References
Arevalo, J., Welty, J., Fan, Y., & Zeng, X. (2021). Implementation of
snowpack treatment in the cpc water balance model and its impact on
drought assessment. Journal of Hydrometeorology , 22 (5),
1235–1247. https://doi.org/10.1175/JHM-D-20-0201.1
Atiah, W. A., Amekudzi, L. K., Akum, R. A., Quansah, E., Antwi-Agyei,
P., & Danuor, S. K. (2022). Climate variability and impacts on maize
(Zea mays) yield in Ghana, West Africa. Quarterly Journal of the
Royal Meteorological Society , 148 (742), 185–198.
https://doi.org/10.1002/qj.4199
Bell, J. E., Palecki, M. A., Baker, C. B., Collins, W. G., Lawrimore, J.
H., Leeper, R. D., et al. (2013). U.S. climate reference network soil
moisture and temperature observations. Journal of
Hydrometeorology , 14 (3), 977–988.
https://doi.org/10.1175/JHM-D-12-0146.1
Benjamin, S. G., Smirnova, T. G., James, E. P., Lin, L.-F., Hu, M.,
Turner, D. D., & He, S. (2022). Land-snow data assimilation including a
moderately coupled initialization method applied to NWP. Journal
of Hydrometeorology . https://doi.org/10.1175/jhm-d-21-0198.1
Carrera, M. L., Bilodeau, B., Bélair, S., Abrahamowicz, M., Russell, A.,
& Wang, X. (2019). Assimilation of passive L-band microwave brightness
temperatures in the Canadian Land data assimilation system: Impacts on
short-range warm season numerical weather prediction. Journal of
Hydrometeorology , 20 (6), 1053–1079.
https://doi.org/10.1175/JHM-D-18-0133.1
Delworth, T. L., & Manabe, S. (1988). The Influence of Potential
Evaporation on the Variabilities of Simulated Soil Wetness and Climate.Journal of Climate , 1 (5), 523–547.
https://doi.org/10.1175/1520-0442(1988)001<0523:TIOPEO>2.0.CO;2
Dirmeyer, P. A., Wu, J., Norton, H. E., Dorigo, W. A., Quiring, S. M.,
Ford, T. W., et al. (2016). Confronting weather and climate models with
observational data from soil moisture networks over the United States.Journal of Hydrometeorology , 17 (4), 1049–1067.
https://doi.org/10.1175/JHM-D-15-0196.1
Dowell, D. C., Alexander, C. R., James, E. P., Weygandt, S. S.,
Benjamin, S. G., Manikin, G. S., et al. (2022). The High-Resolution
Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast
Model. Part 1: Motivation and System Description. Wea.
Forecasting . https://doi.org/10.1175/WAF-D-21-0151.1
Ek, M. B., & Holtslag, A. A. M. (2004). Influence of Soil Moisture on
Boundary Layer Cloud Development. Journal of Hydrometeorology ,5 (1), 86–99.
https://doi.org/10.1175/1525-7541(2004)005<0086:IOSMOB>2.0.CO;2
Fan, Y., & van den Dool, H. (2004). Climate Prediction Center global
monthly soil moisture data set at 0.5° resolution for 1948 to present.Journal of Geophysical Research D: Atmospheres , 109 (10),
1–8. https://doi.org/10.1029/2003JD004345
Ford, T. W., & Quiring, S. M. (2019). Comparison of Contemporary In
Situ, Model, and Satellite Remote Sensing Soil Moisture With a Focus on
Drought Monitoring. Water Resources Research , 1565–1582.
https://doi.org/10.1029/2018WR024039
Guttman, N. B., & Quayle, R. G. (1996). A historical perspective of
U.S. climate divisions. Bulletin of the American Meteorological
Society , 77 (2), 293–303.
https://doi.org/10.1175/1520-0477(1996)077<0293:AHPOUC>2.0.CO;2
Huang, J., Van Den Dool, H. M., & Georgakakos, K. P. (1996). Analysis
of model-calculated soil moisture over the United States (1931-1993) and
applications to long-range temperature forecasts. Journal of
Climate .
https://doi.org/10.1175/1520-0442(1996)009<1350:AOMCSM>2.0.CO;2
James, E. P., Alexander, C. R., Dowell, D. C., Weygandt, S. S.,
Benjamin, S. G., Manikin, G. S., et al. (2022). The High-Resolution
Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast
Model. Part 2: Forecast Performance. Wea. Forecasting.https://doi.org/10.1175/WAF-D-21-0130.1
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P.,
et al. (2004). Regions of Strong Coupling Between Soil Moisture and
Precipitation. Science , 305(5687), 1138–1140.
https://doi.org/10.1126/science.1100217
Lin, L. F., & Pu, Z. (2020). Improving near-surface short-range weather
forecasts using strongly coupled land-atmosphere data assimilation with
gsi-enkf. Monthly Weather Review , 148(7), 2863–2888.
https://doi.org/10.1175/MWR-D-19-0370.1
Liu, J., Zhan, X., Hain, C., Yin, J., Fang, L., Li, Z., & Zhao, L.
(2016). NOAA Soil Moisture Operational Product System (SMOPS) and its
validations. In 2016 IEEE International Geoscience and Remote Sensing
Symposium (IGARSS) (pp. 3477–3480). IEEE.
https://doi.org/10.1109/IGARSS.2016.7729899
Madadgar, S., AghaKouchak, A., Farahmand, A., & Davis, S. J. (2017).
Probabilistic estimates of drought impacts on agricultural production.Geophysical Research Letters , 44 (15), 7799–7807.
https://doi.org/10.1002/2017GL073606
Min, L., Fitzjarrald, D. R., Du, Y., Rose, B. E. J., Hong, J., & Min,
Q. (2021). Exploring Sources of Surface Bias in HRRR Using New York
State Mesonet. Journal of Geophysical Research: Atmospheres ,126 (20), 1–18. https://doi.org/10.1029/2021JD034989
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J.
C., Robock, A., et al. (2004). The multi-institution North American Land
Data Assimilation System (NLDAS): Utilizing multiple GCIP products and
partners in a continental distributed hydrological modeling system.Journal of Geophysical Research: Atmospheres , 109(7), 1–32.
https://doi.org/10.1029/2003jd003823
Muñoz-Sabater, J., Lawrence, H., Albergel, C., Rosnay, P., Isaksen, L.,
Mecklenburg, S., et al. (2019). Assimilation of SMOS brightness
temperatures in the ECMWF Integrated Forecasting System. Quarterly
Journal of the Royal Meteorological Society , 145 (723),
2524–2548. https://doi.org/10.1002/qj.3577
Palecki, M. A., Lawrimore, J.H., Leeper, R. D., Bell, J. E., Embler, S.,
Casey, N. (2013). U.S. Climate Reference Network Products, Daily.
[Dataset]. NOAA National Centers for Environmental Information.
https://doi.org/10.7289/V5H13007.
https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01/. Accessed 11
Nov. 2021.
Pan, M., Cai, X., Chaney, N. W., Entekhabi, D., & Wood, E. F. (2016).
An initial assessment of SMAP soil moisture retrievals using
high-resolution model simulations and in situ observations.Geophysical Research Letters , 43(18), 9662–9668.
https://doi.org/10.1002/2016GL069964
Quiring, S. M., Ford, T. W., Wang, J. K., Khong, A., Harris, E.,
Lindgren, T., et al. (2016). The North American Soil Moisture Database:
Development and Applications. Bulletin of the American
Meteorological Society , 97(8), 1441–1459.
https://doi.org/10.1175/BAMS-D-13-00263.1
Rigden, A. J., Powell, R. S., Trevino, A., McColl, K. A., & Huybers, P.
(2020). Microwave Retrievals of Soil Moisture Improve Grassland Wildfire
Predictions. Geophysical Research Letters , 47 (23), 1–8.
https://doi.org/10.1029/2020GL091410
Robock, A., Luo, L., Wood, E. F., Wen, F., Mitchell, K. E., Houser, P.
R., et al. (2003). Evaluation of the North American Land Data
Assimilation System over the southern Great Plains during the warm
season. Journal of Geophysical Research: Atmospheres ,108 (22). https://doi.org/10.1029/2002jd003245
Robock, A., Vinnikov, K. Y., Schlosser, C. A., Speranskaya, N. A., &
Xue, Y. (1995). Use of Midlatitude Soil Moisture and Meteorological
Observations to Validate Soil Moisture Simulations with Biosphere and
Bucket Models. Journal of Climate , 8 (1), 15–35.
https://doi.org/10.1175/1520-0442(1995)008<0015:UOMSMA>2.0.CO;2
Soil Climate Analysis Network (SCAN) (2016). SCAN Daily Historic
Provisional Data. [Dataset]. USDA National Resources Conservation
Service National Water and Climate Center. www.wcc.nrcs.usda.gov/scan.
Access date 16 Feb. 2022.
Schaefer, G. L., Cosh, M. H., & Jackson, T. J. (2007). The USDA Natural
Resources Conservation Service Soil Climate Analysis Network (SCAN).Journal of Atmospheric and Oceanic Technology , 24 (12),
2073–2077. https://doi.org/10.1175/2007JTECHA930.1
Shellito, P. J., Small, E. E., Colliander, A., Bindlish, R., Cosh, M.
H., Berg, A. A., et al. (2016). SMAP soil moisture drying more rapid
than observed in situ following rainfall events. Geophysical
Research Letters , 43(15), 8068–8075.
https://doi.org/10.1002/2016GL069946
Smirnova, T. G., Brown, J. M., & Benjamin, S. G. (1997). Performance of
different soil model configurations in simulating ground surface
temperature and surface fluxes. Monthly Weather Review ,125 (8), 1870–1884.
https://doi.org/10.1175/1520-0493(1997)125<1870:PODSMC>2.0.CO;2
Smirnova, T. G., Brown, J. M., Benjamin, S. G., & Kim, D. (2000).
Parameterization of cold-season processes in the MAPS land-surface
scheme. Journal of Geophysical Research: Atmospheres , 105(D3),
4077–4086. https://doi.org/10.1029/1999JD901047
Smirnova, T. G., Brown, J. M., Benjamin, S. G., & Kenyon, J. S. (2016).
Modifications to the Rapid Update Cycle Land Surface Model (RUC LSM)
Available in the Weather Research and Forecasting (WRF) Model.Monthly Weather Review , 144(5), 1851–1865.
https://doi.org/10.1175/MWR-D-15-0198.1
Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J.,
et al. (2002). THE DROUGHT MONITOR. Bulletin of the American
Meteorological Society , 83 (8), 1181–1190.
https://doi.org/10.1175/1520-0477-83.8.1181
Taylor, C. M., Gounou, A., Guichard, F., Harris, P. P., Ellis, R. J.,
Couvreux, F., & De Kauwe, M. (2011). Frequency of sahelian storm
initiation enhanced over mesoscale soil-moisture patterns. Nature
Geoscience, 4(7), 430–433. https://doi.org/10.1038/ngeo1173
van den Dool, H., Huang, J., & Fan, Y. (2003). Performance and analysis
of the constructed analogue method applied to U.S. soil moisture over
1981-2001. Journal of Geophysical Research: Atmospheres ,108 (16), 1–16. https://doi.org/10.1029/2002jd003114
Vinnikov, K. Y., & Yeserkepova, I. B. (1991). Soil Moisture: Empirical
Data and Model Results. Journal of Climate , 4 (1), 66–79.
https://doi.org/10.1175/1520-0442(1991)004<0066:SMEDAM>2.0.CO;2
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., et
al. (2012). Continental-scale water and energy flux analysis and
validation for the North American Land Data Assimilation System project
phase 2 (NLDAS-2): 1. Intercomparison and application of model products.
Journal of Geophysical Research Atmospheres, 117(3).
https://doi.org/10.1029/2011JD016048
Xia, Y., Sheffield, J., Ek, M. B., Dong, J., Chaney, N., Wei, H., et al.
(2014). Evaluation of multi-model simulated soil moisture in NLDAS-2.
Journal of Hydrology, 512, 107–125.
https://doi.org/10.1016/j.jhydrol.2014.02.027
Xia, Y., Ek, M. B., Wu, Y., Ford, T., & Quiring, S. M. (2015a).
Comparison of NLDAS-2 simulated and NASMD observed daily soil moisture.
Part I: Comparison and analysis. Journal of Hydrometeorology ,16 (5), 1962–1980. https://doi.org/10.1175/JHM-D-14-0096.1
Xia, Y., Ek, M. B., Wu, Y., Ford, T., & Quiring, S. M. (2015b).
Comparison of NLDAS-2 simulated and NASMD observed daily soil moisture.
Part II: Impact of soil texture classification and vegetation type
mismatches. Journal of Hydrometeorology , 16(5), 1981–2000.
https://doi.org/10.1175/JHM-D-14-0097.1
Yao, Y., Ciais, P., Viovy, N., Li, W., Cresto-Aleina, F., Yang, H., et
al. (2021). A Data-Driven Global Soil Heterotrophic Respiration Dataset
and the Drivers of Its Inter-Annual Variability. Global
Biogeochemical Cycles , 35 (8), 1–23.
https://doi.org/10.1029/2020GB006918