Implementation of an orographic/nonorographic rainfall classification scheme in the GSMaP algorithm for microwave radiometers

Yamamoto, Munehisa K.; Shige, Shoichi

doi:10.1016/j.atmosres.2014.07.024

Cited by 80 publications

(51 citation statements)

References 29 publications

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“…Ngo-Duc et al (2013) improved monthly precipitation data from the Global Satellite Mapping of Precipitation (GSMaP; Aonashi et al (2009) ;Ushio et al (2009)) with a corrective method applying an artificial neural network over central Vietnam. Yamamoto and Shige (2015) (hereafter YS15) introduced an algorithm to distinguish orographic rainfall in the GSMaP.…”

Section: Introductionmentioning

confidence: 99%

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Nodzu

Matsumoto

Trinh-Tuan

et al. 2019

Prog Earth Planet Sci

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The performance of the Global Satellite Mapping of Precipitation data Microwave-Infrared Combined Reanalysis Product (GSMaP RNL), version 6, was evaluated, using northern Vietnam as the test area. The Vietnam Gridded Precipitation (VnGP) Dataset was used for comparison purposes. Particular emphasis was placed on the investigation of heavy-rain days (precipitation over 50 mm day −1 ). Wind data from operational radiosonde observations at Hanoi were also used to examine the effect of interaction between wind and topography, on the GSMaP performance, and the basic relationship between the VnGP precipitation and lower tropospheric wind. Results showed that heavy-rain days generally occurred during May-August. Regions with higher and lower precipitation in the GSMaP, compared to the VnGP, were found to be distributed in a complex manner, at a scale similar to that of tributary basins. The GSMaP and the VnGP showed similar precipitation values when associated with westerly wind in the lower troposphere at Hanoi. Positive influences of westerly wind on estimated precipitation were observed along the northeastern foot of the mountain ranges, whereas negative influences were found along the southeastern foot. The results implied that the GSMaP rainfall estimation algorithm, taking account of orographic effects, may improve the accuracy of rainfall reproducibility, by using a more appropriate wind dataset to evaluate orographic convection.

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Section: Introductionmentioning

confidence: 99%

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Nodzu

Matsumoto

Trinh-Tuan

et al. 2019

Prog Earth Planet Sci

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“…To do so, a Kalman filter refined PMW rainfall estimation propagation by using the atmospheric moving vector derived from two successive IR images [14]. In comparison to the previous GSMaP-v5, GSMaP-v6 includes new algorithms to enhance rainfall estimates over land and ocean [15]. GSMaP-v6 is available in the form of near-to-real-time and post-adjusted versions.…”

Section: Datasetsmentioning

confidence: 99%

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

et al. 2017

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Abstract:The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1 • , these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25 • ) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.

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“…A possible solution for correcting the overestimates with G_Gauge during the summer season would be to adjust the precipitation rate by further tuning of the parameters in the algorithm. The latest version (version 6), which considers the orographic effect, was provided in September 2014, and this occurred after the analysis (Yamamoto and Shige 2015). An extensive study should include the evaluation of G_Gauge data.…”

Section: Spatial Analysismentioning

confidence: 99%

Spatiotemporal Evaluation of the Gauge-Adjusted Global Satellite Mapping of Precipitation at the Basin Scale

Takido

Saavedra

Ryo

et al. 2016

Journal of the Meteorological Society of Japan

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This study evaluated the accuracy of gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP_Gauge version V5.222.1, hereafter G_Gauge) data in Japan's Tone River basin during [2006][2007][2008][2009]. Specifically, the accuracy of a gauge non-adjusted product, GSMaP Moving Vector with Kalman Filter (GSMaP_MVK, hereafter G_MVK), was also evaluated. Both products were also evaluated against ground observation data from rain gaugeradar combined product Radar-Automated Meteorological Data Acquisition System (Radar-AMeDAS) in terms of temporal and spatial variability. Temporal analyses showed that G_Gauge had better accuracy than G_MVK at sub-daily time scales (1, 3, 6, 9, 12, and 24 h) within any range of precipitation intensity and better detection capabilities of rainfall event. Linear regressions with Radar-AMeDAS showed better performance for G_Gauge than G_MVK at any time scales in terms of Pearson's correlation coefficient and the slope of regression. At an hourly scale, in particular, Pearson's correlation coefficient for G_Gauge (0.84) was higher than that for G_MVK (0.72) as well as the slope of linear regression (0.87 and 0.65, respectively). The probability of detection (POD) improved from 0.48 (G_MVK) to 0.70 (G_Gauge) when gauge-adjusted data were used. However, spatial analysis detected that G_Gauge still underestimated the precipitation intensity in high-elevation regions and slightly overestimated it in low elevation regions. The POD and false alarm ratio had a linear relationship with log-transformed elevation data, and the relationships were stronger in the winter seasons than in the summer seasons. At any spatial and temporal scale, the evaluation of these products should consider seasonal changes (especially in winter) and the topographic effects. For further improvements of G_Gauge, we suggest including higher resolution gauge-based network data than the Climate Prediction Center unified gauge-based analysis of global daily precipitation, which is used for G_Gauge.

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Implementation of an orographic/nonorographic rainfall classification scheme in the GSMaP algorithm for microwave radiometers

Cited by 80 publications

References 29 publications

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

Spatiotemporal Evaluation of the Gauge-Adjusted Global Satellite Mapping of Precipitation at the Basin Scale

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