Differences In Wind Direction Research Articles

Constrained synthetic wind fields from high-resolution 3D WindScanner measurements

The limited spatial and temporal resolution of wind measurements within the inflow of a wind turbine requires statistical modeling of synthetic wind fields, integrating actual measurements or derived statistics along with selected turbulence models. The short-range WindScanner technology enhances atmospheric wind measurements with high resolution in both spatial and temporal dimensions. This contribution utilizes 3D turbulent inflow measurements from three synchronized WindScanner to generate synthetic turbulent wind fields. Both, mean wind field parameters and turbulent time series are analyzed, and different input configurations for the constrained wind field generation are evaluated.Our findings indicate the presence of periods characterized by dominant horizontal shear and veer events, with wind speed and direction differences up to 1.53 ms−1 or 8.45° across the rotor span. Additionally, the study reveals that the optimal measurement configuration for constrained turbulence modeling varies depending on the specific evaluated location and velocity component being analyzed. Another observation is that excessive constraints, placed too closely, may lead to overfitting, thereby diminishing the representativeness of the synthetic field for the lateral velocity component.

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.

Uncertainty of Atmospheric Winds in Three Widely Used Global Reanalysis Datasets

Abstract Atmospheric winds are crucial to the transport of heat, moisture, momentum, and chemical species, facilitating Earth’s climate system interactions. Existing weather and climate studies rely heavily on the wind fields from reanalysis datasets. In this study, we analyze the uncertainty of instantaneous atmospheric winds in three reanalysis (ERA5, MERRA-2, and CFSv2) datasets. We show that the mean wind vector differences (WVDs) between the reanalysis datasets are about 3–6 m s−1 in the troposphere. The mean absolute wind direction differences can be more than 50°. Large WVDs greater than 5 m s−1 are found for 30%–50% of the time when the observed precipitation rate is larger than 0.1 mm h−1 over the eastern Pacific Ocean, Indian Ocean, Atlantic Ocean, and some mountain areas. The mean WVDs exhibit seasonal variations but no significant diurnal variations. The uncertainty of vertical wind shear has a correlation of 0.59 with the uncertainty of winds at 300 hPa. The magnitudes of vorticity and horizontal divergence uncertainties are on the order of 1 × 10−5 s−1, which is comparable to the mean values of vorticity and horizontal divergence. In comparison with some limited observations from field campaigns, the reanalysis datasets exhibit a mean WVD ranging from 2 to 4.5 m s−1. Among the three reanalysis datasets, ERA5 shows the closest agreement with the observations while MERRA-2 has the largest discrepancy. The substantial uncertainty and errors of the reanalysis wind products highlight the critical need for new satellite missions dedicated to 3D wind measurements.

Evaluation of the GRAMM/GRAL model for high-resolution wind fields in Heidelberg, Germany

Independent verification of mitigation efforts for climate and air quality action in cities relies on inferring emissions from atmospheric concentration measurements. As emissions are dispersed in the atmosphere before they reach an instrument, the quantitative estimation of emissions requires an understanding of the atmospheric transport and associated uncertainties. In this study, we analyse the catalogue of steady-state flow fields generated by the Graz Mesoscale Model (GRAMM) coupled to the Graz Lagrangian Model (GRAL) for an entire year in Heidelberg, Germany. We use a loss function for the wind field selection, which assigns a best-matching catalogue entry to any given hour by exploiting observation data. We introduce a new loss function which finds an optimal balance between differences in wind speed and wind direction. We evaluate the performance of the model based on 15 meteorological measurement sites, of which 14 are in the inner high-resolution and building-resolving GRAL domain (12.5 km × 12.5 km, 10 m resolution). Performance metrics include mean bias (MB) and root mean square errors (RMSEs) of simulated and observed wind speed and wind direction for all individual stations. On average, we find a mean underestimation of wind speed of 0.14 ms−1 corresponding to about 7 % of the mean wind speed and a mean RMSE of 1.03 ms−1. For wind direction, a mean overall bias smaller than 1° is achieved, but individual stations show larger biases (mean absolute bias: 37°), especially at stations where wind speeds are low on average. Evaluation benchmarks for mean biases of wind direction and wind speed of mesoscale models provided by the European Environmental Agency (EEA) are met at 11 and 14 out of 15 stations at low measurement heights, respectively. Recently suggested extended benchmarks for complex terrain are met at almost all stations. Additionally, for the first time, we analyse the model's ability to simulate the vertical wind profile and we analyse the benefit of implementing a wind profile measurement into the process. We find that the model does not fully capture the vertical profile in our setting. We further study the required measurement network size and find that a high number (> 6) of meteorological stations improves the selection of flow fields over the entire GRAL domain substantially. The conducted comprehensive analysis of the wind fields in the GRAL domain are the basis for detailed quantitative analysis of greenhouse gas and air pollutant emissions using the GRAMM/GRAL modelling framework.

CORRELATION OF ATMOSPHERIC LABILITY INDEX TO VERTICAL WIND SHEAR AT I GUSTI NGURAH RAI AIRPORT

Wind shear is a condition that is detrimental to aircraft because it can cause the aircraft to experience lift, especially during take-off or landing, where wind shear can occur due to bad and unstable weather, so research on the correlation of the atmospheric lability index to wind shear is very important to prevent a plane crash. This research aims to determine the magnitude of the correlation of the atmospheric lability index to the vertical wind shear that occurs at I Gusti Ngurah Rai Airport, Bali. In this research, the Wind Profiler Radar (WPR) brand scintec LAP-3000 was used to obtain wind shear data in the form of wind direction and wind speed data then radiosonde to obtain atmospheric lability index data in the form of Lifted Index (LI), Total-Totals Index (TT), K-Index (KI), and Convective Available Potential Energy (CAPE) index values for each month in 2019-2020. Wind shear measurement was carried out at an altitude of 100-3000 m and data were taken on the difference in wind direction ≥ 60o and the difference in wind speed ≥ 10 knots. Meanwhile, measurement of the atmospheric lability index is carried out by a flying air balloon that has been equipped with a transmitter. All LI, TT, KI, and CAPE index value data and the number of wind shear events were analyzed with Pearson Product Moment correlation. The value of the correlation coefficient (r) between the LI, TT, KI, and CAPE indices was obtained for successive wind shear events of -0.786; 0,250; 0.738, and 0.713. These results show that the LI, KI, and CAPE index values can be used as a reference to predict wind shear events because they have a strong correlation, obtaining a correlation value between 0.60 to 0.79 compared to the TT index.

Planetary boundary layer climatological features over North China Plain: Derived from intensive experiment data

AbstractA field campaign was carried out in the North China Plain (NCP) spanning 4 years and formed a dataset of more than 2,700 planetary boundary layer (PBL) sounding samples (872 in summer and 1,841 in winter). Based on these data, fundamental aspects of the PBL climatology over this region were investigated. First, the ensemble mean features of the PBL were revealed. The maximum PBL height in the daytime reaches 1,079 versus 786 m (summer vs. winter, hereafter the same), while about 200 m at nighttime. The bulk mean temperature and specific humidity across the PBL in summer are significantly higher than those in winter (24.0 vs. −0.5°C; 8.9 vs. 1.7 g⋅kg−1, respectively). Mean wind speed within the PBL keeps almost the same in winter and summer (4.5 m⋅s−1) but with a seasonal difference in wind direction (southerly vs. northwesterly). Second, significant diurnal variations within/above the PBL were quantified. Day‐night temperature contrast is largest near the ground (7.9 vs. 5.1°C), and it decreases with height to almost a constant above the PBL. Diurnal variation amplitude of specific humidity is about 1.0 versus 0.3 g⋅kg−1. Diurnal variation of winds also supports the extended influence of the earth surface across the PBL. Third, the local influence on the PBL over the NCP is also presented. PBL height is higher in the plain centre; temperature is lower in coast than inland; winds in the PBL are strongly modulated by local or mesoscale circulations including the plain wind and sea breeze; low‐level jets are common at summer night over the central and eastern parts of the NCP. Analysis reveals possible energy and water vapour exchange across the PBL, and additional heating mechanism above the PBL (e.g., solar radiation absorption).

Comparison of GRUAN data products for Meisei iMS-100 and Vaisala RS92 radiosondes at Tateno, Japan

Abstract. A total of 99 dual soundings with Meisei iMS-100 radiosonde and Vaisala RS92 radiosondes were carried out at the Aerological Observatory of the Japan Meteorological Agency, known as Tateno (36.06∘ N, 140.13∘ E, 25.2 m; the World Meteorological Organization, WMO, station number 47646), from September 2017 to January 2020. Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) data products (GDPs) from both sets of radiosonde data for 59 flights were subsequently created using a documented processing programme along with the provision of optimal estimates for measurement uncertainty. Differences in radiosonde performance were then quantified using these GDPs. For daytime observations, the iMS-100 temperature is around 0.5 K cooler than RS92-GDP in the stratosphere, with significant differences in the upper troposphere and lower stratosphere in consideration of combined uncertainties. For nighttime observations, the difference is around −0.1 K, and data are mostly in agreement. For relative humidity (RH), iMS-100 is around 1 % RH–2 % RH higher in the troposphere and 1 % RH smaller in the stratosphere than RS92, but both GDPs are in agreement for most of the profile. The mean pressure difference is ≤0.1 hPa, the wind speed difference is from −0.04 to +0.14 m s−1, the wind direction difference is ≤6.4∘, and the root mean square vector difference (RMSVD) for wind is ≤1.04 m s−1.

Study of the Convective Heat Transfer Coefficient of Different Building Envelope Exterior Surfaces

Convective heat transfer on the exterior surface of the building envelope is an important component for building energy consumption. The calculation of energy consumption depends on the convective heat transfer coefficient (CHTC) of the exterior surface of the envelope. The existing research does not fully consider the effects of the airflow field around the building on the CHTC of different envelope exterior surfaces. In this paper, the relationships between the CHTC and influence factors were investigated for the isolated building. Response surface methodology (RSM) and support vector machine (SVM) algorithms were integrated with the single building simulation to build the fitting formulas. Then, the fitting correlation between CHTC and different influencing factors was validated by the heating building simulation. The results showed that the CHTC of the building exterior surface was related to the wind velocity, wind direction and temperature difference. Additionally, the fitting formulas had good accuracy in calculating the CHTC under different conditions. The SVM algorithm (averaged error: 3.34%) performed slightly better than the RSM algorithm (averaged error: 4.84%).

Effects of self-induced gravity waves on finite wind-farm operations using a large-eddy simulation framework

In the present study, we use large-eddy simulation (LES) to investigate how a capping inversion in combination with a stable free atmosphere influences the flow development and energy extraction in a large finite wind farm with a staggered and aligned layout. In the conventionally neutral boundary layer (CNBL), we find that gravity waves induce an unfavourable pressure gradient in the induction region of the farm which contributes to the upstream blockage, decreasing the available energy for first-row turbines. However, a favourable pressure gradient establishes through the farm in such conditions, which redistributes the energy and enhances wake recovery. These results are compared with a farm operating in the neutral boundary layer (NBL). Here, we find that only hydrodynamic effects induced by the turbines drag play a role, which cause minor pressure perturbations across the domain. For the selected atmospheric conditions, the power losses generated by the upstream blockage are balanced by the enhanced wake recovery promoted by the favourable pressure gradient throughout the farm. Consequently, the staggered farm efficiency in the CNBL is 8.8% higher than in the NBL. We note that this difference in efficiency is slightly enhanced by the 0.5? difference in wind direction at the location of the first-row turbines between the CNBL and NBL cases, which is caused by the presence of flow blockage. Since both simulations are forced with an equal turbulent velocity profile, the variation in performance is solely caused by the different vertical temperature profiles in the main domain. Finally, the staggered layout leads to a slightly stronger flow blockage than the aligned one when both farms operate in the CNBL.

Spatial and Temporal Variation of the Near-Surface Wind Environment in the Sahara Desert, North Africa

The Sahara Desert is the largest source of dust on Earth, and has a significant impact on global atmospheric changes. Wind is the main dynamic factor controlling the transport and intensity of dust in the Sahara Desert. This study comprehensively analyzed the spatial and temporal variation in the wind regime of the Sahara Desert from 1980 to 2019 using data from 17 meteorological stations to improve awareness of global atmospheric changes and the intensity of regional aeolian activities. All wind speed parameters decreased from northwest to southeast. While there were significant differences in the trends of temporal variation in wind speed among the different regions, there was an overall decreasing trend across the Sahara Desert, with an average wind speed of 0.09 m s−1 10 a−1. This decrease was closely related to wind frequency. The easterly, westerly, and northerly winds dominated, with more complex wind direction in the northern region. Seasonal differences in wind direction were observed in all regions. The wind direction frequency of wind speeds &amp;gt;6 m s−1 exceeded those with wind speeds &amp;lt;6 m s−1 in the western and northern regions, whereas other regions showed an opposite pattern. The highest drift potential (DP) and resultant drift potential (RDP) were found in the western and northern regions, and during spring and winter. There was a trend of decreasing annual variation in DP and RDP in all regions. The directional variability (RDP/DP) indicated mostly intermediate and high variability in wind direction. Resultant drift direction (RDD) indicated that a mainly southwest wind direction. No apparent trends in temporal variation in RDD and RDP/DP were observed. Total DP was strongly influenced by DP and the magnitude and frequency of strong winds in the prevailing wind direction. No strong correlation between wind regimes and dune types was observed in this desert, indicating the complexity of factors affecting dune morphology.

City Scale Modeling of Ultrafine Particles in Urban Areas with Special Focus on Passenger Ferryboat Emission Impact.

Air pollution by aerosol particles is mainly monitored as mass concentrations of particulate matter, such as PM10 and PM2.5. However, mass-based measurements are hardly representative for ultrafine particles (UFP), which can only be monitored adequately by particle number (PN) concentrations and are considered particularly harmful to human health. This study examines the dispersion of UFP in Hamburg city center and, in particular, the impact of passenger ferryboats by modeling PN concentrations and compares concentrations to measured values. To this end, emissions inventories and emission size spectra for different emission sectors influencing concentrations in the city center were created, explicitly considering passenger ferryboat traffic as an additional emission source. The city-scale chemical transport model EPISODE-CityChem is applied for the first time to simulate PN concentrations and additionally, observations of total particle number counts are taken at four different sampling sites in the city. Modeled UFP concentrations are in the range of 1.5–3 × 104 cm−3 at ferryboat piers and at the road traffic locations with particle sizes predominantly below 50 nm. Urban background concentrations are at 0.4–1.2 × 104 cm−3 with a predominant particle size in the range 50–100 nm. Ferryboat traffic is a significant source of emissions near the shore along the regular ferry routes. Modeled concentrations show slight differences to measured data, but the model is capable of reproducing the observed spatial variation of UFP concentrations. UFP show strong variations in both space and time, with day-to-day variations mainly controlled by differences in air temperature, wind speed and wind direction. Further model simulations should focus on longer periods of time to better understand the influence of meteorological conditions on UFP dynamics.

Comparison of Multiple Surface Ocean Wind Products with Buoy Data over Blue Amazon (Brazilian Continental Margin)

Remote sensing data for space-time characterization of wind fields in extensive oceanic areas have been shown to be increasingly useful. Orbital sensors, such as radar scatterometers, provide data on ocean surface wind speed and direction with spatial and temporal resolutions suitable for multiple applications and air-sea studies. Even considering the relevant role of orbital scatterometers to estimate ocean surface wind vectors on a regional and global scale, the products must be validated regionally. Six different ocean surface wind datasets, including advanced scatterometer (ASCAT-A and ASCAT-B products) estimates, numerical modelling simulations (BRAMS), reanalysis (ERA5), and a blended product (CCMP), were compared statistically with in situ measurements obtained by anemometers installed in fifteen moored buoys in the Brazilian margin (8 buoys in oceanic and 7 in shelf waters) to analyze which dataset best represents the wind field in this region. The operational ASCAT wind products presented the lowest differences in wind speed and direction from the in situ data (0.77 ms−1 &lt; RMSEspd &lt; 1.59 ms−1, 0.75 &lt; Rspd &lt; 0.96, −0.68 ms−1 &lt; biasspd &lt; 0.38 ms−1, and 12.7° &lt; RMSEdir &lt; 46.8°). CCMP and ERA5 products also performed well in the statistical comparison with the in situ data (0.81 ms−1 &lt; RMSEspd &lt; 1.87 ms−1, 0.76 &lt; Rspd &lt; 0.91, −1.21 ms−1 &lt; biasspd &lt; 0.19 ms−1, and 13.7° &lt; RMSEdir &lt; 46.3°). The BRAMS model was the one with the worst performance (RMSEspd &gt; 1.04 m·s−1, Rspd &lt; 0.87). For regions with a higher wind variability, as in the southern Brazilian continental margin, wind direction estimation by the wind products is more susceptible to errors (RMSEdir &gt; 42.4°). The results here presented can be used for climatological studies and for the estimation of the potential wind power generation in the Brazilian margin, especially considering the lack of availability or representativeness of regional data for this type of application.

Evaluation of the First Negative Ion-Based Cloud Seeding and Rain Enhancement Trial in China

Negative ion-based cloud seeding has been shown to be an effective means in the laboratory. China’s first negative ion-based cloud seeding outfield trial was conducted in the northwestern interior. This paper briefly introduces the principle of the ion-based precipitation enhancement, and the trial location is described. The design of the ionization system and meteorological monitoring network are presented. The implementation plan of the outfield trial is explained. In addition, the evaluation of experimental effects is detailed in this paper. We designed various analytical methods to investigate both the overall precipitation variation of the experimental area and the precipitation variation within the experimental area. The overall precipitation of the experimental area was predicted using a neural network, and then the actual precipitation was compared with the predicted precipitation to evaluate the effectiveness of the experiment. The effectiveness of the experiment was also evaluated using historical precipitation data and the result of the randomized comparative trial. This paper also explores the effects of geographic location differences and wind direction differences on the precipitation differences within the trial area. The changes in the number of negative ions and clouds in the sky were also analyzed. From these analyses, we obtained quantitative assessment results. These results could indicate that the outfield trial basically met the expected requirements, which is to increase the rainfall of the trial area by 20%.

Evaluation of uncertainties derived from meteorological forecast inputs in plume directions predicted by atmospheric dispersion simulations

ABSTRACT Atmospheric transport, dispersion, and deposition models (ATDMs) can support decision-making during nuclear emergencies; however, uncertainties in the ATDM results need to be carefully evaluated. To investigate the uncertainties derived from meteorological forecast inputs, we conducted three-day forecast simulations every day for one year with hypothetical releases of radionuclides (one-hour releases every 6 h) from a nuclear facility. The forecast outputs were compared with the analysis outputs during the same period. The difference between the outputs is treated as the uncertainty in the forecasts and is represented as an angle based on the discrepancy in the plume directions between the analysis and forecast outputs. Using meteorological inputs made by Japan Meteorological Agency, the discrepancy angle (Ang) increased by approximately 10 per day on an annual average basis. Meanwhile, the Ang values were occasionally 4–5 times higher than the annual average during short time periods. Since the Ang time series show seasonal and diurnal changes, the statistical characteristics likely depend on the geographical and meteorological conditions, as well as the types of meteorological inputs. Additionally, a main factor in the uncertainty is the wind-direction difference between the analysis and forecast outputs on scales of more than or less than 100 km.

A novel method for ranking CMIP6 global climate models over the southeast Asian region

AbstractThe performance of 28 CMIP6 models in simulating the Southeast Asian (SEA) climate is investigated. An evaluation methodology is developed to evaluate spatiotemporal patterns of precipitation, near‐surface temperature, and 850‐hPa wind. Mean annual cycles of temperature and rainfall are assessed separately over Indochina, the Maritime Continent, and the Philippines; and the summer and winter seasons are selected for studying spatial patterns. Within these distinctions, statistics comparing the models to reference data are calculated. The patterns' shape and phase are notably studied, and wind direction differences are assessed specifically over the most major wind flows. We present a scoring procedure allowing us to give each model a unique score reporting on the diverse evaluation aspects, hence establishing a ranking. The process rests on a scoring function growing exponentially with the performance: it favours the models combining good performances according to the largest number of criteria. The assessment highlights EC‐Earth3 and EC‐Earth3‐Veg, which conduct excellent simulations of the regional patterns of rainfall and wind. Their temperature outputs are also reliable, but CNRM‐CM6 ranks first for this variable, while finishing third globally. Local differences are discussed: we emphasize, among other things, the diverse impact of topography on temperature and rainfall, or the frequent deviation of winter winds circulating from the South China Sea to the Java Sea. In addition, a similarity study reveals major difficulties for simulating near‐equatorial annual cycles of temperature and precipitation whereas the summer monsoon spatial rainfall patterns bring out the largest discrepancies. In any event, the top models pointed out by our ranking constitute a high‐performance subset that further climate modelling experiments in SEA can rely on.

Wind-driven rain on buildings: Accuracy of the ISO semi-empirical model

Previous studies have shown that the accuracy of the ISO semi-empirical wind-driven rain (WDR) model can be improved by applying more detailed wall factors determined based on high-resolution field measurements. However, discrepancies between measurements and predictions still exist. The availability of high-resolution WDR field data collected over a long period of time makes a detailed analysis possible. Field measurements of WDR on two mid-rise and one high-rise buildings in three Canadian regions over a year and a half were analyzed. Two main factors that can significantly improve the ISO WDR model were identified, i.e. the time resolution (5 ​min data vs. hourly data) and the correction of difference in wind speed and wind direction between the building site and weather station using hourly data. It is recommended to take WDR measurements at shorter-duration and calculate wall factors using both shorter-duration records and hourly averaged data. In general, the ISO model works well if all the differences in wind conditions between site and the weather station can be taken into account. The observed discrepancy can be reduced to within 2% for façades facing the prevailing wind-direction, while to about 60% for façades off the prevailing wind direction in complex urban settings.

METEOROLOGICAL FACTORS ASSOCIATED WITH THE SPREAD OF THE COVID-19 VIRUS

SESSION TITLE: Chest Infections Posters SESSION TYPE: Original Investigation Posters PRESENTED ON: October 18-21, 2020 PURPOSE: In December 2019, a SARS-CoV-2 (Severe acute respiratory failure syndrome Coronavirus- 2) virus emerged from Wuhan, China causing SARS and other pulmonary infections (COVID-19) that rapidly spread to other parts of the world, causing a major pandemic. There is data suggesting that this virus will follow similar trends as the 2009 Influenza H1N1 and 2002 SARS-COV, due geographical and meteorological factors, being the most importants the temperature, relative humidity and wind speed. We aim to analyze the impact of geographical and meteorological factors in the incidence of COVID19. METHODS: We analyzed the frequency of COVID19 cases along geographical and meteorological data from 84 regions around the world from January to March 2020, we divided the regions if they had less than 1, 000 cases vs more than 1, 000 cases of COVID-19. The meteorological data was collected from the Climate Re-analyzer platform from the Climate Change Institute at University of Maine, the National Science foundation and Russell Grinnell Memorial Trust. The monthly incidence of the cases was collected by the Mapping 2019-nCOV tool from the Center of Systems Science and Engineering (CSSE) at Johns Hopkins University. We compare the temperature (° Celsius), latitude (° North and ° South), humidity (%) wind speed (km/h) and wind direction (0°-360°) between areas with above 1, 000 cases of COVID19 vs areas with less than 1, 000 cases. Categorical variables were compared using chi-square, and a univariate linear regression model was created. We used SPSS version 25.0 (Armonk, NY: IBM Corp.) for statistical analysis. RESULTS: We found 84 regions around the world affected by COVID-19 from January 1st to March 1st, 2020. Regions with more that 1, 000 COVID-19 cases were more likely to have lower minimum temperature (5.05°C vs 9.18°c; P: <0.05), lower maximum temperature (6.22°C vs 7.97°C; P:<0.05), no difference in wind direction, higher maximum wind speed (3.29km/h vs 2.36km/h; P:<0.05), no difference between average humidity, and to be located above 30 degrees in north latitude (32% vs 11%; P<0.05) presented more cases. See table 1 and table 2. CONCLUSIONS: Regions with more than one thousand cases were statistically significantly more likely to have minimum average temperature of -0.3 ° Celsius, maximum wind speed 14.85 km / h, humidity average of 75.65% and with a latitude greater than 30 ° north. Furthers studies with larger sample are needed to assess the geographical and meteorological impact in COVID-19 pandemic. CLINICAL IMPLICATIONS: COVID19, Pandemic, meteorological data. DISCLOSURES: no disclosure on file for Waldemar Castillo; no disclosure on file for Jose Waldemar Castillo; No relevant relationships by EMILIO CASTILLO, source=Web Response No relevant relationships by Maria Choc, source=Web Response No relevant relationships by Juan Del Cid Fratti, source=Web Response No relevant relationships by Luis Godínez, source=Web Response No relevant relationships by gabriel rios, source=Web Response No relevant relationships by Angel Soto, source=Web Response

Spatial and temporal changes in aeolian redistribution of sediments and nutrients following fire

AbstractWildfires can profoundly alter rates, magnitudes, and ecological influences of aeolian redistribution of sediments and nutrients. This study examines the influence of fire in a semi‐arid ecosystem using 2 years of continuous passive dust trap data in the northern Great Basin, USA. We analyse the mass flux, organic material content, grain size distribution, and geochemistry of the collected samples to trace the fingerprint of the 2015 Soda Fire through space and time. In areas not affected by fire, dust is characterized by silt‐sized median grains, a geochemical signature consistent with a playa source area, and spatially consistent but seasonally variable dust flux rates. Following fire, dust flux increases significantly within and near the burned area. At burned and topographically sheltered sites, dust deposition in the eighth month following fire was 190% higher than dust deposition 2 years post‐revegetation. Topographically exposed sites recorded only modest increases in dust deposition following fire. Analysis of organic matter indicates all dust samples (both burned and unburned) contained an average of 45% organic matter compared to a watershed average of 1.6% organic matter in soils.Geochemical and seasonal dust deposition data from 12 dust traps at a range of elevations indicate that with the removal of stabilizing vegetation after wildfire, differences in topographic position and wind direction lead to preferential redistribution of material across a burned landscape over hillslope scales (0–10 km). We posit post‐fire aeolian redistribution of locally derived material to topographically controlled positions is an important control on the spatial variability of soil depth and characteristics in drylands with complex topography. © 2020 John Wiley &amp; Sons, Ltd.

Influence of Storm Events on Chlorophyll Distribution Along the Oligotrophic Continental Shelf Off South-Western Australia

The role of storm events in controlling chlorophyll distribution along the oligotrophic Rottnest continental shelf is examined data collected by autonomous ocean gliders combined with meteorological data. Spatial and temporal distribution of chlorophyll concentrations were obtained from a repeated transect across the shallow continental shelf that revealed complex relationships among wind events (e.g. differences in wind speed and direction) and chlorophyll concentrations. Data indicated that the water column responded rapidly to changes in wind speeds alternating between stratification, de-stratification and vice-versa over 1-3 days. Under low wind conditions (wind speeds 15 ms-1) higher chlorophyll values were present throughout the vertically mixed water column. Maximum chlorophyll concentrations, more than double that due to climatology, were observed 1-3 days after the passage of storms subsequent to sediment re-suspension. Storm events, with an onshore component, promoted downwelling, the water column retained vertical stratification, and DSWC was present across the shelf. Here, DSWC intensified with higher chlorophyll in the cascaded water extending further offshore. It is concluded that wind speed and direction are the dominant parameters controlling the distribution of chlorophyll particularly during storm events.

Analysis of Wind Speed and Wind Pressure on the Facades in Frequency Domain for the Modelling of Air Change Rate

Air exchange in buildings is driven by pressure difference across the building envelope caused by wind and difference in density between external and internal air. The evaluation of the influence of wind on the air change rate is usually limited to the analysis of the hourly mean wind speed. Wind is a random phenomenon characterized by the broad energy spectrum. The high frequency part can be responsible for the oscillation of the air through the openings resulting in the increased air exchange. Wind pressure coefficient on the leeward site mostly depends on the form characteristics of the object in relation to wind direction. The analysis of wind speed and wind pressure on the facades in frequency domain can deliver interesting data to air change rate model. Some of the results of continuous measurements carried out on a single-family house for 8 months are presented in frequency domain. The statistics of wind speed, wind direction and pressure differences across the 6 building components are calculated. The wind turbulence and the pressure fluctuations on the facades and the roof of the building are being investigated using energy spectra of their signals. Farther analysis of the experimental results is needed to be able to include high frequency wind in the infiltration model.