Progress in Advancing Drought Monitoring and Prediction


Building on the previous special collection entitled “Advancing Drought Monitoring and Prediction,” this special collection focuses on scientific research to advance the U.S.'s capability to monitor and predict drought, including the development of new data and methodologies. The results presented in this collection represent the outcomes of research in large part funded by NOAA’s National Integrated Drought Information System (NIDIS) through NOAA’s Modeling, Analysis, Predictions and Projections (MAPP) program, also leveraging other U.S. agencies’ investments, and coordinated within the framework of the Third MAPP Drought Task Force (DTF3). The collection is divided broadly into papers addressing monitoring and those addressing the prediction problem, but also includes an important focus on improving our understanding of past droughts. The papers provide a state-of-the-practice / state-of-the-science assessment of the modern drought challenge and efforts to understand and manage it. The collection includes an introductory paper, 10.1175/BAMS-D-20-0087.1, that motivates the research and highlights some recent advances in drought monitoring and prediction systems.

Collection organizers:

Christa D. Peters-Lidard, NASA Goddard Space Flight Center
Pierre Gentine, Columbia University
Michael Barlage, NOAA/Environmental Modeling Center
Dennis Lettenmaier, University of California, Los Angeles
David M. Mocko, SAIC at NASA Goddard Space Flight Center
Daniel Barrie, NOAA Climate Program Office

Progress in Advancing Drought Monitoring and Prediction

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Anthony M. DeAngelis, Hailan Wang, Randal D. Koster, Siegfried D. Schubert, Yehui Chang, and Jelena Marshak


Rapid-onset droughts, known as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA’s GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable. While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (3–4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary atmospheric circulation anomalies.

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