Water Science and Engineering 2018, 11(3) 196-204 DOI:   https://doi.org/10.1016/j.wse.2018.09.003  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Observation operator
Unscented particle filter (UPF)
Soil temperature
MODIS LST
Data assimilation
Authors
PubMed

Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land model

Xiao-lei Fu a, b, c, Zhong-bo Yu b, * ,Yong-jian Ding c, Ying Tang d, Hai-shen Lü b, Xiao-lei Jiang b, Qin Ju b

a College of Civil Engineering, Fuzhou University, Fuzhou 350116, China
b State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
c State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
d Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48824, USA

Abstract

An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter (UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) assimilation. The results show that the filter improved the soil temperature simulations significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF’s performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.

Keywords Observation operator   Unscented particle filter (UPF)   Soil temperature   MODIS LST   Data assimilation  
Received 2017-10-07 Revised 2018-06-24 Online: 2018-07-30 
DOI: https://doi.org/10.1016/j.wse.2018.09.003
Fund:

This work was supported by the National Key Research and Development Program of China (Grants No. 2016YFC0402706 and 2016YFC0402710), the National Natural Science Foundation of China (Grants No. 51709046 and 41323001), and the Open Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University (Grant No. 2015490311).

Corresponding Authors: Zhong-bo Yu
Email: zyu@hhu.edu.cn
About author:

References:

Anderson, J.L., 2001. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 129(12), 2884-2903. https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

Bengtsson, T., Snyder, C., Nychka, D., 2003. Toward a nonlinear ensemble filter for high-dimensional systems. Journal of Geophysical Research,108(D24), 8775-8785. https://doi.org/10.1029/2002JD002900.

Burgers, G., van Leeuwen, P.J., Evensen, G., 1998. Analysis scheme in the ensemble Kalman filter. Monthly Weather Review, 126(6), 1719-1724. https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.

Chau, K.W., Wu, C.L., Li, Y.S., 2005. Comparison of several flood forecasting models in Yangtze River. Journal of Hydrologic Engineering, ASCE. 10(6), 485-491. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:6(485).

Dai, Y., Zeng, X., Dickinson, R.E., Baker, I., Bonan, G.B., Bosilovich, M.G., Denning, A.S., Dirmeyer, P.A., Houser, P.R., Niu, G., et al., 2003. The common land model. Bulletin of the American Meteorological Society, 84(8), 1013-1023. https://doi.org/10.1175/BAMS-84-8-1013.

Diak, G.R., Whipple, M.S., 1993. Improvements to models and methods for evaluating the land-surface energy balance and “effective” roughness using radiosonde reports and satellite-measured ‘skin’ temperature data. Agricultural and Forest Meteorology, 63(3-4), 189-218. https://doi.org/10.1016/0168-1923(93)90060-U.

Evensen, G., 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143-10162. https://doi.org/10.1029/94JC00572.

Evensen, G., 1997. Advanced data assimilation for strongly nonlinear dynamics. Monthly Weather Review, 125(6), 1342-1354. https: //doi.org/10.1175/1520-0493(1997)125<1342:ADAFSN>2.0.CO;2.

Fu, X.L., Yu, Z.B., Luo, L.F., Lü, H.S., Liu, D., Ju, Q., Yang, T., Xu, F., Gu, H.H., Yang, C.G., et al., 2014. Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter. Stochastic Environmental Research and Risk Assessment. 28(3), 681-693. https://doi.org/10.1007/s00477-013-0781-3.

Han, X.J., Li, X., 2008. An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation. Remote Sensing of Environment, 112(4), 1434-1449. https://doi.org/10.1016/j.rse.2007.07.008.

Huang, C.L., Li, X., Lu, L., 2008. Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter. Remote Sensing of Environment, 112(4), 1320-1336. https://doi.org/10.1016/j.rse.2007.03.028.

Julier, S.J., Uhlmann, J.K., 2004. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 410-422. https:// doi.org/10.1109/JPROC.2004.837637.

Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering Transactions, 82(1), 35-45. https:// doi.org/ 10.1115/1.3662552.

Keefer, T.O., Moran, M.S., Paige, G.B., 2008. Long-term meteorological and soil hydrology database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resources Research, 44(5), W05S07. https://doi.org/10.1029/2006WR005702.

Kumar, P., Kaleita, A.L., 2003. Assimilation of near-surface temperature using extended Kalman filter. Advances in Water Resources, 26(1), 79-93. https://doi.org/10.1016/S0309-1708(02)00098-2.

Kumar, S.V., Reichle, R.H., Peters-Lidard, C.D., Koster, R.D., Zhan, X., Crow, W.T., Eylander, J.B., Houser, P.R., 2008. A land surface data assimilation framework using the land information system: Description and applications. Advances in Water Resources, 31(11), 1419-1432. https://doi.org/10.1016/j.advwatres.2008.01.013.

Lü, H.S., Yu, Z.B., Zhu, Y.H., Sam, D., Hao, Z.C., Sudicky, E.A., 2010. Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation. Advances in Water Resources, 34(3), 395-406. https://doi.org/10.1016/j.advwatres.2010.12.005.

McLaughlin, D., 2002. An integrated approach to hydrologic data assimilation: Interpolation, smoothing, and filtering. Advances in Water Resources, 25(8-12), 1275-1286. https://doi.org/10.1016/S0309-1708(02)00055-6.

Mihalakakou, G., 2002. On estimating soil surface temperature profiles. Energy and Buildings, 34(3), 251-259. https:// doi.org/10.1016/S0378-7788(01)00089-5.

Miller, R.N., Carter, E.F., Blue, S.T., 1999. Data assimilation into nonlinear stochastic models. Tellus Series A-Dynamic Meteorology and Oceanography, 51(51), 167-194. https://doi.org/10.3402/tellusa.v51i2.12315.

Njoku, E.G., Li, L., 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 79-93. https://doi.org/10.1109/36.739125.

Oleson, K.W., Dai, Y., Bonan, G.B., Bosilovich, M.G., Dickinson, R.E., Dirmeyer, P.A., Hoffman, F., Houser, P.R., Levis, S., Niu, G., et al., 2004. Technical Description of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-461+STR. https://doi.org/10.5065/D6N877R0.

Owe, M., de Jeu, R., 2003. Surface parameter retrieval at global scales by microwave remote sensing. In: SPIE Proceedings Vol. 4879: Remote Sensing for Agriculture, Ecosystems, and Hydrology IV, pp. 202-210. https://doi.org/10.1117/12.466000.

Pham, D.T., Verron, J., Roubaud, M.C., 1998. A singular evolutive extended Kalman filter for data assimilation in oceanography. Journal of Marine Systems, 16(3), 323-340. https://doi.org/10.1016/S0924-7963(97)00109-7.

Reichle, R.H., 2008. Data assimilation methods in the earth sciences. Advances in Water Resources, 31(11), 1411-1418. https://doi.org/10.1016/j.advwatres.2008.01.001.

van der Merwe, R., 2004. Sigma-Point Kalman Filters for Probalistic Inference in Dynamic State-Space Models. Ph. D. Dissertation. Oregon Health and Science University, Portland.

Verlaan, M., 1998. Efficient Kalman Filtering Algorithms for Hydrodynamic Models. Ph. D. Dissertation. TU Delft, Delft.

Verlaan, M., Heemink, A.W., 2001. Nonlinearity in data assimilation applications: A practical method for analysis. Monthly Weather Review, 129(6), 1578-1589. https://doi.org/10.1175/1520-0493(2001)129<1578:NIDAAA>2.0.CO;2.

Wan, Z., Dozier, J., 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 892-905. https://doi.org/10.1109/36.508406.

Whitaker, J.S., Hamill, T.M., 2002. Ensemble data assimilation without perturbed observations. Monthly Weather Review, 130(7), 1913-1924. https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

Wu, C.L., Chau, K.W., Huang, J.S., 2007. Modeling coupled water and heat transport in a soil-mulch-plant-atmosphere continuum (SMPAC) system. Applied Mathematical Modeling, 31(2), 152-169. https://doi.org/10.1016/j.apm.2005.08.018.

Xie, X., Zhang, D., 2010. Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter. Advances in Water Resources, 33(6), 678-690. https://doi.org/10.1016/j.advwatres.2010.03.012.

Yu, Z.B., Fu, X.L., Lü, H.S., Luo, L.F., Liu, D., Ju, Q., Xiang, L., Wang, Z.Z., 2014a. Evaluating ensemble Kalman, particle, and ensemble particle filters through soil temperature prediction. Journal of Hydrologic Engineering, 19(12), https://doi.org/10.1061/(ASCE)HE.1943-5584.0000976.

Yu, Z.B., Fu, X.L., Luo, L.F., Lü, H.S., Ju, Q., Liu, D., Kalin, D.A., Huang, D., Yang, C.G., Zhao, L.L., 2014b. One-dimensional soil temperature simulation with common land model by assimilating in situ observations and MODIS LST with the ensemble particle filter. Water Resources Research, 50(8), 6950-6965. https://doi.org/10.1002/2012WR013473.

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