scholarly journals Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms

2011 ◽  
Vol 15 (12) ◽  
pp. 3651-3658 ◽  
Author(s):  
M. Ćurić ◽  
D. Janc
Keyword(s):  
Cloud Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Spatial And Temporal Variability ◽  
Extreme Rainfall Events ◽  
Small Areas ◽  
Convective Clouds ◽  
Rain Gauges ◽  
Cloud Resolving Model

Abstract. Convective clouds generate extreme rainfall events and flash floods in small areas with both large spatial and temporal variability. For this reason, the monitoring of the total accumulated precipitation fields at the surface with rain gauges and meteorological radars has both strengths and weakness. Alternatively, a numerical cloud model may be a useful tool to simulate convective precipitation for various analyses and predictions. The main objective of this paper is to show that the cloud-resolving model reproduces well the accumulated convective precipitation obtained from the rain gauge network data in the area with frequent split storms. We perform comparisons between observations and model samples of the areal accumulated convective precipitation for a 15-year period over treated area. Twenty-seven convective events have been selected. Statistical analyses reveal that the model areal accumulated convective precipitation closely match their observed values with a correlation coefficient of 0.80.

scholarly journals Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms

2011 ◽  
Vol 8 (4) ◽  
pp. 7237-7259
Author(s):  
M. Ćurić ◽  
D. Janc
Keyword(s):  
Cloud Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Spatial And Temporal Variability ◽  
Extreme Rainfall Events ◽  
Small Areas ◽  
Convective Clouds ◽  
Rain Gauges ◽  
Cloud Resolving Model

Abstract. Convective clouds generate extreme rainfall events and flash floods in small areas with both large spatial and temporal variability. For this reason, the monitoring of the total accumulated precipitation fields at the surface with rain gauges and meteorological radars has both strengths and weakness. Alternatively, a numerical cloud model may be a useful tool to simulate convective precipitation for various analyses and predictions. The main objective of this paper is to show that the cloud-resolving model reproduces well the accumulated convective precipitation obtained from the rain gauge network data in the area with frequent split storms. We perform comparisons between observations and model samples of the areal accumulated convective precipitation for a 15-yr period over treated area. Twenty-seven convective events have been selected. Statistical analyses reveal that the model areal accumulated convective precipitation closely match their observed values with a correlation coefficient of 0.80.

scholarly journals Radar–rain-gauge rainfall estimation for hydrological applications in small catchments

2017 ◽  
Vol 44 ◽  
pp. 61-66 ◽  
Author(s):  
Salvatore Gabriele ◽  
Francesco Chiaravalloti ◽  
Antonio Procopio
Keyword(s):  
Extreme Rainfall ◽  
Mediterranean Area ◽  
Rain Gauge ◽  
Spatial And Temporal Variability ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Extreme Rainfall Events ◽  
Rain Gauges ◽  
Small Catchments ◽  
Hydrological Applications

Abstract. The accurate evaluation of the precipitation's time–spatial structure is a critical step for rainfall–runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall–runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.

scholarly journals Comparison of Modeled and Observed Accumulated Convective Precipitation in Mountainous and Flat Land Areas

2011 ◽  
Vol 12 (2) ◽  
pp. 245-261 ◽  
Author(s):  
Mladjen Ćurić ◽  
Dejan Janc
Keyword(s):  
Size Distribution ◽  
Correlation Coefficient ◽  
Initial Conditions ◽  
Numerical Models ◽  
Cloud Model ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Spatial And Temporal Variability ◽  
Mountainous Area ◽  
Alternative Scheme

Abstract Convective precipitation is the main cause of extreme rainfall events in small areas. Its primary characteristics are both large spatial and temporal variability. For this reason, the monitoring of accumulated precipitation fields (liquid and solid components) at the surface is difficult to carry out through the use of rain gauge networks or remote sensing observations. Alternatively, numerical models may be a useful tool to simulate convective precipitation for various analyses and predictions. This paper focuses on improving quantitative convective precipitation estimates that are obtained with a cloud-resolving model. This aim is attained by using the appropriate cloud drop size distribution and modified single sounding data. The authors perform comparisons between observations and three model samples of the areal-accumulated convective precipitation for a 15-yr period over mountainous and flat land areas with 45 and 29 convective events, respectively. They compare the results from a numerical cloud model that uses 2 different microphysical schemes—the unified Khrgian–Mazin size distribution of cloud drops—and an alternative scheme that is a combination of a monodispersed cloud droplet spectrum and the Marshall–Palmer size distribution for raindrops. The authors’ statistical analysis shows that the model version with the Khrgian–Mazin size distribution and the new initial conditions better simulates the observed areal-accumulated convective precipitation than the alternative microphysical approach for both study areas. The model simulations with the Khrgian–Mazin size distribution most closely match observations for the flat land area with a correlation coefficient of 0.94, while it is somewhat lower (0.89) for the mountainous area. Use of the alternative microphysical approach, on the other hand, underestimates the observed precipitation, and has the lowest correlation coefficient among the methods, 0.82 for the mountainous area and 0.85 for the flat land.

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 Extreme Rainfall in the Mediterranean: What Can We Learn from Observations?

2013 ◽  
Vol 14 (3) ◽  
pp. 906-922 ◽  
Author(s):  
N. Rebora ◽  
L. Molini ◽  
E. Casella ◽  
A. Comellas ◽  
E. Fiori ◽  
...  
Keyword(s):  
Extreme Rainfall ◽  
Flash Floods ◽  
Rain Gauge ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Life Threatening ◽  
The Mediterranean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ground Effects

Abstract Flash floods induced by extreme rainfall events represent one of the most life-threatening phenomena in the Mediterranean. While their catastrophic ground effects are well documented by postevent surveys, the extreme rainfall events that generate them are still difficult to observe properly. Being able to collect observations of such events will help scientists to better understand and model these phenomena. The recent flash floods that hit the Liguria region (Italy) between the end of October and beginning of November 2011 give us the opportunity to use the measurements available from a large number of sensors, both ground based and spaceborne, to characterize these events. In this paper, the authors analyze the role of the key ingredients (e.g., unstable air masses, moist low-level jets, steep orography, and a slow-evolving synoptic pattern) for severe rainfall processes over complex orography. For the two Ligurian events, this role has been analyzed through the available observations (e.g., Meteosat Second Generation, Moderate Resolution Imaging Spectroradiometer, the Italian Radar Network mosaic, and the Italian rain gauge network observations). The authors then address the possible role of sea–atmosphere interactions and propose a characterization of these events in terms of their predictability.

Reconstruction of a realistic rainfall field for extreme events happened in mountain area

2021 ◽  
Author(s):  
Andrea Abbate ◽  
Laura Longoni ◽  
Monica Papini
Keyword(s):  
Extreme Events ◽  
Temporal Distribution ◽  
Extreme Rainfall ◽  
Shallow Landslides ◽  
Mountain Areas ◽  
Mountain Area ◽  
Extreme Rainfall Events ◽  
Lombardy Region ◽  
Rain Gauges ◽  
Rainfall Model

<p>In the field of hydrogeological risk, rainfalls represent the most important triggering factor for superficial terrain failures such as shallow landslides, soil slips and debris flow. The availability of local rain gauges measurements is fundamental for defining the cause-effect relationship for predicting failure scenarios. Unfortunately, these hydrogeological phenomena are typical triggered over mountains regions where the density of the ground-based meteorological network is poor, and the local effects caused by mountains topography can change dramatically the spatial and temporal distribution of rainfall. Therefore, trying to reconstruct a representative rainfall field across mountain areas is a challenge but is a mandatory task for the interpretation of triggering causes. We present a reanalysis of an ensemble of extreme rainfall events happened across central Alps and Pre-Alps, in the northern part of Lombardy Region, Italy. We have investigated around some critical aspects such as their intensity and persistency also proposing a modelling of their meteorological evolution, using the Linear Upslope-Rainfall Model (LUM). We have considered this model because it is designed for describing the mechanism of orographic precipitation intensification that was identified as the main cause of that extreme events. To test and calibrate the LUM model we have considered local rain gauges data because they represent the effective rainfall poured on the ground. These punctual data are generally considered for landslide assessment, in particular for rainfall induced phenomena such as shallow landslides and debris flows. Considering our test cases, the results obtained have shown that the LUM has been able to reproduce accurately the rainfall field. In this regard, LUM model can help to address further information around those ungauged area where rainfall estimation could be critical for evaluating the hazard. We are conscious that our and other studies around this topic would be propaedeutic in the next future for the adoption of an integrated framework among the real-time meteorological modelling and the hydrogeological induced risk assessment and prevision.</p>

Extreme Rainfall Events in the Macro-Metropolis of São Paulo: trends and connection with climate oscillations

2021 ◽  
Author(s):  
Carolyne B. Machado ◽  
Thamiris L. O. B. Campos ◽  
Sameh A. Abou Rafee ◽  
Jorge A. Martins ◽  
Alice M. Grimm ◽  
...  
Keyword(s):  
Urban Areas ◽  
Extreme Rainfall ◽  
Kendall Test ◽  
Rain Gauge ◽  
Sao Paulo ◽  
Annual Rainfall ◽  
São Paulo ◽  
Wet Season ◽  
Extreme Rainfall Events ◽  
Climatic Indices

AbstractIn the present work, the trend of extreme rainfall indices in the Macro-Metropolis of São Paulo (MMSP) was analyzed and correlated with largescale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm per year on average, according to Mann-Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events, while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific Decadal Oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the Temperature of South Atlantic (TSA), the AAO, the El Niño South Oscillation (ENSO) and the Multidecadal Oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R2 = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the ‘70s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than dry months.

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 Statistical Characteristic of Heavy Rainfall Associated with Typhoons near Taiwan Based on High-Density Automatic Rain Gauge Data

2016 ◽  
Vol 97 (8) ◽  
pp. 1363-1375 ◽  
Author(s):  
Chun-Chieh Wu ◽  
Tzu-Hsiung Yen ◽  
Yi-Hsuan Huang ◽  
Cheng-Ku Yu ◽  
Shin-Gan Chen
Keyword(s):  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Statistical Characteristics ◽  
Rain Gauge Data ◽  
Mountainous Regions ◽  
Rain Gauges ◽  
Weather Stations ◽  
Central Weather Bureau

Abstract This study utilizes data compiled over 21 years (1993–2013) from the Central Weather Bureau of Taiwan to investigate the statistical characteristics of typhoon-induced rainfall for 53 typhoons that have impacted Taiwan. In this work the data are grouped into two datasets: one includes 21 selected conventional weather stations (referred to as Con-ST), and the other contains all the available rain gauges (250–500 gauges, mostly automatic ones; referred to as All-ST). The primary aim of this study is to understand the potential impacts of the different gauge distributions between All-ST and Con-ST on the statistical characteristics of typhoon-induced rainfall. The analyses indicate that although the average rainfall amount calculated with Con-ST is statistically similar to that with All-ST, the former cannot identify the precipitation extremes and rainfall distribution appropriately, especially in mountainous areas. Because very few conventional stations are located over the mountainous regions, the cumulative frequency obtained solely from Con-ST is not representative. As compared to the results from All-ST, the extreme rainfall assessed from Con-ST is, on average, underestimated by 23%–44% for typhoons approaching different portions of Taiwan. The uneven distribution of Con-ST, with only three stations located in the mountains higher than 1000 m, is likely to cause significant biases in the interpretation of rainfall patterns. This study illustrates the importance of the increase in the number of available stations in assessing the long-term rainfall characteristic of typhoon-associated heavy rainfall in Taiwan.

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.

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