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Eric F. Wood, Siegfried D. Schubert, Andrew W. Wood, Christa D. Peters-Lidard, Kingtse C. Mo, Annarita Mariotti, and Roger S. Pulwarty

is the near-real-time merging of these satellite data with in situ observations, perhaps using techniques developed in Chirlin and Wood (1982) and utilized in Chaney et al. (2014) to develop a high-resolution meteorological dataset over sub-Saharan Africa. b. Prediction The development of the NMME suite of seasonal model forecasts is a significant success in demonstrating the potential of having an international collaboration of operational and research groups focusing on both the generation

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Johnna M. Infanti and Ben P. Kirtman

demonstrated above. A “warning” is issued when the forecast probability exceeds a certain threshold (say, at least 80% confidence or 80% of ensemble members predict above-normal conditions), and these are used to define hit rates and false alarm rates [discussion of thresholds is found in the in relevant section of Mason and Graham (1999) , and equations are found in WMO SVS]. These values are then aggregated seasonally within the region. The analysis technique is defined in Mason and Graham (1999) and

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M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager

temperatures for the entire 1895–2011 period (Fig. ES4) reveals no statistically significant relationship. The lack of such relationships between summer U.S. precipitation and sea surface temperatures has thwarted efforts at successful seasonal forecasting. Global SSTs have appreciably changed, however, since the occurrence of past major central plains droughts. Figure 6 presents two analyses for the SST anomalies of May–August 2012: one calculated relative to a 1901–90 climatology (top) that brackets

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Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, Rolf Reichle, Yuqiong Liu, Kristi R. Arsenault, Youlong Xia, Michael Ek, George Riggs, Ben Livneh, and Michael Cosh

; Zaitchik et al. 2010 ; Xia et al. 2012c ). These studies note that the model-based estimates suffer from uncertainties in the forcing inputs, model parameters, and model structural errors. Data assimilation (DA) techniques have been employed as an effective strategy to combine the strengths of both modeling and observations to generate superior estimates by appropriately weighting their respective sources of errors ( Reichle 2008 ). There have been several studies that have examined the assimilation

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John M. Peters, Christopher J. Nowotarski, and Gretchen L. Mullendore

updrafts . Mon. Wea. Rev. , 131 , 2394 – 2416 ,<2394:RRFTSO>2.0.CO;2 . 10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 Bunkers , M. J. , B. A. Klimowski , R. L. Thompson , and M. L. Weisman , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79 ,<0061:PSMUAN>2.0.CO;2 . 10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 Dahl , J. M. L. , 2017 : Tilting

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Paul A. Dirmeyer, Jiangfeng Wei, Michael G. Bosilovich, and David M. Mocko

droughts and wet intervals are quantified. The goal of this analysis is to determine when and where extreme precipitation events can be attributed to changes in the sources of moisture supplying the precipitation. Section 2 describes the datasets used, the back-trajectory technique that estimates the distribution of evaporative sources for moisture supplying precipitation over any location, and a robust statistical method to compare distributions of evaporative sources. The basic distributions of

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Hongshuo Wang, Jeffrey C. Rogers, and Darla K. Munroe

those collocated with meteorological and soil moisture observations within 0.5° lat/lon (32 stations). The data from the station with the open circle are used in Fig. 2 . Due to the spatial distribution of soil moisture stations, the islands in the South China sea are not displayed, similarly hereinafter. Soil moisture observations were taken using the gravimetric technique for each 10-cm layer down to a depth of 1 m, with the first layer divided into two 5-cm layers. The data are obtained three

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