Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
Department of Statistics, Universitas Padjajaran, Bandung 45363, Indonesia.
ScientificWorldJournal. 2022 Apr 27;2022:9779829. doi: 10.1155/2022/9779829. eCollection 2022.
Generating an accurate rainfall prediction is a challenging work due to the complexity of the climate system. Numerous efforts have been conducted to generate reliable prediction such as through ensemble forecasts, the North Multi-Model Ensemble (NMME). The performance of NMME globally has been investigated in many studies. However, its performance in a specific location has not been much validated. This paper investigates the performance of NMME to forecast rainfall in Surabaya, Indonesia. Our study showed that the rainfall prediction from NMME tends to be underdispersive, which thus requires a bias correction. We proposed a new bias correction method based on gamma regression to model the asymmetric pattern of rainfall distribution and further compared the results with the average ratio method and linear regression. This study showed that the NMME performance can be improved significantly after bias correction using the gamma regression method. This can be seen from the smaller RMSE and MAE values, as well as higher values compared with the results from linear regression and average ratio methods. Gamma regression improved the value by about 30% higher than raw data, and it is about 20% higher than the linear regression approach. This research showed that NMME can be used to improve the accuracy of rainfall forecast in Surabaya.
生成准确的降雨预测是一项具有挑战性的工作，因为气候系统的复杂性。已经进行了大量的努力来生成可靠的预测，例如通过集合预报，即北美多模式集合（NMME）。许多研究已经研究了 NMME 在全球范围内的性能。然而，它在特定位置的性能并没有得到太多验证。本文研究了 NMME 在印度尼西亚泗水市预测降雨的性能。我们的研究表明，NMME 的降雨预测往往存在低估，因此需要进行偏差修正。我们提出了一种新的基于伽马回归的偏差修正方法，以模拟降雨分布的非对称模式，并进一步将结果与平均比方法和线性回归进行比较。本研究表明，使用伽马回归方法进行偏差修正后，NMME 的性能可以显著提高。这可以从较小的 RMSE 和 MAE 值以及更高的 值与线性回归和平均比方法的结果相比看出。与原始数据相比，伽马回归将 值提高了约 30%，比线性回归方法高约 20%。这项研究表明，NMME 可用于提高泗水市降雨预测的准确性。
