China land soil moisture EnKF data assimilation based on satellite remote sensing data

Shi, Chunxiang; Xie, Zhenghui; Qian, Hui; Liang, Miaoling; Yang, Xiaochun

doi:10.1007/s11430-010-4160-3

Cited by 179 publications

(116 citation statements)

References 17 publications

Supporting

Mentioning

115

Contrasting

Unclassified

Order By: Relevance

“…In this study, four sets of meteorological forcing over mainland China developed by different institutions were used in our multiple forcings ensemble system. They include -Forcing data for China (hereafter FY) developed by Shi (2008) and Shi et al (2011), which introduced inverted precipitation and ground-incident solar radiation products acquired through the high-resolution spatial and temporal FY2C geostationary satellite data for precipitation and solar radiation. The original inverted precipitation products from the China Satellite Data Service Center (http://satellite.cma.gov.cn) are estimated 6-hourly accumulated precipitation output.…”

Section: Multiple Meteorological Forcingsmentioning

confidence: 99%

“…near-surface air temperature, pressure, wind speed and humidity were derived from National Centers for Environmental Prediction (NCEP)/NCAR reanalysis data. Shi et al (2011) and Jia et al (2013) used this information as the external meteorological forcing of land data assimilation system.…”

Section: Multiple Meteorological Forcingsmentioning

confidence: 99%

“…At a regional scale, many more in situ observations and remote sensing measurements are available for LSM use. For example, high-resolution space/time meteorological forcings for mainland China have been developed by He (2010), Shi (2008), Shi et al (2011) and Tian et al (2010), but various errors are still found in soil-moisture simulations by LSM using different forcings. It has been found that the simplest ensemble approach (simple arithmetical averaging of soil moisture from an individual ensemble member) is an effective strategy for improving the simulation of soil moisture, but it is still not superior to the best individual ensemble member in most cases (Guo et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

Liu

Xie

2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The quality of soil-moisture simulation using land surface models depends largely on the accuracy of the meteorological forcing data. We investigated how to reduce the uncertainty arising from meteorological forcings in a simulation by adopting a multiple meteorological forcing ensemble approach. Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland China were conducted using four different meteorological forcings, and the four sets of soil-moisture data related to the simulations were then merged using simple arithmetical averaging and Bayesian model averaging (BMA) ensemble approaches. BMA is a statistical post-processing procedure for producing calibrated and sharp predictive probability density functions (PDFs), which is a weighted average of PDFs centered on the biascorrected forecasts from a set of individual ensemble members based on their probabilistic likelihood measures. Compared to in situ observations, the four simulations captured the spatial and seasonal variations of soil moisture in most cases with some mean bias. They performed differently when simulating the seasonal phases in the annual cycle, the interannual variation and the magnitude of observed soil moisture over different subregions of mainland China, but no individual meteorological forcing performed best for all subregions. The simple arithmetical average ensemble product outperformed most, but not all, individual members over most of the subregions. The BMA ensemble product performed better than simple arithmetical averaging, and performed best for all fields over most of the subregions. The BMA ensemble approach applied to the ensemble simulation reproduced anomalies and seasonal variations in observed soil-moisture values, and simulated the mean soil moisture. It is presented here as a promising way for reproducing long-term, highresolution spatial and temporal soil-moisture data.

show abstract

Section: Multiple Meteorological Forcingsmentioning

confidence: 99%

Section: Multiple Meteorological Forcingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

Liu

Xie

2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…To facilitate product processing, all LST data is resampled to 0.01 • using the nearest neighbor method with the help of the MRT (MODIS Reprojection Tool) provided by NASA. The climate reanalysis dataset used in this study is from the China Meteorological Administration (CMA) land data assimilation system (CLDAS) version 2.0 provided by the China metrological data service center (http://data.cma.cn/) [48,49]. CLDAS is developed by the National Meteorological Information Center (NMIC) of CMA using data fusion and assimilation technology.…”

Section: Modis Lst Productmentioning

confidence: 99%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

View full text Add to dashboard Cite

Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

show abstract

“…The meteorological variables required by the soil moisture model include daily maximum temperature, daily minimum temperature, daily mean temperature, daily mean relative humidity, and daily total precipitation. This study used the meteorological data generated by the CMA Land Data Assimilation System Version 1.0 (CLDAS-V1.0) [40]. The meteorological data were generated by fusing the ground measurements, satellite observations, and numerical model products using data fusion and assimilation techniques.…”

Section: Meteorological Datamentioning

confidence: 99%

Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method

et al. 2016

View full text Add to dashboard Cite

Abstract:Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits.

show abstract

China land soil moisture EnKF data assimilation based on satellite remote sensing data

Cited by 179 publications

References 17 publications

Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method

Contact Info

Product

Resources

About