Reference Weather Station Research Articles

Lisbon Urban Climate: Statistical Analysis/Approach for Urban Heat Island Effect Based on a Pioneering Urban Meteorological Network

The urban heat island (UHI) effect is a widely recognized phenomenon consisting of heat accumulation by dense urban construction and human activities, resulting in higher temperatures across urban areas compared to their surroundings. This article aims to quantify the UHI effect on several areas throughout the city of Lisbon, Portugal, with the main goal of validating, evaluating, and reinforcing urban climate adaptation and resilience strategies proposed in the recent scientific literature. A set of nine quality-controlled weather stations from the “Lisboa Aberta” network that are compliant with the World Meteorological Organization (WMO) standards and installation requirements were used to characterize Lisbon’s UHI, in comparison to a reference weather station from the Instituto Português do Mar e da Atmosfera (IPMA), located at Lisbon Airport. By applying a principal component analysis (PCA) in an innovative way to 10 urban indexes, it is shown that the thermal inertia in Lisbon’s urban areas is positively correlated with the UHI intensity and urban density, regardless of the daily heating/cooling cycle. Furthermore, the results show that land use also has an impact on the UHI effect, with continuous, vertical building areas showing the greatest deviations in comparison to the reference, averaging +1.8 °C. Contrastingly, horizontal building areas reveal an average deviation of +1.3 °C, with sparse, discontinuously built areas representing an average UHI effect of +0.2 °C. Finally, through a climatope analysis, it is determined that, across Lisbon, high-density urban areas and ventilation corridors are responsible for inducing average UHI effects of +1.7 °C and +0.2 °C, respectively.

Measurement of the on-road wind environment in channelled roadways

A road test was undertaken to characterize the complex wind environment experienced by vehicles in channelled roadways. Channelled roadways consist of highways that are bordered by forests, hills, or other terrain features. A test vehicle was outfitted with three fast-response pressure probes in front of the vehicle located at 1 m, 2 m, and 3 m above the ground. Measurements were acquired on three highway routes in Canada with varying degrees of surrounding terrain, roadside forest cover, and in different wind conditions. Reference wind conditions were established using a network of federally-managed weather stations. The study identified that the effects of terrain and roadside forests can cause changes in wind direction of more than 50∘ to 90∘ relative to nearby reference weather stations. Directional channelling of winds was observed in forested and complex terrain, where changes in the wind direction mimicked the changes in highway alignment. The on-road video files were analysed using a machine-learning algorithm to quantitatively assess road-side forests and terrain and to identify fully-channelled sections of highway. Generalized channelled-flow trends were identified by considering the wind conditions in the channelled sections of all three routes. A linear relationship between the flow direction in the channel to that of the reference conditions was observed when the reference wind direction and the highway was within ±50∘. As the reference wind direction approached perpendicularity to the channelled segments, greater variations in the on-road wind direction were observed due to the unsteady flow within the channel. On-road wind speeds on each route were a maximum when the reference wind direction was approximately aligned with the highway and decreased to approximately 20% of the reference wind speed when the reference wind was perpendicular to the channel.

Performance evaluation of MeteoTracker mobile sensor for outdoor applications

Abstract. The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring.

UAV Atmosphere Sounding for Rocket Launch Support.

One of the crucial branches of activity at the Łukasiewicz Research Network-Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth's atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1-2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices-for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system's anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV's rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode.

Exploring Meteorological Conditions and Microscale Temperature Inversions above the Great Barrier Reef through Drone-Based Measurements

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas.

Outdoor micro-climate: Air temperature measurements around an office building in Denmark during summer

The outdoor micro-climate caused by the presence of buildings can significantly differ from that around weather stations located outside of urban areas. However, the latter is often used to design buildings and size building systems. This could lead to significant mistakes and performance gaps. To date, there is a certain lack of experimental studies assessing the micro-climate around buildings, especially in Scandinavian countries. The current paper presents the preliminary results and analysis of a measurement campaign of the temperature gradient in the two-meter air layer around the envelope of a multi-storey office building in Denmark in the summertime. Depending on the orientation of the external building surface (South/North façade or rooftop), the distance from the latter and the weather conditions, the temperature in this two-meter air layer can vary significantly and differ from the air temperature measured at nearby open fields or recorded by the reference weather station. During sunny days, a temperature gradient of up to 3.4 °C and 13.6 °C was measured in the air layer around the South façade and the rooftop, respectively. These results could help to validate urban climate models and bridge the gap between building design and real-condition performance. The curated dataset of this measurement campaign is available in open access.

Minimizing the impact of geographical basis risk on weather derivatives

AbstractIn the last decade, the index-based weather products (also called weather derivatives) have been gaining attention in the climate resilience discussion. Weather derivatives are designed to help companies hedging against climate variability. These products, that can be market-traded or over-the-counter, compensate individuals based on a pre-defined weather index. Thus, pay-offs of a weather derivative depend on a weather index and not, as with traditional types of insurance, on the actual amount of money lost due to adverse weather. One of the major drawbacks that may prevent weather derivatives to catch on is the impact of the Geographical Basis Risk (GBR), that is the deviation of weather conditions at different locations. In fact, when the reference weather station is not located in the immediate vicinity of the site of interest the hedging effectiveness may be reduced. In this paper, we contribute to the existing literature on GBR by proposing an optimization method that may help in offering a tailored solution, while at the same time keeping a standardized instrument as a reference. Using a historical record of Italian temperatures, strikes for temperatures are the choice variables of a penalty function containing pay-offs of a reference station and all other stations. Further, altitude and latitude of meteorological stations are shown to be relevant predictors to explain GBR. This can be an interesting starting point for the design of weather derivatives, since, from a unique station where the “reference” derivative is priced, all the other stations may be easily settled.

Urban Heat Island Mitigation and Urban Green Spaces: Testing a Model in the City of Padova (Italy)

The urban heat island (UHI) is a critical issue in most urbanised areas. Spatial variation of urban air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and the energy demand for cooling. UHIs can be particularly harmful to human health and there are numerous studies that link mortality and morbidity with extreme thermal events, that can be worsened by UHIs. The temperature difference between city centres and the surrounding countryside, which is accentuated in the summer months and at night, is the result not only of a greater production of anthropogenic heat but is mainly due to the properties of urban surfaces. The use of vegetation, and in particular urban tree planting, is one of possible strategies to contrast the heat island effects. In order to analyse the mitigation effects produced by green spaces in the city of Padova, a municipality in the northeast of Italy, simulations of the air temperature variations and their spatial distribution were carried out using the i-Tree Cool Air model. High-resolution RGBir aerial photos were processed to produce a tree canopy and a permeability map and the model was applied on a 10 m × 10 m grid over the entire city, producing a raster map of the aboveground air temperatures. A particularly hot July day with recorded air temperatures of 35 °C at 3 p.m. and 28 °C at 10 p.m. at a reference weather station was chosen for the test. In the daytime, the results show temperature differences up to almost 10 °C between urban open spaces with impervious cover (squares, streets) and green areas under tree canopy. At night, the simulated air temperatures are only slightly cooler in areas with tree cover than those recorded at the reference station, while urban areas with sealed surfaces maintain air temperatures 4.4 °C higher. The study was aimed at testing the applicability of the model as a tool for predicting air temperatures in relation to land use and canopy cover. The results show that the model can potentially be used to compare different urban forest and urban greening planning scenarios, however, further research is necessary to assess the reliability of the temperature predictions.

Analysis of the Skyscraper Wind around High-Rise Buildings in Coastal Region, South Korea, during Typhoon ‘Hinnamnor’

High-rise buildings in cities adversely affect wind regimes by changing the air currents in their surrounding areas. In particular, extreme climate phenomena caused by climate change are stronger and more frequent, causing damage in cities. To better understand skyscraper wind behaviors around high-rise buildings, actual measurements are necessary to determine the environmental assessment of the wind effect. In this study, field measurements were performed with five anemometers at five points in the vicinity of a skyscraper called the LCT residential complex (411.6 m tall) surrounded by high-rise buildings in the coastal city of Busan, South Korea during Typhoon Hinnamnor. The gust was 3.7 times stronger, while the maximum 1-min mean wind speed was 3.1 times stronger than those measured at a nearby reference weather station operated by the Korean Meteorological Administration. The characteristics of downward and canyon winds were shown to depend on the spatiotemporal characteristics of the five points. The turbulence intensity declined as the wind speed increased and converged to a certain value. The gust factor also dropped as the wind speed increased and converged to 2.0, and was considered to be the parameter that best represents the intensity of instantaneous gust caused by the skyscraper wind effect. These results suggest that high-rise buildings should be designed with the consideration of gusts twice as strong as the average wind speed. In addition, field measurements should be accompanied in order to respond to the skyscraper wind effect.

Modeling Potential production and yield gap of potato using modelling and GIS approaches

Understanding yield potential (Yp) and yield gap (Yg) in current intensive potato (solanum tuberosum L.) production is essential to meet future food demand with the limited resources. Evaluating yield gap is a strong approach to estimate maximum production potential when all factors are in the best condition. A complete estimation of yield gap and potential yield across all major potato producing regions in Iran is lacking. The global yield gap atlas (GYGA) protocol was used to estimate potential yield of potato in Iran. This protocol is based on the climatic zones (CZs) and the reference weather stations (RWS) buffer zones, soil types in each buffer zone. Thirty-five RWS buffer zones in potato producing regions were selected, and total potato area in the RWS buffer zones covered 83% of the whole potato harvest area. According to the results, the average Yp was 67.3 t ha–1 and actual yield (Ya) was 30 t ha–1. Therefore, the average tuber yield gap was 37.3 t ha–1. These results indicate that the Potato producers achieved 45% of the potential yield in Iran. Iranian farmers produced 5 million tons of potato from about 164,000 ha. If they can obtain only 80% of Yp (53.8 t ha–1), amount of potato production will be 8.8 million tones. As result, they can produce 5.2 million tons tuber yield of potato in 97,000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands.

Exploring the current status of barley yield and production gap of Iran

Barley (Hordeum vulgare. L) is adapted to drought and salinity as the most critical constrained factors in Iran's crop production. Hence, the grain barley's importance is exhibited via 1.75 million hectares of harvested areas and 3.2 million tons of production in the country with arid and semi-arid climate conditions. However, the current production status meets only about 50% of Iran's barley's grain demands and is globally recognized as one of the largest barley importers in recent years. This study aimed to estimate the potential and yield gap and the potential barley grain production increase under the irrigated and rainfed conditions as the first step in schematizing the promotion of barley production. The impact of determinant climate factors on barley yields was also investigated at dominant barley productions regions over Iran. The present study approach is implemented based on the Global Yield Gap Atlas (GYGA). Accordingly, 12 and 17 designated climatic zones (DCZ), are distinguished as the dominant areas of the irrigated and rainfed barley production in the country, respectively. Afterwards, 48 and 38 reference weather stations (RWSs) within the DCZs were distributed to irrigated and rainfed barley harvested areas, respectively. The SSM-iCrop2 crop model was employed to simulate potential yield in irrigated (Yp) and rainfed (Yw) conditions by utilization of required data in each RWS through 15 barley growing seasons (2000–2014). The yield gap (the difference between simulated potential yield (Yp or Yw) and actual yield (Ya)) were calculated based on the bottom-up approach of GYGA in RWSs, DCZs and national-scale, respectively. Based on the results, the estimated potential yield varies between 5283 and 8286 kg/ha (with an average of 7090 kg/ha) in irrigated condition, and in the case of rainfed barley ranged between 1072 and 4002 kg/ha (with an average of 2723 kg/ha). In contrast, the actual yield in the DCZs was reported in the range of 1406 and 3723 kg/ha (with an average of 3009 kg/ha) for irrigated barley and ranged around 390–1510 kg/ha in rainfed conditions (with an average of 1009 kg/ha). According to the results, the DCZs that are confronted with higher temperatures and shorter growth length periods due to less total received daily solar radiation have low yields in irrigated conditions. In rainfed barley harvested areas, a significant correlation between rainfall distribution, high temperature and Yw has existed during the reproductive phase within 17 DCZs. Ultimately, the results indicated that the calculated yield gap varies between 3237 and 4697 kg/ha (50–76%) with an average of 4081 kg/ha (58%), and also between 615 and 3125 kg/ha (53–82%) with an average of 1714 kg/ha (63%) in the DCZs of irrigated and rainfed barley harvested areas, respectively. Consequently, it has been concluded that by achieving attainable yield (80% of potential yield) via improving production management, irrigated barley yield production can be increased to 5672 kg/ha and 4.17 million tons, respectively, provided that water resources are available. It can also be exploitable to increase rainfed barley's current yield and production from 1009 to 2178 kg/ha and 1.05–2.26 million tons, respectively. Finally, it makes sense to be inspired by increasing total national barley production from 3.26 to 6.43 million tons per year and bring the country closer to obtain full self-sufficiency in supplying the required barley grain.

A NEURAL NETWORK REGRESSION MODEL FOR ESTIMATING MAXIMUM DAILY AIR TEMPERATURE USING LANDSAT-8 DATA

Abstract. Urban Heat Islands (UHI) phenomenon is a pressing problem for highly industrialized areas with serious risks for public health. Weather stations guarantee long-term accurate observations of weather parameters, such Air Temperature (AT), but lack appropriate spatial coverage. Numerous studies have argued that satellite Land Surface Temperature (LST) is a relevant parameter for estimating AT maps, exploring both linear regression and Machine Learning algorithms. This study proposes a Neural Network (NN) regression model for estimating the maximum AT from Landsat-8 data. The approach has been tested in a variegated morphological region (Puglia, Italy) using a large stack of data acquired from 2018 to 2020. The algorithm uses the median values of LST and Normalized Difference Vegetation Index (NDVI) computed using different buffer radius around the location of each reference weather station (250 m, 1000 m, and 2000 m) to train the NN model with a K-fold cross-validation strategy. The reference dataset was split into three sets using a stratified sampling approach considering the different station categories: rural, High- and Low-density Urban areas respectively. The algorithm was tested with different learning rates (LR) (0.001 and 0.005). The results show that our NN model accuracy improves with the increase of the buffer radius, minimizing the difference in terms of R^2 between training and evaluation data, with an overall accuracy consistently higher than 0.84. Future research could investigate more input variables in the NN model such as morphology or climate variables and test the algorithm on larger areas.

Microclimate estimation under different coffee-based agroforestry systems using full-sun weather data and shade tree characteristics

In Central America, coffee is mainly grown in agroforestry systems. This practice modifies the microclimate, which, in turn, influences coffee growth and development. However, modeling these microclimate modifications is a challenge when trying to predict the development of a disease in the understory crop, based on variables usually monitored in weather stations exposed to full sunlight. Furthermore, critical variables for plant disease development, such as leaf wetness duration and leaf temperatures, are generally not measured by weather stations. In our study, we sought to build models explaining daily minimum and maximum coffee leaf temperatures, daily coffee leaf wetness duration, and minimum and maximum air temperatures in agroforestry systems with a single shade tree species, which are common in Central America, and which were characterized by shade tree height, canopy openness and light gap distribution. The modeled variables were mainly explained by one or more meteorological variables provided by reference weather stations exposed to full sunlight. The presence of shade trees resulted in a buffer effect, reducing daily maximum air and leaf temperatures, and increasing daily minimum air and leaf temperatures. Moreover, except for the daily minimum air temperature under shade, shade tree characteristics affected these microclimatic variables. Indeed, the buffer effect on the daily maximum air temperature increased with shade trees 7 m tall or over, whereas for extreme leaf temperatures, this effect seemed to be further intensified by a dense and homogeneous canopy. The tallest shade trees also tended to provide conditions that reduced coffee leaf wetness duration. The coffee leaf stratum affected the daily maximum leaf temperature, with a top layer intercepting radiation for the lower strata, but had no effect on the daily minimum leaf temperature, detected at night. The models developed were simple equations allowing interpretation of shade tree height, the effects of canopy characteristics on the microclimate and were therefore useful for designing and managing agroforestry system. The more accurate models could be incorporated into an early warning system for coffee pests and diseases in the region.

Weather Sensing in an Urban Environment with the Use of a UAV and WebRTC-Based Platform: A Pilot Study.

Thanks to IoT, Internet access, and low-cost sensors, it has become possible to increase the number of weather measuring points; hence, the density of the deployment of sources that provide weather data for the needs of large recipients, for example, weather web services or smart city management systems, has also increased. This paper presents a flying weather station that carries out measurements of two weather factors that are typically included in weather stations (ambient temperature and relative humidity), an often included weather factor (atmospheric pressure), and a rarely included one (ultraviolet index). In our solution, the measurements are supplemented with a visual observation of present weather phenomena. The flying weather station is built on a UAV and WebRTC-based universal platform proposed in our previous paper. The complete, fully operational flying weather station was evaluated in field studies. Experiments were conducted during a 6-month period on days having noticeably different weather conditions. Results show that weather data coming from the flying weather station were equal (with a good approximation) to weather data obtained from the reference weather station. When compared to the weather stations described in the literature (both stationary weather stations and mobile ones), the proposed solution achieved better accuracy than the other weather stations based on low-cost sensors.

Validation of a CFD model for the evaluation of urban microclimate at high latitudes: A case study in Trondheim, Norway

The urban microclimate is a rapidly evolving field of research gaining increasing interest from public authorities and researchers. However, studies at high-latitude cities are scarce and researchers primarily focus on summerly overheating. This study focuses on the validation process of a CFD model that applies the 3D URANS approach with the realisable k-ε turbulence model at a highly complex urban area in Trondheim, Norway (63.4° N) during autumn. The CFD model features a polyhedral grid of the urban environment, including geometrically explicitly modelled buildings and trees in the area of interest. Furthermore, solar radiation, longwave radiation exchange, heat transfer from the buildings, heat storage in the urban surface, and the thermal effects of evapotranspiration from trees and grass surfaces are considered. The CFD model is validated with experimental results from a network of five mobile and one reference weather stations in the study area, providing hourly-averaged measurements for wind speed, wind direction (only reference weather station) and air temperature for two 48-h periods from September 27–28 and October 19–20. The results show that the CFD model is well able to reproduce the measured conditions at the area of interest with a mean R2 of 0.60, 0.63, and 0.96 for wind speed, wind direction and air temperature, respectively, at the reference weather station. It will be used in future studies, including the analysis of the impact of urban microclimate on buildings’ energy performance, outdoor thermal and pedestrian wind comfort.

Effects of urban park design features on summer air temperature and humidity in compact-city milieu

Urban park patronage is intimately linked to the thermal comfort level perceived by visitors. This study quantified the effects of nine park-design and urban-landscape parameters on air temperature and relative humidity in summer by deploying 100 sensors in 14 urban parks in Hong Kong. The field data were compared with a reference weather station and analysed with multiple regression. The sampled parks were on average 0.2 °C warmer and 1.7% less humid than the reference. A notable temperature reduction (mean = 0.6 °C and max. = 4.9 °C) was only observed in the largest park in Hong Kong. The daily variations in cooling and humidifying magnitudes were strongly dependent on background temperature. The distance from sea, shrub cover, tree cover and sky view factor were significant parameters that controlled temperature and relative humidity. Mean temperature could be predicted to cool by 0.6 °C if the park was 1 km closer to the sea. For every 10% increase in shrub and tree covers, mean temperature would drop by 0.07 and 0.04 °C respectively. Larger parks with good coverage of woody vegetation should be developed to improve urban microclimate within parks and mediate the warming effect of nearby built-up areas.

Meteorological observations in tall masts for the mapping of atmospheric flow in Norwegian fjords

Abstract. Since 2014, 11 tall meteorological masts have been erected in coastal areas of mid-Norway in order to provide observational data for a detailed description of the wind conditions at several potential fjord crossing sites. The planned fjord crossings are part of the Norwegian Public Roads Administration (NPRA) Coastal Highway E39 project. The meteorological masts are 50–100 m high and located in complex terrain near the shoreline in Halsafjorden, Julsundet and Storfjorden in the Møre og Romsdal county of Norway. Observations of the three-dimensional wind vector are made at 2–4 levels of each mast with a temporal frequency of 10 Hz. The dataset is corroborated with observed profiles of temperature at two masts, as well as observations of precipitation, atmospheric pressure, relative humidity and dew point at one site. The first masts were erected in 2014, and the measurement campaign will continue until at least 2024. The current paper describes the observational setup, and observations of key atmospheric parameters are presented and put in context with observations and climatological data from a nearby reference weather station. The 10 min and 10 Hz wind data, as well as other meteorological parameters, are publicly available through the Arctic Data Centre (https://doi.org/10.21343/z9n1-qw63; Furevik et al., 2019).

Urban-scale air temperature estimation: development of an empirical model based on mobile transects

The urban microclimate is influenced by many factors which trigger the well-known Urban Heat Island (UHI) phenomenon. Different approaches have been developed in the literature for estimating urban temperatures, but a compromise is always needed, either spatial or temporal. To solve this issue, this work presents a new way for estimating urban temperatures with a fine spatial resolution (even specific streets) while also keeping a high temporal resolution (hourly time-steps) for prolonged periods. This is done by developing an empirical model, based on the measurements of a reference weather station and data taken from mobile transects.The proposed method was tested in Seville (Spain). The validation was done comparing the prediction of the model with the measurements of a fixed temperature sensor, from the 7th of June 2019 until the 7th of December 2019 (4390 hours). The results showed a high R2 coefficient of 0.976 and a low RMSE of 1.21, improving the accuracy of previous literature for estimating urban temperatures. The methodology is applicable for any geographical location around the world, with different climates or population densities. In addition, it offers a precise way to verify the real impact of UHI mitigation strategies and concentrate climate change mitigation efforts.

Annual range of temperature and precipitation forecast for Altai-Sayan mountain country

Analysis and long-term forecasting of climatic characteristics of the mountains is laborious and extremely difficult due to complex vertical and horizontal differentiation of climatic fields and insufficient number of weather stations in the region. We have developed a method for statistical forecast of average monthly temperature in the surface air layer and monthly precipitation for the mountain areas with an annual lead time.
 The method is based on the description of monthly dynamics of the mentioned factors expressed in percent of their average annual monthly values measured in situ. Such a dynamics remains the same throughout the study territory, regardless of its height and exposure. To convert the relative values of temperature and precipitation into their conventional units of measurements (C and mm) one needs just mean annual January and July values of air temperature and precipitation for the territory under study. By the example of the Altai-Sayan mountain country, it is shown that the use of observation data for 67 years obtained from several reference weather stations ensure reliable prediction. The forecast is equally true for any part of the mountainous country due to spatial generalization of relative changes in these factors. The universal criterion A for assessing the quality of various predictive methods (including those, which do not use the model quality indices RSR and NashSutcliffe) is proposed.
 The criterion is the error of predictive method Sdiff normalized by standard deviation Sobs of observations from their average and equals to Sdiff/ Sobs. It is associated with NSE and RSR indices through dependencies RSR = A and NSE = 1RSR2 = 12A2. The proposed criterion was used in assessing the quality of temperature and precipitation forecasts; it was close to the theoretically best one for statistical prognoses.

A method to derive long-term coastal wind data from distant weather station to improve aeolian sand transport rate prediction

Abstract One of the main factors responsible for foredune development is the magnitude of aeolian sand supply. However, there are still many potential sources of error in its estimation resulting among others from long-term prediction of aeolian sand transport solely based on wind data from distant weather stations. Until now, no study has focused on the biases in these predictions caused by differences in wind data collected by weather stations located outside and within a research area. We aim to resolve the following questions: 1) what is the percentage of valid data lost and non-valid data included in such predictions, 2) what are the boundary conditions to determine the best relationship between wind data obtained from on-site and off-site weather stations, and 3) what is the best method of off-site data transformation to improve the prediction of sand transport rate at the research area. The research was based on 8961 hourly wind data from the reference weather station located at the coast and two nearby inland stations. The aeolian sand transport rate predicted on rough data sets significantly differed between each weather station, with the values from the off-site weather stations being two to ten times higher than those in the on-site weather station, depending on wind direction. The transformation of the inland wind data set was successful only if it was based on the formulae established separately for each direction sector proving that the proposed procedure may produce a data set that best reflects the wind conditions at the study site.