A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data

Ushio, Tomoo; Sasashige, Kazushi; Kubota, Takuji; Shige, Shoichi; Okamoto, Ken‐ichi; Aonashi, Kazumasa; Inoue, Toshiro; Takahashi, Nobuhiro; Iguchi, Toshio; Kachi, Misako; Oki, Riko; Morimoto, Takeshi; Kawasaki, Zen‐Ichiro

doi:10.2151/jmsj.87a.137

Cited by 522 publications

(306 citation statements)

References 15 publications

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“…Examples of these products include the Climate Prediction Center (CPC) MORPHing technique (CMORPH) (Joyce et al, 2004) and the Global Satellite Mapping of Precipitation (GSMaP) (Ushio et al, 2009). Analyses are based mostly on the CMORPH data gridded at a threehour and one-quarter degree (latitude and longitude) resolution as these data are available in a consistent form since early 2002.…”

Section: Satellite Precipitation Products In Seamentioning

confidence: 99%

Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program

Reid¹,

Hyer²,

Johnson

et al. 2013

Atmospheric Research

298

264

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Section: Satellite Precipitation Products In Seamentioning

confidence: 99%

Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program

Reid¹,

Hyer²,

Johnson

et al. 2013

Atmospheric Research

298

264

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“…The PERSIANN_CDR has a high R (0.78) but also yields high RMSE (2.93 mm/day) and the discrete degree of point is larger than other products, which PERSIANN uses only IR data to estimate rainfall rate ( Figure 3d). The highest R and lowest RMSE of GSMaP_RENALYSIS are 0.91 and 0.85 mm/day and these points are uniform distributed, because GSMaP_RENALYSIS has inherited CMORPH's morphing algorithm and employs a new Kalman filter approach to assimilate IR-derived rain rates, which it can help to reduce the total errors ( Figure 3e) [11,39,40].…”

Section: Nine-year Daily Mean Precipitationmentioning

confidence: 99%

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

et al. 2018

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Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.

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“…First, Kubota et al [16] developed a simplified, near real-time version of GSMap (GSMap-NRT), which uses fewer PMW input streams and a forward-only cloud advection scheme. To further reduce the total number of retrieval errors, Ushio et al (2009) employed a new Kalman filter approach to assimilate and refine the Vis/IR-based rainfall rates and thus generated an improved version [GSMap moving vector with Kalman filter (GSMap-MVK)] for nearly all available satellite-borne precipitation-related sensors [28]. Another important difference between these two GSMap products is that GSMap-MVK contains a two-way morphing technique (both forwards and backwards) to propagate the area affected by rainfall using microwave radiometry.…”

Section: Gpm-era Satellite-based Precipitation Datasetsmentioning

confidence: 99%

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

Ning

Wang

Jin

et al. 2016

Water

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Abstract:The Global Precipitation Mission (GPM) Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial-temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6) with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1 • and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.

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A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data

Cited by 522 publications

References 15 publications

Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program

Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

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