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首页> 外文期刊>Theoretical and applied climatology >Assessment of EnKF data assimilation of satellite-derived soil moisture over the Indian domain with the Noah land surface model
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Assessment of EnKF data assimilation of satellite-derived soil moisture over the Indian domain with the Noah land surface model

机译：用诺亚地表模型评估印度领域卫星源性土壤水分卫星水分的恩典数据同化

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摘要

Land surface models (LSMs) are typically forced with observed precipitation and surface meteorology and hence the soil moisture estimates obtained from LSM do not reflect the contribution of irrigation to the soil moisture estimates. However, the satellite retrievals of soil moisture estimates do register the signature of the irrigation effects. It is suggested that the soil moisture estimates obtained from LSM may reflect the role of irrigation if they are assimilated with soil moisture estimated from satellites. The present study evaluates the improvement of soil moisture estimates obtained from Noah LSM by ingesting them with the satellite-derived Advanced Scatterometer (ASCAT) soil moisture retrievals over the Indian domain for the year 2012. The above ingesting of soil moisture estimates is performed using the land information system (LIS). The improved soil moisture estimates are validated with the in situ India Meteorological Department (IMD) soil moisture observations and also with the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) regional reanalysis data. The percentages of grid points over the Indian domain where the improvement parameter shows positive values are 59.14% (winter), 69.17% (pre-monsoon), 43.59% (monsoon), and 77.53% (post-monsoon). Furthermore, the forecast impact parameter also indicates the positive impact of data assimilation. Also, 12 of the 22 stations show reduced RMSE soil moisture error after data assimilation is performed while only 6 of the 22 stations show higher correlation coefficient in soil moisture without data assimilation, when validated with the in situ IMD soil moisture observations. The study has also evaluated the irrigation impact of ASCAT in the assimilated soil moisture using triple collocation (TC) method. For the TC analysis, the model-based Global Land Data Assimilation System (GLDAS), Catchment Land Surface Model (CLSM), and MERRA (Modern-Era Retrospective analysis for Research and Applications) Land data set together with soil moisture model outputs with and without ASCAT assimilation are used to calculate the error and correlation coefficient of each of the two set of triplets. The results of the TC analysis further conclusively show the positive impact of irrigation effects in the ASCAT-assimilated soil moisture model output.
机译：陆地表面模型（LSM）通常被迫使观察到的沉淀和表面气象，从而从LSM获得的土壤水分估算不反映灌溉对土壤水分估算的贡献。然而，土壤湿度估计的卫星检索确实记录了灌溉效果的签名。建议，如果从卫星估计的土壤水分同化，从LSM获得的土壤水分估计可能会反映灌溉的作用。本研究评估了通过在2012年的印度领域的卫星衍生的先进散射仪（ASCAT）土壤水分检索，从印度领域摄取到印度领域的土壤水分检索，评估从诺亚LSM获得的土壤水分估算。上述采用卫生水分估算土地信息系统（LIS）。改善的土壤水分估算与原位印度气象部门（IMD）土壤水分观察和高分辨率印度季风数据同化和分析（IMDAA）区域再分析数据验证。改善参数显示阳性值的印度领域的网格点数为阳性值为59.14％（冬季），69.17％（季隆），43.59％（季风）和77.53％（季风后）。此外，预测影响参数也表明数据同化的积极影响。此外，在进行数据同化之后，22个站中的12个中的12个在进行数据同化之后的RMSE土壤水分误差下降，在没有数据同化的情况下显示出较高的有关系数而没有数据同化。该研究还评估了ASCAT在使用三重搭配（TC）方法中ASSIMILATED土壤水分灌溉的影响。对于TC分析，基于模型的全球土地数据同化系统（GLDAS），集水区陆表面模型（CLSM）和MERRA（用于研究和应用的现代回顾性分析）与土壤水分模型输出一起使用和如果没有ASCAT同化，则用于计算两组三元组中的每一个的误差和相关系数。 TC分析的结果进一步得出了促进灌溉作用对亚抗亚升水土壤湿度模型输出的积极影响。

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  • 来源
    《Theoretical and applied climatology》 |2021年第2期|851-867|共17页
  • 作者

    Jose Vibin; Chandrasekar Anantharaman;

  • 作者单位

    Indian Inst Space Sci & Technol Dept Earth & Space Sci Valiamala PO Thiruvananthapuram 695547 Kerala India;

    Indian Inst Space Sci & Technol Dept Earth & Space Sci Valiamala PO Thiruvananthapuram 695547 Kerala India;

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