Heavy rainfall in Mediterranean cyclones. Part I: contribution of deep convection and warm conveyor belt

In this study, we provide an insight to the role of deep convection (DC) and the warm conveyor belt (WCB) as leading processes to Mediterranean cyclones’ heavy rainfall. To this end, we use reanalysis da-ta, lighting and satellite observations to quantify the relative contribution of DC and the WCB to cyclone rainfall, as well as to analyse the spatial and temporal variability of these processes with respect to the cy-clone centre and life cycle. Results for the period 2005-2015 show that the relationship between cyclone rainfall and intensity has high variability and demonstrate that even intense cyclones may produce low rainfall amounts. However, when considering rainfall averages for cyclone intensity bins, a linear relationship was found. We focus on the 500 most intense tracked cyclones (responsible for about 40-50% of the total 11-year Mediterrane-an rainfall) and distinguish between the ones producing high and low rainfall amounts. DC and the WCB are found to be the main cause of rainfall for the former (producing up to 70% of cyclone rainfall), while, for the latter, DC and the WCB play a secondary role (producing up to 50% of rainfall). Further analysis showed that rainfall due to DC tends to occur close to the cyclones’ centre and to their eastern sides, while the WCBs tend to produce rainfall towards the northeast. In fact, about 30% of rainfall produced by DC overlaps with rainfall produced by WCBs but this represents only about 8% of rainfall produced by WCBs. This suggests that a considerable percentage of DC is associated with embedded convection in WCBs. Finally, DC was found to be able to produce higher rain rates than WCBs, exceeding 50 mm in 3-hourly accumulated rainfall compared to a maximum of the order of 40 mm for WCBs. Our results demonstrate in a climatological framework the relationship between cyclone intensity and pro-cesses that lead to heavy rainfall, one of the most prominent environmental risks in the Mediterranean. Therefore, we set perspectives for a deeper analysis of the favourable atmospheric conditions that yield high impact weather

The FLASH project: using lightning data to better understand and predict flash floods

The FLASH project was implemented from 2006 to 2010 underthe EU FP6 framework. The project focused on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms lightning data from the ZEUS network were used together with satellite derived rainfall estimates in orderto understand the storm development and electrification. In addition, these case studies were simulated using mesoscale meteorological models to better understand the meteorological and synoptic conditions leading up to these intense storms. As part of this project tools for short term predictions (nowcasts) of intenseconvection across the Mediterranean and Europe, and long term forecasts (a few days) of the likelihood of intense convection were developed. The project also focused on educationaloutreach through our website http://flashproject.orgsupplying real time lightning observations, real time experimental nowcasts, forecasts and educational materials. While flash floods and intense thunderstorms cannot be preventedas the climate changes, long-range regional lightning networks can supply valuable data, in realtime, for warningend-users and stakeholders of imminent intense rainfall and possible flash floods

Numerical simulation of Tehran dust storm on 2 june 2014: A case study of agricultural abandoned lands as emission sources

On 2 June 2014, at about 13 UTC, a dust storm arrived in Tehran as a severe hazard that caused injures, deaths, failures in power supply, and traffic disruption. Such an extreme event is not considered as common for the Tehran area, which has raised the question of the dust storm’s origin and the need for increasing citizens’ preparedness during such events. The analysis of the observational data and numerical simulations using coupled dust-atmospheric models showed that intensive convective activity occurred over the south and southwest of Tehran, which produced cold downdrafts and, consequently, high-velocity surface winds. Different dust source masks were used as an input for model hindcasts of the event (forecasts of the past event) to show the capability of the numerical models to perform high-quality forecasts in such events and to expand the knowledge on the storm’s formation and progression. In addition to the proven capability of the models, if engaged in operational use to contribute to the establishment of an early warning system for dust storms, another conclusion appeared as a highlight of this research: abandoned agricultural areas south of Tehran were responsible for over 50% of the airborne dust concentration within the dust storm that surged through Tehran. Such a dust source in the numerical simulation produced a PM10 surface dust concentration of several thousand µm/m3, which classifies it as a dust source hot-spot. The produced evidence indivisibly links issues of land degradation, extreme weather, environmental protection, and health and safety

Snow Depth Trends of European Ski Resorts

Snow significantly affects the economy of many European countries, especially in the sector of winter tourism. It affects the operation of ski resorts, the mountain real estate, and can cause disruptions in transportations. The objective of this study is to analyze trends in snow depth at ski resorts in Europe based on the CERRA-land reanalysis data. Time series are computed between 1985–2020 for 4507 European ski resorts. Results show that the majority of ski resorts are severely affected by decreasing trends in snow depth, especially in winter and spring. Spatial patterns based on the elevation of ski resorts are also discussed

A Machine Learning Approach for Rainfall Nowcasting Using Numerical Model and Observational Data

The application of machine learning (ML) algorithms in large datasets in the field of meteorology is at the forefront of research. In this context, the use of satellite data to estimate the amount of rainfall is an important field of research, with operational applications. It is important to accurately predict the amount of rainfall (or rain rate) in a particular area for the proper taking of life and property protection measures. The present work intends to deepen the analysis of meteorological data with ML techniques to improve our capacity in short-range forecasting of rainfall. To this end, relationships between thermodynamic parameters derived by satellite measurements and recorded rainfall by in situ gauges, along with outputs from a numerical atmospheric model are analyzed. The main purpose of the work is to find the best relationships between the atmospheric conditions and the formation of clouds that lead to production of rainfall and build a ML model for nowcasting of rainfall. Several ML methods are used, i.e., Auto Regression, Ensemble Machine Learning, and Deep Learning, and their results are compared in order to find the best fit model

Expected Changes in Heating and Cooling Degree Days over Greece in the near Future Based on Climate Scenarios Projections

The change in heating and cooling needs of Greece in the near future due to the climate change is assessed in the present study. Global and regional climate models and two different representative concentration pathways (RCPs) are used to simulate the expected change in temperature. A widely used methodology of computation of heating degree days (HDDs) and cooling degree days (CDDs) is employed with a base temperature of 18 °C. In agreement with the expected temperature rise in the near future, an HDD decrease and CDD increase under both RCPs is also expected. The changes under RCP8.5 are stronger compared to those under RCP4.5. Differences related to topography are noted. The HDD decrease is stronger than CDD increase but the relative increase in CDDs is higher than the relative increase in HDDs. The highest absolute decreases in HDDs are expected for February and March while the highest absolute increases in CDDs are expected during the three summer months

Long-Term Patterns and Trends of Shortwave Global Irradiance over the Euro-Mediterranean Region

The spatiotemporal patterns and trends of shortwave global irradiance (SWGI) are a crucial factor affecting not only the climate but also sectors of the economy. In this work, the ERA5-Land reanalysis dataset is employed and evaluated against in situ measurements from a dense network of surface stations operated by the National Observatory of Athens over Greece, revealing a good agreement between the two datasets. Then, the spatiotemporal variability of SWGI is investigated over the Euro-Mediterranean region (10° W–42° E and 30° N–52° N) for a 40-year period (1981–2020). SWGI exhibits a smooth latitudinal variability from north to south of −5.4 W/m2/degree on an annual scale, while it varies significantly on a seasonal basis and is almost four times lower in the winter than in the summer. The SWGI trend during the analyzed period was found to be positive and statistically significant at the 95% confidence level. Spring and summer are the periods where positive and the strongest rates of SWGI trends are evident, while in the winter and autumn, negative or neutral trends were found. The increasing SWGI trend shows a slowdown during the beginning of the 2000s in all seasons, except autumn. The SWGI trend decreases by about −0.06 W/m2/decade every 100 m of elevation increase

Heavy rainfall in Mediterranean cyclones, Part II: Water budget, precipitation efficiency and remote water sources

In this study, we use convection-permitting high resolution (3 km) simulations to quantify and analyse the water budget, precipitation efficiency and water sources of 100 intense Mediterranean cyclones. To this end, we calculate the water content, advection and microphysical processes of water vapour and rain water by implementing new diagnostics to the Weather Research and Forecasting (WRF) model. The 100 intense cyclones have been randomly selected from a 500 intense cyclones dataset, identified and tracked in an 11-year time period in part I of this study. Results are presented in a composite approach showing that most rainfall takes place to the north-east side of the cyclones, close to their centre. Rainfall location is concomitant to the area of horizontal moisture flux convergence and is quasi-equal to the amount of water vapour loss due to microphysical processes. Similar results were found regardless if cyclones produce high or low rainfall amounts. Vertical profiles of the water budget terms revealed deeper clouds for the cyclones producing high rainfall, consistent with higher values of vertical advection of both water vapour and rain water. Finally, cyclones were analysed with respect to their precipitation efficiency, i.e. the ratio between the rainwater produced in an atmospheric column and the consequent rainfall, and showed that cyclones tend to be more efficient when their rainfall production takes place over land. Therefore, there is a complex relation between water vapour advection, precipitation efficiency and rainfall which is discussed through the comparison of two tropical-like cyclones with two cyclones that produced low rainfall amounts. Finally, our analysis is complemented by applying a Lagrangian approach to all 100 cyclones in order to quantify the water vapour source regions that contribute to the cyclones’ rainfall due to local surface evaporation. Results showed that these regions are located over both the Atlantic and the Mediterranean, however we show that cyclones producing high rainfall are related with higher water transport from both the subtropical Atlantic and the Mediterranean Sea.Fil: Flaounas, Emmanouil. National Observatory; GreciaFil: Fita Borrell, Lluís. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Lagouvardos, Konstantinos. National Observatory; GreciaFil: Kotroni, Vassiliki. National Obsevatory ; Greci

Investigating the Role of Extreme Synoptic Patterns and Complex Topography During Two Heavy Rainfall Events in Crete in February 2019

During February 2019, two severe storms affected the island of Crete, located in south Greece. Both storms produced excessive rainfall, provoking severe damages, especially in the western part of Crete. The role of the prevailing synoptic patterns and the interaction of the flow with the high mountains of Crete were investigated. For this purpose, a variety of observational and numerical model data were exploited, including data from a dense rain gauge network, satellite imagery, and model analysis of various parameters describing the stability of the impinging flow. The first storm was a long-lasting event, with convective outbreaks embedded in a more stratiform rainfall pattern. The second storm was brief but mostly convection dominated. The analysis of the available data underlined the role of the low-level convergence upstream of the mountains during both storms, highlighting similarities and differences, as well as the role of the stability of the impinging flow. High soil moisture content was also evidenced as a key ingredient for the severe flooding that occurred during the second storm. This work complements similar studies on the role of Mediterranean islands and their topography on the spatial and temporal distribution of extreme rainfall