1. NATS 101-06 Lecture 23 Weather (and Climate) Forecasting

2. Review: ET Cyclones Ingredients for Intensification Strong Temperature Contrast Jet Stream Overhead S/W Trough to West UL Divergence over Surface Low If UL Divergence exceeds LL Inflow, Cyclone Deepens Similar Life Cycles Ahrens, Meteorology Today, 5th Ed. filling deepening

3. Reasons to Forecast Weather & Climate 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 CO 2 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? We will emphasize mid-latitude forecasts out to 15 days where most progress has been made. PLUS comments about climate models where relevant

5. Types of Forecasts Persistence - forecast the future atmospheric state to be the same as current state -Raining today, so forecast rain tomorrow -Useful for few hours to couple days

6. Types of Forecasts Trend - add past change to current condition to obtain forecast for the future state -Useful for few hours to couple days 10 am11 am12 pm 59  F63  F67  F PastNowFuture

7. Types of Forecasts Analog - find past state that is most similar to current state, then forecast same evolution -Difficulty is that no two states exactly alike -Useful for forecasts up to one or two days Can be useful for seasonal forecasts

8. Types of Forecasts Climatology - forecast future state to be same as climatology or average of past weather for date -Forecast July 4th MAX for Tucson to be 100 F -Most accurate for long forecast projections, forecasts longer that 30 days

9. Types of Forecasts Numerical Weather Prediction (NWP) - use mathematical models of physics principles to forecast future state from current conditions. Process involves three major phases 1.Analysis Phase (most expensive piece) 2.Prediction Phase (modeling, computing) 3.Post-Processing Phase (use of products) To justify NWP cost, it must beat forecasts of persistence, trend, analog and climatology

10. Analysis Phase PURPOSE: to generate the best estimate of the state of the atmosphere (e.g. density, press, temp, winds, humidity, clouds), and the state of the ocean and land which are needed to start the next weather forecast cycle The analysis state estimate is a combination of the latest observations and the most recent weather forecast

11. Analysis Phase Current weather conditions are observed around the globe (surface data, radar, weather balloons, satellites, aircraft). Millions of observations are transmitted via the Global Telecommunication System (GTS) to the various weather centers. U.S. center is in D.C. and is named National Centers for Environmental Prediction (NCEP)

12. Analysis Phase The operational weather centers sort, archive, and quality control the observations. Computers then combine the latest observations and most recent weather forecast to generate the weather analysis and draw maps to help us interpret weather patterns. Procedure is called Objective Analysis. Final chart is referred to as an Analysis. Computer models at weather centers make global or national weather forecast maps

13. Courtesy ECMWF Sparse data over oceans and Southern Hemisphere Surface Data

14. Courtesy ECMWF Some buoy data over Southern Hemisphere Surface Buoy Reports

15. Courtesy ECMWF Little data over oceans and Southern Hemisphere Radiosonde Coverage

16. Aircraft Reports Courtesy ECMWF Little data over oceans and Southern Hemisphere

17. Weather Satellites Geostationary Polar Orbit Satellite observations fill data void regions Geostationary Satellites High temporal sampling Lower horizontal resolution Limited vertical information Can’t penetrate clouds (yet) Polar Orbiting Satellites Low temporal sampling Higher horizontal resolution Limited vertical information Ahrens, Figs. 9.5 & 9.6

18. Courtesy ECMWF Obs from Geostationary Satellites

19. Temperature from Polar Satellites Courtesy ECMWF

20. Prediction Phase: Atmospheric Models Weather models are based on mathematical equations that represent the most important aspects of atmospheric behavior - Newton's 2nd Law (density, press, wind) - Conservation of mass (density, wind) - Conservation of energy (temp, wind) - Equation of state (density, press, temp) Governing equations relate time changes of fields to spatial distributions of the fields e.g. warm to south + southerly winds  warming

21. Prediction Phase Analysis of the current atmospheric state (wind, temp, press, moisture) are fed into the model equations Equations are solved for a short time period (~5 minutes) over a large number (10 7 to 10 8 ) of discrete locations called grid points Grid spacing is 5 km to 50 km horizontally and 100 m to 500 m vertically

22. Model Grid Boxes 10-20 km 100-500 m

23. “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

24. 13 km Model Terrain Big mountain ranges, like the Sierra Nevada Range, are resolved. But isolated peaks, like the Catalinas, are not evident! 100 m contour

25. 40 km Model Grid and Terrain

26. NWP Forecasts Next lecture, we will show some analyses and forecasts from the current suite of NCEP forecast products

27. 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

28. Suite of Official NWS Forecasts CPC Predictions Page

29. SST Forecast Example (from January 2006) Forecasts of El Nino and La Nina generally did not forecast present El Nino but some ensemble members did come close

30. 3-Month SST Forecast (Issued October 23, 2006)October 23, 2006 SST forecasts for the El Nino region of tropical Pacific are a crucial component of seasonal and yearly forecasts. Forecasts of El Nino and La Nina show skill out to around 12 months. Current El Nino

31. Winter 2004-2005 Outlook ( Issued 20 October 2005)

32. Winter 2004-2005 Outlook ( Issued 18 March 2004)

34. Day 5 and 7 GFS Model

35. 60 h ETA Forecast (Valid 0000 UTC 5 NOV 2001) NCEP model with finest resolution (12 km grid) ETA model gives the best precipitation forecasts

36. NCEP GFS Forecasts ATMO GFS Link NCEP global forecast; 4 times per day Run on 50 km grid (approximately) GFS gives the best 2-10 day forecasts

37. NCEP GFS Forecasts ATMO NAM Link NCEP CONUS forecast; 4 times per day Run on 12 km grid (approximately) NAM gives the best 24 h precip forecasts

38. Different Forecast Models Different, but equally defensible models produce different forecast evolutions for the same event. Although details of the evolutions differ, the large- waves usually evolve very similarly out to 2 days. Ahrens 2 nd Ed. Akin to Fig 9.1 AVN-ETA-NGM Comparison

39. Forecast Evaluation: Accuracy and Skill Accuracy measures the closeness of a forecast value to a verifying observation Accuracy can be measured by many metrics Skill compares the accuracy of a forecast against the accuracy of a competing forecast A forecast must beat simple competitors: Persistence, Climatology, Random, etc. If forecasts consistently beat these competitors, then the forecasts are said to be “skillful”

40. Example of Accuracy Estimate Absolute Error = | Forecast Value - Observed Value | Error (Tucson) = | 5750 m-5780 m | = | -30 m | = 30 m Error (Newfoundland) = | 5280 m-5540 m | = | -260 m | = 260 m Map average value is around 60 m, a sufficiently small error that the locations of the trough and ridge are accurately forecast 5 Day Forecast Verification Ahrens 2 nd Ed.

41. Example of Skill Estimate Absolute Error (Tucson) = | 5750 m-5780 m | = | -30 m | = 30 m Absolute Error (Climatology) =| 5690 m-5780 m | = | -90 m | = 90 m The error for the model is less than the error for the climatology forecast, so the forecast is said to be skillful relative to climatology. 5 Day Forecast Verification Ahrens 2 nd Ed.

42. Current NWP Performance Aguado and Burt 24 h rainfall forecasts are skillful. Skill decreases with rain amount. Skill varies with season and year. Summer is most difficult season. Skill of NCEP models for rain Seasonal variation in skill for ETA rainfall forecasts

43. How Humans Improve Forecasts Local geography in models is smoothed out. Model forecasts contain small, regional biases. Model surface temperatures must be adjusted, and local rainfall probabilities must be forecast based on experience and statistical models. Small-scale features, such as thunderstorms, must be inferred from long-time experience. If model forecast appears systematically off, human corrects it using current information.

44. Humans Improve Model Forecasts Aguado and Burt Forecasters perform better than automated model and statistical forecasts for 24 and 48 h. Human forecasters play an important role in the forecasting process, especially during severe weather situations that impact public safety. Max Temp Accuracy Rainfall Skill

45. Current Skill 0-12 hrs: Can track individual severe storms 12-48 hrs: Can predict daily weather changes well, including regions threatened by severe weather. 3-5 days: Can predict major winter storms, excessive heat and cold snaps. Rainfall forecasts are less accurate. 6-15 days: Can predict average temp and rain over 5 day period well, but daily changes are not forecast well. 30-90 days: Slight skill for average temp and rainfall over period. Forecasts use combination of model forecasts and statistical relationships (e.g. El Nino). 90-360 days: “Slight” skill for SST anomalies.

46. Why NWP Forecasts Go Awry There are inherent flaws in all NWP models that limit the accuracy and skill of forecasts Computer models idealize the atmosphere Assumptions can be on target for some situations and way off target for others

47. Why NWP Forecasts Go Awry All analyses contain errors Regions with sparse or low quality observations - Oceans have “poorer” data than continents Instruments contain measurement error - A 20 o C reading does not exactly equal 20 o C Even a precise measurement at a point location might not accurately represent the big picture - Radiosonde ascent through isolated cumulus

48. Why NWP Forecasts Go Awry Insufficient resolution Weather features smaller than the grid point spacing do not exist in computer forecasts Interactions between the resolved larger scales and the excluded smaller scales are absent Inadequate representations of physical processes such as friction and heating Energy and moisture transfer at the earth's surface are not precisely known

49. Chaos: Limits to Forecasting We now know that even if our models were perfect, it would still be impossible to predict precisely winter storms beyond 10-14 days There are countless, undetected small errors in our initial analyses of the atmosphere These small disturbances grow with time as the computer projects farther into the future Lorenz posed, “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?”

50. Chaos: Limits to Forecasting After a few days, these initial imperfections dominate forecasts, rendering it useless. Chaotic physical systems are characterized by unpredictable behavior due to their sensitivity to small changes in initial state. Evolutions of chaotic systems in nature might appear random, but they are bounded. Although bounded, they are unpredictable.

51. Chaos: Kleenex Example Drop a Kleenex to the floor Drop a 2 nd Kleenex, releasing it from the same spot Drop a 3 rd Kleenex, releasing it from the same spot, etc. Repeat procedure…1,000,000 times if you like, even try moving closer to the floor Does a Kleenex ever land in the same place as a prior drop? Kleenex exhibits chaotic behavior!

52. Atmospheric Predictability The atmosphere is like a falling Kleenex! The uncertainty in the initial conditions grow during the evolution of a weather forecast. So a point forecast made for a long time will ultimately be worthless, no better than a guess! There is a limited amount of predictability, but only for a short period of time. Loss of predictability is an attribute of nature. It is not an artifact of computer models.

53. Courtesy R. Houze, following Lorenz (1993) A Chaotic System: Ski Slope Many systems in nature are unpredictable Consider a simple ski slope with moguls

54. A Chaotic System: Ski Slope Imagine 7 skis released at top of slope. All skis point in the same direction and have the same velocity, but they start from points separated by 10 cm along top of hill. Paths can be computed from Newton’s 2nd Law and the relevant forces of gravity and friction. The results (on next page) show that the final positions of the skis are unpredictable.

55. All ski tracks are closely bunched prior to 17 m Ski tracks are widely spaced after 17 m Positions at bottom of hill are much farther apart than at top of hill. Final positions of skis are very sensitive to their initial positions. If there is uncertainty in initial position, the final position is unpredictable. Example of Chaotic System The Atmosphere is Chaotic! Lorenz 1993

56. A Smooth Ski Slope Now consider a smooth slope with no moguls. The skis would go downhill in a straight line. The final positions of the skis would always remain 70 cm apart, spaced at 10 cm intervals. Uncertainty in the final prediction, regardless of the forecast length, is no greater than the uncertainty in the initial positions of the skis. A smooth slope is not a chaotic system.

57. Ski Slope Although a chaotic system is ultimately unpredictable, it is somewhat predictable early. Note that the skis are closely spaced to ~17 m. So the positions are fairly predictable at first. After ~17 m, the paths diverge greatly and there is a loss of predictability. The skis have limited predictability.

58. Atmospheric Predictability The atmosphere is like the ski slope with moguls! The uncertainty in the initial conditions grow during the evolution of a weather forecast. So a pinpoint forecast made for a long time in the future is worthless, no better than a guess! There is a limited amount of predictability, but only for a short period of time. Loss of predictability is an attribute of nature. It is not an artifact of computer models.

59. Limits of Predictability What determines the limits of predictability for the atmosphere? Limits dependent on many factors such as: Flow regime Geographic location Spatial scale of disturbance Weather element

60. Sensitivity to Initial Conditions VERIFYING ANALYSIS DAY 3 FORECAST POSITIVE PERTURB DAY 3 FORECAST NEGATIVE PERTURB DAY 3 FORECAST NOT PERTURBED

61. Sensitivity to Initial Conditions VERIFYING ANALYSIS DAY 3 FORECAST POSITIVE DAY 3 FORECAST NEGATIVE DAY 3 FORECAST UNPERTURBED

62. Summary: Key Concepts Forecasts are needed by many users There are several types of forecasts Numerical Weather Prediction (NWP) Use computer models to forecast weather -Analysis Phase -Prediction Phase -Post-Processing Phase Humans modify computer forecasts

63. Summary: Key Concepts National Center for Environment Prediction (NCEP) issues operational forecasts for El Nino tropical SST anomalies Seasonal outlooks 10 to 15 day weather forecasts 2 to 3 day fine scale forecasts

64. Summary: Key Concepts NCEP issues forecasts out to a season. Human forecasters improve NWP forecasts. NWP forecast go awry for several reasons: measurement and analysis errors insufficient model resolution incomplete understanding of physics chaotic behavior and predictability Chaos always limits forecast skill.

65. Assignment for Next Lecture Topic - Thunderstorms Reading - Ahrens pg 257-271 Problems - 10. 1, 3, 4, 5, 6, 7, 16