The Auto‐Tuned Land Data Assimilation System (ATLAS)

[1] Land data assimilation systems are commonly tasked with merging remotely sensed surface soil moisture retrievals with information derived from a soil water balance model driven by observed rainfall. The performance of such systems can be degraded by the incorrect specification of parameters describing modeling and observation errors. Here the Auto-Tuned Land Data Assimilation System (ATLAS) is introduced to simultaneously solve for all parameters required for the application of a simple land data assimilation system to integrate satellite-based rainfall and soil moisture retrievals for drought monitoring applications. The approach is based on combining a triple collocation (TC) strategy with the statistical analysis of filtering innovations and designed to leverage the simultaneous availability of satellite-based soil moisture products acquired from both active and passive microwave remote sensing. A number of variants of the ATLAS approach—each based on a different strategy for leveraging TC and innovation analysis within an adaptive filtering framework—are derived and evaluated through a synthetic twin experiment. In addition, a preliminary real data analysis is conducted using actual satellite-based products and evaluated against independent ground-based observations. Results illustrate the potential of ATLAS to improve the analysis of soil moisture anomalies using data products derived from the Global Precipitation Measurement (GPM) and the NASA Soil Moisture Active/Passive missions.

[1]  Yudong Tian,et al.  Modeling errors in daily precipitation measurements: Additive or multiplicative? , 2013 .

[2]  Wouter Dorigo,et al.  Estimation of the temporal autocorrelation structure by the collocation technique with an emphasis on soil moisture studies , 2013 .

[3]  R. Jeu,et al.  Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .

[4]  Yi Y. Liu,et al.  Error characterisation of global active and passive microwave soil moisture datasets. , 2010 .

[5]  Wade T. Crow,et al.  An ensemble Kalman filter dual assimilation of thermal infrared and microwave satellite observations of soil moisture into the Noah land surface model , 2012 .

[6]  T. Jackson,et al.  III. Measuring surface soil moisture using passive microwave remote sensing , 1993 .

[7]  Wolfgang Wagner,et al.  Estimating root mean square errors in remotely sensed soil moisture over continental scale domains , 2013 .

[8]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[9]  Venkat Lakshmi,et al.  Advances in downscaling soil moisture for use in drought and flood assessments: Implications for data from the Soil Moisture Active and Passive (SMAP) Mission , 2015 .

[10]  Wade T. Crow,et al.  The Optimality of Potential Rescaling Approaches in Land Data Assimilation , 2013 .

[11]  Klaus Scipal,et al.  An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Klaus Scipal,et al.  A possible solution for the problem of estimating the error structure of global soil moisture data sets , 2008 .

[13]  Wade T. Crow,et al.  Enhancing model‐based land surface temperature estimates using multiplatform microwave observations , 2013 .

[14]  Wade T. Crow,et al.  An improved approach for estimating observation and model error parameters in soil moisture data assimilation , 2010 .

[15]  R. Mehra.  On the identification of variances and adaptive Kalman filtering , 1970 .

[16]  Wade T. Crow,et al.  Improved prediction of quasi‐global vegetation conditions using remotely‐sensed surface soil moisture , 2012 .

[17]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[18]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .

[19]  Wade T. Crow,et al.  Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture , 2006 .

[20]  Guanrong Chen,et al.  Kalman filtering: with real-time applications (2nd ed.) , 1991 .

[21]  Thomas J. Jackson,et al.  Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Rolf H. Reichle,et al.  Assimilation of passive and active microwave soil moisture retrievals , 2012 .

[23]  Wade T. Crow,et al.  On the utility of land surface models for agricultural drought monitoring , 2012 .

[24]  Wade T. Crow,et al.  Comparison of adaptive filtering techniques for land surface data assimilation , 2008 .

[25]  A. Stoffelen.  Toward the true near-surface wind speed: Error modeling and calibration using triple collocation , 1998 .

[26]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[27]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .