Summer Rainfall Forecast Spread in an Ensemble Initialized with Different Soil Moisture Analyses

Eric A. Aligo Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa

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William A. Gallus Jr. Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa

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Moti Segal Department of Agronomy, Iowa State University, Ames, Iowa

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Abstract

The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

Corresponding author address: Eric A. Aligo, Iowa State University, 3010 Agronomy Hall, Ames, IA 50011. Email: ealigo@iastate.edu

Abstract

The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

Corresponding author address: Eric A. Aligo, Iowa State University, 3010 Agronomy Hall, Ames, IA 50011. Email: ealigo@iastate.edu

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