Predicting the US Drought Monitor (USDM) using Precipitation, Soil Moisture, and Evapotranspiration Anomalies, Part II: Intraseasonal Drought Intensification Forecasts.

Abstract

robabilistic forecasts of US Drought Monitor (USDM) intensification over two, four and eight 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 non-discrete 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 further from this threshold. The method shows skill over most of the US, but is most skillful over the north-central US where the cross-validated Brier Skill Score averages 0.20 for both two and four week forecasts. The eight-week forecasts are less skillful in most locations. The two and four week probabilities have very good reliability. The eight-week probabilities, on the other hand, are noticeably over-confident. For individual drought events, the method shows the most skill when forecasting high amplitude flash droughts and when large regions of the US are experiencing intensifying drought.