Showing papers on "Weather station published in 2015"

Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature

Abstract: Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.

Wildland fire as a self-regulating mechanism: the role of previous burns and weather in limiting fire progression

Abstract: Theory suggests that natural fire regimes can result in landscapes that are both self-regulating and resilient to fire. For example, because fires consume fuel, they may create barriers to the spread of future fires, thereby regulating fire size. Top-down controls such as weather, however, can weaken this effect. While empirical examples demonstrating this pattern-process feedback between vegetation and fire exist, they have been geographically limited or did not consider the influence of time between fires and weather. The availability of remotely sensed data identifying fire activity over the last four decades provides an opportunity to explicitly quantify-the ability of wildland fire to limit the progression of subsequent fire. Furthermore, advances in fire progression mapping now allow an evaluation of how daily weather as a top-down control modifies this effect. In this study, we evaluated the ability of wildland fire to create barriers that limit the spread of subsequent fire along a gradient representing time between fires in four large study areas in the western United States. Using fire progression maps in conjunction with weather station data, we also evaluated the influence of daily weather. Results indicate that wildland fire does limit subsequent fire spread in all four study areas, but this effect decays over time; wildland fire no longer limits subsequent fire spread 6-18 years after fire, depending on the study area. We also found that the ability of fire to regulate, subsequent fire progression was substantially reduced under extreme conditions compared to moderate weather conditions in all four study areas. This study increases understanding of the spatial feedbacks that can lead to self-regulating landscapes as well as the effects of top-down controls, such as weather, on these feedbacks. Our results will be useful to managers who seek to restore natural fire regimes or to exploit recent burns when managing fire.

Analysis of the urban heat island effects on building energy consumption

Abstract: Urban areas usually experience higher tem- peratures when compared to their rural surroundings. Several studies underlined that specific urban conditions are strictly connected with the Urban heat island (UHI) phenomenon, which consists in the environmental over- heating related to anthropic activities. As a matter of fact, urban areas, characterized by massive constructions that reduce local vegetation coverage, are subject to the absorption of a great amount of solar radiation (short wave) which is only partially released into the atmosphere by radiation in the thermal infrared (long wave). On the contrary, green areas and rural environments in general show a reduced UHI effect, that is lower air temperatures, due to evapo-transpiration fluxes. Several studies demon- strate that urban microclimate affects buildings' energy consumption and calculations based on typical meteoro- logical year could misestimate their actual energy con- sumption. In this study, two different sets of meteorological data are used for the calculation of the heating and cooling energy needs of an existing university building. The building is modeled using TRNSYS v.17 software. The first set of data was collected by a weather station located in the city center of Modena, while the second set of data was collected by another station, located in the surrounding area of the city, near to the studied building. The influence of the different meteorological situations described by the two weather stations are analyzed and assumed to be representative of the UHI effect. Furthermore, the effects of UHI mitigation strate- gies on the building energy needs are evaluated and discussed.

Development of a Global Fire Weather Database

Abstract: . The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

Using LCZ data to run an urban energy balance model

Abstract: In recent years a number of models have been developed that describe the urban surface and simulate its climatic effects. Their great advantage is that they can be applied in environments outside the cities in which they have been developed and evaluated. Thus, they may be applied to cities in the economically developing world, which are growing rapidly, and where the results of such models may have greatest impact with respect to informing planning decisions. However, data requirements, particularly for the more complex urban models, represent a major obstacle to their employment. Here, we examine the potential for running the Surface Urban Energy and Water Balance model (SUEWS) using readily obtained data. SUEWS was designed to simulate energy and water balance terms at a neighbourhood scale (⩾1 km2) and requires site-specific meteorological data and a detailed description of the surface. Here, its simulations are evaluated by comparison with measurements made over a seven month (approximately 3 seasons) period (April–October) at two flux tower sites (representing urban and suburban landscapes) in Dublin, Ireland. However, the main purpose of this work is to test the performance of the model under ‘ideal’ and ‘imperfect’ circumstances in relation to the input data required to run SUEWS. The ideal case uses detailed urban land cover data and meteorological data from the tower sites. The imperfect cases use parameters derived from the Local Climate Zone (LCZ) classification scheme and meteorological data from a standard weather station located beyond the urban area. For the period of record examined, the simulations show good agreement with the observations in both ideal and imperfect cases, suggesting that the model can be used with data that is more easily derived. The comparison also shows the importance of including vegetative cover and of the initial moisture state in simulating the urban energy budget.

The probability distribution of intense daily precipitation

Abstract: The probability tail structure of over 22,000 weather stations globally is examined in order to identify the physically and mathematically consistent distribution type for modeling the probability of intense daily precipitation and extremes. Results indicate that when aggregating data annually, most locations are to be considered heavy tailed with statistical significance. When aggregating data by season, it becomes evident that the thickness of the probability tail is related to the variability in precipitation causing events and thus that the fundamental cause of precipitation volatility is weather diversity. These results have both theoretical and practical implications for the modeling of high-frequency climate variability worldwide.

Using weather forecast data for irrigation scheduling under semi-arid conditions

Abstract: A new methodology based on the use of weather forecast data from freely and easily accessible online information for determining irrigation scheduling has been developed. Firstly, reference evapotranspiration (ETo) was determined with a user-friendly procedure that does not require previous local calibration, knowledge of data acquisition or processing, or a nearby weather station. The comparison of ETo based on short-term (same-day) and long-term (6-day-ahead) weather forecast data with measured data for 50 locations in southern Spain during 2013–2014 season indicated that differences in ETo were relatively low with root-mean-square error (RMSE) equal to 0.65 and 0.76 mm d−1, respectively. The procedure was tested for a wide range of weather conditions in the development of irrigation schedules and yield simulations for maize crop during 2013–2014 season. Irrigation water depths provided by irrigation schedules based on ETo obtained from daily and weekly forecasts and from measured data showed differences of around 1.5 and 0.9 %, respectively. Likewise, yield simulation with irrigation scheduling based on forecast and measured data provided equal averaged values, with a relative RMSE of below 5 %. This similarity of irrigation scheduling and yield estimation based on forecast and measured data has proved the optimal performance of the proposed approach.

An Intelligent Weather Station.

Abstract: Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

Comparison of different models for ground-level atmospheric turbulence strength (Cn 2 ) prediction with a new model according to local weather data for FSO applications

Abstract: Atmospheric parameters strongly affect the performance of free-space optical communication (FSOC) systems when the optical wave is propagating through the inhomogeneous turbulence transmission medium. Developing a model to get an accurate prediction of the atmospheric turbulence strength (Cn2) according to meteorological parameters (weather data) becomes significant to understand the behavior of the FSOC channel during different seasons. The construction of a dedicated free-space optical link for the range of 0.5 km at an altitude of 15.25 m built at Thanjavur (Tamil Nadu) is described in this paper. The power level and beam centroid information of the received signal are measured continuously with weather data at the same time using an optoelectronic assembly and the developed weather station, respectively, and are recorded in a data-logging computer. Existing models that exhibit relatively fewer prediction errors are briefed and are selected for comparative analysis. Measured weather data (as input factors) and Cn2 (as a response factor) of size [177,147×4] are used for linear regression analysis and to design mathematical models more suitable in the test field. Along with the model formulation methodologies, we have presented the contributions of the input factors’ individual and combined effects on the response surface and the coefficient of determination (R2) estimated using analysis of variance tools. An R2 value of 98.93% is obtained using the new model, model equation V, from a confirmatory test conducted with a testing data set of size [2000×4]. In addition, the prediction accuracies of the selected and the new models are investigated during different seasons in a one-year period using the statistics of day, week-averaged, month-averaged, and seasonal-averaged diurnal Cn2 profiles, and are verified in terms of the sum of absolute error (SAE). A Cn2 prediction maximum average SAE of 2.3×10−13 m−2/3 is achieved using the new model in a longer range of dynamic meteorological parameters during the different local seasons.

Comparing farmers’ perception of climate change and variability with historical climate data in the Upper East Region of Ghana

Abstract: Perception of climate change and variability supported by local knowledge has helped to advance understanding of climate change and its impacts on agricultural land-use systems. This study compares farmers’ perception of climate change and variability in four communities of the Upper East Region of Ghana. Using a sample of 186 households from these four communities, farmers’ perception was compared with historical climatic data from the closest weather station of the study area. Also, logistic regressions were used to estimate factors that influence the perception of climate change and variability in the area. Findings show that 71% of respondents perceived an increase in temperature which matches with the climatological evidence. On the other hand, decreasing rainfall with a shortening period was observed by 95% of respondents. From the climatological data, no real evidence of reduction in the amount of rainfall was apparent due to the high inter-annual variability. Unlike the rainfall data, there is an agreement between climatological data and farmers’ observation that the onset of the rainy season is now shifting from April to June, accompanied by an increasing dry spell. In contrast, there is a divergence concerning the length of the growing season which is explained by the strong influence of the onset rather than by the end of the rainy season. From the findings of the binary logistic analyses, the local topography and the information on weather and climate were significant in determining the likelihood of a good perception of climate change and variability. Therefore, for any policy directed at farmers to adopt adaptation measures to climate change, more attention should be given to the role of the local environment and access to climate-related information. Keywords: Adaptation, Climate change, Perception, Climatological evidence, Upper East

Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations

Abstract: Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not...

Winter weather conditions vs. air quality in Tricity, Poland.

Abstract: The principal aim of this paper is to assess the influence of meteorological conditions on the variability of sulfur dioxide and PM10 particulate matter concentration of pollutants during winter with consideration of an excess of admissible standards. The basis for the analysis were hourly concentrations of PM10 and sulfur dioxide as well as the basic meteorological elements automatically recorded at five stations located in the Tricity agglomeration, and operating within the weather station network belonging to the Agency of Regional Air Quality Monitoring in the Gdansk Metropolitan Area (ARMAAG). The analysis covers the calendar winters (December–February) in the years 2004/2005 through 2009/2010. The variability of the concentrations of both pollutants under certain weather conditions, i.e. air temperature and relative humidity, atmospheric pressure, as well as wind speed and direction, were evaluated by means of cluster analysis using k-means belonging to a group of non-hierarchical cluster analysis method. The composite effect of meteorological conditions on the variability of sulfur dioxide and PM10 concentrations in isolated clusters were determined by multiple linear regression, using a stepwise procedure, at the significance level α = 0.05 and α = 0.01. The effect of individual weather elements on the pattern of concentration levels was determined using partial regression coefficients. Clusters grouping the highest concentrations of pollutants were characterised, in most cases, by the lowest air temperature and a lower wind speed, and often a higher pressure, and sometimes slightly lower relative air humidity, i.e. the conditions of anticyclonic weather. Weather conditions had a statistically significant effect on the concentrations of both pollutants in all clusters; however, air temperature and wind speed had the crucial role. Thermal conditions were the decisive factor in the winter season 2005/2006 with the most frequent, overnormative daily particulate matter concentration, yet the inversion layers both lower and upper, occurring almost every day in January 2006 also had a significant influence.

Climatological characteristics of the Ora del Garda wind in the Alps

Abstract: The Ora del Garda is a coupled lake and valley breeze regularly blowing from the northern shorelines of Lake Garda, in the Italian Alps, especially during warm-season clear-sky days. The climatological characteristics of this wind are investigated through the analysis of 10 years of observations collected at two representative surface weather stations – one on Lake Garda's shore and the other 30 km inland. Furthermore, the possible influences of the land-water temperature contrast and of the synoptic wind on the development and the propagation of the Ora del Garda are analysed. Lake-breeze days are identified by means of a set of objective criteria based on observations of solar radiation, wind speed and direction at the two stations. The analysis highlights that, on the lake's shoreline, the breeze develops on about 70% of the days in the warmest months, while it rarely occurs from October to February. Moreover, in the warmest months, the Ora del Garda reaches the inland weather station on about 80–90% of the days on which it blows on the lake's shore, after 3.5 h on average. It displays rather strong intensities, reaching average velocities of 5 m s−1 and gusts of 10 m s−1, respectively, in summer on the lake's shore and in spring at the inland weather station. No clear relationship is found between the land-water temperature contrast and the lake-breeze strength. On the other hand, synoptic winds are observed to affect significantly the development of the breeze. In particular, onshore synoptic winds are associated with stronger intensities at the lake's shore. Moreover, in these situations the Ora del Garda propagates faster and is detected earlier at the inland weather station.

Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies

Abstract: A spatial mismatch exists between regional climate models and conditions experienced by individual organisms. We demonstrate an approach to downscaling air temperatures for site-level studies using airborne LiDAR data and remote microclimate loggers. In 2012–2013, we established a temperature logger network in the forested region of central Missouri, USA, and obtained sub-hourly meteorological measurements from a centrally located weather station. We then used linear mixed models within an information theoretic approach to evaluate hourly and seasonal effects of insolation, vegetation structure, elevation, and meteorological measurements on near-surface air temperatures. The best-supported models predicted fine-scale temperatures with high accuracy during both the winter and growing seasons. We recommend that researchers consider the scales relevant to specific applications when using our approach to develop site-specific spatio-temporal models.

Evaluation of high-resolution precipitation analyses using a dense station network

Abstract: The ability of radar–rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling, thunderstorm warnings, and, as a reference, to spatially validate numerical weather prediction models. However, due to drawbacks of methods like cross-validation and due to the limited availability of reference data sets on high temporal and spatial scales, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high-resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense weather station network (WegenerNet), operated in a limited domain of the southeastern parts of Austria (Styria). Based on case studies and a longer-term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown by both continuous and categorical verification measures, although the temporal and spatial variability of the errors is – by convective nature – high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

Wireless Sensor Network for Monitoring Soil Moisture and Weather Conditions

Abstract: . A wireless sensor network (WSN) was built and deployed in three fields to monitor soil moisture status and collect weather data for irrigation scheduling. The WSN consists of soil moisture sensors, weather sensors, wireless data loggers, and a wireless modem. Soil moisture sensors were installed at three depths below the ground surface in various locations across the fields. Weather sensors were mounted on a 3-m instrument tower. An antenna mount was designed and fabricated for use in the WSN. When field equipment such as a fertilizer or chemical applicator impacted the mount, the mount was capable of protecting the antenna from damage by the equipment. In the WSN, received radio signal strength of Em50R data logger decreased as the distance from the data logger to the receiver increased. It also decreased as the distance between the top of the plant canopy and the logger’s antenna above the plant canopy decreased. The antenna of the Em50R logger required replacement above the plant canopy for effective data communication. The Em50G data logger was capable of transferring data as its antenna was inside the plant canopy. Using the WSN system, soil moisture and weather conditions including precipitation, solar radiation, wind speed, and humidity were measured every minute and the hourly averages were reported and stored at 1-h interval. The soil moisture data and weather data were automatically and wirelessly transmitted to the internet making the data available online. Data collected by the WSN have been used in irrigation scheduling research in cotton, corn and soybean crops.

Challenges and benefits from crowdsourced atmospheric data for urban climate research using Berlin, Germany, as testbed

Abstract: Provision of atmospheric data from observational networks at high spatial resolution and over long time periods remains a challenge in urban climate research. Classical observational networks are designed for detection of synoptic atmospheric conditions, and thus are rarely suitable for city-specific and intra-urban analysis. Therefore, using citizens as data provider offers huge potentials, especially in urban areas due to high population density. The concept of citizen science is not new, especially in the field of ecology (Dickinson et al. 2012). This concept relies on active participation of citizens to contribute to research. A number of efforts have been made in recent years concerning atmospheric applications, e.g. mapping of atmospheric aerosols with smartphones (Snik et al., 2014) or involving citizens in observational networks such as “CoCoRaHS” (Community Collaborative Rain, Hail and Snow Network, http://www.cocorahs.org/) or the “Citizen Weather Observer Program” (http://wxqa.com). Another approach to acquire huge amounts of data is the concept of crowdsourcing, defined by Dickinson et al. (2012) as “…getting an undefined public to do work, usually directed by designated individuals or professionals…” For instance, Overeem et al. (2013) took battery-temperature measurements from smartphones to derive urban air temperatures by using data from the smartphone application ‘OpenSignal’ (opensignal.com), while Mass and Madaus (2014) exploited air-pressure measurements from another application called ‘pressureNET’ (pressurenet.cumulonimbus.ca) to simulate an active convection event in the United States. The netatmo urban weather stations (www.netatmo.com) act as an intermediate between active citizen science and crowdsourcing of passively acquired data. The netatmo company develops and distributes weather stations around the world for interested citizens for monitoring the atmospheric conditions inside and outside their buildings. The netatmo weather station is cost-efficient, and Wi-Fi connection serves for data transfer, storage and visualisation via application software. These smart devices upload automatically their data to the netatmo server. They belong to the ‘Internet of things’, which plays an important role for recent innovations in data mining and crowdsourcing (Muller et al. 2015). While netatmo weather stations offer huge potentials due to dense spatial coverage in many urban areas, the question remains if and how crowdsourced data from this source could be suitable for urban climate research. What are the key challenges and benefits? The focus of this contribution is on crowdsourced air temperature (Ta) records.

Effects of diurnal, intra-seasonal and seasonal climate variability on the energy balance of a small subtropical reservoir

Abstract: In order to gain a full understanding of the importance of interactions between the atmosphere and inland water bodies, there is a need to analyse these exchanges in different regions throughout the world and under a wide range of weather and climate conditions. A 2-year time series of energy balance measurements was made at a small reservoir situated in southeast Queensland, Australia. Measurements were used to establish diurnal, intra-seasonal and seasonal cycles of evaporation, while synoptic weather maps and local weather station data were used to relate variations in water surface-atmosphere energy exchanges to synoptic and mesoscale weather phenomena. Consistent diurnal peaks in latent heat flux during the afternoon were observed throughout this study as a result of strong dry winds coinciding with peak water surface temperatures. Occasional intra-seasonal pulses in latent heat flux (i.e. evaporation) in winter and spring were associated with the passage of cold fronts over southern Queensland, which brought strong dry westerly winds. The average annual evaporative water loss from the reservoir during the 2-year measurement campaign was 991mm year-1. It is thought that differences between the annual evaporation totals for the 2 years of this study may have been related to differences in the frequency of overcast conditions as a result of a change in the phase of the El Nino-Southern Oscillation.

Offset between GPS collar-recorded temperature in moose and ambient weather station data

Abstract: GPS collar-recorded temperature is often considered as a proxy for the ambient temperature in wildlife ecology studies, yet few studies actually test its reliability as well as the correlation with ambient temperature. Here, we address this question and demonstrate a strong correlation between collar temperature and weather station data, indicating that GPS collar sensor data can be regarded as a reliable index of ambient air temperature. Using data obtained from 384 free-ranging moose equipped with GPS collars between latitude 57° N to 68° N in Sweden, we analyzed 1,467,361 paired observations of collar temperature and air temperature of the nearest official Swedish Meteorological and Hydrological Institute station. We found a systematic offset that varied across months, being larger during the warm summer months than during the winter period. We found an average correlation of .91 (r s; range .75 to .93, median .91) between collar and ambient temperature of the nearest weather station. Thus, temperature sensors in, e.g., a GPS collar, may be used to study animal behavior, movement and habitat choice in relation to ambient air temperature. This aligns with the calls for using animals as not only subjects but also as the samplers of the environment. It also opens up possibilities for large-scale projects on animal ecology and physiology in the absence of ground measuring stations on higher spatial scales like home range and landscape. As an application of collar temperature, we show that changes in the movement patterns seem to be highly influenced by temperature-induced heat stress that moose experience during summer.

A fast and spatialized urban weather generator for long-term urban studies at the city-scale

Abstract: In order to make urban climate predictions at the city-scale and on long term experiment accessible to communities such as building engineers or urban planner, a method to calculate meteorological forcing for surface models is presented. This method is computed with weather data files from an operational measurement station outside of the city. The model, called the spatialized urban weather generator (SUWG) calculates the temperature field above the urban canopy level with an energy budget for each volume of the boundary layer considered and 2D lagrangian advection model in order to take wind advection into account.This method has multiple advantages. First, the files from operational weather stations can easily be found for a lot of cities, in most case in airports. Second, the calculated urban heat island (UHI) can be influenced by urban planning scenarios. The method has been validated with an operational weather station network giving temperatures over the region of Paris and by comparing the SUWG simulations to a complete high resolution atmospheric simulation (MesoNH model) done over the Paris region at 2km of resolution during years 2010 and 2011. The full atmospheric model and the SUWG give comparable results with comparison to the data over the period studied for each urban operational station.