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

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.

Assessment of Heavy Rainfall using GPM- IMERG Satellite Product over Nepal

This study evaluates the Integrated Multi-satellite Retrievals from Global Precipitation Measurement (IMERG) final product’s ability to represent the extreme precipitation against 310 observations from Nepal between 2015 and 2017. Additionally, Method of Object-based Diagnostic Evaluation (MODE) analysis was also performed to analyze IMERG ability to capture actual spatial distribution of the rainfall extremes. Both datasets show the extreme rainfall events are mostly concentrated at southern low land areas of the country. MODE tool further revealed the slight shifting of heavy precipitation location by IMERG product as compared to observation. It is also noted that, as precipitation intensity increases (threshold values of rainfall), the number of extreme events decreases. Moreover, this work provides a systematic quantification of the performance of IMERG gauge calibrated product and its applicability in extreme precipitation over mountainous region.

Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Products over Mainland China

Due to the difficulty involved in obtaining and processing a large amount of data, the spatial distribution of the quality and error structure of satellite precipitation products and the climatic dependence of the error sources have not been studied sufficiently. Eight statistical and detection indicators were used to compare and evaluate the accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) precipitation products in China, including IMERG Early, Late, and Final Run. (1) Based on the correlation coefficient between GPM IMERG precipitation products and measured precipitation, the precipitation detection ability is good in eastern China, whereas the root-mean-square error increases from northwest to southeast. (2) Compared with the Early and Late Run, the accuracy of the detection of a light rain of the IMERG Final Run is higher, but the precipitation is overestimated. With the increase in the precipitation intensity, the detection ability weakens, and the precipitation is underestimated. (3) The Final Run has a higher estimation accuracy regarding light rain in western high-altitude areas, whereas the accuracy of the detection of moderate rain, heavy rain, and rainstorms is higher in eastern coastal low-altitude areas. This phenomenon is related to the performance and detection principles of satellites. The altitude and magnitude of the precipitation affect the detection accuracy of the satellite. This study provides guidance for the application of GPM IMERG precipitation products in hydrological research and water resource management in China.

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

Validation of GPM IMERG extreme precipitation in the Peninsular Malaysia and Philippines by station and radar data

Extreme precipitation is ubiquitous in the Maritime Continent (MC) but poorly predicted numerical weather prediction (NWP) models. NWP evaluation against accurate measures of heavy precipitation is essential to improve their forecasting skill. Here we examine the potential utility of the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrieval for GPM (IMERG) for NWP evaluation of extreme precipitation in the MC. For that purpose, we use radar data in Subang (Malaysia) and station data from the Global Historical Climatology Network (GHCN) in Malaysia and the Philippines. We find that earlier studies may have underestimated IMERG performances in the MC due to large spatial sampling errors of ground precipitation measurements, especially during extreme precipitation conditions. We recommend using the 95th percentile for NWP evaluation of extreme daily and sub-daily precipitation against IMERG. At higher percentiles, the IMERG rainfall rates tend to diverge from ground observation and may therefore be treated with caution.

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

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.

Understanding the Global Three-dimensional Distribution of Precipitation Mean Particle Size with the Global Precipitation Measurement Mission

This study addresses the global distribution of precipitation mean particle size using data from the Global Precipitation Measurement (GPM) mission. The mass-weighted mean diameter, Dm, is a characteristic parameter of the precipitation particle size distribution (PSD), estimated from the GPM Combined Radar-Radiometer Algorithm (CORRA) using data from GPM’s dual-frequency precipitation radar and microwave imager. We examine Dm in individual precipitation systems in different climate regimes and investigate a six-year (2014-2020) global climatology within 70° N/S.The vertical structure of Dm is demonstrated with cases of deep convection, frontal rain and snow, and stratocumulus light rain. The Dm values, detectable by GPM, range from ~0.7 mm in stratocumulus precipitation to >3.5 mm in the ice layers of intense convection. Within the constraint of the 12-dBZ detectability threshold, the smallest annual mean Dm (~ 0.8 mm) are found in the eastern oceans, and the largest values (~ 2 mm) occur above the melting levels in convection over land in summer. The standard deviation of the annual mean is generally < 0.45 mm below 6 km.Climate regimes are characterized with Dm annual/seasonal variations, its convective/stratiform components, and vertical variabilities (2-10 km). The US Central Plains and Argentina are associated with the largest Dm in a deep layer. Tropical Africa has larger Dm and standard deviation than Amazon. Large convective Dm occurs at high latitudes of Eurasia and North America in summer; the southern hemisphere high latitudes have shallower systems with smaller Dm. Oceanic storm tracks in both hemispheres have relatively large Dm, particularly for convective Dm in winter. Relatively small Dm occurs over tropical oceans, including ITCZ, requiring further investigation.

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

As 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.