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

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3381
Author(s):  
Linjiang Nan ◽  
Mingxiang Yang ◽  
Hao Wang ◽  
Zhenglin Xiang ◽  
Shaokui Hao
Keyword(s):  
Water Resource Management ◽  
Eastern China ◽  
Heavy Rain ◽  
Estimation Accuracy ◽  
Detection Accuracy ◽  
Detection Ability ◽  
Light Rain ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation

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.

Preliminary Assessment of GPM Satellite Rainfall over Myanmar

2018 ◽  
Vol 13 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Muhammad Mohsan ◽  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Heavy Rain ◽  
Probability Of Detection ◽  
Light Rain ◽  
Precipitation Measurement ◽  
Observation Network ◽  
Satellite Rainfall ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Statistical Metrics

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

Microphysical features indicated by GPM DPR product “flagHeavyIcePrecip” – Case studies on lightning activity

2020 ◽  
Author(s):  
Kenji Suzuki ◽  
Rimpei Kamamoto ◽  
Tetsuya Kawano ◽  
Katsuhiro Nakagawa ◽  
Yuki Kaneko
Keyword(s):  
Heavy Rain ◽  
Lightning Activity ◽  
Dual Frequency ◽  
Convective Clouds ◽  
Precipitation Measurement ◽  
X Band ◽  
Precipitation Type ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>Two products from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) algorithms, a flag of intense solid precipitation above the –10°C height (“flagHeavyIcePrecip”), and a classification of precipitation type (“typePrecip”) were validated quantitatively from the viewpoint of microphysics using ground-based in-situ hydrometeor measurements and X-band multi-parameter (X-MP) radar observations of snow clouds that occurred on 4 February 2018. The distribution of the “flagHeavyIcePrecip” footprints was in good agreement with that of the graupel-dominant pixels classified by the X-MP radar hydrometeor classification. In addition, the vertical profiles of X-MP radar reflectivity exhibited significant differences between footprints flagged and unflagged by “flagHeavyPrecip”. We confirmed the effectiveness of “flagHeavyIcePrecip”, which is built into “typePrecip” algorithm, for detecting intense ice precipitation and concluded that "flagHeavyIcePrecip" is appropriate to useful for determining convective clouds.</p><p>It is well known that the lightning activity is closely related to the convection. We examined the lightning activity using GPM DPR product flagHeavyIcePrecip that indicates the existence of graupel in the upper cloud. On 20 June 2016, we experienced heavy rain with active lightning during Baiu monsoon rainy season, while the GPM DPR passed over Kyushu region in Japan. The distribution of “flagHeavyIcePrecip” obtained from the GPM DPR well corresponded to the CG/IC lightning concentration. On 4 September 2019, isolated thunder clouds observed by the GPM DPR was also similar to the “flagHeavyIcePrecip” distribution. However, partially there was IC lightning without “flagHeavyIcePrecip”, which was positive lightning. It was suggested to have been produced in the upper clouds in which positive ice crystals were dominant.</p>

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

2021 ◽  
pp. 1-62
Author(s):  
Mei Han ◽  
Scott A. Braun
Keyword(s):  
Particle Size ◽  
Standard Deviation ◽  
High Latitudes ◽  
Dual Frequency ◽  
Light Rain ◽  
Mean Particle Size ◽  
Precipitation Measurement ◽  
Using Data ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractThis 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.

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

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.

scholarly journals A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

2020 ◽  
Vol 24 (5) ◽  
pp. 2687-2710 ◽  
Author(s):  
Christian Massari ◽  
Luca Brocca ◽  
Thierry Pellarin ◽  
Gab Abramowitz ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Short Latency ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

scholarly journals IDENTIFIKASI PENGARUH MJO FASE 3 TERHADAP CURAH HUJAN DI PULAU SUMATERA DAN JAWA (STUDI KASUS 14 – 17 OKTOBER 2018)

2019 ◽  
Vol 3 ◽  
pp. 228
Author(s):  
Rahpeni Fajarianti ◽  
Deffi Munadiyat Putri ◽  
Paulus Agus Winarso
Keyword(s):  
Outgoing Longwave Radiation ◽  
Longwave Radiation ◽  
Heavy Rain ◽  
Sea Surface ◽  
Madden Julian Oscillation ◽  
Phase 3 ◽  
Convective Clouds ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p class="AbstractEnglish"><strong>Abstract:</strong>. Madden Julian Oscillation (MJO) is a wave in tropical atmosphere that moving eastward from Indian ocean to Pacific Ocean for a period 30 – 60 days. There are many research that explain when MJO is active in phases 2, 3 and 4 it affects convective activities in the Indonesian Maritime Continent. The purpose of this study is to determine the effect of MJO in phase 3 on temporal rainfall intensity in Sumatra and Java island on 14 – 17 October 2018. This study uses the descriptive analysis method using parameter such as Outgoing Longwave Radiation (OLR) and Phase MJO diagram from Bureau of Meteorology (BOM), Sea Surface Temperature (SST) and vertical velocity data from the National Oceanic and Atmospheric Administration (NOAA) and also raw data of HCAI Himawari-8 satellite to monitor cloud formation on Sumatra and Java island and Global Precipitation Measurement (GPM) data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) to determine its rainfall distribution on 14 – 17 October 2018. The active MJO in phase 3 causing an increase in convective activity on the Sumatra. The SST value of 29.5<sup>0</sup> – 30<sup>0</sup> Celcius supports the occurrence of sufficient evaporation to produce convective clouds with a vertical velocity of less than -0.12 Pa/s (strong updraft) so as to form Cumulonimbus clouds which cause heavy rain intensity which can cause floods. However, in Java Island the influence of MJO was less significant due to the influence of relatively lower sea surface temperatures in the south of Java island so that it is not strong enough to form convective clouds that produce heavy rain.</p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Madden Julian Oscillation (MJO) merupakan gelombang di kawasan tropis yang menjalar dari Barat (Samudera Hindia) ke timur (Samudera Pasifik) dengan periode 30 – 60 hari. Banyak penelitian menjelaskan bahwa pada saat MJO aktif pada fase 2, 3 dan 4 berpengaruh terhadap giatnya aktivitas konvektif di Benua Maritim Indonesia. Penelitian ini bertujuan untuk mengetahui pengaruh MJO di fase 3 terhadap intensitas curah hujan secara temporal di wilayah Pulau Sumatra dan Pulau Jawa pada 14 – 17 Oktober 2018. Penelitian ini menggunakan metode analisis deskriptif dengan parameter antara lain : <em>Outgoing Longwave Radiation</em> (OLR) dan diagram fase MJO yang diambil dari <em>Bureau of Meteorology</em> (BOM), <em>Sea Surface Temperature </em>(SST)<em> </em>dan<em> </em>kecepatan vertikal yang diambil dari <em>National Oceanic and Atmospheric Administration</em> (NOAA) serta <em>raw</em> data HCAI satelit Himawari-8 untuk memonitoring pembentukan awan di Pulau Sumatera dan Jawa dan data <em>Global Precipitation Measurement</em> (GPM) yang didapatkan dari Badan Meteorologi, Klimatologi dan Geofisika (BMKG) untuk mengetahui distribusi curah hujannya pada 14 – 17 Oktober 2018. Aktifnya MJO pada fase 3 menyebabkan peningkatan aktivitas konvektif di Pulau Sumatera. Nilai SST sebesar 29.5<sup>0</sup> – 30<sup>0</sup> Celcius mendukung terjadinya penguapan yang cukup untuk menghasilkan awan konvektif dengan kecepatan vertikal kurang dari -0.12 Pa/s (<em>updraft</em> kuat) sehingga membentuk awan Cumulonimbus yang menyebabkan intensitas hujan lebat yang mampu menimbulkan bencana banjir. Sedangkan di Pulau Jawa pengaruh MJO kurang signifikan akibat pengaruh suhu permukaan laut di selatan Jawa yang relatif lebih rendah sehingga tidak cukup kuat untuk membentuk awan konvektif yang menghasilkan hujan lebat.</p>

PERBANDINGAN SEBARAN CURAH HUJAN HASIL OBSERVASI SATELIT GPM DENGAN MODEL CUACA WRF (Studi Kasus: Siklon Tropis Cempaka Dan Dahlia, 27 Nov – 3 Des 2017)

2019 ◽  
Vol 3 ◽  
pp. 1063
Author(s):  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Weather Research And Forecasting ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Japan Aerospace Exploration Agency ◽  
Weather Research

Satelit GPM (Global Precipitation Measurement) merupakan proyek kerjasama antara NASA (National Aeronautics and Space Administration) dan JAXA (Japan Aerospace Exploration Agency) serta lembaga internasional lainnya untuk membuat satelit generasi terbaru dalam rangka pengamatan curah hujan di bumi sejak 2014. Model Cuaca WRF (Weather Research and Forecasting) merupakan model cuaca numerik yang telah dipakai oleh BMKG (Badan Meteorologi Klimatologi dan Geofisika) untuk pelayan prediksi cuaca harian kepada masyarakat. Pada tanggal 27 November – 3 Desember 2017 telah terjadi bencana alam siklon tropis Cempaka dan Dahlia di samudra Hindia sebelah selatan pulau Jawa. Tujuan Penelitian ialah untuk mengetahui sebaran akumulasi curah hujan antara observasi satelit GPM dan model cuaca WRF, serta keakuratan model WRF terhadap observasi satelit GPM saat terjadinya bencana alam tersebut. Metode yang dipakai ialah dengan melakukan analisa meteorologi pertumbuhan terjadinya siklon tropis tersebut hingga terjadinya hujan sangat lebat secara temporal maupun spasial. Dari hasil analisa disimpulkan bahwa satelit GPM memiliki luasan sebaran curah hujan yang lebih kecil daripada sebaran hujan model cuaca WRF pada saat siklon tropis Cempaka dan Dahlia. Bias akumulasi sebaran hujan model cuaca WRF juga cukup bagus terhadap satelit GPM sehingga dapat dilakukan antisipasi dampak hujan lebat yang terjadi.

scholarly journals Validation of Satellite-Based Precipitation Products from TRMM to GPM

2021 ◽  
Vol 13 (9) ◽  
pp. 1745
Author(s):  
Jianxin Wang ◽  
Walter A. Petersen ◽  
David B. Wolff
Keyword(s):  
Regional Differences ◽  
Tropical Rainfall Measuring Mission ◽  
Decomposition Approach ◽  
Precipitation Product ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Error Decomposition ◽  
Improved Accuracy ◽  
Global Precipitation ◽  
Statistical Metrics

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

Sign in / Sign up

Export Citation Format

Share Document