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Class #32: Monday, March 301 Weather Forecasting (continued)

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Presentation on theme: "Class #32: Monday, March 301 Weather Forecasting (continued)"— Presentation transcript:

1 Class #32: Monday, March 301 Weather Forecasting (continued)

2 Class #32: Monday, March 302 6. Numerical Weather Forecasting Rely on extensive calculations. That’s where “numerical” comes from. Use equations, including the gas law, conservation of mass, conservation of energy (1 st law of thermodynamics), conservation of momentum (Newton’s 2 nd law), and conservation of water vapor. Conservation means “all accounted for”.

3 Class #32: Monday, March 303 Numerical Weather Forecasts Couldn’t be accomplished without computers to do the many calculations Concept was established by L.F. Richardson in England in the early 1900s Start with current observations, called initial conditions Then many small forecasts over short time periods “step forward” in time the equations.

4 Class #32: Monday, March 304 Steps in Numerical Weather Forecasting 1. Weather Observations 2. Data Assimilation 3. Forecast model integration (of the equations with time) 4. Forecast tweaking and Broadcasting Models can be short-range or longer

5 Class #32: Monday, March 305 Lewis F. Richardson English scientist who did the first numerical weather forecast Forecast was for a part of Europe His initial conditions allowed gravity waves to grow His time step was numerically unstable— advection changed the winds with errors during his time step

6 Class #32: Monday, March 306

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11 Class #32: Monday, March 3011 More Problems He made a numerical error early on in the extensive series of calculations The result showed a low pressure center that did not occur But the method he developed is used today with great success, because of computers, better mathematical methods, more and better initial data

12 Class #32: Monday, March 3012

13 Class #32: Monday, March 3013

14 Class #32: Monday, March 3014 Initial conditions Today there are many sources of data, spanning the globe, both surface and upper- air, and remote sensing instruments like radar and satellites. Data assimilation merges all this world- wide data, checking for errors, and assigns it to appropriate positions within a grid of points.

15 Fig. 13-7, p. 386 15Class #32: Monday, March 30

16 Fig. 13-7a, p. 386 16Class #32: Monday, March 30

17 Fig. 13-7b, p. 386 17Class #32: Monday, March 30

18 Fig. 13-7c, p. 386 18Class #32: Monday, March 30

19 Fig. 13-7d, p. 386 19Class #32: Monday, March 30

20 Fig. 13-7e, p. 386 20Class #32: Monday, March 30

21 Fig. 13-7f, p. 386 21Class #32: Monday, March 30

22 Fig. 13-7g, p. 386 22Class #32: Monday, March 30

23 Fig. 13-7h, p. 386 23Class #32: Monday, March 30

24 Fig. 13-7i, p. 386 24Class #32: Monday, March 30

25 Fig. 13-7j, p. 386 25Class #32: Monday, March 30

26 Fig. 13-7k, p. 386 26Class #32: Monday, March 30

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28 Class #32: Monday, March 3028

29 Class #32: Monday, March 3029

30 Class #32: Monday, March 3030


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