There have been several advances in understanding the North Atlantic Oscillation (NAO), but there are still uncertainties regarding its level of influence on the tropical climate. Added to this is the fact that most of the studies related to the subject refer to medium and high latitudes, while for the tropical region there is little research that directly addresses the influence of the NAO on the behavior of the oceanographic and atmospheric variables that make up the climate of this region. That is why this work determines the influence of the NAO on the main hydrometeorological events that affect Cuba in the 1999-2016 period. To comply with this, a regression analysis is carried out in the CurveExpert software where the combined influence of the NAO and El Niño-Southern Oscillation (ENSO) on hydrometeorological events is also examined. It was found that the NAO exerts a greater influence on Cuba when it is in its negative phase during the winter season.

Data from an S-band polarimetric radar (NASA-NPOL) located at a mid-latitude, coastal location are used to compare two different methods for identifying stratiform and convective rain regions. The first method entails the retrievals of two (main) parameters of the rain drop size distributions (DSD) using the radar reflectivity and the differential reflectivity. The second technique is a well-known texture-based method which utilizes the radar reflectivity and its spatial variability. For the DSD-based method, an empirically improved retrieval technique was used, and the separation of the rain types was based on the estimated mass-weighted mean diameter and the normalized intercept parameter. Further, an additional category was also introduced for the DSD-based method to represent ‘mixed’ (or ‘transition’) rain type.

A widespread event with an embedded line convection, which occurred on 30 April 2020, was used as an initial test case. The two methods were compared using 500m by 500m pixel resolution gridded data (120 km by 120 km) constructed from the NPOL radar volume scan taken at 21:05 UTC. The comparison resulted in (a) 56% of the radar pixels being categorized as stratiform rain by both methods; (b) 21% as convective rain by both methods; and (c) a further 11% as the ‘mixed’ category from the DSD-based method. For the remaining 12% of the pixels, there was disagreement between the two methods which largely occurred in regions adjacent to (b).

The rain types are retrieved from data only at the lowest gridded level; however, it will be shown that they all have unique vertical structures indicative of contributions to latent heating through the column. Results will be presented for the gridded data and the error sources corresponding to each of the methods will be discussed.

Measurements of atmospheric pressure by mesoscale transects of vehicle platforms such as the National Severe Storms Lab (NSSL) Mobile Mesonets have previously been collected in various targeted field campaigns. The challenges involved were specifically documented in the very different environments of tornadogenesis (Markowski et al. 2002) and orographic foehn winds (Raab and Mayr 2008). In recent years the Jackson State University Mobile Meteorology Unit (MMU) has been developed with broad ranging applications in mind. The inclusion of barometric pressure was originally expected only to be used for calculation of potential temperature over transects with significant elevation change. As such, careful positioning of a pressure port intake to reduce effects of flow distortion around the vehicle was not considered necessary. However previous studies have determined a dynamic change of measured pressure due to vehicle motion relative to the air that varies quadratically with speed, in agreement with theoretical expectations. This quadratic relationship has been examined for the MMU under a wide variety of conditions, both with and without use of a pressure port. In order to consider least squares regression of this relationship, it was necessary to also have accurate speed and elevation data. Since even quite small elevation changes can produce measurable pressure changes, it was considered necessary to reduce pressures in each transect to the mean elevation using the methodology of Markowski et al. (2002). In most cases this required a combination of digital elevation model (DEM) and geographic positioning system (GPS) data to have sufficiently accurate elevations matched to the locations of the pressure measurements. Since the MMU does not measure wind velocity and most cases were for light winds, only the speed relative to ground from the GPS was used. Types of cases to be discussed include transects from about 10 to 200 km in length: approximately uniform conditions in flat terrain; crossing of significant orographic barriers (> 500 m); cold fronts; drylines; a mesohigh associated with a strong thunderstorm. The impacts for determination of mesoscale pressure gradients, potential temperature, and other derived quantities will be evaluated.

There is growing awareness that the development of optimal strategies to prevent health damages associated with the exposure to the atmospheric pollution requires the assessment of the weather influence on the attainment of air quality objectives. Meteorological conditions, in fact, affect the link between emissions and air pollution over multiple scales in time and space so masking the real trends in the observed pollutants concentrations.

An emerging approach to afford the problem consists in developing machine-learning (ML) based ‘meteorological-normalization’ algorithms establishing the relationship between local meteorology and air pollutants surface concentrations.

In this study, a technique of meteorological-normalization, based on a random forest (RF) ML algorithm, is developed to assess changes in the nitrogen oxides (NO_x and NO₂) and sulfur dioxide (SO₂) time series in a rural area affected by anthropic sources of air pollutants.

The RF model was trained on air pollutants and meteorological parameters daily data, acquired over the period 2013-2019. Several variables representing time predictors were added to the training data to capture seasonal and weekly pollutants variations.

Thanks to the interpretability of the RF model, the functional relationships between each input explanatory variable (i.e. meteorological parameters and time variables) and each response variable (the pollutant concentration) of the model can be provided, pointing out the role of local meteorological processes in the observed pollutants concentrations.

Throughout the metadata publicly available, some hypothesis on the potential link between the change points of the normalised time series and the pollutants sources existing in the area are also discussed. Overall, our findings show that the developed approach proves to be a powerful technique to correctly detecting variations in pollutant concentrations discriminating the contribution of meteorology from those of source’s emissions; this represents a crucial information for the implementation of effective strategies to prevent health impact of air pollution.

The outbreak of the novel coronavirus SARS-CoV-2 (hereafter COVID19) has changed the daily routines of people around the world. The first case of COVID19 was confirmed on December, 2019 whilst, it was confirmed on 11 March 2020 in Turkey. After the number of cases reached 4500 per day by 10 April, the government declared more restrictive lockdown measures for 31 metropolitan cities and it was implemented for the following weekends, national and religious holidays. The change in concentrations of PM₁₀, CO, NO_x and NO₂ during this measures with respect to pre – lockdown period and for different level of measures for Samsun, the biggest city of Karadeniz Region were investigated in this study. The daily mean concentrations of PM₁₀, CO, NO_x and NO₂ obtained for the Tekkekoy station due to data completeness greater than 95 percent for all pollutants. Average CO, NO_x and NO₂ concentrations during lock down period, declined with respect to pre-lock down period whilst PM₁₀ increased 5 percent. The average concentrations of all the pollutants decreased when the level of restrictions increased during COVID19 lockdown period. The number of days exceed WHO limit for PM₁₀ were decreased during lockdown period of 16 days with respect to pre-lockdown period of 19 days. The relationship between concentration levels and mobility change was also investigated.

During 2020, the Dominican Republic received the impact of several tropical organisms. Among those that generated the greatest losses in the country, the Tropical Storm Isaías stands out because caused significant floods. The present work analyzes the ability of the Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (SisPI, for its acronym in Spanish) for the quantitative precipitation forecast (QPF) of the rains generated by the Isaías TT over Dominican Republic. Various traditional verification methods and others based on object identification are used in the study. The results show, that both numerical weather systems are powerful tools for the QPF, and also for prevention and mitigation of disasters caused by the extreme hydro-meteorological event studied.

Atmospheric blocking is a large-scale phenomenon occurs in the mid-latitudes to high latitudes in the troposphere, leading to severe and persistent weather anomalies such as heat waves, cold spells, and extreme dry conditions with large fires and floods. A series of huge wildfires occurred in some regions in the south and north of Lebanon in mid - October 2019, in which the region witnessed a heat wave with high averages of minimum and maximum temperatures, accompanied with dry weather conditions and high speed winds. This situation is attributed to the domination of high blocking pattern (strong Ridge) over eastern Mediterranean region for many days leading to fire outbreak, and the active wind participated in extending the fires to forests and green lands. The present research focuses on Chouf district, in Mount Lebanon Governorate as the study area in which it witnessed the most severe wildfire outbreak, based on ERA5 atmospheric reanalysis data at the surface and upper levels for the period (13-16) of October 2019. It was found that the existence of atmospheric blocking system over the region for many days was the main factor in creating the dry and extremely hot weather, and the ridge outbreak caused the ignition of fire, reinforcing the wildfire intensity and amplifying the fire patches to other regions.

Atmospheric aerosols play essential roles in regional energy balance, hydrological cycle, and air quality, thus greatly influencing the global climate and public health. Rapid economic expansion, industrialization, urbanization, and energy demand have significantly enhanced anthropogenic emissions over the Middle East (ME) that received the utmost scientific attention. Therefore, we present the temporal variability of atmospheric aerosols over ME for the period of 15 years (2005-2019). Here the long-term measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Clouds and the Earth’s Radiant Energy System (CERES) on Aqua are analyzed in order to understand the spatio-temporal variability of aerosols and their impacts on radiation budget over ME. On average, a significant increase in aerosol optical depth (AOD) trend is observed by ~0.01 per year over ME. The peak aerosol loading is observed in summer (March-September) followed by the winter (October-February). A similar trend has been observed in the CALIOP derived extinct aerosol coefficients over ME. In addition, MODIS retrievals are validated against the AErosol RObotic NETwork (AERONET)’s ground-based sun photometers. Overall, MODIS AOD showed good agreement against AERONET AOD, with ~70% of the retrievals falling within the expected error and high correlation coefficient (R > 0.8). Furthermore, the associated changes in clear-sky Shortwave (SW) radiative flux indicates the enhanced aerosol loading over ME further increases the surface cooling (~1.2 W m^-2 per year) and atmospheric warming (~1.8 W m^-2 per year). Overall, the results suggest that enhanced aerosol emissions have significantly impacted the regional energy budget over ME during 2005-2019. The assessment also demonstrates the potential of synergetic use of multi-platform measurements for climate system studies.

This study aims to investigate flash floods in Iraq by plotting the cartographic maps by using synoptic and dynamical analysis of meteorological reanalysis data, obtains from the European Centre for Medium-Range Weather Forecasts (ECMWF) and statistical analysis of daily precipitation records from the Iraqi Meteorological and Seismology Organization for selected Iraqi stations (Mosul, Kirkuk, Khanaqin, Baghdad and Al-Rutba, Al-Hayy, Al-Nasiriyah, and Basra), as well as the use of Geographic information system (GIS) techniques. Three models, create to investigate and map flash floods in Iraq. The results of the first model (The longest period of time) show that the station of Mosul record the longest period for a rainstorm, 9 days in 2014, while the lowest period was in Rutba,6 days in 2012, and the other stations varied between these two stations. The results of the second model (the highest total rainfall), present that Kirkuk station recorded the highest amount of rain (117.2 mm in 2013), while Al-Rutba station,47.2 mm in 2011, the lowest station. Finally, the results of the third model (the highest frequency of rainstorms per month) show that the lowest frequency of rainstorms per month was in Basra,29 rainstorms in 2009, while Mosul station has 40 rainstorms in2007 and the other stations within these two values.

The prediction of fog is a challenging task in operational weather forecast. Due to its dependency on small scale processes, numerical weather models struggle to deal with under scale features resulting in uncertainties on the fog forecast. Unawareness of the onset time and the duration of fog leads to disproportionate impact on open air activities, especially in aviation. Nevertheless, in a small sized country such as Portugal mainland, the fog varies greatly. The traffic of the two busiest Portuguese international airports of Porto and Lisbon is affected by the occurrence of fog in different times of the year. The fog occurrence at Porto is a predominant winter phenomenon and a summer one at Lisbon. A conceptual model supported by observational evidence associate the fog formation in the Tagus estuary followed by its slow advection towards the airport. At Porto the fog formation is highly dependent on local wind distribution, as an indication of the dominant role of local advection. Observational variables and their trend are local indicators of favouring conditions to the fog onset, like cooling, water vapour saturation and turbulent mixing. The dataset of 17 years of half hourly METAR from the airports of Porto and Lisbon are used to diagnose the pre fog conditioning. Two diagnostic models are proposed to assess pre fog conditions. The first model is adapted from the statistical method proposed by Menut et al (2014), which performs a diagnosis from key variables trend, such as temperature, wind speed, relative humidity and base height of very low clouds. Thresholds are defined from the METAR samples in the six-hours period prior to the formation of fog. Due to the local character of fog, the presented thresholds are the most appropriate ones for each airport. The predictability of fog is then assessed using observations. In the second approach will encompass networks such as the Multilayer Perceptron and also Recurrent Neural Networks (RNN), which are especially well suited for time series. By experimenting with different types of RNN, we will try to capture the connection between the temporal evolution of measured variables in the dataset and the fog onset. These experiments will include different time windows to measure its influence in the prediction performance. An ablation study will also be performed to measure the sensitivity of the models not only to the architecture but also to the measured variables in the dataset.