Estimating hourly incoming solar radiation from limited meteorological data

Weed Science ◽  
2006 ◽  
Vol 54 (1) ◽  
pp. 182-189 ◽  
Author(s):  
Kurt Spokas ◽  
Frank Forcella
Keyword(s):  
Solar Radiation ◽  
Growth Models ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Incident Radiation ◽  
Climatic Data ◽  
Agricultural Field ◽  
Radiation Beam ◽  
Soil System

Two major properties that determine weed seed germination are soil temperature and moisture content. Incident radiation is the primary variable controlling energy input to the soil system and thereby influences both moisture and temperature profiles. However, many agricultural field sites lack proper instrumentation to measure solar radiation directly. To overcome this shortcoming, an empirical model was developed to estimate total incident solar radiation (beam and diffuse) with hourly time steps. Input parameters for the model are latitude, longitude, and elevation of the field site, along with daily precipitation with daily minimum and maximum air temperatures. Field validation of this model was conducted at a total of 18 sites, where sufficient meteorological data were available for validation, allowing a total of 42 individual yearly comparisons. The model performed well, with an average Pearson correlation of 0.92, modeling index of 0.95, modeling efficiency of 0.80, root mean square error of 111 W m−2, and a mean absolute error of 56 W m−2. These results compare favorably to other developed empirical solar radiation models but with the advantage of predicting hourly solar radiation for the entire year based on limited climatic data and no site-specific calibration requirement. This solar radiation prediction tool can be integrated into dormancy, germination, and growth models to improve microclimate-based simulation of development of weeds and other plants.

scholarly journals Forecasting the Carsharing Service Demand Using Uni and Multivariable Models

2020 ◽  
Author(s):  
Victor Aquiles Alencar ◽  
Lucas Ribeiro Pessamilio ◽  
Felipe Rooke da Silva ◽  
Heder Soares Bernardino ◽  
Alex Borges Vieira
Keyword(s):  
Short Term Memory ◽  
Meteorological Data ◽  
Absolute Error ◽  
Climatic Data ◽  
Short Term ◽  
Car Sharing ◽  
Urban Centers ◽  
Service Demand ◽  
Boosting Algorithms

Abstract Car-sharing is an alternative to urban mobility that has been widely adopted. However, this approach is prone to several problems, such as fleet imbalance, due to the variance of the daily demand in large urban centers. In this work, we apply two time series techniques, namely, Long Short-Term Memory (LSTM) and Prophet, to infer the demand for three real car-sharing services. We also apply several state-of-the-art models on free-floating data in order to get a better understanding of what works best for this type of data. In addition to historical data, we also use climatic attributes in LSTM applications. As a result, the addition of meteorological data improved the model’s performance, especially on Evo: an average Mean Absolute Error (MAE) of approximately 61.13 travels was obtained with the demand data on Evo, while MAE equals 32.72 travels was observed when adding the climatic data, the other datasets also improved but none other improved this much. For the free-floating data test, we got the Boosting Algorithms (XGBoost, Catboost, and LightGBM) got the best performance short term, the worst one has an improvement of around 22% of MAE over the next best-ranked (Prophet). Meanwhile in the long term Prophet got the best MAE result, around 22.5% better than the second-best (LSTM).

scholarly journals Evaluation of the PV energy production determined by measurements, simulation and analytical calculations

2019 ◽  
Vol 85 ◽  
pp. 04002
Author(s):  
Dorin Petreuş ◽  
Mugur Bălan ◽  
Octavian Pop ◽  
Radu Etz ◽  
Toma Patărău
Keyword(s):  
Solar Radiation ◽  
Ambient Temperature ◽  
Analytical Model ◽  
Energy Production ◽  
Meteorological Data ◽  
Experimental System ◽  
Absolute Error ◽  
Simulation Software ◽  
Accuracy Evaluation ◽  
Tilted Plane

The study provides a comparative analysis of the energy production of a 3 kW peak PV array connected in an islanded microgrid, in correlation with solar radiation and ambient temperature measurements. The experimental system is located in Cluj-Napoca Romania and was monitored during the year 2017, based on a graphical user interface. It was also evaluated the capability to predict the PV energy production by using the PV*SOL simulation software and an analytical model, developed at the Technical University of Cluj-Napoca. As input data in the analytical model was used the measured solar radiation and ambient temperature while in the simulations was used alternatively measured data and average meteorological data available in the software database. Besides energy production it was compared the solar radiation on the tilted plane of the PV panels, the PV panel's temperature and the system efficiency. For the predictions accuracy evaluation it was used the weighted mean absolute error based on total energy production, which was found to be lower than 1%, in good agreement with the values reported in literature. The outcomes of this study are valuable for expanding the PV installations in this area and for predictive energy management developments.

scholarly journals COMPARAÇÃO ENTRE DADOS METEOROLÓGICOS OBTIDOS POR ESTAÇÕES CONVENCIONAIS E AUTOMÁTICAS NO ESTADO DO PIAUÍ, BRASIL*

Irriga ◽  
2017 ◽  
Vol 22 (2) ◽  
pp. 220-235
Author(s):  
Aureliano De Albuquerque Ribeiro ◽  
Aderson Soares De Andrade Júnior ◽  
Everaldo Moreira Da Silva ◽  
Marcelo Simeão ◽  
Edson Alves Bastos
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Atmospheric Pressure ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Average Relative Humidity ◽  
Operational Issues ◽  
Meteorological Elements

COMPARAÇÃO ENTRE DADOS METEOROLÓGICOS OBTIDOS POR ESTAÇÕES CONVENCIONAIS E AUTOMÁTICAS NO ESTADO DO PIAUÍ, BRASIL*  AURELIANO DE ALBUQUERQUE RIBEIRO1; ADERSON SOARES DE ANDRADE JÚNIOR2; EVERALDO MOREIRA DA SILVA3; MARCELO SIMEÃO4 E EDSON ALVES BASTOS2 1Doutorando em Engenharia Agrícola, Universidade Federal do Ceará, Av. Mister Hull, s/n - Pici, bloco 804, 60455-760, Fortaleza - CE, [email protected] Embrapa Meio-Norte, Teresina, PI, [email protected], [email protected] Professor Adjunto II da Universidade Federal do Piauí, Campus Professora Cinobelina Elvas, Bom Jesus, PI, [email protected] Mestre em Agronomia: Solos e Nutrição de Plantas, Universidade Federal do Piauí, Campus Professora Cinobelina Elvas, Bom Jesus, PI, [email protected]*Extraído da dissertação de mestrado do primeiro autor  1 RESUMOO registro de elementos climáticos é efetuado por estações meteorológicas convencionais e automáticas. Porém, por questões operacionais e de custo, as estações automáticas estão substituindo as convencionais. Contudo, para que as séries de dados dessas estações sejam únicas, há a necessidade de estudos comparativos entre as duas estações. O estudo teve como objetivo comparar dados meteorológicos obtidos por estações convencionais (EMC) e automáticas (EMA) em municípios do Estado do Piauí (Paulistana, Picos, São João do Piauí, Floriano, Parnaíba e Piripiri). Os elementos meteorológicos avaliados foram: temperaturas do ar máxima (°C) mínima (ºC) e média (ºC), umidade relativa média do ar (%), velocidade do vento a 10 m (m s-1), precipitação pluviométrica (mm) e pressão atmosférica média (hPa). As comparações dos dados foram feitas por meio dos seguintes indicadores estatísticos: precisão (R2), erro absoluto médio (EAM), coeficiente de correlação (r), índice de concordância de Willmott (d) e índice de confiança (c). Os melhores ajustes dos dados foram constatados para a precipitação e pressão atmosférica; intermediários, para a temperatura do ar, umidade relativa do ar média e os piores, para a velocidade do vento. A umidade relativa média do ar foi o elemento analisado que mostrou as maiores diferenças entre a EMC e a EMA. Palavras-chave: Agrometeorologia, elementos climáticos, sensores. RIBEIRO, A. A.; ANDRADE JÚNIOR, A. S.; SILVA, E.M.; SIMEÃO, M.; BASTOS, E.A.COMPARISON OF METEOROLOGICAL DATA RECORDED BY CONVENTIONAL AND AUTOMATIC STATIONS IN PIAUÍ STATE, BRAZIL   2 ABSTRACTClimatic elements are recorded by both conventional and automatic weather stations. However, due to cost and operational issues, automatic stations are replacing the conventional. So that  data sets from these stations are unique, there is a need for comparative studies between the two types of stations. The aim of this study was to compare meteorological data obtained by conventional and automatic stations in towns of the State of Piauí, Brazil (Paulistana, Picos, São João do Piauí, Floriano and Piripiri).The meteorological elements evaluated were: maximum (°C) minimum (°C) and average (°C) air temperature, average relative humidity (%), wind speed at 10 m (m s-1), rainfall (mm) and average atmospheric pressure (hPa). Data comparison was by the following statistical indicators: precision (R2), mean absolute error (EAM), Pearson correlation coefficient (r), Willmott’s index of agreement (d) and confidence index (c).  The best data adjustments were observed for rainfall and atmospheric pressure; intermediates for the air temperature, average relative humidity and worst for the wind speed.  The air average relative humidity was the analyzed element that showed the greatest differences between EMC and EMA. Keywords: Agrometeorology, meteorological elements, sensors 

A Repetitive Day Method for Predicting the Long-Term Thermal Performance of Passive Solar Buildings

1990 ◽  
Vol 112 (1) ◽  
pp. 34-42 ◽  
Author(s):  
D. Feuermann
Keyword(s):  
Solar Radiation ◽  
Ambient Temperature ◽  
Thermal Performance ◽  
Meteorological Data ◽  
Climatic Data ◽  
Temperature Model ◽  
Governing Equations ◽  
Model Calculations ◽  
Passive Solar

The long-term thermal performance of passively-heated solar buildings is predicted by a single repetitive meteorological day which contains judiciously chosen solar radiation and ambient temperature functions. These are used as the driving functions of the governing equations that describe the passive solar building under study. The solar radiation and ambient temperature functions are chosen such that they include, both qualitatively and quantitatively, the essential radiation and temperature statistics of the climate in which the building is to be located. The relevant statistics are determined from hourly meteorological data. When hourly meteorological data are not available for a given location, the solar radiation and ambient temperature functions can be constructed from the knowledge of only two climatic data, namely, the monthly average horizontal radiation and the ambient temperature. Model calculations compare favorably with experimental data from Los Alamos solar test cells and with computer simulations.

scholarly journals Application of different calibration methods for the Hargreaves-Samani model in Southeast Brazil

2020 ◽  
Vol 9 (8) ◽  
pp. e368984811
Author(s):  
Rafael Esteves Dohler ◽  
Sidney Sara Zanetti ◽  
Roberto Avelino Cecílio ◽  
José Eduardo Macedo Pezzopane ◽  
Alexandre Cândido Xavier
Keyword(s):  
Linear Regression ◽  
Hydrological Cycle ◽  
Mean Absolute Error ◽  
Meteorological Data ◽  
Absolute Error ◽  
Important Variable ◽  
Climatic Data ◽  
Calibration Methods ◽  
Potential Alternative ◽  
Main Components

The evapotranspiration is an important variable in the hydrological cycle and one of the main components of water balance in the soil. The use of simplified equations is a potential alternative to estimate reference evapotranspiration (ET0) when there is limited meteorological data. The objective of this study was to test different methods to estimate ET0 using the Hargreaves-Samani equation (HS) under different meteorological conditions. ET0 was calibrated with HS, adjusting the HS coefficient (HC), using different methods. Adjustment by linear regression was also performed. ET0 was also estimated using the original HS and Penman-Monteith FAO-56 methods with limited climatic data (PML). The performance of the methods (mean absolute error, mm day-1) to estimate evapotranspiration, based on Penman-Monteith, were: PML (1.46); HS (0.68); Vanderlinden et al. (2004) (0.81); Martí et al. (2015) (0.77); and linear regression (0.53). The PML method presented the worst performance. Adjustment by linear regression presented a better performance than the adjustments of HC, improving the ET0 estimates by up to 30%, and it is considered the most recommendable of the methods tested.

scholarly journals COEFICIENTES DE ANGSTRÖM-PRESCOTT PARA ESTIMAR A RADIAÇÃO SOLAR GLOBAL EM CAMPOS DOS GOYTACAZES, RJ

Irriga ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 481-491
Author(s):  
José Carlos Mendonça ◽  
Andre Dalla Bernardina Garcia ◽  
Jonathan Nogueira Franco
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Radiation ◽  
Absolute Error ◽  
Maximum Error ◽  
Global Solar Radiation ◽  
Study Region ◽  
Emax Model ◽  
Mass Coefficient ◽  
The City

COEFICIENTES DE ANGSTRÖM-PRESCOTT PARA ESTIMAR A RADIAÇÃO SOLAR GLOBAL EM CAMPOS DOS GOYTACAZES, RJ     JOSÉ CARLOS MENDONÇA¹; ANDRE DALLA BERNARDINA GARCIA² E JONATHAN NOGUEIRA FRANCO³   ¹ Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil. [email protected] ² Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil. [email protected] ³ Laboratório de Engenharia Agrícola – LEAG, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego, 2000, Parque Califórnia, Campos dos Goytacazes, RJ, Brasil. [email protected]     1 RESUMO   Para reduzir custos com equipamentos utilizados na estimativa da radiação solar, geralmente são realizadas estimativas a partir de um modelo utilizando a irradiância solar, como Angström-Prescott. Esse trabalho teve por objetivo obter os coeficientes, mensais, utilizados na equação de Angström-Prescott para estimativa da Radiação Solar Global no município de Campos dos Goytacazes, RJ. Foram utilizados dados meteorológicos entre os anos de 1998 a 2020 para obtenção dos valores de horas de insolação (n), máximo de horas de insolação diária (N), radiação solar global no topo da atmosfera (Ra) e radiação solar global (Rs) mensalmente estimados. Os resultados foram validados por meio dos índices de concordância (D), erro médio absoluto (MAE), erro máximo (EMAX), eficiência do modelo ou método (EF), índice de desempenho (c) e coeficiente residual de massa (CRM). Para a região de estudo, os coeficientes “a” e “b” encontrados foram 0,0191 e 0,9486, respectivamente, considerando-se os valores anuais. Os índices estatísticos “D” e “c” foram 0,977 e 0,94, respectivamente, sendo classificados como ótimos. A partir dos resultados e obtidos é possível utilizar apenas um heliógrafo para estimar a radiação global com ótima precisão, em cada um dos meses individualmente, para a cidade de Campos dos Goytacazes.   Palavras-chave: climatologia, agrometeorologia, modelagem.     MENDONÇA, J. C., GARCIA, A. D. B., FRANCO, J. N. MONTHLY ANGSTRÖM-PRESCOTT COEFFICIENTS TO ESTIMATE GLOBAL SOLAR RADIATION IN CAMPOS DOS GOYTACAZES, RJ     2 ABSTRACT   To reduce costs with equipment used to estimate solar radiation, estimates are usually made based on a  model that uses solar irradiance, such as Angström-Prescott. This work aimed to obtain monthly coefficients, using the Angström-Prescott equation to estimate Global Solar Radiation in the city of Campos dos Goytacazes, RJ. Meteorological data from 1998 to 2020 were used to obtain values ​​of hours of sunshine (n), maximum hours of daily sunshine (N), global solar recording at the top of the atmosphere (Ra) and global solar radiation (Rs) monthly estimated. The results were validated using  agreement indexes (D), mean absolute error (MAE), maximum error (EMAX), model or method efficiency (EF), performance index (c) and residual mass coefficient (CRM). For the study region, coefficients "a" and "b" found were 0.0191 and 0.9486, respectively, considering the annual values. The statistical indexes "D" and "c" were 0.977 and 0.94, respectively, and were classified as great. Based on the results, it is possible to use only a heliograph to estimate global radiation with maximum precision, in each month, individually, for the city of Campos dos Goytacazes.   Keywords: climatology, agrometeorology, modeling.

scholarly journals Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 389
Author(s):  
Asaad M. Armanuos ◽  
Nadhir Al-Ansari ◽  
Zaher Mundher Yaseen
Keyword(s):  
Linear Regression ◽  
Correlation Coefficient ◽  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Similarity Index ◽  
Absolute Error ◽  
Skill Score ◽  
Environmental Data ◽  
Climatic Data ◽  
Precipitation Data

The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with rPearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.

scholarly journals Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1365 ◽  
Author(s):  
Tao ◽  
Ebtehaj ◽  
Bonakdari ◽  
Heddam ◽  
Voyant ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Burkina Faso ◽  
Pearson Correlation ◽  
Research Work ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Climate Variables ◽  
Western Africa ◽  
Intelligent Models

Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions.

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

2020 ◽  
Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic
Keyword(s):  
Human Capital ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Growth Models ◽  
Pearson Correlation ◽  
Small Companies ◽  
Statistical Process ◽  
Production Staff ◽  
Footwear Industry

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.

scholarly journals Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 802
Author(s):  
Kristian Skeie ◽  
Arild Gustavsen
Keyword(s):  
Solar Radiation ◽  
Web Service ◽  
Meteorological Data ◽  
Surface Wind ◽  
Building Energy ◽  
Energy Performance ◽  
Geospatial Data ◽  
Weather Data ◽  
Building Site ◽  
Incident Solar Radiation

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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