Abstract
The active layer of frozen ground data assimilation system adopts the SHAW (Simulteneous Heat and Water) model as the model operator. It employs an ensemble kalman filter to fuse state variables predicted by the SHAW model with in situ observation and the SSM/I 19 GHz brightness temperature for the purpose of optimizing model hydrothermal state variables. When there is little water movement in the frozen soil during the winter season, the unfrozen water content depends primarily on soil temperature. Thus, soil temperature is the crucial state variable to be improved. In contrast, soil moisture is heavily influenced by precipitation during the summer season. The simulation accuracy of soil moisture has a strong and direct impact on the soil temperature. In this case, the crucial state variable to be improved is soil moisture. One-dimensional assimilation experiments that have been carried out at AMDO station show that land data assimilation method can improve the estimation of hydrothermal state variables in the soil by fusing model information and observation information. The reasonable model error covariance matrix plays a key role in transferring the optimized surface state information to the deep soil, and it provides improved estimations of whole soil state profiles. After assimilating the 4-cm soil temperature by in situ observation, the soil temperature RMSE (Root Mean Square Error) of each soil layer decreased by 0.96°C on average relative to the SHAW simulation. After assimilating the 4-cm soil moisture in situ observation, the soil moisture RMSE of each soil layer decreased by 0.020 m3·m−3. When assimilating the SSM/I 19 GHz brightness temperature, the soil temperature RMSE of each soil layer during the winter decreased by 0.76°C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m3·m−3.
Similar content being viewed by others
References
Jin H J, Zhao L, Wang S L, et al. Thermal regimes and degradation modes of permafrost along the Qinghai-Tibet Highway. Sci China Ser D-Earth Sci, 2006, 49(11): 1170–1183
Allison I, Barry R G, Goodison B E. Climate and Cryosphere (CliC) Project Science and Co-ordination Plan. WCRP-114/WMO/TD, No. 1053, 2001
Cheng G D. Special issue of studies on the dynamic changes of cryosphere—Preface. J Glaciol Geocryol, 1996, 18(Special Issue): 1–3
Wang S L. Study of permafrost degration in the Qinghai-Xizang Plateau. Adv Earth Sci, 1997, 12(2): 164–167
Anisimov O A, Shiklomanov N I, Nelson F E. Global warming and active-layer thickness: Results from transient general circulation models. Glob Planet Change, 1997, 15(3–4): 61–78
Goodison B E, Brown R D, Crane R G. EOS Science Plan: Chapter 6 Cryospheric System. NASA, 1998
Jin R, Li X. A review on the algorithm of frozen/thaw boundary detection by using passive microwave remote sensing. Remote Sens Technol Appl, 2002, 17(6): 370–375
Li X, Cheng G D. Review on the interaction models between climatic system and frozen soil. J Glaciol Geocryol, 2002, 24(3): 315–321
Talagrand O. Assimilation of observations, an introduction. J Meteor Soc Japan, 1997, 75(1B): 191–209
Li X, Huang C L. Data assimilation: A new means for multi-source geospatial data integration. Sci Technol Rev, 2004, 12: 13–16
Li X, Huang C L, Che T, et al. Development of a Chinese land data assimilation system: Its progress and prospects. Prog Nat Sci, 2007, 17(8): 881–892
Huang C L, Li X. A review of Land Data Assimilation System (in Chinese). Remote Sens Technol Appl, 2004, 19(5): 424–430
Houser P R, Shuttleworth W J, Famiglietti J S, et al. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour Res, 1998, 34(12): 3405–3420
Reichle R H, Walker J P, Koster R D, et al. Extended versus ensemble Kalman filtering for land data assimilation. J Hydrometeor, 2002, 3: 728–740
Walker J P, Willgoose G R, Kalma J D. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithms. Adv Water Res, 2001, 24: 631–650
Walker J P, Houser P R. A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations. J Geophys Res, 2001, 106: 11761–11774
Walker J P, Willgoose G R, Kalma J D. Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application. Water Resour Res, 2002, 38(12): 1301, doi: 10.1029/2002WR001545
Zhang S W, Li H R, Zhang W D, et al. Estimating the soil moisture profile by assimilating near-surface observations with the ensemble Kalman filter. Adv Atmos Sci, 2005: 22(6): 936–945
Huang C L, Li X, Lu L, et al. Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter. Remote Sens Environ, 2008, 112(3): 888–900
Entekhabi D, Galantowicz J F, Njoku E G. Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE Trans Geosci Remote Sensing, 1994, 32: 438–448
Galantowicz J F, Entekhabi D, Njoku E G. Tests of sequential data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness. IEEE Trans Geosci Remote Sensing, 1999, 37: 1860–1870
Pathmathevan M, Koike T, Li X. A new satellite-based data assimilation algorithm to determine spatial and temporal variations of soil moisture and temperature profiles. J Meteor Res Japan, 2003, 81(5): 1111–1135
Crow W T, Wood E F. The assimilation of remotely sensed brightness imagery into a land surface model using ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97. Adv Water Res, 2003, 26: 137–149
Huang C L, Li X, Lu L, et al. Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter. Remote Sens Environ, 2008, 112(3): 888–900
Hoeben R, Troch P A. Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour Res, 2000, 36(10): 2805–2819
England A W, DeRoo R. Active layer thickness and moisture content of arctic tundra from SVAT/Radiobrightness models and assimilated 1.4 or 6.9 GHz brightness. Final Report of NSF Award, ID 0240747, 2006
Tian X J, Xie Z H. A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing. Sci China Ser D-Earth Sci, 2008, 51(7): 992–1000
Flerchinger G N, Saxton K E. Simultaneous heat and water model of a freezing snow-residue-soil system I. Theory and development. Trans ASAE, 1989, 32(2): 565–571
Nassar I N, Horton R, Flerchinger G N. Simultaneous heat and mass transfer in soil columns exposed to freezing/thawing conditions. Soil Sci, 2000, 165(3): 208–216
Fung A K. Microwave Scattering and Emission Models and Their Applications. Norwood, MA: Artech House, 1994
Wu T D, Chen K S. A reappraisal of the validity of the IEM model for backscattering from rough surfaces. IEEE Trans Geosci Remote Sens, 2004, 42(4): 743–753
Chen K S, Wu T D, Tsang L, et al. Emission of rough surface calculated by the integral equation method with comparison to three-dimensional moment method simulations. IEEE Trans Geosci Remote Sens, 2003, 41(1): 90–101
Liou Y A, England A W. Annual temperature and radiobrightness signature for bare soils. IEEE Trans Geosci Remote Sens, 1996, 34: 981–990
Dobson M C, Ulaby F T, Hallikainen M, et al. Microwave dielectric behavior of wet soil, part II: Four-component dielectric mixing models. IEEE Trans Geosci Remote Sens, 1985, GE-23: 35–46
Evensen G. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn, 2003, 53: 343–367
Evensen G. Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn, 2004, 54: 539–560
Zhang Y, Lü S H. Development and validation of a simple frozen soil parameterization scheme used for climate model. Adv Atmos Sci, 2002, 19(3): 513–527
Comprehensive Scientific Expedition Team of Qinghai-Tibet Plateau, Chinese Academy of Sciences. Tibet Climate. Beijing: Science Press, 1984. 165–166
Song K C. The hydrothermal transfer observation and simulation research of typical vegetation Landscapes zone in Heihe River Basin. Doctoral Dissertation. Beijing: Graduate University of Chinese Academy of Sciences, 2005
Che T. Study on passive microwave remote sensing of snow and snow data assimilation method (in Chinese). Doctoral Dissertation. Beijing: Graduate University of Chinese Academy of Sciences, 2006
Du Y, chen X F, Zhang Y L, et al. Reliability of calibration curves measured by TDR in frozen soils verified by NMR (in Chinese). J Glaciol Geocryol, 2004, 26(6): 788–794
Spaans E J A, Baker J M. Examining the use of time domain reflectometry for measuring liquid water content in frozen soil. Water Resour Res, 1995, 31(12): 2917–2925
England A W. The effect upon microwave emissivity of volume scattering in snow, in ice and in frozen soil. Proc URSI Special Meeting on Microwave Scattering and Emission from the Earth, Berne, Switzerland, 1974. 273–287
Moradkhani H, Sorooshian S, Gupta H V, et al. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv Water Resour, 2005, 28(2): 135–147
Yang K, Watanabe T, Koike T, et al. Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget. J Meteor Res Japan, 2007, 85A: 229–242
Tsang L, Chen C T, Chang A, et al. Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow. Radio Sci, 2000, 35(3): 731–749
Han X J, Li X. An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation. Remote Sens Environ, 2008, 112(4): 1434–1449
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-Q10-2-4), National Natural Science Foundation of China (Grant No. 40701113) and “Western Light” Program of Talent Cultivation of the Chinese Academy of Sciences (2008)
Rights and permissions
About this article
Cite this article
Jin, R., Li, X. Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data. Sci. China Ser. D-Earth Sci. 52, 1732–1745 (2009). https://doi.org/10.1007/s11430-009-0174-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11430-009-0174-0