scholarly journals ANALYSIS OF CHANGES IN AIR TEMPERATURE AND PRECIPITATION ACCORDING TO THE DATA OF WEATHER STATIONS STARITSA AND TVER OVER A LONG PERIOD

2020 ◽  
pp. 57-78
Author(s):  
Лариса Эдуардовна Лапина ◽  
Ирина Леонидовна Григорьева
Keyword(s):  
Air Temperature ◽  
Least Squares Method ◽  
Average Rate ◽  
Statistical Characteristics ◽  
Monthly Precipitation ◽  
Weather Station ◽  
Regional Variability ◽  
Temperature And Precipitation ◽  
Significant Difference ◽  
Weather Stations

Анализируется региональная изменчивость среднемноголетних характеристик температуры воздуха в приземном слое и осадков по данным метеостанций в Старице и Твери, расположенных в бассейне верхней Волги. Среднегодовые и среднемесячные значения характеристик проанализированы с использованием метода скользящего среднего. Рассмотрен 30-летний период осреднения. Проанализированы данные по метеостанции Старица с 1962 по 2017 гг., а по метеостанции Тверь - с 1944 по 2017 гг. Для Старицы использованы суточные данные, для Твери - только среднемесячные. Данные температуры приземного слоя воздуха для каждого года наблюдений аппроксимировались простой синусоидальной функцией. Показано, что среднемноголетние значения амплитуд годовых колебаний для обеих метеостанций имеют тенденцию к снижению, а среднегодовые значения температур - тенденцию к повышению. Методом наименьших квадратов найдены параметры уравнений прямых, описывающих изменчивость среднемноголетних величин амплитуд годовых колебаний и среднемноголетних годовых температур воздуха. Сравнивались периоды наблюдений с 1961 по 1990 гг. и с 1991 по 2017 гг. для Твери и Старицы. Приводятся статистические характеристики температуры воздуха и осадков для двух периодов, сопоставлены значения метеостанций в Старице и Твери. Показано, что существенная разница между значениями температуры воздуха для обеих метеостанций наблюдается только во втором периоде. Среднемноголетние годовые суммы атмосферных осадков в этих городах имеют тенденцию к повышению со средней скоростью 18 мм/год. Месячные суммы атмосферных осадков однозначной тенденции, одинаковой для всех месяцев, не имеют. Скорость роста среднемноголетних значений температуры воздуха в Твери оценивается в 0.04˚С/год, в Старице - 0.03˚С/год. Regional variability of the average annual characteristics of air temperature in the surface layer and precipitation is analyzed based on data from weather stations in Staritsa and Tver, located in the Upper Volga basin. The average annual and average monthly values of the characteristics are analyzed using the moving average method. The 30-year averaging period is considered. Data on the Staritsa weather station from 1962 to 2017 and on the Tver weather station from 1944 to 2017 were analyzed. For Staritsa daily data were used, while for Tver only the average monthly data was used. The surface air temperature data for each year of observations was approximated by a simple sinusoidal function. It is shown that the average annual values of the amplitudes of annual fluctuations for both weather stations tend to decrease, and the average annual values of temperatures tend to increase. The parameters of the linear equations describing the variability of the average annual values of the amplitudes of annual fluctuations and the average annual air temperatures are found using the least squares method. We compared the observation periods from 1961 to 1990 and from 1991 to 2017 for Tver and Staritsa. Statistical characteristics of air temperature and precipitation for both periods are given. Values for both weather stations are compared. It is shown that a significant difference between the air temperature values for both weather stations is observed only in the second period. The average annual precipitation in both cities tends to increase at an average rate of 18 mm /year. Monthly precipitation totals do not have the same trend for all months. The rate of growth of the average annual air temperature in Tver is estimated at 0.04˚C/year, in Staritsa - 0.03˚C/year.

scholarly journals The Street Air Warming Phenomenon in a High-Rise Compact City

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 402 ◽  
Author(s):  
Xiaoxue Wang ◽  
Yuguo Li ◽  
Xinyan Yang ◽  
Pak Chan ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Air Temperature ◽  
Thermal Environment ◽  
Urban Climate ◽  
Pollutant Dispersion ◽  
Urban Morphology ◽  
Air Temperatures ◽  
Significant Difference ◽  
Sensitivity Studies ◽  
Weather Stations

The street thermal environment is important for thermal comfort, urban climate and pollutant dispersion. A 24-h vehicle traverse study was conducted over the Kowloon Peninsula of Hong Kong in summer, with each measurement period consisting of 2–3 full days. The data covered a total of 158 loops in 198 h along the route on sunny days. The measured data were averaged by three methods (direct average, FFT filter and interpolated by the piecewise cubic Hermite interpolation). The average street air temperatures were found to be 1–3 °C higher than those recorded at nearby fixed weather stations. The street warming phenomenon observed in the study has substantial implications as usually urban heat island (UHI) intensity is estimated from measurement at fixed weather stations, and therefore the UHI intensity in the built areas of the city may have been underestimated. This significant difference is of interest for studies on outdoor air temperature, thermal comfort, urban environment and pollutant dispersion. The differences were simulated by an improved one-dimensional temperature model (ZERO-CAT) using different urban morphology parameters. The model can correct the underestimation of street air temperature. Further sensitivity studies show that the building arrangement in the daytime and nighttime plays different roles for air temperature in the street. City designers can choose different parameters based on their purpose.

scholarly journals Annual range of temperature and precipitation forecast for Altai-Sayan mountain country

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuriy Borisovich Kirsta ◽  
Olga Volfova Loucka
Keyword(s):  
Precipitation Forecast ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Model Quality ◽  
Temperature And Precipitation ◽  
Annual Range ◽  
Weather Stations ◽  
Sayan Mountain ◽  
Mountain Country

Analysis and long-term forecasting of climatic characteristics of the mountains is laborious and extremely difficult due to complex vertical and horizontal differentiation of climatic fields and insufficient number of weather stations in the region. We have developed a method for statistical forecast of average monthly temperature in the surface air layer and monthly precipitation for the mountain areas with an annual lead time. The method is based on the description of monthly dynamics of the mentioned factors expressed in percent of their average annual monthly values measured in situ. Such a dynamics remains the same throughout the study territory, regardless of its height and exposure. To convert the relative values of temperature and precipitation into their conventional units of measurements (C and mm) one needs just mean annual January and July values of air temperature and precipitation for the territory under study. By the example of the Altai-Sayan mountain country, it is shown that the use of observation data for 67 years obtained from several reference weather stations ensure reliable prediction. The forecast is equally true for any part of the mountainous country due to spatial generalization of relative changes in these factors. The universal criterion A for assessing the quality of various predictive methods (including those, which do not use the model quality indices RSR and NashSutcliffe) is proposed. The criterion is the error of predictive method Sdiff normalized by standard deviation Sobs of observations from their average and equals to Sdiff/ Sobs. It is associated with NSE and RSR indices through dependencies RSR = A and NSE = 1RSR2 = 12A2. The proposed criterion was used in assessing the quality of temperature and precipitation forecasts; it was close to the theoretically best one for statistical prognoses.

Assessment of changes in air temperature and precipitation in Transbaikalia

2021 ◽  
Vol 2 ◽  
pp. 138-146
Author(s):  
V.K. Smakhtin ◽  
Keyword(s):  
Air Temperature ◽  
High Water ◽  
Total Precipitation ◽  
Significance Level ◽  
Temperature And Precipitation ◽  
Annual Total Precipitation ◽  
Lena Basin ◽  
Annual Total ◽  
Weather Stations

Assessment of changes in air temperature and precipitation in Transbaikalia/ Smakhtin V.K. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 138-146. The paper analyzes long-term fluctuations in average air temperature and annual total precipitation in Transbaikalia. Between 1951 and 2020, air temperature increased by 2.3 °C according to 40 weather stations. Warming is mainly manifested in the air temperature rise in February, March and April. From 1955 to 2017, the decrease in annual total precipitation was 56 mm in the Amur basin and 39 mm in the Yenisei basin. The trends are reliable at the 5% significance level. In the Lena basin, annual total precipitation during the mentioned period increased by 7 mm, the trend is not reliable at the 5% significance level. The high-water phase has been observed since 2017. Taking into account that two previous high-water phases lasted 16‒17 years, it may be supposed that a risk of precipitation above the normal will be kept in the next 13–14 years. Keywords: climate change, air temperature, precipitation, phases of water content, trendsRef. 81.

scholarly journals Regional Climate Change (Karelia, Russia)

2015 ◽  
Vol 2 ◽  
pp. 356
Author(s):  
Larisa Nazarova
Keyword(s):  
Air Temperature ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climatic Conditions ◽  
Temperature And Precipitation ◽  
The Mean ◽  
Weather Stations ◽  
Meteorological Observations ◽  
Mean Annual Air Temperature

The overview of climatic conditions in Karelia is based on the data from meteorological observations carried out in 1951-2009 at Roskomgidromet weather stations situated in the study area. Taking the period in question into account, the mean annual air temperature norm has increased by 0.2-0.3°C. The greatest deviation from multiyear averages of mean monthly air temperature is observed in January and March. The investigation of the changes the basic regional climate characteristics is very important in present time because the global climate is changed. The analysis the data about air temperature and precipitation, that were obtained for the different meteorological stations in the investigated region, shows that the regional climate is changed and the main tendencies are directly proportional to the change of the global characteristics.

scholarly journals Spatially detailed urban climatology for temperature and precipitation.

2021 ◽  
Author(s):  
Cristina Lavecchia ◽  
Enea Montoli ◽  
Samantha Pilati ◽  
Giuseppe Frustaci
Keyword(s):  
Spatial Resolution ◽  
Air Temperature ◽  
High Spatial Resolution ◽  
Urban Climate ◽  
Temperature Fields ◽  
Statistical Characteristics ◽  
Near Surface ◽  
Urban Climatology ◽  
Temperature And Precipitation ◽  
Recent Climate

<p>With the growing relevance of urbanized environments in the framework of adaptation and mitigation plans, improvements in monitoring the urban weather, and specially in the knowledge of the urban climatology and its evolution, are urgently needed. A basic difficulty arises from the fact that dedicated surface observational networks with the desired characteristics of measurement quality and continuity are often lacking in cities, while remote sensing data are mainly used for specific aspects, as for instance the Surface Urban Heat Island, while air temperature is more important for applications. After the experience gained, and the methodologies developed in Milan during a locally co-funded project (ClimaMi: https://www.progettoclimami.it/), the possibility was investigated of a medium- to high-resolution urban climatology mainly derived from observed air temperature and precipitation data.</p><p>The urban specialized surface network (by Fondazione Osservatorio Meteorologico Milano Duomo: FOMD), in operation since 2011 and “metrologically” tested during MeteoMet Project (Merlone et al., 2015), was considered as a reliable basis for a new and more detailed analysis of the most recent urban climate in Milan. To complement the necessarily limited number of high quality measurements by this urban Climate Network (CN),  other  automatic weather stations  (as homogenous as possible to CN) were accurately selected from third-party networks, in particular from the regional (ARPA Lombardy) meso-synoptic one, and from a private citizens association (MeteoNetwork): this helped in setting up a database of reliable hourly observational data (and metadata) in urban and peri-urban environments, dense enough for a mesoscale description of the city main statistical characteristics and for an already significative time span of 5 years.</p><p>Nevertheless, resilience plans by local authorities and professionals often require a spatial resolution of the order of tens of meters: to significantly improve the spatial resolution, space-borne sensors are an obvious and nowadays practical possibility. Furthermore, to make the best use of the quality of (under sampled) surface measurements, and of the high spatial resolution offered by remote sensed data, a cokriging-based methodology (Goovaerts, 1999) was developed and tested for air temperature. While direct correlation methods between Land Surface Temperature (LST) and the (more interesting and required) near-surface air temperature are not straightforward and generally unreliable, the encouraging results obtained in reconstructing air temperature fields by cokriging allowed an analysis of the recent climate of the cities and neighborhoods at medium to high spatial resolution for selected weather types of particular relevance in the definition of resilience measures.</p><p>The same methodology is now under test for precipitation measurements by different sensors and networks, and first results will be presented together with the unprecedented climatological description of temperature in the greater Milan, and analysis of micro-scale urban climate variations in consideration of (present and future) climate monitoring and assessment needs.</p>

scholarly journals Air temperature and precipitation regime in Ukraine in 2021-2050 by CORDEX model ensemble

2020 ◽  
pp. 17-27
Author(s):  
M. S. Zamfirova ◽  
V. M. Khokhlov
Keyword(s):  
Air Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Monthly Precipitation ◽  
Precipitation Regime ◽  
Maximum Increase ◽  
Temperature And Precipitation ◽  
Maximum Temperatures ◽  
Southern Ukraine

Global temperatures over the period of 2081–2100 are expected to rise by 0.3–4.8 °C compared to the period of 1986–2005. According to the previous studies, the average annual air temperature in all regions of Ukraine will keep increasing in the near future and the maximum increase in precipitation is expected mainly in the western and northern regions during winter and spring, whereas the decrease in precipitation will be registered in the central, eastern and southern regions during summer and autumn. This article aims to identify the features of changes in air temperature and precipitation for different regions of Ukraine in 2021–2050 based on the modelling results of the ensemble of CORDEX models as per the RCP4.5 scenario. 16 simulation runs for 7 regional climate models were selected for the analysis and the results were presented for five regional centers of Ukraine: Kyiv, Lviv, Kropyvnytskyi, Kharkiv and Odesa. It is shown that future monthly precipitation in all regions tends to increase by an average of 20–40 mm during autumn, winter and spring, whereas the decrease is expected to occur in summer. According to some models, the monthly precipitation will be close to zero in the Southern Ukraine in July and August, which is typical for the Mediterranean climate. Compared to the period of 1961–1990, the average monthly temperature will undergo small changes (up to 1 °C) in spring and autumn, while the temperature in summer and winter will increase by 2.5–3.5 °C. In Odesa, in contrast to the present-day situation, a positive average monthly air temperature will be expected to be recorded throughout the whole year, and only 25% of the runs show negative average monthly minimum temperatures. In the Northern Ukraine, the average monthly minimum and maximum temperatures in winter will increase by 2.0–2.5 °C, and in summer only the maximum air temperature will increase significantly. Thus, we can assume a change in the regime of moisture supply in Ukraine over the next thirty years. One can also assume a high probability of snow cover absence throughout the whole winter in the Southern Ukraine as a result of positive temperatures.

scholarly journals Meteorological effects of the solar eclipse of 20 March 2015: analysis of UK Met Office automatic weather station data and comparison with automatic weather station data from the Faroes and Iceland

2016 ◽  
Vol 374 (2077) ◽  
pp. 20150212 ◽  
Author(s):  
Edward Hanna ◽  
John Penman ◽  
Trausti Jónsson ◽  
Grant R. Bigg ◽  
Halldór Björnsson ◽  
...  
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Solar Eclipse ◽  
Temperature Drop ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Station Data ◽  
Weather Station Data ◽  
The Mean ◽  
Weather Stations

Here, we analyse high-frequency (1 min) surface air temperature, mean sea-level pressure (MSLP), wind speed and direction and cloud-cover data acquired during the solar eclipse of 20 March 2015 from 76 UK Met Office weather stations, and compare the results with those from 30 weather stations in the Faroe Islands and 148 stations in Iceland. There was a statistically significant mean UK temperature drop of 0.83±0.63°C, which occurred over 39 min on average, and the minimum temperature lagged the peak of the eclipse by about 10 min. For a subset of 14 (16) relatively clear (cloudy) stations, the mean temperature drop was 0.91±0.78 (0.31±0.40)°C but the mean temperature drops for relatively calm and windy stations were almost identical. Mean wind speed dropped significantly by 9% on average during the first half of the eclipse. There was no discernible effect of the eclipse on the wind-direction or MSLP time series, and therefore we can discount any localized eclipse cyclone effect over Britain during this event. Similar changes in air temperature and wind speed are observed for Iceland, where conditions were generally clearer, but here too there was no evidence of an eclipse cyclone; in the Faroes, there was a much more muted meteorological signature. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’.

scholarly journals The frequency of occurrence of extreme and detrimental meteorological conditions for vegetation of crops in Poland (1971–2010)

2016 ◽  
Vol 48 (2) ◽  
pp. 165-172
Author(s):  
Agnieszka Ziernicka-Wojtaszek
Keyword(s):  
Air Temperature ◽  
Extreme Values ◽  
Thermal Conditions ◽  
Vegetation Period ◽  
Meteorological Conditions ◽  
Monthly Precipitation ◽  
Frequency Of Occurrence ◽  
Mean Values ◽  
Temperature And Precipitation ◽  
June July

Abstract The frequency of occurrence of extreme and detrimental meteorological conditions for vegetation of crops in Poland (1971-2010). The subject, and aim of this study is the comparison of the frequency of occurrence of thermal, precipitation and pluvio-thermal conditions detrimental to agriculture in Poland during two periods: 1971-2000 and 1981-2010, constituting the former, and the current climate normal, respectively. Each month of the vegetation period (April-October) was, in accordance with the current accounts carried out by agriculture correspondents, assigned to one of the following categories: favorable for vegetation, dry, dry and cool, cool, cool and humid, humid, dry and hot. An identical classification of meteorological vegetation conditions was also carried out for months characterized by extreme air temperature and precipitation values. Extreme values were defined as those monthly temperature mean values, and monthly precipitation totals, the probability of exceeding of which is lower than 10%, i.e. their probability of occurrence, or the socalled recurrence interval, is once every 10 years. The differences existing between the analyzed 30-year periods, can be attributed to the present day climate change - a significant increase in air temperature in April, June, July, and August, with a lack of significant precipitation trends. In the two compared periods, an increase in the number of extreme months from 74 to 82 was stated. The biggest changes during the extreme months were observed for precipitation deficits combined with hot air temperatures, namely, an increase from 15 to 29 months. In general, all the analyzed months of the vegetation period showed an increase in dry months (90 to 105 cases) and a decrease in cool months (44 to 24 cases).

scholarly journals Experiences in using low-cost sensors to observe road weather

2021 ◽  
Author(s):  
Virve Karsisto ◽  
Lasse Latva ◽  
Janne Miettinen ◽  
Marjo Hippi ◽  
Kari Mäenpää ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Explanatory Variable ◽  
Low Cost ◽  
Temperature Measurements ◽  
Sensor Technology ◽  
Spatial Density ◽  
Weather Station ◽  
Weather Stations ◽  
Temperature Observations

<p>Road weather information is essential for keeping the roads well maintained and safe during wintertime. Main source of road weather observations are road weather stations, but IoT (Internet of things) sensor technology provides new ways to observe road weather. Finnish Meteorological Institute (FMI) and Fintraffic Road are studying whether such IoT technology could help increase spatial density and/or improve coverage in the observation network and whether these additional observations could also be used to improve road weather forecasts. Around 100 autonomous battery-operated low-cost IoT sensors based on LoRaWAN communication technology were installed into the roadside area of a motorway in southern Finland and at the Sodankylä airport test track during winter 2020. Most of the sensors were of the types UC11-T1 from Ursalink and ELT-2 from ELSYS AB, but there were a few MCF-LW12TERWP sensors from MCF88 as well. All sensors measure air temperature and humidity and the MCF sensors also measure air pressure. Some of the sensors were installed at a weather station and some at road weather stations to enable data comparison with reference stations. During wintertime the IoT sensors’ air temperature measurements correspond rather well to the reference measurements. However, during other times of the year the solar radiation often causes warm bias to the measurements. The bias is reduced when the sensors are installed inside radiation shields. However, the reliability of the IoT devices needs improvement, as several sensors stopped working during the measurement campaign. This was probably caused by a firmware bug, that led to excess power consumption and emptying of batteries in some of the devices.</p><p>The FMI road weather model uses surface temperature observations in the model initialization to improve the forecasts. As the model surface temperature is forced to the observed surface temperature, the air temperature measurements don’t have that much effect in the initialization. When there are no surface temperature observations available at the forecast location, the model uses values interpolated from road weather station observations. The interpolation is done with the universal kriging method, where elevation is used as an explanatory variable. In this project we studied whether air temperature observations from IoT sensors could be used as explanatory variable as well. The results thus far show that use of air temperature observations from road weather stations improves the interpolated surface temperature values at least in some situations. However, this is rather location dependent. Initial results suggest that IoT observations would be useful this way as well. According to the results, IoT observations show potential to improve road weather monitoring and forecasting, but more studies are still needed.</p>

scholarly journals STATISTICAL MODELS FOR FORECASTING AVERAGE MONTHLY TEMPERATURE AND MONTHLY PRECIPITATION AMOUNT IN PERM

2021 ◽  
pp. 84-95
Author(s):  
Valery N. Aptukov ◽  
◽  
Victor Yu. Mitin ◽  
Keyword(s):  
Precipitation Amount ◽  
Forecast Accuracy ◽  
Statistical Characteristics ◽  
Monthly Precipitation ◽  
Total Precipitation ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Precipitation Anomalies ◽  
Fractality Index ◽  
Climate Series

The article proposes an approach to forecasting mean temperature and total precipitation for the upcoming month, based on the study of the regularities of the influence of statistical characteristics of temperature and precipitation of previous periods on them. Among the predictors, along with the basic statistical characteristics, we use the fractality index which is an indicator of the randomness/ determinism of the climate series. Within the framework of this approach, we have developed models of different levels to predict the temperature and total precipitation amount in the upcoming month. The main parameters of these models are described and the possibilities of their variation are indicated. Examples are given to illustrate the forecasting methodology using various types of models and include the results of quality control of the models, calculation of forecast accuracy and dependence of forecast accuracy of average temperature and precipitation on the month (climate season). When tested in 2020, models for forecasting temperature and precipitation for the upcoming month give good results: 9 correct forecasts of temperature anomalies out of 10 (90%) and 7 correct forecasts of precipitation anomalies out of 9 (77,7%).

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