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contain large errors attributable to land surface complexities and temporally frequent snowmelt processes in the western United States (e.g., Tait and Armstrong 1996 ; Rodell et al. 2004 ; Foster et al. 2005 ; Dong et al. 2005 ; Tong et al. 2010 ), the 500-m daily Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) snow cover area (SCA) product has been widely used as an important constraint on snowpack processes in land surface and hydrological models. Assimilation
contain large errors attributable to land surface complexities and temporally frequent snowmelt processes in the western United States (e.g., Tait and Armstrong 1996 ; Rodell et al. 2004 ; Foster et al. 2005 ; Dong et al. 2005 ; Tong et al. 2010 ), the 500-m daily Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) snow cover area (SCA) product has been widely used as an important constraint on snowpack processes in land surface and hydrological models. Assimilation
to the visible or near-infrared-based snow cover fraction (SCF) products, the passive microwave–based SWE or snow depth products are typically coarser in resolution and lower in accuracy ( Foster et al. 2005 ; Dong et al. 2005 ). As a result, studies that employ the assimilation of passive microwave–based retrievals have reported only limited success ( Andreadis and Lettenmaier 2006 ; Dong et al. 2007 ). In a more recent study, De Lannoy et al. (2012) present results from the joint
to the visible or near-infrared-based snow cover fraction (SCF) products, the passive microwave–based SWE or snow depth products are typically coarser in resolution and lower in accuracy ( Foster et al. 2005 ; Dong et al. 2005 ). As a result, studies that employ the assimilation of passive microwave–based retrievals have reported only limited success ( Andreadis and Lettenmaier 2006 ; Dong et al. 2007 ). In a more recent study, De Lannoy et al. (2012) present results from the joint
typically driven by the combination of warm temperatures, low rainfall, strong winds, and below-normal cloud cover that together act to enhance evaporation and rapidly dry the soil. The study underscored the findings of Anderson et al. (2013) in showing that the remotely sensed ESI captures these phenomena and can provide an early warning of drought impacts on agricultural systems. Finally, Dong et al. (2014) focused on quantifying errors in MODIS fractional snow cover (FSC) datasets, which have
typically driven by the combination of warm temperatures, low rainfall, strong winds, and below-normal cloud cover that together act to enhance evaporation and rapidly dry the soil. The study underscored the findings of Anderson et al. (2013) in showing that the remotely sensed ESI captures these phenomena and can provide an early warning of drought impacts on agricultural systems. Finally, Dong et al. (2014) focused on quantifying errors in MODIS fractional snow cover (FSC) datasets, which have
1. Introduction Smith and Katz (2013) report that during the period 1980–2011, droughts and heat waves ranked only behind tropical cyclones in the cost of damages associated with weather and climate disasters in the United States that individually caused more than a billion dollars worth of damage. This estimate did not include the 2012 drought, which covered much of the central United States. While the absolute magnitude of the economic losses is greatest in the developed world, the relative
1. Introduction Smith and Katz (2013) report that during the period 1980–2011, droughts and heat waves ranked only behind tropical cyclones in the cost of damages associated with weather and climate disasters in the United States that individually caused more than a billion dollars worth of damage. This estimate did not include the 2012 drought, which covered much of the central United States. While the absolute magnitude of the economic losses is greatest in the developed world, the relative
ALEXI period of record is currently limited to the MODIS era (2000 and following), but can be extended back to the early 1980s using VI data from the Advanced Very High Resolution Radiometer (AVHRR) series flown by the National Oceanic and Atmospheric Administration (NOAA) and geostationary data from the International Satellite Cloud Climatology Project (ISCCP) B1 data rescue project ( Knapp 2008 ). Snow-covered regions have been masked using the 24-km resolution Daily Northern Hemisphere Snow and
ALEXI period of record is currently limited to the MODIS era (2000 and following), but can be extended back to the early 1980s using VI data from the Advanced Very High Resolution Radiometer (AVHRR) series flown by the National Oceanic and Atmospheric Administration (NOAA) and geostationary data from the International Satellite Cloud Climatology Project (ISCCP) B1 data rescue project ( Knapp 2008 ). Snow-covered regions have been masked using the 24-km resolution Daily Northern Hemisphere Snow and
Infiltration Capacity (VIC) 4.0.6 ( Liang et al. 1994 ), Noah 2.8 ( Koren et al. 1999 ; Ek et al. 2003 ), Sacramento/SNOW-17 (SAC) ( Burnash et al. 1973 ; Anderson 1973 ), and the Community Land Model 3.5 (CLM3.5; Oleson et al. 2007 ). Model descriptions and properties can be found in Wang et al. (2009) . 2) The NCEP system Precipitation forcing for the NCEP system was derived from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) unified precipitation analysis
Infiltration Capacity (VIC) 4.0.6 ( Liang et al. 1994 ), Noah 2.8 ( Koren et al. 1999 ; Ek et al. 2003 ), Sacramento/SNOW-17 (SAC) ( Burnash et al. 1973 ; Anderson 1973 ), and the Community Land Model 3.5 (CLM3.5; Oleson et al. 2007 ). Model descriptions and properties can be found in Wang et al. (2009) . 2) The NCEP system Precipitation forcing for the NCEP system was derived from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) unified precipitation analysis
North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent . J. Geophys. Res. , 108 , 8849 , doi:10.1029/2002JD003274 . Sheffield, J. , Xia Y. , Luo L. , Wood E. F. , Ek M. , and Mitchell K. E. , 2012 : The North American Land Data Assimilation System (NLDAS). Remote Sensing of Drought: Innovative Monitoring Approaches, B. Wardlow, M. Anderson, and J. Verdin, Eds., CRC Press, 227–260 . Svoboda, M. , and Coauthors , 2002 : The
North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent . J. Geophys. Res. , 108 , 8849 , doi:10.1029/2002JD003274 . Sheffield, J. , Xia Y. , Luo L. , Wood E. F. , Ek M. , and Mitchell K. E. , 2012 : The North American Land Data Assimilation System (NLDAS). Remote Sensing of Drought: Innovative Monitoring Approaches, B. Wardlow, M. Anderson, and J. Verdin, Eds., CRC Press, 227–260 . Svoboda, M. , and Coauthors , 2002 : The
of 20 in SPEI). Most of the exceptions are in northeastern China. Fig . 3. The optimum time scale of SPI for a soil layer depth of (a) 0–5 and (b) 90–100 cm. (c),(d) As in (a),(b), but for SPEI. The circles without color filling are those for which the soil moisture observations in a given layer cover less than 40 months or for which the correlation is not significant at p = 0.05. Fig . 4. Correlation between the optimum time scales (1–12 months) and soil depth for the full soil layer of 0
of 20 in SPEI). Most of the exceptions are in northeastern China. Fig . 3. The optimum time scale of SPI for a soil layer depth of (a) 0–5 and (b) 90–100 cm. (c),(d) As in (a),(b), but for SPEI. The circles without color filling are those for which the soil moisture observations in a given layer cover less than 40 months or for which the correlation is not significant at p = 0.05. Fig . 4. Correlation between the optimum time scales (1–12 months) and soil depth for the full soil layer of 0
1. Introduction Rain or snow falling over any particular location is composed of condensed water vapor that entered the atmosphere as surface evaporation from a range of upstream locations. Surface and atmospheric conditions along the paths of moisture advection determine the ultimate sources of evaporative moisture, which generally have a combination of oceanic and terrestrial origins. Knowledge of the sources of moisture supplying precipitation over a particular location could be used to
1. Introduction Rain or snow falling over any particular location is composed of condensed water vapor that entered the atmosphere as surface evaporation from a range of upstream locations. Surface and atmospheric conditions along the paths of moisture advection determine the ultimate sources of evaporative moisture, which generally have a combination of oceanic and terrestrial origins. Knowledge of the sources of moisture supplying precipitation over a particular location could be used to
cold seasons led to the presence of very little snow during the winter and spring, though this was largely confined to the western and northern United States, outside our region of interest. The central U.S. soil moisture anomalies preceding the summer drought ( Figs. 1p,q ) gave little indication of what was to come, with average moisture conditions in April (during which precipitation anomalies in the central and upper Great Plains were indeed positive) and dry conditions only developing in May
cold seasons led to the presence of very little snow during the winter and spring, though this was largely confined to the western and northern United States, outside our region of interest. The central U.S. soil moisture anomalies preceding the summer drought ( Figs. 1p,q ) gave little indication of what was to come, with average moisture conditions in April (during which precipitation anomalies in the central and upper Great Plains were indeed positive) and dry conditions only developing in May
