15 May 2023
 | 15 May 2023
Status: this preprint is open for discussion.

Comparative Study of Strongly and Weakly Coupled Soil Moisture Data Assimilation with a Global Coupled Land-Atmosphere Model

Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi

Abstract. This study explores coupled land-atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. To assimilate land data with a coupled land-atmosphere model, weakly-coupled DA has been a common approach, in which land (atmospheric) data are not used to analyze atmospheric (land) model variables. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF), so that we can perform strongly-coupled land-atmosphere DA experiments. We perform various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves SM analysis and forecasts and mitigates a warm temperature bias in the lower troposphere where a dry SM bias exists. However, analyzing SM by assimilating atmospheric observations has detrimental impacts on SM analysis and forecasts.

Kenta Kurosawa et al.

Status: open (until 10 Jul 2023)

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Kenta Kurosawa et al.

Kenta Kurosawa et al.


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Short summary
In this study, we improve weather forecasts by incorporating land and atmospheric data in a model. We focus on soil moisture, crucial for predicting droughts and floods. By using soil moisture data, we enhance temperature and precipitation predictions. However, challenges remain, and further research is needed to refine the approach using satellite data and higher-resolution models.