1. NATS 101 Lecture 26 Weather Forecasting 2

2. Review: Key Concepts 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

3. Suite of Official NWS Forecasts CPC Predictions Page

4. 3-Month SST Forecast Most recent 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. 1997-98 El Nino forecast was fairly accurate once El Nino was established Strong La Nina

5. Winter 2007-2008 Outlook latest prediction

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

8. Day 5 and 7 GFS Model

9. 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

10. 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

11. 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

12. 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

13. 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”

14. 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.

15. 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.

16. 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

17. 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.

18. 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

19. 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: Some skill for average temp but not so much for rainfall over period. Forecasts use combination of model forecasts and statistical relationships (e.g. El Nino). 90-360 days: “Slight” skill for SST anomalies.

20. 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

21. 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

22. 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

23. 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?”

24. 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.

25. 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!

26. 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.

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

28. 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.

29. 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

30. 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.

31. 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.

32. 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.

33. 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

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

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

36. 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.

37. Assignment for Next Lecture Topic - Thunderstorms Reading - Ahrens pg 257-271 Problems – 10.1, 10.3, 10.4, 10.5, 10.6, 10.7, 10.16