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 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.

scholarly journals The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

2020 ◽  
Vol 21 (2) ◽  
pp. 161-182 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Andrés Navarro ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
José Luis Sánchez ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Surface Precipitation ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Level 3 ◽  
Hydrologic Impacts ◽  
A Minor ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

scholarly journals Validation of the First Years of GPM Operation over Cyprus

2018 ◽  
Vol 10 (10) ◽  
pp. 1520 ◽  
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
First Results ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

scholarly journals Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

2019 ◽  
Vol 11 (6) ◽  
pp. 697 ◽  
Author(s):  
Fenglin Xu ◽  
Bin Guo ◽  
Bei Ye ◽  
Qia Ye ◽  
Huining Chen ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Accurate Estimation ◽  
Detection Capability ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Daily Precipitation Data ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Precipitation Events

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

scholarly journals Validation of rainfall data estimated by GPM satellite on Southern Amazon region

2019 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Luiz Octavio Fabricio dos Santos ◽  
Carlos Alexandre Santos Querino ◽  
Juliane Kayse Albuquerque da Silva Querino ◽  
Altemar Lopes Pedreira Junior ◽  
Aryanne Resende de Melo Moura ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
High Rate ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Surface Data ◽  
Precipitation Estimates ◽  
Amazonas State ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.

scholarly journals Seasonal Effect on Spatial and Temporal Consistency of the New GPM-Based IMERG-v5 and GSMaP-v7 Satellite Precipitation Estimates in Brazil’s Central Plateau Region

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 668 ◽  
Author(s):  
Leandro Salles ◽  
Frédéric Satgé ◽  
Henrique Roig ◽  
Tati Almeida ◽  
Diogo Olivetti ◽  
...  
Keyword(s):  
Daily Basis ◽  
Seasonal Effect ◽  
Temporal Consistency ◽  
Time Step ◽  
Central Plateau ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation

This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product.

scholarly journals Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

2009 ◽  
Vol 48 (9) ◽  
pp. 1843-1857 ◽  
Author(s):  
David T. Bolvin ◽  
Robert F. Adler ◽  
George J. Huffman ◽  
Eric J. Nelkin ◽  
Jani P. Poutiainen
Keyword(s):  
High Latitude ◽  
Global Precipitation Climatology Project ◽  
Precipitation Event ◽  
Finnish Meteorological Institute ◽  
Monthly Scale ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Wide Variability ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Monthly and daily products of the Global Precipitation Climatology Project (GPCP) are evaluated through a comparison with Finnish Meteorological Institute (FMI) gauge observations for the period January 1995–December 2007 to assess the quality of the GPCP estimates at high latitudes. At the monthly scale both the final GPCP combination satellite–gauge (SG) product is evaluated, along with the satellite-only multisatellite (MS) product. The GPCP daily product is scaled to sum to the monthly product, so it implicitly contains monthly-scale gauge influence, although it contains no daily gauge information. As expected, the monthly SG product agrees well with the FMI observations because of the inclusion of limited gauge information. Over the entire analysis period the SG estimates are biased low by 6% when the same wind-loss adjustment is applied to the FMI gauges as is used in the SG analysis. The interannual anomaly correlation is about 0.9. The satellite-only MS product has a lesser, but still reasonably good, interannual correlation (∼0.6) while retaining a similar bias due to the use of a climatological bias adjustment. These results indicate the value of using even a few gauges in the analysis and provide an estimate of the correlation error to be expected in the SG analysis over ocean and remote land areas where gauges are absent. The daily GPCP precipitation estimates compare reasonably well at the 1° latitude × 2° longitude scale with the FMI gauge observations in the summer with a correlation of 0.55, but less so in the winter with a correlation of 0.45. Correlations increase somewhat when larger areas and multiday periods are analyzed. The day-to-day occurrence of precipitation is captured fairly well by the GPCP estimates, but the corresponding precipitation event amounts tend to show wide variability. The results of this study indicate that the GPCP monthly and daily fields are useful for meteorological and hydrological studies but that there is significant room for improvement of satellite retrievals and analysis techniques in this region. It is hoped that the research here provides a framework for future high-latitude assessment efforts such as those that will be necessary for the upcoming satellite-based Global Precipitation Measurement (GPM) mission.

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