Search

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50). [ABSTRACT FROM AUTHOR]

The paper compares air and sea surface temperatures in recent years on two islands in the Adriatic Sea. The data measured at the climatological station Krk on the island of Krk and the main meteorological station Lastovo on the island of Lastovo are used. The island of Krk is located in the north of the Adriatic Sea and Lastovo in the south. Since a significant increase in air and sea surface temperatures has been observed over the last thirty years, the goal is to establish how they reflect at these two stations, 313 km apart. The goal of the analysis is to monitor the changes in these two islands to reduce the negative impacts they may cause. The analysis of sea temperatures showed that global warming has a greater impact in the northern Adriatic than in the southern Adriatic. Air and sea surface temperatures have a faster upward trend on Krk than on Lastovo. Similar to the Mediterranean Sea, a positive trend was observed in the Adriatic Sea for both sea surface temperature and air temperature. [ABSTRACT FROM AUTHOR]

The study of climate element distribution models under complex terrain conditions is a popular topic in the field of GIS application in climatology, especially in plateau areas with a complex topography and scarce meteorological station information. In this paper, the spatial and temporal heterogeneity of temperature and precipitation at the northeastern edge of the Tibetan Plateau was analyzed by taking the northeastern edge of the plateau as the study area and constructing a topographic spatial statistical model using 47 meteorological stations and digital elevation models from 1981 to 2010. The following conclusions were drawn from the study: (1) The ME of the temperature distribution model for each month is below 0.9 °C; the maximum ME of the precipitation distribution model for each month is −5.808 mm in July, and the precipitation distribution model has similar error characteristics with the temperature distribution model, which can reflect the horizontal zone distribution pattern of meteorological data and can clearly show the changes of temperature and precipitation as the altitude increases. (2) The spatial distribution pattern of temperature is as follows: the temperature in the study area gradually increases from the southwest to the northeast, with Zhouqu County in Linxia Prefecture and Gannan Prefecture as the main high-temperature areas; the spatial distribution of precipitation is as follows: the precipitation in the southwest of the study area is significantly higher than that in the north, and the precipitation in Linxia Prefecture is significantly lower than that in Gannan Prefecture. (3) The temporal distribution pattern of the temperature distribution model is as follows: the overall temperature in the study area is at its lowest level in January, and the maximum temperature is only 2.6 °C, until July, when the maximum temperature rises to 24.2 °C and then gradually starts to decline; the spatial distribution of precipitation is as follows: the precipitation in the study area gradually rises from January, and the maximum precipitation rises to July and then starts to decline, and in December the precipitation falls to the lowest level. The temporal distribution characteristics of the precipitation distribution model are similar to those of the air temperature model, with obvious hydrothermal synchronization characteristics. [ABSTRACT FROM AUTHOR]

Dust storms are one of the important natural hazards that affect the lives of inhabitants all around the world, especially in North Africa and the Middle East. In this study, wind speed, wind direction, and air temperature patterns are investigated in one of the dustiest cities in Sistan Basin, Zahedan City, located in southeast Iran, over a 17-year period (2004–2020) using a WRF model and ground observation data. The city is located near a dust source and is mostly affected by local dust storms. The World Meteorology Organization (WMO) dust-related codes show that the city was affected by local dust, with 52 percent of the total dust events occurring during the period (2004–2021). The city's weather station reported that 17.5% and 43% were the minimum and maximum dusty days, respectively, during 2004–2021. The summer and July were considered the dustiest season and month in the city. Since air temperature, wind speed, and wind direction are important factors in dust rising and propagation, these meteorological factors were simulated using the Weather Research and Forecasting (WRF) model for the Zahedan weather station. The WRF model's output was found to be highly correlated with the station data; however, the WRF simulation mostly overestimated when compared with station data during the study period (2004–2020). The model had a reasonable performance in wind class frequency distribution at the station, demonstrating that 42.6% of the wind was between 0.5 and 2, which is in good agreement with the station data (42% in the range of 0.5–2). So, the WRF model effectively simulated the wind class frequency distribution and the wind direction at Zahedan station, despite overestimating the wind speed as well as minimum, maximum, and average air temperatures during the 17-year period. [ABSTRACT FROM AUTHOR]

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas. [ABSTRACT FROM AUTHOR]

Mountains are highly sensitive to the effects of climate change, including extreme short- and long-term weather phenomena. Therefore, in spite of relatively high annual precipitation totals, mountains might become endangered by droughts. The paper presents drought trends in the Polish Carpathians located in Central Europe. Data from the period 1991–2020 from 12 meteorological stations located in various vertical climate zones of the mountains were used to define drought conditions using the following indices: Standardized Precipitation (SPI), Standardized Precipitation Evapotranspiration (SPEI), Relative Precipitation (RPI) and Sielianinov. Additionally, four forest drought indices were used in order to estimate the impact of drought on beech as a typical Carpathian tree species, i.e., the Ellenberg (EQ), Forestry Aridity (FAI), Mayr Tetratherm (MT) and De Martonne Aridity (AI) indices. Statistically significant but weak trends were obtained for the 6-month SPI for four stations (indicating an increase in seasonal to mid-term precipitation), for the 1-month SPEI for three stations, for the 3-month SPEI for four stations, and for MT for all stations (indicating an increase in drought intensity). The analysis of dry month frequency according to particular indices shows that at most of the stations during the last decade of the study period, the frequency of dry months was much higher than in previous decades, especially in the cold half-year. Two zones of the Polish Carpathians are the most prone to drought occurrence: the peak zone due to the shift in climatic vertical zones triggered by the air temperature increase, and the forelands and foothills, together with basins located about 200–400 m a.s.l., where the mean annual air temperature is the highest in all the vertical profile, the annual sums of precipitation are very diversified, and the conditions for beech are already unfavorable. [ABSTRACT FROM AUTHOR]

The aim of this research was to determine relationships between solar activity and variability of discharges of three Central European rivers: the Vistula, Odra and Warta in Poland in the multi-annual period of 1901–2020. Changes in precipitation and air temperature at Poznań meteorological station in the same period were also analyzed. The long-term variations in river runoff were investigated both from the point of view of temporal variability in relation to climate variations in the study area, and from the point of view of linear/non-linear links to solar activity, as described by the Wolf sunspot number. The wavelet transform analysis was used to highlight the frequency-time distribution of the coherences between solar and discharge variability. It was found that most of the links between solar activity and discharges were non-linear. [ABSTRACT FROM AUTHOR]

Analysis of a long-term temperature variability of mountains has been neglected for a long time. Here we homogenised and reconstructed four temperature series (1961–2020) of selected mountain stations situated above 1000 m representing the area of northern Moravian mountain ridges (the Czech Republic) and their spatiotemporal variability was examined. A statistically significant increase of 10-year linear trends of annual (0.26–0.38), summer/winter half-year and seasonal mean temperatures were found at most stations. Summer (JJA) was detected as the season with the highest mean temperature increase between 1961–1990 and 1991–2020 normal periods (1.5 °C). From the spatial point of view, the climate conditions were becoming warmer faster in higher elevations compared to the lower ones. At all stations, a statistically significant decrease in ice and frost days was observed (−9.1–19.9 and −9.5–16.3, respectively). Moreover, it was proved that the temperature series of a newly established Vysoká hole station are representative enough and comparable with measurements of other meteorological stations in the studied region. This research provided valuable insight into the temperature variability of mountain ridges and the results completed our sparse knowledge about temperature changes in the mountain regions in Central Europe. [ABSTRACT FROM AUTHOR]

Roof surfaces on which air conditioning equipment is installed cause significant air temperature increases around the condensers due to roof thermal properties, resulting in excess electrical energy consumption and greenhouse gas (GHG) emissions. An experiment to quantify such excesses during the summer was conducted at a university campus in Mexicali, Mexico. The air temperatures and surface temperatures for three types of roofs were recorded. Temperatures (condenser inlet air temperatures) were used as the input data to a priori estimate the electrical consumption of a 5-ton A/C equipment working over these different roofs. Temperatures recorded by a nearby meteorological station were used as a reference. The results indicate a differential of up to 4.81 °C, resulting in an excess of electricity consumption of 5.55 kWh and an additional 3.9 kg of CO2 emissions, representing an 8.2% energy surplus and differences of up to 2.1% in electricity consumption provoked by microclimate. [ABSTRACT FROM AUTHOR]

Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The deep learning neural networks are more flexible but with high variance. Here, we proposed a stacking ensemble model named FLT, which consists of a fully connected neural network with embedded layers (ED-FCNN), a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to overcome the high variance of a single neural network and to improve prediction of maximum air temperature. The case study of daily maximum temperature forecast evaluated with observation of almost 2400 weather stations shows substantial improvement over that of single neural network model, ECMWF-IFS and statistical post-processing model. The FLT model can more effectively improve the forecast bias of the ECMWF-IFS model than that of any of the above single neural network model, with the RMSE reduced by 52.36% and the accuracy of temperature forecast increased by 43.12% compared with the ECMWF-IFS model. The average RMSEs of the FLT model decreases by 8.39%, 1.50%, 2.96% and 16.03%, respectively, compared with ED-FCNN, LSTM, TCN and the decaying average method. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%. [ABSTRACT FROM AUTHOR]

Short-term fluctuations in air temperature, called the daily temperature range (DTR), or its daily amplitude, have a strong impact on ecosystems, as well as on the health and well-being of people. The pronounced effect of DTR on mortality from all causes and especially on cardiovascular mortality is well documented in the scientific literature, but little is known about spatial, inter-annual, and inter-seasonal fluctuations of DTR in the Russian Far East (RFE), an area with high annual dynamics of air temperature. Data from 99 weather stations for the period from 2000 to 2019 was used to evaluate spatial and temporal DTR patterns at the RFE. A higher DTR, up to 16 °C in Toko on the border with Sakha (Yakutia) as a mean for the entire period, is typical for continental areas further to the north. Lower values are observed at continental weather stations in the south and in coastal climates (4.7 °C in Mys Alevina, Magadan Region). In general, a distinct seasonal change in DTR was found for continental locations with a significant difference from month to month throughout the year. On the annual cycle, the maximum DTR at the continental northern stations is observed in April and June–July, and at the continental southern areas in February; the minimum DTR is shown in November and December. The DTR ranges as much as from 6 °C in December to 17 °C in April in the continental Korkodon in the far north. Locations with a marine climate are characterized by a smoothed seasonal change in DTR, with obvious peaks in February–March and October, and a minimum in July–August. The downward trend in DTR for the period from 2000 to 2019, up to −0.7 °C in coastal Bolsheretsk, is based on a faster increase in the minimum daily temperature compared to the maximum, which is typical for most weather stations at the RFE and is known worldwide as a diurnal asymmetry of global warming. At the same time, an increase in the daily amplitude of air temperature (up to +0.6 °C in continental Dolinovka) was found for some localities, associated with a higher positive trend of maximum temperatures, which contradicts global patterns. [ABSTRACT FROM AUTHOR]

The change in the spatial distribution of agroclimatic conditions based on the sum of active temperatures (SAT), growing degree days (GDD), and latitude–temperature index (LTI) is discussed in this article. Data from 20 meteorological stations of IMGW-PIB (Institute of Meteorology and Water Management—National Research Institute) in Poland from the years 1966–2020 were used. The temporal and spatial diversity of mean air temperature and the chosen indices were analyzed for the period from April to October. Designating areas of diverse thermal conditions with respect to plant comfort on the basis of agroclimatic indices was attempted, together with mean air temperature and its temporal changes. The clustering, using the Ward's method, yielded four regions with different thermal resources in Poland. The study period showed an increase in the values of all agroclimatic indices and air temperature during the growing season, suggesting an increase in the thermal resources in the territory of Poland. [ABSTRACT FROM AUTHOR]

Glaciers and snow in the Caucasus are major sources of runoff for populated places in many parts of this mountain region. These glaciers have shown a continuous area decrease; however, the magnitude of mass balance changes at the regional scale need to be further investigated. Here, we analyzed regional changes in surface elevation (or thickness) and geodetic mass balance for 1861 glaciers (1186.1 ± 53.3 km2) between 2000 and 2019 from recently published dataset and outlines of the Caucasus glacier inventory. We used a debris-covered glacier dataset to compare the changes between debris-free and debris-covered glaciers. We also used 30 m resolution ASTER GDEM (2011) to determine topographic details, such as aspect, slope, and elevation distribution of glaciers. Results indicate that the mean rate of glacier mass loss has accelerated from 0.42 ± 0.61 m of water equivalent per year (m w.e. a−1) over 2000–2010, to 0.64 ± 0.66 m w.e. a−1 over 2010–2019. This was 0.53 ± 0.38 m w.e. a−1 in 2000–2019. Mass loss rates differ between the western, central, and eastern Greater Caucasus, indicating the highest mean annual mass loss in the western section (0.65 ± 0.43 m w.e. a−1) in 2000–2019 and much lower in the central (0.48 ± 0.35 m w.e. a−1) and eastern (0.38 ± 0.37 m w.e. a−1) sections. No difference was found between the northern and southern slopes over the last twenty years corresponding 0.53 ± 0.38 m w.e. a−1. The observed decrease in mean annual geodetic mass balance is higher on debris-covered glaciers (0.66 ± 0.17 m w.e. a−1) than those on debris-free glaciers (0.49 ± 0.15 m w.e. a−1) between 2000 and 2019. Thickness change values in 2010–2019 were 1.5 times more negative (0.75 ± 0.70 m a−1) than those in 2000–2010 (0.50 ± 0.67 m a−1) in the entire region, suggesting an acceleration of ice thinning starting in 2010. A significant positive trend of May-September air temperatures at two selected meteorological stations (Terskol and Mestia) along with a negative trend of October-April precipitation might be responsible for the negative mass balances and thinning for all Caucasus glaciers over the study period. These results provide insight into the change processes of regional glaciers, which is key information to improve glaciological and hydrological projections in the Caucasus region. [ABSTRACT FROM AUTHOR]

This study was designed to better understand vegetation's impact on air maximum (Tmax), minimum (Tmin), and daily temperature range (DTR), as well as seasonality and variability. We selected a flat, under synoptic-scale, northern Serbian region with an operational network of automated weather stations (AWS) for the study. Data were collected directly from the eighteen AWSs placed in the orchard canopy during 2013–2018. Meteorological data, plant phenological data in the form of the BBCH scale, and orchards' soil characteristics data were collected. Environmental factors influencing the temperature were classified as static (slow or unchangeable) and dynamic (fast-changing). The impact of both factors on maximum, minimum, and daily temperature range and its variability were analyzed. Results show that static factors (like soil texture) affect the annual variation of Tmax, Tmin, and DTR rather than its variability over the season. The dynamic factors, mainly coming from the plant's phenology, substantially affected the seasonal variability of these variables. Studies like this suffer from missing data and sparse spatial coverage by the AWS network. Therefore, the alternatives of orchard micrometeorological data, nearest climatological station, and ERA5-Land reanalysis data are tested. Both data sets showcased limitations in their applicability, while reanalysis data deviated more from the in-situ measurements, both seasonally and regionally. [ABSTRACT FROM AUTHOR]