scholarly journals About simulated influence of roof- and wall-greening on an old people’s home in Helsinki, Finland, during the 2018 heatwave event - Drebs, Achim, …

2021 ◽  
Author(s):  
Achim Drebs ◽  
Tim Sinsel ◽  
Kirsti Jylhä
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Geographical Location ◽  
Daily Maximum ◽  
Old People ◽  
Meteorological Forcing ◽  
Synoptic Weather ◽  
Wave Event ◽  
Standard Set ◽  
Construction Type

<p>Due to geographical location extended heat-waves occurs in coastal high-latitude areas or cities rather seldom.</p><p>In our research we describe influences of roof- and wall-greening on micro-meteorological conditions around (and in?) a moderate insulated stand-alone six-storey concrete building in Helsinki, Finland. The block of flats serves as an old people's home and being built in the late 1970’s, it represents the prevailing construction type of that era. The building is located on a slightly southwards declining slope, and the neighbouring buildings are 30 meters away.</p><p>We applied the holistic ENVI-met simulation soft-ware and used real meteorological forcing-data as input for the simulations.  The study focused on a 24-day heat-wave event in summer 2018. During the period from July, 13<sup>th</sup> until August, 5<sup>th</sup>, the daily maximum air temperature reached almost every day 25 °C and more, sometimes even more than 30 °C. All one-hour air temperature, wind, humidity, and solar radiation (cloudiness) measurements were conducted at a near-by synoptic weather station.</p><p> The ENVI-met soft-ware has standard set-ups for green roof and green wall properties for simulations of their impacts on the thermal condition. We simulated different levels of insulation (poor, moderate, and good) and used the roof- and wall-greening separately or together in order to find the optimal combination of different greening options. Furthermore, we analysed the physical environment around the old people’s home from the aspect of human comfort, especially the influences of the simulated green infrastructures in front of the building.</p><p>The research is part of the HEATCLIM-project financed by the Academy of Finland Science Program CLIHE (2020-2023).</p>

Micro-climatological influences on temperature condition in an old people’s home in Helsinki, Finland, caused by extended heat-waves 

2021 ◽  
Author(s):  
Achim Drebs ◽  
Tim Sinsel ◽  
Kirsti Jylhä
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Daily Maximum ◽  
Old People ◽  
Concrete Walls ◽  
Meteorological Forcing ◽  
Clear Sky ◽  
Wave Event ◽  
Construction Type ◽  
Maximum Air Temperature

<p>In our research we describe the micro-climatological influences of two heat-waves around and the air temperature development in a certain old people’s home in Helsinki, Finland. The stand-alone six-storey concrete building was erected in the late 1970’s and represents the prevailing construction type of this area. The building is located on a slightly southwards declining slope.</p><p>The first simulation used real meteorological forcing-data from the heat-wave event in summer 2018, which lasted from July, 13<sup>th</sup> until August, 5<sup>th</sup>. In this period the daily maximum air temperature reached almost every day 25 °C and more, sometimes even more than 30 °C. All air temperature, wind, humidity, and solar radiation (cloudiness) measurements were conducted at a near-by synoptical weather station.</p><p>The second simulation used fourteen-day constructed meteorological forcing-data, based on a clear-sky, slowly increasing air temperature, higher than normal humidity, and low wind conditions assumption starting on July, 13<sup>th</sup> (day 194 of the year).</p><p>We used the holistic ENVI-met simulation soft-ware to simulate the physical environment around the old people’s home and especially the energy fluxes inside the concrete walls to explain the needs for cooling demands.</p><p>The research is part of the HEATCLIM-project financed by the Academy of Finland Science Program CLIHE (2020-2023).</p>

scholarly journals The occurrence of heat waves in Europe and their circulation conditions

Geografie ◽  
2019 ◽  
Vol 124 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Arkadiusz M. Tomczyk ◽  
Ewa Bednorz ◽  
Marek Półrolniczak
Keyword(s):  
Sea Level ◽  
Heat Wave ◽  
Air Temperature ◽  
Atmospheric Pressure ◽  
Geopotential Height ◽  
Heat Waves ◽  
Daily Maximum ◽  
Isobaric Surface ◽  
Hot Days ◽  
Maximum Air Temperature

The objective of the paper was to characterize the occurrence of heat waves in Europe between 1976 and 2015 and to determine circulation conditions causing their occurrence. The heat waves were recognized as a sequence of at least 5 consecutive hot days. The hot day was defined as a day on which daily maximum air temperature was higher than 95th percentile of all the values in the analyzed period. The conducted research showed an increase in the number of heat waves and their duration in the analyzed period. The longest heat wave occurred in 2010, in Moscow, which lasted 45 days. The most intense changes were observed in the eastern and south-eastern regions. The occurrence of heat waves was mainly connected with positive anomalies of atmospheric pressure at sea level, geopotential height of 500 hPa, and temperature on isobaric surface 850 hPa.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Modeling Northern Hemisphere Summer Heat Extreme Changes and Their Uncertainties Using a Physics Ensemble of Climate Sensitivity Experiments

2006 ◽  
Vol 19 (17) ◽  
pp. 4418-4435 ◽  
Author(s):  
Robin T. Clark ◽  
Simon J. Brown ◽  
James M. Murphy
Keyword(s):  
Structural Changes ◽  
Heat Waves ◽  
Full Range ◽  
Daily Maximum ◽  
Model Parameters ◽  
Mean Values ◽  
Summer Heat ◽  
Modeling Uncertainties ◽  
Physics Ensemble ◽  
Projected Changes

Abstract Changes in extreme daily temperature events are examined using a perturbed physics ensemble of global model simulations under present-day and doubled CO2 climates where ensemble members differ in their representation of various physical processes. Modeling uncertainties are quantified by varying poorly constrained model parameters that control atmospheric processes and feedbacks and analyzing the ensemble spread of simulated changes. In general, uncertainty is up to 50% of projected changes in extreme heat events of the type that occur only once per year. Large changes are seen in distributions of daily maximum temperatures for June, July, and August with significant shifts to warmer conditions. Changes in extremely hot days are shown to be significantly larger than changes in mean values in some regions. The intensity, duration, and frequency of summer heat waves are expected to be substantially greater over all continents. The largest changes are found over Europe, North and South America, and East Asia. Reductions in soil moisture, number of wet days, and nocturnal cooling are identified as significant factors responsible for the changes. Although uncertainty associated with the magnitude of expected changes is large in places, it does not bring into question the sign or nature of the projected changes. Even with the most conservative simulations, hot extreme events are still expected to substantially increase in intensity, duration, and frequency. This ensemble, however, does not represent the full range of uncertainty associated with future projections; for example, the effects of multiple parameter perturbations are neglected, as are the effects of structural changes to the basic nature of the parameterization schemes in the model.

Temperature anomalies of a cold period on the territory of Ukraine in 2010-2019

2020 ◽  
Vol 101-102 (3-4) ◽  
pp. 19-25
Author(s):  
Olena Nashmudinova
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Negative Temperature ◽  
Cold Period ◽  
Positive Anomaly ◽  
Temperature Anomalies ◽  
Gradient Fields ◽  
Air Temperature Anomalies

Regional climate change in Ukraine in recent decades is accompanied by an increase in the repetitiveness of intense waves, both heat and cold; there is a tendency to increase the frequency of warm winters, but sometimes there are periods with significant decreases in temperature. The aim of the study is to determine the specifics of the formation of air temperature anomalies in the cold period 2010–2019. According to the distribution of the average monthly air temperature at the stations Odessa, Kiev, Kharkiv, Lviv investigated positive and negative deviations from the climate norm. In January, the average monthly air temperature in most cases was above normal, except for 1–3 years. The maximum positive anomaly was 4–5°C in Kyiv and Lviv (2015), the largest negative deviations were 3.8°C. In February, the trend continues – only 2–3 years with negative anomalies, the largest deviations to 3–6°C in 2011 and 2012, and positive deviations maximum in 2016. In March, negative temperature anomalies were observed 3–4 years, with a maximum of 2–3°C in 2018, positive anomalies in 4–6°C were observed in 2014, 2017. Temperatures in November were variable, with the prevailing positive anomaly, a high of 6–8°C in 2010. The distribution of air temperature in December was characterized by positive deviations of a maximum of 5–6°C in 2011, 2015, 2017 and 2019. Months of the greatest positive and negative air temperature anomalies over Europe have been highlighted. Among the colder months, the biggest anomaly stood out in January 2010 and February 2012 to 5–6°C. Among the warm months, the temperature anomaly was observed in February 2016, positive deviations from the norm to 8°C. Heat waves formed in winter with a zonal type of circulation, when warm moist air from the Atlantic shifted across the periphery of the Icelandic low. In March, waves of heat formed in low–gradient fields. Powerful waves of cold over the European sector were mainly formed under the influence of “eastern processes” in the spread of the Siberian anticyclone to Europe. In some years, significant cooling over Ukraine is formed in cyclonic systems with a high–altitude thermobaric field characterized by polar or ultrapolar hollow.

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

scholarly journals Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017 ◽  
Author(s):  
Sara C. Pryor ◽  
Ryan C. Sullivan ◽  
Justin T. Schoof
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
Air Temperature ◽  
Energy Content ◽  
Daily Maximum ◽  
Synoptic Scale ◽  
Near Surface ◽  
Artificial Neural ◽  
Static Energy

Abstract. The static energy content of the atmosphere is increasing at the global scale, but exhibits important sub-global and sub-regional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming-holes (i.e. locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. A new non-linear statistical model for summertime daily maximum and minimum θe is developed and used to advance understanding of drivers of historical change and variability over the eastern USA. It is shown that soil moisture (SM) is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using Artificial Neural Networks (ANN) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in min- and max-θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme max-θe. Over the entire domain the ANN with 3 hidden layers exhibits high accuracy in predicting max-θe > 347 K. The median hit rate for max-θe > 347 K is > 0.60, while the median false alarm rate ≈ 0.08.

scholarly journals Extreme Temperature Events in Serbia in Relation to Atmospheric Circulation

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1584
Author(s):  
Ivana Tošić ◽  
Suzana Putniković ◽  
Milica Tošić ◽  
Irida Lazić
Keyword(s):  
Atmospheric Circulation ◽  
Heat Waves ◽  
Extreme Temperature ◽  
Daily Maximum ◽  
Absolute Maximum ◽  
Extreme Temperature Events ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Maximum Temperatures ◽  
The Relationship

In this study, extremely warm and cold temperature events were examined based on daily maximum (Tx) and minimum (Tn) temperatures observed at 11 stations in Serbia during the period 1949–2018. Summer days (SU), warm days (Tx90), and heat waves (HWs) were calculated based on daily maximum temperatures, while frost days (FD) and cold nights (Tn10) were derived from daily minimum temperatures. Absolute maximum and minimum temperatures in Serbia rose but were statistically significant only for Tx in winter. Positive trends of summer and warm days, and negative trends of frost days and cold nights were found. A high number of warm events (SU, Tx90, and HWs) were recorded over the last 20 years. Multiple linear regression (MLR) models were applied to find the relationship between extreme temperature events and atmospheric circulation. Typical atmospheric circulation patterns, previously determined for Serbia, were used as predictor variables. It was found that MLR models gave the best results for Tx90, FD, and Tn10 in winter.

scholarly journals Relationships of surface air temperature anomalies over Europe to persistence of atmospheric circulation patterns conducive to heat waves

2008 ◽  
Vol 14 ◽  
pp. 243-249 ◽  
Author(s):  
J. Kyselý ◽  
R. Huth
Keyword(s):  
Atmospheric Circulation ◽  
Air Temperature ◽  
Heat Waves ◽  
Temperature Extremes ◽  
Human Society ◽  
Positive Temperature ◽  
Temperature Anomalies ◽  
Circulation Types ◽  
Circulation Patterns ◽  
Air Temperature Anomalies

Abstract. Heat waves are among natural hazards with the most severe consequences for human society, including pronounced mortality impacts in mid-latitudes. Recent studies have hypothesized that the enhanced persistence of atmospheric circulation may affect surface climatic extremes, mainly the frequency and severity of heat waves. In this paper we examine relationships between the persistence of the Hess-Brezowsky circulation types conducive to summer heat waves and air temperature anomalies at stations over most of the European continent. We also evaluate differences between temperature anomalies during late and early stages of warm circulation types in all seasons. Results show that more persistent circulation patterns tend to enhance the severity of heat waves and support more pronounced temperature anomalies. Recent sharply rising trends in positive temperature extremes over Europe may be related to the greater persistence of the circulation types, and if similar changes towards enhanced persistence affect other mid-latitudinal regions, analogous consequences and implications for temperature extremes may be expected.

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