Representation of atmospheric boundary layer (ABL) over sea ice is a challenge for numerical weather prediction (NWP) models. Particularly, when it comes to stable or near neutral ABL, where models tend to erroneously simulate 2-m temperature, 10 m wind speed and other variables strongly affected by atmosphere-surface coupling. Studies indicate that submesoscale processes, not resolved directly by the models, might be responsible for those inaccuracies. Development of new parameterizations that would improve models performance is hampered by the limited amount of field data from the sea ice covered areas. To increase our knowledge about relevant processes a Hailuoto Atmospheric Observations over Sea Ice (HAOS) campaign was carried out over the sea ice in the Bothnian Bay (Baltic Sea) on 27 February - 2 March 2020. Observations included 27 Unmanned Aerial Vehicles (UAV) flights collecting meteorological data (temperature, wind speed, air pressure) and 4 photogrammetry missions over a stretch of sea ice, along with continuous, shore based automatic weather station (AWS) and LIDAR wind measurements. Obtained dataset is used for the validation of several mesoscale models results including WRF, run operationally by the Interdisciplinary Centre for Mathematical and Computational Modelling at University of Warsaw, AROME-ARCTIC run by Norwegian Meteorological Institute and HIRLAM from Finnish Meteorological Institute. Furthermore, the WRF Single Column Model (SCM) is launched multiple times for the comparison of different planetary boundary layer (PBL) and microphysics schemes influence on the model capability to reproduce the observed conditions. Considering that throughout the campaign the weather was dominated by low winds, clear skies and low temperatures, our research provides a valuable insight into the model strengths and shortcomings in the numerical modeling of stable boundary layer, together with the evaluation of alternative ABL parameterizations.

Stratiform and convective rain are associated with different microphysical processes and generally produce drop size distributions (DSDs) with different characteristics. Identification of these two rain types is also important for estimating rainfall rates from ground-based polarimetric radars as well as spaceborne radars. Previous studies have investigated DSD characteristics using disdrometer data along with radar observations and/or vertically pointing Doppler radar observations. One such study, using data from Darwin, Australia (a tropical coastal location), found that the two rain types could be separated in the N_W – D_m space, where Dm is the mass-weighted mean diameter and N_W is the normalized intercept parameter. Since then, the separation method has been tested using data and observations from Greeley, Colorado, a mid-latitude continental location with semi-arid climate, and Huntsville, Alabama, a sub-tropical continental location. In this paper, we investigate the same separation technique using data and observations from a mid-latitude coastal region, situated in the Delmarva peninsula in Virginia at NASA Wallops Flight Facility (WFF). Two different types of disdrometers were used to construct the full DSD spectra and the NW versus Dm based classification is compared with simultaneous observations from a S-band polarimetric radar 38 km away from the disdrometer site. Three-minute DSDs were used for the classification and RHI (range-height indicator) radar scans over the disdrometer were used for validation. Events which occurred on 14 and 16 October 2019 were chosen. Results show, surprisingly, that there was no need to modify the separation criteria from those used in Darwin, AU. Also considered were the outer rain bands of Hurricane Dorian (as Category-1) which occurred on 06 September 2019. However, in this case, it was largely stratiform rain during the major part of the storm affecting WFF. Scattering (T-matrix) calculations using the 3-minute DSD spectra were used to derive retrieval equations for N_W and D_m for the S-band radar data. These were applied to the radar scans to identify convective and stratiform rain regions as well as mixed or transition regions. Chosen RHI scans from two events (14 October 2019 and 06 September 2019) will be used as illustrative examples. Vertical profiles of reflectivity, differential reflectivity and copolar correlation coefficient over the disdrometer site will be extracted to establish whether or not the melting layer can be clearly distinguished. Specific times will be chosen from the two events and compared against the disdrometer data based classification as well for those corresponding times.

Atmospheric moisture transport plays an important role in the genesis of tropical cyclones (TCs). In this study were investigated the moisture sources associated to its genesis in the tropical Atlantic Ocean near West Africa, from 1980 to 2018 from June to November. To detect the TCs it was used the HURDAT2 database from the National Hurricane Center, and the outputs of the Lagrangian FLEXPART model were tracked to determine their moisture sources. This model permits to track backward in time the air masses from the genesis region of the TCs and to identify those regions where the moisture is uptake. The results reveal that a 18.3% (109 TC) of the total number of TCs that were formed in the North Atlantic (NATL) basin were originated in the region of study. The major frequency for the TCs genesis was observed in August and September, each one representing approximately 45% of the total. The transport of moisture for TCs mainly comes from the eastern of the North and South Atlantic Ocean, as well as from the Sahel region, providing the necessary water vapor to initiate the convective activity. A complementary analysis of atmospheric and oceanic conditions confirmed a path of moisture transport and vorticity linked to the West African Monsoon development and the African easterly jet to the Atlantic Ocean. In 1980 and 2010 were formed the major number of TCs in the study region (6 in each season). The average pattern of (E – P) > 0 for the 1980 TCs revealed an enhanced contribution of moisture from the north Atlantic Ocean from the African coast to the Iberian Peninsula. However, in 2010 was observed positive anomalies of oceanic and land evaporation near the coast of the southern West Africa, and across the Sahel, respectively. Consequently, the content of humidity raised and the wind patterns favored an interhemispheric moisture transport (south-north) along the tropical eastern Atlantic Ocean. These moisture transport mechanisms, combined with a sea surface temperature higher than 26 ºC during the NATL hurricane season leads to favorable conditions for the TC genesis.

The variability in the intensity and frequency of extreme hydrometeorological events due to climate change have an enormous impact in managing water resources in developing countries. Frequently it has been recognized sudden droughts and a severe floods. This study analyzed the spatial and temporal trends of five meteorology indices: annual maximum precipitation (AMP),annual precipitation (AP), mean areal precipitation (MAP),annual maximum temperature (AMaT) and annual minimum temperature (AMiT) and three stream flow indices: mean annual flow(MAF), annual maximum flow (AMaxF) and total mean annual flow (TMAF) over the Tendaho Catchment. Mann-Kendall (MK), Spearman Rho (SR), Sen’s slope test in R-program modifiedmk package and Sen innovative trend test were used to detect trends of 16 meteorological and one stream flow stations from 1979-2017. The result showed that there is a statistically significant temporal trends only in AMP, AP, AMaT and AMiT at 6, 19, 56 and 50% of the stations respectively. The remaining indices have no statistically significant trend in all the stations. It also dictates that the catchment is characterized by a slightly increasing AP and AMP; a slightly decreasing MAP and a significant increasing AMaT and AMiT trends. Except AMaT, there is no particular spatial pattern of AP, MAP and AMiT on the majority of the stations. Whereas, all stream flow indices showed a slightly decreasing temporal trend at 95% confidence. From this, we can conclude that the decreasing flow trend could be due to the decrement of MAP, an increment of temperature and construction of reservoirs in the catchment.

This study examines how the mass concentrations of gaseous species (NO, NO₂, NOx, O₃, SO₂, CO, C₆H₆) and particulate matter PM₁₀, PM_2.5 (particulate matter less than 10 µm and less than 2.5 μm) might be linked with precipitation characteristics using an observational data set for five years (2015-2019) in Bucharest metropolitan area. Particulate matter data and meteorological parameters at each site (atmospheric pressure, relative humidity, temperature, solar radiation, wind speed and direction) were extracted from the public available Romanian National Air Quality Database. Meteorology was complemented with radar products (images, reflectivity, echotops) from the C-band meteorological radar from National Meteorological Administration in Bucharest. Change of aerosol mass concentration during the evolution of the precipitation events was investigated. The aerosol scavenging coefficients were estimated and compared with those in scientific literature. Correlations between meteorological parameters and ambient pollutant levels were analyzed. Connection of meteorological phenomena occurrence and air mass origin was investigated by computing air mass backward trajectories using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model for 72 hours back. It was found a good capability of the convective precipitating systems to clear the atmosphere from fine aerosol and gaseous pollutant species. The obtained results are important for modeling of air quality and for investigations of aerosol wet deposition processes.

Previous studies have demonstrated that El Nino and Southern Oscillation (ENSO) have a distinct impact on the occurrence of severe weather and the attendant environment in the eastern two thirds of the USA. Typically, La Nina years have been shown to be more active in the central USA. Here, a previous study of tornado activity in Missouri from 1948 – 1999, as well as the neighboring states of Iowa, Nebraska, and Kansas, is updated to included the most recent two decades. The datasets used in this study were the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP / NCAR) re-analyses and the National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center (SPC) event archive were used. The results demonstrated that recently tornado activity in this region was higher than that of the late 20^th century suggesting interdecadal variability in the time series. The interannual variability for the latest two decades is similar to that of the last half of the 20^th century. Finally, these results will show that there is a correlation between the in-season soil moisture and tornado activity, but it is not clear whether the correlation was a lead or lag

This study investigated the number of tropical cyclones (TCs) that affected Puerto Rico during the June-November hurricane season in the period 1980-2016, and their contribution to total precipitation. Special attention was dedicated to assessing the role of atmospheric-oceanic teleconnections in the formation of TCs that affect this island. The HURDAT2 tropical cyclone climatology database and the multi-source weighted set precipitation (MSWEP) v2 data with a spatial resolution of 0.1 ° x 0.1 ° were used. A total of 92 TCs within a 500-km radius of Puerto Rico were identified for the study period. Contrary to what was expected, a similar percentage of affectation was found between those TCs formed under El Niño (17.39 %) and La Niña (17.39 %) conditions. Regarding the North Atlantic Oscillation, a 23.91 % of the 92 TCs that affected Puerto Rico formed under the negative phase, while the 13.04 % during the positive phase, which is explained in agreement with previous findings with the weakening and shift to the south and west of the Azores High during the negative phase of NAO. The role of the Sea Surface Temperature on the genesis of TCs that affected Puerto Rico was also assessed through the Atlantic Meridional Mode (AMM) and the Atlantic Warm Pool area. In the positive (negative) phase of the AMM was formed the 23.91 % (3.26 %) of the total number of TCs, while under larger (smaller) AWP formed the 61.96 % (16.3 %). It confirms that the affectation of TCs in Puerto Rico is highly related to the eastward extension of the AWP and the SST gradient in the tropical North Atlantic region. Indeed, a westward shift in genesis position was found from July to November. The mean TC rainfall contribution for the cyclonic season was ~ 9 %. It experimented a great interannual variability during the study period and a positive but non-significant trend. As expected, the correlation of the TC rainfall contribution is positively correlated (r = 0.54) with the amount of TCs. Finally, a monthly analysis revealed that in August and September the maximum contribution of rain from CT occurred (~ 17 %), followed by October (~ 6 %) and November (~ 4 %).

In the south of Eastern Siberia (Russia), there are three optical observatories of the Institute of Solar-Terrestrial Physics of the Siberian Branch of the Russian Academy of Sciences. The observatories are equipped with instruments to observe the Sun, near and deep space. The first one, Sayan Solar Observatory, is located in the mountains at the height of 2,000 m (51.67 N, 100.99 E). The second, Baikal Astrophysical Observatory, is on the southern bank of Lake Baikal near the settlement of Listvyanka (51.86 N, 104.86 E). These two observatories were built in the 60s — 70s of the last century. The third observatory is now being built in settl. Tory, Buryatia (51.78 N, 103.00 E).

During space observations in the optical range, atmospheric conditions are of great importance. The purpose of this work is to study the long-term dynamics of cloud cover within the region of the optical observatories. We used the following data: Monthly mean High cloud cover, Low cloud cover, Medium cloud cover and Total cloud cover (TCC) values derived from ERA-Interim 1979–2018 with spatial resolution 1º×1º and Monthly mean TCC values derived from NCEP Reanalysis Gaussian Grid 1948–2019. We calculated cloud cover values averaged along 50,475º N (NCEP) and along 51º N (ERA) over 101º–105º E (5 grid points for ERA and 3 grid points for NCEP).

We found that since about the mid-1990s, the amount of high, medium, and low cloud cover has been decreasing in summer. While only positive cloud cover anomalies were detected during construction of the observatories and the first observations at them, since 2005, the cloud cover anomalies are predominantly negative.

Electricity production through renewable energy sources, such as wind energy, is dependent of the variability of weather conditions. Thus, this work aims to assess the wind resource available and the wind energy potential during recent December months (the three years 2017, 2018, 2019). These winters were characterized by the occurrence of high impact storms. To understand the effect of the strong winds associated with the passage of the storms during these months, 10m wind components (10-meter U and V wind components) are used. The fields were extracted at 00, 06, 12 and 18 UTC ( 6-hourly data), for the 2017, 2018 and 2019 December months over a geographical sector that covers the Iberian Peninsula (IP) region (35°N–45°N;10°W–4°E) and compared to climatological values for the 1981-2010 period. The obtained results show an increase of wind intensity of up to 1.5 m.s^-1 in IP during December 2017 and 2019, and a decrease of 1.5m.s^-1 in December 2018, when compared with the respective climatology for the 1981-2010 period. This increase corresponds to an increase in the corresponding wind energy potential. Therefore, our results agree with the values of the wind energy produced during the analyzed months for the two countries, Portugal and Spain. Finally, it is notably that the highest values of wind energy production occurred on the days of the storms passage through the IP.

The study is a first attempt to quantitatively evaluate an existing satellite-based rain estimation algorithm using a network of ground-based meteorological stations. The study domain is the Epirus Region (Greece), which is one of the rainiest areas of Greece, and where the laboratory of Meteorology and Climatology (Ioannina University) operates eight meteorological stations distributed across the study domain. The utilized version of the rain estimation algorithm is using the Meteosat-11 Brightness Temperature in the 10.8 μm channel (BT_10.8μm) to estimate the rain intensity on a 4 Κm pixel basis, after discriminating the rain/non-rain pixels with a simple thresholding method. The validation procedure consists, at first, in a selection of dates of the year 2019 during which a wide range of rainfall values were recorded. Subsequently, the rain recordings of the meteorological stations’ network were spatiotemporally correlated with the Meteosat-11 data. These correlations finally led to a dataset with 1323 pairs of rain recordings and their relative rain estimations from the satellite-based algorithm. A statistical analysis of these pairs of values was conducted revealing a Mean Error (ME) of 0.22 mm/hr (14% error regarding the mean value of the recordings). Also, basic categorical statistics were calculated to assess the accuracy of the satellite-based algorithm in providing rainfall estimations (in cases that rain recordings from the network of the meteorological stations exist). The computed Probability of False Detection (POFD), Probability of Detection (POD) and the bias score are equal to 0.22, 0.69 and 0.88, respectively. The evaluation statistics are promising with regards to operationally using this algorithm for rain estimation on a real-time basis.