2. Some of your guesses for my Halloween costume…

12. Announcements Those of you who submitted a guess for my costume will all get an extra point on Exam #2! Last lab groups hand in kits today.

13. Which statement best describes your opinion of those who forecast the weather? A)They’re almost always right and I always believe them B)They’re right some of the time, but I still believe them. C)They’re only right occasionally and probably can’t be trusted. D)They’re always wrong and I don’t understand what they get paid for.

14. NATS 101 Section 4: Lecture 24 Weather Forecasting Part I

15. A few of you asked in your questions at the beginning of the semester about how the weather affects health… There are some people out there that claim their bodies are affected by the weather—and they even claim they can even predict what’s coming!

16. A Weather Forecaster? Ms. Betty Martin of Newburgh, IN Age 94 Betty claims to know exactly when it’s going to rain because her knee starts to hurt. X NEWBURGH, IN

17. So should the meteorologists at the National Weather Service in Paducah, KY all quit and let dear old Betty do the job? In my professional opinion, probably not. Though she’d certainly crack a few jokes to lighten them up.

18. Forecasting weather and climate is REALLY important—and that is the main reason why use our tax dollars to do it! Goes to the core of one of reasons to study weather and climate I mentioned the first day of class.

19. Like death and taxes, weather and climate affects all of us every day in big and small ways. …and human beings may be affecting it! Hurricane Katrina on August 29, 2005 (NOAA imagery).

20. So how can we solve the problem? Simple approach vs. complex approach The simple forecasting approaches should be used as a “sanity check” to see if the complex approach are worth it.

21. Simple Approach #1 Persistence Forecast Persistence: Future atmospheric state is the same as the current state. Good Example: Tropical rainforest during wet season when the ITCZ is around. It’s raining today, so predict rain for tomorrow. HIGH: 83°F LOW: 70°F HIGH: 83°F LOW: 70°F HIGH: 83°F LOW: 70°F TODAYTHURSDAYFRIDAY

22. Simple Approach #2 Trend forecast Trend: Add past change to current condition to obtain forecast for future state Good Example: Temperature in Tucson increasing at 3°F per hour in the morning on a clear, calm day. Use this to forecast temperatures later in afternoon because the surface heats at a steady rate due to solar heating. 9 AM12 PM3 PM 93°F 96°F99°F

23. Simple Approach #3 Climatology forecast Climatology: Forecast future state as the average of past weather for a given period Good example: Forecast about six inches of rain to occur during the monsoon in Tucson, the average for the 1971-2000 period.

24. Simple approach #4: Analog forecast Analog: Find a previous atmospheric state that is like the current state and forecast the same evolution. This one does require some more skill because no two situations are EVER exactly alike… Good example: If a surface low pressure forms in the eastern Gulf of Mexico with a deep upper-level trough to the west, a Nor’ester will roll up the Eastern seaboard—like the 1993 Superstorm 500-mb MAP: 1993 SuperstormSURFACE MAP: 1993 Superstorm LOW TRACK

25. One more simple approach—and perhaps the one meteorologists and climatologists like me try their hardest to beat but are always asked about…

26. Simple Approach #5 FORECAST FROM SOMEBODY WHO IS NOT A METEOROLOGIST BASED ON WHATEVER THEY PLEASE!

27. The complicated way to make a forecast is to use a physical and mathematical model of the atmosphere, starting from an observed state at an initial time. This is called Numerical Weather Prediction (NWP)

28. Why do Numerical Weather Prediction? NUMERICAL WEATHER PREDICTION IS ONLY USEFUL IF YOU CAN SHOW IT GIVES A BETTER FORECAST THAN ALL THE SIMPLE WAYS TO MAKE A FORECAST: PERSISTENCE TREND CLIMATOLOGY ANALOG BETTY MARTIN’S KNEE, THE OLD FARMER’S ALMANAC…etc.

29. Steps in Numerical Weather Prediction 1.ANALYSIS: Gather the data (from various sources) 2.PREDICTION: Run the NWP model 3.POST-PROCESSING: Display and use products

30. Analysis Phase: Surface data Surface data comes from surface meteorological stations and ships at sea.

31. ASOS: Automated Surface Observing System Electronic sensors to measure all elements of weather: Temperature Pressure Moisture Wind speed and direction Visibility Precipitation and precipitation type Located at virtually every major airport. Many observations you see on a surface map are taken from ASOS.

32. Analysis phase: Ocean data Drifting and moored ocean buoys. TAO ARRAY

33. Analysis Phase: Upper air data from radiosondes (weather balloons)

34. Analysis Phase: Aircraft reports

35. Analysis Phase: Satellites Geostationary Polar Orbit Geostationary: Fixed over one location at all times directly over equator. Polar: Orbit over the poles, covering the Earth in swaths.

36. Geostationary satellite data coverage

37. Polar satellite data coverage

38. So we get all that data, say about every six hours or so. Now what?

39. Objective Analysis Data must be interpolated to some kind of grid so we can run the numerical weather prediction model—this is called the initial analysis. For a regional model these are equally spaced points. Grid spacing = 35 km

40. Now the “fun” begins— actually running the model to make a prediction! But how do NWP models work? Not a simple answer!!

41. Structure of atmospheric models Dynamical Core Mathematical expressions of Conservation of motion (i.e. Newton’s 2 nd law F = ma) Conservation of mass Conservation of energy Conservation of water These must be discretized to solve on a grid at given time interval, starting from the initial conditions (analysis). Parameterizations One dimensional column models which represent processes that cannot be resolved on the grid. Called the model “physics”—but it is essentially engineering code.

42. Still, some out there would look at all that and say it’s not worth it… Betty Martin, for example, would probably be getting a bit ornery by this point in my lecture!

43. Oh, phooey…come on, forecastin’ ain’t that hard! Now listen here, I just ask my knee! It ain’t been wrong in thirty years! What’s your model got on that? TO KINDLY EDIFY YOU, BETTY: IT IS VERY, VERY HARD—AND WORTH IT!

44. Equations represented in dynamic core MUST SOLVE AT EVERY GRID POINT! MASS CONSERVATION ENERGY CONSERVATION CONSERVATION OF MOTION CONSERVATION OF MOISTURE Why is just doing this REALLY, REALLY HARD? Have discretize the equations, so they can be solved on a grid. Equations are non-linear. We haven’t even accounted for parameterizations yet! (Pielke 2002)

45. Parameterized processes One-dimensional models MOST OF THESE REPRESENTED AS 1-D PROCESSES—WITH ESSENTIALLY ENGINEERING CODE.

46. Dynamic core Discretized dynamical equations Precipitation processes Radiation Land surface energy balance Boundary layer Turbulent diffusion Boundary conditions

48. “A Lot Happens Inside a Grid Box” (Tom Hamill, CDC/NOAA) Approximate Size of One Grid Box for NCEP Global Ensemble Model Note Variability in Elevation, Ground Cover, Land Use Source: www.aaccessmaps.co Rocky Mountains Denver 50 km

49. 13 km Model Terrain 100 m contour Big mountain ranges, like the Sierra Nevada, are resolved. But isolated peaks, like the Catalina’s, are not evident.

50. What’s Betty got to say now?

51. WHEEEW….LORDY!! Well you can just keep playin’ on your computer! Hey, why are you dressed all in black like that today? You ain’t goin’ to my funeral just yet!!

52. Summary of Lecture 24 Weather and climate forecasting is really important, but a very challenging problem. Simple approaches to forecasting include: persistence, trend, climatology, and analog. It must be demonstrated that any other forecasting methodology can beat these to show it’s useful. NWP is the use of a physical and mathematical model to represent the atmosphere, starting from an observed state at an initial time. In the analysis phase of NWP, data is gathered from a variety of sources, such as: surface stations, buoys, radiosondes, aircraft, and satellites. These data are then objectively analyzed to a grid. A NWP model consists of a dynamical core and (one-dimensional) parameterizations to represent sub-grid scale processes. “Run” a NWP model by solving the dynamical equations and parameterizations forward in time.