Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

doi:10.1007/s10483-016-2103-6

Abstract

Abstract:

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature (Tb) simulation.How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging (BMA) ensemble approach.The simulations by the community microwave emission model (CMEM) coupled with the community land model version 4.5(CLM4.5) over mainland China are conducted by the 24 configurations from four vegetation opacity parameterizations (VOPs),three soil dielectric constant parameterizations (SDCPs),and two soil roughness parameterizations (SRPs).Compared with the simple arithmetical averaging (SAA) method,the BMA reconstructions have a higher spatial correlation coefficient (larger than 0.99) than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system (AMSR-E) at the vertical polarization.Moreover,the BMA product performs the best among the ensemble members for all vegetation classes,with a mean root-mean-square difference (RMSD) of 4 K and a temporal correlation coefficient of 0.64.

Key words: community microwave emission model (CMEM), community land model version 4.5(CLM4.5), microwave brightness temperature, Bayesian model averaging (BMA)

2010 MSC Number:

78A40

Binghao JIA, Zhenghui XIE. Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach. Applied Mathematics and Mechanics (English Edition), 2016, 37(11): 1501-1516.

TrendMD

References

[1] Dobson, M., Ulaby, F., Hallikainen, M., and El-Rayes, M. Microwave dielectric behavior of wet soil, part Ⅱ:dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, 38, 1635-1643(1985)
[2] Shi, J. C., Jiang, L., Zhang, L., Chen, K. S., Wigneron, J. P., and Chanzy, A. A parameterized multifrequency-polarization surface emission model. IEEE Transactions on Geoscience and Remote Sensing, 43, 2831-2841(2005)
[3] Wigneron, J. P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M. J., Richaume, P., Ferrazzoli, P., de Rosnay, P., Gurney, R., Calvet, J. C., Grant, J. P., Guglielmetti, M., Hornbucklei, B., Mätzler, C., Pellarin, T., and Schwank, M. L-band microwave emission of the biosphere (L-MEB) model:description and calibration against experimental data sets over crop fields. Remote Sensing of Environment, 107, 639-655(2007)
[4] Holmes, T., Drusch, M., Wigneron, J. P., and de Jeu, R. A global simulation of microwave emission:error structures based on output from ECMWF's operational integrated forecast system. IEEE Transactions on Geoscience and Remote Sensing, 46, 846-856(2008)
[5] De Lannoy, G., Reichle, R., and Pauwels, V. Global calibration of the GEOS-5 L-band microwave radiative transfer model over non-frozen land using SMOS observations. Journal of Hydrometeorology, 14, 765-785(2013)
[6] De Rosnay, P., Drusch, M., Boone, A., Balsamo, G., Decharme, B., Harris, P., Kerr, Y., Pellarin, T., Polcher, J., and Wigneron, J. P. AMMA land surface model intercomparison experiment coupled to the community microwave emission model:ALMIP-MEM. Journal of Geophysical Research, 114, D05108(2009)
[7] Drusch, M., Holmes, T., de Rosnay, P., and Balsamo, G. Comparing ERA-40 based L-band brightness temperatures with Skylab observations:a calibration/validation study using the community microwave emission model. Journal of Hydrometeorology, 10, 213-226(2009)
[8] De Lannoy, G., Reichle, R., and Vrugt, J. Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations. Remote Sensing of Environment, 148, 146-157(2014)
[9] Balsamo, G., Mahfouf, J. F., Belair, S., and Deblonde, G. A global root-zone soil moisture analysis using simulated L-band brightness temperature in preparation for the hydros satellite mission. Journal of Hydrometeorology, 7, 1126-1146(2006)
[10] Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Mialon, A., and Delwart, S. The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50, 1384-1403(2012)
[11] Parinussa, R. M., Meesters, A. G., Liu, Y. Y., Dorigo, W., Wagner, W., and de Jeu, R. A. Error estimates for near-real-time satellite soil moisture as derived from the land parameter retrieval model. IEEE Transactions on Geoscience and Remote Sensing, 8, 779-783(2011)
[12] Jia, B., Tian, X., Xie, Z., Liu, J., and Shi, C. Assimilation of microwave brightness temperature in a land data assimilation system with multi-observation operators. Journal of Geophysical Research:Atmospheres, 118, 1-14(2013)
[13] Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133, 1155-1174(2005)
[14] Viallefont, V., Raftery, A. E., and Richardson, S. Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine, 20, 3215-3230(2001)
[15] Fernandez, C., Ley, E., and Steel, M. Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100, 381-427(2001)
[16] Duan, Q. Y., Ajami, N. K., Gao, X. G., and Sorooshian, S. Multi-model ensemble hydrologic prediction using Bayesian model averaging. Advances in Water Resources, 30, 1371-1386(2007)
[17] Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M. Y., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J. F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J. Y., and Yang, Z. L. Technical Description of Vrsion 4.5 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, Colorado (2013)
[18] Jia, B. and Xie, Z. Evaluation of the community microwave emission model coupled with the community land model over East Asia. Atmospheric and Oceanic Science Letters, 4, 209-215(2011)
[19] Jones, A., Vukivi, T., and vonder Haar, T. A microwave satellite observational operator for variational data assimilation of soil moisture. Journal of Hydrometeorology, 5, 213-229(2004)
[20] Parrens, M., Calvet, J. C., de Rosnay, P., and Decharme, B. Benchmarking of L-band soil microwave emission models. Remote Sensing of Environment, 140, 407-419(2014)
[21] Chen, Y., Yang, K., He, J., Qin, J., Shi, J., Du, J., and He, Q. Improving land surface temperature modeling for dryland of China. Journal of Geophysical Research:Atmospheres, 116, D20104(2011)
[22] Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D. The global land data assimilation system. Bulletin of the American Meteorological Society, 85, 381-394(2004)
[23] Yang, K., He, J., Tang, W. J., Qin, J., and Cheng, C. C. K. On downward shortwave and longwave radiations over high altitude regions:observation and modelling in the Tibetan Plateau. Agricultural and Forest Meteorology, 150, 38-46(2010)
[24] Masson, V., Champeaux, J. L., Chauvin, F., Meriguet, C., and Lacaze, R. A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate, 97, 1261-1282(2003)
[25] Sabater, J. M., de Rosnay, P., and Balsamo, G. Sensitivity of L-band NWP forward modelling to soil roughness. International Journal of Remote Sensing, 32, 5607-5620(2011)
[26] Mironov, V., Dobson, M., Kaupp, V., Komarov, S., and Kleshchenko, V. Generalized refractive mixing dielectric model for moist soils. IEEE Transactions on Geoscience and Remote Sensing, 42, 773-785(2004)
[27] Wang, J. R. and Schmugge, T. An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Transactions on Geoscience and Remote Sensing, 18, 288-295(1980)
[28] Choudhury, B., Schmugge, T., Chang, A., and Newton, R. Effect of surface roughness on the microwave emission from soils. Journal of Geophysical Research, 84, 5699-5706(1979)
[29] Wegmüller, U. and Mätzler, C. Rough bare soil reflectivity model. IEEE Transactions on Geoscience and Remote Sensing, 37, 1391-1395(1999)
[30] Jackson, T. and Schmugge, T. Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, 36, 203-212(1991)
[31] Kirdyashev, K., Chukhlantsev, A., and Shutko, A. Microwave radiation of the Earth's surface in the presence of vegetation cover. Radiotekhnika i Elektronika, 24, 256-264(1979)
[32] Wegmüller, U., Mätzler, C., and Njoku, E. Canopy opacity models. Passive Microwave Remote Sensing of Land-Atmosphere Interactions, Utrecht, Netherlands, 375-387(1995)
[33] Njoku, E. G., Jackson, T. L., Lakshmi, V., Chan, T., and Nghiem, S. V. Soil moisture retrieval from AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 41, 215-229(2003)
[34] Fujii, H. Development of a Microwave Radiative Transfer Model for Vegetated Land Surface Based on Comprehensive In-Situ Observations, Ph. D. dissertation, University of Tokyo, Tokyo (2005)
[35] Jia, B., Xie, Z., Tian, X. and Shi, C. A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature. Science in China, Series D:Earth Sciences, 52, 1835-1848(2009)
[36] Tian, X., Xie, Z., Dai, A., Jia, B., and Shi, C. A microwave land data assimilation system:scheme and preliminary evaluation over China. Journal of Geophysical Research, 115, D21113(2010)
[37] Li, L., Njoku, E. G., Im, E., Chang, P. S., and Germain, K. S. A preliminary survey of radiofrequency interference over the U.S. in Aqua AMSR-E data. IEEE Transactions on Geoscience and Remote Sensing, 42, 380-390(2004)
[38] Njoku, E. G., Ashcroft, P., Chan, T. K., and Li, L. Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Transactions on Geoscience and Remote Sensing, 43, 938-947(2005)
[39] Vrugt, J. A., Diks, C. G. H., and Clark, M. P. Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling. Environmental Fluid Mechanics, 8, 579-595(2008)