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 Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

2021 ◽  
Author(s):  
Jaroslav Pastorek ◽  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Intensity ◽  
Rain Gauge ◽  
Model Parameters ◽  
Rainfall Events ◽  
Precipitation Estimates ◽  
Attenuation Models ◽  
Using Data ◽  
Quantitative Precipitation Estimates ◽  
Rain Gauge Network ◽  
Commercial Microwave Links

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

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 Improving an Extreme Rainfall Detection System with GPM IMERG data

2019 ◽  
Vol 11 (6) ◽  
pp. 677 ◽  
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Simone Balbo ◽  
Piero Boccardo ◽  
Franca Disabato
Keyword(s):  
Detection System ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Mean Annual Precipitation ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Time Aggregation ◽  
Spatial Coverage ◽  
Rainfall Detection

Many studies have shown a growing trend in terms of frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a key point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG products proved to be the most appropriate source of information for precipitation retrievement by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall events for varying time aggregation intervals. This is performed by comparing the IMERG data with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h aggregation interval ensures a probability of detection (defined as the number of hits divided by the total number of observed events) greater than 80%. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall events using a threshold methodology based on the mean annual precipitation.

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.

scholarly journals Estimation of convective precipitation: the meteorological radar versus an automatic rain gauge network

2005 ◽  
Vol 2 ◽  
pp. 103-109 ◽  
Author(s):  
M. C. Llasat ◽  
T. Rigo ◽  
M. Ceperuelo ◽  
A. Barrera
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Space Distribution ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Convective Rainfall ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Meteorological Radar ◽  
Rain Gauge Network

Abstract. The estimation of convective precipitation and its contribution to total precipitation is an important issue both in hydrometeorology and radio links. The greatest part of this kind of precipitation is related with high intensity values that can produce floods and/or damage and disturb radio propagation. This contribution proposes two approaches for the estimation of convective precipitation, using the β parameter that is related with the greater or lesser convective character of the precipitation event, and its time and space distribution throughout the entire series of the samples. The first approach was applied to 126 rain gauges of the Automatic System of Hydrologic Information of the Internal Basins of Catalonia (NE Spain). Data are series of 5-min rain rate, for the period 1996-2002, and a long series of 1-min rain rate starting in 1927. Rainfall events were classified according to this parameter. The second approach involved using information obtained by the meteorological radar located near Barcelona. A modified version of the SCIT method for the 3-D analysis and a combination of different methods for the 2-D analysis were applied. Convective rainfall charts and β charts were reported. Results obtained by the rain gauge network and by the radar were compared. The application of the β parameter to improve the rainfall regionalisation was demonstrated.

Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China

2009 ◽  
Vol 48 (11) ◽  
pp. 2227-2241 ◽  
Author(s):  
Zifeng Yu ◽  
Hui Yu ◽  
Peiyan Chen ◽  
Chuanhai Qian ◽  
Caijun Yue
Keyword(s):  
Heavy Rainfall ◽  
Rain Rate ◽  
Mainland China ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Horizontal Distribution ◽  
Rainfall Events ◽  
Light Rain ◽  
Geostationary Meteorological Satellite ◽  
Heavy Rainfall Events

Abstract To evaluate the abilities of satellite retrievals in reflecting precipitation features related to tropical cyclones (TCs) affecting mainland China, four years of 6- and 24-h precipitation retrievals from three datasets, namely the Tropical Rainfall Measuring Mission satellite algorithm 3B42, version 6 (3B42), Climate Prediction Center morphed (CMORPH) product, and one based on the Geostationary Meteorological Satellite-5 infrared brightness temperature (GMS5-TBB), are compared statistically with direct measurements from surface gauge rainfall data during the periods affected by TCs. The GMS5-TBB dataset was set up by a method of considering the GMS5-TBB characteristics, hourly precipitation intensity, and horizontal distribution for landfalling TCs. The results show that in a general sense, all three satellite-retrieved rainfall datasets give quite reasonable 6- and 24-h rainfall distributions, with skill decreasing with the increase in both latitude and rainfall amount. The 3B42 has a little bit better skill than CMORPH, which is likely related to the fact that the 3B42 product has a rain gauge adjustment and CMORPH does not. Further analyses show that both 3B42 and CMORPH considerably underestimate the moderate and heavy rainfall and overestimate the very light precipitation. The overestimation of the GMS5-TBB data for the light rain is larger than that for 3B42 and CMORPH, probably due to the fact that the GMS5-TBB method considers stratiform and convective rainfall separately with a fixed stratiform rain rate of 2 mm h−1. For the heavy rainfall events, the GMS5-TBB data perform much better than the 3B42 and CMORPH data with an almost halved bias, owing to the fact that the GMS5-TBB method adopted the adjustment of the convective rain rate by considering TBB characteristics of landfalling TCs and using hourly gauge rainfall in the setup process. Since the heavy rainfall events associated with landfalling TCs are of the most concern, the compared GMS5-TBB data could be useful as an operational/research reference.

Assessment of »True« Variability of Small Rainfall Events in a Small Mountainous Watershed in the Summer

1982 ◽  
Vol 13 (4) ◽  
pp. 205-212 ◽  
Author(s):  
Lawrence C. Nkemdirim ◽  
Brian D. Meller
Keyword(s):  
Spatial Distribution ◽  
Standard Error ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Rainfall Events ◽  
Mountainous Watershed ◽  
Rainfall Volume ◽  
Areal Rainfall ◽  
The Relationship ◽  
Rain Gauge Network

The standard error of mean areal rainfall was calculated for various densities of rain gauge network in a small mountainous watershed in the summer of 1978. It is shown that a) the optimum gauge density required to assess mean rainfall is about 3 gauges/km2; b) the »true« variability in the spatial distribution of rainfall decreases with increasing rainfall amount; and c) the relationship between »true« variability and rainfall volume is linear in that watershed.

scholarly journals Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula

2014 ◽  
Vol 31 (9) ◽  
pp. 1902-1921 ◽  
Author(s):  
Ji-Hye Kim ◽  
Mi-Lim Ou ◽  
Jun-Dong Park ◽  
Kenneth R. Morris ◽  
Mathew R. Schwaller ◽  
...  
Keyword(s):  
Korean Peninsula ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
International Partnerships ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Since 2009, the Korea Meteorological Administration (KMA) has participated in ground validation (GV) projects through international partnerships within the framework of the Global Precipitation Measurement (GPM) Mission. The goal of this work is to assess the reliability of ground-based measurements in the Korean Peninsula as a means for validating precipitation products retrieved from satellite microwave sensors, with an emphasis on East Asian precipitation. KMA has a well-developed operational weather service infrastructure composed of meteorological radars, a dense rain gauge network, and automated weather stations. Measurements from these systems, including data from four ground-based radars (GRs), were combined with satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and used as a proxy for GPM GV over the Korean Peninsula. A time series of mean reflectivity differences (GR − PR) for stratiform-only and above-brightband-only data showed that the time-averaged difference fell between −2.0 and +1.0 dBZ for the four GRs used in this study. Site-specific adjustments for these relative mean biases were applied to GR reflectivities, and detailed statistical comparisons of reflectivity and rain rate between PR and bias-adjusted GR were carried out. In rain-rate comparisons, surface rain from the TRMM Microwave Imager (TMI) and the rain gauges were added and the results varied according to rain type. Bias correction has had a positive effect on GR rain rate comparing with PR and gauge rain rates. This study confirmed advance preparation for GPM GV system was optimized on the Korean Peninsula using the official framework.

scholarly journals Comparison of TMPA-3B42RT Legacy Product and the Equivalent IMERG Products over Mainland China

2018 ◽  
Vol 10 (11) ◽  
pp. 1778 ◽  
Author(s):  
Lei Wu ◽  
Youpeng Xu ◽  
Siyuan Wang
Keyword(s):  
Rainfall Intensity ◽  
Mean Absolute Error ◽  
Detection Threshold ◽  
Mainland China ◽  
Absolute Error ◽  
Rain Gauge ◽  
Dry Season ◽  
False Alarms ◽  
Light Rain ◽  
Dry Areas

The near-real-time legacy product of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (3B42RT) and the equivalent products of Integrated Multi-satellite Retrievals for Global Precipitation Measurement mission (IMERG-E and IMERG-L) were evaluated and compared over Mainland China from 1 January 2015 to 31 December 2016 at the daily timescale, against rain gauge measurements. Results show that: (1) Both 3B42RT and IMERG products overestimate light rain (0.1–9.9 mm/day), while underestimate moderate rain (10.0–24.9 mm/day) to heavy rainstorm (≥250.0 mm/day), with an increase in mean (absolute) error and a decrease in relative mean absolute error (RMAE). The IMERG products perform better in estimating light rain to heavy rain (25.0–49.9 mm/day), and heavy rainstorm, while 3B42RT has smaller error magnitude in estimating light rainstorm (50.0–99.9 mm/day) and moderate rainstorm (100.0–249.9 mm/day). (2) Higher rainfall intensity associates with better detection. Threshold values are <2.0 mm/day, below which 3B42RT is unreliable at detecting rain; and <1.0 mm/day, below which both 3B42RT and IMERG products are more likely to cause false alarms. (3) Generally, both 3B42RT and IMERG products perform better in wet areas with relatively heavy rainfall intensity and/or during wet season than in dry areas with relatively light rainfall intensity and/or during dry season. Compared with 3B42RT, IMERG-E and IMERG-L constantly improve performance in space and time, but it is not obvious in dry areas and/or during dry season. The agreement between IMERG products and rain gauge measurements is low and even negative for different rainfall intensities, and the RMAE is still at a high level (>50%), indicating the IMERG products remain to be improved. This study will shed light on research and application during the transition in multi-satellite rainfall products from TMPA to IMERG and future algorithms improvement.

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