대기 (Atmosphere)

  • 제32권4호
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  • Pages.395-409
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  • 2022
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  • 1598-3560(pISSN)
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  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
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기상청 GloSea의 위성관측 기반 토양수분(SMAP) 동화: 예비 실험 분석

Assimilation of Satellite-Based Soil Moisture (SMAP) in KMA GloSea6: The Results of the First Preliminary Experiment

  • 지희숙 (국립기상과학원 기후연구부) ;
  • 황승언 (국립기상과학원 기후연구부) ;
  • 이조한 (국립기상과학원 기후연구부) ;
  • 현유경 (국립기상과학원 기후연구부) ;
  • 류영 (환경부 영산강홍수통제소 예보통제과) ;
  • 부경온 (국립기상과학원 기후연구부)
  • Ji, Hee-Sook (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Hwang, Seung-On (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Lee, Johan (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Hyun, Yu-Kyung (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Ryu, Young (Forecast and Control Division, Yeongsan River Flood Control Office) ;
  • Boo, Kyung-On (Climate Research Division, National Institute of Meteorological Sciences)
  • 투고 : 2022.10.08
  • 심사 : 2022.10.28
  • 발행 : 2022.12.31
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초록

A new soil moisture initialization scheme is applied to the Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6). It is designed to ingest the microwave soil moisture retrievals from Soil Moisture Active Passive (SMAP) radiometer using the Local Ensemble Transform Kalman Filter (LETKF). In this technical note, we describe the procedure of the newly-adopted initialization scheme, the change of soil moisture states by assimilation, and the forecast skill differences for the surface temperature and precipitation by GloSea6 simulation from two preliminary experiments. Based on a 4-year analysis experiment, the soil moisture from the land-surface model of current operational GloSea6 is found to be drier generally comparing to SMAP observation. LETKF data assimilation shows a tendency toward being wet globally, especially in arid area such as deserts and Tibetan Plateau. Also, it increases soil moisture analysis increments in most soil levels of wetness in land than current operation. The other experiment of GloSea6 forecast with application of the new initialization system for the heat wave case in 2020 summer shows that the memory of soil moisture anomalies obtained by the new initialization system is persistent throughout the entire forecast period of three months. However, averaged forecast improvements are not substantial and mixed over Eurasia during the period of forecast: forecast skill for the precipitation improved slightly but for the surface air temperature rather degraded. Our preliminary results suggest that additional elaborate developments in the soil moisture initialization are still required to improve overall forecast skills.

키워드

과제정보

본 연구를 위해 자료동화시스템을 제공해주신 울산과학기술대학교 도시환경공학과 이명인 교수 연구팀과 서은교 박사님께 감사드립니다. 이 연구는 기상청 국립기상과학원 「기후예측 현업시스템 개발」(KMA2018-00322)의 지원으로 수행되었습니다.

참고문헌

  1. Beck, H. E., A. I. J. M. van Dijk, V. Levizzani, J. Schellekens, D. G. Miralles, B. Martens, and A. De Roo, 2017: MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21, 589-615, doi:10.5194/hess-21-589-2017.
  2. Chan, S. K., and Coauthors, 2016: Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sens., 54, 4994-5007, doi:10.1109/TGRS.2016.2561938.
  3. Crow, W. T., and Coauthors, 2012: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys., 50, doi:10.1029/2011RG000372.
  4. de Rosnay, P., M. Drusch, D. Vasiljevic, G. Balsamo, C. Albergel, and L. Isaksen, 2013: A simplified extended Kalman filter for the global operational soil moisture analysis at ECMWF. Q. J. R. Meteorol. Soc., 139, 1199-1213, doi:10.1002/qj.2023.
  5. Dirmeyer, P. A., 2003: The role of the land surface back- ground state in climate predictability. Clim. Hydrometeorol., 4, 599-610. https://doi.org/10.1175/1525-7541(2003)004<0599:TROTLS>2.0.CO;2
  6. Dirmeyer, P. A., 2006: The hydrologic feedback pathway for landclimate coupling. J. Hydrometeorol., 7, 857-867. https://doi.org/10.1175/JHM526.1
  7. Entekhabi, D., and Coauthors, 2010: The soil moisture active passive (SMAP) mission. Proc. IEEE, 98, 704-716, doi:10.1109/JPROC.2010.2043918.
  8. Entekhabi, D., N. Das, E. Njoku, J. Johnson, and J. Shi, 2012: Soil Moisture Active Passive (SMAP) algorithm theoretical basis document L2 & L3 radar/radiometer soil moisture (active/passive) data products [Available online at http://smap.jpl.nasa.gov/files/smap2/L2&3_SM_AP_InitRel_v11.pdf].
  9. Entin, J. K., A. Robock, K. Y. Vinnikov, S. E. Hollinger, S. Liu, and A. Namkhai, 2000: Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res., 105, 11865-11877. https://doi.org/10.1029/2000JD900051
  10. Fairbairn, D., P. de Rosnay, and P. A. Browne, 2019: The new stand-alone surface analysis at ECMWF: implications for land-atmosphere DA coupling. J. Hydrometeorol., 20, 2023-2042, doi:10.1175/JHM-D19-0074.1.
  11. Giorgi, F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Clim. Dyn., 16, 169-182. https://doi.org/10.1007/PL00013733
  12. He, L., J. M. Chen, G. Mostovoy, and A. Gonsamo, 2021: Soil moisture active passive improves global soil moisture simulation in a land surface scheme and reveals strong irrigation signals over farmlands. Geophys. Res. Lett., 48, e2021GL092658, doi:10.1029/2021GL092658.
  13. Hersbach, H., and D. Dee, 2016: ERA5 reanalysis is in production. ECMWF Newsletter [Available online at https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production].
  14. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys. D Nonlinear Phenomena, 230, 112-126. https://doi.org/10.1016/j.physd.2006.11.008
  15. Hutt, A., 2020: Divergence of the ensemble transform Kalman filter (LETKF) by nonlocal observations. Front. Appl. Math. Stat., 6, 42, doi:10.3389/fams.2020.00042.
  16. Kim, H., J. Lee, Y. K. Hyun, and S.-O. Hwang, 2021: The KMA global seasonal forecasting system (GloSea6) - part 1: operational system and improvements. Atmosphere, 31, 341-359, doi:10.14191/Atmos.2021.31.3.341 (in Korean with English abstract).
  17. Koster, R. D., and M. J. Suarez, 2001: Soil moisture memory in climate models. J. Hydrometeorol., 2, 558-570. https://doi.org/10.1175/1525-7541(2001)002<0558:SMMICM>2.0.CO;2
  18. Koster, R. D., and Coauthors, 2004: Realistic initialization of land surface states: impacts on subseasonal forecast skill. J. Hydrometeorol., 5, 1049-1063. https://doi.org/10.1175/JHM-387.1
  19. Koster, R. D., and Coauthors, 2006: GLACE: the global land-atmosphere coupling experiment. Part I: overview. J. Hydrometeorol., 7, 590-610. https://doi.org/10.1175/JHM510.1
  20. Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Clim., 22, 4322-4335. https://doi.org/10.1175/2009JCLI2832.1
  21. Koster, R. D., and Coauthors, 2010: Contribution of land surface initialization to subseasonal forecast skill: first results from a multi-model experiment. Geophys. Res. Lett., 37, L02402, doi:10.1029/2009GL041677.
  22. Koster, R. D., and Coauthors, 2011: The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J. Hydrometeorol., 12, 805-822, doi:10.1175/2011JHM1365.1.
  23. Koukoula, M., C. S. Schwartz, E. I. Nikolopoulos, and E. N. Anagnostou, 2021: Understanding the impact of soil moisture on precipitation under different climate and meteorological conditions: a numerical sensitivity study over the CONUS. J. Geophys. Res. Atmos., 126, e2021JD035096, doi:10.1029/2021JD035096.
  24. Lahoz, W.A., and G. J. M. De Lannoy, 2014: Closing the gaps in our knowledge of the hydrological cycle over land: conceptual problems. Surv. Geophys., 35, 623-660, doi:10.1007/s10712-013-9221-7.
  25. Lim, S., Y. K. Hyun, H. Ji, and J. Lee, 2021: Application of land initialization and its impact in KMA's operational climate prediction system. Atmosphere, 31, 327-340, doi:10.14191/Atmos.2021.31.3.327 (in Korean with English abstract).
  26. L'vovich, M. I., 1979: World Water Resources and Their Future. American Geophysical Union, 415 pp.
  27. Lopez, N., 2021: Copernicus: 2020 warmest year on record for Europe; globally, 2020 ties with 2016 for warmest year recorded [Available online at https://climate.copernicus.eu/copernicus-2020-warmest-year-record-europe-globally-2020-ties-2016-warmest-year-recorded].
  28. Materia, S., A. Borrelli, A. Bellucci, A. Alessandri, P. Di Pietro, P. Athanasiadis, A. Navarra, and S. Gualdi, 2014: Impact of atmosphere and land surface initial conditions on seasonal forecasts of global surface temperature, J. Clim., 27, 9253-9271, doi:10.1175/JCLID-14-00163.1.
  29. Miyoshi, T., and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861. https://doi.org/10.1175/2007mwr1873.1
  30. Paolino, D. A., J. L. Kinter III, B. P. Kirtman, D. Min, and D. M. Straus, 2012: The impact of land surface and atmospheric initialization on seasonal forecasts with CCSM. J. Clim., 25, 1007-1021, doi:10.1175/2011JCLI3934.1.
  31. Peng, J., and Coauthors, 2021: A roadmap for high-resolution satellite soil moisture applications - confronting product characteristics with user requirements. Remote Sens. Environ., 252, 112162, doi:10.1016/j.rse.2020.112162.
  32. Reichle, R. H., and R. D. Koster, 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501. https://doi.org/10.1029/2004GL020938
  33. Reichle, R. H., W. T. Crow, and C. L. Keppenne, 2008: An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resour. Res., 44, W03423. https://doi.org/10.1029/2007WR006357
  34. Reichle, R. H., and Coauthors, 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Clim., 24, 6322-6338, doi:10.1175/JCLI-D-10-05033.1.
  35. Reichle, R. H., and Coauthors, 2017: Global assessment of the SMAP level-4 surface and root-zone soil moisture product using assimilation diagnostics. J. Hydrometeorol., 18, 3217-3237, doi:10.1175/JHM-D-17-0130.1.
  36. Santanello JA Jr., J. A., P. Lawston, S. Kumar, and E. Dennis, 2019: Understanding the impacts of soil moisture initial conditions on NWP in the context of land-atmosphere coupling. J. Hydrometeorol., 20, 793-819, doi:10.1175/JHM-D-18-0186.1.
  37. Scipal, K., M. Drusch, and W. Wagner, 2008: Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system. Adv. Water Resour., 31, 1101-1112. https://doi.org/10.1016/j.advwatres.2008.04.013
  38. Seneviratne, S. I., and Coauthors, 2006: Soil moisture memory in AGCM simulations: analysis of global land-atmosphre coupling experiment (GLACE) data. J. Hydrometeorol., 7, 1090-1112. https://doi.org/10.1175/JHM533.1
  39. Seneviratne, S. I., and Coauthors, 2010: Investigating soil moisture-climate interactions in a changing climate: a review. Earth Sci. Rev., 99, 125-161, doi:10.1016/j.earscirev.2010.02.004.
  40. Seo, E., M.-I. Lee, J.-H. Jeong, H.-S. Kang, and D.-J. Won, 2016: Improvement of soil moisture initialization for a global seasonal forecast system. Atmosphere, 26, 35-45, doi:10.14191/Atmos.2016.26.1.035 (in Korean with English abstract).
  41. Seo, E., and Coauthors, 2019: Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events. Clim. Dyn., 52, 1695-1709, doi:10.1007/s00382-018-4221-4.
  42. Seo, E., M.-I. Lee, S. D. Schubert, R. D. Koster, and H. S. Kang, 2020: Investigation of the 2016 Eurasia heat wave as an event of the recent warming. Environ. Res. Lett., 15, 114018, doi:10.1088/1748-9326/abbbae.
  43. Seo, E., M.-I. Lee, and R. H. Reichle, 2021: Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the local ensemble transform Kalman filter. Remote Sens. Environ., 253, 112222, doi:10.1016/j.rse.2020.112222.
  44. Seo, E., and P. A. Dirmeyer, 2022: Improving the ESA CCI daily soil moisture time series with physically based land surface model datasets using a Fourier time-filtering method. J. Hydrometeorol., 23, 473-489, doi:10.1175/JHM-D-21-0120.1.
  45. Shi, P., and Coauthors, 2022: Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe. J. Clim., 35, 517-535, doi:10.1175/JCLI-D20-0506.1.
  46. Sospedra-Alfonso, R., and W. J. Merryfield, 2018: Initialization and potential predictability of soil moisture in the Canadian seasonal to interannual prediction system. J. Clim., 31, 5205-5224, doi:10.1175/JCLI-D-17-0707.1.
  47. Stocker, T. F., and Coauthors, 2014: Climate Change 2013 - The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1552 pp.
  48. Trenberth, K. E., L. Smith, T. Qian, A. Dai, and J. Fasullo, 2007: Estimates of the global water budget and its annual cycle using observational and model data. J. Hydrometeorol., 8, 758-769. https://doi.org/10.1175/JHM600.1
  49. Xie, P., and P. A. Arkin, 1997: Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc., 78, 2539-2558. https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2
  50. Yoon, J. H., and L. R. Leung, 2015: Assessing the relative influence of surface soil moisture and ENSO SST on precipitation predictability over the contiguous United States. Geophys. Res. Lett., 42, 5005-5013, doi:10.1002/2015GL064139.