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NATS 101 Lecture 26 Weather Forecasting 2

NATS 101 Lecture 26 Weather Forecasting 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 (today) Humans modify computer forecasts.

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NATS 101 Lecture 26 Weather Forecasting 2

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  1. NATS 101Lecture 26Weather 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 (today) Humans modify computer forecasts

  3. 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

  4. Suite of Official NWS Forecasts CPC Predictions Page

  5. 3-Month SST Forecasts (Issued 19 Oct 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. 1997-98 El Nino forecast wasvery accurate Moderate El Nino CPC Forecasts

  6. Winter 2006-2007 Outlook(Issued 19 October 2006) CPC 90 Day Predictions

  7. Winter 2004-2005 Outlook(Issued 18 March 2004) CPC Skill

  8. Winter 2004-2005 Outlook(Issued 18 March 2004) CPC Skill

  9. 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

  10. NCEP NAM 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

  11. NCEP NMM Forecasts NWS NMM Link NCEP ZONE forecasts; 4 times per day Run on ~5 km grid (approximately) NMM should give the best Cb 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-wavesusuallyevolve very similarly out to 2 days. Ahrens 2nd 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. 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.

  15. Humans Can Improve Model Forecasts 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. Lower Better Max Temp Accuracy Aguado and Burt Higher Better Rainfall Skill

  16. 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.

  17. 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

  18. 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 20oC reading does not exactly equal 20oC Even a precise measurement at a point location might not accurately represent the big picture - Radiosonde ascent through isolated cumulus

  19. 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

  20. Chaos: Limits to Forecasting • We now know that even if our models wereperfect, 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?”

  21. 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,theyareunpredictable.

  22. Chaos: Kleenex Example • Drop a Kleenex to the floor • Drop a 2nd Kleenex, releasing it from the same spot • Drop a 3rd 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 exact location as a prior drop? NO! AND IT NEVER WILL! Kleenex exhibits chaotic behavior!

  23. 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’s not an artifact of “computers” or “models.”

  24. 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

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

  26. 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.

  27. 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

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