scholarly journals A Remotely Deployable Wind Sonic Anemometer

2017 ◽  
Vol 1 (2) ◽  
pp. 1
Author(s):  
Muhammad Awais ◽  
Syed Suleman Abbas Zaidi ◽  
Murk Marvi ◽  
Muhammad Khurram
Keyword(s):  
Wind Speed ◽  
Systems Engineering ◽  
Business Models ◽  
Short Term Memory ◽  
Weather Prediction ◽  
Sonic Anemometer ◽  
Ground Truth ◽  
Weather Conditions ◽  
Average Wind Speed ◽  
Ground Truth Data

Communication and computing shape up base for explosion of Internet of Things (IoT) era. Humans can efficiently control the devices around their environment as per requirements because of IoT, the communication between different devices brings more flexibility in surrounding. Useful data is also gathered from some of these devices to create Big Data; where, further analysis assist in making life easier by developing good business models corresponding to user needs, enhance scientific research, formulating weather prediction or monitoring systems and contributing in other relative fields as well. Thus, in this research a remotely deployable IoT enabled Wind Sonic Anemometer has been designed and deployed to calculate average wind speed, direction, and gust. The proposed design is remotely deployable, user-friendly, power efficient and cost-effective because of opted modules i.e., ultrasonic sensors, GSM module, and solar panel. The testbed was also deployed at the roof of Computer & Information Systems Engineering (CIS) department, NED UET. Further, its calibration has been carried out by using long short-term memory (LSTM), a deep learning technique; where ground truth data has been gathered from mechanical wind speed sensor (NRG-40 H) deployed at top of Industrial & Manufacturing (IM) department of NED UET. The obtained results are satisfactory and the performance of designed sensor is also good under various weather conditions.

scholarly journals Simultaneous contact and aerodynamic force estimation (s-CAFE) for aerial robots

2020 ◽  
Vol 39 (6) ◽  
pp. 688-728
Author(s):  
Teodor Tomić ◽  
Philipp Lutz ◽  
Korbinian Schmid ◽  
Andrew Mathers ◽  
Sami Haddadin
Keyword(s):  
Wind Speed ◽  
Aerodynamic Force ◽  
Ground Truth ◽  
Angular Speed ◽  
Location Estimation ◽  
Contact Forces ◽  
Force Estimation ◽  
Real World Data ◽  
Ground Truth Data ◽  
Flying Robots

In this article, we consider the problem of multirotor flying robots physically interacting with the environment under influence of wind. The results are the first algorithms for simultaneous online estimation of contact and aerodynamic wrenches acting on the robot based on real-world data, without the need for dedicated sensors. For this purpose, we investigated two model-based techniques for discriminating between aerodynamic and interaction forces. The first technique is based on aerodynamic and contact torque models, and uses the external force to estimate wind speed. Contacts are then detected based on the residual between estimated external torque and expected (modeled) aerodynamic torque. Upon detecting contact, wind speed is assumed to change very slowly. From the estimated interaction wrench, we are also able to determine the contact location. This is embedded into a particle filter framework to further improve contact location estimation. The second algorithm uses the propeller aerodynamic power and angular speed as measured by the speed controllers to obtain an estimate of the airspeed. An aerodynamics model is then used to determine the aerodynamic wrench. Both methods rely on accurate aerodynamics models. Therefore, we evaluate data-driven and physics-based models as well as offline system identification for flying robots. For obtaining ground-truth data, we performed autonomous flights in a 3D wind tunnel. Using this data, aerodynamic model selection, parameter identification, and discrimination between aerodynamic and contact forces could be performed. Finally, the developed methods could serve as useful estimators for interaction control schemes with simultaneous compensation of wind disturbances.

scholarly journals Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

2020 ◽  
Vol 5 (2) ◽  
pp. 15 ◽  
Author(s):  
Javier López Gómez ◽  
Francisco Troncoso Pastoriza ◽  
Enrique Granada Álvarez ◽  
Pablo Eguía Oller
Keyword(s):  
Local Level ◽  
Weather Prediction ◽  
Ground Truth ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Accurate Information ◽  
Numerical Weather ◽  
Weather Model ◽  
Geostatistical Interpolation ◽  
Solid Foundation

Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures.

scholarly journals A Review on the Building Wind Impact through On-site Monitoring in Haeundae Marine City: 2021 12th Typhoon OMAIS Case Study

2021 ◽  
Vol 35 (6) ◽  
pp. 414-425
Author(s):  
Jongyeong Kim ◽  
Byeonggug Kang ◽  
Yongju Kwon ◽  
Seungbi Lee ◽  
Soonchul Kwon
Keyword(s):  
Wind Speed ◽  
Weather Conditions ◽  
Speed Ratio ◽  
Wind Effect ◽  
Strong Wind ◽  
Average Wind Speed ◽  
Wind Environment ◽  
High Rise ◽  
Site Monitoring ◽  
Surrounding Areas

Overcrowding of high-rise buildings in urban zones change the airflow pattern in the surrounding areas. This causes building wind, which adversely affects the wind environment. Building wind can generate more serious social damage under extreme weather conditions such as typhoons. In this study, to analyze the wind speed and wind speed ratio quantitatively, we installed five anemometers in Haeundae, where high-rise buildings are dense, and conducted on-site monitoring in the event of typhoon OMAIS to determine the characteristics of wind over skyscraper towers surround the other buildings. At point M-2, where the strongest wind speed was measured, the maximum average wind speed in 1 min was observed to be 28.99 m/s, which was 1.7 times stronger than that at the ocean observatory, of 17.0 m/s, at the same time. Furthermore, when the wind speed at the ocean observatory was 8.2 m/s, a strong wind speed of 24 m/s was blowing at point M-2, and the wind speed ratio compared to that at the ocean observatory was 2.92. It is judged that winds 2–3 times stronger than those at the surrounding areas can be induced under certain conditions due to the building wind effect. To verify the degree of wind speed, we introduced the Beaufort wind scale. The Beaufort numbers of wind speed data for the ocean observatory were mostly distributed from 2 to 6, and the maximum value was 8; however, for the observation point, values from 9 to 11 were observed. Through this study, it was possible to determine the characteristics of the wind environment in the area around high-rise buildings due to the building wind effect.

scholarly journals Impact of weather conditions on cheetah monitoring with scat detection dogs

2021 ◽  
pp. 1-6
Author(s):  
Noreen M. Mutoro ◽  
Jonas Eberle ◽  
Jana S. Petermann ◽  
Gertrud Schaab ◽  
Mary Wykstra ◽  
...  
Keyword(s):  
Wind Speed ◽  
Weather Conditions ◽  
Assessment Tools ◽  
Average Wind Speed ◽  
Strong Negative Correlation ◽  
Detection Rates ◽  
Average Wind ◽  
Invasive Method ◽  
Detection Dog ◽  
The Impact

Abstract Knowledge on cheetah population densities across their current range is limited. Therefore, new and efficient assessment tools are needed to gain more knowledge on species distribution, ecology and behaviour. Scat detection dogs have emerged as an efficient and non-invasive method to monitor elusive and vulnerable animal species, like cheetahs, due to the dog’s superior olfactory system. However, the success of locating scat using detection dogs can be significantly improved under suitable weather conditions. We examined the impact of temperature, humidity and wind speed on detection rates of scat from cheetahs during a scat detection dog survey in Northern Kenya. We found that average wind speed positively influences the scat detection rate of detection dogs working on leash. Humidity showed no significant influence. Temperature showed a strong negative correlation with humidity and thus was excluded from our model analyses. While it is likely that wind speed is especially invalid for dogs working off leash, this study did not demonstrate this. Wind speed could thus influence the success of monitoring cheetahs or other target species. Our findings help to improve the survey and thus maximise the coverage of study area and the collection of target samples of elusive and rare species.

scholarly journals An intercomparison of mesoscale models at simple sites for wind energy applications

2016 ◽  
Author(s):  
Bjarke Tobias Olsen ◽  
Andrea Noemi Hahmann ◽  
Anna Maria Sempreviva ◽  
Jake Badger ◽  
Hans Ejsing Jørgensen
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Best Practice ◽  
Weather Prediction ◽  
Average Wind Speed ◽  
Small Shift ◽  
Mesoscale Models ◽  
Wind Speeds ◽  
Energy Applications ◽  
Mean Wind Speed

Abstract. An intercomparison of model results from 25 different Numerical Weather Prediction (NWP) models is presented for the year 2011 at six sites in Northern Europe characterized by simple terrain. The model results and a detailed description of each model was submitted by 18 different modeling groups to a open call for data, and serves as a rare quantitative overview of the model uncertainties associated with state-of-the-art mesoscale models used for wind energy applications today. At three of the sites the model intercomparison was verified with observations from nearby meteorological masts. The intercomparison was based on statistical properties of the wind for a number of heights at each site. The results show better performance of the models and a smaller inter-model spread offshore and aloft (2–4 % mean wind speed bias above 40 meters), and greater errors and more spread for inland sites and closer to the surface (up to 7–9 % wind speed bias). For the distributions of wind speed, wind direction, and wind shear only small deviations exist between the observations and the average of the models, but a small shift of the average wind speed distribution towards high wind speeds at Cabauw, and an underrepresentation of strong shear cases was observed. Although the model setup options were studied to determine a 'best practice', no significant indicator was found.

scholarly journals Correlation between weather conditions and COVID-19 pandemic in the southeast area of Iran

2021 ◽  
Author(s):  
Najmeh Parvaz ◽  
Fatemeh Amin ◽  
Ali Esmaeili Nadimi ◽  
Hadi Eslami
Keyword(s):  
Wind Speed ◽  
Weather Conditions ◽  
Inverse Association ◽  
Medical Sciences ◽  
Average Wind Speed ◽  
Positive Correlation ◽  
Affective Factors ◽  
Average Temperature ◽  
Average Wind ◽  
The Relationship

Abstract The Coronavirus disease 2019 (COVID-19) has influenced the live of all people around the world. This study analyzed the relationship between the weather elements (daily temperature, wind speed and humidity) and daily active, recovered and dead cases of covid-19 in Rafsanjan, in the southeast area of Iran. Covid-19 data and meteorological variables were obtained from 29 February 2020 to 20 March 2021 (386 days) from Rafsanjan University of Medical Sciences and Meteorological Organization of Iran, respectively. The results showed that there is a significant inverse association between daily average temperature with the number of daily active cases (r: -0.293, p<0.01), recovered cases (r: -0.301, p<0.01) and dead cases (r: -0.198, p<0.01). With decreasing the average wind speed, the number of daily active cases (r: -0.224, p<0.01), recovered cases (r: -0.232, p<0.01) and dead cases has been increased. A nonsignificant positive correlation between daily humidity average and daily active cases (r:0.033, p=0.518) and recovered cases (r:0.044, p=0.390), and significant positive correlation with the number of daily dead cases (r: 0.254, p<0.01) was observed. Therefore, temperature and wind speed can be considered as affective factors in COVID-19 as an auxiliary solution.

scholarly journals On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice

2013 ◽  
Vol 54 (62) ◽  
pp. 87-96 ◽  
Author(s):  
Marko Mäkynen ◽  
Bin Cheng ◽  
Markku Similä
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Barents Sea ◽  
Weather Prediction ◽  
Weather Conditions ◽  
The Arctic ◽  
Ice Thickness ◽  
Limited Area ◽  
Night Time ◽  
Modis Data

AbstractWe have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of Ts-based hi retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS hi charts are used.

scholarly journals Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2351 ◽  
Author(s):  
Yusupujiang Aimaiti ◽  
Wen Liu ◽  
Fumio Yamazaki ◽  
Yoshihisa Maruyama
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Ground Truth Data ◽  
Active Sensor ◽  
Optical Images ◽  
Comparison Results ◽  
Landslide Mapping ◽  
Aperture Radar

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

Measuring Sub-second Wind Velocity Changes for Agricultural Drift One Meter Above the Ground

2019 ◽  
Vol 35 (5) ◽  
pp. 697-704
Author(s):  
Matthew W. Schramm ◽  
H Mark Hanna ◽  
Matt J. Darr ◽  
Steven J. Hoff ◽  
Brian L. Steward
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Wind Velocity ◽  
Wind Direction ◽  
Weather Conditions ◽  
Spray Drift ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
Velocity Changes

Abstract. Agricultural spray drift is affected by many factors including current weather conditions, topography of the surrounding area, fluid properties at the nozzle, and the height at which the spray is released. During the late spring/summer spray seasons of 2014 and 2015, wind direction, speed, and solar radiation (2014 only) were measured at 10 Hz, 1 m above the ground to investigate conditions that are typically encountered by a droplet when released from a nozzle on an agricultural sprayer. Measurements of wind velocity as the wind passed from an upwind sensor to a downwind sensor were used to evaluate what conditions wind may be most likely to have a significant direction or speed change which affects droplet trajectory. For two individual datasets in which the average wind speed was 3.6 and 1.5 m/s (8.0 and 3.4 mi/h), there exists little linear correlation of wind speed or wind direction between an upwind and downwind anemometer separated by 30.5 m (100 ft). The highest observed correlation, resulting from a 12-s lag between the upwind and downwind datasets, was 0.29 when the average wind speed was 3.6 m/s (8.0 mi/h). Correlations greater than 0.1 were only found for wind speeds exceeding 3 m/s. Using this lag time, it was observed that the wind direction 30 s into the future had a 30% chance to be different by more than 20° from current conditions. A wind speed difference of more than 1 m/s (2.2 mi/h) from current conditions [mean wind speed was 3.6 m/s (8.0 mi/h)] was observed about 50% of the time. Analyzing 36 days of the 2014 and 2015 spray season wind velocity data showed that the most variability in wind direction occurred with wind speeds below 2 m/s (4.5 mi/h). Greater wind direction variability occurred in the mid-afternoon with higher solar radiation. Keywords: Sprayers, Spray drift, Spray droplets, Turbulence, Wind effects.

scholarly journals Weather Forecasting Based on Data-Driven and Physics Informed Reservoir Computing Models

2021 ◽  
Author(s):  
Yslam D. Mammedov ◽  
Ezutah Udoncy Olugu ◽  
Guleid A. Farah
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Weather Forecasting ◽  
Renewable Energy Sources ◽  
Weather Prediction ◽  
Prediction Method ◽  
Weather Conditions ◽  
Forecasting Analysis ◽  
Global Energy Supply ◽  
Atmospheric System

Abstract In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

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