Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

The case study on strong temperature inversion event in Armenia has been presented in this paper using observed temperatures and simulations from both global (ERA-Interim) and regional high-resolution (WRF) models. According to the observations the December of 2013 was ranked as the second coldest since 1961. Monthly mean temperature anomaly for entire Armenia consisted of -4.4 °C in December, while that for inversion affected basins such as Ararat valley was -9-7 °C. The persistent temperature inversion in Ararat valley lasted more than two weeks resulted in unprecedented cold wave events. The ability of the global ERA-Interim and regional WRF models to simulate this dramatic temperature event in Armenia has been examined. The results show that high resolution WRF model has a clear advantage over ERA-Interim model in representation of spatial temperature pattern in Armenia. The small-scale variations of temperature and surface inversion basins are simulated by WRF model while ERA-Interim model provides very coarse results underestimating the spatial variability and influence of topography. However, it should be noted that there are significant uncertainties and errors in WRF temperature forecasts. Significant RMSE values and negative correlation coefficients obtained for the area covering inversion basin indicate that WRF model fails to capture the temporal variability of temperature during the inversion event. It is worth noting the significant positive bias for daytime WRF temperatures during the strongest phase of the inversion.

This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

The ultimate goal of the study is to develop an early warning system for the south and southeast regions of Armenia (11 in total) by defining specific thresholds for issuing alerts for adverse and severe weather phenomena. In the article the high temperature, wind and precipitation weather elements are discussed based on the experiments performed during the summer periods of 2011 and 2014. The system has been implemented based on the mesoscale Weather Research and Forecasting (WRF) model [1], which is adapted to the territory of Armenia and used for operational weather forecasting. The verification methodology is to analyze the model results against observations received from four ground hydrometeorological stations located in the south and southeast regions of Armenia. The correlation coefficients, standard deviations of the differences and biases are calculated for the air temperature and wind speed and for precipitation amount and yes/no contingency tables are constructed.

The study aims to evaluate the Landsat 8 Thermal Infrared Sensor land surface temperature (LST) products and Visible Infrared Imager Radiometer Suite (VIIRS) LST environmental data record against ground observations in Armenia and Belarus. The data from the Landsat-8 satellite is used due to its high spatial resolution. In contrast, the VIIRS information is obtained due to its high temporal resolution, accessible with a minimal delay after the flyby of satellites. Algorithms to calculate LST were analyzed to find the best performance for both daytime and nighttime data. The evaluation shows that the current VIIRS LST products demonstrate a reasonable accuracy, with a root mean squared error average of 2.77 $\mathbf{K}$ and an average coefficient of determination of 0.92 K.

The number of extreme weather events including strong frosts, cold waves, heat waves, droughts, hails, strong winds has increased in Armenia by 20% during the last 20 years. The paper studies the formation of cold-air pools in Ararat Valley, Armenia during the winter season. Observational data from 47 meteorological stations of Armenia were used, and daily minimum temperatures lower than -10 °C were assessed over the period 1966-2017. December, 2016 was considered as the 4-th coldest month, after the years 2013, 2002 and 1973. The focus area of this study is the low-elevated basin of Ararat Valley for which climatological analysis of winter temperature regime has been performed. Monthly average temperatures for December were significantly below normal values, particularly, for low-elevated part of Ararat Valley. 24-hour simulations derived from Weather Research and Forecasting model (WRF) were used to assess the WRF model's capabilities to reproduce strong cold-air pool (CAP) over the Ararat Valley observed on 20 December 2016 when minimum temperatures decreased up to -20 °C and lower. The WRF model was applied with spatial resolutions of 9 and 3 km and 65 vertical levels based on Global Forecast System model's (GFS) initial and boundary conditions at 0.25×0.25 deg. resolution.