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Keyhan Gavahi, Peyman Abbaszadeh, Hamid Moradkhani, Xiwu Zhan, and Christopher Hain

fully parallelized system to cope with the computational complexity. At this resolution, large-scale as well as local drought conditions can be monitored, hence a more realistic assessment of actual drought can be made. Optimal assimilation methods are needed to maximize information content from observations and model simulations ( Abbaszadeh et al. 2019a ). However, most assimilation algorithms are suboptimal for complex real-world problems to which they are applied. However, the sequential Monte

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Kingtse C. Mo and Dennis P Lettenmaier

model forcings (e.g., downward solar and longwave radiation and humidity) were not archived in the reforecast dataset, so instead we computed them using algorithms summarized by Bohn et al. (2013) . In general, these algorithms index downward solar radiation to the daily temperature range, downward longwave radiation to the daily mean temperature, and the dewpoint hence vapor pressure deficit to the daily minimum temperature with a correction for arid areas. Application of these procedures resulted

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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

conditions. These data are generated using the Thunderstorm Observation by Radar Algorithm (ThOR; Houston et al. 2015 ), a Lagrangian method to automatically identify and track convective events. Detailed descriptions of this method are provided in Houston et al. (2015) . These data have been used to assess the relationship between soil moisture and convective initiation in the previous studies ( Ford et al. 2018 ; Yuan et al. 2020 ). However, because ThOR is computationally intensive, we only used 3

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Chul-Su Shin, Paul A. Dirmeyer, Bohua Huang, Subhadeep Halder, and Arun Kumar

was derived by interpolating GTS (Global Telecommunication System) station values through the Shepard algorithm that is a distance-weight technique with directional correction. Finally, gridded analyses of total temperature were computed by adding the anomaly to the CRU climatology ( ftp://ftp.cpc.ncep.noaa.gov/precip/PEOPLE/wd52ws/global_temp/CPC-GLOBAL-T.pdf ). More details about the data, including maps of typical station distribution, can be found there. As one may expect, these verification

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