Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX

Khodayar, S.; Davolio, Silvio; Girolamo, Paolo Di; Brossier, Cindy Lebeaupin; Flaounas, Emmanouil; Fourrié, Nadia; Lee, Keun-Ok; Ricard, Didier; Vié, Benoît; Bouttier, François; Caldas-Álvarez, Alberto; Ducrocq, Véronique

doi:10.5194/acp-21-17051-2021

Cited by 16 publications

(5 citation statements)

References 200 publications

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“…Probably latent heat flux (not tried yet) would be a better indicator, with variability on the same temporal scales of EPEs. In addition, water vapour sources from remote areas are sometimes more important for EPE development, as confirmed by other recent studies (Duffourg & Ducrocq, 2011; Khodayar et al ., 2021; Khodayar et al ., 2022), so that the direct Mediterranean Sea contribution for some EPEs is less important, like in winter.…”

Section: Methodsmentioning

confidence: 99%

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

Grazzini,

Dorrington,

Grams

et al. 2024

Quart J Royal Meteoro Soc

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The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.

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Section: Methodsmentioning

confidence: 99%

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

Grazzini,

Dorrington,

Grams

et al. 2024

Quart J Royal Meteoro Soc

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“…Then from November to February, the Gulf of Genoa (just north of Corsica) becomes an active cyclogenesis region and the nearby Corsica region registers a high cyclone track density (Trigo et al, 2002). HPEs have been studied in the framework of the HyMeX (Drobinski et al 2014;Ducrocq et al 2014) and the main results have helped to identify several factors responsible for their occurrence (Khodayar et al, 2021): (i) instability and moisture supply from the evaporation coming from the Mediterranean Sea after the warm season, (ii) slow-evolving synoptic-scale conditions and (iii) deep convection triggered by orographic lifting, cold-pool or mesoscale low-level convergence.…”

Section: Mediterranean Heavy Precipitation Eventsmentioning

confidence: 99%

Evaluation of the Convection Permitting Regional Climate Model CNRM-AROME on the complex orographic island of Corsica

Hernández,

Caillaud,

Bellon

et al. 2023

Preprint

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Meteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land-sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000-2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and winter months and ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain.

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“…HPEs and associated flash floods are the most dangerous meteorological hazards affecting Mediterranean countries, causing extensive mortality and hundreds of millions of euros in damages every year. The Mediterranean basin and, in particular, the surrounding mountainous coastal regions are often affected by these phenomena, especially in autumn [93]. The Mediterranean Sea is a source of heat and moisture surrounded by steep orography, which favours the frequent occurrence of heavy precipitation, mainly of convective nature [94].…”

Section: Validation and Evaluation Of Convection-permitting Simulationsmentioning

confidence: 99%

Non-Hydrostatic Regcm4 (Regcm4-NH): Evaluation of Precipitation Statistics at the Convection-Permitting Scale over Different Domains

et al. 2022

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Recent studies over different geographical regions of the world have proven that regional climate models at the convection-permitting scale (CPMs) improve the simulation of precipitation in many aspects, such as the diurnal cycle, precipitation frequency, intensity, and extremes at daily—but even more at hourly—time scales. Here, we present an evaluation of climate simulations with the newly developed RegCM4-NH model run at the convection-permitting scale (CP-RegCM4-NH) for a decade-long period, over three domains covering a large European area. The simulations use a horizontal grid spacing of ~3 km and are driven by the ERA-Interim reanalysis through an intermediate driving RegCM4-NH simulation at ~12 km grid spacing with parameterized deep convection. The km-scale simulations are evaluated against a suite of hourly observation datasets with high spatial resolutions and are compared to the coarse-resolution driving simulation in order to assess improvements in precipitation from the seasonal to hourly scale. The results show that CP-RegCM4-NH produces a more realistic representation of precipitation than the coarse-resolution simulation over all domains. The most significant improvements were found for intensity, heavy precipitation, and precipitation frequency, both on daily and hourly time scales in all seasons. In general, CP-RegCM4-NH tends to correctly produce more intense precipitation and to reduce the frequency of events compared to the coarse-resolution one. On the daily scale, improvements in CP simulations are highly region dependent, with the best results over Italy, France, and Germany, and the largest biases over Switzerland, the Carpathians, and Greece, especially during the summer seasons. At the hourly scale, the improvement in CP simulations for precipitation intensity and spatial distribution is clearer than at the daily timescale. In addition, the representation of extreme events is clearly improved by CP-RegCM4-NH, particularly at the hourly time scale, although an overestimation over some subregions can be found. Although biases between the model simulations at the km-scale and observations still exist, this first application of CP-RegCM4-NH at high spatial resolution indicates a clear benefit of convection-permitting simulations and encourages further assessments of the added value of km-scale model configurations for regional climate change projections.

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Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX

Cited by 16 publications

References 200 publications

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

Evaluation of the Convection Permitting Regional Climate Model CNRM-AROME on the complex orographic island of Corsica

Non-Hydrostatic Regcm4 (Regcm4-NH): Evaluation of Precipitation Statistics at the Convection-Permitting Scale over Different Domains

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