Comparison of Multisatellite Precipitation Data from the Global Precipitation Measurement Mission and Tropical Rainfall Measurement Mission Datasets: Seasonal and Diurnal Cycles

The Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation product derived from the Global Precipitation Measurement (GPM) constellation offers a unique opportunity of observing the diurnal cycle of precipitation in the latitudinal band 60 ° N–S at unprecedented 0.1 ° × 0.1 ° and half-hour resolution. The diurnal cycles of occurrence, intensity and accumulation are determined using four years of data at 2 ° × 2 ° resolution; this study focusses on summertime months when the diurnal cycle shows stronger features. Harmonics are fitted to the diurnal cycle using a non-linear least squares method weighted by random errors. Results suggest that mean-to-peak amplitudes for the diurnal cycles of occurrence and accumulation are greater over land (generally larger than 25% of the diurnal mean), where the diurnal harmonic dominates and peaks at ~16–24 LST, than over ocean (generally smaller than 25%), where the diurnal and semi-diurnal harmonics contribute comparably. Over ocean, the diurnal harmonic peaks at ~0–10 LST (~8–15 LST) over open waters (coastal waters). For intensity, amplitudes of the diurnal and semi-diurnal harmonics are generally comparable everywhere (~15–35%) with the diurnal harmonic peaking at ~20–4 LST (~3–12 LST) over land (ocean), and the semi-diurnal harmonic maximises at ~5–8 LST and 17–20 LST. The diurnal cycle of accumulation is dictated by occurrence as opposed to intensity.

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Comparison of Oceanic Multisatellite Precipitation Data from Tropical Rainfall Measurement Mission and Global Precipitation Measurement Mission Datasets with Rain Gauge Data from Ocean Buoys

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0152.1 ◽

2019 ◽

Vol 36 (5) ◽

pp. 903-920 ◽

Cited By ~ 2

Author(s):

Qiaoyan Wu ◽

Yilei Wang

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Tropical Pacific ◽

Tropical Rainfall ◽

High Rainfall ◽

Precipitation Measurement ◽

The Tropics ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

The Tropical Pacific

AbstractThree satellite-derived precipitation datasets [the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) dataset, the NOAA Climate Prediction Center morphing technique (CMORPH) dataset, and the newly available Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) dataset] are compared with data obtained from 55 rain gauges mounted on floating buoys in the tropics for the period 1 April 2014–30 April 2017. All three satellite datasets underestimate low rainfall and overestimate high rainfall in the tropical Pacific Ocean, but the TMPA dataset does this the most. In the high-rainfall (higher than 4 mm day−1) Atlantic region, all three satellite datasets overestimate low rainfall and underestimate high rainfall, but the IMERG dataset does this the most. For the Indian Ocean, all three rainfall satellite datasets overestimate rainfall at some gauges and underestimate it at others. Of these three satellite products, IMERG is the most accurate in estimating mean precipitation over the tropical Pacific and Indian Oceans, but it is less accurate over the tropical Atlantic Ocean for regions of high rainfall. The differences between the three satellite datasets vary by region and there is a need to consider uncertainties in the data before using them for research.

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Precipitation Data Key to Food Security and Public Health

Eos ◽

10.1029/2016eo045583 ◽

2016 ◽

Vol 97 ◽

Cited By ~ 5

Author(s):

Dalia Kirschbaum ◽

Kasha Patel

Keyword(s):

Public Health ◽

Food Security ◽

Precipitation Data ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation

2015 Global Precipitation Measurement (GPM) Mission Applications Workshop; Hyattsville, Maryland, 9–10 June 2015

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A Ground Validation Network for the Global Precipitation Measurement Mission

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1403.1 ◽

2011 ◽

Vol 28 (3) ◽

pp. 301-319 ◽

Cited By ~ 64

Author(s):

Mathew R. Schwaller ◽

K. Robert Morris

Keyword(s):

Attenuation Correction ◽

Radar Data ◽

Radar Reflectivity ◽

Retrieval Algorithm ◽

Precipitation Data ◽

Precipitation Measurement ◽

Trmm Pr ◽

Ground Validation ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA’s Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to “matchup” the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR–GR bias was found to increase with the amount of PR attenuation correction applied, with the PR–GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is >6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%–40%.

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The Global Precipitation Measurement (GPM) Mission for Science and Society

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-00306.1 ◽

2017 ◽

Vol 98 (8) ◽

pp. 1679-1695 ◽

Cited By ~ 219

Author(s):

Gail Skofronick-Jackson ◽

Walter A. Petersen ◽

Wesley Berg ◽

Chris Kidd ◽

Erich F. Stocker ◽

...

Keyword(s):

Climate Models ◽

Microwave Radiometer ◽

Three Dimensional ◽

Current Status ◽

Precipitation Data ◽

Dual Frequency ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

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Hydrologic Evaluation of TRMM and GPM IMERG Satellite-based Precipitation in a Humid Basin of China

Remote Sensing ◽

10.3390/rs11040431 ◽

2019 ◽

Vol 11 (4) ◽

pp. 431 ◽

Cited By ~ 15

Author(s):

Zengxin Zhang ◽

Jiaxi Tian ◽

Yuhan Huang ◽

Xi Chen ◽

Sheng Chen ◽

...

Keyword(s):

Hydrological Model ◽

Watershed Scale ◽

Hydrological Models ◽

Tropical Rainfall Measurement Mission ◽

Precipitation Measurement ◽

Precipitation Structure ◽

Precipitation Analysis ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Hydrologic Evaluation

Tropical Rainfall Measurement Mission (TRMM) is one of the most popular global high resolution satellite-based precipitation products with a goal of measuring precipitation over the oceans and tropics. However, in recent years, the TRMM mission has come to an end. Its successor, Global Precipitation Measurement (GPM) mission was launched to measure the earth's precipitation structure, with an aim to improve upon the TRMM project. Both of the precipitation products have their own strengths and weaknesses in resolution, accuracy, and availability. The aim of this study is to evaluate the hydrologic utilization of the TRMM and GPM products in a humid basin of China. The main findings of this study can be summarized as follows: (1) 3B42V7 generally outperforms 3B42V6 in terms of hydrologic performance. Meanwhile, 3B42RTV7 significantly outperforms 3B42RTV6, and showed close performance with the bias-adjusted TRMM Multi-satellite Precipitation Analysis (TMPA) products. (2) The GPM showed better agreement with gauge observation than the TMPA products with lower RB and higher correlation coefficient (CC) values at different time scales. (3) The VIC hydrological model generally outperformed the XAJ hydrological model with lower RB, higher Nash–Sutcliffe Coefficient of Efficiency (NSCE) and CC values; though the 3B42RTV6 and 3B42RTV7 showed higher CC values in simulating the streamflow hydrograph by using the VIC and XAJ hydrological models. It can be found that the conceptual hydrological model was enough for the hydrologic evaluation of TRMM and GPM IMERG satellite-based precipitation in a humid basin of China. This study provides a reference for the comparison of multiple models on watershed scale.

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Evaluation and Correction of GPM IMERG Precipitation Products over the Capital Circle in Northeast China at Multiple Spatiotemporal Scales

Advances in Meteorology ◽

10.1155/2018/4714173 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Wei Sun ◽

Yonghua Sun ◽

Xiaojuan Li ◽

Tao Wang ◽

Yanbing Wang ◽

...

Keyword(s):

Cumulative Distribution ◽

Precipitation Data ◽

Evaluation Indexes ◽

Ground Station ◽

Critical Success ◽

Precipitation Measurement ◽

Long Time ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Better Than

Accurate remote-sensed precipitation data are crucial to the effective monitoring and analysis of floods and climate change. The Global Precipitation Measurement (GPM) satellite product offers new options for the global study of precipitation. This paper evaluates the applicability of GPM IMERG products at different time resolutions in comparison to ground-measured data. Based on precipitation data from 107 meteorological stations in the Beijing-Tianjin-Hebei region, GPM products were analysed at three timescales: half-hourly (GPM-HH), daily (GPM-D), and monthly (GPM-M). We use a cumulative distribution function (CDF) model to correct GPM-D and GPM-M products to analyse temporal and spatial distributions of precipitation. We came to the following conclusions: (1) The GPM-M product is strongly correlated with ground station data. Based on five evaluation indexes, NRMSE (Normalized Root Mean Square Error), NSE (Nash-Sutcliffe), FAR (False Alarm Ratio), UR (Underreporting Rate), and CSI (Critical Success Index), the monthly GPM products showed the best performance, better than GPM-HH products and GPM-D products. (2) The performance of GPM products in summer and autumn was better than in winter and spring. However, the GPM satellite’s precision in undulating terrain was poor, which could easily lead to serious errors. (3) CDF models were successfully used to modify GPM-D and GPM-M products and improve their accuracy. (4) The range of 0–100 mm precipitation could be corrected best, but the GPM-M products were underestimated. Corrected GPM-M data in the range >100 mm were overestimated. According to this analysis, the GPM IMERG Final Run products at daily and monthly timescales have good detection ability and can provide data support for long-time series analyses in the Beijing-Tianjin-Hebei region.

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Evaluation of the precipitation-type dependent uncertainty in rain/no-rain classification using PCT from GPM/GMI data

10.5194/egusphere-egu21-1908 ◽

2021 ◽

Author(s):

Jiseob Kim ◽

Dong-Bin Shin

Keyword(s):

Passive Microwave ◽

Precipitation Data ◽

Precipitation Measurement ◽

The Difference ◽

Microwave Imager ◽

New Perspective ◽

Passive Microwave Radiometry ◽

The Tropics ◽

Global Precipitation Measurement ◽

Global Precipitation

<p>Spaceborne passive microwave sensors have been developed to improve the knowledge of precipitation systems based on channels that interact directly with hydrometeors in clouds. In particular, understanding the global distribution of precipitation is one of the main missions. Prior to these precipitation studies, many researchers tend to implement the rain/no-rain classification (RNC) procedure. As a simple way, the polarized corrected temperature at 89 GHz (PCT89) from passive microwave radiometry has been widely used to identify rain pixels. The PCT89 can estimate the scattering intensity accompanied by precipitating clouds while minimizing the effects of the surface at high resolution, however, the diversity of the hydrometeor distributions can be a problem in the use of a consistent cut-off threshold. Therefore, the purpose of this study is to evaluate differences in the accuracy of the PCT-based RNC method induced by the various hydrometeor distributions and to present a new perspective to users so that it can be used appropriately. Precipitation data observed by the global precipitation measurement (GPM) microwave imager (GMI) for the period from January to December of 2015 in the tropics were used in the study. Based on the classification algorithm of the GPM dual precipitation radar (DPR), the precipitation data were subdivided into 11 types (3 stratiform types, 4 convective types, and others), and then a statistical verification was attempted to ensure that the cut-off threshold was appropriate. The PCT89-based RNC method leads to an increase of 70% and 54% in the number of two significant stratiform types compared to the DPR precipitation flag. On the other hand, the convective types decreased by up to 53%. Although regional diversity could lead to systematic differences in the verification, they did not exceed magnitudes of the difference between precipitation types. Therefore, this study suggests that the precipitations identified by the PCT89-based RNC method have features that enhance the bias toward the stratiform type.</p>

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