Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran

Abstract Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over the conterminous United States (CONUS). This paper directly addresses these concerns by developing a methodology that adjusts existing TMPA products utilizing ground-based precipitation data. The approach is not a simple bias adjustment but a three-step process that transforms a satellite precipitation product. Ground-based precipitation is used to develop a filter eliminating FAR in the authors’ adjusted product. The probability distribution function (PDF) of the satellite-based product is adjusted to the PDF of the ground-based product, minimizing bias. Failure of precipitation detection (POD) is addressed by utilizing a ground-based product during these periods in their adjusted product. This methodology has been successfully applied in the hydrological modeling of the San Pedro basin in Arizona for a 3-yr time series, yielding excellent streamflow simulations at a daily time scale. The approach can be applied to any satellite precipitation product (i.e., TRMM 3B42 version 7) and will provide a useful approach to quantifying precipitation in regions with limited ground-based precipitation monitoring.

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Metabolomics Reveals Discrimination of Chinese Propolis from Different Climatic Regions

Foods ◽

10.3390/foods9040491 ◽

2020 ◽

Vol 9 (4) ◽

pp. 491 ◽

Cited By ~ 1

Author(s):

Tongtong Wang ◽

Quanhui Liu ◽

Min Wang ◽

Limin Zhang

Keyword(s):

1H Nmr ◽

Chemical Shifts ◽

Biological Activities ◽

Climatic Regions ◽

Geographic Origins ◽

Chemical Profiles ◽

Ethanol Extracts ◽

Chinese Propolis

The chemical profiles of propolis vary greatly due to the botanic sources and geographic origins, which limit its standardization for modern usages. Here, we proposed a reliable 1H NMR-based metabolomic approach, to discriminate the function and quality of Chinese propolis. A total 63 Chinese propolis samples from different temperate regions were collected and extracted for NMR analysis. Twenty-one compositions in ethanol extracts were assigned based on characteristic chemical shifts and previous literature reports. Significant geographic indicators were identified after the PCA and orthogonal partial least squares discriminant analysis (OPLS-DA) analysis of the obtained 1H NMR data. It was found that the composition discriminations arose from long-term acclimation of the different climates of botanic origin and caused the differences in the biological activities. This study provides us a reasonable instruction for the quality control of Chinese propolis.

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Quasi-Global Evaluation of IMERG and GSMaP Precipitation Products over Land Using Gauge Observations

Water ◽

10.3390/w12010243 ◽

2020 ◽

Vol 12 (1) ◽

pp. 243 ◽

Cited By ~ 3

Author(s):

Wang ◽

Yong

Keyword(s):

Climate Models ◽

Input Parameter ◽

Poor Performance ◽

Global Evaluation ◽

Satellite Precipitation ◽

Precipitation Product ◽

Precipitation Measurement ◽

Highly Correlated ◽

Global Precipitation Measurement ◽

Global Precipitation

Understanding the error distribution of satellite precipitation products is conducive to obtaining accurate precipitation data, which is a very important input parameter in hydrological models and climate models. The error characteristics of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) uncalibrated products on quasi-global land and six continents are evaluated, and the effects of latitude, elevation, and season on satellite precipitation product accuracy are analyzed. In order to be consistent with the Climate Prediction Center (CPC), the selected products are resampled at 0.5° and daily resolutions from 1 January 2015 to 31 August 2018. We find out that (1) GSMaP performs worse than IMERG mainly due to systematic errors and poor performance at high latitudes; (2) overestimation is obvious in high latitude areas of the northern hemisphere and also in areas with low rainfall intensity; (3) IMERG and GSMaP show good performance in summer and poor performance in winter; (4) where elevation is lower than 1500 m, the error metrics are highly correlated with the elevation; (5) the correlation coefficient is relatively high in areas with high rainfall, and the dispersion of satellite data and gauge data is also high. IMERG is a high-quality satellite precipitation product in the GPM era, but some uncertainties mentioned above are still worthy of attention by product developers and users.

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