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<p>Low-pressure systems (LPs) are characterized by their associated surface circulation. Scatterometer winds help identify and track these surface wind patterns, providing insights into the structure and dynamics of the LPs. Oceansat-3 is the third-generation polar sun-synchronous orbiting satellite in the Oceansat series launched by ISRO. Understanding the influence of Oceansat-3 surface winds on LPs during the crucial monsoon period is imperative for improving weather predictions. This study employs the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) for data assimilation using the 3DVAR method. Two distinct experiments namely, control (CNTL) assimilate only (conventional) PrepBUFR data except the Scatterometer winds, while the experimental setup (EXP) includes the assimilation of surface winds from Oceansat Scatterometer (OSCAT-3) and PrepBUFR. The assessment focuses on well-marked lows and deep depressions formed over the North Indian Ocean, during the monsoon season of 2023. The model outcomes are compared against ECMWF Reanalysis V5 (ERA5). Results indicate a noteworthy impact of OSCAT-3 surface winds on the 72-hour forecast for the identified well-marked lows in the July and August months. Notably, the temperature forecast displays improvement at the 24-hour and 48-hour mark. These findings provide valuable insights into the potential benefits of assimilating OSCAT-3 surface winds for enhancing the short-term forecast using the regional model for low-pressure systems. Assimilation of OSCAT-3 winds leads to improved rainfall verification scores for Day 2 and Day 3 forecasts, aiding in the capture of weather events associated with Weather Monitoring Lows (WML) and Deep Depressions (DD). Corrections in track and minimum central pressure of DD are observed post-assimilation. This study forms the basis for further assessments, contributing to the ongoing efforts to refine and optimize the assimilation of satellite-derived winds in numerical weather prediction models for more accurate and timely predictions during the monsoon period.</p>

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Assessing the Impact of Oceansat-3 Surface Winds on Monsoonal Low-Pressure Systems Using WRF ARW Model

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • Meenakshi Shenoy,
  • Suryakanti Dutta,
  • Srinivas Desamsetti,
  • S. Indira Rani,
  • V. S. Prasad

摘要

Low-pressure systems (LPs) are characterized by their associated surface circulation. Scatterometer winds help identify and track these surface wind patterns, providing insights into the structure and dynamics of the LPs. Oceansat-3 is the third-generation polar sun-synchronous orbiting satellite in the Oceansat series launched by ISRO. Understanding the influence of Oceansat-3 surface winds on LPs during the crucial monsoon period is imperative for improving weather predictions. This study employs the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) for data assimilation using the 3DVAR method. Two distinct experiments namely, control (CNTL) assimilate only (conventional) PrepBUFR data except the Scatterometer winds, while the experimental setup (EXP) includes the assimilation of surface winds from Oceansat Scatterometer (OSCAT-3) and PrepBUFR. The assessment focuses on well-marked lows and deep depressions formed over the North Indian Ocean, during the monsoon season of 2023. The model outcomes are compared against ECMWF Reanalysis V5 (ERA5). Results indicate a noteworthy impact of OSCAT-3 surface winds on the 72-hour forecast for the identified well-marked lows in the July and August months. Notably, the temperature forecast displays improvement at the 24-hour and 48-hour mark. These findings provide valuable insights into the potential benefits of assimilating OSCAT-3 surface winds for enhancing the short-term forecast using the regional model for low-pressure systems. Assimilation of OSCAT-3 winds leads to improved rainfall verification scores for Day 2 and Day 3 forecasts, aiding in the capture of weather events associated with Weather Monitoring Lows (WML) and Deep Depressions (DD). Corrections in track and minimum central pressure of DD are observed post-assimilation. This study forms the basis for further assessments, contributing to the ongoing efforts to refine and optimize the assimilation of satellite-derived winds in numerical weather prediction models for more accurate and timely predictions during the monsoon period.