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<p>Thailand frequently faces meteorological hazards, such as storms and floods, necessitating accurate rainfall monitoring for disaster management. This study presents an open-source radar-based rainfall mosaic framework to estimate rainfall at a national scale, integrating data from 12 C-band radars operated by the Thailand Meteorological Department (TMD). The framework combines data from both conventional Doppler and dual-polarimetric radars, calibrated with rain gauge measurements within each radar’s 240&#xa0;km observation radius. These data are mosaicked to produce hourly Constant Altitude Plan Position Indicator (CAPPI) maps. A key innovation is the application of Mean Field Bias (MFB) correction, which significantly improves radar-derived rainfall accuracy. Validation during Tropical Storm Son-Tinh (2018) demonstrated that the Rosenfeld-Tropical Z-R relationship yielded superior performance. Post-MFB correction, radar estimates achieved correlation coefficients exceeding 0.7 in most regions and exhibited reduced bias, supporting operational applications in flood forecasting and water resource management. The outputs include a GIS-compatible spatial rainfall database for hydrological analysis at basin and sub-basin scales. The open-source code promotes customization and collaboration, providing a scalable tool for national meteorological agencies and researchers. This study advances radar-based quantitative precipitation estimation (QPE) in Thailand and highlights its potential for enhancing real-time disaster response and long-term climatological studies.</p>

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An open-source framework for mosaic radar-based rainfall estimation across Thailand's watersheds

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Nattapon Mahavik,
  • Fatah Masthawee,
  • Wirachart Promta,
  • Pattara Suktawee,
  • Nuttapong Panthong,
  • Manoon Do-Ove,
  • Sarawut Arthayakun,
  • Rangsan Ketord,
  • Sarintip Tantanee

摘要

Thailand frequently faces meteorological hazards, such as storms and floods, necessitating accurate rainfall monitoring for disaster management. This study presents an open-source radar-based rainfall mosaic framework to estimate rainfall at a national scale, integrating data from 12 C-band radars operated by the Thailand Meteorological Department (TMD). The framework combines data from both conventional Doppler and dual-polarimetric radars, calibrated with rain gauge measurements within each radar’s 240 km observation radius. These data are mosaicked to produce hourly Constant Altitude Plan Position Indicator (CAPPI) maps. A key innovation is the application of Mean Field Bias (MFB) correction, which significantly improves radar-derived rainfall accuracy. Validation during Tropical Storm Son-Tinh (2018) demonstrated that the Rosenfeld-Tropical Z-R relationship yielded superior performance. Post-MFB correction, radar estimates achieved correlation coefficients exceeding 0.7 in most regions and exhibited reduced bias, supporting operational applications in flood forecasting and water resource management. The outputs include a GIS-compatible spatial rainfall database for hydrological analysis at basin and sub-basin scales. The open-source code promotes customization and collaboration, providing a scalable tool for national meteorological agencies and researchers. This study advances radar-based quantitative precipitation estimation (QPE) in Thailand and highlights its potential for enhancing real-time disaster response and long-term climatological studies.