scholarly journals Performance Assessment of GSMaP and GPM IMERG Products during Typhoon Mangkhut

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 134
Author(s):  
Xiaoyu Li ◽  
Sheng Chen ◽  
Zhenqing Liang ◽  
Chaoying Huang ◽  
Zhi Li ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Southern China ◽  
Eastern China ◽  
Mainland China ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Critical Success ◽  
Rainfall Occurrence ◽  
Precipitation Measurement ◽  
Global Precipitation

This paper evaluated the latest version 6.0 Global Satellite Mapping of Precipitation (GSMaP) and version 6.0 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products during 2018 Typhoon Mangkhut in China. The reference data is the rain gauge datasets from Gauge-Calibrated Climate Prediction Centre (CPC) Morphing Technique (CMORPHGC). The products for comparison include the GSMaP near-real-time, Microwave-IR merged, and gauge-calibrated (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) and the IMERG Early, Final, and Final gauge-calibrated (IMERG_ERUncal, IMERG_FRUncal, and IMERG_FRCal) products. The results show that (1) both GSMaP_Gauge and IMERG_FRCal considerably reduced the bias of their satellite-only products. GSMaP_Gauge outperforms IMERG_FRCal with higher Correlation Coefficient (CC) values of about 0.85, 0.78, and 0.50; lower Fractional Standard Error (FSE) values of about 18.00, 18.85, and 29.30; and Root-Mean-Squared Error (RMSE) values of about 12.12, 33.35, and 32.99 mm in the rainfall centers over mainland China, southern China, and eastern China, respectively. (2) GSMaP products perform better than IMERG products, with higher Probability of Detection (POD) and Critical Success Index (CSI) and lower False Alarm Ratio (FAR) in detecting rainfall occurrence, especially for high rainfall rates. (3) For area-mean rainfall, IMERG performs worse than GSMaP in the rainfall centers over mainland China and southern China but shows better performance in the rainfall center over eastern China. GSMaP_Gauge and IMERG_FRCal perform well in the three regions with a high CC (0.79 vs. 0.94, 0.81 vs. 0.96, and 0.95 vs. 0.97) and a low RMSE (0.04 vs. 0.06, 0.40 vs. 0.59, and 0.19 vs. 0.34 mm). These useful findings will help algorithm developers and data users to better understand the performance of GSMaP and IMERG products during typhoon precipitation events.

scholarly journals Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

2019 ◽  
Vol 11 (19) ◽  
pp. 2193 ◽  
Author(s):  
Negin Hayatbini ◽  
Bailey Kong ◽  
Kuo-lin Hsu ◽  
Phu Nguyen ◽  
Soroosh Sorooshian ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Probability Of Detection ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Critical Success ◽  
Adversarial Network ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

scholarly journals Multiscale assessment of TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products over a Mediterranean mountainous watershed with sparse rain gauges in the Moroccan High Atlas (case study of Zat basin)

2021 ◽  
Author(s):  
Myriam Benkirane ◽  
Nour-Eddine Laftouhi ◽  
Said Khabba ◽  
Bouabid El Mansouri
Keyword(s):  
Probability Of Detection ◽  
Satellite Precipitation ◽  
Critical Success ◽  
Precipitation Measurement ◽  
Catchment Areas ◽  
Mountainous Watershed ◽  
Short Time ◽  
Global Precipitation ◽  
Trmm 3B42 ◽  
Precipitation Events

Abstract. The performance of Tropical Precipitation Measurement Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), has provided hydrologists with a source of critical precipitation data for hydrological applications in basins where ground-based observations of precipitation are sparse, or spatially undistributed. The very high temporal and spatial resolution satellite precipitation products have therefore become a reliable alternative that researchers are increasingly using in various hydro-meteorological and hydro-climatological applications. This study aims to evaluate statistically and hydrologically the TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitations products (SPPs), at multiple temporal scales from 2010 to 2017, in a mountainous watershed characterized by the Mediterranean climate. The results show that TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products have a significant capacity for detecting precipitation at different time steps. However, the statistical analysis of SPPs against ground observation shows good results for both statistical metrics and contingency statistics with notable values (CC > 0.8), and representative values relatively close to 0 for the probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR). Moreover, the sorting of the events implemented on the hydrological model was performed seasonally, at daily time steps. The calibrated episodes showed very good results with Nash values ranging from 53.2 % to 95.5 %. Nevertheless, the (IMERG V5) product detects more efficiently precipitation events at short time steps (daily), while (3B42 V7) has a strong ability to detect precipitation events at large time steps (monthly and yearly). Furthermore, the modeling results illustrate that both satellite precipitation products tend to underestimate precipitation during wet seasons and overestimate them during dry seasons, while they have a better spatial distribution of precipitation measurements performance, which shows the importance of their use for basin modeling and potentially for flood forecasting in Mediterranean catchment areas.

scholarly journals How Does the Evaluation of the GPM IMERG Rainfall Product Depend on Gauge Density and Rainfall Intensity?

2018 ◽  
Vol 19 (2) ◽  
pp. 339-349 ◽  
Author(s):  
Fuqiang Tian ◽  
Shiyu Hou ◽  
Long Yang ◽  
Hongchang Hu ◽  
Aizhong Hou
Keyword(s):  
Rainfall Intensity ◽  
Mainland China ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Evaluation Metrics ◽  
Retrieval Algorithm ◽  
Rainfall Events ◽  
Ground Validation ◽  
Global Precipitation ◽  
Rain Gauge Network

Abstract This study investigates the dependency of the evaluation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) rainfall product on the gauge density of a ground-based rain gauge network as well as rainfall intensity over five subregions in mainland China. High-density rain gauges (1.5 gauges per 100 km2) provide exceptional resources for ground validation of satellite rainfall estimates over this region. Eight different gauge networks were derived with contrasting gauge densities ranging from 0.04 to 4 gauges per 100 km2. The evaluation focuses on two warm seasons (April–October) during 2014 and 2015. The results show a strong dependency of the evaluation metrics for the IMERG rainfall product on gauge density and rainfall intensity. A dense rain gauge network tends to provide better evaluation metrics, which implies that previous evaluations of the IMERG rainfall product based on a relatively low-density gauge network might have underestimated its performance. The decreasing trends of probability of detection with gauge density indicate a limited ability to capture light rainfall events in the IMERG rainfall product. However, IMERG tends to overestimate (underestimate) light (heavy) rainfall events, which is a consistent feature that does not show strong dependency on gauge densities. The results provide valuable insights for the improvement of a rainfall retrieval algorithm adopted in the IMERG rainfall product.

scholarly journals Evaluation of Gridded Precipitation Datasets in Malaysia

2020 ◽  
Vol 12 (4) ◽  
pp. 613 ◽  
Author(s):  
Afiqah Bahirah Ayoub ◽  
Fredolin Tangang ◽  
Liew Juneng ◽  
Mou Leong Tan ◽  
Jing Xiang Chung
Keyword(s):  
Heavy Rainfall ◽  
Mean Squared Error ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Probability Of Detection ◽  
Intensity Level ◽  
Critical Success ◽  
Extreme Flooding

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

scholarly journals Comprehensive Comparisons of State-of-the-Art Gridded Precipitation Estimates for Hydrological Applications over Southern China

2020 ◽  
Vol 12 (23) ◽  
pp. 3997
Author(s):  
Zhen Gao ◽  
Bensheng Huang ◽  
Ziqiang Ma ◽  
Xiaohong Chen ◽  
Jing Qiu ◽  
...  
Keyword(s):  
Southern China ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
Vital Role ◽  
Probability Of Detection ◽  
Precipitation Estimates ◽  
Accumulated Rainfall ◽  
Daily Scale ◽  
Global Precipitation ◽  
Better Than

Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning.

scholarly journals An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

2021 ◽  
Author(s):  
Eva–Maria Walz ◽  
Marlon Maranan ◽  
Roderick van der Linden ◽  
Andreas H. Fink ◽  
Peter Knippertz
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Predictive Ability ◽  
Window Size ◽  
Predictive Performance ◽  
Ensemble Forecast ◽  
High Mountain ◽  
Rainfall Occurrence ◽  
Precipitation Measurement ◽  
Global Precipitation

AbstractCurrent numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

scholarly journals The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

2020 ◽  
Vol 21 (2) ◽  
pp. 161-182 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Andrés Navarro ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
José Luis Sánchez ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Surface Precipitation ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Level 3 ◽  
Hydrologic Impacts ◽  
A Minor ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

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

2019 ◽  
Vol 11 (1) ◽  
pp. 70 ◽  
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.

scholarly journals Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

2019 ◽  
Vol 11 (21) ◽  
pp. 2463
Author(s):  
Arthur Moraux ◽  
Steven Dewitte ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Deep Learning ◽  
Multiscale Analysis ◽  
Mean Squared Error ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Precipitation Rate ◽  
Rain Gauges ◽  
Squared Error ◽  
Precipitation Estimation ◽  
Deep Learning Model

This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.

scholarly journals Satellite-based high-resolution mapping of rainfall over southern Africa

2017 ◽  
Vol 10 (6) ◽  
pp. 2009-2019 ◽  
Author(s):  
Hanna Meyer ◽  
Johannes Drönner ◽  
Thomas Nauss
Keyword(s):  
Southern Africa ◽  
Ground Truth ◽  
Skill Score ◽  
Probability Of Detection ◽  
Rainfall Events ◽  
Precipitation Measurement ◽  
Hourly Rainfall ◽  
Model Probability ◽  
Spatio Temporal ◽  
Global Precipitation

Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

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