用于预测印度地区季风降雨和极端降雨事件的多模式集合工具

IF 0.7 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES MAUSAM Pub Date : 2023-03-29 DOI:10.54302/mausam.v74i2.6118
M. Bushair, D. Pattanaik, M. Mohapatra
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引用次数: 0

摘要

6月至9月的西南季风季节(JJAS)是印度大部分地区的主要降雨期。准确的降雨预报是数值天气预报(NWP)模型中最关键、最不可预测的参数之一,因为其在全球的分布和模式不均匀。在过去的十年里,人们使用不同的NWP模型来预测降雨事件,进行了许多研究,发现预测技巧有了很大的提高。在本研究中,开发了一种基于多模式集合(MME)的工具,用于预测印度地区一级为期五天的西南季风降雨量。来自五个运行中的NWP建模系统的降水预报,即:(i)印度气象部运行的全球预报系统(GFS)和(ii)全球综合预报系统(GEFS),(iii)国家环境预测中心运行的全球预测系统模型,(iv)国家中期天气预报中心(NCMRWF)运行的统一模型和(v)日本气象厅(JMA)运行的全球光谱模型(GSM)已用于制定2021年西南季风的MME预测。MME和单个模型预测的预测技巧是根据观测到的地区降雨量进行评估的。个别模型和MME的地区级强降雨预报也根据观测到的降雨事件进行了评估,这些降雨事件对预警服务有用。为了验证目的,计算了不同的验证分数,如相关系数(CC)、均方根误差(RMSE)、平均偏差、检测概率(POD)、误报率(FAR)、公平对待分数(ETS)、关键成功指数(CSI)等。不同的验证分数表明,MME降雨预测在不同的空间域和时间尺度上都比单独的模型表现良好。观测到的降雨量与第1天MME预测之间的CC为0.58,而GFS、GEFS、NCEP、NCUM和JMA分别为0.43、0.47、0.49、0.49和0.46。与IMD观测到的降雨量相比,MME、GFS、GEFS、NCEP、NCUM和JMA观测到的RMSE分别为12.7、15.2、14.1、14.3、16.6和14.1 mm/天。模型预测的相互比较表明,MME方法可以生成印度熟练的地区降雨量预测,供季风季节使用。
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A multi-model ensemble tool for predicting districts level monsoon rainfall and extreme rainfall events over India
The southwest (SW) monsoon season from June to September (JJAS) is the major rainfall period over most parts of Indian regions. Accurate rainfall forecast is one of the most crucial and least predictable parameters of the numerical weather prediction (NWP) models because of its uneven distribution and patterns over the globe. During the last decade many studies have been carried out using different NWP models to predict rainfall incidents, and it is found that the forecast skill has been improved considerably. In the present study, a multi-model ensemble (MME) based tool has been developed for the prediction of SW monsoon rainfall at the district level over India for five days. The precipitation forecasts from five operational NWP modelling systems, viz., (i) Global Forecast System (GFS) and          (ii) Global Ensemble Forecasting System (GEFS) running at India Meteorological Department, (iii) Global Forecast System model running at National Centre for Environment Prediction (NCEP), (iv) Unified Model running at National Centre for Medium-Range Weather Forecasting (NCMRWF) and (v) Global Spectral Model (GSM) running at Japan Meteorological Agency (JMA) have been used for developing the MME forecasts for SW monsoon 2021. The prediction skill of the MME and the individual model forecast is evaluated against observed district rainfall.  The district-level heavy rainfall forecast from individual models and MME is also evaluated against the observed rainfall events useful for warning services. Different verification scores like Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Bias, Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Treat Score (ETS), Critical Success Index (CSI), etc. are calculated for the verification purpose. The different verification score shows that MME rainfall forecast has performed well than the individual models in different spatial domains and temporal scales. The CC between observed rainfall and day 1 MME forecast is 0.58, whereas GFS, GEFS, NCEP, NCUM and JMA are showing 0.43, 0.47, 0.49, 0.49 and 0.46 respectively. The RMSE observed for MME, GFS, GEFS, NCEP, NCUM and JMA are 12.7, 15.2, 14.1, 14.3, 16.6 and 14.1 mm/day respectively when compared with IMD observed rainfall. The inter-comparison of the model forecasts reveal that the MME method can generate skillful district rainfall forecast over India for operational use during the monsoon season.
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来源期刊
MAUSAM
MAUSAM 地学-气象与大气科学
CiteScore
1.20
自引率
0.00%
发文量
1298
审稿时长
6-12 weeks
期刊介绍: MAUSAM (Formerly Indian Journal of Meteorology, Hydrology & Geophysics), established in January 1950, is the quarterly research journal brought out by the India Meteorological Department (IMD). MAUSAM is a medium for publication of original scientific research work. MAUSAM is a premier scientific research journal published in this part of the world in the fields of Meteorology, Hydrology & Geophysics. The four issues appear in January, April, July & October.
期刊最新文献
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