The analogy of heterogenic ice nucleation in bulk immersion freezing of the atmospheric droplets with aerosol particle in it and freezing of moisture within the wet grounds was considered. For the experiment there were taken modeled sandy and kaolinite grounds with the weight wetness of about 25% and 90% and with the total mass of about 100 g and 80 g respectively. The samples were placed in the stainless steel or plastic dishes and were cooled down in the refrigerated chamber with ambient temperature of about -5°C. The ground samples’ temperatures were measured and recorded. The temperatures of the moment of ice nucleation in the grounds were determined. The performed experiments of the ground samples freezing indicated that ice nucleation in the considered experimental samples of sandy and kaolinite grounds happen at the temperatures of about -4°C (close to the ambient temperature of refrigerated chamber (-5°C)). This may be compared to the results of the heterogenic ice nucleation on the particles of sand and kaolinite immersed in the water droplet presented in the works (Marcolli et al., 2007, Pinti et al., 2012, Kaufmann et al., 2016).

Jaipur has seen a rapid development in the last two and a half decades being the capital city of Rajasthan and its proximity to the National capital region of India, directly impacting its environment. This systematic review and meta-analysis aimed to evaluate the status of air pollution based on available literature. A review based on status of air pollution beginning with works of researchers in 1996-97 period for Jaipur city, till the recent developments through published literature is presented here in the light of abrupt and extreme situations arising due to COVID-19. High Volume Samplers having respirable dust sampler with dust collector and filter paper were utilized in these studies and it was conducted by dividing the city into various categories such as: industrial area, commercial area, residential area, and sensitive area. Sulphur dioxide and nitrogen dioxide were measured by doing gas sampling and passing the gas through absorbing solution of sodium tetrachloromercurate and sodium hydroxide – sodium arsenite solution respectively. Carbon monoxide monitors of type CO-200 were being used to detect presence of CO and indicate the concentration in ppm. Researchers have found that the recorded mean values of PM2.5 and PM10 were much higher than the specified limit by National Ambient Air Quality Standards (NAAQS). Sound level meters were used for the measurement of noise levels. Currently, daily AQI results are provided through online services based on PM2.5, PM10, NO₂, NH₃, SO₂, CO and ozone. The AQI on 15^th May 2020 is 92, 98, and 100 at 9:00 AM, 11:00 AM, and 2:00 PM representing satisfactory category. However, AQI was 102 (moderate) at 3:00 PM and 4:00 PM. Jaipur is witnessing a major improvement in the air quality index (AQI) and noise levels during the COVID-19 crisis period due to limited anthropogenic activity since mid-March 2020.

Using a database on wildfires recorded by remote sensing for 1996–2020, we assessed the seasonal variation of direct carbon emissions from the burning in Siberian forests. We have implemented an approach that takes into account the combustion parameters and the changing intensity of the fire (in terms of Fire Radiative Power, FRP), which affects the accuracy of the emission estimate. For the last two decades, the range of direct carbon emissions from wildfires was 20–250 Тg С per year. Sporadic maxima were fixed in 2003 (> 150 Тg С/yr), in 2012 (> 220 Тg С/yr), and in 2019 (> 190 Тg С/yr). Preliminary estimation of emissions for 2020 (on 30th of September) was ~180 Tg/year. Fires in the larch forests of the flat-mountainous taiga region (Central Siberia) made the greatest contribution (>50%) to the budget of direct fire emission, affecting the quality of the atmosphere in vast territories during summer period. According to the temperature rising and forest burning trend in Siberia, the fire emissions of carbon may double (220 Тg С/yr) or even increase by an order of magnitude (>2000 Тg С/yr) at the end of the 21st century, which was evaluated depending on IPCC scenario.

Quantifying the spatiotemporal variability of rainfall is the principal component for the assessment of the impact of climate change on the hydrological cycle. A better understanding of the quantification of variability and its trend is vital for water resources planning and management. Therefore, a multitude of studies has been dedicated to quantifying the rainfall variability over the years. Despite their importance for modelling rainfall variability, the studies mainly focused on the amount of rainfall and its spatial patterns. The studies investigating the spatial and temporal variability of rainfall across Central India, in general, and at multiscale, in particular, are limited. In this study, we used a Standardized Variability Index (SVI), based on information theory to investigate the spatiotemporal variability of rainfall. The proposed measure is independent of the temporal scale, the length of the data and therefore, is able to compare the rainfall variability at multiple timescales. Distinct spatial patterns were observed for information entropies at the monthly and seasonal scale. Stations with statistically significant trends were observed and vary from monthly to seasonal scale. There is an increase in the variability of precipitation amount from South to North, indicating that spread of the rainfall is high in the South when compared to North of Central India. Trend analysis revealed there is changing behaviour in the rainfall amount as well as rainy days, showing an increase in variability of rainfall over Central India, hence the high probability of occurrence of extreme events in near future.

One of the most crucial variables in Agricultural Meteorology is Solar Radiation (Rs), although it is measured in a very limited number of weather stations due to its high cost in both installation and maintenance. Moreover, the quality of the data is usually low because of sensor failure and/or lack of calibration, which made scientists search for new approaches such as neural network models. Thus, the improvement of traditional solar radiation estimation models with minimum data availability is still needed for different purposes. In this work, several neural network models have been developed and assessed (Multilayer perceptron -MLP-, Support Vector Machines -SVM-, Extreme Learning Machine, Convolutional Neural Networks -CNN- and Long Short-Term Memory -LSTM-) with different temperature-based input variables configurations in Southern Spain (weather station located in the Mediterranean Sea coast). The performances have been analyzed using different statistical indices (Root Mean Square Error -RMSE-, Mean Bias Error -MBE-, correlation coefficient -R2- and Nash-Sutcliffe model efficiency coefficient -NSE-).

Spatiotemporal distribution has been assessed using time series of the monthly area-averaged soil moisture content of 0-10 cm underground, generated by NASA GLDAS_NOAH025_M model (Giovanni online data system), for period 2000-2019. Calculated Soil Moisture Anomaly Index (SMAI) was used to characterize the degree of saturation of the soil, comparing to normal conditions.

The clear annual soil moisture content (SM) course is observed in all agroclimatic zones of Ukraine, when the maximum is observed in February, and the minimum is in August or September. The lowest SM values are fixed in the Western Steppe and the maximum in the Carpathian region and Polesie. The analysis of time series of the SMAI showed that during the study period in summer and autumn months there was a tendency to reduce the interannual amplitude of index fluctuations and observed the transition from mostly positive values to negative ones. In winter and spring, no significant trends were found in the SMAI values.

Analysis of the correlation coefficients between the SMAI and the North Atlantic Oscillation index (NAO) showed that the statistical relationship is weak, but in some months it is significant. For all regions, except the Eastern Steppe, significant correlation coefficients are observed in March and May (-0.45 ...-0.77). The inverse statistical relationship indicates that under increasing zonal flow (NAO> 0), the SM in Ukranian areas decreases and contrariwise.

Analysis of the correlation coefficients between the SMAI and the blocking index ECBI showed that significant statistic relationship is observed in July and autumn. In July, a direct correlation was found, i.e. blocking of the zonal flow (ECBI>0) leads to an increase the SM. In October and November, negative correlation is fixed (-0.69 ...-0.49), i.e. blocking processes lead to decrease of the SM due to prevailing anticyclonic circulation in Eastern Europe this season.

ISRO's Chandrayaan-1 and NASA's Lunar Reconnaissance Orbiter (LRO) were launched on 22 October 2008 and June 18, 2009, respectively with an aim of exploration along with identification of possible safe sites for future robotic and human lunar missions. These missions have used Miniature Synthetic Aperture Radar (Mini SAR) and Miniature Radio Frequency (Mini-RF) instrument payloads developed by NASA and flown aboard India's Chandrayaan-1 spacecraft and LRO, NASA respectively. These sensors are used for hybrid polarimetry to image shadowed polar regions, where there is a possibility of water to exist. NASA estimates that there are chances of over 600 million metric tons of water ice on the moon, which further prompts the interest of researchers in exploring the moon. The hybrid polarimetric architecture of mini-SAR supports the calculation of Stokes parameters of the backscattered signal, which further allows the derivation of decomposition parameters required to obtain the sub-surface information of the craters. This research aims to study the surface properties of polar and non-polar regions of the Moon and find an approach to detect the boundaries of the Lunar craters. The ArcGIS add-in crater tool was used for delineating the boundaries of the lunar craters using the openly accessible digital elevation model (DEM) generated from Terrain Mapping Camera (TMC) data. The polar craters Nobile, Haworth, Gioja, and an unnamed crater near Byrd along with non-polar craters named Arago and Moltke were investigated for surface characteristics. The Eigenvalues and Eigenvectors were estimated to calculate entropy (H) and the Poincare ellipticity (α) which were used to characterize the scattering mechanism. The three polarimetric decomposition techniques m-δ, m-χ, and H-α applied along with the circular polarization ratio (CPR) were used to obtain the sub-surface information, and identify the shreds of evidence of water ice deposits inside the craters near the polar regions. The results found the dominance of surface scattering inside the craters and double-bounce scattering in the outer region of craters which were graphically analyzed using a transect and box plot. The analytical view of the results proved the hypothesis of the possibility of water ice deposits for the polar craters with the use of decomposition techniques with CPR greater than 1, which further needs validation through other techniques, such as dielectric constant analysis.

This brief research report is aimed to make a first approach to the study of the type of precipitation associated with a set of atmospheric river (AR) events over the Atlantic region of Galicia. Fifteen ARs that made landfall in the Spanish region of Galicia have been analyzed using the 2B-GEOPROF and 2C-PRECIP-COLUMN from the CloudSat cloud profiling radar (CPR). An estimation of the relative ratio between warm and cold precipitation associated with each event is provided. Broadly speaking, cold precipitation accounts for 80% of the total. This value is slightly higher than the already stated for Pacific AR events. However, similar mean rain rates (0.35 mm/h for the warm precipitation and 1.16 mm/h for the cold counterpart) to those reported by the literature have been obtained. In the absence of a more comprehensive and conclusive statistic, it seems that cold precipitation is predominant along the central axis of a well-developed AR. In this central core of the AR, the bulk of the moisture remains in the lower levels, and the freezing level (FL) is low. According to these results, the interaction between the warm conveyor and the cold conveyor belt may eventually raise the FL to upper levels, leading the warm fraction to play a more important —even though still secondary— role.

The Selenga is one of the crucial transboundary rivers of the semi-arid Northern Eurasia belt. The Selenga basin is located in Mongolia and Russia, and it is 83.4% of the Lake Baikal catchment area. Atmospheric precipitation is the primary source of the river supply, most of its amount (of about 450 mm per year, about 70% of the annual) falls like rain from June to August (about 70% of the annual). In the present paper, the relationship between the heaviest rains (HR) around Selenga River basin in July (above 90th percentile) and Rossby wave breaking (both cyclonic and anticyclonic type, AWB, and CWB) was examined. The total number of HR events from 1982 to 2019 was 83. For each event, the synoptic analysis and automatic detection of breaking based on potential vorticity from 2 to 9 PVU on the 350 K were utilized. In most cases (86%) of HR, events were accompanied to the RWB. It was revealed that waves are propagating along the subtropical jet (PVU overturning on 350 K) were the most important. Precipitation was observed both for the period of amplitude growth and period of waves breaking (CWB or AWB) up to the barotropic stage. However, CWBs on the subtropical jet stream that occurred east to Lake Baikal were observed in most of the HR events. The high vertical instability and precipitable water were characteristic of these CWBs. The case study is shown that the high precipitable water in part was due to the “East Asian summer monsoon” northward jump.

A sensitivity study has developed with Lin, Morrison 2-moment, WSM5 (WRF Single-Moment 5-class) and WSM6 (WRF Single-Moment 6-class) microphysics schemes available in the WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) for the numerical forecast of the wind field at José Martí International Airport. The selection of these schemes was based on their use in numerical weather forecast systems operating in Cuba. As case studies, five thunderstorms associated with synoptic patterns that cause dangerous conditions at this aerodrome were selected. The simulations were initialized at 0000 UTC with the forecast outputs of the GFS (Global Forecast System) model. The schemes were evaluated from the representation of the wind field in the region where airport is located, the headlands and center of the runway. The microphysics exhibit largest errors from 0600 to 1200 UTC, with a mean absolute error for wind speed between 2 and 4 m/s, and variations from 50 to 120 degrees for direction. The errors observed are strongly dependent on the occurrence of convection, especially on the intensity and the factors that cause it. The microphysics schemes studied presented limitations in the representation of the thunderstorms radar characteristics as a consequence of the stage of storm development. The maximum wind speed overestimation on the runway was 5 m/s. The numerical forecast at the airport was more efficient for wind speed than for wind direction. On the other hand, during the dry season (November-April) the biggest errors are located in the first hours of the forecast with WSM5 and WSM6 schemes. In addition, both parametrizations showed the worst performance between 1800 and 2700 forecast hours for the rainy period (May-October). From the evaluations, Lin microphysic scheme is the one that best reproduces the behavior of the wind field on the aerodrome.