Decision-Oriented Sub-seasonal Forecasts for Alaska

Transcription

1 Decision-Oriented Sub-seasonal Forecasts for Alaska Richard James, Jeremy Ross, and John Dutton Prescient Weather Ltd Climate Prediction Applications Science Workshop 2017 Anchorage, AK

2 Sub-seasonal Forecasts: Accelerating Demand Vitart et al. (2017) Demands are growing rapidly for forecasts that fill the gap between medium-range and seasonal forecasts. This time scale is critical for proactive disaster mitigation efforts U.S. Bureau of Reclamation (2017) Lacking skillful sub-seasonal information limits water managers ability prepare for shifts in hydrologic regimes, such as the onset of drought or occurrence of wet weather extremes National Academy of Sciences committee (2016) Our committee puts forward a vision that S2S forecasts will be as widely used a decade from now as weather forecasts are today

3 Sub-seasonal Forecasts: But Notable Challenges - Relatively Low Skill - Deterministic Mindset of Users/Providers - Perception of Minimal Value Benefits to Society are Limited by Low Forecast Uptake and Inappropriate Use

4 A discouraging example July 4 Anchorage Forecast issued April 24

5 Sub-seasonal Forecasts: A Conceptual Framework - The Value of Forecasts Lies in Decisions - The Ideal Decision Process is Quantitative - Reliable Probabilities are Essential We propose: a reliable low-skill forecast can be more valuable than a deterministic or unreliable higher-skill forecast

6 Sub-seasonal Forecasts: A Conceptual Framework Reliability: correspondence between forecast probability and observed frequency - The Value of Forecasts Lies in Decisions - The Ideal Decision Process is Quantitative - Reliable Probabilities are Essential We propose: a reliable low-skill forecast can be more valuable than a deterministic or unreliable higher-skill forecast

7 Sub-seasonal Forecasts: A Conceptual Framework - The Value of Forecasts Lies in Decisions - The Ideal Decision Process is Quantitative - Reliable Probabilities are Essential We propose: a reliable low-skill forecast can be more valuable than a deterministic or unreliable higher-skill forecast

8 Example of under-confidence

9 Improved Calibra;on Model Improvements Mul;-Model Ensemble

10 Economic Value of Forecasts (Wilks 2001) Cost/Loss Ra;o

11 The CPC forecasts are valuable

12 The CPC forecasts are valuable Peak value where forecasts most ogen affect the decision

13 but they could be improved

14 Implications of under-confidence Users decline protec;on when they shouldn t Users protect when there s no need

15 Decision-Oriented Forecasts: How? - Calibrate the Probability Forecasts - Motivate the Users - Predict Decision Variables Reliable Probabilities FORECASTS Of Decision Variables ACTION

16 Forecast Examples for Alaska - Fairbanks 2m Temperature - Bering Strait MSLP - Deadhorse 10m Wind Speed Retrospec;ve Ensemble Forecasts Historical Observa;ons CALIBRATION Calibrated Probability Forecasts VERIFICATION

17 Weekly Tercile Probability Forecasts Fairbanks 2m Temperature Dec-Feb

18

19 Dec-Feb Mar-May Jun-Aug Sep-Nov

20 Weekly Tercile Probability Forecasts Bering Strait MSLP Dec-Feb

21 Weekly Tercile Probability Forecasts Deadhorse 10m Wind Speed Dec-Feb

22 Let s talk about extremes - Many users are particularly sensitive to extremes - Forecast value is optimized when the forecast categories match user decision thresholds - Calibration for extremes is difficult but essential

23 Weekly Decile Probability Forecasts Fairbanks 2m Temperature Dec-Feb

24 Dec-Feb Mar-May Jun-Aug Sep-Nov

25 Weekly Decile Probability Forecasts Bering Strait MSLP Dec-Feb

26 Weekly Decile Probability Forecasts Deadhorse 10m Wind Speed Dec-Feb

27 Conclusions Sub-seasonal dynamical model forecasts are skillful enough to provide actionable guidance Forecasts are decision-oriented when expressed as reliable probabilities of action variables The expected long-term value of such forecasts can be quantified from verification statistics