نتایج جستجو برای: satellite precipitation products spps
تعداد نتایج: 421522 فیلتر نتایج به سال:
[1] Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite‐retrieved precipitation products with high spatial resolutions and global coverage have resulted...
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 s...
Performance assessment of satellite-based precipitation products (SPPs) is critical for their application and development. This study assessed the accuracies four (PERSIANN-CDR, PERSIANN-CCS, PERSIANN-DIR, PERSIANN) using data in situ weather stations installed over Himalayan Mountains Pakistan. All SPPs were evaluated on annual, seasonal, monthly, daily bases from 2010 to 2017, whole spatial d...
Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is crucial to quantify and document their error characteristics. In this study, four sa...
The real-time availability of several satellite-based precipitation products has recently provided hydrologists with an unprecedented opportunity to improve current hydrologic prediction capability for vast river basins, particularly for ungauged regions. However, the accuracy of real-time satellite precipitation data remains uncertain. This study aims to use three widely used real-time satelli...
An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction center MORPHing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMP...
Global precipitation data sets with high spatial and temporal resolution are needed for many applications, but they were unavailable before the recent creation of several such satellite products. Here, we evaluate four different satellite data sets of hourly or 3-hourly precipitation (namely CMORPH, PERSIANN, TRMM 3B42 and a microwave-only product referred to as MI) by comparing the spatial pat...
In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH) were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of a 1232 km watershed in the Guadalupe River basin, Texas. Storm events in 2007 were used to analyze...
Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing techni...
The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological (RCMs), has provided the means to establish baselines for quantification, propagation, reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed improve operational real-time forecasts Upper Zambezi River Basin (UZRB), Africa, utilizing...
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