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Is Weather and Climate Prediction Deterministic or Stochastic? Ensemble Forecasting: a new era forweather and climate study
Dr. Jun Du NCEP/NOAA [email protected]
Based on the talk to the U.S. National Academy of Sciences on August 4 th, 2005, Washington, DC
Using weather forecasting as example but the same principle
should be applied to climate prediction since both weather and
climate study use same model (only with different forcing such as doubling CO2 or deforestation for
climate change study)
NWS National Digital Forecast Database (NDFD)
Deterministic !!!!!!!!!!!!!!!!!!!!!!!!
Deterministic too!!!!!!!!!!!!!!!!!!!!!!!!
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X = Xm( current deterministic practice and scientists’ dream too)
X = Xm + X0( actual realization, X0 is unknown)
Numerical Weather Prediction system
• Observation• Data assimilation (prepare initial
conditions to initiate model integration)
• Prediction (model integration: model dynamics, physics)
• Application to real world situation
EarthObservations
Models need to represent many irresolvable processes
One thing certain: Uncertainties in all steps!
They are intrinsic, unavoidable and could be random
Lorenz
• “… one flap of a sea-gull’s wing may forever change the future course of the weather” (Lorenz, 1963): butterfly effect
(A) 00z, Oct. 3 – 00z, Oct. 19, 2006 (B) 06z, Oct. 3 – 06z, Oct. 19, 2006
Two consecutive NCEP operational Global Forecasting System (GFS) 16-day 500mb HGT/VORT forecasts (with only 6hr-hour apart in initial conditions)!
X=> {Xm + X’} (X’ is a kind of distribution)
How to estimate X’?• Traditionally, using statistical approach based on
model’s historical performance over a period of time in the past to estimate either X’ (PoP) or X0 (MOS, Perfect Prog)
• Problem 1: not flow-dependent but model’s systematic error in the past (not necessary to reflect “error of the day”)
• Problem 2: not work well if a model changes frequently (a common practice)
Our Fundamental Problem:a dynamical approach: starting from some current states to be projected to some future states within
the limits of predictability
Chaos as a motivator for probabilistic forecasting
Ensemble Product type in general:
1. single outcome type: mean, median, mode, extremes, consensus, …
2. uncertainty: spread, confidence factor, predictability measure …
3. distribution type: probability, spaghetti, clustering, envelope …
(mode)
Four Major Tasks:
1. How to capture uncertainty in a forecasting system?
2. How to convey uncertainty to users and public?
3. How to use uncertainty and probabilistic information in decision-making? 4. How to possibly reduce uncertainty to better serve people and society?
Task 1: How to capture uncertainty in a forecasting system?
*IC aspect: (1) perturb analysis (bred vector, ET/ETKF, singular vector, random), (2) multi-analysis (gdas, ndas)
*Model aspect:(1) multi-model (Eta, RSM, NMM, ARW) (2) multi-physics (GFS, Eta, MM5/ BMJ, KF, SAS, RAS, LSM, cloud, PBL, radiation …), (3) stochastic physics
*Residual Part: statistical post processing
http://www.emc.ncep.noaa.gov/mmb/SREF/SREF.html
How NCEP Short-Range Ensemble Forecasting (SREF) system to capture uncertainties?
f12 f12
f24 f24
worst member best member
~10”
2.5-5”
Task 2: How to convey uncertainty to users and public?
Psychological Effects
• 100 balls in a jar: 10 red and 90 blue.
• Driving after Sept. 11, 2001 US terrorist attack.
• Riding bicycle after July 7, 2005 London Bombing.
• 10 balls in a jar: 1 red and 9 blue.
• Flying after Sept. 11, 2001 US terrorist attack.
• Taking bus/metro after July 7, 2005 London Bombing.
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11/18 12/00 06 12 18 13/00 06 12 18 14/00 06 Valid Time (UTC)
Misawa AB, JapanMisawa AB, Japan
Win
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AFWA Forecast MultimeteogramJME Cycle: 11Nov06, 18ZRWY: 100/280
15km Resolution
Win
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90%CI
Extreme Min
ExtremeMax
Mean
Multimeteogram(uses Confidence Intervals, as opposed to a plume plot that shows data from all members)
Task 3: How to use Uncertainty and probabilistic information in decisionmaking? (a forecast’s value depends
on user’s decision and action!)
About probabilistic forecast
• What does a probabilistic forecast mean?
e.g. 60% of an event. If the same forecast is predicted 100 times either in time or location, 60 times will realize and 40 time will not in a perfect ensemble system or perfectly reliable probabilistic
forecast.
• How to verify a probabilistic forecast?
Based on the above, “event occurs” not= correct fcst; “does not occur” not= wrong; but reliability measure is needed
• How to use a probabilistic forecast?
Use economic value or social significance
Obs freq. (%) 50
Fcst prob. (%)1
A perfect prob fcst
10050
100
Reliability diagram
Decision-making based on a probabilistic forecastIf event occurs without action taken bigger loss (Lp+Lu)
If event occurs with action taken smaller loss (Lu) + cost
If event doesn’t occur but with action taken cost
save = (Lp+Lu)– (Lu + cost)=Lp-cost
For a reliable P% probability forecast:Benefit = save * P%Risk = cost * (1-P%)
Benefit
Risk=
>1.0, take action
= 1.0, neutral
< 1.0, no action
Which is a function of location (cities, rural area …), time (rush hour, weekend …), social impact or event significance etc., an area needs our more attention and efforts!
How to Convert Probabilistic Weather Forecast Information into Decision/Action?o convert
User 1
User 2User 3
User 4
Benefit/Risk ratio
1.0
0.0 10 20 30 40 50 60 70 80 90 100%prob
Economic value (Benefit/Risk ratio) based decision-making diagram
Task 4: How to possibly reduce forecast uncertainty to better serve people and society?
Single deterministic run Stochastic ensemble runs
Comparison between traditional single-forecast
based NWP and new ensemble-based NWP
• Does not consider uncertainty in both observation and prediction (single-value, deterministic view)
• One-way system: observation>forecast>application
• Consider uncertainty in both observation and prediction (PDF distribution, stochastic view)
• Two-way system: observation<>forecast<>application
Interactive targeting: tracing uncertainty source region by using previous SREF cycle
09z, May 11, 2005 SREF SLP spread forecast (previous cycle run)
f63 f57 f48
f36 f24 f12
09z, May 11, 2005 SREF SLP control forecast (previous cycle run)
Interactive targeting: using previous ensemble run to track uncertainty source region
f63 f57 f48
f36 f24 f12
Without extra observation within uncertainty source area:
With Extra observation within uncertainty source area (uncertainty reduced):
f00
f00
F51 – targeted time
F51 – targeted time
21z, May 11, 2005 SREF SLP spread forecast (current cycle)
rms error of SLP by ensemble mean forecast (error reduced in targeted region)
Without extra observation within uncertainty source area:
With extra observation within uncertainty source area:
Winter Storm Reconnaissance Program
Objective:
Improve Forecasts of Significant Winter Weather Events Through Targeted Observations in Data Sparse Northeast Pacific Ocean
Adaptive approach to collection of observational data:
1) Only Prior to Significant Winter Weather Events of Interest
2) Only in Areas that Influence high impact event Forecasts
Results:
70+% of Targeted Numerical Weather Predictions Improve
10-20% error reduction for high impact event forecasts
12-hour gain in predicting high impact events – earlier warnings possible
Operational since January 2001
Summary
(1) Weather forecasting is a stochastic process but not deterministic and need to quantify its uncertainty. Without quantifying forecast uncertainty, a forecast is incomplete.
(2) It’s a fundamental transition and revolutionary change from the current single-forecast based deterministic NWP paradigm to the ensemble-based probabilistic NWP paradigm. It is a new frontier in research and development. Many need to be done at all hands including scientists/developers, forecasters/media and end-users/public:
For Researchers and Developers:(a) Best ways to incorporate initial condition and model uncertainties into NWP forecasting system.(b) How to best convey forecast uncertainty info to users.(c) Nobody like uncertainty, we need to reduce it as much as possible such as the adaptive observation technique.For Forecasters and media: Training is needed on how to better use and interpret ensemble products and to increase forecaster’s human role in possibly reducing uncertainty.For End-Users: Further understanding the nature of probabilistic forecasts and quantify their probability-based decision making process using economic value measure (such as benefit/risk ratio).
References
• Completing the forecast: characterizing uncertainty for better decisions using weather and climate forecasts, 2006 by US National Research Council of the National Academies. The National Academies Press.
• Ensemble forecasting: conveying forecast uncertainty, 2008 by Jun Du. To be included as a new chapter of the book “Handbook of Weather, Climate and Water” edited by Potter and Colman, John Willey & Sons press, (in revision).
