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The evaluation of AMSR-E soil moisture data in atmospheric modeling using a suitable time series iteration to derive land surface fluxes over the Tibetan Plateau


Autoři: Weiqiang Ma aff001;  Yaoming Ma aff001
Působiště autorů: Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China aff001;  CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China aff002;  University of Chinese Academy of Sciences, Beijing, China aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226373

Souhrn

In this study, the initial soil moisture in an atmospheric model was varied by assimilating AMSR-E (The Advanced Microwave Scanning Radiometer for EOS) products, and the results were compared with the default model scenario and in-situ data based on long-term CAMP/Tibet (Coordinated Enhanced Observing Period (CEOP) Asia-Australia Monsoon Project (CAMP) Tibet) observations. The differences between the obtained results (i.e., the new simulation, default model configuration and in-situ data) showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model simulation. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data assimilation was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in-situ data and the results of the default model configuration. Assimilating the AMSR-E SM products was important for determining the land surface heat fluxes in the WRF model. All the assimilation work substantially improved the modeling of land surface heat fluxes. Land surface heat fluxes are related to atmospheric interactions. Therefore, land surface heat fluxes are very important land surface parameters during these processes. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model. It is important to study the surface heating sources that are related to both the water and energy cycles over the Tibetan Plateau.

Klíčová slova:

Algorithms – Atmospheric layers – Remote sensing – Simulation and modeling – Latent heat – Tibetan Plateau – Atmospheric models – Monsoons


Zdroje

1. Duan A, Wang M, Lei Y, Cui Y. Trends in summer rainfall over China associated with the Tibetan Plateau sensible heat source during 1980–2008. Journal of Climate. 2013;26(1):261–75.

2. Yanai M, Li C, Song Z. Seasonal heating of the Tibetan Plateau and its effects on the evolution of the Asian summer monsoon. Journal of the Meteorological Society of Japan. 1992;79(1B):419–34.

3. Ma Y, Zhong L, Wang B, Ma W, Chen X, Li M. Determination of land surface heat fluxes over heterogeneous landscape of the Tibetan Plateau by using the MODIS and in situ data. Atmospheric Chemistry and Physics. 2011;11(20):10461–9.

4. Ma Y, Su Z, Li Z, Koike T, Menenti M. Determination of regional net radiation and soil heat flux over a heterogeneous landscape of the Tibetan Plateau. Hydrological Processes. 2002;16(15):2963–71.

5. Ma Y. Remote sensing parameterization of regional net radiation over heterogeneous land surface of Tibetan Plateau and arid area. International Journal of Remote Sensing. 2003;24(15):3137–48.

6. Yang K, Koike T, Yang D. Surface flux parameterization in the Tibetan Plateau. Boundary-Layer Meteorology. 2003;106(2):245–62.

7. Oku Y, Ishikawa H. Estimation of land surface temperature over the Tibetan Plateau using GMS data. Journal of Applied Meteorology. 2004;43(4):548–61.

8. Li M, Ma Y, Ma W, Hu Z, Ishikawa H, Su Z, et al. Analysis of turbulence characteristics over the northern Tibetan Plateau area. Advances in Atmospheric Sciences. 2006;23(4):579–85.

9. Ma W, Ma Y. The annual variations on land surface energy in the northern Tibetan Plateau. Environmental Geology. 2006;50(5):645–50.

10. Ma Y, Zhong L, Su Z, Ishikawa H, Menenti M, Koike T. Determination of regional distributions and seasonal variations of land surface heat fluxes from Landsat-7 Enhanced Thematic Mapper data over the central Tibetan Plateau area. Journal of geophysical research. 2006;111(D10):D10305.

11. Ma Y, Kang S, Zhu L, Xu B, Tian L, Yao T. ROOF OF THE WORLD: Tibetan Observation and Research Platform. Bulletin of the American Meteorological Society. 2008;89(10):1487–92. doi: 10.1175/2008bams2545.1

12. Ma Y, Wang Y, Wu R, Hu Z, Yang K, Li M, et al. Recent advances on the study of atmosphere-land interaction observations on the Tibetan Plateau. Hydrology and Earth System Sciences. 2009;13(7):1103–11.

13. Oku Y, Ishikawa H. Land Surface Energy Budget Over the Tibetan Plateau Based on Satellite Remote Sensing Data. Atmospheric Science (AS). 2010;16:147.

14. Ma W, Ma Y, Su B. Feasibility of Retrieving Land Surface Heat Fluxes from ASTER Data Using SEBS: a Case Study from the NamCo Area of the Tibetan Plateau. Arctic, Antarctic, and Alpine Research. 2011;43(2):239–45. doi: 10.1657/1938-4246-43.2.239

15. Kustas WP, Choudhury BJ, Moran MS, Reginato RJ, Jackson RD, Gay LW, et al. Determination of sensible heat flux over sparse canopy using thermal infrared data. Agricultural and forest meteorology. 1989;44(3):197–216.

16. Menenti M, Choudhury B. Parameterization of land surface evaporation by means of location dependent potential evaporation and surface temperature range. Exchange processes at the land surface for a range of space and time scales, IAHS Publ. 1993;212:561–8.

17. Gallus WA Jr, Bresch JF. Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season rainfall forecasts. Monthly weather review. 2006;134(9):2632–41.

18. Fujii H, Koike T, Imaoka K. Improvement of the AMSR-E algorithm for soil moisture estimation by introducing a fractional vegetation coverage dataset derived from MODIS data. Journal of The Remote Sensing Society of Japan. 2009;29.

19. Hu Y, QI Y. The combinatory method to determine the turbulent fluxes and the universal functions in the surface layer. Acta Meteorologica Sinica. 1991;49:46–53.

20. Wang Wei, Cindy Bruyère, al. MDe. User’s Guide describes the Advanced Research WRF (ARW) Version 3.4 modeling system 2012.

21. Niu GY, Yang ZL, Mitchell KE, Chen F, Ek MB, Barlage M, et al. The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. Journal of Geophysical Research: Atmospheres (1984–2012). 2011;116(D12).

22. Xue Y, Sellers P, Kinter J, Shukla J. A simplified biosphere model for global climate studies. Journal of Climate. 1991;4(3):345–64.


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