John A. Dutton, Richard P. James, Jeremy D. Ross, Prescient Weather Ltd
Short-term climate variability on the scale of weeks to seasons creates both risk and opportunity for a wide range of private and public endeavors. Some climate variations will facilitate favorable operational and financial performance; others may hinder activities or lead to unfavorable financial returns.
Sufficiently skillful subseasonal to seasonal (S2S) probability forecasts can assist in mitigating the adverse effects of climate variability by answering the key question for decision makers who face climate risk or opportunity:
What consequences can we expect if we act at predicted
probabilities >= p for adverse or favorable conditions?
S2S probability forecasts are created by analyzing the statistical distributions of ensemble forecasts generated for weeks or months ahead by computer Earth System simulations. Dividing the range of possible events into a set of potentially adverse events and a set of favorable (or not adverse) events simplifies the verification and the analysis of possible outcomes and creates some useful statistical symmetries. The performance and value of S2S probability forecasts can be improved significantly with sufficiently robust calibration of forecasts based on comparison of extended forecast histories with verification data.
The three critical metrics for assessing forecast performance are the reliability, which demonstrates whether the forecast calibration is successful, the fraction of adverse forecasts, and the fraction of correct forecasts—all three dependent on the predicted probability. The fraction of correct forecasts for the above and below normal terciles will exceed the predicted probability p for p >1/3 for successful calibration processes.
A compact model describing possible outcomes of a business or other activity combines with a matrix of probabilities based on the forecast performance functions to estimate the expected values and volatility of key business variables in response to climate variability. The model will also demonstrate the statistical consequences of hedges or other action taken to mitigate predicted adverse S2S conditions.
Verification statistics for the S2S probability forecasts of The World Climate Service (WCS) provide statistical illustrations of the consequences of climate variability and mitigation strategies. The WCS offers a calibrated and optimized multi-model combination of the S2S ensemble forecasts of the U.S. National Weather Service (NWS) and the European Centre for Medium-Range Weather Forecasts (ECMWF), and it computes and publishes verification and skill information for both retrospective and recent forecasts. With NOAA small business support, WCS developed and is offering S2S forecasts for degree days, wind, and solar insolation, in addition to traditional meteorological variables like temperature, precipitation, surface pressure, and 500 mb height.
The results demonstrate that the WCS forecasts are sufficiently skillful to confer advantage to users in hedge transactions and other mitigation strategies, thus enabling decision makers to act on forecast with confidence in the consequences.
Today, the creation, use, and value of S2S forecasts are all advancing rapidly because of two important developments. First, firms preparing value-added forecasts can now choose from a broader array of forecast models, including the NOAA CFS, the seasonal NMME and the subseasonal SubX forecasts assembled by NOAA, the ECMWF S2S forecasts, and a growing suite of S2S models offered by the European Union through the Copernicus project. Second, preparation of user-focused forecasts is increasingly facilitated by digital transmission of customized forecast information directly from the private forecast firm to the user’s computer decision systems via the Internet.
Acknowledgments
Prescient Weather Ltd research reported here was supported by the National Oceanic and Atmospheric Agency with Contracts WC133R-11-CN-0147 and WC-133R-16-CN-0103.
This presentation draws on some concepts and results presented previously, including:
Dutton, John A., Richard P. James, Jeremy D. Ross, 2018, Transforming Risk Management with Probability Forecasts: Weeks to a Season or More. National Earth System Prediction Workshop, Metrics, Post-Processing, and Products for S2S, College Park, MD., 28 Feb-2 Mar 2018.
Dutton, John A., Richard P. James, Jeremy D. Ross, 2017. Probability Forecasts for Energy–Weeks to a Century or More, in Weather and Climate Services for the Energy Industry, Alberto Troccoli, ed., 197 pp., an open source Palgrave Pivot book available at
https://link.springer.com/content/pdf/10.1007%2F978-3-319-68418-5.pdf.
Dutton, John A., Richard P. James, Jeremy D. Ross, 2015. Bridging the Gap Between Subseasonal and Seasonal Forecasts and Decisions to Act, AMS Annual Meeting, 7 January 2015, https://ams.confe12.com/ams/95Annual/webprogram/Paper260171.html
Dutton, John A., Richard P. James, Jeremy D. Ross, 2014. A Probabilistic View of Weather, Climate, and the Energy Industry, Weather Matters for Energy, Alberto Troccoli et al., eds., 353-378, Springer.
Dutton, John A., Richard P. James, Jeremy D. Ross, 2013. Calibration and Combination of Dynamical Seasonal Forecasts to Enhance the Value of Predicted Probabilities for Managing Risk, Climate Dynamics 40, 3089-3105.
James, Richard P., Jeremy D. Ross, John A. Dutton, 2014. Skill of a New Two- to -Six Week Forecast System. AMS Annual Meeting, 4 February 2014, https://ams.confe12.com/ams/94Annual/webprogram/Paper233913.html
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