N: 90 S: -90 E: 180 W: -180
Description
Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.
The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.
The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.
The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.
The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.
The IMERG system is run twice in near-real time:
"Early" multi-satellite product ~4 hr after observation time using only forward morphing and
"Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing
and once after the monthly gauge analysis is received:
"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.
In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.
Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing | Safari, Ziauddin, Rahimi, Sayed Tamim, Ahmed, Kamal, Sharafati, Ahmad, Ziarh, Ghaith Falah, Shahid, Shamsuddin, Ismail, Tarmizi, Al-Ansari, Nadhir, Chung, Eun-Sung, Wang, Xiaojun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Aplikasi Metode Nash Pada Perhitungan Limpasan Langsung Menggunakan Data Hujan GPM 3IMERGHH Studi Kasus SubDAS Winongo Hulu | Harsanto, Puji, Prihatmanti, Hanan Eko, Wisnulingga, Bayu Krisna | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A new approach for bias adjustment of IMERG remotely sensed snowfall product | Sadeghi, Leili, Saghafian, Bahram, Moazami, Saber | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model | Wu, Tongwen, Yu, Rucong, Lu, Yixiong, Jie, Weihua, Fang, Yongjie, Zhang, Jie, Zhang, Li, Xin, Xiaoge, Li, Laurent, Wang, Zaizhi, Liu, Yiming, Zhang, Fang, Wu, Fanghua, Chu, Min, Li, Jianglong, Li, Weiping, Zhang, Yanwu, Shi, Xueli, Zhou, Wenyan, Yao, Junchen, Liu, Xiangwen, Zhao, He, Yan, Jinghui, Wei, Min, Xue, Wei, Huang, Anning, Zhang, Yaocun, Zhang, Yu, Shu, Qi, Hu, Aixue | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Bias Correction Method Based on Artificial Neural Networks for Quantitative Precipitation Forecast | Fuentes-Barrios, Adrian, Sierra-Lorenzo, Maibys | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comparative analysis of TMPA and IMERG precipitation datasets in the arid environment of El-Qaa plain, Sinai | Morsy, Mona, Scholten, Thomas, Michaelides, Silas, Borg, Erik, Sherief, Youssef, Dietrich, Peter | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of the assimilation of conventional and satellite-based observations in simulating heavy rainfall event using WRFDA over the North-West Himalayan region | Budakoti, Sachin, Singh, Charu, Pal, P.K., Navale, Ashish | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The semi-diurnal cycle of deep convective systems over Eastern China and its surrounding seas in summer based on an automatic tracking algorithm | Li, Wenwen, Zhang, Feng, Yu, Yueyue, Iwabuchi, Hironobu, Shen, Zhongping, Wang, Guoyin, Zhang, Yijun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of satellite estimated convective rainfall products : A case study for the summer cyclone season of 2020 | SATEESH, M., KHADKE, CHINMAY, PRASAD, V. S., GOYAL, SUMAN | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of satellite-based precipitation products from TRMM to GPM | Wang, Jianxin, Petersen, Walter A., Wolff, David B. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Cross-examination of similarity, difference and deficiency of gauge, radar and satellite precipitation measuring uncertainties for extreme events using conventional ... | Li, Zhi, Chen, Mengye, Gao, Shang, Hong, Zhen, Tang, Guoqiang, Wen, Yixin, Gourley, Jonathan J., Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Contribution of Tropical Cyclones to Precipitation around Reclaimed | Yao, Dongxu, Song, Xianfang, Yang, Lihu, Ma, Ying | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data | Kumar, Ashutosh, Islam, Tanvir, Sekimoto, Yoshihide, Mattmann, Chris, Wilson, Brian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of a Customized Variable-Resolution Global Model and its | Lui, Yuk Sing, Tam, ChiYung, Tse, Louis KwanShu, Ng, KaKi, Leung, WaiNang, Cheung, Chi Chiu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference | Mandapaka, Pradeep V., Lo, Edmond Y. M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying and characterizing tropical oceanic mesoscale cold pools using spaceborne scatterometer winds | Garg, Piyush, Nesbitt, Stephen W., Lang, Timothy J., Priftis, George, Chronis, Themis, Thayer, Jeffrey D., Hence, Deanna A. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Increased Likelihood of Appreciable Afternoon Rainfall Over Wetter or | Welty, Josh, Stillman, Susan, Zeng, Xubin, Santanello, Joseph | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| GPM-Based Multitemporal Weighted Precipitation Analysis Using | Ullah, Sana, Zuo, Zhengkang, Zhang, Feizhou, Zheng, Jianghua, Huang, Shifeng, Lin, Yi, Iqbal, Imran, Sun, Yiyuan, Yang, Ming, Yan, Lei | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Development of aerosol activation in the double-moment Unified Model and | Gordon, Hamish, Field, Paul R., Abel, Steven J., Barrett, Paul, Bower, Keith, Crawford, Ian, Cui, Zhiqiang, Grosvenor, Daniel P., Hill, Adrian A., Taylor, Jonathan, Wilkinson, Jonathan, Wu, Huihui, Carslaw, Ken S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Enabling smart dynamical downscaling of extreme precipitation events with machine learning | Shi, Xiaoming | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Case study of a convective cluster over the rain shadow region of Western Ghats using multi-platform observations and WRF model | Samanta, Soumya, Gayatri, Kulkarni, Murugavel, P., Balaji, B., Malap, N., Jaya Rao, Y., Deshpande, S. M., Sonbawne, S. M., Suneetha, P., Prabha, Thara V. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| AIMERG: a new Asian precipitation dataset (0.1/half-hourly, 20002015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE | Ma, Ziqiang, Xu, Jintao, Zhu, Siyu, Yang, Jun, Tang, Guoqiang, Yang, Yuanjian, Shi, Zhou, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China | Ma, Ziqiang, Xu, Jintao, He, Kang, Han, Xiuzhen, Ji, Qingwen, Wang, TseChun, Xiong, Wentao, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A spatial downscaling method for SMAP soil moisture through visible and | Hu, Fengmin, Wei, Zushuai, Zhang, Wen, Dorjee, Donyu, Meng, Lingkui | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, RADAR IMAGERY, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Reflectance, Land Surface Temperature, Emissivity | |
| Accuracy analysis of IMERG and CMORPH precipitation data over North China | Shen, L, Lin, R, Lu, L, Xu, C, Liu, Y | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate |