A new perspective in understanding rainfall from satellites over a complex topographic region of India

Thakur, Manoj Kumar; Kumar, T. V. Lakshmi; Rao, K. Koteswara; Barbosa, Humberto Alves; Rao, V. Brahmananda

doi:10.1038/s41598-019-52075-y

Cited by 17 publications

(7 citation statements)

References 58 publications

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“…The complex topography and mountainous regions with orographic convection and low‐troposphere winds represent a challenge in rainfall estimation by satellites (Shige et al., 2013). Evaluation studies of SPDs on complex topography are still required to further advance the product algorithms (Bartsotas et al., 2018; Derin & Yilmaz, 2014; Lu & Yong, 2018; Thakur et al., 2019; Wang & Yong, 2020). Various studies that have compared the abilities of SPDs to detect heavy rainfall caused by landfalling typhoons have suggested performing a comparison in terms of rainfall distribution and spatiotemporal variability (S. Chen et al., 2013; C. Huang et al., 2019; Pham & Vu, 2020; Yu et al., 2009), analyzing rain gauges in areas within 400 km of the center of a typhoon (D. Wang et al., 2016), rainfall detection on typhoon and non‐typhoon events (W.‐R.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

Aryastana

Liu

Jou

et al. 2022

Earth and Space Science

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Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground‐based radar because these instruments have an uneven distribution in remote areas. Satellite precipitation data sets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. However, the speed and direction of the wind has the interaction with terrain, which leads the uncertainty of the SPDs. This study performed sub‐daily assessments of near‐real‐time and high resolution SPDs (i.e., IMERG, GSMaP, and PERSIANN data sets) during five typhoon‐related heavy precipitation events in the Philippines, with the analysis under the impact due to wind and terrain effect. The aforementioned assessments were performed through a point‐to‐grid comparison by using continuous and volumetric statistical validation indices for the 34‐knot wind radii of the typhoons, rainfall intensity, terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall. The GSMaP data set overestimated the gauge observations during peak rainfall, while the IMERG and PERSIANN data sets considerably underestimated rainfall. The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds.

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Section: Introductionmentioning

confidence: 99%

Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

Aryastana

Liu

Jou

et al. 2022

Earth and Space Science

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“…Other P products based on satellites, reanalysis, weather simulators, or a combination of the above sources are also available (Sun et al, 2018). Several studies have analyzed such products across the whole of India -e.g., Rana et al (2015), Prakash (2019), and Gupta et al (2020), and specific regions of India -e.g., Thakur et al (2019) and Kanda et al (2020). Within these studies, gauge-based P products are often treated as reference products, or benchmarks, when evaluating satellite-based and other non-traditional datasets.…”

Section: Introductionmentioning

confidence: 99%

Extent of gross underestimation of precipitation in India

Goteti,

Famiglietti

2024

Preprint

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Abstract. Underestimation of precipitation (UoP) in the hilly and mountainous parts of South Asia is estimated by some studies to be as large as the observed precipitation (P). For instance, correction factors (CFs) developed by the recent PBCOR dataset have values exceeding 2.0 across the wettest regions of India, some of which have experienced catastrophic flooding in the recent past. However, UoP has been analyzed only to a limited extent across India. Towards bridging this gap, this study analyzes watershed-scale UoP using various P datasets within a water imbalance analysis. Among these P datasets, the often-used Indian Meteorological Department (IMD) dataset is of primary interest. Gross UoP was identified by analyzing the extent of imbalance in the annual water budget of watersheds corresponding to 242 river gauging stations where quality controlled data on catchment boundaries and streamflow is available. Water year (WY) based volume of observed annual P was compared against observed annual streamflow (R) and satellite-based actual evapotranspiration (ET). Across many watersheds of both Northern and Peninsular India, the spurious water imbalance scenarios of P ≤ R, or P << R + ET, were realized. It is shown that management of water, such as groundwater extraction, reservoir storage and water diversion (imports or exports), is generally minimal compared to annual P in such watersheds. It is also shown that annual changes in terrestrial water storage are also minimal compared to annual P in such watersheds. Assuming data on R (and ET to a lesser extent) to be reliable, it is concluded that UoP is very likely the cause of such imbalance. All 12 of the P datasets analyzed here suffer from UoP, but the extent of UoP varies by dataset and region. The reanalysis-based datasets ERA5-Land and IMDAA are less affected by UoP than IMD, and the spatial patterns of estimated CFs based on these two datasets are also consistent with those made independently by the PBCOR dataset. Based on the 30-year period of WY 1985–2014, P for the whole of India could be up to 19 % (ERA5-Land) to 37 % (IMDAA) higher than IMD, with substantial variability within years and river basins. For instance, P for the Indian portion of the Ganga River Basin, for the same 30-year period, could be up to 36 % (ERA5-Land) to 54 % (IMDAA) higher than IMD. The actual magnitude of UoP is speculated to be even greater. Moreover, trends in IMD's P are not always present in ERA5-Land and IMDAA. Studies using IMD should exercise caution since UoP could lead to misrepresentation of water budgets and long-term trends. The empirical approach of identifying watersheds affected by UoP using a water imbalance approach is contingent on data availability. It is speculated that if additional data on R becomes available, particularly in Northern India, many other watersheds affected by UoP would be identified. While the scientific community is striving to continually improve P products, India's water agencies can help the community better quantify UoP by making observed hydrometeorological data more widely available. Limitations of this study are discussed.

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“…A comprehensive evaluation of multiple precipitation products (SREs and reanalysis) in China, revealed that IMERG outperformed other datasets except for GSMaP [51]. Furthermore, only a few studies have evaluated GPM-era SREs focusing on complex terrain areas such as Ethiopia, Italy [53], and India [18,48,54], revealing that GSMaP-V07 and IMERG-V05 tend to underestimate rainfall over complex mountainous areas. Similarly, the GSMaP-V07 product surpasses IMERG-V06B over the multiple complex regions of the world [31].…”

Section: Introductionmentioning

confidence: 99%

Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

et al. 2021

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The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.

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A new perspective in understanding rainfall from satellites over a complex topographic region of India

Cited by 17 publications

References 58 publications

Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

Extent of gross underestimation of precipitation in India

Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

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