Micrometeorological Variables Research Articles

Micrometeorological drivers affecting the variability of CO2 uptake in the Himalayan Oak and Pine dominated ecosystems: An assessment of causal relationships

Micrometeorological variability significantly impacts the structures, functions, and dynamics of ecosystems. However, the assessment of feedback and causal relationships among microclimatic drivers and various ecosystems in the Himalayan region is rarely evaluated. Here, we studied the micrometeorological drivers controlling the variability in the net ecosystem exchange (NEE) of Himalayan Oak (Banj-Oak/Quercus leucotrichophora) and Pine (Chir-Pine/Pinus roxburghii) dominated ecosystems, as NEE is an indicator of ecosystem functioning. We used half-hourly eddy covariance flux data of CO2 fluxes from two sites established over Pine and Oak dominated ecosystems in Uttarakhand, India. We conducted the analysis with the information theory-based Temporal Information Partitioning Networks (TIPNets) approach to generate weekly process networks. TIPNets represent directed lag-structured causal graphs to identify the causal relationships and capture the temporal association among the variables. Our analysis aimed to capture fluctuations in variables with up to 6 h of memory. Based on the data availability, we generated the weekly networks at both the sites for the monsoon and post-monsoon seasons of 2016 and 2017. In both ecosystems, the sub-daily scale variations among the micrometeorological variables are responsible for the fluctuations in NEE. The Pine ecosystem is found to be more sensitive to changes in air temperature (TA) and uptakes more CO2 as compared to the Oak ecosystem throughout the study period. The transfer entropy links show that the NEE of the Oak ecosystem is moisture-driven (precipitation and relative humidity), while the Pine ecosystem is heat-driven (TA and net solar radiation) in both seasons. The influence of precipitation is not observed within a short memory of 6 h in the Pine ecosystem. This is because lesser fine roots take time to show the precipitation signature on NEE through infiltration, soil moisture, and root water uptake, compared to Oak. However, the impacts of moisture stress are evident in the network structure of both ecosystems, with more causal links observed in the network during dry periods compared to wet periods.

Correlation dimension for temperature and other micrometeorological variables in different ground cover from six biomes of the Amazon basin

Correlation dimension () for embedding dimensions, varying between 3 to 11, were estimated for a 1- or 2-month time series of air temperature and other micrometeorological variables, such as energy fluxes, solar radiation and concentration, in six ecosystems in Amazon and the central region of Brazil. Dc remained within the 1.5 - 3.0 range, for variables locally coupled to ecosystems, and outside the range for variables not locally coupled, such as precipitation and wind velocity. Results indicate that the feature of the variables does not have an important stochastic component. There is a small amount of data available concerning estimates of correlation dimension () of micrometeorological variables. The measurement of for several variables from six Brazilian biomes indicates that this parameter is sensitive to environmental conditions as the rainfall. The obtained results also indicates that the natural processes in the ecosystems remain in a non-chaotic regime. In certain circumstances, as in conditions of high humidity, natural ecosystems may stay inside a limit which is close but lower than chaos. Moreover, an important stochastic component was not observed.

Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing

Accurate evapotranspiration (ET) assessment is crucial for agricultural water management, encompassing crop water requirements, irrigation scheduling, water budgeting and drought monitoring. This study integrates remote sensing-based surface energy balance model with in-situ ET measurements to evaluate surface energy fluxes and ET in Pantnagar, Tarai region. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model, using high-resolution remote sensing data, was validated against observations from an ET station equipped with large aperture scintillometer and micrometeorological tower, situated in sugarcane farm at Govind Ballabh Pant University of Agriculture & Technology (GBPUA&T), Pantnagar. On November 13, 2021, METRIC and Landsat-9 satellite data estimated an instantaneous ET of 7.39 mm day-1, closely aligned with the observed value of 6.72 mm day-1 recorded by the ET station. The findings confirm the METRIC model's high accuracy for spatial ET estimation and its associated micrometeorological variables. This study underscores the utility of the METRIC model, ET station and remote sensing in determining ET and energy flux which may be further utilised in the estimation of crop water requirement, energy fluxes and irrigation water management for sugarcane cultivation in the Tarai region.

Evapotranspiration of an abandoned grassland in the Italian Alps: Modeling the impact of shrub encroachment

This study analyzes the effect of shrub encroachment on actual evapotranspiration (ETa), a still poorly studied phenomenon in the Alps. The effect of shrub encroachment is investigated on an Alpine grassland in Western Italy using both data and a soil hydrological model (Hydrus 1D), which is used to model three different land covers: grassland, shrubland, and a mixture of the two land covers with a novel double vegetation approach recently introduced. Four growing seasons of eddy covariance measurements are used as an approximate reference for the interpretation and consistency of the model outputs. Also, the impact of meteorological inter-annual variability and of different environmental conditions on both modeled and measured evapotranspiration is analyzed. The modeling results show that the model is able to capture the inter-annual variability of ETa. The double vegetation approach suggests that the percentage of total transpiration flux assigned to the shrubland is between 20 and 60 %. Single-vegetation simulations show that shrubs lead to an enhancement of ETa equal to + 27.1 %, +26.0 %, +26.8 %, and + 23.9 % (range 2014–2017) compared to grassland, which could lead to an alteration of the hydrological cycle. Moreover, chambers measurements of shrubs transpiration show a good agreement with the eddy covariance measurement, suggesting that the ecosystem’s behavior is already close to a shrubland, which yields an increased ETa if compared to grassland. The evaporative index from the modeled shrubland is higher (range +24–27 %) than the case of a modeled grassland.Finally, ETa and the evaporative fraction (EF) are in the energy-limited regime in most cases. This result was obtained from the analysis of the relationship between ETa (and EF) and either meteorological variables or soil water content including the simulated one in the 0–100 cm horizon. The following analysis, more focused on micrometeorological variables, namely vapor pressure deficit, net radiation, wind speed, air temperature, and ground heat flux, indicates that ETa is mostly affected by the vapor pressure deficit.

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

Deep learning to extract the meteorological by-catch of wildlife cameras.

Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

A dataset of water, heat, and carbon fluxes of an oasis agroecosystem in the middle areas of the Hexi Corridor (2012–2015)

The exchange of water, heat, and carbon dioxide between the terrestrial ecosystems and the atmosphere is a fundamental process that underlies mass and energy transfer at the Earth’s surface. The eddy covariance technique has become one of the preferred and state-of-the-art approaches for measuring and calculating the exchange of water, heat, and carbon between the land surface and the atmosphere. This dataset covers the fluxes of water, heat, and carbon dioxide in an oasis agroecosystem, as well as the auxiliary micrometeorological variables in the middle areas of the Hexi Corridor during 2012–2015. The experimental observation campaign was carried out by the Linze Inland River Basin Research Station, a member station of the China Flux Observation and Research Network (ChinaFLUX). The dataset serves as a fundamental source of data, enabling in-depth comprehension water transfer, energy exchange, carbon cycle processes in the oasis agroecosystem in arid regions, and even their environmental and vegetation controlling mechanisms. It offers valuable insights into better understanding hydrological processes, ecosystem-hydrology reaction and discovering the coupling between carbon, water and heat in the context of climate change. Furthermore, the dataset holds significant scientific value in addressing chronic practical challenges related to fragile environment and shortage of water resource, such as water and land resources management, the preservation of oasis stability and sustainable development, the conservation of oasis ecosystems, etc.

Intercomparison of micrometeorological variables, surface energy fluxes, and evapotranspiration in different landscapes of the Brazilian semi-arid region

The comparison of energy exchanges between different landscapes is very valuable for understanding the consequences of deforestation and land use change on the climate. In this study, we analyzed micrometeorological variables, energy fluxes, and actual evapotranspiration over four landscapes of the Brazilian semi-arid region (preserved Caatinga – CAA, regenerating Caatinga – REGE, cactus landscape – CAM and deforested landscape – DEFA). Micrometeorological measurements (air temperature and relative humidity, vapor pressure deficit), net radiation (RN), latent heat (LE), sensible (H) and soil heat (G) fluxes, actual evapotranspiration (ET), and soil water storage variation (ΔS) were obtained in landscapes from October 2018 to September 2020. LE and H were calculated based on the Bowen ratio method. Significant differences were noted between micrometeorological variables (temperature and relative humidity, and vapor pressure deficit) and energy flux among the four landscapes. The four surfaces exhibited, on average, lower LE values when compared to H. At CAA landscape, 35% of the RN was used for LE, 64% for H, and 1% for G, versus 33% for LE, 65% for H, and 2% for G at REGE; 33% RN for LE, 65% for H and 2% for G at CAM, and 40% for LE, 59% for H and 1% for G at DEFA. The evapotranspiration for regenerating Caatinga (1.62 mm day−1), cactus (1.59 mm day−1), and deforested area (1.90 mm day−1) were higher than the preserved Caatinga (1.40 mm day−1). However, seasonal variations of the ET were more associated with fluctuations in RN, rainfall, and ΔS than with the type of land use. It is concluded that the removal of Caatinga by landscapes such as regenerating Caatinga, cactus plantations, and deforested area significantly modifies micrometeorological variables, energy fluxes, and the evapotranspiration, but their seasonality depends on the regime availability of water and energy.

Temporal Evolution of Vapor Pressure Deficit Observed in Six Locations of Different Brazilian Ecosystems and Its Relationship with Micrometeorological Variables

In this study, data collected from 2000 to 2019 on vapor pressure deficit (VPD) and its relationship with micrometeorological variables (fire occurrences, aerosol concentration, temperature, and carbon flux) were analyzed in six locations situated in different Brazilian ecosystems: Rio Branco, AC; Manaus, AM; Alta Floresta, MT (within the Amazon Rainforest); Baia das Pedras, MT (Pantanal); Fazenda Miranda, MT (Cerrado); and Petrolina, PE (northeastern semiarid region). Temporal series analysis of VPD was conducted by determining the principal component of singular spectrum analysis (SSA) for this variable in all locations. It was observed that the main component of SSA for VPD is sensitive to local land-use changes, while no evidence of large-scale influences related to global climate change was observed. A strong coupling between VPD values and local maximum temperature with monthly fire occurrence and logarithmic aerosol concentration profiles was also observed. The results of the study are discussed in the context of the ecosystems’ carbon sequestration capacity. The combined results of the study indicate a scenario in which local land-use changes can compromise the capacity of Brazilian ecosystems to absorb carbon.

Effects of deforestation on microclimate in a Cerrado-Amazonia Transition area

The Cerrado-Amazon Transition region has a high deforested area in Brazil. Given the importance of the forest in maintaining the climate of this region, were evaluated the patterns of micrometeorological variables in forested and deforested areas in the Cerrado-Amazon Transition region in Mato Grosso, Brazil. Precipitation, solar radiation, average, minimum and maximum air temperature, relative air humidity, soil temperature and wind speed were measured into a forest (FOR) and in a deforested area (DEF). Precipitation in the studied region has a hyper-seasonal pattern with 95% of the volume in the wet season (October to April), which influenced the seasonality of the micrometeorological variables. Solar radiation in DEF was 8-folds higher than in FOR, air temperature in DEF was up to 11% higher than in FOR, relative humidity in FOR was up to 14% higher than in DEF, soil temperature in DEF was 18% greater than in FOR and wind speed in DEF was 22-folds greater than in FOR. Deforestation significantly influenced the seasonality and magnitude of the analyzed micrometeorological variables.

Field study to estimate exposure to vehicle exhaust during idling and starting

Several studies indicate that exposure to pollutants emitted by vehicles is linked to several adverse health effects. There are few measurements that can be used to examine exposure to emissions from idling or slowly moving vehicles exposed to atmospheric flow and turbulence; such situations occur when people are waiting to be picked up by public or private vehicles. We report on a field study that collected tracer concentration data at distances of a few meters from the tailpipe of a vehicle when the accelerator pedal was controlled to simulate idling and starting conditions. Analysis of the data shows that the measurements are within a factor of two of results from a dispersion model that uses micrometeorological variables as inputs and includes plume rise associated with the buoyancy of the exhaust plume. The data suggest that people situated a few meters from an idling vehicle are likely to be exposed to levels of NO2 that are above the 1-h standard of 100 ppb.

Stem growth of multipurpose tree species: net effect of micrometeorological variability assessed by principal component regression

ABSTRACT In a typical year in the central Amazon, there is a mild dry season, but its effect on stem growth of multipurpose forest tree species is not yet well known. This study aimed to determine the individual effect of microclimatic parameters on stem growth after removing the influence of intercorrelation among microclimatic variables. Monthly stem diameter increment was measured in six species (46 trees) from January 2018 to December 2020. Microclimatic variables recorded were irradiance, air temperature, rainfall, and vapor pressure deficit. Principal component regression was used to assess the effect of micrometeorological variability on stem growth. On average, stem growth increased with an increase in rainfall and soil water content, but decreased with rise in maximum temperature and maximum vapor pressure deficit. These findings indicate that, when removing the effect of intercorrelation between microclimatic parameters, vapor pressure deficit may, in fact, affect stem growth. We demonstrate that the reduction in stem growth during the dry season can also be related to an increase in maximum temperature and maximum vapor pressure deficit, and not only to a decline in soil water content.

In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model

In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability.

Improving simulation of the fog life cycle with high-resolution land data assimilation: A case study from WiFEX

The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24–25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017–18 winter months (December–January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13°C, 0.10% and 0.75 m s−1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.

Site-Specific Evaluation of Canopy Resistance Models for Estimating Evapotranspiration over a Drip-Irrigated Potato Crop in Southern Chile under Water-Limited Conditions

The evapotranspiration (ET) process is an essential component in many agricultural water management systems, and its estimation is even more determinant when crops are grown under water-limited environments. The traditional canopy resistance (rc) approaches were evaluated to simulate potato evapotranspiration (ETcp) using the original Penman–Monteith equation under different irrigation levels. A field study was carried out on a drip-irrigated potato crop (var. Puyehue INIA) located in the Research Center Carillanca (INIA), La Araucanía Region, Chile (38°41′ S, 72°24′ W, 188 m above sea level) during the 2018/2019 and 2019/2020 growing seasons. The different irrigation levels were full irrigation (IL1), 75% of IL1 (IL2), and 60% of IL1 (IL3). The soil water content, morphological, physiological, meteorological, and micrometeorological variables were measured to calculate the different rc approaches and estimate ET for both growing evaluated seasons. The final values of estimated ETcp were compared to the soil water balance method (ETcpWB). The use of amphistomatous (LA) and hypostomatous (LH) rc approaches are the best alternative to estimate the ETcp on potato crops. The best estimation of ET was found for ETcpLA with an overestimation of 0.6% for IL1, 7.0% for IL2, and 13.0% for IL3, while for ETcpLH with underestimations of 12.0, 11.0 and 31.0% for IL1, IL2, and IL3, respectively. The lowest average values of root mean square error (RMSE), mean absolute error (MAE), and index of agreement (d) were observed for ETcpLA in both IL1 and IL2 conditions, with values of 4.4 and 3.2 mm, 3.2 and 2.5 mm, and 0.82 and 0.87, respectively. More investigation is necessary on the plasticity of the morphological features of potato leaves and canopy geometry, as the stomatal water vapor flowing on the canopy surface could be affected, which is a key factor in the canopy resistance model for accurate ET estimation under soil-water-limited conditions.

Evapotranspiration of an Abandoned Grassland in the Italian Alps: Influence of Local Topography, Intra- and Inter-Annual Variability and Environmental Drivers

Evapotranspiration is a key variable of the hydrological cycle but poorly studied in Alpine ecosystems. The current study aimed to characterise the impact of topography and temporal variability on actual evapotranspiration (ETa) and its environmental drivers at an Alpine abandoned grassland encroached by shrubs on a steep slope. Eddy covariance, meteorological, hydrological and soil data were analysed over four growing seasons, of which two had wet and two dry conditions. The topography caused a systematic morning inflexion of ETa in all growing seasons, reflecting the valley wind system. Inter-annual differences of ETa exceeded 100 mm, and ETa means and cumulative values were significantly different between wet and dry growing seasons in the four years. Besides, ETa had a larger temporal variability in wet growing seasons. A bimodality of ETa was found in all years, caused by the onset of plant activity in the morning hours. Energy- and water-limited ETa periods were identified by comparing ETa to potential evapotranspiration (ETo). Periods of fifteen days revealed the main intra- and inter-annual differences of the environmental variables (air temperature, vapour pressure deficit—VPD, precipitation and ETa). The fixed effects of a linear mixed model based on ETa drivers explained 56% of ETa variance. The most important ETa drivers were net radiation and VPD, followed by wind speed. In growing seasons characterised by dry conditions, air temperature and the ground heat flux at the surface (either both or one of them) influenced ETa as well. The current study contributed to the understanding of topographical and temporal effects on evapotranspiration and other micrometeorological variables in an Alpine ecosystem still rarely studied.

Does deadwood moisture vary jointly with surface soil water content?

AbstractDeadwood moisture plays a major role in regulating deadwood decomposition rates and may also affect forest microclimate. Despite this, the temporal variability of deadwood moisture at 15‐min time scales remains relatively unknown because techniques for using high‐frequency sensors for tracking moisture at appropriate spatial and temporal intensities have been lacking. We installed a high‐density sensor array in and around a downed log to gain a detailed assessment regarding the temporal variation of volumetric water content at multiple locations within one downed dead log, the source snag, and the surrounding soil. We also measured micrometeorological variables near the log in order to predict variability of the deadwood moisture. We found that the deadwood varied in similar fashion as the soil moisture, and that this similarity allowed us to make accurate predictions of deadwood moisture using micrometeorological data and soil moisture. The primary driver of deadwood moisture was rainfall; however, diurnal cycles of subcanopy humidity and temperature seemed to cause variation in the deadwood moisture between rain events. Our findings highlight the need for applying similar techniques to a variety of forest types, deadwood types, and different forest management strategies to gain a broader understanding of high‐frequency deadwood moisture dynamics and their feedbacks with ecosystem processes, like decomposition and forest fire hazards.

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

In this paper, two different computationally inexpensive methods for nowcasting/data filling spatially varying meteorological variables (wind velocity components, specific humidity, and virtual potential temperature) covering scales ranging from 100 m to 5 km in regions marked by complex terrain are compared. Multivariable linear regression and artificial neural networks are used to predict micrometeorological variables at eight locations using the measurements from three nearby weather stations. The models are trained using data gathered from a system of eleven low-cost automated weather stations that were deployed in the Cadarache Valley of southeastern France from December 2016 to June 2017. The models are tested on two held-out periods of measurements of thermally-driven flow and synoptically forced flow. It is found that the models have statistically significant performance differences for the wind components during the synoptically driven flow period (p = 6.6 × 10−3 and p = 2.0 × 10−2 for U and V, respectively), but perform the same otherwise. These methods can be used to spatially fill gaps in micrometeorological datasets. Recommended future work should include statistically interpreting the predictive models and testing their capabilities on meteorological datasets from different locations.

A Relationship between Micro-Meteorological and Personal Variables of Outdoor Thermal Comfort: A Case Study in Kitakyushu, Japan

Outdoor thermal comfort is an important indicator to create a quality and livable environment. This study examines a relationship between micro-meteorological and personal variables of outdoor thermal comfort conditions in an urban park. The data collection of outdoor thermal comfort is carried out using two methods in combination: micro-meteorological measurement and questionnaire survey. This finding shows that most of the respondents were comfortable with the thermal, wind, and humidity condition. The acceptability and satisfaction level of thermal comfort were positive. The most significant micro-meteorological variable for the physiologically equivalent temperature (PET) value is mean radiant temperature (Tmrt). As the Tmrt value is influenced by how much shading is produced from the presence of vegetation or buildings around the measurement location, this finding shows that the shadow was very important to the thermal comfort conditions in the Green Park Kitakyushu. The most influential micro-meteorological variable for the three different personal variables (TSV, WFSV, and HSV) is air temperature. The strongest relationship among the four variables is between TSV and PET. The findings will be the basis for the city authorities in preparing regional development plans, especially those related to the planning of city parks or tourist attractions.

Evaluating outdoor thermal comfort in urban open spaces in a humid subtropical climate: Chandigarh, India

This study deals with the assessment of outdoor thermal comfort in three distinct urban open space typologies (i.e. plaza, green and waterfront) in Chandigarh, India. Micrometeorological monitoring and transverse surveys eliciting 2523 valid responses were conducted during summer and winter seasons. PET was computed based on the measured micrometeorological variables. Benchmarks pertaining to thermoneutrality and thermal acceptability were established. The thermoneutrality of the respondents corresponded to a physiological equivalent temperature of 24.09 °C. Neutral temperatures and thermal sensation varied significantly between summer and winter. The respondents were more sensitive to the prevalent thermal environment in summer than winter. In addition, they considered the green spaces to be the most thermally comfortable among the open space typologies evaluated. The acceptable thermal comfort range was 20.2–36.6 °C. Thermal neutrality, acceptability and comfort varied significantly based on context and the attributes of open spaces.