Spatial Variability Of Air Temperature Research Articles

Simulating the meteorology during persistent Wintertime Thermal Inversions over urban areas. The case of Madrid

Persistent wintertime inversions over cities are the most critical conditions for air quality, and also one of the most challenging situations to simulate with a meteorological model. In this study, the ability of the meteorological Weather Research and Forecasting (WRF) model, coupled with the multilayer urban canopy parameterization BEP-BEM (Building Effect Parameterization - Building Energy Model), to simulate the evolution of the Planetary Boundary Layer (PBL) structure over the city of Madrid, Spain, during a multiday inversion episode, is assessed. The model results are evaluated against airport soundings, fourteen meteorological stations within and around the urban area, and remotely sensed surface temperatures. The study indicates that the PBL structure is determined by the interaction between the urban and rural heat and momentum fluxes, the topography, and the downward turbulent transport of heat. The best air temperature spatial distribution is obtained when the 6th order horizontal filter is applied only to wind and not to temperature, and when the soil moisture is reduced to 25% of the initial value provided by the global scale model. However, the comparison against satellite surface temperature data indicates that with such a dry soil the model underestimates the surface temperatures during the night and overestimates them during the day in rural areas. This compensating error points to a likely deficiency in the PBL and surface schemes during the persistent inversions. In urban areas, the simulations with the urban canopy parameterization tend to overestimate the nocturnal surface and air temperatures, while they reproduce wind speed correctly. Finally, a new methodology, based on a comparison of the differences between maximum and minimum temperatures for couples of stations, to assess the model's capability to reproduce the spatial variability of air temperature, is introduced, and the results show that the use of the multilayer urban canopy scheme and the adjustment of the soil moisture are both needed to improve the reproduction of such spatial distribution.

Energy partitioning and spatial variability of air temperature, VPD and radiation in a greenhouse tunnel shaded by semitransparent organic PV modules

A study related to the application of organic photovoltaic (OPV) modules in greenhouses is presented. It considers the impact of nonhomogeneous shading by semitransparent OPV modules, placed on the cover of a greenhouse tunnel housing a tomato crop, on energy partitioning and the spatial variability of radiation, air temperature and vapour pressure deficit (VPD) within the tunnel. Experiments were conducted in two similar tunnels covered by a diffuse polyethylene sheet. Flexible semitransparent strips of OPV modules were placed on 37% of the roof area of one tunnel, creating an approximately 23% nonhomogeneous shading, while the other tunnel, homogeneously shaded by a 25% black shading net, served as a control greenhouse. The results show that on cloudy days (high diffuse radiation), spatial variability of radiation in the OPV tunnel was smaller than on sunny days (low diffuse radiation). Conversely, variability in air temperature and VPD did not change much with the change in diffuse radiation. Except when diffuse radiation was high, no significant difference in the energy partitioning between nonhomogeneous shading by OPVs and homogeneous shading was observed. Most of the net radiation in the tunnels was converted into latent heat. With a high solar elevation angle, the spatial variability of radiation within the tunnel was higher than with a low solar elevation angle. Additional experiments are needed to determine the best arrangement of semitransparent OPV modules on the roof, without resulting in any significant increase in spatial variability. Agronomic aspects of plant growth under the OPV modules are briefly presented.

Estimating monthly air temperature using remote sensing on a region with highly variable topography and scarce monitoring in the southern Ecuadorian Andes

Monitoring of air temperature has implications in a wide range of environmental applications. Air temperature commonly measured with meteorological stations provides a high accuracy and temporal resolution for specific monitoring sites. However, in regions with highly variable topography and scare monitoring such as the case of the southern Ecuadorian Andes, these in situ data poorly describe the spatial variability of air temperature. Thus, remote sensing data has a great potential to estimate the spatial distribution of climatological variables due to the spatial continuity of the information. This research aims to estimate the spatial distribution of the monthly air temperature in the Paute river basin for the period 2014–2017, using statistical and geostatistical methods: linear regression (LR), random forest regression (RF), and regression kriging (RK), in addition to evaluate the use of altitude and other auxiliary variables (land surface temperature, latitude, and longitude). Cross-validation showed that RF performed better than LR as well as when using auxiliary variables compared to only the altitude (LR-altitude: RMSE=1.325 °C, P bias= −0.150%, r=0.775; LR-auxiliary variables: RMSE=1.265 °C, P bias=0.000% r=0.795; RF-altitude: RMSE=1.235 °C, P bias =0.200%, r=0.810; RF-auxiliary variables RMSE=1.205 °C, P bias =0.200%, r=0.820). The application of regression kriging was limited since less than 50% of the months had spatial autocorrelation in the regression model residuals. Nevertheless, in these months, regression kriging increased the estimation performance. The outcomes of this research work increase the understanding of the spatial distribution of monthly air temperature in the Paute river basin, which will improve hydrological modeling.

Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

Mapping daily evapotranspiration over a large irrigation district from MODIS data using a novel hybrid dual-source coupling model

Accurate knowledge of spatially distributed evapotranspiration (ET) is essential for better understanding the availability and utilization of water resources. Over the past several decades, remote sensing (RS) technique has been widely applied to acquire indispensable input data for the estimation of ET at different spatial and temporal scales. Several dual-source ET models that can partition evaporation and transpiration have been developed by utilizing remote sensing data and ancillary meteorological observations. However, due to the significant spatial variability of air temperature over heterogeneous land surface, these models typically cannot be applied in heterogeneous regions with scarce meteorological observations. In the present study, a new model, referred to as the HTEM-ABL model, is developed by coupling the Hybrid dual-source scheme and Trapezoid framework-based ET Model (HTEM) with a simple atmospheric boundary layer (ABL) model for ET estimation without ancillary air temperature observations. This model uses Moderate Resolution Imaging Spectroradiometer (MODIS) data as the main input and is evaluated with field observations over a large irrigation district in North China. Results show that the HTEM-ABL model can be well applied to the study area, and the accuracy of HTEM-ABL model is slightly superior to the original HTEM model. The root mean square errors between the calculated and measured ET are 0.63 mm/day and 29.6 mm/year at the field scale and the regional scale, respectively. Furthermore, the estimated air temperature has been proved to be accurate qualitatively and quantitatively, and provides another aspect to evaluate the HTEM-ABL model. The spatial and temporal distributions of ET produced by the HTEM-ABL model are in good agreement with both the distribution of land use types and the variations of crop growth stages. In addition, the HTEM-ABL model shows less sensitivity to land surface temperature compared with the original HTEM model.

Copula-based interpolation methods for air temperature data using collocated covariates

This paper introduces two copula-based interpolation methods to produce air temperature maps in a data-scarce area: a spatial copula interpolator including covariates, and a mixed copula interpolator. The methods allow a construction of the conditional distribution of air temperature given the collocated covariates. Our study compared the new methods with the spatial copula interpolator, the ordinary kriging predictor and the cokriging predictor. Daily mean air temperature was used from weather stations and ERA_Interim reanalysis weather data at 174 locations in the Qazvin Plain, Iran. Spatial copula interpolator including covariates resulted in more precise predictions as shown by leave-two-out cross-validation. Visual inspection of air temperature maps demonstrated that the new methods well represented spatial variability of air temperature at a 1 km spatial resolution. The results showed an improved performance of the new methods to describe both spatial variability and co-variability between variables. The methods are potentially useful for other sparsely and irregularly distributed weather data.

Hillslope-scale prediction of terrain and forest canopy effects on temperature and near-surface soil moisture deficit

Soil moisture has important effects on fuel availability, but is often assessed using drought indices at coarse spatial resolution, without accounting for the fine-scale spatial effects of terrain and canopy variation on forest floor moisture. In this study, we examined the spatial variability of air temperature, litter temperature and near-surface soil moisture (θ, 0–100 mm) using data from field experiments at 17 sites in south-east Australia, covering a range of topographic aspects and vegetation types, within climates from semiarid to wet montane. Temperatures and θ in mountainous environments were found to vary at much finer spatial scales than typical drought index grid dimensions (several kilometres). Using terrain elevation, local insolation ratio and plant area index, we developed semi-empirical microclimate models for air and litter temperatures, then used modelled temperatures as input into calculations of the Keetch–Byram Drought Index, a widely used index of soil moisture deficit. Drought index results based on predicted litter temperature were found to explain 91% of the spatial variation in near-surface soil moisture at our experimental sites. These results suggest the potential for routine hillslope-scale predictions of forest floor moisture status, which may be useful in the management of fire, particularly prescribed burning, in complex terrain.

Temporal and spatial patterns of air temperature in a coastal city with a slope base setting

AbstractIn coastal cities with a slope base setting, effects of sea breeze and cold air drainage superpose on top of the urban heat island (UHI) effect. It is difficult to articulate the urbanization effect which, however, is important in developing summer heat mitigation solutions. The aim of this study is to address this challenge by investigating the temporal and spatial distribution of air temperature in the Adelaide metropolitan area of South Australia based on data from a monitoring network in 2011–2013. The results indicate that the dominant influence on the spatial variability of air temperature in winter is the urban heat island effect, followed by the prevailing nighttime northeasterly wind and cold air drainage. In summer, the dominant influence comes from daytime sea breezes, followed by the urban heat island effect, and the prevailing southeasterly nighttime wind. The urban heat island intensity is estimated based on various references where the influence of other factors is minimal. A clear nighttime urban heat island is observed over the Adelaide metropolitan area, with a mean intensity of 2.5°C for both summer and winter. The daytime urban heat island is not clear. On hot days, the city center develops a dominant nighttime urban heat island about 4°C warmer than the coast, stronger than an average summer night. Given multiple influences on the thermal climate in the Adelaide metropolitan area, it is suggested that the unique 500 m wide Adelaide parkland belt surrounding the city center be used as a reference to evaluate the Adelaide city center urban heat island in summer. A rural station, Edinburgh, can be used to evaluate the nighttime urban heat island intensity in the study area.

Spatial variability of air temperature in a free-stall in the Northeastern semi-arid region of Brazil

ABSTRACT The knowledge on the spatial variability of climatic attributes and the building of Kriging maps can assist in the design and management of confined animal facilities, by allowing a spatial visualization that is helpful for the planning and control of information from the production environment. The study aimed to characterize the spatial variability of air temperature in a free-stall barn used for dairy cattle confinement located in Petrolina-PE, Brazil, in different seasons and at different times. The variable air temperature was recorded at 136 points distributed in the areas under the shed and the shade cloth for the study of spatial variability and the construction of maps by Kriging. Air temperature data was collected in the winter and in the summer, in the months of July and August (2013) and January and February (2014), at different times (9 and 15 h). According to the results, the use of geostatistics enabled to define areas with different spatial variabilities in air temperature and specific areas in the free-stall with values higher than the recommended levels for thermal comfort. In addition, the central part of the facility is the region with the lowest values of air temperatures, due to the presence of a ridge vent.

The spatial variability of air temperature and nocturnal urban heat island intensity in the city of Brno, Czech Republic

Abstract This study seeks to quantify the effects of a number of factors on the nocturnal air temperature field in a medium-sized central European city located in complex terrain. The main data sources consist of mobile air temperature measurements and a geographical database. Temperature measurements were taken along several profiles through the city centre and were made under a clear sky with no advection. Altogether nine sets of detailed measurements, in all seasons, were assembled. Altitude, quantity of vegetation, density of buildings and the structure of the transportation (road) system were considered as explanatory variables. The result is that the normalized difference vegetation index (NDVI) and the density of buildings were the most important factors, each of them explaining a substantial part (more than 50%) of overall air temperature variability. Mobile measurements with NDVI values as a covariate were used for interpolation of air temperature for the entire study area. The spatial variability of nocturnal air temperature and UHI intensity in Brno is the main output presented. Air temperatures interpolated from mobile measurements and NDVI values indicate that the mean urban heat island (UHI) intensity in the early night in summer is at its highest (approximately 5 °C) in the city centre and decreases towards the suburban areas.

Investigation of spatial variability of air temperature, humidity and velocity in cold stores by using management zone analysis

The main objective of this research was to determine spatial variability of ambient temperature, relative humidity and air velocity in a cold storage using management zone analysis methods. Ambient temperature, relative humidity and air velocity of an experimental cold store were measured using 36 temperature-relative humidity sensors and air velocity measurement probe. Sensor installation was performed on grid scale at three levels (base, middle and top) such as to monitor the storage conditions in the cold store. The measured data were analysed by MZA software which performed fuzzy clustering to delineate the full cold storage, half full cold storage and empty cold storage. In addition, Surfer mapping software was utilized to create the maps of the measured parameters. The results indicated that there is significant spatial variation of the basic storage parameters such as ambient temperature, relative humidity and air velocity in the cold storage, which denotes the significance of management zones delineation in these facilities so as to maximize the storage.

Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters

This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.

Temporal and spatial variability of air temperature and precipitation at the Polish coastal zone of the southern Baltic Sea

The study concerns the analysis of temporal and spatial variability of thermal and precipitation conditions at the Polish Baltic coastal zone in the second half of the twentieth century and the first decade of the twenty–first century. The paper also presents its annual and daily extreme values of air temperature and precipitation. The study area includes the Polish coast of the Baltic Sea at which long–term measurements and observations of weather conditions have been performed within the national meteorological services at seven Baltic coastal stations. Their daily data of air temperature and precipitation taken from the measuring period of 1966–2009 were used in the study. For each meteorological station was set thresholds of annual average air temperature and annual total precipitation. Temporal variability of air temperature was determined by the thermal classification made by Lorenc (1998). Temporal variability of precipitation was determined by the precipitation classification made by Kaczorowska (1998).

Development of technologies and methods for monitoring the spatial variability of air temperature in greenhouse environment

Los factores climáticos influyen directamente en el crecimiento de las plantas y la productividad en los invernaderos, siendo que la temperatura puede ser considerad como uno de los factores más importantes en este contexto. El objetivo de este trabajo fue desarrollar un dispositivo de bajo costo para el sensoriamento térmico y adquisición de datos, y su utilización en la recolección de datos y análisis de la variabilidad espacial de la temperatura dentro de un invernadero con clima tropical. El dispositivo físico de medición térmica desarrollado mostró un alto grado de precisión y respuestas inmediatas en las mediciones, lo que demuestra su eficiencia. Las interpretaciones de los datos se hicieron con el desarrollo de variogramas y mapas tridimensionales generadas por un software geoestadístico. Los datos analizados mostraron que un invernadero sin control térmico presenta variaciones espaciales de la temperatura del aire, tanto en las capas horizontales muestreadas, como en las tres alturas de columnas verticales analizadas, mostrando variaciones de hasta 3,6 ºC en ciertos momentos.

Análise espacial das condições térmicas do ambiente pré-ordenha de bovinos leiteiros sob regimes de climatização

Propõe-se, com este trabalho, caracterizar a variabilidade espacial da temperatura do ar na sala de pré-ordenha a partir de diferentes tempos de operação do sistema de resfriamento adiabático evaporativo automatizado. A variável temperatura do ar foi registrada em 35 pontos equidistantes 1 m, na forma de malha, para o estudo da variabilidade espacial e a construção de mapas por krigagem. Foram considerados diferentes tempos de exposição dos animais a climatização no curral de espera, 20, 30, 40 min e controle (0 min). Por meio dos resultados obtidos foi possível verificar que o uso da geoestatística possibilitou definir áreas com diferentes variabilidades espaciais para temperatura do ar, definindo áreas específicas na sala de pré-ordenha que apresentaram valores acima do recomendado para o conforto térmico animal. O sistema de climatização garantiu melhor acondicionamento térmico no tempo de exposição de 40 min, obtendo temperatura média dentro da condição de conforto térmico para vacas em lactação.

ANALISE DO CAMPO TÉRMICO DA CIDADE DE IRATI/PR: PRIMEIROS EXPERIMENTOS PARA A DEFINIÇÃO DO CLIMA URBANO.

O presente trabalho trata do planejamento urbano de cidades de porte médio apequeno e, de forma particular, analisa a formação do campo térmico urbanocom base na análise climática. A cidade de Irati, localizada na porção centrosudestedo estado do Paraná, constitui-se no objeto deste estudo.Considerando-se o referencial teórico que trata sobre a temática, tanto noaspecto mais geral (planejamento urbano), como no específico (clima urbano),foi efetuado um levantamento preliminar das características essenciais do sítiourbano da cidade em questão. Após tal levantamento, foi possível escolherpontos de coletas de dados de temperatura para 12 locais diferentes, os quaisforam monitorados por acadêmicos do curso de Geografia da Unicentro, noperíodo de 23 a 30 de abril de 2008. As características físicas da cidadepermitiram concluir que a mesma apresenta um relevo ondulado e orientaçõesde vertentes variadas, não sendo possível definir um padrão uniforme. Por outrolado, as características socioeconômicas permitem afirmar que o adensamentourbano está concentrado na área central da cidade, com uma incipienteocupação nas áreas periféricas, pelo menos no que se refere aimpermeabilização do solo. Dessa forma, os 12 pontos escolhidos para coleta dedados possibilitaram a análise de diferentes realidades socioambientais dacidade, dando condições para se analisar a influência da dinâmica urbana navariação do campo térmico. As análises dos resultados e as conclusões obtidaspermitiram identificar a formação de ilhas de calor dentro da área de estudo;em princípio, a área mais quente está localizada na região central da cidade,mas, ao mesmo tempo no período noturno, se torna uma área semdiferenciação com o seu entorno e, até mesmo, apresentando valores detemperatura que induzem à formação de uma ilha de frescor. A influência daestrutura urbana na variabilidade espacial da temperatura do ar foi verificada, embora sobre ela paire dúvidas quanto à gênese, pois os fatores físicos (relevo,vegetação e água) parecem influenciar tanto quanto a estrutura urbana, na suaformação. Para uma maior segurança quanto à analise, novos experimentosdevem ser realizados, utilizando métodos e metodologias diferenciadas,procurando uma melhor explicação para a variação dos elementos climáticos nacidade de Irati.

Spatial variability of air temperature and appropriate resolution for satellite‐derived air temperature estimation

This study investigates the spatial variability of air temperature over Hong Kong using in situ air temperature recorded from a mobile traverse combined with an ASTER thermal satellite image. Three different degrees of urbanization in Hong Kong, including city downtown (Kowloon), suburban areas (Yuen Long and Shatin), and rural countryside (Tai Mo Shan and Lam Tsuen) are analysed. The spatially variable relationship between air and surface temperature was evaluated using two spatial averaging techniques, namely spatial resampling and buffering around air temperature points. The strength of the correlation coefficient was tested for every decreasing resolution and the appropriate spatial scales of air temperature in urban, suburban and rural areas were found to be 200 m, 450 m and 700 m, respectively. The differences in the spatial scales of air temperature in these regions are attributed mainly to structural factors of land cover such as city block size, building density and percentage of green areas, and secondarily to the climatic conditions being operating in, and which commonly typify these individual regions. Thus small scale lengths in the urban area corresponded to heterogeneous land cover, a well developed urban boundary layer, low wind speeds and a low lapse rate, whereas longer scale lengths were observed in suburban and rural areas having more homogeneous land cover, higher wind speeds and higher lapse rate.

Evapotranspiration Derived from Satellite Observed Surface Temperatures

Evapotranspiration is calculated from surface temperatures using an energy balance method. This method is sensitive to the temperature difference between the surface and the air above, and somewhat to the windspeed. In this study we consider the influence of the spatial variability of air temperature on the interpretation of surface temperatures. It is argued that small-scale atmospheric variations can be corrected by using temperature and wind data at a height of 50 m. Three models have been used to calculate these 50 m data from standard weather observations. The first model uses a very simple concept of constant temperature and wind over the test area (zero-dimensional). In the second model windspeed is also taken constant, but air temperature is evaluated from the initial vertical temperature in the atmosphere with a one-dimensional slab layer model. The third model is a two-dimensional primitive equations model in which wind velocity is calculated from the geostrophic wind and air temperature similar to model 2. A homogeneous grassland area was selected in the north of the Netherlands close to the sea. Several days in the summer of 1983 with clear skies and winds from the sea were selected. Surface temperatures were derived from the NOAA-7 satellite overpass in the early afternoon using the split-window technique. On most days an almost linear increase of both surface and air temperature is found with increasing distance to the sea. This study reveals that model 1 results in an unrealistic decrease of the calculated evapotranspiration with increasing distance to the coast. Furthermore evapotranspiration is underestimated. The evapotranspiration as calculated with models 2 and 3 is almost constant in the test area and agrees well with measurements. Model 3 gave more scatter, probably due to the fact that uncalibrated wind velocities were used. For practical calculation of evapotranspiration the Priestley-Taylor parameter α is often used. This study shows how this parameter can be derived from satellite observations of surface temperature.