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) techniques with these models has been highly recommended because it improves the accuracy of water and energy balance computations and increases the model’s predictive skills ( Reichle et al. 2014 ; Sawada et al. 2015 ; Seo et al. 2003 ). Earth system DA seeks to exploit real-time observations for more accurate hydrologic forecasts ( Kumar et al. 2014 ; Reichle et al. 2014 ). DA aims at merging current and past observations with a dynamical model, using the model’s prognostic equations to estimate
) techniques with these models has been highly recommended because it improves the accuracy of water and energy balance computations and increases the model’s predictive skills ( Reichle et al. 2014 ; Sawada et al. 2015 ; Seo et al. 2003 ). Earth system DA seeks to exploit real-time observations for more accurate hydrologic forecasts ( Kumar et al. 2014 ; Reichle et al. 2014 ). DA aims at merging current and past observations with a dynamical model, using the model’s prognostic equations to estimate
1. Introduction Useful predictability of deterministic weather forecasts is usually no more than 2 weeks, limited by the sensitivity to the atmospheric initial state, while longer memory from ocean heat content plays a dominant role in the climate predictability on seasonal and longer time scales (e.g., Lorenz 1963 , 1975 ; Shukla 1985 ; Lorenz 1993 ). There is a gap between the two time scales of weather and climate predictions, where inertia in the land surface, such as soil moisture
1. Introduction Useful predictability of deterministic weather forecasts is usually no more than 2 weeks, limited by the sensitivity to the atmospheric initial state, while longer memory from ocean heat content plays a dominant role in the climate predictability on seasonal and longer time scales (e.g., Lorenz 1963 , 1975 ; Shukla 1985 ; Lorenz 1993 ). There is a gap between the two time scales of weather and climate predictions, where inertia in the land surface, such as soil moisture
temperature (e.g., Yoon et al. 2012 ; Yuan and Wood 2013 ; Dutra et al. 2014 ; Mo and Lyon 2015 ). Current seasonal coupled forecast systems can predict major oceanic and atmospheric anomalies at useful lead times (e.g., Jin et al. 2008 ; Kirtman et al. 2014 ; Huang et al. 2017a ; Shin et al. 2019 ). However, the current level of skill in forecasting drought onset, development, and demise is limited (e.g., Quan et al. 2012 ; Mo and Lyon 2015 ), possibly because other sources of predictability
temperature (e.g., Yoon et al. 2012 ; Yuan and Wood 2013 ; Dutra et al. 2014 ; Mo and Lyon 2015 ). Current seasonal coupled forecast systems can predict major oceanic and atmospheric anomalies at useful lead times (e.g., Jin et al. 2008 ; Kirtman et al. 2014 ; Huang et al. 2017a ; Shin et al. 2019 ). However, the current level of skill in forecasting drought onset, development, and demise is limited (e.g., Quan et al. 2012 ; Mo and Lyon 2015 ), possibly because other sources of predictability
surface models that run routinely for monitoring or forecasting has opened up many more possibilities for calculating drought indicators. Because the availability of data and/or model outputs determines which indicators are possible, we broadly classify indicators into traditional and land surface model based, with a third category—remotely sensed—to be discussed in a later section. Traditional drought indicators As noted in the Introduction, dozens of drought indicators are in common use (e.g., Heim
surface models that run routinely for monitoring or forecasting has opened up many more possibilities for calculating drought indicators. Because the availability of data and/or model outputs determines which indicators are possible, we broadly classify indicators into traditional and land surface model based, with a third category—remotely sensed—to be discussed in a later section. Traditional drought indicators As noted in the Introduction, dozens of drought indicators are in common use (e.g., Heim
; Miralles et al. 2014 ), and they may also affect the intensity, frequency, and distribution of precipitation ( Findell et al. 2011 ; Guillod et al. 2015 ; Taylor et al. 2012 ). Further, a shortage or excess of SM could trigger the occurrence of droughts ( Wang et al. 2011 ) or floods ( Koster et al. 2010 ). As such, SM is crucial for weather prediction, climate forecasting and ecosystem dynamics assessment. Moreover, SM is vital for agricultural production since it is the only direct source for crop
; Miralles et al. 2014 ), and they may also affect the intensity, frequency, and distribution of precipitation ( Findell et al. 2011 ; Guillod et al. 2015 ; Taylor et al. 2012 ). Further, a shortage or excess of SM could trigger the occurrence of droughts ( Wang et al. 2011 ) or floods ( Koster et al. 2010 ). As such, SM is crucial for weather prediction, climate forecasting and ecosystem dynamics assessment. Moreover, SM is vital for agricultural production since it is the only direct source for crop
regression models. 2. Data and methodology a. Data We used the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) ( Dee et al. 2011 ) for the moisture budget analysis described in section 2c . The ERAI reanalysis provides 6-hourly upper-air parameters from 1979 to near-real-time and its data are available online ( http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ ). The atmospheric model has a hybrid sigma-pressure vertical coordinate system with 60
regression models. 2. Data and methodology a. Data We used the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) ( Dee et al. 2011 ) for the moisture budget analysis described in section 2c . The ERAI reanalysis provides 6-hourly upper-air parameters from 1979 to near-real-time and its data are available online ( http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ ). The atmospheric model has a hybrid sigma-pressure vertical coordinate system with 60
