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.

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

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012020 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012020

Author(s):

Nicolas A Da Silva ◽

Benjamin G M Webber ◽

Adrian J Matthews ◽

Matthew M Feist ◽

Thorwald H M Stein ◽

...

Keyword(s):

Extreme Precipitation ◽

Weather Prediction ◽

Heavy Precipitation ◽

Radar Data ◽

The Philippines ◽

Peninsular Malaysia ◽

Sampling Errors ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract 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.

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Are Storm Characteristics the Same When Viewed Using Merged Surface Radars or a Merged Satellite Product?

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0187.1 ◽

2021 ◽

Vol 22 (1) ◽

pp. 43-62

Author(s):

Hooman Ayat ◽

Jason P. Evans ◽

Steven Sherwood ◽

Ali Behrangi

Keyword(s):

High Resolution ◽

Flash Flood ◽

Saturation Properties ◽

Precipitation Measurement ◽

Object Based ◽

Evaluation Mode ◽

Storm Characteristics ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Droplet Aggregation

AbstractHigh-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane’s lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes.

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How Well Can IMERG Products Capture Typhoon Extreme Precipitation Events over Southern China?

Remote Sensing ◽

10.3390/rs11010070 ◽

2019 ◽

Vol 11 (1) ◽

pp. 70 ◽

Cited By ~ 11

Author(s):

Chaoying Huang ◽

Junjun Hu ◽

Sheng Chen ◽

Asi Zhang ◽

Zhenqing Liang ◽

...

Keyword(s):

Extreme Precipitation ◽

Southern China ◽

Correlation Coefficients ◽

Heavy Precipitation ◽

Spatiotemporal Variability ◽

Bias Analysis ◽

Extreme Precipitation Events ◽

Precipitation Measurement ◽

Global Precipitation ◽

Precipitation Events

This study assesses the performance of the latest version 05B (V5B) Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Early and Final Runs over southern China during six extremely heavy precipitation events brought by six powerful typhoons from 2016 to 2017. Observations from a dense network composed of 2449 rain gauges are used as reference to quantify the performance in terms of spatiotemporal variability, probability distribution of precipitation rates, contingency scores, and bias analysis. The results show that: (1) both IMERG with gauge calibration (IMERG_Cal) and without gauge correction (IMERG_Uncal) generally capture the spatial patterns of storm-accumulated precipitation with moderate to high correlation coefficients (CCs) of 0.57–0.87, and relative bias (RB) varying from −17.21% to 30.58%; (2) IMERG_Uncal and IMERG_Cal capture well the area-average hourly series of precipitation over rainfall centers with high CCs ranging from 0.78 to 0.94; (3) IMERG_Cal tends to underestimate precipitation especially the rainfall over the rainfall centers when compared to IMERG_Uncal. The IMERG Final Run shows promising potentials in typhoon-related extreme precipitation storm applications. This study is expected to give useful feedbacks about the latest V5B Final Run IMERG product to both algorithm developers and the scientific end users, providing a better understanding of how well the V5B IMERG products capture the typhoon extreme precipitation events over southern China.

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Characterising extreme rainfall over mountain regions with a network of tipping bucket rain gauges and GPM satellite data

10.5194/egusphere-egu2020-20371 ◽

2020 ◽

Author(s):

Wouter Buytaert ◽

Jonathan Paul ◽

Boris Ochoa-Tocachi ◽

Keyword(s):

Spline Interpolation ◽

Extreme Rainfall ◽

Data Availability ◽

Mountain Regions ◽

Satellite Mission ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

Rain Gauges ◽

Precipitation Measurement ◽

Global Precipitation

Mountain regions such as the Andes and the Himalayas are a hotspot of natural hazards. Many of them, in particular floods, landslides, and soil degradation, are related to extreme rainfall events. However, characterising rainfall is complicated by the extreme spatiotemporal gradients, and the scarcity of in situ observations. Characterising extreme rainfall events is particularly problematic because most existing rainfall records are only available at a low temporal resolution (daily or coarser). Here, we analyse records of a network of 77 tipping bucket rain gauges located in Ecuador, Peru, Bolivia and Nepal, with a data availability ranging between 1 and 10 years.From the raw data we derive rainfall intensities at 5 and 10 minute intervals using composite cubic spline interpolation and smoothing. We then compare those intensities with instantaneous measurements from the Global Precipitation Measurement (GPM) satellite mission. Although correlations are generally low, it is possible to find significant trends that make it possible to interpolate the observed intensities in space, and to generate rainfall intensity quantile maps for the wider high Andean region.

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

Water ◽

10.3390/w11061139 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1139 ◽

Cited By ~ 5

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.

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Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

Remote Sensing ◽

10.3390/rs13122264 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2264

Author(s):

F. Joseph Turk ◽

Sarah E. Ringerud ◽

Andrea Camplani ◽

Daniele Casella ◽

Randy J. Chase ◽

...

Keyword(s):

Heavy Precipitation ◽

Cold Season ◽

Dual Frequency ◽

Light Rain ◽

Triple Frequency ◽

W Band ◽

Precipitation Measurement ◽

Precipitation Structure ◽

Global Precipitation Measurement ◽

Global Precipitation

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.

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Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Remote Sensing ◽

10.3390/rs13071241 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1241

Author(s):

Peng Li ◽

Zongxue Xu ◽

Chenlei Ye ◽

Meifang Ren ◽

Hao Chen ◽

...

Keyword(s):

Extreme Precipitation ◽

Rain Gauge ◽

Precipitation Measurement ◽

Precipitation Estimation ◽

Daily Scale ◽

Extreme Precipitation Indices ◽

Precipitation Indices ◽

Jinan City ◽

Global Precipitation Measurement ◽

Global Precipitation

In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

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Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

Atmosphere ◽

10.3390/atmos12020254 ◽

2021 ◽

Vol 12 (2) ◽

pp. 254

Author(s):

Bikash Nepal ◽

Dibas Shrestha ◽

Shankar Sharma ◽

Mandira Singh Shrestha ◽

Deepak Aryal ◽

...

Keyword(s):

Extreme Precipitation ◽

Daily Precipitation ◽

Precipitation Amount ◽

Probability Of Detection ◽

Precipitation Extremes ◽

Extreme Precipitation Events ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Precipitation Events

The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.

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Seasonal and Regional Differences in Extreme Rainfall Events and Their Contribution to the World’s Precipitation: GPM Observations

Advances in Meteorology ◽

10.1155/2019/4631609 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 9

Author(s):

Shailendra Kumar ◽

Yamina Silva ◽

Aldo S. Moya-Álvarez ◽

Daniel Martínez-Castro

Keyword(s):

South America ◽

South Asia ◽

Extreme Rainfall ◽

Maximum Height ◽

Gangetic Plain ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

Precipitation Measurement ◽

Subtropical Oceans ◽

Global Precipitation

In the present study, five-year of precipitation features (PFs) datasets, based on Global Precipitation Measurement (GPM), are used to investigate the global and regional characteristics of extreme rainfall events (EREs). The EREs are defined based on the PFs area, depth (maximum height of radar reflectivity), and the rain rate and called them largest, deepest, and intense EREs, respectively. The EREs are divided into top 10%, 1%, 0.1%, and 0.01% based on their frequency of occurrences. It is observed that occurrences of EREs belonging to less than top 0.01% EREs follow the tropical rainfall climatology over the tropics based on all the parameters. Subtropical oceanic areas consist of a higher frequency of largest EREs, whereas tropical land areas consist of the higher number of deepest EREs. The most intense EREs (top 0.01%) are uniformly distributed over tropical areas and subtropical oceans, and spatial distribution shows that a deepest ERE belongs to intense EREs in the tropical land areas. Large differences between the precipitation contribution from the largest and deepest EREs are seen; for example, the top 1% of largest EREs contribute to ∼80.7% of Earth’s precipitation, whereas the corresponding percentage for deepest EREs is only 53%. On the regional and seasonal scale, South Asia (SAsia) and South America (SA) nearly show common features, as oceanic and land areas consist of largest and deepest EREs, respectively, and contribute to higher precipitation. Subtropical latitudes over South America, including Sierra de Cordoba and La Plata basin, consist of deepest and intense EREs and match with those of the Indo-Gangetic plain over South Asia, which also shows the similar characteristics. EREs based on various parameters are strongly linked over SAsia compared to SA. For example, the largest top 10% EREs have a higher probability to be part of the top 10% deepest and intense EREs over SAsia. The seasonal and regional water budget reveals different characteristics, as in the southern hemisphere, the deeper EREs contribute to the higher fraction of rainfall, but over SAsia, the shallower EREs could also contribute to significant rainfall.

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A Global Assessment of Non-Stationarity in Extreme Precipitation

10.5194/egusphere-egu2020-11943 ◽

2020 ◽

Author(s):

Razi Sheikholeslami ◽

Simon Michael Papalexiou ◽

Martyn Clark

Keyword(s):

Extreme Precipitation ◽

Extreme Rainfall ◽

Heavy Precipitation ◽

Global Scale ◽

Flood Frequency ◽

Human Society ◽

Extreme Rainfall Events ◽

The Future ◽

Resilient Infrastructure ◽

Earth System Models

Rapid urban development, along with human modifications in river discharge (both frequency and magnitude) increase the need to design safe and resilient infrastructure. In addition, continental-domain studies show that there are significant changes in the intensity and frequency of the extreme rainfall events. Importantly, Earth System Models predict that these changes will continue to grow in the future. Consequently, flood frequency from heavy precipitation events is expected to increase, thereby threatening human society and the environment. Therefore, the stationary climate assumption &#8212; the idea that the future variability of the system will remain within the limits observed in the past record &#8212; may not be valid and should be carefully examined. Despite the existing awareness of potential non-stationarity, there has been a limited research on analysis of non-stationary of extreme precipitation at the global scale. This motivated us to conduct a comprehensive global study to compare the performance of non-stationary and stationary models in describing precipitation extremes.

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