Search Results (9)

Weather survival poses a significant challenge for the utilization of tethered balloons. The dynamic modeling of tethered balloon systems presents challenges due to the flexible nature of the cables and the intricate nature of gust forces. The present study introduces a new approach for modeling near-ground tethered balloon systems, which enables the analysis of their dynamic responses and performance evaluation under complex boundary conditions. First, finite cylindrical rigid bodies that are joined together by bushing forces to describe the dynamics of the tethered cable. The properties of the flexible cables under severe bending and translation can be illustrated by the dynamics model. Second, a three-dimensional dynamics model based on the multibody dynamics theory is created to deal with the interaction of the tethered balloon system and flexible cables. The dynamic responses of the tethered balloon system under challenging operating conditions are investigated, focusing on the number of cable segments and the place and direction of gust wind impacts. This model allows for precise assessment and optimization of the system’s overall performance to improve weather resistance. The results show that compared to computational fluid dynamics (CFDs) methods, the multibody system dynamics-based balloon model improved the solution time by 80%, with a pitch angle deviation of only 0.0016°. Moreover, the bushing model effectively reduced cable force and enabled accurate reflection of the system’s motion characteristics. Full article

Stable boundary layer height (SBLH) is an important parameter to characterize the characteristics and vertical structure of the nocturnal lower atmosphere at night. The distribution of SBLH has obvious spatial and temporal differences, and there are many meteorological factors affecting the SBLH, but at present, there are few quantitative studies on the effects of near-surface meteorological factors on the SBLH in the desert hinterland. This study was based on GPS sounding balloon data, near-surface meteorological observation data, and ERA5 data from Tazhong Station (TZ) in the Taklamakan Desert (TD) collected in July 2017, 2019, and 2021. The variation characteristics of the SBLH and its relationship with near-surface meteorological factors are described. We quantitatively analyzed the degree of influence of near-surface meteorological factors affecting the SBLH and verified it using a model. The study also elucidates the possible formation mechanism of the SBLH in the TD hinterland. The SBLH in the TD hinterland trended upward in July 2017, 2019, and 2021, which is consistent with the changes in meteorological factors, according to the near-surface meteorological observation and ERA5 data. Therefore, we think that an inherent connection exists between near-surface meteorological factors and the SBLH. The results of correlation analysis show that complex internal connections and interactions exist among the meteorological factors near the ground; some thermal, dynamic, and other meteorological factors strongly correlate with the SBLH. Having established the change in SBLH (ΔSBLH) and in major thermal, dynamic, and other meteorological factors (Δ), the linear regression equation between them revealed that near-surface meteorological factors can affect the SBLH. The dynamic factors have a stronger influence on the ΔSBLH than thermal and other factors. The results of model validation based on the variable importance projection (VIP) also confirmed that the SBLH in the TD hinterland is jointly affected by dynamic and thermal factors, but the dynamic factors have a stronger impact. The mechanism through which the SBLH forms is relatively complex. At night, surface radiative cooling promotes the formation of a surface inversion layer, and low-level jets strengthen wind shear, reducing atmospheric stability. The combined effects of heat and dynamics play an important role in dynamically shaping the SBLH. This study helps us with accurately predicting and understanding the characteristics of the changes in and the factors influencing the SBLH in the TD hinterland, providing a reference for understanding the mechanism through which the SBLH forms in this area. At the same time, it provides a scientific basis for regional weather and climate simulation, meteorological disaster defense, air quality forecasting, and model parameterization improvement. Full article

One of the crucial branches of activity at the Łukasiewicz Research Network—Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth’s atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1–2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices–for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system’s anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV’s rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode. Full article

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (MobileNet) was trained to identify key features of various satellite images that contained fire or without fire. Then, the trained system is used to classify new satellite imagery and sort them into fire or no fire classes. A cloud-based development studio from Edge Impulse Inc. is used to create a NN model based on the transferred learning algorithm. The effects of four hyperparameters are assessed: input image resolution, depth multiplier, number of neurons in the dense layer, and dropout rate. The computational cost is evaluated based on the simulation of deploying the neural network model on an Arduino Nano 33 BLE device, including Flash usage, peak random access memory (RAM) usage, and network inference time. Results supported that the dropout rate only affects network prediction performance; however, the number of neurons in the dense layer had limited effects on performance and computational cost. Additionally, hyperparameters such as image size and network depth significantly impact the network model performance and the computational cost. According to the developed benchmark network analysis, the network model MobileNetV2, with 160 × 160 pixels image size and 50% depth reduction, shows a good classification accuracy and is about 70% computationally lighter than a full-depth network. Therefore, the proposed methodology can effectively design an ML application that instantly and efficiently analyses imagery from a spacecraft/weather balloon for the detection of wildfires without the need of an earth control centre. Full article

In recent years, NOAA Earth System Research Laboratories (ESRL) have been launching very high quality and high resolution ozonesondes from eight sites across the globe: Antarctica; Greenland; American Samoa; Fiji; and several sites in USA (Alabama, California, Colorado and Hawai’i). These locations collectively cover the tropics, mid-latitudes and polar regions. The balloons provide in situ measurements approximately every second throughout their vertical ascent and descent in the troposphere, tropopause and stratosphere (up to ~30–35 km altitude). This unique high quality and publicly archived dataset allows direct inter-comparisons between various new and old techniques for analyzing the troposphere/stratosphere transitions that were not previously possible. With this in mind, we have analyzed one complete year (2016) of ozonesonde data from these eight locations in terms of several definitions of the tropopause. We find a surprising cohesiveness between many of the independent definitions of the tropopause that does not appear to have been properly recognized until now. These definitions appear to hold over all eight locations—from the tropics to the poles—for all seasons. Full article

Super Typhoon Lekima (2019) was the third strongest tropical cyclone (TC) that has ever made landfall in Jiangsu and Zhejiang Provinces, China. During its passage, the storm resulted in catastrophic disasters to mainland China, which made it one of the costliest typhoons in Chinese history. This article presents an observational study on the thermodynamic and kinematic structures of Typhoon Lekima at landfall, mainly based on measurements from radiosonde balloons that were released at different periods from a coastal site located with a nearest distance of ~200 km to the track of Lekima. Observations from a weather radar are first discussed to demonstrate the horizontal structure of the typhoon, and the concentric eyewall structure of Lekima is highlighted. Then, Lekima’s pressure field is analyzed, and a two-dimensional model is proposed to quantify both the radial and height dependence of the pressure distribution. The subsequent analysis focuses on the warm-core like structure in the rainband region. The maximum perturbation of measured equivalent potential temperature with respect to the one of environment reached 25 K at ~5 km. Some factors contributing to the warm-core like feature are discussed. The authors of this article finally investigate the TC wind field. Low-level jets of vertical wind profile in rainband areas were observed at heights of around 1–3 km. Dramatic wind shears were observed in the range of 15–17 km where the outflow layer existed, while wind became considerably weak at the tropopause. Full article

In order to verify the accuracy of precipitable water vapor (PWV) in remote sensing and reanalysis datasets under different climatic conditions and ensure the reliability of analysis results, the performances of ERA-5 reanalysis PWV data and the Atmospheric Infrared Sounder (AIRS) remotely-sensed PWV data were tested in the northern Qinghai-Tibet Plateau by using weather balloon radiosonde data from meteorological stations from 2002 to 2016. The coincidence degree of total cloud cover was also verified, and then the PWV data precision with different levels of cloud cover was analyzed. The results show that: (1) Both ERA-5 and AIRS data underestimate PWV in the studied high plateau region, and higher altitude leads to greater deviation. (2) Compared with AIRS data, ERA-5 data have better consistency with radiosonde data in PWV and total cloud cover. (3) For the long-term trend of PWV, the ERA-5 data are the opposite to the radiosonde data with a clear sky, but both datasets showed a significant increasing trend in cloudy skies. It can be concluded that in high altitude areas, the ERA-5 data can be used for general analysis, but are not well qualified to reflect the changing trend of PWV under climate change. Full article

The lowest portion of the Earth’s atmosphere, known as the atmospheric boundary layer (ABL), plays an important role in the formation of weather events. Simple meteorological measurements collected from within the ABL, such as temperature, pressure, humidity, and wind velocity, are key to understanding the exchange of energy within this region, but conventional surveillance techniques such as towers, radar, weather balloons, and satellites do not provide adequate spatial and/or temporal coverage for monitoring weather events. Small unmanned aircraft, or aerial, systems (sUAS) provide a versatile, dynamic platform for atmospheric sensing that can provide higher spatio-temporal sampling frequencies than available through most satellite sensing methods. They are also able to sense portions of the atmosphere that cannot be measured from ground-based radar, weather stations, or weather balloons and have the potential to fill gaps in atmospheric sampling. However, research on the vertical sampling scales for collecting atmospheric measurements from sUAS and the variabilities of these scales across atmospheric phenomena (e.g., temperature and humidity) is needed. The objective of this study is to use variogram analysis, a common geostatistical technique, to determine optimal spatial sampling scales for two atmospheric variables (temperature and relative humidity) captured from sUAS. Results show that vertical sampling scales of approximately 3 m for temperature and 1.5–2 m for relative humidity were sufficient to capture the spatial structure of these phenomena under the conditions tested. Future work is needed to model these scales across the entire ABL as well as under variable conditions. Full article

This paper assesses impacts of three wind farms in northern Illinois using land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites for the period 2003–2013. Changes in LST between two periods (before and after construction of the wind turbines) and between wind farm pixels and nearby non-wind-farm pixels are quantified. An areal mean increase in LST by 0.18–0.39 °C is observed at nighttime over the wind farms, with the geographic distribution of this warming effect generally spatially coupled with the layout of the wind turbines (referred to as the spatial coupling), while there is no apparent impact on daytime LST. The nighttime LST warming effect varies with seasons, with the strongest warming in winter months of December-February, and the tightest spatial coupling in summer months of June-August. Analysis of seasonal variations in wind speed and direction from weather balloon sounding data and Automated Surface Observing System hourly observations from nearby stations suggest stronger winds correspond to seasons with greater warming and larger downwind impacts. The early morning soundings in Illinois are representative of the nighttime boundary layer and exhibit strong temperature inversions across all seasons. The strong and relatively shallow inversion in summer leaves warm air readily available to be mixed down and spatially well coupled with the turbine. Although the warming effect is strongest in winter, the spatial coupling is more erratic and spread out than in summer. These results suggest that the observed warming signal at nighttime is likely due to the net downward transport of heat from warmer air aloft to the surface, caused by the turbulent mixing in the wakes of the spinning turbine rotor blades. Full article