Microwave SWE Retrieval with A Coupled Hydrology and Microwave Forward Simulator
Abstract
Knowledge on terrestrial snow distribution and its phase change is significantly lacking to understand the sophisticated global water cycle. Specifically, there is a lack of reliability in the current global snow observation and assimilation system using microwave satellite observations. It is also known that retrieving snow water equivalent (SWE) from microwave observations suffers from low sensitivity and non-uniqueness. This paper demonstrates a general framework to combine the power of hydrology modeling and microwave remote sensing. A column-based multi-layer snow hydrology model is coupled with the state-of-art Dense Media Radiative Transfer (DMRT) model. The model predicted multi-layered snowpack variables drive the DMRT model to estimate the microwave observations. The snow variables are then updated by an Ensemble Kalman Filter (ENKF) algorithm to incorporate information from microwave observations. These modified snow variables are subsequently used to update the snow hydrology model in deriving the snow state in the next observation cycle. The proposed approach has potential to improve the accuracy of SWE remote sensing taking advantage from multi-source data fusion in a data assimilation framework. The Finland Nordic Snow Radar Experiment (NoSREx) dataset with controlled ground observations are used to verify the performance of the proposed algorithm. This work also demonstrates a general framework for other applications that might benefit from combing microwave observations with hydrology modeling, and additionally by combing multi-source observations.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH082...01L
- Keywords:
-
- 1836 Hydrological cycles and budgets;
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY;
- 1878 Water/energy interactions;
- HYDROLOGY;
- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS