Predicting the U.S. Drought Monitor Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part II: Intraseasonal Drought Intensification Forecasts

Predicting the U.S. Drought Monitor Using Precipitation, Soil Moisture, and Evapotranspiration... AbstractProbabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Predicting the U.S. Drought Monitor Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part II: Intraseasonal Drought Intensification Forecasts

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
eISSN
1525-7541
D.O.I.
10.1175/JHM-D-16-0067.1
Publisher site
See Article on Publisher Site

Abstract

AbstractProbabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Jul 15, 2017

References

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