Impacts of 3DVAR data assimilation on the near surface weather variables and predictability of rainfall over the mid-hills of central Nepal Himalaya during Pre-Monsoon Season
Systematic under-prediction of near-surface humidity and precipitation is a major issue in numerical weather prediction (NWP) within the complex terrain of Nepal Himalayas. Inaccurate initialization of weather models is a major cause of errors in weather prediction. Data Assimilation (DA) is essenti...
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Published in: | Meteorology and atmospheric physics 2025, Vol.137 (1), p.1, Article 1 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Systematic under-prediction of near-surface humidity and precipitation is a major issue in numerical weather prediction (NWP) within the complex terrain of Nepal Himalayas. Inaccurate initialization of weather models is a major cause of errors in weather prediction. Data Assimilation (DA) is essential for improving the initial conditions used in weather models, thus reducing uncertainties in weather forecasts. Though the global initial conditions driving the regional model undergo some DA, downscaling the coarser analysis for regional forecasting often leads to inaccurate forecasts. This study uses the Three-Dimensional Variational (3DVAR) DA to assimilate observations into a high-resolution regional weather model and systematically examines the improvement in the accuracy of near-surface weather variable predictions and the predictability of rainfall over the Central Nepal Himalayas during the pre-monsoon season, compared to forecast generated without DA. Three-day forecasts over the Kathmandu Valley were performed in seven DA experiments using different combinations of publicly available instrument data. The changes in initial conditions over an area after assimilation varied with the assimilated observations. Assimilating surface and upper-air weather observations, along with satellite radiance data in all the domains of the nested system produced a better forecast of humidity, temperature, rainfall, and cloud cover over the valley despite fewer observations in child-domains. The forecast successfully predicted the previously missing rainfall in the control simulation. The study demonstrated the significant positive impacts of 3DVAR-DA on the regional model forecast over the Himalayan region, even with a small domain and also beyond 24 h after assimilation of publicly available data. |
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ISSN: | 0177-7971 1436-5065 |
DOI: | 10.1007/s00703-024-01050-y |