One-dimensional soil temperature assimilation experiment based on unscented particle filter and Common Land Model

Xiao Lei Fu; Bao Ming Jin; Xiao Lei Jiang; Cheng Chen

doi:10.1051/e3sconf/20183803009

All issues

Volume 38 (2018)

E3S Web Conf., 38 (2018) 03009

References

Open Access

Issue		E3S Web Conf. Volume 38, 2018 2018 4^th International Conference on Energy Materials and Environment Engineering (ICEMEE 2018)


Article Number		03009
Number of page(s)		5
Section		Water Conservancy and Civil Engineering
DOI		https://doi.org/10.1051/e3sconf/20183803009
Published online		04 June 2018

Yu Z, Carlson T N, Barron E J, et al. On evaluating the spatial-temporal variation of soil moisture in the Susquehanna River Basin [J]. Water Resources Research, 2001, 34: 1313-1326. [CrossRef] [Google Scholar]
Yu Z, Fu X, Lü H, et al. Evaluating ensemble Kalman, particle and ensemble particle filters through soil temperature prediction [J]. Journal of Hyrologic Engineering, 2014, 19: 04014027. [CrossRef] [Google Scholar]
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Huang C, Li X, Lu L. Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter [J]. Remote Sensing of Environment, 2008, 112(4): 1320-1336. [CrossRef] [Google Scholar]
Yu Z, Fu X, Luo L. One-dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODIS LST with the ensemble particle filter [J]. Water Resources Research, 2014, 50: 6950-6965. [CrossRef] [Google Scholar]
Mihalakakou G. On estimating soil surface temperature profiles [J]. Energy and Buildings, 2002, 34(3): 251-259. [CrossRef] [Google Scholar]
Wu C L, Chau K W, Huang J S. Modeling coupled water and heat transport in a soil-mulch-plant-atmosphere continuum (SMPAC) system [J]. Applied Mathematical Modeling, 2007, 31 (2): 152-169. [CrossRef] [Google Scholar]
Wan Z. Dozier J. A generalized split-window algorithm for retrieving land-surface temperature from space [J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 892-905. [CrossRef] [Google Scholar]
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Reichle R H. Data assimilation methods in the earth sciences [J]. Advances in Water Resources, 2008, 31(11): 1411-1418. [CrossRef] [Google Scholar]
Fu X, Yu Z, Luo L, et al. Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter [J]. Stochastic Environmental Research and Risk Assessment, 2014, 28: 681-693. [CrossRef] [Google Scholar]
McLaughlin D. Recent developments in hydrologic data assimilation [J]. Reviews of Geophysics, 1995, 33: 977-984. [CrossRef] [Google Scholar]
Van der Merwe R. Sigma-Point Kalman Filters for Probalistic Inference in Dynamic State-Space Models [D]. Oregon Health and Science University, 2004. [Google Scholar]
Han X, Li X. An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation [J]. Remote Sensing of Environment, 2008, 112(4): 1434-1449. [CrossRef] [Google Scholar]
Keefer T O, Moran M S, Paige G B. Long-term meteorological and soil hydrology database, Walnut Gulch Experimental Watershed, Arizona, United States [J]. Water Resources Research, 2008, 44: W05S07. [Google Scholar]
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[1] Yu Z, Carlson T N, Barron E J, et al. On evaluating the spatial-temporal variation of soil moisture in the Susquehanna River Basin [J]. Water Resources Research, 2001, 34: 1313-1326. [CrossRef] [Google Scholar]

[2] Yu Z, Fu X, Lü H, et al. Evaluating ensemble Kalman, particle and ensemble particle filters through soil temperature prediction [J]. Journal of Hyrologic Engineering, 2014, 19: 04014027. [CrossRef] [Google Scholar]

[3] Yu Z, Barron E J, Yarnal B, et al. Evaluation of basin scale hydrologic response to a multi storm simulation [J]. Journal of Hydrology, 2002, 257: 212-225. [CrossRef] [Google Scholar]

[4] Huang C, Li X, Lu L. Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter [J]. Remote Sensing of Environment, 2008, 112(4): 1320-1336. [CrossRef] [Google Scholar]

[5] Yu Z, Fu X, Luo L. One-dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODIS LST with the ensemble particle filter [J]. Water Resources Research, 2014, 50: 6950-6965. [CrossRef] [Google Scholar]

[6] Mihalakakou G. On estimating soil surface temperature profiles [J]. Energy and Buildings, 2002, 34(3): 251-259. [CrossRef] [Google Scholar]

[7] Wu C L, Chau K W, Huang J S. Modeling coupled water and heat transport in a soil-mulch-plant-atmosphere continuum (SMPAC) system [J]. Applied Mathematical Modeling, 2007, 31 (2): 152-169. [CrossRef] [Google Scholar]

[8] Wan Z. Dozier J. A generalized split-window algorithm for retrieving land-surface temperature from space [J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 892-905. [CrossRef] [Google Scholar]

[9] Njoku E G, Li L. Retrieval of land surface parameters using passive microwave measurements at 6-18GHz [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1): 79-93. [CrossRef] [Google Scholar]

[10] Owe M, Van de Griend A A. On the relationship between thermodynamic surface temperature and high frequency (37GHz) vertical polarization brightness temperature under semi-arid conditions [J]. International Journal of Remote Sensing, 2001, 22(17): 3521-3532. [CrossRef] [Google Scholar]

[11] Kumar S V, Reichle R H, Peters-Lidard C D, et al. A land surface data assimilation framework using the land information system: Description and applications [J]. dvances in Water Resources, 2008, 31(11): 1419-1432. [CrossRef] [Google Scholar]

[12] McLaughlin D. An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering [J]. Advances in Water Resources, 2002, 25(8-12):1275-1286. [CrossRef] [Google Scholar]

[13] Reichle R H. Data assimilation methods in the earth sciences [J]. Advances in Water Resources, 2008, 31(11): 1411-1418. [CrossRef] [Google Scholar]

[14] Fu X, Yu Z, Luo L, et al. Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter [J]. Stochastic Environmental Research and Risk Assessment, 2014, 28: 681-693. [CrossRef] [Google Scholar]

[15] McLaughlin D. Recent developments in hydrologic data assimilation [J]. Reviews of Geophysics, 1995, 33: 977-984. [CrossRef] [Google Scholar]

[16] Van der Merwe R. Sigma-Point Kalman Filters for Probalistic Inference in Dynamic State-Space Models [D]. Oregon Health and Science University, 2004. [Google Scholar]

[17] Han X, Li X. An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation [J]. Remote Sensing of Environment, 2008, 112(4): 1434-1449. [CrossRef] [Google Scholar]

[18] Keefer T O, Moran M S, Paige G B. Long-term meteorological and soil hydrology database, Walnut Gulch Experimental Watershed, Arizona, United States [J]. Water Resources Research, 2008, 44: W05S07. [Google Scholar]

[19] Oleson K, Dai Y, Bonan G, et al. Technical description of the Community Land Model (CLM) [M]. NCAR Technical Note NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder, Colo, 2004. [Google Scholar]