An assessment of GPM IMERG Version 7 rainfall estimates over the North West Himalayan region

IF 4.5 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Sreyasi Biswas, Charu Singh, Vidhi Bharti
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引用次数: 0

Abstract

The latest Global Precipitation Mission (GPM) IMERG V07B Final Run (Hereafter IMERG) has been validated for rainfall estimates in the North West Himalayan (NWH) region during the Indian Summer Monsoon (ISM) season (June–September) of 2000–2022. The validation is assessed against daily 0.25o x 0.25o India Meteorological Department (IMD) gridded rainfall data. We found that IMERG inherently underestimates rainfall, particularly low-intensity (< 50 mm day−1) rainfall. However, an overestimation is evident in high-intensity rainfall (50 mm day−1–200 mm day−1). This is consistent across all elevation ranges, from <1000 m to >5000 m, with the most significant negative bias observed at lower elevations. The proportion of such underestimated rainfall events increases with elevation, while the proportion of overestimated rainfall events decreases. Conclusively, IMERG is negatively skewed (−0.94). The proportion of accurate estimation of rainfall intensity is low and lies between 3 mm day−1 to 21 mm day−1 for <1000 m. IMERG performs the best in classifying a ‘rain event’ in Uttarakhand (UK) and Himachal Pradesh (HP), which is evident from near-optimal values of categorical metrics like False Alarm Ratio (FAR) (0.19 and 0.30 respectively), Probability of Detection (POD) (0.86 and 0.86 respectively), and Critical Success Index (CSI) (0.71 and 0.62 respectively). The classification of a “no rain event” by IMERG exhibits relatively low accuracy in the two states (Probability of False Detection (POFD) = 0.54 and 0.65 respectively). Overall, the Accuracy (ACC) in classifying a ‘rainfall event’, irrespective of it being a ‘rain event’ or a ‘no rain event’, is fairly good in UK (ACC = 0.75) and HP (ACC = 0.67) including the estimation of ‘rain event’ (Frequency Bias Index FBI = 1.07 and 1.23 respectively). The manifestation of stratiform rainfall in UK and HP could account for the underestimation of rainfall intensity and the discrepancies in the categorical metrics, owing to them being unrecognized by satellite due to warm cloud top temperatures. IMERG estimates are moderate over Jammu and Kashmir (JK) (FAR = 0.40, ACC = 0.1, CSI = 0.53, POFD = 0.64), while a large uncertainty in the performance of IMERG exists over Ladakh (LD) due to the paucity of IMD rain gauges (FAR = 0.62, ACC = 0.49, CSI = 0.34, POFD = 0.65).
对西北喜马拉雅地区GPM IMERG第7版降雨估计的评估
最新的全球降水任务(GPM) IMERG V07B最终运行(以下简称IMERG)已对2000-2022年印度夏季风(ISM)季节(6 - 9月)西北喜马拉雅(NWH)地区的降雨量进行了验证。根据印度气象部门(IMD)每日0.25 × 0.25的网格化降雨数据评估了验证。我们发现IMERG固有地低估了降雨量,特别是低强度(<;50毫米日降雨量−1)。然而,在高强度降雨(50毫米天−1 - 200毫米天−1)中,估计过高是明显的。这在所有海拔范围内都是一致的,从1000米到5000米,在较低海拔处观察到最显著的负偏差。随海拔高度的增加,被低估的降水事件的比例增加,而被高估的降水事件的比例减少。最后,IMERG呈负偏(- 0.94)。对于1000 m,准确估计降雨强度的比例很低,在3 ~ 21 mm之间。IMERG在北阿坎德邦(英国)和喜马偕尔邦(HP)的“降雨事件”分类中表现最好,这从接近最优的分类指标中可以看出,如虚警率(FAR)(分别为0.19和0.30)、检测概率(POD)(分别为0.86和0.86)和关键成功指数(CSI)(分别为0.71和0.62)。IMERG对“无雨事件”的分类在两种状态下的准确率相对较低(POFD分别为0.54和0.65)。总体而言,无论“降雨事件”是“降雨事件”还是“无雨事件”,分类“降雨事件”的准确度(ACC)在英国(ACC = 0.75)和HP (ACC = 0.67)都相当好,包括“降雨事件”的估计(频率偏差指数FBI分别= 1.07和1.23)。英国和HP的层状降水的表现可以解释降雨强度的低估和分类指标的差异,因为它们由于温暖的云顶温度而无法被卫星识别。在查谟和克什米尔(JK)地区,IMERG估算值中等(FAR = 0.40, ACC = 0.1, CSI = 0.53, POFD = 0.64),而在拉达克(LD)地区,由于IMD雨量计的缺乏(FAR = 0.62, ACC = 0.49, CSI = 0.34, POFD = 0.65), IMERG的性能存在很大的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Atmospheric Research
Atmospheric Research 地学-气象与大气科学
CiteScore
9.40
自引率
10.90%
发文量
460
审稿时长
47 days
期刊介绍: The journal publishes scientific papers (research papers, review articles, letters and notes) dealing with the part of the atmosphere where meteorological events occur. Attention is given to all processes extending from the earth surface to the tropopause, but special emphasis continues to be devoted to the physics of clouds, mesoscale meteorology and air pollution, i.e. atmospheric aerosols; microphysical processes; cloud dynamics and thermodynamics; numerical simulation, climatology, climate change and weather modification.
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