scholarly journals Evaluation of CMPA precipitation estimate in the evolution of typhoon-related storm rainfall in Guangdong, China

2016 ◽  
Vol 18 (6) ◽  
pp. 1055-1068 ◽  
Author(s):  
Dashan Wang ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dagang Wang ◽  
Huabing Huang ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Spatiotemporal Evolution ◽  
Light Rain ◽  
Storm Rainfall ◽  
Climate Prediction Center ◽  
Rain Gauge Network

The merged precipitation data of Climate Prediction Center Morphing Technique and gauge observations (CMPA) generated for continental China has relatively high spatial and temporal resolution (hourly and 0.1°), while few studies have applied it to investigate the typhoon-related extreme rainfall. This study evaluates the CMPA estimate in quantifying the typhoon-related extreme rainfall using a dense rain gauge network in Guangdong Province, China. The results show that the event-total precipitation from CMPA is generally in agreement with gauges by relative bias (RB) of 2.62, 10.74 and 0.63% and correlation coefficients (CCs) of 0.76, 0.86 and 0.91 for typhoon Utor, Usagi and Linfa events, respectively. At the hourly scale, CMPA underestimates the occurrence of light rain (<1 mm/h) and heavy rain (>16 mm/h), while overestimates the occurrence of moderate rain. CMPA shows high probability of detection (POD = 0.93), relatively large false alarm ratio (FAR = 0.22) and small missing ratio (0.07). CMPA captures the spatial patterns of typhoon-related rain depth, and is in agreement with the spatiotemporal evolution of hourly gauge observations by CC from 0.93 to 0.99. In addition, cautiousness should be taken when applying it in hydrologic modeling for flooding forecasting since CMPA underestimates heavy rain (>16 mm/h).

Heavy precipitating events in satellites and rain-gauge products over the Sahel

2021 ◽  
Author(s):  
Sidiki Sanogo ◽  
Philippe Peyrillé ◽  
Romain Roehrig ◽  
Françoise Guichard ◽  
Ousmane Ouedraogo
Keyword(s):  
Burkina Faso ◽  
West African Monsoon ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Extreme Rainfall Events ◽  
Microwave Sensor ◽  
Sahel Region ◽  
Distribution Intensity ◽  
Rain Gauge Network

<p>The Sahel has experienced an increase in the frequency and intensity of extreme rainfall events over the recent decades. These trends are expected to continue in the future. However the properties of these events have so far received little attention. In the present study, we define a heavy precipitating event (HPE) as the occurrence of daily-mean precipitation exceeding a given percentile (e.g., 99<sup>th</sup> and higher) over a 1°x1° pixel and examine their spatial distribution, intensity, seasonality and interannual variability. We take advantage of an original reference dataset based on a rather high-density rain-gauge network over Burkina Faso (142 stations) to evaluate 22 precipitation gridded datasets often used in the literature, based on rain-gauge-only measurements, satellite measurements, or both. Our reference dataset documents the HPEs over Burkina Faso. The 99<sup>th</sup> percentile identifies events greater than 26 mm d<sup>-1</sup> with a ~2.5 mm confidence interval depending on the number of stations within a 1°x1° pixel. The HPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. The evaluation of the gridded rainfall products reveals that only two of the datasets, namely the rain-gauge-only based products GPCC-DDv1 and REGENv1, are able to properly reproduce all of the HPE features examined in the present work. A subset of the remaining rainfall products also provide satisfying skills over Burkina Faso, but generally only for a few HPE features examined here. In particular, we notice a general better performance for rainfall products that include rain-gauge data in the calibration process, while estimates using microwave sensor measurements are prone to overestimate the HPE intensity. The agreement among the 22 datasets is also assessed over the entire Sahel region. While the meridional gradient in HPE properties is well captured by the good performance subset, the zonal direction exhibit larger inter-products spread. This advocates for the need to continue similar evaluation with the available rain-gauge network available in West Africa, both to enhance the HPE documentation and understanding at the scale of the region and to help improve the rainfall dataset quality.</p>

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 Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

2012 ◽  
Vol 13 (6) ◽  
pp. 1784-1798 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Tamiru Haile ◽  
Yudong Tian ◽  
Robert J. Joyce
Keyword(s):  
Rain Gauge ◽  
Random Errors ◽  
Event Scale ◽  
Entire Study ◽  
Rain Gauges ◽  
Fine Resolution ◽  
Rain Rates ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Insight Into

Abstract This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates >2 mm h−1 and probability of false detection <3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.

Gridded Hourly Precipitation Analysis from High-Density Rain Gauge Network over the Yangtze–Huai Rivers Basin during the 2007 Mei-Yu Season and Comparison with CMORPH

2013 ◽  
Vol 14 (4) ◽  
pp. 1243-1258 ◽  
Author(s):  
Yali Luo ◽  
Weimiao Qian ◽  
Renhe Zhang ◽  
Da-Lin Zhang
Keyword(s):  
Heavy Rainfall ◽  
Rain Gauge ◽  
Rainfall Analysis ◽  
Hourly Precipitation ◽  
Spatial Consistency ◽  
Hourly Rainfall ◽  
Reflectivity Data ◽  
Climate Prediction Center ◽  
Rain Gauge Network

Abstract Heavy rainfall hit the Yangtze–Huai Rivers basin (YHRB) of east China several times during the prolonged 2007 mei-yu season, causing the worst flood since 1954. There has been an urgent need for attaining and processing high-quality, kilometer-scale, hourly rainfall data in order to understand the mei-yu precipitation processes, especially at the mesoβ and smaller scales. In this paper, the authors describe the construction of the 0.07°-resolution gridded hourly rainfall analysis over the YHRB region during the 2007 mei-yu season that is based on surface reports at 555 national and 6572 regional automated weather stations with an average resolution of about 7 km. The gridded hourly analysis is obtained using a modified Cressman-type objective analysis after applying strict quality control, including not only the commonly used internal temporal and spatial consistency and extreme value checks, but also verifications against mosaic radar reflectivity data. This analysis reveals many convectively generated finescale precipitation structures that could not be seen from the national station reports. A comprehensive quantitative assessment ensures the quality of the gridded hourly precipitation data. A comparison of this dataset with the U.S. Climate Prediction Center morphing technique (CMORPH) dataset on the same resolution suggests the dependence of the latter's performance on different rainfall intensity categories, with substantial underestimation of the magnitude and width of the mei-yu rainband as well as the nocturnal and morning peak rainfall amounts, due mainly to its underestimating the occurrences of heavy rainfall (i.e., >10 mm h−1).

scholarly journals Self-Nowcast Model of extreme precipitation events for operational meteorology

2015 ◽  
Vol 8 (10) ◽  
pp. 10635-10661
Author(s):  
G. B. França ◽  
M. V. de Almeida ◽  
A. C. Rosette
Keyword(s):  
Neural Network ◽  
Heavy Rain ◽  
Local Area ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Warning Signs ◽  
Short Term ◽  
Validation Data ◽  
Extreme Precipitation Events ◽  
The Impact

Abstract. Nowadays many social activities require short-term (one to two hours) and local area forecasts of extreme weather. In particular, air traffic systems have been studying how to minimize the impact of meteorological events, such as turbulence, wind shear, ice, and heavy rain, which are related to the presence of convective systems during all flight phases. This paper presents an alternative self-nowcast model, based on neural network techniques, to produce short-term and local-specific forecasts of extreme meteorological events in the area of the landing and take-off region of Galeão, the principal airport in Rio de Janeiro, Brazil. Twelve years of data were used for neural network training and validation. Data are originally from four sources: (1) hourly meteorological observations from surface meteorological stations at five airports distributed around the study area, (2) atmospheric profiles collected twice a day at the meteorological station at Galeão Airport, (3) rain rate data collected from a network of twenty-nine rain gauges in the study area; and (4) lightning data regularly collected by national detection networks. An investigation was done about the capability of a neural network to produce early warning signs – or as a nowcasting tool – for extreme meteorological events. The self-nowcast model was validated using results from six categorical statistics, indicated in parentheses for forecasts of the first, second, and third hours, respectively, namely: proportion correct (0.98, 0.96, and 0.94), bias (1.37, 1.48, and 1.83), probability of detection (0.84, 0.80, and 0.76), false-alarm ratio (0.38, 0.46, and 0.58), and threat score (0.54, 0.47, and 0.37). Possible sources of error related to the validation procedure are discussed. Two key points have been identified in which there is a possibility of error: i.e., subjectivity on the part of the meteorologist making the observation, and a rain gauge measurement error of about 20 % depending on wind speed. The latter was better demonstrated when lightning data were included in the validation. The validation showed that the proposed model's performance was quite encouraging for the first and second hours.

scholarly journals On CMORPH Rainfall for Streamflow Simulation in a Small, Hortonian Watershed

2011 ◽  
Vol 12 (3) ◽  
pp. 456-466 ◽  
Author(s):  
Dawit A. Zeweldi ◽  
Mekonnen Gebremichael ◽  
Charles W. Downer
Keyword(s):  
Peak Flow ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Small Watersheds ◽  
Hydrologic Analysis ◽  
Comparable Performance ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Streamflow Simulation

Abstract The objective is to assess the use of the Climate Prediction Center morphing method (CMORPH) (~0.073° latitude–longitude, 30 min resolution) rainfall product as input to the physics-based fully distributed Gridded Surface–Subsurface Hydrologic Analysis (GSSHA) model for streamflow simulation in the small (21.4 km2) Hortonian watershed of the Goodwin Creek experimental watershed located in northern Mississippi. Calibration is performed in two different ways: using rainfall data from a dense network of 30 gauges as input, and using CMORPH rainfall data as input. The study period covers 4 years, during which there were 24 events, each with peak flow rate higher than 0.5 m3 s−1. Streamflow simulations using CMORPH rainfall are compared against observed streamflows and streamflow simulations using rainfall from a dense rain gauge network. Results show that the CMORPH simulations captured all 24 events. The CMORPH simulations have comparable performance with gauge simulations, which is striking given the significant differences in the spatial scale between the rain gauge network and CMORPH. This study concludes that CMORPH rainfall products have potential value for streamflow simulation in such small watersheds. Overall, the performance of CMORPH-driven simulations increases when the model is calibrated with CMORPH data than when the model is calibrated with rain gauge data.

scholarly journals The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015

2021 ◽  
Vol 13 (15) ◽  
pp. 2931
Author(s):  
Jhonatan Ureña ◽  
Oliver Saavedra ◽  
Takuji Kubota
Keyword(s):  
Amazon Basin ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Correction Factors ◽  
The Public ◽  
Rain Gauges ◽  
Basin Scale ◽  
Surface Measurements ◽  
Rain Gauge Network ◽  
Water Assessment

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia.

scholarly journals Verification of APHRODITE Precipitation Data Set in Bangladesh

2021 ◽  
Vol 12 (1) ◽  
pp. 51-56
Author(s):  
Md Atiqul Islam ◽  
Asif Ahmed ◽  
Md Munirujjaman Munir ◽  
Zarif Zaman Khandakar
Keyword(s):  
Extreme Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
False Alarms ◽  
Data Set ◽  
Study Region ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Precipitation Threshold

We investigated the preformance of Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) of water resources precipitation products in Bangladesh taking rain gauge data as reference for a 3-year period (2003-2005). Various statistical and categorical indices such as coefficient of correlation (CC), bias, relative bias (RB), mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and false alarm ratio (FAR), were applied to measure the performance of the product. With CC value of 0.85, bias of 0.91, RB of -9.5%, MAE of 7.7 mm, and RMSE of 15.2 mm the product tended to underestimate rainfall values during the study period. Although, the POD score of 1.00 demonstrated very good skill in detecting the occurrence of rainfall events, FAR value of 0.25 indicated a considerable amount of false alarms. Moreover, as the precipitation threshold increased, the underestimation became more prominent over the study region. Analysis on the basis of location of the rain gauges also showed that APHRODITE consistently underestimated rainfall values with the increase of extreme rainfall thresholds. Journal of Engineering Science 12(1), 2021, 51-56

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.

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