Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan

2018 ◽  
Vol 205 ◽  
pp. 134-146 ◽  
Author(s):  
Muhammad Naveed Anjum ◽  
Yongjian Ding ◽  
Donghui Shangguan ◽  
Ijaz Ahmad ◽  
Muhammad Wajid Ijaz ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Northern Highlands

Validation of the final monthly Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 05 and Version 06 with ground-based precipitation gauge measurements across the Canadian Arctic

2022 ◽  
Author(s):  
David Hudak ◽  
Éva Mekis ◽  
Peter Rodriguez ◽  
Bo Zhao ◽  
Zen Mariani ◽  
...  
Keyword(s):  
Canadian Arctic ◽  
Correlation Coefficients ◽  
Mean Error ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Precipitation Estimates ◽  
The Mean ◽  
Precipitation Gauge ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract To assess the performance of the most recent versions of the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), namely V05 and V06, in Arctic regions, comparisons with Environment and Climate Change Canada (ECCC) Climate Network stations north of 60°N were performed. This study focuses on the IMERG monthly final products. The mean bias and mean error-weighted bias were assessed in comparison with twenty-five precipitation gauge measurements at ECCC Climate Network stations. The results of this study indicate that IMERG generally detects higher precipitation rates in the Canadian Arctic than ground-based gauge instruments, with differences ranging up to 0.05 mm h−1 and 0.04 mm h−1 for the mean bias and the mean error-weighted bias, respectively. Both IMERG versions perform similarly, except for a few stations, where V06 tends agree slightly better with ground-based measurements. IMERG’s tendency to detect more precipitation is in good agreement with findings indicating that weighing gauge measurement suffer from wind undercatch and other impairing factors, leading to lower precipitation estimates. Biases between IMERG and ground-based stations were found to be slightly larger during summer and fall, which is likely related to the increased precipitation rates during these seasons. Correlations of both versions of IMERG with the ground-based measurements are considerably lower in winter and spring than during summer and fall, which might be linked to issues that Passive Microwave (PMW) sensors encounter over ice and snow. However, high correlation coefficients with medians of 0.75-0.8 during summer and fall are very encouraging for potential future applications.

scholarly journals Performance Evaluation of Version 5 (V05) of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) over the Tianshan Mountains of China

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1139 ◽  
Author(s):  
Min Yang ◽  
Zhongqin Li ◽  
Muhammad Naveed Anjum ◽  
Yayu Gao
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Tianshan Mountains ◽  
Spatiotemporal Variability ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Better Than

This study evaluated the performance of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) version 5 (V05) Early-run and Final-run (IMERG-E and IMERG-F, respectively) products over the Tianshan Mountains. For comparison, the accuracies of two Tropical Rainfall Measuring Mission (TRMM) products (3B42RT and 3B42V7) were also analyzed. Performance of the satellite-based precipitation products (SPPs) was analyzed at daily to annual scales from April 2014 to October 2017. Results showed that: (1) IMERG-F and 3B42V7 performed better than IMERG-E and 3B42RT in the characterization of spatiotemporal variability of precipitation; (2) Precipitation estimates from IMERG-F were in the best overall agreement with the gauge-based data, followed by IMERG-E and 3B42V7 on all temporal scales; (3) IMERG-E and 3B42RT products were failed to provide accurate precipitation amounts, whereas IMERG-F and 3B42V7 were able to provide accurate precipitation estimates with the lowest relative biases (4.98% and −1.71%, respectively) and RMSE (0.58 mm/day and 0.76 mm/day, respectively); (4) The enhancement from the IMERG Early-run to the Final-run to capture the moderate to heavy precipitation events was not evident; (5) On seasonal scale, IMEGR-F performed better than all other SPPs, particularly during the spring season with negligible bias (0.28%). It was deduced that IMERG-F was capable of replacing TRMM products.

scholarly journals Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus

2020 ◽  
Vol 12 (19) ◽  
pp. 3212
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Annual Basis ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Gauge Station ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations’ records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of −1.6 and −3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations.

The quasi-global geographical distribution of precipitation system scale

2020 ◽  
Author(s):  
Yan Zhang ◽  
Kaicun Wang
Keyword(s):  
Geographical Distribution ◽  
Precipitation Amount ◽  
Statistical Average ◽  
Precipitation System ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
The World ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Frequency Intensity

<p>The scale of precipitation systems can provide important information to acquire a better understanding of formation mechanism and environmental effects of precipitation as well as model promotion. However, the global geographical distribution of precipitation system scale remains poorly known from previous studies. This study uses the latest Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data to get global patterns of precipitation system scale by grouping the contiguous rainy gridboxes during 2015-2018. Our results show that the large precipitation systems (>10<sup>3</sup> km) occur more frequently over ocean and the midlatitude land areas with low precipitation amount such as Siberia as well as the western and central parts of North America. The most apparent seasonal variation of precipitation system scale occurs over midlatitude ocean along with the northern and southern coast of South America. Most regions of the world have the highest peak in the late afternoon at around 17:00 local time (LT). In a statistical average, the relationships between scale and other precipitation properties including amount, frequency, intensity and duration all seem to be positive. The strongest associations of scale with amount, frequency, intensity and duration all occur over tropics and ocean with the highest correlation coefficient greater than 0.8.</p>

scholarly journals IMERG-Based Meteorological Drought Analysis over Italy

Climate ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 65
Author(s):  
Tommaso Caloiero ◽  
Giulio Nils Caroletti ◽  
Roberto Coscarelli
Keyword(s):  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Drought Characteristics ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Grid Points ◽  
The Standardized Precipitation Index ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Italian Territory

The Mediterranean region is an area particularly susceptible to water scarcity and drought. In this work, drought has been analyzed in Italy using multiple timescales of the standardized precipitation index (SPI) evaluated from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement product from 2000 to 2020. In particular, drought characteristics (severity, duration, and intensity) have been estimated by means of the run theory applied to the SPI values calculated in 3325 grid points falling within the Italian territory. Results clearly indicate that although a high number of drought events has been identified for the short timescale, these events present a lower duration and lesser severity than the long-timescale droughts. The main outcomes of this study, with the indication of the spatial distribution of the drought characteristics in Italy, allow identifying the areas that could also face water stress conditions in the future, thus requiring drought monitoring and adequate adaptation strategies.

scholarly journals Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2714
Author(s):  
Daniele Tôrres Rodrigues ◽  
Cláudio Moisés Santos e Silva ◽  
Jean Souza dos Reis ◽  
Rayana Santos Araujo Palharini ◽  
Jório Bezerra Cabral Júnior ◽  
...  
Keyword(s):  
River Basin ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
P Value ◽  
Sao Francisco River ◽  
Precipitation Measurement ◽  
São Francisco River ◽  
Satellite Retrievals ◽  
Global Precipitation Measurement ◽  
Global Precipitation

The São Francisco River basin is one of the largest in the Brazilian territory. This basin has enormous economic, social and cultural importance for the country. Its water is used for human and animal supply, irrigation and energy production. This basin is located in an area with different climatic characteristics (humid and semiarid) and studies related to precipitation are very important in this region. In this scenario, the objective of this investigation is to present an assessment of rainfall estimated through the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) product compared with rain gauges over the São Francisco river basin in Brazil. For that, a period from of 20 years and 18 surface weather stations were used to evaluate the product. Based on different evaluation techniques, the study found that the IMERG is appropriate to represent precipitation over the basin. According to the results, the performance of the IMERG product depends on the location where the rain occurs. The bias ranged from −1.67 to 0.34 mm, the RMSE ranged from 5.36 to 10.36 mm and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from 0.28 to 0.61. The results obtained by Student t-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. The exception is the eastern portion of the basin, where the product, on average, underestimates the precipitation (p-value < 0.05) and presents the worst statistical metrics.

scholarly journals Introducing and evaluating the Climate Hazards center IMErg with Stations (CHIMES) - Timely station-enhanced Integrated Multi-satellitE Retrievals for Global Precipitation Measurement

2021 ◽  
pp. 1-52
Author(s):  
Chris C. Funk ◽  
Pete Peterson ◽  
George J. Huffman ◽  
Martin Francis Landsfeld ◽  
Christa Peters-Lidard ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Rain Gauge ◽  
Station Data ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
The Mean ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Variance Explained

AbstractAs human exposure to hydro-climatic extremes and the number of in situ precipitation observations declines, precipitation estimates, such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG), provide a critical source of information. Here, we present a new gauge-enhanced data set (CHIMES) designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center’s (CHC) high-resolution climatology (CHPclim) and low-latency rain-gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES.The CHC’s curated rain-gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERG late fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERG late performs well (r=0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50 to 63 percent and decreases the mean absolute error from 48 to 39 mm month−1. Gauge enhancement then increases the variance explained to 75 percent, reducing the mean absolute error to 27 mm month−1.

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