Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction

• URL: http://arxiv.org/abs/2206.15241v1
• Date: Thu, 30 Jun 2022 12:41:32 GMT
• Title: Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction
• Authors: Taehyeon Kim, Namgyu Ho, Donggyu Kim, Se-Young Yun
• Abstract summary: We present a hybrid NWP-DL workflow to fill the gap between standalone NWP and DL approaches. Under this workflow, the NWP output is fed into a deep model, which post-processes the data to yield a refined precipitation forecast. We present a novel dataset focused on the Korean Peninsula, comprised of NWP predictions and AWS observations.
• Score: 11.52104902059751
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Precipitation forecasting is an important scientific challenge that has wide-reaching impacts on society. Historically, this challenge has been tackled using numerical weather prediction (NWP) models, grounded on physics-based simulations. Recently, many works have proposed an alternative approach, using end-to-end deep learning (DL) models to replace physics-based NWP. While these DL methods show improved performance and computational efficiency, they exhibit limitations in long-term forecasting and lack the explainability of NWP models. In this work, we present a hybrid NWP-DL workflow to fill the gap between standalone NWP and DL approaches. Under this workflow, the NWP output is fed into a deep model, which post-processes the data to yield a refined precipitation forecast. The deep model is trained with supervision, using Automatic Weather Station (AWS) observations as ground-truth labels. This can achieve the best of both worlds, and can even benefit from future improvements in NWP technology. To facilitate study in this direction, we present a novel dataset focused on the Korean Peninsula, termed KoMet (Korea Meteorological Dataset), comprised of NWP predictions and AWS observations. For NWP, we use the Global Data Assimilation and Prediction Systems-Korea Integrated Model (GDAPS-KIM).

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