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 of Conferences, 38 (2018) 03009

References

Open Access

Issue		E3S Web of Conferences 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]
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]
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]
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]
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]
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]
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]
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]
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[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]