Average Wind Direction Research Articles

About Height of the Surface Air Layer by Sodar Data

Average empirical estimations of the surface air layer height in Moscow have been received by the data of long-term acoustic remote sensing of the atmosphere using MODOS Doppler sodar of METEK (Germany) production. Based on the assumption that the average conditions are close to neutral stratification, this height, as the top of the quasi-linear section of the average long-term wind velocity profile in semi-logarithmic coordinates, is 40–60 m. The wind rotation height, i. e. the height of intersection of day and night wind profiles is on average 95 meters per year. The roughness length in conditions of loose but high urban development in vicinity of Moscow State University in Moscow is 5 m. According to the criterion of the constant wind direction in the surface air layer, its height appears in the average monthly wind direction profiles over the “dead zone” of the sodar (40 m) in approximately one case out of three and usually amounts to 60 m, less often 80 or 100 m. In the rest cases, it is apparently masked by “dead zone”. The average height of the surface layer according to this approach is probably a little less than 50 m, which is close to the estimate obtained from the logarithmic distribution of wind velocity with height in this layer. The daily course of the surface air layer height is noted by the largest values in the afternoon (80–100 m in summer under conditions of prevailing unstable stratification and 60–80 m in winter) and the smallest ones (less than 40 m) in the late evening and at night in summer and from evening to noon in winter.

Multi-step ahead forecasting of wind vector for multiple wind turbines based on new deep learning model

Obtaining wind speed and direction predictions with high accuracy is of vital significance for the utilization of wind energy. This research focuses on three problems: how to realize multiple station prediction, multi-step ahead prediction and wind vector prediction. Firstly, a 4-D time series variable scene is proposed for multi-step ahead forecasting of multiple station wind vector. Then, a wind vector prediction model (CM-G) based on Convolutional Minimum Gate Memory Neural Network and Graph Convolution Neural Network is constructed to improve the prediction accuracy. Further, three multi-step ahead forecasting modes are compared and their advantages, disadvantages and application scenarios are analyzed. Finally, the model and method proposed in this study are applied to two datasets in Tibet, China. The experimental results and conclusions are as follows: (1) The prediction accuracy of CM-G proposed in this study is higher than that of the existing state-of-the-art model in 90 % of the metrics. (2) From single-step to multi-step ahead forecasting, the prediction accuracy of wind vector gradually decreases. (3) In wind vector prediction, the prediction accuracy of wind speed is higher than that of wind direction. (4) The direction of accuracy improvement for multiple stations is relatively consistent with the average wind direction.

Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model.

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.

Evaluation and correction of wind energy forecast in Yangjiang

Wind power generation is one of the most promising new energy sources. It is important to study effective short-term wind speed forecasting methods. In this paper, the CMA-GD model is applied to forecast the wind speed over South China in four typical months (January, April, August, and October) in 2022. Taking Longyue Wind Farm in Yangjiang as an example, the simulated wind speed and wind direction are evaluated based on the observation of this station at four heights (10 m, 30 m, 50 m, and 70 m). The results show that the CMA-GD model has a good effect on forecasting the next day’s 24 h wind speed of Longyue Wind Farm. Overall, the correlation coefficients (R) between forecast and observation are 0.77-0.81, and the root-mean-square errors (RMSE) are 1.80-2.08 m·s-1. With the increase in altitude, the simulation effect is a little better. For different months, the R is as follows: October>August>April>January, and the RMSE and mean absolute error (MAE) are as follows: October>January>August>April. For diurnal variation, the wind speed simulation effect in the night is better than that in the day. Due to the influence of the subtropical monsoon climate and local mountain microclimate in April and August, there are some deviations in the wind speed simulation during 4-8 p.m. The difference in wind direction between forecast and observation are as follows: August<January<October<April, the average wind direction differences are between 2-17°, with little difference in the vertical direction. After the forecast wind speed is corrected by the random forest (RF) algorithm, the R of wind speed at each height is 0.92, and RMSEs are 1.07-1.27 m·s-1. The revised wind speed forecast has the same diurnal variation characteristics as the observation, indicating that the random forest algorithm can effectively reduce the model forecast error.

Deep learning and tree-based models for earth skin temperature forecasting in Malaysian environments

Predicting the Earth Skin Temperature (TS) using artificial intelligence (AI) has the potential to offer valuable insights into environmental changes and their impacts. TS has significant nonlinearity due to several meteorological parameters, including average temperature, maximum temperature, minimum temperature, relative humidity, surface pressure, wind speed, and wind direction. This study introduced four machine learning (ML) models, namely solo Long Short-Term Memory (LSTM), Autoencoder-LSTM, Classification and Regression Tree (CART), and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM), to predict daily averaged TS at different locations over the Malaysian region. In the first stage of the modeling development, Pearson Correlation (PC) was adopted to measure the strength and direction of the relationship between input and output variables. The statistical analysis and visual interpretations demonstrated that the Autoencoder-LSTM model outperformed the CNN-LSTM, LSTM, and CART models for each simulated city. The Autoencoder-LSTM model showed outstanding performance at Kuantan in comparison with the seven cities, achieving a coefficient of determination (R2 = 0.965), Root Mean Square Error (RMSE = 0.183), Mean Absolute Error (MAE = 0.142), and Nash-Sutcliffe Efficiency (NSE = 0.96). The research findings suggested that the coordinates of each station are crucial in determining the level of data randomness, which ultimately affects the learning process of the ML models.

Determinants of spring migration departure dates in a New World sparrow: Weather variables reign supreme.

Numerous factors influence the timing of spring migration in birds, yet the relative importance of intrinsic and extrinsic variables on migration initiation remains unclear. To test for interactions among weather, migration distance, parasitism, and physiology in determining spring departure date, we used the Dark-eyed Junco (Junco hyemalis) as a model migratory species known to harbor diverse and common haemosporidian parasites. Prior to spring migration departure from their wintering grounds in Indiana, USA, we quantified the intrinsic variables of fat, body condition (i.e., mass ~ tarsus residuals), physiological stress (i.e., ratio of heterophils to lymphocytes), cellular immunity (i.e., leukocyte composition and total count), migration distance (i.e., distance to the breeding grounds) using stable isotopes of hydrogen from feathers, and haemosporidian parasite intensity. We then attached nanotags to determine the timing of spring migration departure date using the Motus Wildlife Tracking System. We used additive Cox proportional hazard mixed models to test how risk of spring migratory departure was predicted by the combined intrinsic measures, along with meteorological predictors on the evening of departure (i.e., average wind speed and direction, relative humidity, and temperature). Model comparisons found that the best predictor of spring departure date was average nightly wind direction and a principal component combining relative humidity and temperature. Juncos were more likely to depart for spring migration on nights with largely southwestern winds and on warmer and drier evenings (relative to cooler and more humid evenings). Our results indicate that weather conditions at take-off are more critical to departure decisions than the measured physiological and parasitism variables.

Assessment of NASA POWER reanalysis products as data resources alternative for weather monitoring in West Sumbawa, Indonesia

The climatic reanalysis datasets represent a crucial form of data that can help to address the shortage of data observations. This study evaluates the accuracy of NASA POWER reanalysis data by comparing it with AMMAN Environmental Department in-situ observations from four weather stations in West Sumbawa, collected from 2013 to 2022. The reanalysis data includes surface daily average temperatures, average wind speed, wind direction, relative humidity, and rainfall. The statistical analysis used in this research includes Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to measure the differences between the observed and predicted values, both of which expressed in the same unit as the variable being measured. The comparison of 10 years of historical data revealed that the temperature parameter had the lowest RMSE and MAE values of 0.003 and 0.18, respectively, while the highest values were 0.46 and 5.11. For humidity, the lowest RMSE and MAE values were 0.001 and 3.6, respectively, and the highest values were 1.3 and 14.2. The RMSE and MAE values for rainfall were 0.02 and 0.9 (lowest) and 2.2 and 5.7 (highest). Regarding wind speed, the lowest RMSE and MAE values were 0.001 and 0.27, while the highest values were 0.07 and 1. Finally, the lowest RMSE and MAE values for wind direction were 0.1 and 6.6, and the highest values were 15.7 and 67.6. The comparison between the predicted and observed values showed a relatively high level of similarity for temperature, rainfall and wind speed parameters. However, parameters such as humidity and wind direction resulted varying degrees of deviation between the weather station data and the NASA POWER reanalysis data. These results highlight both the accuracy and discrepancy in the reanalysis data, emphasizing the importance of considering the limitations of such datasets when addressing the shortage of observational data.

Correlating chlorophyll movement with wind speed and direction using satellite imagery: A case study of Devils Lake, North Dakota

Estimating the locations of high concentrations of Chlorophyll-a (Chl-a) is essential for accurate analysis of Harmful Algal Blooms (HABs). This paper focused on identifying the locations of higher concentrations of Chl-a in Devils Lake, North Dakota via satellite images by applying different segmentation techniques. The segmentation evaluation metrics indicate that Random Forest (RF) yielded better accuracy than other methods. The segmented images were validated with ground-sampled Chlorophyll data (location matching) and provided a matching percentage of 88.14% on August 08, 2023. The centroids of the contours from the segmented images acquired between 2017 and 2021 were analyzed to study the shift of chlorophyll against the average wind direction. The results indicate that there was a positive correlation of Chl-a against the wind direction. The wind velocity for the same periods was compared against the percentage change of the total area of Chl-a concentration. The results proved that the days having wind velocity greater than 3 m/s had a decrease in the area of Chl-a concentration.

Aeolian bedrock ridges in Gale crater, Mars

Fields of parallel, regularly-spaced, bedrock ridges observed on Mars and rarely on Earth have been interpreted as aeolian surface features oriented perpendicular to a formative wind. The exact formation process for these ridges, however, including the role of aeolian erosion versus deposition, continues to be debated. We identified forty fields of bedrock ridges on and within the sedimentary strata of Gale crater's central mound, Aeolis Mons. To better constrain the development of these landforms and the winds responsible for their formation, we characterized ridge morphology, orientation, geographic distribution, stratigraphic relationships, and interactions with other aeolian landforms. The study area of Aeolis Mons was chosen to leverage the extensive high-resolution image coverage from orbit, as well as to build upon the detailed stratigraphy compiled from in situ exploration with the Mars Science Laboratory Curiosity rover. Using the orientation of the ridges, we present a wind vector map of Aeolis Mons. The orientation of bedrock ridges in the Mound Skirting Unit, which unconformably drapes Aeolis Mons, suggests the average wind direction in Gale crater may have remained consistent since that unit was emplaced (∼3.8–3.1 Ga). This study explores the implications of these ridges for the history of wind, deposition, and erosion in Gale crater.

Framework of wind joint analysis for different lake regions and its effects on the water quality

Water quality of Lake Taihu (LT) is paid wide attention, and the wind field makes wind-induced flow, significantly impacting the water quality. As a wide lake, the wind field suffers spatial and temporal changes. The correlated relationship among the wind fields for different lake regions has not been studied, and the joint effects of wind field on the water quality is still unknown. Hence, this paper proposed a framework of wind joint analysis for different lake regions and modelling its effects on the water quality, including joint analysis of wind field for different lake regions using Copula function, random time series generation of wind field using Monte-Carlo simulation, and hydrodynamic and water quality numerical simulation. Taking the Lake Taihu (LT) as study case, the joint relationships of wind field for four lake regions were analyzed, and the CODMN, TP and TN distributions under different wind fields were simulated. This paper showed the following: (1) The relationship between the wind speed and direction was weak, and the correlated relationships among the wind fields for four lake regions were significant (ρ > 0.5). (2) The water quality of LT was more influenced by the wind direction rather than the wind speed, with an impacting ratio of ∼4.5. (3) The discharged weighted angle of runoff outflow (DWARO) was a practical index to improve the water quality. When regulating the input/output runoff discharge, water diversion project and water intakes in practice, the administrations should make the DWARO less than or equal to the average wind direction, to decrease the average pollutant concentration and improve the water quality of lake and intake. The proposed framework and results of LT could be extended to other wide shallow lakes to support the environmental management and basin runoff regulation.

Analysis of Surface Wind Speed at Minangkabau International Airport for the period 2011-2020 using the Windrose Method

Weather information is very much needed in flight operations because it is related to the smooth running of the aircraft from takeoff to landing. Weather information required by the runway surface wind data needs as important information in the landing process. Information on the surface wind components determines Minangkabau International Airport’s direction and average surface wind speed pattern. Analysis of changes in surface winds also determines the effect of wind on flight safety, especially in the process of taking off and landing aircraft. Wind direction and speed data obtained are from synoptic observations of the Minangkabau Meteorological Station for ten years (2011-2020). Surface wind data is processed using the windrose method with the WRPLOT (Wind Rose Plots for Meteorological Data) application which shows the direction and difference in wind speed and produces a wind pattern image in the form of a windrose diagram. The results of this study note that the dominant wind blows from the Southwest (203°-248°) with a percentage of 41.97%. The west direction (248°-293°) with a percentage of 27.93%. The south (158°-203°) with a percentage of 13.81%. The prevailing wind speed is 4.08 – 7 knots, with a percentage of 54.8%. The rate of 0.97 – 4.08 knots with a percentage of 38.1%, and the speed that occurs is 7-11.08 knots with a percentage of 6.1%. The condition of the overall average wind direction and speed that occurred from 2011-2022 at Minangkabau International Airport was still in the safe category for the takeoff and landing process.

Characteristics and Establishment of Objective Identification Criteria and Predictors for Foehn Winds in Urumqi, China

The special terrain of Urumqi (in the valley area) often triggers strong foehn winds, causing huge losses to local people’s lives and social economies. By using the surface observation data of the hourly temperature, pressure, humidity, and wind from the downwind Urumqi Meteorological Station and the upwind Dabancheng Meteorological Station in the Middle Tianshan Canyon and the NCEP/NCAR reanalysis data during 2008–2022, this paper establishes the dataset of foehn processes at Urumqi Station in the past 15 years and analyzes the variation rules of the associated meteorological variables during the foehn processes. In addition, based on the physical mechanism of the occurrence of foehn, a three-element identification criterion (i.e., 94° ≤ 2 min average wind direction ≤ 168°, 2 min average wind speed ≥ 2.0 m/s, and Δθ between Urumqi station and Dabancheng station ≥ 0.29 K) for foehn in Urumqi is established by comparing and analyzing the variations of wind direction (WD), wind speed (WS), and the potential temperature difference (Δθ) between the two weather stations during the periods of foehn and non-foehn winds from 2013 to 2022. In addition, the performance of the three-element identification criterion is verified, and the results suggest that this criterion has an accuracy of 82.96% and a hit rate of 89.50% for the 2008–2012 foehn events in Urumqi. Moreover, the hit rate of this criterion for foehn wind of gale or above level (i.e., a 2 min wind ≥ 10.8 m/s on average) is 100%. In addition, combined with two predictors of sea-level pressure difference (ΔP) and Δθ between downwind stations and upwind stations, the foehn forecast can be more accurate than that provided by a single predictor. With ΔP ≤ −12 hPa and Δθ ≥ 5 K, the chances for foehn to occur are over 90%. This finding would have some reference and application values for the foehn forecasting.

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

An evaluation of meteorological data prediction over Washington, D.C.: Comparison of DCNet observations and NAM model outputs

This study presents an example of how outputs of operational and readily-available mesoscale numerical models can be adapted to initialize dispersion calculations within the urban surface roughness layer. Three years of urban meteorological observations from central Washington, DC, are compared against forecast outputs of the North American Mesoscale (NAM) model. NAM wind speed predictions underestimate the observations in light winds and overestimate the measurements in high winds. Average wind directions are consistent. However, an adjustment of the predicted direction of the plume by −20° is needed. The uncertainty associated with this adjustment is large in light NAM wind speed with no evident variation by season. The values of the standard deviation of the wind direction, σθ derived from NAM model outputs underestimate the observations by a small amount (about −1.5 to −2.5°). The results presented here indicate that mesoscale numerical model outputs can provide information adequate for dispersion calculations. However, levels of uncertainty associated with implementation of the suggested procedures increase with decreasing wind speed, causing considerable uncertainty in the implementation of adjustments as low wind speed conditions are approached. Results and recommendations reported here should not be extended to other numerical models or other cities without further testing.

Effects of weather on temperatures of the grain bin components and headspace of a 10-m diameter corrugated steel bin.

The mean global temperatures are increasing as a result of climate change. To understand how the change in ambient weather influences the temperature of the stored grain, the temperature fluctuation patterns of the floor, roof, sidewalls, and headspace were monitored from mid-August 2019 to the end of October 2021 in Winnipeg, Canada. The bin was filled with 300 t of wheat at an initial average moisture content of 12.5 ± 0.1% (wet basis). The thermocouples were installed at 17, 9, and 12 locations on the floor, roof (outside), and sidewalls (outside) of the bin, respectively. Sixteen temperature and relative humidity sensors were installed at different locations with varying distances from the surface of the grain in the headspace. The ambient weather (air temperature (°C), relative humidity (%), barometric pressure (kPa), average solar radiation (W/m2), precipitation (mm), wind speed (m/s), and wind direction (degrees with reference to the north)) were also measured near the bin during the study period. The temperatures of the roof, sidewalls, and headspace were influenced by the ambient temperature and solar radiation. In Year II (November 2020 – October 2021), the floor, roof, sidewalls, and headspace temperatures were higher by 2.1 ± 0.1°C, 3.9 ± 0.1°C, 3.5 ± 0.2°C, and 1.9 ± 0.1°C than that in Year I (November 2019 - October 2020), respectively. The ambient temperature increased by 1.8°C in year II, compared to year I. These results can be used in the prediction of temperatures in grain bins caused by weather changes.

Analysis of the Method for Enhancing Safety by Predicting and Comparing Damage Ranges Due Owing to Chemical Leakages

To predict the extent of damage caused by leakages at a place where chemicals are wholesaled, four of the most commonly stored substances in the corresponding place were selected. Leakage volumes were increased by 50 ㎘, and methyl alcohol, ethyl acetate, and methyl ethyl ketone were confirmed by increasing the leakage volume to 100 ㎘ and to 160 ㎘ for toluene. A total of 92 scenarios were created by applying the region’s average temperature, wind speed, wind direction, and humidity over the last 10 years to confirm each substance’s diffusion distance. The prepared scenario was analyzed by applying ALOHA air dispersion models. Regarding toxicity, criteria were analyzed based on each substance’s PAC-1, PAC-2, and PAC-3 concentration, and a fire occurrence possibility range was used to maintain the concentration of the lower flammable limit. Under normal conditions, in the case of the same amount of leakage, the concentration of the lower flammable limit and PAC-1 was maintained farthest from toluene, but the danger distance of methyl alcohol was farther in PAC-2 and PAC-3. Results showed that the diffusion distance increases the log function with the amount of leakage. However, owing to the substances’ toxicity and physical characteristics, the PAC levels of some substances showed non-uniform results. In addition, diffusion widths differed for each substance. Therefore, to prescribe countermeasures for chemical leakages in storage locations, all substances being stored must be checked. Disaster prevention measures must also be established by considering not only diffusion distances of the substances but also their diffusion widths.

Contributions of nearby agricultural insecticide applications to indoor residential exposures

BackgroundPesticide exposure has been associated with adverse health effects. We evaluated relationships between proximity to agricultural insecticide applications and insecticides in household dust, accounting for land use and wind direction. MethodsWe measured concentrations (ng/g) of nine insecticides in carpet-dust samples collected from 598 California homes. Using a geographic information system (GIS), we integrated the California Pesticide Use Reporting (CPUR) database to estimate agricultural use within residential buffers with radii of 0.5 to 4 km. We calculated the density of use (kg/km2) during 30-, 60-, 180-, and 365-day periods prior to dust collection and evaluated relationships between three density metrics (CPUR unit-based, agricultural land area adjusted, and average daily wind direction adjusted) and dust concentrations. We modeled natural-log transformed concentrations using Tobit regression for carbaryl, chlorpyrifos, cypermethrin, diazinon, and permethrin. Odds of detection were modeled with logistic regression for azinphos-methyl, cyfluthrin, malathion, and phosmet. We adjusted for season, year, occupation, and home/garden uses. ResultsChlorpyrifos use within 1–4 km was associated with 1 to 2-times higher dust concentrations in both the 60- and 365-day periods. Carbaryl applications within 2–4 km of homes 60-days prior to dust collection were associated with 3 to 7-times higher concentrations and the 4 km trend was strongest using the wind-adjusted metric (p-trend = 0.04). For diazinon, there were 2-times higher concentrations for the 60-day metrics in the 2 km buffer and for the CPUR and wind-adjusted metrics within 4 km. Cyfluthrin, phosmet, and azinphos-methyl applications within 4 km in the prior 365-days were associated with 2-, 6-, and 3-fold higher odds of detection, respectively. ConclusionsAgricultural use of six of the nine insecticides within 4 km is an important determinant of indoor contamination. Our findings demonstrated that GIS-based metrics for quantifying potential exposure to fugitive emissions from agriculture should incorporate tailored distances and time periods and support wind-adjustment for some, but not all insecticides.

Applicability of wind products retrieved from wind lidar in the winter games

Abstract Based on the 10-min average wind speed and wind direction data observed by automatic stations at the starting point and midway of the snow track in the “14th National Winter Games”, the wind product accuracy of wind lidar from December 1, 2019 to January 31, 2020 was analyzed. The results show that the mean relative deviation (MRD), standard deviation of relative deviation (STDRD ) and correlation coefficient (R) of wind direction data are better than wind speed data. The MRD is largest when the average wind speed of the ground observation is about 3 m/s, reaching 55%. The wind speed retrieved by the lidar is higher than that observed by automatic station when the average wind speed is lower than 10 m/s. When the average wind speed is higher than 10 m/s, the wind speed retrieved by the lidar is lower than ground observations. The detection accuracy of the lidar wind direction at the automatic station No.4 is better than that at the automatic station No.3. For the detection of wind speed, the mean absolute error (MAE) is larger, the R was slightly lower, but the mean error (ME), the MRD and the STDRD are better at the automatic station No.4. As the SNR increases, ME of the average wind speed decreases, and the relative deviations (RD) of wind speed and wind direction gradually concentrate on zero.

Characteristics of Climate Change in the Lake Basin Area of Gangcha County

This paper mainly was based on the average temperature, precipitation, humidity, and wind direction of Gangcha county from 1960 to 2013. By using wavelet analysis and Mann-Kendall (M-K) mutation analysis, specifically analyzed the climate change characteristics in the lake basin area of Gangcha county. The result showed that the climatic change in the lake basin area of Gangcha county is noticeable. The average temperature, average minimum temperature, average maximum temperature, and evaporation showed an increasing trend. But the evaporation in the study area was higher than precipitation. The average relative humidity showed a decreasing trend. And the sunshine and the average wind speed percentage showed a significant decreasing trend. Utilizing the Morelet wavelet, The time series of annual mean temperature, annual evaporation and annual sunshine percentage all have quasi3a and quasi4A periods, and the annual mean precipitation has quasi2–3A, quasi2–5A and quasi2–6A periods, which appear in 1996–2005, 1962–1978 and 1978–1996 respectively. The mean annual relative humidity has obvious quasi2–7A and quasi3A time series, which appear from 1960 to 1996, 1997 to 2005 and after 2008, respectively. The annual mean wind speed has quasi3–4A and quasi5A time series characteristics, which appear in 1964–1967, 1984–1995, after 2009 and 1971–1983, respectively. Through Mann-Kendall (M-K) mutation analysis, it is found that the mutations of evaporation and the average speed of wind are significant. The mutation of evaporation started in 2004, and the mutation of average started in 2003.

An integrated method for atmospheric dispersion and corresponding risks: Application to ITU TRIGA Mark II research reactor

The prediction of the present and future impact of radioactivity release is important. However, there are several models and options to choose from. An integrated approach is proposed in this study and it is implemented to Istanbul Technical University (ITU) TRIGA Mark II Research Reactor. The procedure was divided into six steps including quantification of the source term, specification of meteorological data, atmospheric dispersion model selection, atmospheric dispersion and deposition calculation, biosphere and geosphere transport calculation, and finally dose and risk calculation. The acquisition of data and parameters for these steps were discussed in detail to provide insight to users of the integrated method. The evaluation of wind direction and speed defined in the safety analysis report of the reactor showed that the average wind direction at the reactor site altered from NE direction to NE-NNE over time but speed stayed almost the same. The maximum-total cloudshine and groundshine as a result of annual exposure rate were found as 21.1 μSv/yr at ITU Ayazağa Campus. For inhalation, Ba-140 and I-131 were found to have the highest risk in mortality and morbidity, respectively. The same is true for Sr-90 and I-131 in the lifetime intake of food while in the lifetime intake of tap water, Sr-90 is the leading radioisotope for both mortality and morbidity risks. However, all calculated values are quite low considering the survival rates. Moreover, the results of the study provides useful insight into the understanding of necessities while shedding light on limitations.