The Contribution of Orographically Driven Banded Precipitation to the Rainfall Climatology of a Mediterranean Region

AbstractStudies carried out worldwide show that topography influences rainfall climatology. As in most western Mediterranean regions, the mountainous Cévennes–Vivarais area in France regularly experiences extreme precipitation that may lead to devastating flash floods. Global warming could further aggravate this situation, but this possibility cannot be confirmed without first improving the understanding of the role of topography in the regional climate and, in particular, for extreme rainfall events. This paper focuses on organized banded rainfall and evaluates its contribution to the rainfall climatology of this region. Stationary rainfall systems made up of such bands are triggered and enhanced by small-scale interactions between the atmospheric flow and the relief. Rainbands are associated with shallow convection and are also present in deep-convection events for specific flux directions. Such precipitation patterns are difficult to observe both with operational weather radar networks, which are not designed to observe low-level convection within complex terrain, and with rain gauge networks, for which gauge spacing is typically larger than the bandwidth. A weather class of banded orographic shallow-convection events is identified, and the contribution of such events to annual or seasonal precipitation over the region is assessed. Moreover, a method is also proposed to quantify the contribution of banded convection during specific deep-convection events. It is shown that even though these orographically driven banded precipitation events produce moderate precipitation intensities they have long durations and therefore represent a significant amount of the rainfall climatology of the region, producing up to 40% of long-term total precipitation at certain locations.

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

10.5194/egusphere-egu21-8261 ◽

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

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., 99th and higher) over a 1&#176;x1&#176; 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 99th percentile identifies events greater than 26 mm d-1 with a ~2.5 mm confidence interval depending on the number of stations within a 1&#176;x1&#176; 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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-083.1 ◽

2013 ◽

Vol 14 (3) ◽

pp. 906-922 ◽

Cited By ~ 79

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.

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

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-20-0173.1 ◽

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.

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Improving an Extreme Rainfall Detection System with GPM IMERG data

Remote Sensing ◽

10.3390/rs11060677 ◽

2019 ◽

Vol 11 (6) ◽

pp. 677 ◽

Cited By ~ 10

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.

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Finding homogeneous regions of extreme rainfall in reanalysis data

10.5194/egusphere-egu2020-8985 ◽

2020 ◽

Author(s):

Marjanne Zander ◽

Frederiek Sperna Weiland ◽

Albrecht Weerts

Keyword(s):

Climate Models ◽

Climate Adaptation ◽

Regional Climate ◽

Extreme Rainfall ◽

Regional Climate Models ◽

Rain Gauge ◽

Reanalysis Data ◽

Regional Frequency Analysis ◽

Homogeneous Regions ◽

Rainfall Statistics

In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017). &#160;For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009).&#160; To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.References:Gabriele, S., & Chiaravalloti, F. (2013). &#8220;Searching regional rainfall homogeneity using atmospheric fields&#8221;. Advances in Water Resources, 53, 163&#8211;174. https://doi.org/https://doi.org/10.1016/j.advwatres.2012.11.002Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., &#8230;, Zuo, H. (2018). &#8220;Operational global reanalysis: progress, future directions and synergies with NWP&#8221;, ECMWF.Hosking, J. R. M., & Wallis, J. R. (2009). &#8220;Regional Frequency Analysis: An Approach Based on L-Moments&#8221;. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., &#8230; Wilkinson, J. M. (2017). &#8220;Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?&#8221; BAMS, 98(1), 79&#8211;93. https://doi.org/10.1175/BAMS-D-15-0004.1&#160;Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). &#8220;A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.&#8221; Journal of Hydrology, 547(Supplement C), 413&#8211;427. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.02.019

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Fire and torrential rainfall: effects on seedling establishment in Mediterranean gorse shrublands

International Journal of Wildland Fire ◽

10.1071/wf05037 ◽

2005 ◽

Vol 14 (4) ◽

pp. 413 ◽

Cited By ~ 25

Author(s):

Martín de Luís ◽

José Raventós ◽

Josí C. González-Hidalgo

Keyword(s):

Seedling Survival ◽

Seedling Emergence ◽

Extreme Rainfall ◽

Mediterranean Ecosystems ◽

Western Mediterranean ◽

Rainfall Simulation ◽

Vegetation Recovery ◽

Precipitation Event ◽

Extreme Rainfall Events ◽

Torrential Rainfall

Soil degradation and desertification are key problems facing Mediterranean ecosystems and these have worsened recently with the increasing frequency of fires. Moreover, rainfall in western Mediterranean areas is characterized by high intensity, and extreme rainfall events after fire strongly affect the equilibrium between soil erosion and vegetation recovery. These facts led us to carry out experimental fires in a fire-prone vegetation community (dominated by Mediterranean gorse, Ulex parviflorus Pourr.) to study the effect of a torrential rainfall event (through rainfall simulation) on the short-term vegetation regeneration process (seedling emergence and survival). The results indicate that, in Mediterranean gorse shrublands after fire, an extreme precipitation event does not affect seedling emergence (either through seed loss or from seed germination). In contrast, it has a significant effect on the reduction of seedling survival both from direct impact (seedlings buried by sediment or seedlings totally or partially unearthed) and indirectly as a result of soil and litter losses. As a consequence, the combination of fire and torrential rainfall has an important and persistent effect on vegetation recovery, and may cause further degradation, which eventually becomes irreversible.

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Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

Acta Horticulturae et Regiotecturae ◽

10.2478/ahr-2021-0007 ◽

2021 ◽

Vol 24 (s1) ◽

pp. 31-36

Author(s):

Peter Valent ◽

Roman Výleta

Keyword(s):

Soil Erosion ◽

Climate Models ◽

Regional Climate ◽

Multiple Linear Regression Model ◽

Rain Gauge ◽

Rainfall Erosivity ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

R Factor ◽

Geostatistical Approach

Abstract Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (r 2 = 0.98) between monthly EI 30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site R-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create R-factor maps using a geostatistical approach. The results showed that the mean R-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.

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Evaluation of high resolution WRF forecasts for Extreme Rainfall Events over Karnataka against high density in-situ observations

10.5194/egusphere-egu21-15786 ◽

2021 ◽

Author(s):

Ajay Bankar ◽

Rakesh Vasudevan

Keyword(s):

Prediction Models ◽

Initial Conditions ◽

Weather Prediction ◽

Wrf Model ◽

Extreme Rainfall ◽

Rain Gauge ◽

Distinct Advantage ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

Synoptic Conditions

Extreme Rainfall Events (EREs) in India has increased many folds in recent decades. These severe weather events are generally destructive in nature causing flash floods, catastrophic loss of life and property over densely populated urban cities. Various cities in Karnataka, a southern state in India, witnessed many EREs recently. Appropriate advanced warning systems to predict these events are crucial for preparedness of mitigation strategy to reduce human casualty and socio economic loss. Mesoscale models are essential tools for developing an integrated platform for disaster warning and management. From a stakeholder/user pint of view, primary requirement to tackle ERE related damages is accurate prediction of the observed rainfall location, coverage and intensity in advance. Weather prediction models have inherent limitations imposed primarily by approximations in the model and inadequacies in data. Hence, it is important to evaluate the skill of these models for many cases under different synoptic conditions to quantify model skill before using them for operational applications. The objective of the study is to evaluate performance of the Weather Research and Forecasting (WRF) model for several ERE cases in Karnataka at different model initial conditions. The EREs were identified from the distribution of rainfall events over different regions in Karnataka and those events comes under 1% probability were considered. We examined 38 ERE&#8217;s distributed over Karnataka for the period June to November for the years 2015-2019. WRF model is configured with 3 nested domains with outer, inner and innermost domains having resolution of 12 km, 9 km and 3 km respectively. Two sets of simulations are conducted in this study, i) staring at 12 hours prior to the ERE day (i.e. -1200 UTC) & ii) starting at 0000 UTC of the ERE day. Performance of the WRF model forecast is validated against 15 minutes rainfall observations from ~6000 rain gauge stations over Karnataka. During initial hours forecasts initiated at 1200 UTC has distinct advantage in terms of accuracy compared to those initiated at 0000 UTC for most of the cases. In general, model underpredict EREs and underprediction is relatively low for forecasts initiated at 12 00 UTC.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-15-3651-2011 ◽

2011 ◽

Vol 15 (12) ◽

pp. 3651-3658 ◽

Cited By ~ 16

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.

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The 20 February 2010 Madeira flash-floods: synoptic analysis and extreme rainfall assessment

Natural Hazards and Earth System Science ◽

10.5194/nhess-12-715-2012 ◽

2012 ◽

Vol 12 (3) ◽

pp. 715-730 ◽

Cited By ~ 52

Author(s):

M. Fragoso ◽

R. M. Trigo ◽

J. G. Pinto ◽

S. Lopes ◽

A. Lopes ◽

...

Keyword(s):

Return Period ◽

Flash Flood ◽

Deep Convection ◽

Extreme Rainfall ◽

Flash Floods ◽

Rain Gauge ◽

Reanalysis Data ◽

Sources Of Information ◽

Precipitation Records ◽

Flood Dynamics

Abstract. This study aims to characterise the rainfall exceptionality and the meteorological context of the 20 February 2010 flash-floods in Madeira (Portugal). Daily and hourly precipitation records from the available rain-gauge station networks are evaluated in order to reconstitute the temporal evolution of the rainstorm, as its geographic incidence, contributing to understand the flash-flood dynamics and the type and spatial distribution of the associated impacts. The exceptionality of the rainstorm is further confirmed by the return period associated with the daily precipitation registered at the two long-term record stations, with 146.9 mm observed in the city of Funchal and 333.8 mm on the mountain top, corresponding to an estimated return period of approximately 290 yr and 90 yr, respectively. Furthermore, the synoptic associated situation responsible for the flash-floods is analysed using different sources of information, e.g., weather charts, reanalysis data, Meteosat images and radiosounding data, with the focus on two main issues: (1) the dynamical conditions that promoted such anomalous humidity availability over the Madeira region on 20 February 2010 and (2) the uplift mechanism that induced deep convection activity.

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