Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model

Abstract

This study characterizes the space-time structure of soil moisture background error covariance and paves the way for the development of a soil moisture variational data assimilation system for the Noah land surface model coupled to the Weather Research and Forecasting (WRF) model. The soil moisture background error covariance over the contiguous United States exhibits strong seasonal and regional variability with the largest values occurring in the uppermost soil layer during the summer. Large background error biases were identified, particularly over the southeastern United States, caused mainly by the discrepancy between the WRF-Noah simulations and the initial conditions derived from the used operational global analysis data set.

Characteristics of Background Error Covariance of Soil Moisture and Atmospheric States in Strongly Coupled Land–Atmosphere Data Assimilation

“…The B matrix contains information about the weights of the control states and multivariate error correlation, which allows the spread of the information from the observations to the control states with balance (Bannister 2008a,b). Similar to Lin et al (2017b), this study uses the NMC method (Parrish and Derber 1992) to compute the B matrix:…”

“…This approach has been tested and is operational in a land surface model coupled to Météo-France's Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited area model (Draper et al 2009(Draper et al , 2011Mahfouf 2010;Mahfouf and Bliznak 2011;Parrens et al 2014;Schneider et al 2014;Duerinckx et al 2017) and the Integrated Forecast System at ECMWF (Drusch et al 2009;De Rosnay et al 2013). In addition, several studies have developed a coupled data assimilation system with the Weather Research and Forecasting (WRF) Model to assimilate soil moisture information (Rasmy et al 2011(Rasmy et al , 2012Santanello et al 2016;Seto et al 2016;Lin et al 2017a). Nonetheless, none of these studies addresses the problems in strongly coupled data assimilation.…”

“…Specifically, instead of only specifying the error characteristics for either soil moisture state (Lin et al 2017b) or atmospheric state variables, we adopt the National Meteorological Center (NMC) method (Parrish and Derber 1992) to obtain background error statistics from multiple numerical experiments using the WRF Model with the Noah land surface model and to generate the covariance across both the atmosphere and land surface models. The NMC method has been adopted by the National Centers for Environmental Prediction (NCEP; Parrish and Derber 1992;Wu et al 2002), the Met Office (Lorenc et al 2000;Ingleby 2001), and the WRF data assimilation community (e.g., Li et al 2014;Wang et al 2014;Lin et al 2015;Sun et al 2016;Yang et al 2016;Choi et al 2017). Several ensemble Kalman filter-based atmospheric data assimilation systems have also used NMC-based climatological error covariance statistics to perturb the model initial conditions (e.g., Zhang et al 2006;Zhang 2007, 2008;Wu et al 2010).…”

Does Soil Moisture Have an Influence on Near‐Surface Temperature?

“…Commonly, uncertainties in representing land use, soil moisture (SM), and terrain conditions on the underlying surface, which affect the land-atmosphere interaction directly, are identified as the major sources of error in near-surface weather forecasting (Cheng & Steenburgh, 2005;Massey et al, 2014;Zhang et al, 2013). Hence, the impacts of underlying surface characteristics and bias corrections on NWP have been investigated in recent years (e.g., Fan & van den Dool, 2011;Massey et al, 2016;Chen et al, 2017;Lin et al, 2017). Results indicated that the bias correction of SM could help the near-surface temperature prediction in those case studies.…”

A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band

“…The Earth's vegetation, and thus the global food security, depends on the soil moisture climatology [2]. Trends in intensity, frequency and duration of the global precipitation [3], resulting from a changing climate, makes monitoring of soil moisture a key factor to extend forecast skill of land-atmosphere models [4,5]; improve drought modeling and management [6,7]; and further unravel processes that regulate evapotranspiration [8,9], as well as carbon [10] and nitrogen cycles [11].…”