Number Of Meteorological Parameters Research Articles

Comparative Estimation of Global Solar Radiation over Two Nigerian Cities, Using Artificial Neural Network and Empirical Models

The estimation of solar radiation intensity has been a focus of many researchers due to the cost of setting up its actual measurements. While many of them employed empirical models, this study utilizes the artificial neural network for the analysis and estimation of global solar radiation over two Nigerian cities. The model developed using sunshine hours, temperatures and relative humidity were compared with the existing empirical models. Model performance indicators comparing the measured data and the computed data for the derived and selected models, using the same number of input meteorological parameters showed that ANN having average values of RMSE, MBE, and MPE of 0.0744 MJm-2day-1, -0.0020 MJm-2day-1, and -0.0043%, respectively, performed slightly better. When different number of input meteorological parameters were used, the ANN gave the following error indicators for RMSE, MBE, MPE of 0.0394MJm-2day-1, -0.0023MJm-2day-1 and -0.0144% respectively. Also, in the result of solar radiation in Abuja, using the same number of meteorological parameters, the model with the best performance in the estimation of solar radiation is the ANN model with average values of RMSE, MBE, MPE of 0.1301MJm-2day-1, 0.0053MJm-2day-1 and 0.0441% respectively. Hence, the models are versatile for predicting global solar radiation in locations in the same climatic zones as locations studied in this study, where direct measurements of solar radiation is scarce and widely separated but there is availability of commonly measured meteorological parameters such as sunshine duration, minimum temperature, maximum temperature and relative humidity.

Modelling of Reference Evapotranspiration using Neural Network and Regression Approaches for Semi-humid Region of Sikkim

This study compared the applicability of multiple linear regression (MLR) and artificial neural network (ANN) approaches for estimating the weekly reference evapotranspiration (ET0) in a semi-humid area of Tadong in Gangtok district of Sikkim state in India. Daily meteorological data (1991 - 2020) were used for developing MLR and ANN model using various combinations of meteorological data. Predicted ET0 values were compared with the FAO-56 Penman Monteith (PM) equation estimated ET0. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Nash Sutcliffe efficiency (NSE) were used to assess the performance of MLR and ANN models. For ANN model, one hidden layer and four neurons was found as the best ANN architecture. ANN-4 model with inputs parameters of maximum air temperature (Tmax), minimum air temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), and sunshine hours (SSH) had the smallest RMSE (0.609 mm.day-1 and 0.880 mm.day-1), MAE (0.473 mm.day-1 and 0.551mm.day-1) and the highest R2 (0.752 and 0.657) and NSE (0.782 and 0.696) during training and testing phases, respectively. The MLR-4 model with same combination of input parameters (Tmax, Tmin, RHmax, RHmin, SSH) resulted in smallest RMSE (0.704 mm.day-1 and 0.714 mm.day-1), MAE (0.562 mm.day-1and 0.583 mm.day-1); and the highest R2 (0.679 and 0.602) and NSE (0.708 and 0.597) during training and testing phase, respectively. Study also showed increase in model performance of ANN and MLR models with increase in number of meteorological parameters. All the ANN models gave comparable results, but ANN 4 model resulted in higher prediction accuracy for the sub-humid region of Tadong in Gangtok district of North-east India. The developed ANN-4 model could be helpful for preparing irrigation schedules and planning, and management of water resources in the data scarce northeast region of India and other identical climatic regions.

Observing volcanoes with drones: studies of volcanic plume chemistry with ultralight sensor systems

The study of the chemical composition of volcanic emissions is an important method for obtaining information about volcanic systems and providing indirect and unique insights into magmatic processes. However, there is a non-negligible risk associated with sampling directly at volcanic craters or maintaining geochemical monitoring stations at such locations. Spectroscopic remote sensing methods, in turn, can measure only a few species. Here, drones offer the opportunity to bring measurement systems to the scene. Standard parameters that are commonly measured are SO2 and CO2 concentrations, as well as a number of meteorological parameters. The in-flight transmission of data by radio telemetry plays an important role, since visual localization of the volcanic plume from a distance of several kilometers is practically impossible. Until now, larger and quite cost-intensive drones have been used for this purpose, which must first be transported to the site of operation at great expense. Here, we present the development and successful deployment of a very small drone system (empty weight < 0.9 kg) for chemical characterization of volcanic plumes that can be easily transported on foot to difficult-to-access terrain and, moreover, requires only minimal flight and administrative preparations for operation as an aerial observation platform.

Reference Evapotranspiration Prediction for Smart Irrigation

Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.

Urban-rural fog differences in Belgrade area, Serbia

Urban/rural fog appearance during the last 27 years in the Belgrade region is analysed using hourly meteorological records from two meteorological stations: an urban station at Belgrade-Vracar (BV) and a rural station at Belgrade-Airport (BA). The effects of urban development on fog formation are discussed through analysis of fog frequency trends and comparison with a number of meteorological parameters. The mean annual and the mean annual minimum temperatures were greater at the urban BV station than at the rural BA station. The mean monthly relative humidity and the mean monthly water vapour pressure were greater at the rural than urban station. During the period of research (1988–2014), BA experiences 425 more days with fog than BV, which means that BV experiences fog for 62.68% of foggy days at BA. Trends in the number of days with fog were statistically non-significant. We analysed the fog occurrence during different types of weather. Fog in urban BV occurred more frequently during cyclonal circulation (in 52.75% of cases). In rural BA, the trend was the opposite and fog appeared more frequently during anticyclonic circulation (in 53.58% of cases). Fog at BV occurred most frequently in stable anticyclonic weather with light wind, when a temperature inversion existed (21.86% of cases). Most frequently, fog at BA occurred in the morning and only lasted a short time, followed by clearer skies during the anticyclonic warm and dry weather (22.55% of cases).

From climate simulations to statistics - Introducing the wux package

We present the R package wux, a toolbox to analyze projected climate change signals by numerical climate model simulations and the associated uncertainties. The focus of this package is to automatically process big amounts of climate model data from multi-model ensembles in a user-friendly and flexible way. For this purpose, climate model output in common binary format (NetCDF) is read in and stored in a data frame, after first being aggregated to a desired temporal resolution and then being averaged over spatial domains of interest. The data processing can be performed for any number of meteorological parameters at one go, which allows multivariate statistical analysis of the climate model ensemble.

Quasi-decadal variations in total ozone content, wind velocity, temperature, and geopotential height over the Arosa station (Switzerland)

We present the results of the analysis of the phase relationships between the quasi-decadal variations (QDVs) (in the range from 8 to 13 years) in the total ozone content (TOC) at the Arosa station for 1932–2012 and a number of meteorological parameters: monthly mean values of temperature, meridional and zonal components of wind velocity, and geopotential heights for isobaric surfaces in the layer of 10–925 hPa over the Arosa station using the Fourier methods and composite and cross-wavelet analysis. It has been shown that the phase relationships of the QDVs in the TOC and meteorological parameters with an 11-year cycle of solar activity change in time and height; starting with cycle 24 of solar activity (2008–2010), the variations in the TOC and a number of meteorological parameters occur in almost counter phase with the variations in solar activity. The periods of the maximum growth rate of the temperature at isobaric surfaces 50–100 hPa nearly correspond to the TOC’s maximum periods, and the periods of the maximum temperature correspond the periods of the decrease of the peak TOC rate. The highest correlation coefficients between the meridional wind velocity and temperature are observed at 50 hPa at positive and negative delays of ~27 months. The times of the maxima (minima) of the QDVs in the meridional wind velocity nearly correspond to the periods of the maximum amplification (attenuation) rate of the temperature of the QDVs. The QDVs in the geopotential heights of isobaric surfaces fall behind the variations in the TOC by an average of 1.5 years everywhere except in the lower troposphere. In general, the periods of variations in the TOC and meteorological parameters in the range of 8–13 years are smaller than the period of variations in the level of solar activity.

Modelling aerosol concentration distributions from transient (puff) sources

A field trial involving 50 separate releases of inert aerosol (kaolin) was conducted to determine the concentration distribution within aerosol puffs resulting from near-instantaneous releases. Atmospheric conditions during the trial fell within Pasquill stability classes A and B (very and moderately unstable, respectively). Aerosol concentration measurements were made using a scanning lidar system operating at 1·06 μm. Artificial neural network (ANN) models were developed using the data to predict concentration distributions, given a number of meteorological parameters. The ANN predictions were compared to those from traditional Gaussian puff models, and provided better predictions than the Gaussian model parameterizations examined. The ANN models were also used to develop Gaussian fitting parameters to replace traditional Pasquill and Slade dispersion coefficients. The ANN-derived dispersion coefficients provided better predictions of measured puff concentration distributions than either the Pasquill or Slade parameterizations, though the full multi-input ANN models provided even better predictions than the Gaussian puff model using ANN-derived dispersion coefficients.

Decadal regional air quality simulations over Europe in present climate: near surface ozone sensitivity to external meteorological forcing

Abstract. Regional climate-air quality decadal simulations over Europe were carried out with the RegCM3/CAMx modeling system for the time slice 1991–2000, in order to study the impact of different meteorological forcing on surface ozone. The RegCM3 regional climate model was firstly constrained by the ERA40 reanalysis dataset which is considered as an experiment with perfect meteorological boundary conditions and then it was constrained by the global circulation model ECHAM5. A number of meteorological parameters were examined including the 500 mb geopotential height, solar radiation, temperature, cloud liquid water path, planetary boundary layer height and surface wind. The different RegCM meteorological forcing resulted in changes of near surface ozone over Europe ranging between ± 4 ppb for winter and summer. The area showing the greatest sensitivity in O3 during winter is central and southern Europe while in summer central north continental Europe. The different meteorological forcing impacts on the atmospheric circulation, which in turn affects cloudiness and solar radiation, temperature, wind patterns and the meteorology depended biogenic emissions. For comparison reasons, the impact of chemical boundary conditions on surface ozone was additionally examined with a series of sensitivity studies, indicating that surface ozone changes are comparable to those caused by the different meteorological forcing. These findings suggest that, when it comes to regional climate-air quality simulations, the selection of external meteorological forcing can be as important as the selection of adequate chemical lateral boundary conditions.

Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters

Remote sensors designed specifically for studying the atmosphere have been widely used to derive timely information on air pollution at various scales. Whether the satellite-generated aerosol optical thickness (AOT) data can be used to monitor air pollution, however, is subject to the effect of a number of meteorological parameters. This study analyses the influence of four meteorological parameters (air pressure, air temperature, relative humidity, and wind velocity) on estimating particulate matter (PM) from MODIS AOT data for the city of Nanjing, China during 2004–2006. After the PM data were correlated with the AOT data that had been divided into four chronological seasons, a minimum correlation coefficient of 0.47 was found for the winter season, but a much stronger correlation (r > 0.80) existed in summer and autumn. Similar analyses were carried out after all observations were clustered into four groups based on their meteorological similarity using the K-Means analysis. Grouping caused more observations to be useable in the monitoring of air pollution than season-based analysis. Of the four groups, three had a correlation coefficient higher than 0.60. Grouping-based analysis enables the pollution level to be determined more accurately from MODIS AOT data at a higher temperature and relative humidity, but a lower air pressure and wind velocity. The accuracy of monitoring air pollution is inversely related to the pollution level. Thus, remote sensing monitoring of air pollution has its limits.

A Statistical Approach to Examine the Impact of Various Meteorological Parameters on Pan Evaporation

Evaporation from surface water bodies is influenced by a number of meteorological parameters. The rate of evaporation is primarily controlled by incoming solar radiation, air and water temperature and wind speed and relative humidity. In the present study, influence of weekly meteorological variables such as air temperature, relative humidity, bright sunshine hours, wind speed, wind velocity, rainfall on rate of evaporation has been examined using 35 years(1971-2005) of meteorological data. Statistical analysis was carried out employing linear regression models. The developed regression models were tested for goodness of fit, multicollinearity along with normality test and constant variance test. These regression models were subsequently validated using the observed and predicted parameter estimates with the meteorological data of the year 2005. Further these models were checked with time order sequence of residual plots to identify the trend of the scatter plot and then new standardized regression models were developed using standardized equations. The highest significant positive correlation was observed between pan evaporation and maximum air temperature. Mean air temperature and wind velocity have highly significant influence on pan evaporation whereas minimum air temperature, relative humidity and wind direction have no such significant influence.

Modelling aerosol concentration distributions from transient (puff) sourcesA paper submitted to the Journal of Environmental Engineering and Science.

A field trial involving 50 separate releases of inert aerosol (kaolin) was conducted to determine the concentration distribution within aerosol puffs resulting from near-instantaneous releases. Atmospheric conditions during the trial fell within Pasquill stability classes A and B (very and moderately unstable, respectively). Aerosol concentration measurements were made using a scanning lidar system operating at 1.06 μm. Artificial neural network (ANN) models were developed using the data to predict concentration distributions, given a number of meteorological parameters. The ANN predictions were compared to those from traditional Gaussian puff models, and provided better predictions than the Gaussian model parameterizations examined. The ANN models were also used to develop Gaussian fitting parameters to replace traditional Pasquill and Slade dispersion coefficients. The ANN-derived dispersion coefficients provided better predictions of measured puff concentration distributions than either the Pasquill or Slade parameterizations, though the full multi-input ANN models provided even better predictions than the Gaussian puff model using ANN-derived dispersion coefficients.

Impacts of urbanization on long-term fog variation in Anhui Province, China

Variability of urban fog during the last 50 years in Anhui Province (China) and its possible underlying mechanisms are analyzed based on daily meteorological records from 78 stations. The effects of urban development and aerosols on fog formation are discussed through an analysis of trends in fog frequency and comparison with a number of meteorological parameters, coal consumption and vehicle usage as indicators of economic development and aerosol production, and visibility as an indicator of aerosol load. The impact of urbanization on fog is different at various stages of urban development. The number of annual fog days in most cities studied has increased since the 1960s but decreased after the mid 1980s in large, old cities. New cities, on the other hand, are characterized by still increasing fog frequency. During the last thirty years, fog dissipation time occurred later, average fog duration increased and visibility decreased at most urban stations. The annual total coal combustion of Anhui Province correlates negatively with the average visibility within fog and average annual fog frequency in old cities. The difference in minimum temperature between cities and towns is negatively (positively) correlated with fog frequency in old (new) cities. Our study therefore supports previous findings that the number of fog days in cities is influenced by increased (i) temperatures associated with urban development and (ii) aerosol concentration due to urban development and industrial activity.

The effect of rainfall intensity on surface runoff and sediment yield in the grey dunes along the Dutch coast under conditions of limited rainfall acceptance

Surface runoff and sediment yield in the grey dunes along the Dutch coast are rare events restricted to conditions of water repellency in summer. The sediment moves in mudflow-like tongues. From March 1989 to December 1990, weekly surface runoff and sediment yield have been measured on an experimental plot, along with a number of meteorological parameters. Rainfall intensity is more important than total amount of precipitation for the production of runoff and sediment. Rainfall intensity affects the erosion process in two ways: by supplying the large amount of water needed for the high water: sand ratio in the sand flow, and by providing the high frequency drop impact needed to maintain a high hydraulic pressure in the sand flow.

Frequency of occurrence of transmittance in several wavelength regions during a 3-year period

Within the framework of the OPAQUE project the transmittances in the visible, the far-infrared, and the millimeter wavelength regions as well as a number of meteorological parameters have been measured at the Ypenburg Airfield near the Hague, The Netherlands. These transmittances are presented by means of monthly histograms showing frequency and cumulative distributions of surveys over three years. Scatter-plots are used to explain the interrelationships of the transmittances. Also the relations between transmittances and rain rate, density of fog, relative and absolute humidity are shown graphically.

FIELD MEASUREMENT OF DROPLET DRIFT FROM GROUND SPRAYERS. I. SAMPLING, ANALYTICAL, AND DATA INTEGRATION TECHNIQUES

A procedure for the evaluation of ground deposits and droplet drift characteristics from a typical farm sprayer under actual field conditions is described. It involves sampling both the ground deposits on the swath and the air-borne cloud mass, the latter at various heights and distances downwind from the target area. The system was evaluated with the commonly used 65° flat-fan nozzles operated at 280 kPa which provided an application rate of 0.56 kg/ha of 2,4-D amine (2,4-dichlorophenoxyacetic acid) in 56 ℓ water/ha. The spray solution also contained a fluorescent dye as a tracer. A portable meteorological station, where a number of meteorological parameters can be measured or recorded during the course of a field trial, is also described. Results of four field trials are presented with discussion regarding on-swath deposit density and its distribution over the swath, off-swath drifting cloud mass and its deposition and decay as a function of distance, and mass balance.

Behavior of radiostrontium in rain and air after the ninth Chinese Atmospheric Nuclear Test

Strontium 89 and 90 were measured in rain water and air at Fayetteville, Arkansas (36°N, 94°W), for 9 months after the ninth Chinese atmospheric nuclear test. The 89Sr/90Sr activity ratios showed behavior very similar to the pattern of variation observed after the eighth Chinese atmospheric test. Ratios of 89Sr or 90Sr activity in rains to activity in air samples were computed, and these scavenging ratios were found to vary widely, but those for 89Sr tended to be higher than those for 90Sr. These ratios did not have a well-defined relationship with time, amount of rainfall, or a number of meteorological parameters. The data, however, indicate that a consistent relationship between fallout and rainfall may be necessary for scavenging ratios to be meaningful and useful on a short-term basis. This relationship apparently does not exist in areas of high cumulative deposition of 90Sr, such as Fayetteville.