NATS 101 Lecture 19 Weather Forecasting

1. NATS 101Lecture 19Weather Forecasting Keep Clickers Handy 108 h ensemble forecast valid 1200 utc 20100401 http://www.weatheroffice.gc.ca/ensemble/index_e.html

2. Important Upcoming Deadlines • Apr 5 Monday Homework08 submissions deadline 6:00 PM • Apr 6 Tuesday Quiz 3 • Apr 12 Monday Project Due at turnitin.com submissions deadline 11:00 PM

3. Reasons to Forecast Weather • Should I bring my umbrella to work today? • Should Miami be evacuated for a hurricane? • How much heating oil should a refinery process for the upcoming winter? • Will the average temperature change if CO2 levels double during the next 100 years? • How much to charge for flood insurance? These questions require weather-climate forecasts for today, a few days, months, years, decades

4. Forecasting Questions • How are weather forecasts made? • How accurate are current weather forecasts? • How accurate can weather forecasts be?

5. How can we solve the problem? Simple Approach vs. Complex Approach (a.k.a. Cheap vs. Costly) Simple forecasting approaches should be used as a “sanity check” to see if complex approaches are worth it.

6. Simple Approach #1Persistence 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. TODAY THURSDAY FRIDAY HIGH: 83°F LOW: 70°F HIGH: 83°F LOW: 70°F HIGH: 83°F LOW: 70°F

7. Simple Approach #2Trend 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 AM 12 PM 3 PM 96°F 99°F 93°F

8. Simple Approach #3Climatology 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.

9. 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 Superstorm SURFACE MAP: 1993 Superstorm LOW TRACK

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

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

12. 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)

13. 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 ACHY KNEES; THE OLD FARMER’S ALMANAC…etc.

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

15. Analysis Phase: Surface Data Sparse data over oceans and Southern Hemisphere Courtesy ECMWF

16. 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.

17. Analysis Phase: Surface Buoys Drifter (red) and moored (blue) buoys

18. Analysis Phase: Weather Balloons

19. Analysis Phase: Aircraft Reports Little data over oceans and Southern Hemisphere Courtesy ECMWF

20. Geostationary Polar Orbit Analysis Phase: Satellites Geostationary Fixed over one location at all times over equator Polar Orbiting Orbit over the poles covering earth in swaths The most important source of data for NWP models Ahrens, Figs. 9.5 & 9.6

21. Geostationary Data Coverage Courtesy ECMWF

22. Polar Orbiter Data Coverage Courtesy ECMWF

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

24. 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

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

26. Structure of atmospheric models Dynamical Core Mathematical expressions of Conservation of motion (i.e. Newton’s 2nd 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.

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

28. Physical Processes in Models Stuff falls between the cracks

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

30. Model Grids

31. ~50 km “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 Rocky Mountains Denver Developing Thunderstorm Cell Source: www.aaccessmaps.co

32. 13 km Model Terrain Big mountain ranges, like the Sierra Nevada, are resolved. But isolated peaks, like the Catalinas, are not evident! 100 m contour

33. By now, it may be be evident that… To run a numerical weather prediction model you need a big HUGE number cruncher!

34. NWP’s First Baby Steps: Mid-Twentieth Century It wasn’t until the development of computers in the 1940s and 1950s that NWP could be even attempted. Even at that, the very first NWP models were pretty basic (simple dynamical core, no parameterizations) Hardware unstable: vacuum tubes in the giant computers often blew. BEFORE THIS TIME, THE METEOROLOGISTS MADE FORECASTS JUST BY READING MAPS AND EXPERIENCE! ENIAC One of the first computers

35. Modern NWP NCAR SUPERCOMPUTER (Millions of $$) LINUX PC CLUSTER (Tens of thousands of $$) Today, NWP models are typically run on supercomputers or networked clusters of PCs. A Linux PC cluster within the UA Atmospheric Sciences Dept. is used to generate forecasts during the monsoon season and for significant weather events during the winter. AND YOU NEED TO HAVE EXCELLENT TECH SUPPORT!!

36. Post-Processing Phase • Computer then draws maps of projected state to help humans interpret weather forecast • Observations, analyses and forecasts are disseminated to private and public agencies, such as the local NWS Forecast Office and UA • Forecasters use the computer maps, along with knowledge of local weather phenomena and model performance, to issue regional forecasts • News media broadcast these forecasts to public

37. Weather vs. Climate Forecasts Climate Forecast Run NWP model for a period longer than two weeks. Objective: Forecast probability of deviation from average conditions, or climatology. Example: In the fall before an El Niño winter, a NWP model forced with warm sea surface temperatures in eastern tropical Pacific projects a circulation pattern favorable for above-average winter precipitation in Arizona. NOT DESIGNED TO PREDICT EXACT WEATHER FOR SPECIFIC PLACES/TIMES MONTHS IN ADVANCE. Weather Forecast Run NWP model for a period up to two weeks (synoptic timescale) Objective: Forecast relatively precise weather conditions at a specific time and place Example: NWP model suggests it will likely rain tomorrow afternoon because mid-latitude cyclone will occur over the U.S.

38. Suite of Official NCEP Forecasts Climate Change Projections CLIMATE FORECASTS WEATHER FORECASTS CPC Predictions Page

39. COMMON PUBLIC MISPERCEPTIONBecause the short-term weather forecast is sometimes wrong, why should we even trust climate forecasts, like seasonal forecasts or global warming projections? LOGICAL FALLACY: The purpose of the climate forecast is confused with that of the weather forecast. A COMMON ARGUMENT MADE BY THE UNINFORMED DON’T FALL VICTIM TO IT!!

40. NWP model types to generate weather and climate forecasts General Circulation Model Vs. Limited Area Model

41. NWP model run over the entire globe Utility: Forecast the evolution of large-scale features, like ridges and troughs. Use to generate long-range weather forecasts (beyond three days), climate forecasts and climate change projections. Disadvantage: Can’t get the local details right because of course resolution and model physics. General Circulation Model (GCM) NCEP Global Forecast System (GFS) Model Grid spacing = ~100 km

42. NWP model run over a specific region Utility: Very good for short-term weather forecasting (up to 3 days) Provides high enough spatial resolution for a detailed local forecast (like thunderstorms in AZ). May also be useful for climate forecasting. Disadvantage: Dependent on a larger-scale model (GCM) for information on its lateral boundaries. Limited Area Model (LAM) Weather Research and Forecasting (WRF) Model

43. Forecast Surface TemperatureGCM vs. LAM General Circulation Model Limited Area Model

44. Different Models, Different Forecasts! Why different? Due to all of the various components of the specific modeling system Analysis scheme Model dynamical core + parameterizations

45. So should we just let the computer do all the job of forecasting? NO! The meteorologist DOES add value and can play a VERY important role in improving forecasts IF he/she keeps to appropriate applications!

46. Value Added of the Meteorologist Knowledge of local weather and climate Experience Can correct for model biases Knows how the model works and realizes it isn’t just a black box! MOST IMPORTANT: ISSUE WATCHES AND WARNINGS WHEN SEVERE WEATHER THREATENS PUBLIC SAFETY. (Agudo and Burt)

47. So why do forecasts go wrong? Think about ALL the possible caveats we’ve already discussed: Model sensitivity Inadequate data to specify the initial state (analysis) Unresolved scaled scales and physical processes Still is a lot about processes in weather and climate we don’t understand An inexperienced meteorologist EVEN IF WE COULD “FIX” ALL OF THE ABOVE, IT WOULD STILL BE IMPOSSIBLE TO MAKE SKILLFUL AND ACCURATE WEATHER FORECASTS USING A NUMERICAL MODEL BEYOND ABOUT TWO WEEKS.

48. Chaos: A Fixed Limit to Weather Forecasting—Independent of the specific model Chaos: System exhibits erratic behavior in that small errors in the specification of the initial state lead to unpredictable changes sometime in the future. In NWP, there will ALWAYS uncertainty in the specification of the initial state—no way around it! Bottom line: After about two weeks, can’t rely on NWP to provide an accurate and skillful weather forecast. Sometimes called the “butterfly effect” Dr. Ed Lorenz Professor, MIT First one to describe chaos

49. Beyond the two week limit, any forecast with a NWP model is a climate forecast because it has lost the sensitivity to the initial state. Why is there STILL is value in the climate forecast? These can project the probability of departure from average conditions due to factors that vary on a long-time scale Examples of long term forcing: ocean temperatures, soil moisture, increase in CO2

50. CPC Winter Climate Forecast vs. Obs. Temperature forecast Precipitation forecast Observed temperature anomalies Observed precipitation anomalies Why was this 2010 forecast a bust in Arizona?