AOSC 200Lesson 21

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.

Fig. 13-1, p. 375

Climatology Forecast of a White Christmas

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.

Fig. 13-3, p. 378

Trend forecast based on the assumption that a mid-latitude cyclone moves up the East coast unchanged.

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

Weather patterns leading up to D-day

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!

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

Data Assimilation

Water vapor image

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

Fig. 13.9

Fig. 13.13

Concept of a stretched-grid model

Richardson’s Model Grid

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

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

ETA 48 hr Prediction – 0Z Nov 20 2004

MRF 48 hr Prediction- 0Z Nov 20, 2004

Forecasting

• Let’s consider a car that travels at constant speed v from point B towards point C

• We can use the equation x = x0 + vt (1) to determine its location (the distance x) at a given time t. x0 is the distance from point A to point B at t=0

Forecasting

INITIAL CONDITION

• This equation comes from a MODEL or idealization of reality.

• If for any reason x0 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

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

Ensemble Forecast