Meteorological Data Sources Research Articles

Estimation of some Climatological Parameters by WEKA Software for Selective Regions in Iraq

An Estimation is a direct summation that can produce new data from past measurements. In this study, we confirm the possibility of using the (WEKA) program in estimating the monthly values of some climatological parameters and investigate the influence of the time series' length parameters on the accuracy of estimation for selected regions of Iraq. Satellite data were used, which represent the monthly values for each of the minimum and maximum temperature, wind velocity, and relative humidity for the 1981 – 2021 time period, the absolute error rate (MAE), and the square root of the error rate (RMSE) with the correlation ( R2) are also identified to test the confidence of the prediction. Using (WEKA) software, which depends only on the time series of the parameters in the estimation, 12 months were estimated for the mentioned parameters. The estimated values by WEKA were close to the satellite data, thus we can depend on the software as a good source of meteorological data. Also, the study indicates that the time series' length strongly affects the accuracy of the estimation, as the increase in the time series of weather parameters increases the estimate's accuracy, and this increase fluctuates among parameters and varies from one region to another.

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

AbstractAgrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

SENSITIVITY OF A 1D LAKE MODEL TO THE INPUT METEOROLOGICAL DATA

The paper evaluates how the choice of a source of meteorological data used as boundary conditions and the changes in model calibration parameters affect the adequate performance of a model of lake thermo- and hydrodynamics. The quality of simulation of water temperature dynamics in a small lake by a onedimensional GLM model using data from an automatic meteorological station on the lake shore, a state weather station and two global reanalyses was quantitatively assessed. It is shown that the best modeling result (RMSE of water temperature calculation 0,8-2,0°C, Nash-Sutcliffe efficiency >0,7) can be achieved using data sets that explicitly provide incoming shortwave and longwave radiation fluxes. At the same time, good representation of the bottom water temperature requires correct setting of wind regime, which is only possible if data from instrumental ground-based observations at a stationary weather station are applied. Combining ground-based observations of wind speed, air temperature and humidity with radiation fluxes from global reanalyses makes it possible to achieve a calculation error ≤1°C at all depths.The analysis of model sensitivity to calibration parameters in case when the reanalysis data are applied showed that parameters related to wind mixing, i.e. the scaling factor for wind speed and constants related to the in-model description of wind impact energy, have the greatest influence on the final calculation error. If we change the intensity coefficients of other types of mixing during calibration it does not lead to an obvious trend in the quantitative estimates of modeling quality. It could also be reasonable to introduce correction multipliers to the values of air temperature and solar radiation fluxes, thus neutralizing the effect of unrepresentativeness of the meteorological data applied for a particular lake, as well as to correct the value of solar radiation extinction coefficient relative to the value obtained from the Secchi depth. The results of the study could be used to justify the choice of meteorological data source and to optimize the methods of calibration of lake thermal stratification models

Analysis of tower structure for fatigue taking into account wind vortex shedding

Vortex shedding is manifested in all tower structures with a solid or lattice cross-section with relatively equal width and depth of the cross-section. The phenomenon is associated with the alternate breakdown of vortices from the edges of the structure, which cause it to oscillate under the influence of inertial masses in a plane perpendicular to the wind. The main condition for the manifestation of vortex shedding is the action of wind with a speed exceeding a certain critical one. As shown by calculations of a real tower structure, oscillations according to the first natural frequency and shape occur already under the influence of a moderate wind. This requires attention, assessment of the number of oscillation cycles and the implementation of criteria for taking into account the combined effect of frontal wind and vortex shedding on the structure and calculation of the structure for endurance. The paradox is that this phenomenon is not widely known in engineering practice, and instructions on the mandatory calculation of structures for vortex excitation appeared only with the implementation of amendment 2 to DBN B.1.2-2:2006 "Loads and influences. Design norms" in 2020. In this analyzed state of research on this issue, an available method for calculating vortex excitation, introduced in EN 1991-1-4:2005, is presented. The calculation and analysis of the McDonald's advertising totem with a height of 25.575 m, implemented in Kyiv in 2016, was performed. Calculations showed that this structure is characterized by oscillations due to vortex shedding. From the available sources of meteorological data, an approximate number of oscillation cycles was determined, criteria for calculating the structure's durability were determined, and generalizations were made that can be applied to all structures. In particular, it is pointed out the need to limit stresses beyond the endurance limit in two ranges - parametric resonance from the frontal wind, and sign-changing cyclic oscillations from vortex excitation. An analysis of the effective cross-sections and effective structural forms of building details when working under the action of a frontal wind with vortex shedding was carried out.

Meteoropathy: myth or reality? Assessment of the relationship between cardiovascular diseases and weather conditions according to data from a Moscow hospital

Many patients and some doctors associate the worsening of cardiovascular diseases with meteoropathy (changes in atmospheric pressure and air temperature). Opinion about lack of weather influence on the increase in overall morbidity and the lack of related research data indicate the need to study the relationship between cardiovascular diseases and meteorological conditions.Aim. To assess the prevalence of hospitalizations with symptomatic increases in blood pressure (BP), atrial fibrillation (AF), and decompensated heart failure (HF) in a Moscow hospital depending on meteorological conditions.Material and methods. This retrospective analysis of the number of emergency hospitalizations of patients with symptomatic increases in blood pressure, AF, and decompensated HF in Moscow City Clinical Hospital № 24 was carried out in the period from October 1, 2018 to September 30, 2019. Information from the website www.weather.com was used as a source of meteorological data. The following indicators were assessed daily: daytime air temperature (t), nighttime air temperature, maximum diurnal air temperature variation, atmospheric pressure, maximum diurnal atmospheric pressure variation, amount of precipitation. To assess statistical significance, the Mann-Whitney and Kruskal-Wallis tests were used. A correlation analysis was carried out, and a prognostic model was created using multiple linear regression.Results. During the period from October 1, 2018 to September 30, 2019, 4354 patients were hospitalized at City Clinical Hospital № 24 with symptomatic BP increase and/or AF and/or decompensated HF. The highest number of hospitalizations occurred in November, December and January. The median number of admissions per day was 12 patients. When comparing the mean number of patients admitted to the hospital per day, depending on day- and nighttime air temperature, following statistically significant differences were obtained (p<0,001): with day- and nighttime air temperature ≥0о C, the number of admissions was less compared to sub-zero temperatures. Significant inverse correlations were established between the number of patients hospitalized with symptomatic BP increase, AF and HF per day, with air temperature at night (correlation coefficient r=-0,339; p<0,001), air t during the day (r=-0,316; p<0,001), temperature difference over 48 hours (r=-0,205; p<0,001); direct correlations with atmospheric pressure (r=0,106; p=0,044) and its changes (r=0,115; p=0,028) were identified. All identified relationships were of moderate strength. According to prognostic model, only nighttime air temperature turned out to be a significant indicator associated with the hospitalization rate per day. With a decrease in air temperature at night for every 7,5о C <0, one more patient is expected to be hospitalized with a symptomatic BP increase, decompensated HF, or paroxysmal AF.Conclusion. Negative air temperature in a metropolis is associated with an increase in hospitalizations for symptomatic BP increase, AF or decompensated HF. Atmospheric pressure and precipitation do not affect these conditions.

Factor analysis of hydrologic services in water-controlled grassland ecosystems by InVEST model and geodetector.

Water conservation is highly important for a successful desert grassland ecosystem, but there was no comprehensive view on how to assess influencing factors in managing and addressing water yield and water conservation in desert steppe. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is specifically used for the assessment of ecosystem services, was combined with geographic detectors to identify the priority areas for water conservation function and analyze the driving factors of water conservation in the Tabu River Basin, Inner Mongolia Autonomous Region, China, using different meteorological data sources. (i) The InVEST model has the advantage of modeling water yield and water conservation at spatial scales by fusion downscaling data. High water yield mainly occurs in the southern hilly mountainous areas, low water yield in the northern desert and grassland areas, and between the two in the central agro-pastoral areas; the multi-year average water conservation and water yield based on the InVEST model are 3.3 and 16mm, respectively. (ii) Water yield and water conservation roughly show a transitional phenomenon of "high in the south and low in the north." The water yield and water conservation per unit area of the Tabu River Basin are relatively large for construction land, unused land, and cropland, relatively small for grassland and forestland, and basically zero for water bodies. Forest land has the strongest water conservation capacity, followed by grassland and farmland, while the order of water yield capacity is the opposite. (iii) Precipitation shows the strongest explanatory power for water yield (q = 0.427), followed by land use types (q = 0.411). The precipitation ∩ actual evapotranspiration has the strongest explanatory power for water yield (q = 0.87). The explanatory power of water yield on water conservation is the strongest (q = 0.752), followed by precipitation (q = 0.4), and the water yield ∩ soil has the greatest explanatory power on water conservation (q = 0.91). These findings are crucial for promoting regional hydrologic services and can provide a water resources management strategy for decision-makers.

Impacts of anemometer changes, site relocations and processing methods on wind speed trends in China

Abstract. In situ surface wind observation is a critical meteorological data source for various research fields. However, data quality is affected by factors such as surface friction changes, station relocations and anemometer updates. Previous methods to address discontinuities have been insufficient, and processing methods have not always adhered to World Meteorological Organization (WMO) World Climate Programme guidelines. We analyzed data discontinuity caused by anemometer changes and station relocations in China's daily in situ near-surface (∼ 10 m) wind speed observations and the impact of the processing methods on wind speed trends. By comparing the wind speed discontinuities with the recorded location changes, we identified 90 stations that showed abnormally increasing wind speeds due to relocation. After removing those stations, we followed a standard quality control method recommended by the World Meteorological Organization to improve the data reliability and applied Thiessen polygons to calculate the area-weighted average wind speed. The result shows that China's recent reversal of wind speed was reduced by 41 % after removing the problematic stations, with an increasing trend of 0.017 m s−1 yr−1 (R2 = 0.64, P < 0.05), emphasizing the importance of robust quality control and homogenization protocols in wind trend assessments.

Experimental statistical analysis of radar signals reflected from weather hazards

The nature of the causes of prerequisites for flight accidents due to meteorological conditions has remained unchanged for many years. For the effective operation of aviation, it is necessary to solve the issues of the timely detection of weather hazards and the identification of their intensity. This article discusses the research into the atmospheric reflectivity and turbulence in cumulonimbus clouds using a near-field meteorological radar system. Experimental studies are carried out with the aim of obtaining estimates of statistical characteristics of the reflectivity distribution and turbulence for weather hazards. The methodology for conducting an experimental study to obtain information about the weather hazards (rain shower, thunderstorm and hail), special features of the radar reflectivity propagation and the specific rate of turbulent energy dissipation in the considered conditions for the Tver region is presented. The analysis of the obtained results has been carried out: comparisons of meteorological data derived, using the meteorological radar complex of the near airfield zone (WR BZ), with the reliable sources of meteorological observation data obtained during experimental studies. Studies of horizontal sections and vertical profiles of the weather hazards parameters under consideration, associated with the cumulonimbus area, have been carried out. A data bank has been formed. Histograms of the information parameter distribution have been constructed. The article considers the results of experimental data approximation, using the χ2 criterion for various distribution laws. It is shown that the distributions of reflectivity and turbulence can be described by the generalized Rice’s law. The results obtained can be used to correct the classification criteria of weather hazards to subsequently increase the justification and reliability of the weather hazard classification. The further development of the method to process meteorological information is also of paramount importance to more correctly interpretate experimental data processing results of remote meteorological phenomena sensing.

Meteorological data source comparison—a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability.

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Evaluating the Impact of Meteorological Data Sources on Moisture Prediction Accuracy of Eucalyptus Nitens Log Pile Natural Drying Models

Drying forest biomass at roadside can reduce transport costs and greenhouse gas emissions by reducing its weight and increasing its net calorific value. Drying models are required for forest supply chain analysis to determine optimum storage times considering storage costs and returns. The study purpose was to evaluate the impact of the source of meteorological data on the goodness of fit and practical application of Eucalyptus nitens log pile drying models. The study was conducted in Long Reach, NE Tasmania, Australia from the 6th of February to 6th of August 2020. Four data sources were compared: the nearest meteorological station, interpolated meteorological data, a portable weather station, and digital temperature/RH sensors. Predicted moisture content (MC) values from the only previously published E. nitens log pile drying model were also evaluated using the current study data sources as inputs.Log pile MC changes were determined from weight changes measured by placing the study logs on a steel frame bolted to load cells at each corner. As the study was based on debarked logs, dry matter losses were assumed to be negligible. Initial MC of the logs was determined by extracting samples using an electric drill and drying them until constant weight was achieved.Initial log pile drying rates were high with several daily MC losses >2%. Portable weather station data produced the best goodness of fit drying model. The second-best goodness of fit model was based on meteorological station data. From a user acceptability perspective (highest proportion of results within ±5% of measured values), the best model was based on temperature/RH sensor data. Goodness of fit measures for the temperature/RH sensor data model were poorer than for the other data sources, but still acceptable. The published E. nitens log drying model had the poorest results for goodness of fit and user acceptability.In conclusion, portable weather stations are best suited to research trials due to the expense of placing a weather station at each log pile. Drying models based on data from the nearest meteorological station or temperature/RH sensors are best suited for practical applications, such as forest supply chain analysis. Additional benefits could accrue from a forest estate-wide network of low cost temperature/RH sensors potentially supplying data to forest supply chain analysis as well as fire prediction and tree growth models.

Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna

In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2).

A Study on the Impact of Various Meteorological Data on the Design Performance of Rooftop Solar Power Projects in Vietnam: A Case Study of Electric Power University

People are increasingly using clean energy sources, contributing to environmental protection according to the general trend of the world. In the form of renewable energy, solar energy has contributed to solving current pressing problems, such as environmental pollution and air pollution, improving people’s quality of life. The design of solar power projects in Vietnam is mainly based on meteorological data sources from Meteonorm and NASA. However, the accuracy assessment of two data sources compared to the actual solar power data in Vietnam is not available, so there is no basis to determine better meteorological data source quality to serve the design of rooftop solar power projects. The content of this paper analyzes the simulation results of a typical rooftop solar power station at the Electric Power University, Hanoi city based on meteorological data sources from Meteonorm and NASA. After that, the simulation results will be compared with the actual operating data of a rooftop solar power station near the Electric Power University and other real PV systems in the world. The study results showed that the amount of electricity production using the Meteonorm meteorological data was closer to the actual data than the NASA data source. Therefore, solar power projects in Vietnam should use Meteonorm data source for the design process to determine the best economic and technical efficiency for investors.

Data fusion and machine learning for ship fuel efficiency modeling: Part II – Voyage report data, AIS data and meteorological data

When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources. These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models. Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments. For each ship, nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested. Experimental results revealed the benefits of fusing voyage report data, AIS data, and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate. Over the best datasets, the performances of several decision tree-based models are promising, including Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG). With the best datasets, their R2 values over the training sets are all above 0.96 and mostly reach the level of 0.99–1.00, while their R2 values over the test sets are in the range from 0.75 to 0.90. Fit errors of ET, AB, GB, and XG on daily bunker fuel consumption, measured by RMSE and MAE, are usually between 0.8 and 4.5 ton/day. These results are slightly better than our previous study, which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data, compared with the estimated geographical positions derived from the great circle route, in retrieving weather and sea conditions the ship sails through.

The use of the WRF numerical weather forecasting model in mode ling emergency situations at Belarusian NPP

The expediency of using Weather Research and Forecasting (WRF) numerical weather forecast model as a source of meteorological data during modeling of radioactive substances transfer in the atmosphere is considered, WRF technical details are briefly described. As the reference emergency scenario, the scenario of the maximum design accident hypothetically occurring at Belarusian Nuclear Power Plant (BelNPP) 12 UTC 21.03.2021 was chosen. Four numerical experiments with Global Forecast System (GFS) and WRF meteorological data and RIMPUFF and LASAT atmospheric diffusion models were performed in JRODOS Decision Support Systems (DSS). For each experiment, maps of underlying surface potential radioactive contamination of I131 were created. The potential contamination of the underlying surface I131 from 5 to 100 km from the BelNPP was assessed. The dependency graph of the maximum underlying surface contamination with I131 on the distance from the BelNPP was plotted based on the experiments results. The obtained results indicate the prospects of using the WRF meteorological model in the DSS at various stages of the emergency situations development at the BelNPP.

EirGrid's met mast and alternatives study

The current grid code in Ireland requires wind farms to deliver meteorological data to the two transmission system operators. The importance of accurate on-site measured wind speed signals increases as a function of the installed wind capacity. Since wind farms have been installed with varying technologies over the past 20 years, the system does not have a single point of failure. The lack of uniformity has been found to be both a handling challenge and reliability strength. In order to investigate the future compatibility of the current quality of MET data and to verify, whether and which types of new technology may be an acceptable source of meteorological data to be delivered to the TSOs, a study has been carried out. The study verified whether the quality of meteorological data is sufficient and reliable enough for the 2030 penetration targets set by the Irish government. This paper will describe the methods used to verify the quality of meteorological data signals, along with the various types of meteorological measurements that were found to be acceptable under varying conditions. The authors will also present several recommendations based on the findings of the study, and discuss how these recommendations are being implemented by EirGrid.

PVSYST-Based Solar Power Plant Planning

This research uses the PVSyst software which can plan an off-grid SPP system and find out how much electricity is generated in the Auditorium Building Campus 2, Ujung Pandang State Polytechnic. PVSyst is a software package that is used for the learning process, sizing, and data analysis of the PV mini-grid system. PVSyst is divided into grid connected systems, standalone systems, pumping systems. PVSyst is also equipped with a database from a wide and diverse range of meteorological data sources, as well as data on PV mini-grid components. In planning a PVSyst-based solar power plant, it can be used to find out how much electrical energy is generated to meet the electricity needs of the Campus 2 Auditorium Building, Ujung Pandang State Polytechnic. In this study using the PVSyst software according to the proposed load requires an average energy of 482 kWh/day, therefore, the panel module used is the polycrystalline type model CS3W-410P-HE manufacturer Canadian Solar Inc, where each panel unit used has a nominal power of 410 WP requires 422 modules, 2 modules in series and 211 modules in parallel. The battery used is a lead acid type with a 12-CS-11PS model manufactured from Rolls. The batteries used are 195 batteries, of which 3 batteries are installed in series and 64 batteries are installed in parallel. The controllers needed in this design are 50 units with the FLEXmax 80-36V model manufactured from Outback. The technology of this controller is an MPPT converter. In planning Solar Power Plant using PVSyst in the Auditorium Building Campus 2 of Ujung Pandang State Polytechnic, it produces electricity of 227,122 kWh per year.

Influence of Planetary Boundary Layer (PBL) Parameterizations in the Weather Research and Forecasting (WRF) Model on the Retrieval of Surface Meteorological Variables over the Kenyan Highlands

Regional climate models (RCMs) are crucial for climate studies and may be an alternative source of meteorological data in data-scarce regions. However, the effectiveness of the numerical weather prediction (NWP) models applied in RCMs is hampered by the parameterization of unresolved physical processes in the model. A major source of uncertainties in NWP models is the parameterization of the planetary boundary layer (PBL). This study evaluates the influence of seven PBL parameterization schemes in the Weather Research and Forecasting (WRF) model on the retrieval of four meteorological variables over the Kenyan highlands. The seven PBL schemes consist of four local schemes: the Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN), Bougeault-Lacarrere (BouLac), quasinormal scale elimination (QNSE), and three nonlocal schemes: asymmetrical convective model version 2 (ACM2), Shin and Hong (SHIN) and Yonsei University (YSU). The forcing data for the WRF model was obtained from the fifth generation of the European ReAnalysis (ERA5) dataset. The results were validated against observational data from the Trans-African Hydro-Meteorological Observatory (TAHMO). WRF was found to simulate surface meteorological variables with spatial details coherent with the complex topography within the Kenyan highlands, irrespective of the PBL scheme. A comparison between 2-meter temperature (T2) derived from the YSU scheme and T2 from the land component of ERA5 (ERA5-Land) indicates that surface meteorological variables derived from WRF are better suited for applications over the Kenyan highlands. The choice of the PBL scheme was found to primarily influence the simulation of the 10-meter wind speed (WS10) and rainfall as opposed to T2 and the 2-meter relative humidity (RH2). The insensitivity of the 2-meter variables to the choice of the PBL scheme is attributed to the influence of the surface layer parameterization near the surface. Results from the rainfall simulation indicate that the YSU scheme provides a more realistic depiction of PBL dynamics within the study area. Hence, the YSU scheme is best suited for simulating surface meteorological variables over the Kenyan highlands.

Vadose zone modeling to identify controls on groundwater recharge in an unconfined granular aquifer in a cold and humid environment with different meteorological data sources

Groundwater recharge (GR) is a complex process that is difficult to quantify. Increasing attention has been given to unsaturated zone modeling to estimate GR and better understand the processes controlling it. Continuous soil-moisture time series have been shown to provide valuable information in this regard. The objectives of this study were to (i) analyze the processes and factors controlling GR in an unconfined granular aquifer in a cold and humid environment and (ii) assess the uncertainties associated with the use of data from different sources. Soil moisture data monitored over three years at three experimental sites in southern Quebec (Canada) were used to calibrate the HYDRUS-1D model and to estimate ranges of possible GR in a region where groundwater is increasingly used as a source of fresh water. The simulations identified and quantified important factors responsible for the near-surface water balance that leads to GR. The resulting GR estimates from 2016 to 2018 showed marked differences between the three sites, with values ranging from 347 to 735 mm/y. Mean GR for the three sites was 517 mm/y for 2016–2018 and 455 mm/y for the previous 12-year period. GR was shown to depend on monthly variations in precipitation and on soil textural parameters in the root zone, both controlling soil-water retention and evapotranspiration. Monthly recharge patterns showed distinct preferential GR periods during the spring snowmelt (38–45% of precipitation) and in the fall (29% of precipitation). The use of different meteorological datasets was shown to influence the GR estimates.

Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.