scholarly journals Analysis of extreme rainfall in Barcelona using a microscale rain gauge network

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
M. Carmen Casas ◽  
Raül Rodríguez ◽  
Ángel Redaño
Keyword(s):  
Extreme Rainfall ◽  
Rain Gauge ◽  
Rain Gauge Network

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 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).

scholarly journals Kecenderungan dan Perubahan Hujan Ekstrem Harian di Pulau Madura

2020 ◽  
Vol 18 (1) ◽  
pp. 89-96
Author(s):  
Ahmad Nur Akma Juangga Fura ◽  
Retno Utami Agung Wiyono ◽  
Indarto Indarto
Keyword(s):  
Flood Hazard ◽  
Parametric Method ◽  
Extreme Rainfall ◽  
Kendall Test ◽  
Random Processes ◽  
Rain Gauge ◽  
Rainfall Trend ◽  
Significance Level ◽  
High Level ◽  
Crossing Test

Madura subject to a high level of flood hazard. One of the main causes of flood is extreme rainfall. Global warming generates changes in the amount of extreme rainfall. This research is conducted to identify and to analyze the trends, changes, and randomness of 24-hour extreme rainfall data on Madura Island. The method used is a non-parametric method which includes the Median Crossing test, the Mann-Kendall test, and the Rank-Sum test at the significance level of α =0.05. The analysis was carried out on 31 rain gauge stations. The recording period observed is between 1991-2015. The results of the analysis show that based on the Median Crossing test, most rainfall stations have data originating from random processes. The result shows also that the maximum 24-hour extreme rainfall trend is significantly decreased in a few locations, while for the majority of other stations have no experience a significant trend.

scholarly journals Impacts of Polarimetric Radar Observations on Hydrologic Simulation

2010 ◽  
Vol 11 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Scott E. Giangrande ◽  
Yang Hong ◽  
Zachary L. Flamig ◽  
Terry Schuur ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Model Parameters ◽  
Polarimetric Radar ◽  
Storm Events ◽  
Hydrologic Simulation ◽  
Radar Observations

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

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