Ninth International Conference on Permafrost ... - IARC Research

Ni n t h In t e r n at i o n a l Co n f e r e n c e o n Pe r m a f r o s tTable 1. The soil temperature RMSE (K) of SHAW simulation against the result of assimilating the SSM/I brightness temperature.RMSE (K)Soil Layer (cm)0 4 10 20 40 60 80 100 130 160 200 258SHAW Simulation 10.09 3.03 2.43 2.07 1.65 1.75 1.67 1.61 1.20 0.81 0.36 0.04Assimilating SMTMS 8.05 1.61 0.91 0.60 0.99 0.79 0.63 0.38 0.42 0.51 0.31 0.04Assimilating SSM/I 8.60 1.89 1.23 1.15 1.44 1.06 0.73 0.61 0.62 0.64 0.34 0.04observations. The RMSE (root mean square error) of theassimilated result of 4 cm soil temperature is 0.16 K, whichis significantly less than that (3.03 K) by running the SHAWmodel alone.Assimilating the 4 cm depth observation not only improvesthe state estimation of the corresponding layer, but also canimprove the estimation of the whole soil profile state whengiven reasonable model error covariance matrix (Hoeben &Troch 2000).Figure 1 showed the assimilation results of the nondiagonalelements equal to zero (Fig. 1a) and equaling thevalues determined by the correlation analysis (Fig. 1b).For clarity, part of the assimilation result at the 4 cm layerwas shown here. The zero non-diagonal elements meanthe soil temperature of each layer is independent; only soiltemperature at 4 cm can be updated after assimilating thecorresponding soil temperature observation. The deep soiltemperature can only be influenced slowly by the processof interlayer flow described in the SHAW, so the dataassimilation efficiency is very low. The error covariancematrix with non-diagonal elements not equal to zero canplay the key role of transferring the updated surface stateinformation to the deep soil quickly, achieving improvedestimation of the soil temperature profile. After assimilatingthe 4 cm depth soil temperature observation, the RMSEof soil temperature decreased 1 K on average, comparedto SHAW simulation. The RMSE of assimilation withreasonable covariance decreased about 0.7 K compared toassimilation with covariance as zero.Assimilating the 19 GHz SSM/I brightness temperatureFor the regionally frozen ground research, the availablein situ observation is sparse. Remote sensing, especiallythe passive microwave radiometers, would be a promisingobservation method because of its frequent revisit cycle andglobal/regional coverage.The key to merging the brightness temperature observationinto the assimilation system is the microwave radiativetransfer model, which can act as the bridge between themodel state variables predicted by SHAW and brightnesstemperature observed by the radiometer. The volumescatteringeffect was not considered in the LSP/R model,so the 19 GHz brightness temperatures, having the longestwavelength in the SSM/I frequencies, were chosen as theobservations to be assimilated.After assimilating the SSM/I 19 GHz brightnesstemperature, the RMSE of soil temperature decreases 0.76 Kon average (Table 1). Especially the improvement of 0–100cm layer soil temperature was obvious, nearly 1–2 K. Theimprovement by assimilating the brightness temperatureis lower than assimilating in situ observation, because thebrightness temperature is an indirect observation, and thereexist uncertainties in the microwave radiative transfermodel.ConclusionThe one-dimensional assimilation experiments showedthat assimilating the in situ observations and the passivemicrowave brightness temperature can remarkably improvethe estimation of a soil temperature profile.The regional four-dimensional active layer data assimilationsystem can be developed based on the current onedimensionasystem.It will be able to provide soil temperature,water content, ice content, and other datasets with spatiotemporaland physical consistence. The datasets can beused in frozen soil and climate change interaction research,promoting the in-depth and quantitative understanding offrozen soil dynamics.AcknowledgmentsThe authors thank the support from the National NaturalScience Foundation of China (40701113; 40601065). Thedata used in the paper are generously provided by the CEOPand NSIDC.ReferencesEvensen, G. 2003. The ensemble kalman filter: Theoreticalformulation and practical implementation. OceanDynamics 53: 343-367.Flerchinger, G.N. & Saxton, K.E. 1989. Simultaneous heatand water model of a freezing snow-residue-soilsystemⅠ: Theory and development. Transactions ofthe ASAE 32(2): 565-571.Hoeben, R. & Troch, P.A. 2000. Assimilation of activemicrowave observation data for soil moisture profileestimation. Water Resources Research 36(10): 2805-2819.Li, X., Huang, C.L., Che, T., Jin, R., Wang, S.G., Wang, J.M.,Gao, F., Zhang, S.W., Qiu, C.J. & Wang, C.H. 2007.Development of a Chinese land data assimilationsystem: Its progress and prospects. Progress inNatural Science 17(8): 881-892.Liou, Y.A. & England, A.W. 1998. A land surface process/radiobrightness model with coupled heat and moisturetransport in soil. IEEE Transactions on Geoscienceand Remote Sensing 36(1): 273-286.118