Development and applications of land data assimilation systems for China [INVITED]

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Tuesday, 25 January 2011: 1:30 PM
Development and applications of land data assimilation systems for China [INVITED]
611 (Washington State Convention Center)
Kun Yang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITP-CAS), Beijing, China; and J. Qin, L. Zhao, and Y. Chen

Assimilating satellite data into a hydrological model or a land surface model is a promising tool to improve our understanding to land hydrology at regional scales. China comprises a number of geographic elements, from the Tibetan Plateau and deserts in the West China to the lowland and humid regions in the East China. It is challenging to develop a LDAS applicable for China territory, due to its diverse climate regimes and land use. Nevertheless, three research LDASs are being developed in CAS (Chinese Academy of Sciences) institutes. Their strategies are different: one addresses more the importance of land model development and model parameter estimation, and the other two focus on mathematical skill of data assimilation technique. This paper first briefly introduces the three systems and there applications, and then presents a dual-pass data assimilation system developed by the authors, including the following four aspects.

1. Dual-pass LDAS. This LDAS assimilates AMSR-E brightness temperature of vertical polarization of 6.9 GHz and 18.7 GHz. The system consists of a land surface model (LSM) used to calculate surface fluxes and soil moisture, a radiative transfer model (RTM) to estimate the microwave brightness temperature, and an optimization scheme to search for optimal values of soil moisture by minimizing the difference between modeled and observed brightness temperature. The LSM is an improved simple biosphere model and the RTM is a Q-h model that can account for the effects of surface roughness and vegetation. Several parameters in the LSM and RTM can significantly affect the outputs of the land data assimilation system but their values are either highly variable or unavailable. To solve this problem, we developed a dual-pass assimilation technique. Pass 1 inversely estimates the optimal values of the model parameters with long-term (~months) forcing data and brightness temperature data, while Pass 2 estimates the near-surface soil moisture in a daily assimilation cycle.

2. LDAS validation. This system was tested at two CEOP (Coordinate Enhanced Observing Period) reference sites, respectively, on the Tibetan Plateau and Mongolian Plateau. The application in the Tibetan Plateau shows that simulations of soil moisture and the surface energy budget were improved compared with the case with no assimilation. In particular, the soil moisture and energy partition simulated using the assimilation system is less contaminated by negative biases in input precipitation data than the case with no assimilation. This result is encouraging in terms of producing reliable surface-energy budgets in remote regions such as Tibet where precipitation-monitoring networks are sparse and input precipitation data are prone to large errors. The application in the Mongolian Plateau focused on the validation of soil moisture and model parameter estimates in semi-arid regions, where soil moisture is very heterogeneous. Validation data of soil moisture were collected in a semi-arid region. Results show that (1) the LDAS-estimated soil moistures are comparable to areal averages of in situ measurements, though the measured soil moistures were highly variable from site to site; (2) the LSM-simulated soil moistures show less biases when the LSM uses LDAS-calibrated parameter values instead of default parameter values, indicating that the satellite-based calibration does contribute to soil moisture estimations; (3) compared to the LSM, the LDAS produces more robust and reliable soil moisture when forcing data become worse; the lower sensitivity of the LDAS output to precipitation is particularly encouraging for applying this system to regions where precipitation data are prone to errors.

3. LDAS forcing data. To facilitate the application of the LDAS, we developed a high-resolution (0.25 deg, 3-hr), long-term (1996-2008) forcing data for the mainland of China, including wind speed, air temperature, humidity, pressure, downward shortwave radiation, downward longwave radiation, and precipitation. The new forcing data set is a fusion of global satellite products (GEWERX-SRB, TRMM rainfall) and global forcing data (Princeton data, GLDAS) with surface observations at 716 CMA (China Meteorological Administration) stations. As far as we know, this is the first forcing data set based on CMA observations and specifically developed for China. Preliminary evaluations show better performance of this data set than available global reanalysis or forcing data sets. This data set has been applied to drive the LDAS and soil moisture maps for the mainland of China have been available.

4. Soil moisture observing network. To validate LDASs and remote sensed soil moisture, a soil moisture and temperature observing network was constructed in a mesoscale region (100km by 100km) of central Tibetan Plateau in 2010. The network is composed of 39 sites, and each of them has 4-levels of soil moisture and temperature sensors located at 0-5 cm, 10, 20, and 40 cm soil depths, respectively. Soil samples were taken at each level of each site, and the soil moisture sensor was calibrated based on the gravimetric method. This network will be used for validating ASCAT, AMSR-E, SMOS, and SMAP soil moisture products.


(1) Yang, K., T. Koike, I. Kaihotsu, and J. Qin, 2009: Validation of a dual-pass microwave land data assimilation system for estimating surface soil moisture in semi-arid regions, Journal of Hydrometeorology, 10(3), 780-794.

(2) Yang, K., T. Watanabe, T. Koike, X. Li, H. Fujii, K. Tamagawa, Y. Ma, and H. Ishikawa, 2007: An auto-calibration system to assimilate AMSR-E data into a land surface model for estimating soil moisture and surface energy Budget, Journal of the Meteorological Society of Japan, 85A, 229-242.