Numerical study of properties of air heat content indicators based on stochastic models of the joint meteorological series

Kargapolova, Nina; Khlebnikova, E. I.; Огородников, В. А.

doi:10.1515/rnam-2019-0008

Cited by 8 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The obtained stochastic models may be used both to study the properties of a bioclimatic index and to forecast it. For example, in [11,12] two stochastic models of the bioclimatic index of severity of climatic regime are proposed and in [13][14][15] the same approach was used for the time series of the heat index.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model of Spatial Fields of the Average Daily Wind Chill Index

Kargapolova

2020

Information

View full text Add to dashboard Cite

The objective of this paper was to construct a numerical stochastic model of the spatial field of the average daily wind chill index on an irregular grid defined by the location of the weather stations. It is shown in the paper that the field in question was heterogeneous and non-Gaussian. A stochastic model based on the real data collected at the weather stations located in West Siberia and on the method of the inverse distribution function that sufficiently well reproduce different characteristics of the real field of the average daily wind chill index is proposed in this paper. I also discussed several questions related to the simulation of the field on a regular grid. In the future, my intention is to transform the model proposed to a model of the conditional spatio-temporal field defined on a regular grid that allows one to forecast the wind chill index.

show abstract

Section: Introductionmentioning

confidence: 99%

Stochastic Model of Spatial Fields of the Average Daily Wind Chill Index

Kargapolova

2020

Information

View full text Add to dashboard Cite

show abstract

“…Specifically, a first approach considers the diurnal fluctuation of the climatological processes, by assuming them as periodically correlated random processes with a 1-day period. Through a second approach, the meteorological events are thought as non-stationary random processes, to which the method of the inverse distribution functions can be applied for simulating non-Gaussian variables 32 . A similar procedure can also be employed to analyse previously deseasonalized variables in order to remove their cyclical variability 33,34 .…”

mentioning

confidence: 99%

Combining stochastic models of air temperature and vapour pressure for the analysis of the bioclimatic comfort through the Humidex

Sirangelo

Caloiero

Coscarelli

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Several studies evidenced the importance of the knowledge of the bioclimatic comfort for improving people's quality of life. temperature and relative humidity are the main variables related to climatic comfort/discomfort, influencing the environmental stress in the human body. In this study, a stochastic approach is proposed for characterizing the bioclimatic conditions through the Humidex values in six sites of calabria (southern italy), a region often hit by heat waves in summer months. the stochastic approach is essential, because the available time series of temperature and relative humidity are not long enough and present several missing values. the model allowed the characterization of sequences of extreme values of the Humidex. Results showed different behaviours between inner and coastal stations. For example, a sequence of 20 consecutive days with maximum daily Humidex values greater than 35 has a return period ranging from 10 to 20 years for the inner stations, while it exceeds 100 years for the coastal ones. The maximum yearly Humidex values for the inner stations have a larger range (40-50) than the coastal ones (38-45), reaching higher occurrence probabilities of serious danger conditions. Besides, the different influence of temperature and relative humidity on the Humidex behaviour has been evidenced. Bioclimatic comfort is a cognitive process that mixes many stimuli influenced by physical, physiological, and psychological factors 1. Previous researches evidenced the fundamental importance of the knowledge of the bioclimatic comfort for improving people's quality of life. In fact, in the last years temperatures over 36 °C, especially in situations of heat waves, severely affected people's health, mainly if children, elderly and chronically ill 2,3. An increase of severe heat waves in the current century has been forecasted in areas already affected by heat stress, such as the southern part of the European continent 4-6 and the Mediterranean basin, which is expected to face particularly high impacts from global warming and climate change and to be most vulnerable to their deleterious effects 7. The effects of heat can be various: increase of mortality 8 , working/exercise capacity 9,10 and cognitive performance 11,12. For the analysis of bioclimatic comfort, in studies conducted at different spatial scales in various parts of the world (e.g. in America 13-16 , in Europe 17-19 , in Asia 20-22 and in Oceania 23) several parameters such as temperature, relative humidity of the air, wind and radiation have been analysed. In particular, given their influence on the environmental stress in the human body, temperature and the relative humidity of the air are the key basic parameters related to climatic comfort/discomfort 1. In fact, as stated by Masterton and Richardson 24 , with different humidity conditions the same temperature can provide very different feelings to people. For this reason, in order to detect the effect of heat on human health, an index that combines the effects of temperature and humidit...

show abstract

Stochastic Model of Conditional Non-stationary Time Series of the Wind Chill Index in West Siberia

Kargapolova

Огородников

2021

Methodol Comput Appl Probab

View full text Add to dashboard Cite

Numerical study of properties of air heat content indicators based on stochastic models of the joint meteorological series

Cited by 8 publications

References 11 publications

Stochastic Model of Spatial Fields of the Average Daily Wind Chill Index

Stochastic Model of Spatial Fields of the Average Daily Wind Chill Index

Combining stochastic models of air temperature and vapour pressure for the analysis of the bioclimatic comfort through the Humidex

Stochastic Model of Conditional Non-stationary Time Series of the Wind Chill Index in West Siberia

Contact Info

Product

Resources

About