Showing papers on "Weather station published in 2016"

Evaluation of downscaled, gridded climate data for the conterminous United States.

Abstract: Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Spatial downscaling of European climate data

Abstract: E-OBS(European Observations) is a gridded climate data set which contains maximum temperature, minimum temperature, and precipitation on a daily time step. The data can be as fine as 0.25° in resolution and extends over the entire European continent and parts of Africa and Asia. However, for studying regional or local climatic effects, a finer resolution would be more appropriate. A continental data set with resolution would allow research that is large in scale and still locally relevant. Until now, a climate data set with high spatial and temporal resolution has not existed for Europe. To fulfil this need, we produced a downscaled version of E-OBS, applying the delta method, which uses WorldClim climate surfaces to obtain a 0.008° (about 1 × 1 km) resolution climate data set on a daily time step covering the European Union. The new downscaled data set includes minimum and maximum temperature and precipitation for the years 1951–2012. It is analysed against weather station data from six countries: Norway, Germany, France, Italy, Austria, and Spain. Our analysis of the downscaled data set shows a reduction in the mean bias error of 3 °C for mean daily minimum temperature and of 4 °C for mean daily maximum temperature. Daily precipitation improved by 0.15 mm on average for all weather stations in the validation. The entire data set is freely and publically available at ftp://palantir.boku.ac.at/Public/ClimateData.

IoT based weather station

Abstract: A weather station can be described as an instrument or device, which provides us with the information of the weather in our neighbouring environment. For example it can provide us with details about the surrounding temperature, barometric pressure, humidity, etc. Hence, this device basically senses the temperature, pressure, humidity, light intensity, rain value. There are various types of sensors present in the prototype, using which all the aforementioned parameters can be measured. It can be used to monitor the temperature or humidity of a particular room/place. With the help of temperature and humidity we can calculate other data parameters, such as the dew point. In addition to the above mentioned functionalities, we can monitor the light intensity of the place as well. We have also enabled to monitor the atmospheric pressure of the room. We can also monitor the rain value. The brain of the prototype is the ESP8266 based Wi-fi module Nodemcu (12E). Four sensors are connected to the NodeMCU namely temperature and humidity sensor(DHT11), pressure sensor(BMP180), raindrop module, and light dependent resistor(LDR). Whenever these values exceed a chosen threshold limit for each an SMS, an E-mail and a Tweet post is published alerting the owner of the appliance to take necessary measures.

Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index-based Insurance?

Abstract: Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public insti...

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States

Abstract: Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center) in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts.

Daily indoor-to-outdoor temperature and humidity relationships: a sample across seasons and diverse climatic regions

Abstract: The health consequences of heat and cold are usually evaluated based on associations with outdoor measurements collected at a nearby weather reporting station However, people in the developed world spend little time outdoors, especially during extreme temperature events We examined the association between indoor and outdoor temperature and humidity in a range of climates We measured indoor temperature, apparent temperature, relative humidity, dew point, and specific humidity (a measure of moisture content in air) for one calendar year (2012) in a convenience sample of eight diverse locations ranging from the equatorial region (10 °N) to the Arctic (64 °N) We then compared the indoor conditions to outdoor values recorded at the nearest airport weather station We found that the shape of the indoor-to-outdoor temperature and humidity relationships varied across seasons and locations Indoor temperatures showed little variation across season and location There was large variation in indoor relative humidity between seasons and between locations which was independent of outdoor airport measurements On the other hand, indoor specific humidity, and to a lesser extent dew point, tracked with outdoor, airport measurements both seasonally and between climates, across a wide range of outdoor temperatures These results suggest that, in general, outdoor measures of actual moisture content in air better capture indoor conditions than outdoor temperature and relative humidity Therefore, in studies where water vapor is among the parameters of interest for examining weather-related health effects, outdoor measurements of actual moisture content can be more reliably used as a proxy for indoor exposure than the more commonly examined variables of temperature and relative humidity

Arduino based automatic wireless weather station with remote graphical application and alerts

Abstract: In this paper we have proposed, developed and tested a hardware module based on Arduino Uno Board and Zigbee wireless technology, which measures the meteorological data, including air temperature, dew point temperature, barometric pressure, relative humidity, wind speed and wind direction. This information is received by a specially designed application interface running on a PC connected through Zigbee wireless link. The proposed system is also a mathematical model capable of generating short time local alerts based on the current weather parameters. It gives an on line and real time effect. We have also compared the data results of the proposed system with the data values of Meteorological Station Chandigarh and Snow & Avalanche Study Establishment Chandigarh Laboratory. The results have come out to be very precise. The idea behind to this work is to monitor the weather parameters, weather forecasting, condition mapping and warn the people from its disastrous effects.

At the Mercy of the Gods: Associations Between Weather, Physical Activity, and Sedentary Time in Children.

Abstract: Objectives: This study investigated associations between weather conditions, physical activity, and sedentary time in primary school-aged children in Australia and Canada. Methods: Cross-sectional data on 9–11-year-old children from the Australian (n = 491) and Canadian (n = 524) sites of the International Study of Childhood Obesity, Lifestyle and the Environment were used. Minutes of daily moderate-to-vigorous-physical-activity (MVPA) and sedentary time were determined from 7-day, 24-h accelerometry (Actigraph GT3X+ triaxial accelerometer). Day-matched weather data (temperature, rainfall, snowfall, relative humidity, wind speed) were obtained from the closest weather station to participants’ schools. Covariates included parental highest education level, day type, sex, and BMI z-scores. Generalized mixed model analyses allowing for clustering of participants within schools were completed. Scatterplots with Loess curves were created for maximum temperature, MVPA, and sedentary time. Results: Daily maximum ...

Evaporation estimates using Weather Station data and Boundary Layer theory

Abstract: Global estimates of evapotranspiration remain a challenge. In this study, we show that the daily course of air temperature and specific humidity available at routine weather stations can be used to estimate evapotranspiration and the evaporative fraction, the ratio of latent heat flux to available energy at the surface. Indeed, the diurnal increase in air temperature reflects the magnitude of the sensible heat flux and the increase of specific humidity after sunrise reflects the amplitude of evapotranspiration. The method is physically constrained and based on the budget of heat and moisture in the boundary layer. Unlike land surface-based estimates, the proposed boundary layer estimate does not rely on ad hoc surface resistance parameterizations (e.g., Penman-Monteith). The proposed methodology can be applied to data collected at weather stations to estimate evapotranspiration and evaporative fraction under cloudy conditions and in the pre–remote sensing era.

Weather datasets generated using kriging techniques to calibrate building thermal simulations with TRNSYS

Abstract: Transient thermal simulations are a good method for predicting the energy consumption of buildings. Meteorological data are critical for proper simulation as they constitute the boundary conditions of the building. Meteorological data are not always available in the precise building location, and thus, data from the nearest weather station, are used widely. This paper studies the errors in the results obtained using three different techniques based on kriging interpolation (ordinary kriging, universal kriging with an altitude predictor and universal kriging with the altitude and distance from coast predictors) with data from the meteorological station located by the building under consideration. The data obtained from the weather station of Peinador (an airport located seven kilometers away from the building) are also analyzed. A transient thermal simulation is also performed (using TRNSYS software) to calculate and compare the thermal heating and cooling demands in a library building at the University of Vigo, as well as the errors in the heating and cooling demands obtained with the different weather files generated. The kriging technique results in lower errors in the heating and cooling demands of the building thermal simulation, with a 25% reduction in the RMSE compared with using the nearest meteorological station data of the Vigo airport.

Recent trends in the climate of Namaqualand, a megadiverse arid region of South Africa

Abstract: Namaqualand is especially vulnerable to future climate change impacts. Using a high-resolution (0.5°x0.5°) gridded data set (CRU TS 3.1) and individual weather station data, we demonstrated that temperatures as well as frequency of hot extremes have increased across this region. Specifically, minimum temperatures have increased by 1.4 °C and maximum temperatures by 1.1 °C over the last century. Of the five weather stations analysed, two showed evidence of a significant increase in the duration of warm spells of up to 5 days per decade and a reduction in the number of cool days (TX10P) by up to 3 days per decade. In terms of rainfall, we found no clear evidence for a significant change in annual totals or the frequency or intensity of rainfall events. Seasonal trends in rainfall did, however, demonstrate some spatial variability across the region. Spatial trends in evapotranspiration obtained from the 8-day MOD16 ET product were characterised by a steepening inland-coastal gradient where areas along the coastline showed a significant increase in evapotranspiration of up to 30 mm per decade, most notably in spring and summer. The increase in temperature linked with the increases in evapotranspiration pose significant challenges for water availability in the region, but further research into changes in coastal fog is required in order for a more reliable assessment to be made. Overall, the results presented in this study provide evidence-based information for the management of climate change impacts as well as the development of appropriate adaptation responses at a local scale.

Evaluating weather observations and the Climate Forecast System Reanalysis as inputs for hydrologic modelling in the tropics

Abstract: Correctly representing weather is critical to hydrological modelling, but scarce or poor quality observations can often compromise model accuracy. Reanalysis datasets may help to address this basic challenge. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally available records, and CFSR data have produced satisfactory hydrological model performance in some temperate and monsoonal locations. However, the use of CFSR for hydrological modelling in tropical and semi-tropical basins has not been adequately evaluated. Taking advantage of exceptionally high rainfall station density in the catchments of the Rio Grande de Loiza above San Juan, Puerto Rico, we compared model performance based on CFSR records with that based on publicly available weather stations in the Global Historical Climate Network (GHCN, n = 21) and on a dataset of rainfall records maintained by the United States Geological Survey Caribbean Water Science Center (USGS, n = 24). For an implementation of the Soil and Water Assessment Tool (SWAT) with subbasins defined at 11 streamflow gages, uncalibrated measures of Nash–Sutcliffe efficiency (NSE) were >0 at 8 of 11 gages using USGS precipitation data for daily simulations over the period 1998–2012, but were 0 using all precipitation inputs, including CFSR. However, the ground weather station closest to the geographic basin centre produced the highest NSE values in only 5 of 11 cases. The spatially interpolated CFSR data performed as well or better than single ground observations made further than 20–30 km, and sometimes better than individual weather stations <10 km from the basin centroid. In addition to demonstrating the need to evaluate available weather inputs, this research reinforces the value of CFSR data as a means to supplement ground records and consistently determine a baseline for hydrologic model performance. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison between Total Cloud Cover in Four Reanalysis Products and Cloud Measured by Visual Observations at U.S. Weather Stations

Abstract: A homogeneity-adjusted dataset of total cloud cover from weather stations in the contiguous United States is compared with cloud cover in four state-of-the-art global reanalysis products: the Climate Forecast System Reanalysis from NCEP, the Modern-Era Retrospective Analysis for Research and Applications from NASA, ERA-Interim from ECMWF, and the Japanese 55-year Reanalysis Project from the Japan Meteorological Agency. The reanalysis products examined in this study generally show much lower cloud amount than visual weather station data, and this underestimation appears to be generally consistent with their overestimation of downward surface shortwave fluxes when compared with surface radiation data from the Surface Radiation Network. Nevertheless, the reanalysis products largely succeed in simulating the main aspects of interannual variability of cloudiness for large-scale means, as measured by correlations of 0.81–0.90 for U.S. mean time series. Trends in the reanalysis datasets for the U.S. mean...

Weather index insurance and shock coping : evidence from Mexico's CADENA Program

Abstract: Weather risk and incomplete insurance markets are significant contributors to poverty for rural households in developing countries. Weather index insurance has emerged as a possible tool for overcoming these challenges. This paper provides evidence on the impact of weather index insurance from a pioneering, large-scale insurance program in Mexico. The focus of this analysis is on the ex-post effects of insurance payments. A regression discontinuity design provides find evidence that payments from weather index insurance allow farmers to cultivate a larger land area in the season following a weather shock. Households in municipalities receiving payment also appear to have larger per capita expenditures and income in the subsequent year, although there is suggestive evidence that some of this increase is offset by a decrease in remittances. While the cost of insurance appears to be high relative to the payouts, the benefits exceed the costs for a substantial range of outcomes.

A Deterministic Model for Predicting Hourly Dissolved Oxygen Change: Development and Application to a Shallow Eutrophic Lake

Abstract: Predicting dissolved oxygen (DO) change at a high frequency in water bodies is useful for water quality management. In this study, we developed a deterministic model that can predict hourly DO change in a water body with high frequency weather parameters. The study was conducted during August 2008–July 2009 in a eutrophic shallow lake in Louisiana, USA. An environment monitoring buoy was deployed to record DO, water temperature and chlorophyll-a concentration at 15-min intervals, and hourly weather data including air temperature, precipitation, wind speed, relative humidity, and solar radiation were gathered from a nearby weather station. These data formed a foundation for developing a DO model that predicts rapid change of source and sink components including photosynthesis, re-aeration, respiration, and oxygen consumption by sediments. We then applied the model to a studied shallow lake that is widely representative of lake water conditions in the subtropical southern United States. Overall, the model successfully simulated high-time fluctuation of DO in the studied lake, showing good predictability for extreme algal bloom events. However, a knowledge gap still exists in accurately quantifying oxygen source produced by photosynthesis in high frequency DO modeling.

Capability of a regional climate model to simulate climate variables requested for water balance computation: a case study over northeastern France

Abstract: This paper documents the capability of the ARW/WRF regional climate model to regionalize near-surface atmospheric variables at high resolution (8 km) over Burgundy (northeastern France) from daily to interannual timescales. To that purpose, a 20-year continuous simulation (1989–2008) was carried out. The WRF model driven by ERA-Interim reanalyses was compared to in situ observations and a mesoscale atmospheric analyses system (SAFRAN) for five near-surface variables: precipitation, air temperature, wind speed, relative humidity and solar radiation, the last four variables being used for the calculation of potential evapotranspiration (ET0). Results show a significant improvement upon ERA-Interim. This is due to a good skill of the model to reproduce the spatial distribution for all weather variables, in spite of a slight over-estimation of precipitation amounts mostly during the summer convective season, and wind speed during winter. As compared to the Meteo-France observations, WRF also improves upon SAFRAN analyses, which partly fail at showing realistic spatial distributions for wind speed, relative humidity and solar radiation—the latter being strongly underestimated. The SAFRAN ET0 is thus highly under-estimated too. WRF ET0 is in better agreement with observations. In order to evaluate WRF’s capability to simulate a reliable ET0, the water balance of thirty Douglas-fir stands was computed using a process-based model. Three soil water deficit indexes corresponding to the sum of the daily deviations between the relative extractible water and a critical value of 40 % below which the low soil water content affects tree growth, were calculated using the nearest weather station, SAFRAN analyses weather data, or by merging observation and WRF weather variables. Correlations between Douglas-fir growth and the three estimated soil water deficit indexes show similar results. These results showed through the ET0 estimation and the relation between mean annual SWDI and Douglas-fir growth index that the main difficulties of the WRF model to simulate soil water deficit is mainly attributable to its precipitation biases. In contrast, the low discrepancies between WRF and observations for air temperature, wind speed, relative humidity and solar radiation make then usable for the water balance and ET0 computation.

Application of DSSAT Crop Simulation Model to Identify the Changes of Rice Growth and Yield in Nilwala River Basin for Mid-centuries under Changing Climatic Conditions

Abstract: Changes of climate will be one of the deciding factors that affect for future food production in the world because crop growth is highly sensitive to any changes of climatic conditions. As the rice is staple food of Sri Lankans, it is essential to identify the impacts of climate changes on country's rice production. This study was conducted to identify the yield and growth changes of most popular two rice varieties (At362 and Bg357) cultivated in Nilwala river basin at Yala season under the global climate change scenario Representative Concentrate Pathway (RCP) 8.5. The Decision Support System for Agro technology Transfer (DSSAT) software is used to forecast the rice yield for Yala season in mid-centuries. To simulate the rice yield DSSAT requires data sets of crop growth and management, daily weather data and soil data. Crop management data were obtained from an experiment which was conducted in Palatuwa area at Nilwala downstream in Matara district. Daily weather data were collected from Mapalana weather station and soil data were collected from wet zone soil classification. Model was calibrated using experimental data for Yala season 2014 and model was validated using collected data in Yala season 2013. Future yield was predicted using forecasted weather data under climate change scenario RCP 8.5 for Mapalana area. The results show that increasing temperature and solar radiation and decreasing rainfall in mid-centuries affects both yield and growth of rice. Grain yield in mid-centuries shows decreasing trend in both varieties by 25% to 35% than the yield at 2014 and growth period will be shorter than the present conditions.

Variation in diurnal and seasonal urban land surface temperature: landuse change impacts assessment over Lagos metropolitan city

Abstract: Removal of vegetation to give space for urban expansion might result in the temperature rise in cities. The present study compares the LSTs derived from Moderate Resolution Imaging Spectroradiometer (MODIS) with observed air temperature from ground weather data. The natures of the materials that are usually found in the urban area are typically concrete and asphalt materials which affect the urban atmospheric system. In this study, variation in urban land surface temperatures (LST) using MODIS and in-situ meteorological data were examined. MODIS data and daily rainfall, minimum (Tm) and maximum (Tmx) temperature from ground weather station were used. The results reveal that average LSTs during the dry season are noticeably higher for both daytime during November: 34.62 °C, December: 33.75 °C, January: 34.68 °C, February: 35.02 °C and March: 34.87 °C. There are notable differences in the LST observed between daytime and nighttime for both MOD11A2 and MYD11A2 and that of maximum and minimum air temperature from in-situ meteorological data. MOD11A2 is a better proxy for daily maximum and minimum air temperature than MYD11A2, though seasonal variations in the extent of LST occurs during the wet and dry season. The study shows that the contribution of the urban LSTs was comparatively smaller at night than the day, perhaps as result of the variations in the amount of solar radiation received by the day and night times.

When it gets cold, plant size matters – a comment on treeline

Abstract: Many questions in vegetation science related to species ranges and species performance could be resolved by appropriate bioclimatological data. In the case of small-stature plants, such as herbs and grasses, but also seedlings of trees, weather station data are inappropriate to describe their life conditions – an important point not only in empirical research, but also for modellers.

Estimation of non-catastrophic weather impacts for retail industry

Abstract: Purpose – Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. The purpose of this paper is to develop a model and to analyze the relationship between weather and retail shopping behavior (i.e. store traffic and sales). Design/methodology/approach – The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represen...

Estimation of solar irradiance using ground-based whole sky imagers

Abstract: Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation. In this paper, we show how images taken by WSIs can be used to estimate solar radiation. Sky cameras are useful here because they provide additional information about cloud movement and coverage, which are otherwise not available from weather station data. Our setup includes ground-based weather stations at the same location as the imagers. We use their measurements to validate our methods.

A method to estimate freezing rain climatology from ERA-Interim reanalysis over Europe

Abstract: . A method for estimating the occurrence of freezing rain (FZRA) in gridded atmospheric data sets was evaluated, calibrated against SYNOP weather station observations, and applied to the ERA-Interim reanalysis for climatological studies of the phenomenon. The algorithm, originally developed at the Finnish Meteorological Institute for detecting the precipitation type in numerical weather prediction, uses vertical profiles of relative humidity and temperature as input. Reanalysis data in 6 h time resolution were analysed over Europe for the period 1979–2014. Mean annual and monthly numbers of FZRA events, as well as probabilities of duration and spatial extent of events, were then derived. The algorithm was able to accurately reproduce the observed, spatially averaged interannual variability of FZRA (correlation 0.90) during the 36-year period, but at station level rather low validation and cross-validation statistics were achieved (mean correlation 0.38). Coarse-grid resolution of the reanalysis and misclassifications to other freezing phenomena in SYNOP observations, such as ice pellets and freezing drizzle, contribute to the low validation results at station level. Although the derived gridded climatology is preliminary, it may be useful, for example, in safety assessments of critical infrastructure.

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Abstract: Solar radiation is a key factor in the Earth’s energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010–2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from −30 to 40 W·m−2, absolute root mean square errors (RMSE) from 25 to 60 W·m−2, relative bias ranging from −11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from −50 to 160 W·m−2, absolute RMSE from 60 to 240 W·m−2, relative bias ranging from −30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.

Wavelet Study of Meteorological Data Collected by Arduino-Weather Station: Impact on Solar Energy Collection Technology

Abstract: Meteorological data collected by an automated LSI Lastem weather station connected with an Arduino device for remote acquisition are reported and discussed. Weather station, located at 38° 15’ 35.10’’ N latitude and 15° 35’ 58.86’’ E longitude, registered data which were analysed by wavelet transform to obtain time-frequency characterization of the signals. Such an approach allowed to highlight the correlation existing among the registered meteorological data. The results show a positive correlation between the minimum temperature and the maximum temperature values whereas a negative correlation emerges between daily rainfall and minimum temperature values as well as for daily rainfall and maximum temperature values. These results suggest the possibility to estimate the global and diffuse solar radiation using more reliable climatologic parameters for optimizing solar energy collected by solar panels.