scholarly journals A comparison of methods for a priori bias correction in soil moisture data assimilation

2012 ◽  
Vol 48 (3) ◽  
Author(s):  
Sujay V. Kumar ◽  
Rolf H. Reichle ◽  
Kenneth W. Harrison ◽  
Christa D. Peters-Lidard ◽  
Soni Yatheendradas ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Bias Correction ◽  
A Priori ◽  
Comparison Of Methods ◽  
Soil Moisture Data

scholarly journals Potential of bias correction for downscaling passive microwave and soil moisture data

2015 ◽  
Vol 120 (13) ◽  
pp. 6460-6479 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Michael H. Cosh ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Passive Microwave ◽  
Soil Moisture Data

scholarly journals The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

2020 ◽  
Vol 12 (9) ◽  
pp. 1490 ◽  
Author(s):  
Calum Baugh ◽  
Patricia de Rosnay ◽  
Heather Lawrence ◽  
Toni Jurlina ◽  
Matthias Drusch ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
The United States ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Skill Scores ◽  
High Flows ◽  
The Impact ◽  
Awareness System

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.

scholarly journals A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5

2019 ◽  
Vol 33 (6) ◽  
pp. 1182-1193
Author(s):  
Tariq Mahmood ◽  
Zhenghui Xie ◽  
Binghao Jia ◽  
Ammara Habib ◽  
Rashid Mahmood
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Moisture Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation

2015 ◽  
Vol 42 (16) ◽  
pp. 6710-6715 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Christopher R. Hain ◽  
Jicheng Liu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Ensemble Size ◽  
Soil Moisture Data ◽  
Optimal Ensemble

Application and improvement of an adaptive ensemble Kalman filter for soil moisture data assimilation

2010 ◽  
Vol 53 (11) ◽  
pp. 1700-1708 ◽  
Author(s):  
XiaoKang Shi ◽  
Jun Wen ◽  
JianWen Liu ◽  
Hui Tian ◽  
Xin Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Adaptive Ensemble Kalman Filter

scholarly journals Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

2017 ◽  
Vol 145 (12) ◽  
pp. 4997-5014 ◽  
Author(s):  
Liao-Fan Lin ◽  
Ardeshir M. Ebtehaj ◽  
Alejandro N. Flores ◽  
Satish Bastola ◽  
Rafael L. Bras
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Specific Humidity ◽  
Surface Model ◽  
Precipitation Data ◽  
Near Surface ◽  
Satellite Precipitation ◽  
Soil Moisture Data ◽  
Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

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