1. AOSC 200 Lesson 21

2. WEATHER FORECASTING FOLKLORE –Red sky at night, shepherd’s delight, –Red sky in morning, shepherd’s warning –When spiders’ webs in air do fly –The spell will soon be very dry PERSISTENCE –The weather tomorrow will be the same as the weather today (two times out of three) CLIMATOLOGY –This takes persistence one step further –The average weather say for a particular month is the same each year * ‘COLD in December – HOT in July’ –English saying: In July the Sun is hot, Is it raining? No it’s not.

5. Fig. 13-1, p. 375

6. Climatology Forecast of a White Christmas

7. TREND AND ANALOG We know that persistence forecasts will eventually be wrong because weather does change. A trend forecast assumes that the weather-causing patterns are themselves unchanging in speed, size, intensity, and direction of movement (this is called steady-state). –For instance: we know that an approaching cyclone will bring rain (weather does change) but assume that the amount of rain or its speed will not change during all the path the cyclone will travel. The analog forecast also acknowledges that weather changes, but unlike the trend method, it assumes that weather patterns can evolve with time. –The main assumption is that weather repeats itself. –Therefore, this method “searches” for past weather patterns that are similar (analog) to the current situation. –In this sense, the future weather patterns “should” be similar to those that happened in the past.

8. Fig. 13-3, p. 378 Trend forecast based on the assumption that a mid-latitude cyclone moves up the East coast unchanged.

9. The D-Day Forecast: June 1944 –Suitable weather for the invasion: Initial invasion around sunrise Initial invasion at low tide Nearly clear skies At least 3 miles of visibility Close to full Moon Relatively light winds Non-stormy seas Good conditions persisting for at least 36 hours, preferably for 4 days –Three meteorology groups worked independently: Analog forecast Bergen Schools: air masses, cyclones and upper level patterns Waves forecast

10. Weather patterns leading up to D-day

11. The D-Day Forecast: June 1944 –First question: What are the odds, month-by-month, that the weather required for the invasion would actually occur? May: 24-to-1 June: 13-to-1 July: 33-to-1 –However, the weather changed from a placid and calm May to a very stormy June. A winter-like pattern not seen in the Atlantic in June in past forty years! –At the beginning the invasion was planned for June 5th but postponed to the 6th due to the weather forecast. This decision turned out to be correct!

12. NUMERICAL WEATHER PREDICTION Step One: Weather Observations Surface observations, Rocket and balloon observations, Satellite observations Step Two: Data Assimilation Model grid and grid points Measurements do not cover all of the globe and are not at set grid points The input data need to be interpolated, smoothed and filtered. This process is called Data Assimilation

13. Data Assimilation

14. Water vapor image

15. NUMERICAL WEATHER PREDICTION Step Three: Forecast Model Integration The measured data (initial conditions) and the “primitive equations” of the atmosphere are used to forecast what the status of the atmosphere will be in the future. In order to get a “good” (accurate and precise) forecast enormous computational resources are needed Step Four: Tweaking and Broadcasting –Current forecasts do not sample the atmosphere on a grid size that picks local events or resolve small scale phenomena Local forecasters use local knowledge and experience to tweak the final forecast for the public

16. Fig. 13.9

17. Fig. 13.13 Concept of a stretched-grid model

18. Richardson’s Model Grid

19. Numerical Weather Prediction Models Short-Range Forecast Models US government uses two models for this purpose ETA model – Run four times per day Rapid Update Cycle (RUC) model– Run every three days Forecast out to 48 hours Medium-Range Forecast Models –Spectral-models –Medium range Forecast (MRF) model Forecast out to 10 days

20. Numerical Weather Prediction Models Why Do Forecasts Still Go Wrong Today? Imperfect data Models cannot solve small scale phenomena: parameterization* Chaos: The atmosphere could react very differently to slightly different initial conditions (non-linear system) – butterfly flapping its wings. Is there any solution? Ensemble forecast Vary initial conditions* Use different models

21. ETA 48 hr Prediction – 0Z Nov 20 2004

22. MRF 48 hr Prediction- 0Z Nov 20, 2004

23. Forecasting Let’s consider a car that travels at constant speed v from point B towards point C We can use the equation x = x 0 + vt (1) to determine its location (the distance x) at a given time t. x 0 is the distance from point A to point B at t=0

24. Forecasting  INITIAL CONDITION This equation comes from a MODEL or idealization of reality. If for any reason x 0 is NOT well known, or there is an “error” in determining the exact location of B, then the equation will give us a different distance to point C

25. Forecasting If we now ask the driver “to go straight” but we don’t give him/her any point of reference (there is no road, trees or anything to use as a reference), the final path could be not as straight as the driver might think Numerical integration takes one small step at a time to move forward

26. Ensemble Forecast