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Susan Frankenstein, Maria Stevens, and Constance Scott


This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System–based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the “average” soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results.

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Yanan Meng, Jianhua Sun, Yuanchun Zhang, and Shenming Fu

2003 ) was used to specify the elevation of the MCS positions. The CMORPH-AWS (automatic weather station) merged hourly gridded precipitation product [combined precipitation (COMB)] ( Pan et al. 2012 ; Shen et al. 2013 ; Zhang and Jiang 2013 ) at a spatial resolution of 0.1° × 0.1°, provided by the National Meteorological Information Center of the China Meteorological Administration, was used to study the precipitation associated with the MCSs. The data were combined with CMORPH data from the

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Kichul Jung, Taha B. M. J. Ouarda, and Prashanth R. Marpu

. (2016) analyzed the delineation of homogenous regions based on reference variables representing nonlinear relationships among hydrological data. Abdi et al. (2017a) used the growing neural gas network to determine hydrological data clustering in RFA. Various physiographical and meteorological variables have been employed to define homogeneous regions in RFA, such as basin area, mean basin slope, main channel length, percentage of area covered by forest, percentage of area covered by lakes, main

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Youcun Qi, Jian Zhang, Qing Cao, Yang Hong, and Xiao-Ming Hu

, which leads to large property and crop damages: interruptions of transportation; and, in the most extreme cases, injuries and deaths. Therefore, obtaining an accurate QPE for MCSs is very important. MCSs consist of both convective and stratiform precipitation ( Houze 2004 ; Howard 2007 ). A bright band (BB) is often found in the stratiform precipitation, and inflated reflectivity intensities in the BB region often cause positive biases in radar QPEs. A vertical profile of reflectivity (VPR

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Ayumi Fujisaki-Manome, Greg E. Mann, Eric J. Anderson, Philip Y. Chu, Lindsay E. Fitzpatrick, Stanley G. Benjamin, Eric P. James, Tatiana G. Smirnova, Curtis R. Alexander, and David M. Wright

1. Introduction Severe winter weather events involving ice and snow kill dozens of people every year around the Great Lakes region and impact a wide range of socioeconomic activities, such as commercial shipping, winter recreation, transportation, and utilities (e.g., Lake Carriers’ Association 2019 ; Ayon 2017 ; Niziol 1987 ). Accurate and timely forecasts of hazardous winter weather are critical for safety and support mitigation activities intended to reduce associated losses. However

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Ben S. Pickering, Ryan R. Neely III, Judith Jeffery, David Dufton, and Maryna Lukach

PW codes—typically variations of intensity and longevity—whereas this investigation only concerns the type of hydrometeor detected. The motivation for the PW code’s existence was to reduce the bandwidth of descriptive information, only upheld today for consistency with existing data. PT can have great impact on transportation, agriculture and infrastructure but is poorly forecasted ( Ralph et al. 2005 ; Reeves 2016 ). PT has become more prominent in the field of operational meteorology in the

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Xiaoyin Liu, Junzeng Xu, Shihong Yang, Yuping Lv, and Yang Zhuang

assess an ecosystem’s energy balance ( Liu et al. 2017 ) or to improve an agroecosystem’s water management protocols ( Kang et al. 2003 ; Mu et al. 2011 ). Variations in meteorological conditions and crop physiology traits result in a significant temporal variation in ET and energy balance ( Farah et al. 2004 ; Gentine et al. 2007 ). Finding that ET in wheat ( Triticum æstivum L.) fields varied significantly across temporal scales, Gentine et al. (2007) noted that canopy coverage and soil

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Lucas Bohne, Courtenay Strong, and W. James Steenburgh

-season precipitation, with an average of 53% (with estimates ranging from 35% to 90% by catchment) of surface runoff deriving from the melting of accumulated cool-season snow during spring and summer ( Li et al. 2017 ; Serreze et al. 1999 ). Not only are regions of complex terrain important for water resources, but liquid and frozen precipitation produced by storms in these regions strongly affect recreation, transportation, tourism, and local economies. The diversity in terrain features across the western United

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Xuejin Wang, Baoqing Zhang, Feng Li, Xiang Li, Xuliang Li, Yibo Wang, Rui Shao, Jie Tian, and Chansheng He

variability in the East Asia ( Lin et al. 2014 ). The China Meteorological Forcing Dataset (CMFD) produced by the Institute of Tibetan Plateau Research, the Chinese Academy of Sciences was also used in this study from 1979 to 2015 at 0.1° spatial resolution ( Yang et al. 2010 ; Chen et al. 2011 ; J. He et al. 2020 ; Yang and He 2019 ). The CMFD has been widely used for land surface modeling, hydrological modeling, and terrestrial data assimilation ( Chen et al. 2011 ). Land use and cover data derived

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Guiling Wang, Christine J. Kirchhoff, Anji Seth, John T. Abatzoglou, Ben Livneh, David W. Pierce, Lori Fomenko, and Tengyu Ding

1. Introduction State and local governments and decision-makers across a range of sectors including agriculture, transportation, energy, and water are increasingly seeking place-based climate change information to inform climate adaptation planning ( Bierbaum et al. 2013 ; Mach and Field 2017 ; Kirchhoff et al. 2019 ). Yet, climate change projections derived from global climate models (GCMs) with a typical spatial resolution of ~100–300 km, or from dynamical downscaling using regional climate

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