Remote sensing data assimilation

被引：10
|
作者：
Nair, Akhilesh S. [1 ]
Mangla, Rohit [1 ]
Thiruvengadam, P. [1 ]
Indu, J. [1 ,2 ]
机构：
[1] Indian Inst Technol, Dept Civil Engn, Mumbai, Maharashtra, India
[2] Indian Inst Technol, Interdisciplinary Ctr Climate Studies, Mumbai, Maharashtra, India
来源：
HYDROLOGICAL SCIENCES JOURNAL | 2022年 / 67卷 / 16期
关键词：
remote sensing; data assimilation; land surface model (LSM); Weather Research Forecast (WRF); radiance; SOIL-MOISTURE DATA; HYDROLOGICAL DATA ASSIMILATION; ENSEMBLE STREAMFLOW FORECASTS; PHYSICALLY-BASED MODEL; LAND-SURFACE MODEL; KALMAN FILTER; SNOW COVER; PART I; VARIATIONAL ASSIMILATION; BRIGHTNESS TEMPERATURE;
D O I：
10.1080/02626667.2020.1761021
中图分类号：
TV21 [水资源调查与水利规划];
学科分类号：
081501 ;
摘要：
Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology.
引用
收藏
页码：2457 / 2489
页数：33
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