Are the Latest GSMaP Satellite Precipitation Products Feasible for Daily and Hourly Discharge Simulations in the Yellow River Source Region?

Shi, Jiayong; Wang, Bing; Wang, Guoqing; Yuan, Fei; Shi, Chunxiang; Zhou, Xiong; Zhang, Limin; Zhao, Chenxiao

doi:10.3390/rs13214199

Cited by 12 publications

(7 citation statements)

References 70 publications

(98 reference statements)

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“…However, hourly GSMaP V08 data tend to overestimate with a relatively large bias. This signi cant bias of GSMaP data was also found for previous versions of the GSMaP data(Shi et al 2021;Li et al 2022). This signi cant bias of GSMaP data needs to be identi ed and adjusted rst to obtain more satisfactory hourly data results in the IMC region(Tan et al 2018;Chen et al 2022…”

supporting

confidence: 77%

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

Ramadhan

Marzuki

Muharsyah

et al. 2022

Preprint

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This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022.Assessmentswere carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical metrics and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observation vary from low (CC=0.14-0.29), moderate (CC=0.33-0.45), and good correlation (CC=0.72-0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive RB. Even though the correlation of hourly datais still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount andfrequency. GSMaP data also managed to observe heavy rainfall, as indicated by the good probability of detection (POD) values for daily data ranging from 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR<0.5). GSMaP daily data accuracy also dependson topographic conditions at IMC, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, post-real time non-gauge calibrated (GSMaP_MVK) outperformed, followed by post-real time gauge calibrated (GSMaP_Gauge), near-real-time gauge calibrated (GSMaP_NRT_G), near-real time non-gauge callibrated (GSMaP_NRT), real time gauge callibrated (GSMaP_Now_G), and real time non-gauge callibrated (GSMaP_Now). Thus, GSMaP near real-time data has the potential for observing rainfall in IMC with faster latency.

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supporting

confidence: 77%

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

Ramadhan

Marzuki

Muharsyah

et al. 2022

Preprint

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“…Remote sensing and numerical models can provide continuous precipitation fields with varying spatial scales. These different types of precipitation products have proven to be useful in various research fields, including the study of changes in climatic means and extremes, as well as the monitoring of droughts and floods [5][6][7]. However, they remain limited to rather coarse spatial resolutions such as 0.5 • -0.25 • , which is coarser than the scale of many environmental and ecological processes and the associated data requirements for multiple scientific and operational applications.…”

Section: Introductionmentioning

confidence: 99%

A Simple Method of Coupled Merging and Downscaling for Multi-Source Daily Precipitation Data

Zhao,

Chen

2023

Remote Sensing

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High accuracy and a high spatiotemporal resolution of precipitation are essential for the hydrological, ecological, and environmental fields. However, the existing daily gridded precipitation datasets, such as remote sensing products, are limited both by the coarse resolution and the low accuracy. Despite considerable efforts having been invested in downscaling or merging, a method of coupled and simultaneously downscaling and merging multiple datasets is currently lacking, which limits the wide application of individual popular satellite precipitation products. For the first time, in this study, we propose a simple coupled merging and downscaling (CMD) method for simultaneously obtaining multiple high-resolution and high-accuracy daily precipitation datasets. A pixel-repeated decomposition method was first proposed, and the random forest (RF) method was then applied to merge multiple daily precipitation datasets. The individual downscaled dataset was obtained by multiplying the result of merging by an explanatory rate obtained by RF. The results showed that the CMD method exhibited significantly better performance compared with the original datasets, with the mean absolute error (MAE) improving by up to 50%, the majority of the values of bias ranging between −1 mm and 1 mm, and the majority of the Kling–Gupta efficiency (KGE) values being greater than 0.7. CMD was more accurate than the widely used dataset, Multi-Source Weighted-Ensemble Precipitation (MSWEP), with a 43% reduction in the MAE and a 245% improvement in the KGE. In addition, the long-term estimation suggested that the proposed method exhibits stable good performance over time.

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“…E-mail: a18.7asf@g.chuo-u.ac.jp of Precipitation (GSMaP) improves accuracy by interpolating precipitation rate using the Kalman filter and the cloud motion vector from IR band observations, along with position mapping using the brightness temperature from IR band observations (Ushio et al, 2009;Kubota et al, 2020). GSMaP has provided standard, near-real-time, groundbased rain gauge-corrected versions of data products; however, their implementation in a hydrological context remains challenging (Shi et al, 2021). The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) Morphing Technique (CMORPH) uses wind vectors calculated from IR band observations to correct precipitation data observed by microwave radiometers (Joyce et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

A new image segmentation model for precipitation estimation using meteorological satellite infrared images and geographic information

Fujimoto,

Tebakari

2023

Hydrological Research Letters

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Satellite products are expected to play important roles in water-related management and public welfare, particularly in developing countries. Higher-resolution precipitation products are required to cope with increasingly severe water-related disasters. In this study, we propose a new satellite precipitation estimation algorithm based on deep learning that uses data from multiple satellite infrared (IR) bands and geographic information (e.g. elevation, latitude, and longitude) as input. For the deep learning model component, we use various image segmentation models, including U-Net, PSPNet, and DeepLabv3+. Cosine similarity and correlation coefficients for precipitation rate were used to determine the IR bands of the input data; five bands were used as IR. Four input datasets were constructed: IR alone; IR and elevation data; IR and latitude/longitude; and IR, elevation data, and latitude/longitude. When PSPNet or DeepLabv3+ was used as the deep learning model, and elevation and latitude/longitude were added to IR as input data, the mean square error and fraction skill score showed improved accuracy over GSMaP_MVKv7 and PERSIANN-CCS; precipitation overestimation was eliminated. These results indicate that deep learning models can be used to estimate precipitation from satellite IR observations with high resolution and accuracy.

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Are the Latest GSMaP Satellite Precipitation Products Feasible for Daily and Hourly Discharge Simulations in the Yellow River Source Region?

Cited by 12 publications

References 70 publications

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

A Simple Method of Coupled Merging and Downscaling for Multi-Source Daily Precipitation Data

A new image segmentation model for precipitation estimation using meteorological satellite infrared images and geographic information

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