Precipitation Seasonality over the Indian Subcontinent: An Evaluation of Gauge, Reanalyses, and Satellite Retrievals

Rana, Sapna; McGregor, James; Renwick, James

doi:10.1175/jhm-d-14-0106.1

Cited by 108 publications

(89 citation statements)

References 69 publications

(53 reference statements)

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“…CPC-UNI exhibites lowest precipitation over Myanmar and the neighboring regions. This is similar to the findings of Rana et al (2015), who showed that CPC-UNI poorly simulates seasonal mean precipitation over the same region. ERA-Interim also tends to show lower values over India and the maritime continent.…”

Section: Climatological Features Over the Asian Domainsupporting

confidence: 91%

“…Figure 9 shows the decadal trends of extreme rainfall (i.e., 95th percentile precipitation for JJA) in the datasets over the period 1998-2007 calculated through a linear regression coefficient. Except for APHRODITE, ERA-Interim, and JRA55, the rest shows a remarkable increasing trend over central India, which is consistent with the finding of Rana et al (2015) and Prakash et al (2014). Singh et al (2013) found that APHRODITE has opposite trend signs for extreme precipitation intensity from the 2000s onwards when compared with the IMD dataset (2140 rain-gauge station).…”

Section: Interannual Variabilitysupporting

confidence: 81%

“…Despite CPC-UNI (0.5° × 0.5°) having double the horizontal resolution of GPCP (1.0° × 1.0°), GPCP is more similar to APRODITE. This could reflect the discrepancy in number of stations, with APHRODITE using a more extensive gauge network than CPC-UNI (Gebregiorgis and Hossain 2015;Rana et al 2015).…”

Section: Ratio Of Extremes To Climatologymentioning

confidence: 99%

“…This may reflect insufficient reference datasets (i.e., observational datasets) on daily precipitation as compared with monthly mean data with finer grid resolution over longer periods and/or it may also be due to a shortage of long-term high-resolution climate simulations. Previous studies have restricted their investigations on extreme precipitation over Asia by using diverse observational and reanalysis datatsets (Rana et al 2015;Ceglar et al 2017;Huang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

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In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and lowresolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

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Section: Climatological Features Over the Asian Domainsupporting

confidence: 91%

Section: Interannual Variabilitysupporting

confidence: 81%

Section: Ratio Of Extremes To Climatologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

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“…These findings also suggest that satellite algorithm estimations from TMPA 3B42 v7 are suitable for low to medium ground elevations (>40 MASL) during extreme rainfalls. This proves that TMPA 3B42 v7 estimations have better agreement with gauging observations for higher ground levels than lower levels, which matches with other's study findings [27][28][29]. Generally, overall results indicate the tendency for underestimations by TMPA 3B42 v7 when compared to rain gauge observations during seasonal heavy precipitation, regardless of varying elevations.…”

Section: Performance Assessment Using Continuous Statisticssupporting

confidence: 87%

TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms

et al. 2016

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Abstract. On-site measurements from rain gauge provide important information for the design, construction, and operation of water resources engineering projects, groundwater potentials, and the water supply and irrigation systems. A dense gauging network is needed to accurately characterize the variation of rainfall over a region, unfitting for conditions with limited networks, such as in Sarawak, Malaysia. Hence, satellite-based algorithm estimates are introduced as an innovative solution to these challenges. With accessibility to dataset retrievals from public domain websites, it has become a useful source to measure rainfall for a wider coverage area at finer temporal resolution. This paper aims to investigate the rainfall estimates prepared by Tropical Rainfall Measuring Mission (TRMM) to explain whether it is suitable to represent the distribution of extreme rainfall in Sungai Sarawak Basin. Based on the findings, more uniform correlations for the investigated storms can be observed for low to medium altitude (>40 MASL). It is found for the investigated events of Jan [5][6][7][8][9][10][11] 2009: the normalized root mean square error (NRMSE = 36.7 %); and good correlation (CC = 0.9). These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.

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Uncertainties in TRMM‐Era multisatellite‐based tropical rainfall estimates over the Maritime Continent

Rauniyar

Protat

Kanamori

2017

Earth and Space Science

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This study investigates the regional and seasonal rainfall rate retrieval uncertainties within nine state‐of‐the‐art satellite‐based rainfall products over the Maritime Continent (MC) region. The results show consistently larger differences in mean daily rainfall among products over land, especially over mountains and along coasts, compared to over ocean, by about 20% for low to medium rain rates and 5% for heavy rain rates. However, rainfall differences among the products do not exhibit any seasonal dependency over both surface types (land and ocean) of the MC region. The differences between products largely depends on the rain rate itself, with a factor 2 difference for light rain and 30% for intermediate and high rain rates over ocean. The rain‐rate products dominated by microwave measurements showed less spread among themselves over ocean compared to the products dominated by infrared measurements. Conversely, over land, the rain gauge‐adjusted post–real‐time products dominated by microwave measurements produced the largest spreads, due to the usage of different gauge analyses for the bias corrections. Intercomparisons of rainfall characteristics of these products revealed large discrepancies in detecting the frequency and intensity of rainfall. These satellite products are finally evaluated at subdaily, daily, monthly, intraseasonal, and seasonal temporal scales against high‐quality gridded rainfall observations in the Sarawak (Malaysia) region for the 4 year period 2000–2003. No single satellite‐based rainfall product clearly outperforms the other products at all temporal scales. General guidelines are provided for selecting a product that could be best suited for a particular application and/or temporal resolution.

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Precipitation Seasonality over the Indian Subcontinent: An Evaluation of Gauge, Reanalyses, and Satellite Retrievals

Cited by 108 publications

References 69 publications

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms

Uncertainties in TRMM‐Era multisatellite‐based tropical rainfall estimates over the Maritime Continent

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