 10AM)

13: avn*

11: ngps*

12: cmcg*

10: tcwb*

9: ukmo*

8: eta*

Verification

1: cent

7: avn

5: ngps

6: cmcg

4: tcwb

3: ukmo

2: eta

- Reveals high uncertainty in storm track and intensity- Indicates low probability of Puget Sound wind event

SLP and winds

Ensemble Prediction

•Can use ensembles to give the probabilities that some weather feature will occur.

•Can also predict forecast skill!

•It appears that when forecasts are similar, forecast skill is higher.

•When forecasts differ greatly, forecast skill is less.

Probabilistic Prediction

• So instead of saying the temperature in two days will be 67F. We might tell you:

50% probability it will be between 64 and 69F90% probability it will be between 62 and 72F.

ENSEMBLE SYSTEMS

http://www.atmos.washington.edu/~ens/uwme.cgi

http://www.esrl.noaa.gov/psd/map/images/ens/ens.html#us

The Future

• As computer get faster and faster and our understanding of atmospheric processes improve, there will be a transition to higher resolution, more specific, forecasts and more probabilistic information.

• Too much information for TV…so the web and other online media will dominate.

The Limit

• Forecast skill will be pushed out in time—more skill at longer projections.

• But there are theoretical limits and we will probably never be able to forecast specific weather features out past about 2 weeks.

• But we do have some skill out months for average conditions.