scholarly journals Forcing mechanisms of a heavy precipitation event in the southeastern Adriatic area

2014 ◽  
Vol 72 (2) ◽  
pp. 1231-1252 ◽  
Author(s):  
Branka Ivančan-Picek ◽  
Kristian Horvath ◽  
Nataša Strelec Mahović ◽  
Marjana Gajić-Čapka
Keyword(s):  
Heavy Precipitation ◽  
Precipitation Event ◽  
Heavy Precipitation Event ◽  
Forcing Mechanisms

scholarly journals Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1727
Author(s):  
Valerio Capecchi ◽  
Andrea Antonini ◽  
Riccardo Benedetti ◽  
Luca Fibbi ◽  
Samantha Melani ◽  
...  
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Flash Floods ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Limited Area ◽  
Heavy Precipitation Event ◽  
The Town

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.

scholarly journals Contrasting stable water isotope signals from convective and large-scale precipitation phases of a heavy precipitation event in southern Italy during HyMeX IOP 13: a modelling perspective

2019 ◽  
Vol 19 (11) ◽  
pp. 7487-7506
Author(s):  
Keun-Ok Lee ◽  
Franziska Aemisegger ◽  
Stephan Pfahl ◽  
Cyrille Flamant ◽  
Jean-Lionel Lacour ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Front ◽  
Heavy Precipitation ◽  
Convective Precipitation ◽  
Water Isotopes ◽  
Precipitation Event ◽  
Stable Water Isotopes ◽  
Heavy Precipitation Event ◽  
The North ◽  
Upper Level

Abstract. The dynamical context and moisture transport pathways embedded in large-scale flow and associated with a heavy precipitation event (HPE) in southern Italy (SI) are investigated with the help of stable water isotopes (SWIs) based on a purely numerical framework. The event occurred during the Intensive Observation Period (IOP) 13 of the field campaign of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) on 15 and 16 October 2012, and SI experienced intense rainfall of 62.4 mm over 27 h with two precipitation phases during this event. The first one (P1) was induced by convective precipitation ahead of a cold front, while the second one (P2) was mainly associated with precipitation induced by large-scale uplift. The moisture transport and processes responsible for the HPE are analysed using a simulation with the isotope-enabled regional numerical model COSMOiso. The simulation at a horizontal grid spacing of about 7 km over a large domain (about 4300 km ×3500 km) allows the isotopes signal to be distinguished due to local processes or large-scale advection. Backward trajectory analyses based on this simulation show that the air parcels arriving in SI during P1 originate from the North Atlantic and descend within an upper-level trough over the north-western Mediterranean. The descending air parcels reach elevations below 1 km over the sea and bring dry and isotopically depleted air (median δ18O ≤-25 ‰, water vapour mixing ratio q≤2 g kg−1) close to the surface, which induces strong surface evaporation. These air parcels are rapidly enriched in SWIs (δ18O ≥-14 ‰) and moistened (q≥8 g kg−1) over the Tyrrhenian Sea by taking up moisture from surface evaporation and potentially from evaporation of frontal precipitation. Thereafter, the SWI-enriched low-level air masses arriving upstream of SI are convectively pumped to higher altitudes, and the SWI-depleted moisture from higher levels is transported towards the surface within the downdrafts ahead of the cold front over SI, producing a large amount of convective precipitation in SI. Most of the moisture processes (i.e. evaporation, convective mixing) related to the HPE take place during the 18 h before P1 over SI. A period of 4 h later, during the second precipitation phase P2, the air parcels arriving over SI mainly originate from north Africa. The strong cyclonic flow around the eastward-moving upper-level trough induces the advection of a SWI-enriched African moisture plume towards SI and leads to large-scale uplift of the warm air mass along the cold front. This lifts moist and SWI-enriched air (median δ18O ≥-16 ‰, median q≥6 g kg−1) and leads to gradual rain out of the air parcels over Italy. Large-scale ascent in the warm sector ahead of the cold front takes place during the 72 h preceding P2 in SI. This work demonstrates how stable water isotopes can yield additional insights into the variety of thermodynamic mechanisms occurring at the mesoscale and synoptic scale during the formation of a HPE.

Mechanisms for convection development in a long-lasting heavy precipitation event over southeastern Italy

2011 ◽  
Vol 100 (4) ◽  
pp. 586-602 ◽  
Author(s):  
D. Mastrangelo ◽  
K. Horvath ◽  
A. Riccio ◽  
M.M. Miglietta
Keyword(s):  
Heavy Precipitation ◽  
Precipitation Event ◽  
Heavy Precipitation Event

scholarly journals Dependence of Predictability of Precipitation in the Northwestern Mediterranean Coastal Region on the Strength of Synoptic Control

2020 ◽  
Author(s):  
Christian Keil ◽  
Lucie Chabert ◽  
Olivier Nuissier ◽  
Laure Raynaud
Keyword(s):  
Coastal Region ◽  
Heavy Precipitation ◽  
Forecast Model ◽  
Precipitation Event ◽  
Heavy Precipitation Event ◽  
Precipitation Characteristics ◽  
Synoptic Forcing ◽  
Convective Adjustment ◽  
Forecast Quality

Abstract. The weather regime dependent predictability of precipitation in the convection permitting kilometric scale AROME-EPS is examined for the entire HyMeX SOP1 employing the convective adjustment timescale. This diagnostic quantifies variations in synoptic forcing on precipitation and is associated with different precipitation characteristics, forecast skill and predictability. During strong synoptic control, which is dominating the weather on 80 % of the days in the 2-months period, the domain integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the spatial forecast quality of most intense precipitation in the afternoon, as quantified with its 95th percentiles, is superior during weakly forced synoptic regimes. The study also considers a prominent heavy precipitation event that occurred during the NAWDEX field campaign in the same region, and the predictability during this event is compared with the events that occurred during HyMeX. It is shown that the unconditional evaluation of precipitation widely parallels the strongly forced weather type evaluation and obscures forecast model characteristics typical for weak control.

scholarly journals Comparison of Eulerian and Lagrangian moisture source diagnostics – the flood event in eastern Europe in May 2010

2013 ◽  
Vol 13 (11) ◽  
pp. 29333-29373 ◽  
Author(s):  
A. Winschall ◽  
S. Pfahl ◽  
H. Sodemann ◽  
H. Wernli
Keyword(s):  
Eastern Europe ◽  
Land Surface ◽  
Heavy Precipitation ◽  
Precipitation Event ◽  
Eulerian Model ◽  
Heavy Precipitation Event ◽  
Moisture Source ◽  
Moisture Sources ◽  
Source Regions ◽  
The Mediterranean

Abstract. Moisture convergence from different sources is an important prerequisite for a heavy precipitation event. The contributions from different source regions can, however, hardly be quantified from observations, and their assessment based on model results is complex. Two conceptually different numerical methods are widely used for the quantification of moisture sources: Lagrangian approaches based on the analysis of humidity variations along backward trajectories and Eulerian methods based on the implementation of moisture tracers into a numerical model. In this study the moisture sources for a high-impact heavy precipitation event that affected eastern Europe in May 2010 are studied with both Eulerian and Lagrangian moisture source diagnostics. The precipitation event was connected to a cyclone that developed over northern Africa, moved over the Mediterranean towards eastern Europe and induced transport of moist air towards the Carpathian mountains. Heavy precipitation and major flooding occurred in Poland, the Czech Republic and Slovakia between 16 and 18 May 2010. The Lagrangian and Eulerian diagnostics consistently indicate a~wide spatial and temporal range of moisture sources contributing to the event. The most important source is local evapotranspiration from the European land surface, followed by moisture from the North Atlantic. Further relevant contributions come from tropical Western Africa (10–20° N). Contrary to expectations, the Mediterranean Sea contributes only about 10% to the precipitation event. A detailed analysis of exemplary trajectories corroborates the general consistency of the two approaches, and underlines their complementarity. The Lagrangian method allows for mapping out moisture source regions with computational efficiency, whereas the more elaborate Eulerian model requires predefined moisture sources, but includes also processes such as precipitation, evaporation and turbulent mixing. However, in the Eulerian model, uncertainty concerning the relative importance of remote versus local moisture sources arises from different options to parameterise moisture tagging at the surface. Ultimately a more sophisticated parameterisation scheme will be required to reduce this uncertainty.

scholarly journals ESTIMATIVA DA PROBABILIDADE DE OCORRÊNCIA DE PRECIPITAÇÃO, A PARTIR DE TÉCNICAS ESTATÍSTICAS NÃO PARAMÉTRICAS APLICADAS A SIMULAÇÕES NUMÉRICAS DE WRF. UM CASO DE ESTUDO

2016 ◽  
Vol 38 ◽  
pp. 491
Author(s):  
Lissette Guzmán Rodríguez ◽  
Vagner Anabor ◽  
Franciano Scremin Puhales ◽  
Everson Dal Piva
Keyword(s):  
Wrf Model ◽  
Random Variable ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Precipitation Forecast ◽  
Precipitation Event ◽  
Ensemble Prediction ◽  
False Alarms ◽  
Heavy Precipitation Event ◽  
Skill Scores

In this paper was  used the  kernel density estimation (KDE),  a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic  precipitation forecast, from an ensemble prediction with the  WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities  obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For  accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables,  for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.

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