Limited Number Of Weather Stations Research Articles

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

Scalable Lightweight IoT-Based Smart Weather Measurement System

The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost weather station, where human activities can be significantly affected by enhancing the production of clean energy based on the known direction of the wind. Meanwhile, common weather stations are neither affordable nor customizable for specific applications. Moreover, due to weather forecast changes over time and location within the same city, it is not efficient to rely on a limited number of weather stations that may be located far away from a recipient’s location. Therefore, in this paper, we focus on presenting a low-cost weather station that relies on an artificial intelligence (AI) algorithm that can be distributed across a WTEG area with minimal cost. The proposed study measures multiple weather parameters, such as wind direction, wind velocity (WV), temperature, pressure, mean sea level, and relative humidity to provide current measurements to recipients and AI-based forecasts. In addition, the proposed study consists of several heterogeneous nodes and a controller for each station in a target area. The collected data can be transmitted through Bluetooth low energy (BLE). The experimental results reveal that the proposed study matches the standard of the National Meteorological Center (NMC), with a nowcast measurement of 95% accuracy for WV and 92% for wind direction (WD).

Impact of urban morphology on urban microclimate and building energy loads

Due to insufficient records and limited number of weather stations, prediction models must be used to forecast local climate conditions. Accurate prediction is required in the case of emerging cities because rapid growth in urban development causes changes in the attributes of the urban environment, particularly the local microclimate. With the help of the Urban Weather Generator (UWG) and a locally established weather station, this research explores the validity of UWG processed open weather data (i.e., World Weather Online and Open Weather Map datasets). The Marina district in the city of Lusail near Doha, Qatar, saw a 26% increase in temperature prediction accuracy. A more detailed analysis of a representative residential building load prediction reveals that cooling estimate gaps are reduced by 2.7% to 7.3% when compared to the underestimated loads from the rural weather dataset. The impact of urban morphology on urban climate is further studied. The results show that increasing building construction, which results in increased building footprint density in the studied area, increases cooling consumption of the representative residential building by more than 11,000 kWh under certain conditions. Whereas, increase in greenery only results in savings of around 250 kWh. Additionally, a uniform random sensitivity analysis of 10 UWG characteristics showed that cooling consumption can vary between 10,000 kWh and 47,500 kWh compared to the predicted cooling consumption when the baseline weather dataset is used.

Calculation of Potential Evapotranspiration and Calibration of the Hargreaves Equation Using Geostatistical Methods over the Last 10 Years in Central Italy

Potential evapotranspiration (ET0) is an indicator of great interest for water budget analysis and the agricultural sector. Therefore, the purpose of this study was to make the calculation reliable even if only the temperature data were present. In this research, the ET0 was initially calculated for a limited number of weather stations (12) using the Penman–Monteith method. In some cases, the simplified Penman–Monteith formula was adopted, while in others, as in the case of mountain weather stations, the complete formula was employed to consider the differences in vegetation, deduced from satellite surveys. Subsequently, the ET0 was calculated with the Hargreaves–Samani (HS) formula, calibrating the Hargreaves coefficient, through the spatialization of ET0, by the geostatistical method. The results showed a high reliability of the HS method in comparison with simplified PM (PM) method, and complete Penman–Monteith (cPM) method, with a minimum calibration of the empirical Hargreaves coefficient. In particular, a very good correlation between the results obtained in the mountain environment with the uncalibrated HS method and the cPM method was also observed in this area, while PM showed discordant and much higher results than ET0 compared with the other methods. It follows that this procedure allowed a more accurate estimate of potential evapotranspiration with a view to territory management, both in terms of water resources and the irrigation needs of the vegetation.

Effective Range and Driving Factors of the Urban Ventilation Corridor Effect on Urban Thermal Comfort at Unified Scale with Multisource Data

Urban ventilation corridors serve as channels of fresh air flow between the city and suburbs, helping to improve the wind and thermal environments and thermal comfort. However, owing to the limited number of weather stations, it is impossible to quantitatively reveal the effective effect range of urban ventilation corridors on urban thermal comfort at the scale of 100 × 100 m, which is optimal for urban ventilation corridors. In this study, we integrated building data, the European Centre for Medium-Range weather forecast data (ECMWF), MOD13Q1, and other multisource data to analyse the effect of urban ventilation corridors on urban thermal comfort at a unified scale of 100 × 100 m. The results showed that ECMWF and Landsat8 data could be used as substitute factors to improve the universal thermal climate index (UTCI) urban spatial resolution. The effective range of urban ventilation corridor effects on the urban surface temperature and urban comfort was ≤1000 m, with building density and vegetation coverage as the main factors limiting this range. Therefore, attention should be paid to the effective range of urban ventilation corridors, the surrounding building density, vegetation coverage, and the rational use of urban ventilation corridors to reduce the energy consumption of air conditioning in summer.

Actual precipitation, predicted precipitation, and large herbivore condition in arid and semi-arid southern New Mexico

Precipitation affects large herbivore performance, but data is often available from only a limited number of weather stations that may not correspond with home ranges. Remotely-sensed estimates could potentially address this by providing estimates of precipitation at any point on the landscape. We tested whether 4-km resolution precipitation estimates derived from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) were related to nutritional condition of 3 species of large herbivores and accurately predicted precipitation as measured at weather stations in arid New Mexico. Canonical correlation analyses indicated that condition of elk, mule deer, and pronghorn was related to both PRISM estimates and actual precipitation, but lactation status always had a stronger effect on condition with the exception of pronghorn. Both PRISM and actual precipitation showed comparable influence of precipitation, and the same general ranking of importance of seasonal precipitation, on condition. PRISM estimates were generally positively related to actual precipitation, but deviance was often high and PRISM tended to underestimate actual precipitation. For our study populations, PRISM provided no advantage over the use of dispersed weather stations for assessing nutritional condition. Performance of PRISM may have been hindered because of lack of spatial autocorrelation in precipitation within our sites.

Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method

Due to limited number of weather stations and interruption of data collection, the temperature field data may be incomplete. In the past, spatial interpolation is usually used for filling the data gap. However, the interpolation method does not work well for the case of the large-scale data loss. Matrix completion has emerged very recently and provides a global optimization for temperature field data reconstruction. A recovery method is proposed for improving the accuracy of temperature field data by using sparse low-rank matrix completion (SLR-MC). The method is tested using continuous gridded data provided by ERA Interim and the station temperature data provided by Jiangxi Meteorological Bureau. Experimental results show that the average signal-to-noise ratio can be increased by 12.5%, and the average reconstruction error is reduced by 29.3% compared with the matrix completion (MC) method.

Evaluation of the Performance of ENACTS MAP-ROOM Products over Tanzania

Tanzania has inadequate weather stations (28-synoptic weather stations), which are sparsely distributed over complex topographic terrain. Many places, especially rural areas, have no stations to monitor weather and climate. In this study, we evaluate the performance of ENACT-MAPROOM products over Tanzania with the aim of assessing their potential to supplement observed weather and climate data, especially over areas where there is limited number of weather stations. Monthly rainfall total and monthly averaged minimum and maximum temperatures from ENACT-MAPROOM are evaluated against observed data from 23 weather stations. The evaluation is limited to analyze how well the ENACT-MAPROOM products reproduce climatological trends, annual cycles and inter-annual variability of rainfall, minimum and maximum temperatures. Statistical analysis recommended by the World Meteorological Organization (WMO) that includes that correlation and trend analysis are used. It is found that ENACT-MAPROOM products reproduce the climatological trends, annual cycles and inter-annual variability of rainfall, minimum and maximum temperatures over most stations. The statistical relationship between ENACT-MAPROOM products against observed data from 23 weather stations using Pearson correlation coefficient indicates that ENACT-MAPROOM products bear strong and statistically significant correlation coefficient to observed data. The overall evaluation here finds high skills of ENACT-MAPROOM products in representing rainfall and temperature over Tanzania, suggesting their potential use in planning and decision making especially over areas with limited number of weather stations.

A global dataset of air temperature derived from satellite remote sensing and weather stations

Air temperature at 2 m above the land surface is a key variable used to assess climate change. However, observations of air temperature are typically only available from a limited number of weather stations distributed mainly in developed countries, which in turn may often report time series with missing values. As a consequence, the record of air temperature observations is patchy in both space and time. Satellites, on the other hand, measure land surface temperature continuously in both space and time. In order to combine the relative strengths of surface and satellite temperature records, we develop a dataset in which monthly air temperature is predicted from monthly land surface temperature for the years 2003 to 2016, using a statistical model that incorporates information on geographic and climatic similarity. We expect this dataset to be useful for various applications involving climate monitoring and land-climate interactions.

Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada

Abstract. Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer – Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ω-τopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012–2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 68 % compared to the original SWE simulations, from 23.7 kg m−2 without assimilation to 7.5 kg m−2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1 % (19 % without assimilation) for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50 % of the PMW signal.

Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends

This paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering a non-representative (biased) range of altitudes. As evidence mounts for altitude-dependent global warming, this bias is a significant problem when temperatures at high altitudes are under-represented. We correct this bias by using altitude as a secondary variable and using a novel clustering method for identifying geographical regions (clusters) that maximize the correlation between altitude and mean temperature. In addition, the paper provides an improved regression kriging estimator, which is optimally determined by the cluster analysis. The optimal areal values of near-land-surface temperature are used to generate time series of areal temperature averages in order to assess regional changes in temperature trends. The methodology is applied to records of annual mean temperatures over the period 1950–2011 across mainland Spain. The robust non-parametric Theil-Sen method is used to test for temperature trends in the regional temperature time series. Our analysis shows that, over the 62-year period of the study, 78% of mainland Spain has had a statistically significant increase in annual mean temperature.

Quantifying the spatial and temporal relationship between air and land surface temperatures of different land-cover types in Southeastern China

ABSTRACTNear surface air temperature (Ta) is an important variable utilized for various fields of research such as agriculture, epidemic forecasting, and climate change. It is usually obtained from a limited number of weather stations that are unevenly distributed within a region. Thus, the spatial information of Ta is rarely provided especially in complex topographical and underdeveloped regions. To compensate for this shortcoming, Ta can be estimated by using the land surface temperature (Ts) from satellite data because remote sensing has an advantage of describing the spatial heterogeneity over a large geographical area. In previous research, authors focused on analysing the effect of limited factors on Ta observations by using a small number of weather stations. However, this study explores a thorough sensitivity analysis of several factors on the relationship between Ta (obtained from a dense network of automatic weather stations) and Ts of different land-cover types using statistical analysis. This article discovered the relationships between Ta and Ts. First, comparisons between daytime and night-time Moderate Resolution Imaging Spectroradiometer (MODIS) Ts data with Ta data showed that a better agreement was achieved during the night than the day. Second, when comparing the results of different land-cover types, the correlation coefficient of the vegetated area was higher than that of water and impervious surface. Third, a comparison of statistical results for different seasons indicated that the correlation between Ta and Ts was weakest in a hot season. Fourth, altitude seemed to have no significant effect on the Ta–Ts relationship. Finally, the relationship between Ta and Ts strengthened with increasing window size except for the vegetated area but tended to saturate as the Ts window increased to the optimal size. These results will be helpful for building an appropriate model to derive air temperature directly from the remotely sensed data in the future.

Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998–2015), PERSIANN-CDR (1983–2015) and CHIRPS 2.0 (1981–2015) over Chile to assess their performance across the country and for the case of the two long-term products the applicability for agricultural drought were evaluated when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in situ rainfall measurements across Chile were initially compared to the satellite data. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite products, and nine statistics were used to evaluate their performance to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated rainfall in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, while CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR datasets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of rainfall geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

Reference evapotranspiration (ETo) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ETo assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed.Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ETo assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ETo assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas.The proposed approaches not only enabled accurate calculations of seasonal and daily ETo values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ETo, other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.

British Exploring Society: 2014 expedition to Ladakh in the Indian Himalayas

British Exploring Society: 2014 expedition to Ladakh in the Indian Himalayas

Calibration of solar radiation models for Europe using Meteosat Second Generation and weather station data

Solar radiation is a key input variable for crop growth models. However, direct measurement of solar radiation is performed operationally for only a limited number of weather stations. Instead of direct measurements, empirical solar radiation models are used that link solar radiation to more commonly measured meteorological variables. Coefficients for these models are site-dependent and therefore generally interpolated from the few locations where solar radiation is measured. In this study, three solar radiation models were calibrated (Ångström–Prescott, Supit–Van Kappel, and Hargreaves) using a daily solar radiation product derived from Meteosat Second Generation data. This satellite-based calibration of model coefficients led to a higher accuracy when estimating daily solar radiation, as compared to the use of interpolated ground-based model coefficients. The average relative root mean square error for Meteosat Second Generation-based calibrated models was 1.9% lower for the Supit–Van Kappel model (p<0.001, n=137), and 1.8% lower for the Hargreaves model (p<0.001, n=222). There was no significant improvement using the Ångström–Prescott model. The Meteosat Second Generation-based model coefficients were interpolated to create continuous coefficient maps for Europe. From these maps it is possible to estimate solar radiation from the sunshine duration, cloud coverage and air temperature range for every location in Europe without prior calibration. We conclude that Meteosat Second Generation-based calibration of model coefficients improves the accuracy of solar radiation estimates.

Evaluation and Regional Calibration of Solar Radiation Prediction Models in Southern Spain

AbstractSolar radiation (Rs) is a very important variable in agricultural meteorology, but it is measured at a very limited number of weather stations. In addition, Rs data are usually low quality because of to sensor failure or lack of calibration. Rs can be predicted from other meteorological measurements (e.g., air temperature), but the accuracy of these methods needs to be tested. In this work, the mechanistic model of Hargreaves-Samani, three modified versions of this model, a modified Bristow-Campbell equation, and the Mahmood-Hubbard model were evaluated at 56 sites in the Andalusian region in southern Spain. All of these methods use the daily temperature range of the air (ΔT=Tmax-Tmin) to estimate Rs. The estimated values of Rs were compared with measurements of Rs to check their suitability. The accuracy of the methods was primarily affected by the magnitude of ΔT, with larger ΔT resulting in a greater accuracy. Finally, the parameter of the Hargreaves-Samani equation (Kr) was adjusted to regiona...

Evaluation of MSG-derived global radiation estimates for application in a regional crop model

Crop monitoring systems that rely on agrometeorologic models require estimates of global radiation. These estimates are difficult to obtain due to the limited number of weather stations that measure this variable. In the present study, we validated the global radiation estimates derived from MeteoSat Second Generation (MSG) and evaluated their use in the European Crop Growth Monitoring System (CGMS). A validation with measurements from four CarboEurope flux towers showed that the MSG estimates are accurate and unbiased (standard deviation between 30 and 51W/m2). Moreover, a comparison with global radiation estimates from about 300 operational weather stations throughout Europe confirmed that the quality of the MSG product is high and spatially uniform. We also made an intercomparison between the MSG product and the ECMWF (ERA-INTERIM) and CGMS products at 25km resolution, thus demonstrating that the CGMS and ECMWF products generally underestimate radiation. Nevertheless, the CGMS product showed irregular spatial patterns of local over- and underestimation, while the ECMWF product consistently underestimated. A trend analysis using a seasonal Mann-Kendall test between 2005 and 2009 did not reveal any significant monotonic trends in the MSG radiation estimates, except for 1 location out of 15. Finally, when we applied the WOFOST crop model for maize throughout Europe, the simulated potential total biomass increased due to higher estimates of global radiation made by MSG. In contrast, the water-limited simulated total-biomass generally decreased due to a higher reference evapotranspiration, causing faster depletion of soil moisture and increased water stress.

Sensitivity analysis of performance of crop growth simulation models to daily solar radiation estimation methods in Iran

Solar radiation is the single most important environmental factor driving canopy photosynthesis and transpiration. This weather characteristic is measured only in a limited number of weather stations. Hence, in many situations it has to be estimated from other weather characteristics such as sunshine duration and temperature using empirical relations. In this study, the Ångstrom and Hargreaves formulas have been used for solar radiation estimation, based on monthly and annual weather data for three weather stations in Esfahan province, Iran. Deviations of estimated solar radiation from measured values (both absolute and relative) varied with month of the year and with estimation method. Estimated and measured radiation values were used in a crop growth simulation model to explore sensitivity of simulated production with respect to radiation estimation method. Maximum deviation for winter barley and silage maize was around 9%.

Climate change at the landscape scale: predicting fine‐grained spatial heterogeneity in warming and potential refugia for vegetation

AbstractCurrent predictions of how species will respond to climate change are based on coarse‐grained climate surfaces or idealized scenarios of uniform warming. These predictions may erroneously estimate the risk of extinction because they neglect to consider spatially heterogenous warming at the landscape scale or identify refugia where species can persist despite unfavourable regional climate. To address this issue, we investigated the heterogeneity in warming that has occurred in a 10 km × 10 km area from 1972 to 2007. We developed estimates by combining long‐term daily observations from a limited number of weather stations with a more spatially comprehensive dataset (40 sites) obtained during 2005–2006. We found that the spatial distribution of warming was greater inland, at lower elevations, away from streams, and at sites exposed to the northwest (NW). These differences corresponded with changes in weather patterns, such as an increasing frequency of hot, dry NW winds. As plant species were biased in the topographic and geographic locations they occupied, these differences meant that some species experienced more warming than others, and are at greater risk from climate change. This species bias could not be detected at coarser scales. The uneven seasonal nature of warming (e.g. more warming in winter, minimums increased more than maximums) means that climate change predictions will vary according to which predictors are selected in species distribution models. Models based on a limited set of predictors will produce erroneous predictions when the correct limiting factor is not selected, and this is difficult to avoid when temperature predictors are correlated because they are produced using elevation‐sensitive interpolations. The results reinforce the importance of downscaling coarse‐grained (∼50 km) temperature surfaces, and suggest that the accuracy of this process could be improved by considering regional weather patterns (wind speed, direction, humidity) and topographic exposure to key wind directions.