The influence of vegetation and building morphology on shadow patterns and mean radiant temperatures in urban areas: model development and evaluation

The solar and longwave environmental irradiance geometry (SOLWEIG) model simulates spatial variations of 3-D radiation fluxes and mean radiant temperature (T _mrt) as well as shadow patterns in complex urban settings. In this paper, a new vegetation scheme is included in SOLWEIG and evaluated. The new shadow casting algorithm for complex vegetation structures makes it possible to obtain continuous images of shadow patterns and sky view factors taking both buildings and vegetation into account. For the calculation of 3-D radiation fluxes and T _mrt, SOLWEIG only requires a limited number of inputs, such as global shortwave radiation, air temperature, relative humidity, geographical information (latitude, longitude and elevation) and urban geometry represented by high-resolution ground and building digital elevation models (DEM). Trees and bushes are represented by separate DEMs. The model is evaluated using 5?days of integral radiation measurements at two sites within a square surrounded by low-rise buildings and vegetation in G?teborg, Sweden (57°N). There is good agreement between modelled and observed values of T _mrt, with an overall correspondence of R ²?=?0.91 (p?<?0.01, RMSE?=?3.1?K). A small overestimation of T _mrt is found at locations shadowed by vegetation. Given this good performance a number of suggestions for future development are identified for applications which include for human comfort, building design, planning and evaluation of instrument exposure.     

Transmissivity of solar radiation through crowns of single urban trees—application for outdoor thermal comfort modelling

Trees play an important role in mitigating heat stress on hot summer days, mainly due to their ability to provide shade. However, an important issue is also the reduction of solar radiation caused by trees in winter, in particular at high latitudes. In this study, we examine the transmissivity of total and direct solar radiation through crowns of single street trees in Göteborg, Sweden. One coniferous and four deciduous trees of species common in northern European cities were selected for case study. Radiation measurements were conducted on nine clear days in 2011–2012 in foliated and leafless tree conditions using two sunshine pyranometers—one located in shade of a tree and the other one on the roof of an adjacent building. The measurements showed a significant reduction of total and direct shortwave radiation in the shade of the studied trees, both foliated and leafless. Average transmissivity of direct solar radiation through the foliated and defoliated tree crowns ranged from 1.3 to 5.3 % and from 40.2 to 51.9 %, respectively. The results confirm the potential of a single urban tree to reduce heat stress in urban environment. However, the relatively low transmissivity through defoliated trees should be considered while planning street trees in high latitude cities, where the solar access in winter is limited. The results were used for parameterisation of SOLWEIG model for a better estimation of the mean radiant temperature (T_mrt). Measured values of transmissivity of solar radiation through both foliated and leafless trees were found to improve the model performance.     

Comparison of human radiation exchange models in outdoor areas

Results from the radiation components of seven different human thermal exchange models/methods are compared. These include the Burt, COMFA, MENEX, OUT_SET* and RayMan models, the six-directional method and the new Park and Tuller model employing projected area factors (f _p) and effective radiation area factors (f _eff) determined from a sample of normal- and over-weight Canadian Caucasian adults. Input data include solar and longwave radiation measured during a clear summer day in southern Ontario. Variations between models came from differences in f _p and f _eff and different estimates of longwave radiation from the open sky. The ranges between models for absorbed solar, net longwave and net all-wave radiation were 164, 31 and 187?W?m^?2, respectively. These differentials between models can be significant in total human thermal exchange. Therefore, proper f _p and f _eff values should be used to make accurate estimation of radiation on the human body surface.     

Comparison of models calculating the sky view factor used for urban climate investigations

The sky view factor (SVF) describes the surface geometry and is a commonly used and important measure in urban climate investigations whose aim is the exploration of effects of a complex urban surface on climatological processes in built-up areas. A selection of methods and models for calculating the SVF was compared. For this purpose, fish eye images were taken at several locations in the city of Szeged, southern Hungary. The fish eye images equidistantly follow linear transects to cover a range of SVF values and to analyze the reaction of the methods to a continuously changing environment. The fish eye pictures were evaluated by three methods: the method according to Steyn (Atmos-Ocean 18(3):245?C258, 1980) implemented in a GIS-Script, the ??Edit free sky view factor?? tool of the RayMan model and BMSkyView. The SVF values at the coordinates of the fish eye pictures were calculated with three numerical models (SkyHelios, ArcView SVF extension, and SOLWEIG) with a 3D building data base as input. After comparing the results of the first run, a deviation occurs. The deviation disappears after implementing an option to include a weighting factor in some of the models.     

GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions

Accurate estimation of reference evapotranspiration (ET₀) becomes imperative for better managing the more and more limited agricultural water resources. This study examined the feasibility of developing generalized artificial neural network (GANN) models for ET₀ estimation using weather data from four locations representing different climatic patterns. Four GANN models with different combinations of meteorological variables as inputs were examined. The developed models were directly tested with climatic data from other four distinct stations. The results showed that the GANN model with five inputs, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed, performed the best, while that considering only maximum temperature and minimum temperature resulted in the lowest accuracy. All the GANN models exhibited high accuracy under both arid and humid conditions. The GANN models were also compared with multivariate linear regression (MLR) models and three conventional methods: Hargreaves, Priestley–Taylor, and Penman equations. All the GANN models showed better performance than the corresponding MLR models. Although Hargreaves and Priestley–Taylor equations performed slightly better than the GANN models considering the same inputs at arid and semiarid stations, they showed worse performance at humid and subhumid stations, and GANN models performed better on average. The results of this study demonstrated the great generalization potential of artificial neural techniques in ET₀ modeling.     

Retrieving air humidity,global solar radiation,and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part III: reference evapotranspiration

We evaluated two methods to estimate evapotranspiration (ETo) from minimal weather records (daily maximum and minimum temperatures) in Mexico: a modified reduced set FAO-Penman-Monteith method (Allen et al. 1998, Rome, Italy) and the Hargreaves and Samani (Appl Eng Agric 1(2): 96–99, 1985) method. In the reduced set method, the FAO-Penman-Monteith equation was applied with vapor pressure and radiation estimated from temperature data using two new models (see first and second articles in this series): mean temperature as the average of maximum and minimum temperature corrected for a constant bias and constant wind speed. The Hargreaves-Samani method combines two empirical relationships: one between diurnal temperature range ΔT and shortwave radiation Rs, and another one between average temperature and the ratio ETo/Rs: both relationships were evaluated and calibrated for Mexico. After performing a sensitivity analysis to evaluate the impact of different approximations on the estimation of Rs and ETo, several model combinations were tested to predict ETo from daily maximum and minimum temperature alone. The quality of fit of these models was evaluated on 786 weather stations covering most of the territory of Mexico. The best method was found to be a combination of the FAO-Penman-Monteith reduced set equation with the new radiation estimation and vapor pressure model. As an alternative, a recalibration of the Hargreaves-Samani equation is proposed.     

Comparison of a simple logarithmic and equivalent neutral wind approaches for converting buoy-measured wind speed to the standard height: special emphasis to North Indian Ocean

The difference between the transferred wind speed to 10-m height based on the equivalent neutral wind approach (U _n) and the logarithmic approach (U _log) is studied using in situ observations from the Indian, Pacific, and Atlantic Oceans, with special emphasis given to the North Indian Ocean. The study included U _n ? U _log variations with pressure, relative humidity, wind speed, air temperature, and sea surface temperature (SST). U _n ? U _log variation with respect to air temperature (T _a) reveals that U _n ? U _log is out of phase with air temperature. Further analysis found that U _n ? U _log is in phase with SST (T _s) ? T _a and varies between ?1.0 and 1.0 m/s over the North Indian Ocean, while for the rest of the Oceans, it is between ?0.3 and 0.8 m/s. This higher magnitude of U _n ? U _log over the North Indian Ocean is due to the higher range of T _s ? T _a (?4 to 6 °C) in the North Indian Ocean. Associated physical processes suggested that the roughness length and friction velocity dependence on the air–sea temperature difference contributes to the U _n ? U _log difference. The study is further extended to evaluate the behavior of U _n ? U _log under cyclonic conditions (winds between 15 and 30 m/s), and it was found that the magnitude of U_n ? U _log varies 0.5–1.5 m/s under the cyclonic wind conditions. The increasing difference with the wind speed is due to the increase in the momentum transfer coefficient with wind speed, which modifies the friction velocity significantly, resulting in U _n higher than U _log. Thus, under higher wind conditions, U _n ? U _log can contribute up to half the retrieval error (5 % of the wind speed magnitude) to the satellite validation exercise.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: comparing multi-model simulations with observations

Observations show that the surface diurnal temperature range (DTR) has decreased since 1950s over most global land areas due to a smaller warming in maximum temperatures (T _max) than in minimum temperatures (T _min). This paper analyzes the trends and variability in T _max, T _min, and DTR over land in observations and 48 simulations from 12 global coupled atmosphere-ocean general circulation models for the later half of the 20th century. It uses the modeled changes in surface downward solar and longwave radiation to interpret the modeled temperature changes. When anthropogenic and natural forcings are included, the models generally reproduce observed major features of the warming of T _max and T _min and the reduction of DTR. As expected the greenhouse gases enhanced surface downward longwave radiation (DLW) explains most of the warming of T _max and T _min while decreased surface downward shortwave radiation (DSW) due to increasing aerosols and water vapor contributes most to the decreases in DTR in the models. When only natural forcings are used, none of the observed trends are simulated. The simulated DTR decreases are much smaller than the observed (mainly due to the small simulated T _min trend) but still outside the range of natural internal variability estimated from the models. The much larger observed decrease in DTR suggests the possibility of additional regional effects of anthropogenic forcing that the models can not realistically simulate, likely connected to changes in cloud cover, precipitation, and soil moisture. The small magnitude of the simulated DTR trends may be attributed to the lack of an increasing trend in cloud cover and deficiencies in charactering aerosols and important surface and boundary-layer processes in the models.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

Analysis of the Weighted Mean Temperature of China Based on Sounding and ECMWF Reanalysis Data

Weighted mean temperature (Tm) is one of the most important conversion parameters for calculating precipitable water vapor by the signal path wet delay in ground-based GPS meteorology. This paper first discusses the Tmregression models for Hong Kong (HK) and the associated error statistics relative to the true values of Tmfrom the numerical method. The results show that there is little difference in precision between annual and seasonal Tmregression models for HK. The Bevis Tm-Ts(surface temperature) regression model is more suitable for northeastern China and the Qinghai-Tibetan Plateau than the local models. For areas lack of historical sounding data, the Kriging interpolation method and the ECMWF reanalysis product ERA-interim were employed to set up local Tm-Ts models. The results indicate that the Tmderived by the ERA-interim data coincides well with that by the sounding data, and the Kriging interpolation method can successfully obtain the coefficients of local Tm-Tsmodels, suggesting that these two approaches may serve as effective ways in the acquisition and localization of Tm.     

Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region

Downward longwave radiation (LW _↓ ) is a relevant variable for meteorological and climatic studies. Good estimates of this term are vitally important in correct determining of the net radiation, which, in turn, modulates the magnitude of the terms in the surface energy budget (e.g., evaporation). In remote sensing applications, the determination of daytime LW_↓ is required for estimation of the net radiation using satellite data. LW_↓ is not directly measured in weather stations and then is estimated using models with surface air temperature and humidity as input. In this paper, we identify the best models to estimate daytime downward longwave radiation from meteorological data in the sub-humid Pampean region. Several well-known models to estimate LW_↓ under clear and cloudy skies were tested. We use downward radiation components and meteorological data registered at Tandil (Argentina) from 2006 to 2010 (840 days). In addition, we propose two multiple linear regression models (MLRM-1 and MLRM-2) to estimate LW_↓ at the surface for all sky conditions. The new equations show better performance than the others models tested with root mean square errors between 12 and 16 W m^?2, bias close to zero and best agreements with measured data (r ²?≥?0.85).     

Error assessment of climate variables for FAO-56 reference evapotranspiration

Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ET_o) using the Food and Agriculture Organization Penman?CMonteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ET_o estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ET_o showed small differences [i.e., mean bias error (MBE) varying ?0.22 to 0.25?mm?day^?1 and root-mean-square-error (RMSE) varying 0.06?C0.50?mm?day^?1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ET_o (i.e., MBE of ?0.50?mm?day^?1 and RMSE of 0.60?C0.73?mm?day^?1). Estimated ET_o showed different sensitivity to variations of the meteorological parameters??in order of vapor pressure?>?wind speed?>?radiation. It is clear that the FAO56-PM method is applicable for reasonable ET_o estimation at a daily time scale especially in data-limited regions in Korea.     

Weibull-k Revisited: “Tall” Profiles and Height Variation of Wind Statistics

The Weibull distribution is commonly used to describe climatological wind-speed distributions in the atmospheric boundary layer. While vertical profiles of mean wind speed in the atmospheric boundary layer have received significant attention, the variation of the shape of the wind distribution with height is less understood. Previously we derived a probabilistic model based on similarity theory for calculating the effects of stability and planetary boundary-layer depth upon long-term mean wind profiles. However, some applications (e.g. wind energy estimation) require the Weibull shape parameter (k), as well as mean wind speed. Towards the aim of improving predictions of the Weibull- \(k\) profile, we develop expressions for the profile of long-term variance of wind speed, including a method extending our probabilistic wind-profile theory; together these two profiles lead to a profile of Weibull-shape parameter. Further, an alternate model for the vertical profile of Weibull shape parameter is made, improving upon a basis set forth by Wieringa (Boundary-Layer Meteorol, 1989, Vol. 47, 85–110), and connecting with a newly-corrected corollary of the perturbed geostrophic-drag theory of Troen and Petersen (European Wind Atlas, 1989, Risø National Laboratory, Roskilde). Comparing the models for Weibull-k profiles, a new interpretation and explanation is given for the vertical variation of the shape of wind-speed distributions. Results of the modelling are shown for a number of sites, with a discussion of the models’ efficacy and applicability. The latter includes a comparative evaluation of Wieringa-type empirical models and perturbed-geostrophic forms with regard to surface-layer behaviour, as well as for heights where climatological wind-speed variability is not dominated by surface effects.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Urban heat island and differences in outdoor comfort levels in Glasgow, UK

From extensive outdoor comfort campaigns, preliminary outdoor comfort ranges have been defined for the local population of Glasgow, UK, in terms of two thermal indices: ‘Temperature Humidity Sun Wind’ (THSW) and ‘Physiological Equivalent Temperature’ (PET). A series of measurements and surveys was carried out from winter through summer 2011 during 19 monitoring campaigns. For data collection, a Davis Vantage Pro2 weather station was used, which was equipped with temperature and humidity sensors, cup anemometer with wind vane, silicon pyranometer and globe thermometer. From concurrent measurements using two weather stations, one located close to the city core and another located at a rural setting, approximately at a 15-km distance from the urban area of Glasgow, comparisons were made with regard to thermal comfort levels and to urban–rural temperature differences for different periods of the year. It was found that the two selected thermal indices (THSW and PET) closely correlate to the actual thermal sensation of respondents. Moreover, results show that the urban site will have fewer days of cold discomfort, more days of ‘neutral’ thermal sensation and slightly higher warm discomfort. The most frequent urban heat island intensity was found to be around 3° C, whereas the fraction of cooling to heating degree-hours for a T _base of 65 °F was approximately 1/12th.     

Spatial gridding of daily maximum and minimum temperatures in Europe

We developed an operationally applicable land-only daily high-resolution (5?km?×?5?km) gridding method for station observations of minimum and maximum 2?m temperature (T _min/T _max) for Europe (WMO region VI). The method involves two major steps: (1) the generation of climatological T _min/T _max maps for each month of the year using block regression kriging, which considers the spatial variation explained by applied predictors; and (2) interpolation of transformed daily anomalies using block kriging, and combination of the resulting anomaly maps with climatological maps. To account for heterogeneous climatic conditions in the estimation of the statistical parameters, these steps were applied independently in overlapping climatic subregions, followed by an additional spatial merging step. Uncertainties in the gridded maps and the derived error maps were quantified: (a) by cross-validation; and (b) comparison with the T _min/T _max maps estimated in two regions having very dense temperature observation networks. The main advantages of the method are the high quality of the daily maps of T _min/T _max, the calculation of daily error maps and computational efficiency.     

Estimating net radiation at surface using artificial neural networks: a new approach

This study describes the results of artificial neural network (ANN) models to estimate net radiation (R _n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R _n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R _n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m^?2 and correlation coefficient (R ²) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m^?2 and R ² between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R _n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R _n prediction using commonly measured meteorological parameters.     

Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

Turbulence characteristics in the atmospheric surface layer during summer monsoon of 1997 over a semi-arid location in India

A land surface processes experiment (LASPEX) was conducted in the semi-arid region of Northwest India during January 1997–February 1998. Analysis of turbulent components of wind and air temperature collected in the surface layer (SL) at Anand (22°35′N, 72°55′E) during the Indian summer monsoon season from June to September 1997 is presented. Turbulent fluctuation of wind components and air temperature observed at Anand varied as a function of terrain features and stability of the surface layer. Under neutral conditions, the standard deviation of vertical velocity (σ _w ) and temperature (σ _T ) were normalized using respective surface layer scaling parameter u _* and T _* which fitted the expressions σ _w /u _* = 1.25 and σ _T /T _* ≈ 4. Micrometeorological spectrum of wind and temperature at 5 m above ground level (AGL) at Anand showed peaks at time scale of 1–3 min at the low-frequency end. The inertial sub-range characteristics (?2/3 slope) of the spectrum are exhibited mostly. However, in some occasions, slope of ?1 denoting brown noise was depicted by the wind and temperature spectrum, which indicated anisotropy in turbulence.