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Remote sensing data assimilation.
- Source :
-
Hydrological Sciences Journal/Journal des Sciences Hydrologiques . Dec2022, Vol. 67 Issue 16, p2457-2489. 33p. - Publication Year :
- 2022
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02626667
- Volume :
- 67
- Issue :
- 16
- Database :
- Academic Search Index
- Journal :
- Hydrological Sciences Journal/Journal des Sciences Hydrologiques
- Publication Type :
- Academic Journal
- Accession number :
- 160933548
- Full Text :
- https://doi.org/10.1080/02626667.2020.1761021