Abstract. The ALADIN System is a numerical weather prediction (NWP) system developed by the international AL-ADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 partner institutes of this consortium. These configurations are called the ALADIN canonical model configurations (CMCs). There are currently three CMCs: the AL-ADIN baseline CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations.The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs, (iii) to document their most recent versions, and (iv) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the partner institutes of the AL-ADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

The aim of the study was to investigate the temporal and spatial variability of last spring and first autumn frost events as well as the length of the frost‐free season (FFS) in Central Europe in relation to atmospheric circulation. Studies were conducted for the period 1951–2010 using gridded, daily minimum air temperature data obtained from the E‐OBS dataset at 0.25° spatial resolution. To assess the possible impact of air temperature variability on plants, late spring frost events and severe frost events were also examined with respect to the beginning of the thermal growing season. The role of atmospheric circulation was described using Grosswetterlagen circulation types and NAO index, and finally estimated using empirical orthogonal function analysis (EOF). The results confirm a significant increase in the length of the FFS, up to 10 days per decade in the western parts of Europe. This is mostly a result of earlier occurrence of last spring frost in the west up to 5 days. The occurrence of first autumn frost shows no significant trend in most of the studied regions. The obtained spatial pattern of the trends reflects oceanic (west) and continental (east) climatic conditions of the study area. Detailed analysis of circulation types favouring the occurrence of frost in Central Europe indicates that anti‐cyclonic situations are mainly responsible. EOF analyses for the springtime confirm that the first mode, which accounts for 56% of total variance, is related to an extensive high pressure system over eastern Ukraine and Belarus, which brings an inflow of cold, continental air masses to Central Europe. The results provide a broaden information on the region climatologically important due to its transitional location, which may be relevant for investigating past and future trends in spring freeze risk for perennial crops, as changes in the frequency of these airflow patterns will result in changes in the risk of frost damage.

Water vapor plays a major role in the process of radiation, cloud formation, energy exchange within a system, and remains a key component of the Earth's atmosphere. The purpose of this study is to examine the water vapor content of the troposphere over Europe and the Northern Atlantic. Both temporal and spatial differences were examined for total column water vapor (TCWV) and vertically integrated water vapor flux (IWVF) based on ERA-Interim reanalysis data. The statistical relationship between circulation patterns, as expressed by empirical orthogonal function (EOF) modes, and TCWV were examined, as were statistical relationships between distinguished advection types and TCWV and IWVF. The study confirmed the significance of atmospheric circulation in the formation of moisture content in the winter season (i.e., January) and its markedly lower impact in other seasons. The relationships noted in the study are characterized by statistically significant spatial differentiation. Spatial pattern analysis was used to identify six regions with different moisture content over the course of the year. The boundaries of the regions confirm the significant role of local factors impacting moisture content.

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the novel coronavirus. The role of environmental factors in COVID-19 transmission is unclear. This study aimed to analyze the correlation between meteorological conditions (temperature, relative humidity, sunshine duration, wind speed) and dynamics of the COVID-19 pandemic in Poland. Data on a daily number of laboratory-confirmed COVID-19 cases and the number of COVID-19-related deaths were gatheredfrom the official governmental website. Meteorological observations from 55 synoptic stations in Poland were used. Moreover, reports on the movement of people across different categories of places were collected. A cross-correlation function, principal component analysis and random forest were applied. Maximum temperature, sunshine duration, relative humidity and variability of mean daily temperature affected the dynamics of the COVID-19 pandemic. An increase intemperature and sunshine hours decreased the number of confirmed COVID-19 cases. The occurrence of high humidity caused an increase in the number of COVID-19 cases 14 days later. Decreased sunshine duration and increased air humidity had a negative impact on the number of COVID-19-related deaths. Our study provides information that may be used by policymakers to support the decision-making process in nonpharmaceutical interventions against COVID-19.