Search Results (22)

Pollen allergies represent a growing public health concern that necessitates enhancements to the network of instruments and modeling calculations in order to facilitate a more profound comprehension of pollen transportation. The Beenose instrument quantifies the light scattered by particles that traverse a laser beam at four angles. This methodology enables the differentiation of pollen particles from other particulate matter, predominantly mineral and carbonaceous in nature, thereby facilitating the retrieval of pollen concentrations. The Beenose instrument has been installed on the tourist balloon known as “Ballon de Paris” in a large park situated in the southwest of Paris, France. The measurement period is from April to November 2024, coinciding with the pollen seasons of trees and grasses. The balloon conducts numerous flights per day, reaching an altitude of 150 m when weather conditions are conducive, which occurs approximately 58% of the time during this period. The data are averaged to produce vertical profiles with a resolution of 30 m. Concentrations of the substance decrease with altitude, although a secondary layer is observed in spring. This phenomenon may be attributed to the presence of emissions from a proximate forest situated at a higher altitude. The average decrease in concentration of 11 ± 8% per 10 m is consistent with the findings of previous studies. The long-term implementation of Beenose measurements on this tourist balloon is intended to enhance the precision of the results and facilitate the differentiation of the various parameters that can influence the vertical transportation of pollen. Full article

Weather balloons are a popular tool to obtain atmospheric data. One of the biggest advantages of using this type of vehicle for scientific research is their inexpensiveness, as they are only composed of an inflated envelope, a parachute, and a sonde. However, their flight is dependent on the atmospheric conditions, and their life cycle is short. Thus, altitude control for weather balloons, along with trajectory planning, is a major area of interest, as it would allow one to mitigate the disadvantages while maintaining the benefits. This article presents a novel, efficient, lightweight, and cost-effective framework for weather balloon control and path planning. The proposed solution integrates a P-D cascade controller for altitude control, adapted specifically to the dynamics and actuation constraints of weather balloons, with a wind-based trajectory planner built on the A* algorithm. To the best of the authors’ knowledge, this planner is the first to incorporate wind constraints in a grid-based search tailored for weather balloon navigation. By commanding the ballast release for ascent and helium venting for descent, the developed control solution proves efficient and robust in simulation, guiding the balloon to reach defined goals while traveling through predetermined waypoints. However, it demonstrates limitations in maintaining the balloon over a fixed area. Full article

The D-region ionosphere (60–100 km altitude) is critical for radio communication and space weather research but cannot be easily measured because it is too low for satellites and too high for balloons. The most effective technique is to remotely sense by measuring Very-Low-Frequency (VLF, 3–30 kHz) waves emitted from man-made transmitters, a technique that was traditionally utilized to estimate the average ionospheric condition between the transmitter and receiver. Recently, various methods have been proposed to remotely sense the D-region ionosphere in large areas using network observation of VLF transmitter signals. The key component of these methods is the VLF propagation model, and the Long-Wavelength Propagation Capability (LWPC) model is employed in most cases due to its relatively fast computation speed. However, it is still too long and thus insufficient for real-time remote sensing. To overcome this limitation, we have proposed a neural network model to replace the LWPC model and to shorten the computation time of VLF propagation. This model is specifically obtained using the transfer learning method by retraining the last three layers of the well-established VGG16, GoogLeNet, and ResNet architectures. We have tested different methods to organize the input data for these neural network models and verified their performance using the validation dataset and real measurements. Among the three models, GoogLeNet outperforms the other two, and the root mean squared error (RMSE), with respect to LWPC results, is as low as 0.334. Moreover, the proposed neural network model can dramatically reduce the computation time. The computation time to calculate the signal distribution near the transmitter is 1184 s if one uses the LWPC model but 0.87 s if the present neural network model is used. The performance of this model is also excellent for ionospheric conditions that are not included in the validation dataset. Therefore, this model is robust and can be used to remotely sense, in real time, the D-region ionosphere in large areas, as well as various scientific and engineering needs. Full article

This article describes the main features of the impacts of global observations on the reduction of errors in the data assimilation (DA) cycle carried out in the Brazilian Global Atmospheric Model (BAM) at Center for Weather Forecast and Climate Studies [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)]. These results show the importance of studying and evaluating the contribution of each observation to the DA system, therefore, two experiments (exp1/exp2) were performed with different configurations of the BAM model, with exp2 presenting the best fit between the Gridpoint Statistical Interpolation (GSI) and BAM systems. The BAM model was validated by the statistical metrics of root mean-square error and correlation anomaly, but this validation is not explored in this paper. A metric was applied that does not depend on the adjoint-based method, but only on the residuals that are made available in the GSI system for the observation space, given by the total impact, the fractional impact and the fractional beneficial impact. In general, the average daily showed that the observations of the global system that contribute most to the reduction of errors in the DA cycle are from the pilot balloon data (−3.54/−3.45 J kg⁻¹)and the profilers (−2.13/−1.97 J kg⁻¹), and the smallest contributions came from the land (−0.28/−0.29 J kg⁻¹) and sea (−0.44/−0.44 J kg⁻¹) surfaces. The same pattern was observed for the synoptic times presented. However, when verifying the fraction of the impact by each type of observation, it was found that the radiance data (64.88/30.30%), followed by radiosondes (14.85/27.42%) and satellite winds (11.03/22.70%), are the most important fractions for both experiments. These results show that the DA system is working to generate the best analyses at the research center and that the deficiencies found in some observations can be adjusted to improve the development of the GSI and the BAM model, since together, the entire database used is evaluated, as well as the forecast model itself, indicating the relationship between the assertiveness of the atmospheric model and the DA system used at the research center. Full article

Wind estimation is crucial for studying the atmospheric boundary layer. Traditional methods such as weather balloons offer limited in situ capabilities; besides an Air Data System (ADS) combined with inertial measurements and satellite positioning is required to estimate the wind on fixed-wing drones. As pressure probes are an important constituent of an ADS, they are susceptible to malfunctioning or failure due to blockages, thus affecting the capability of wind sensing and possibly the safety of the drone. This paper presents a novel approach, using low-fidelity aerodynamic models of drones to estimate wind synthetically. In our work, the aerodynamic model parameters are derived from post-processed flight data, in contrast to existing approaches that use expensive wind tunnel calibration for identifying the same. In sum, our method integrates aerodynamic force and moment models into a Vehicle Dynamic Model (VDM)-based navigation filter to yield a synthetic wind estimate without relying on an airspeed sensor. We validate our approach using two geometrically distinct drones, each characterized by a unique aerodynamic model and different quality of inertial sensors, altogether tested across several flights. Experimental results demonstrate that the proposed cross-platform method provides a synthetic wind velocity estimate, thus offering a practical backup to traditional techniques. 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

The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection. Full article

Astronomical seeing is important for monitoring the atmospheric environment, observation scheduling and management, and selecting astronomical sites. This study first attempts to establish a near-global astronomical seeing map by employing the fifth European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) data combined with the estimated model. Then, some example sites’ results from ERA5 were compared against the astronomical seeing results from the balloon-borne microthermal measurements and the differential image motion monitor (DIMM) instrument. The global astronomical seeing variations exhibit large spatial dependence. The best seeing areas are generally discerned over the mid-latitude regions, consistent with the regions of the European Southern Observatory and Tibetan Plateau. In addition, the astronomical seeing values of the Tibetan Plateau in spring are better than in the other seasons. The site results from ERA5 show that the astronomical seeing values of some example sites are generally consistent with the measurements. Overall, the global astronomical seeing map presented in this study can provide a reference and basis to further understand the astronomy site selection and optoelectronics equipment observation path selection. Full article

Halo-Photonics StreamLine XR Doppler LiDAR measurements are performed using several scan configurations (Velocity Azimuth Display-VAD and Doppler Beam Swing-DBS) and elevation angles of 60° and 80°. The measurements are compared to wind observations conducted by various in situ instruments (tethered balloon, meteorological mast, and radiosondes). Good agreement is obtained, with R² over 0.90 for wind speed and a standard error ≤ 18.6° for wind direction. The best performance was attained for lower elevation scans (60°), which is consistent with the higher spatial horizontal homogeneity exhibited by lower angle scans. VAD and DBS scans performed almost equally well with slight advantage for VAD in higher altitudes and for DBS for lower altitudes. The boundary layer structure along a diurnal cycle is analyzed by utilizing retrieved backscatter data and wind measurements in conjunction with Weather Research and Forecast (WRF) simulations. The presence of multiple inversions which allow the coexistence of different layers within the studied profile is also verified using data acquired by several radiosondes. Synergic use of LiDAR data with WRF simulations for low SNR regions is demonstrated. Full article

Cosmic rays were discovered by the Austrian physicist Victor Hess in 1912 in a series of balloon experiments performed between 1911 and 1912. Cosmic rays are an integral part of fundamental and applied research in the field of solar–terrestrial physics and space weather. Cosmic ray data are applied in different fields from the discovery of high-energy particles coming to Earth from space, and new fundamental symmetries in the laws of nature, to the knowledge of residual matter and magnetic fields in interstellar space. The properties of interplanetary space are determined from intensity variations, angular distribution, and other characteristics of galactic cosmic rays. The measure of cosmic ray flux intensity variability is used as one of the significant space weather factors. The negative impact of cosmic rays is also known. The negative impact can significantly increase the level of radiation hazard and pose a threat to astronauts, crews, and passengers of high-altitude aircraft on polar routes and to modern space equipment. Therefore, methods aimed at timely detection and identification of anomalous manifestations in cosmic rays are of particular practical relevance. The article proposes a method for analyzing cosmic ray variations and detecting anomalous changes in the rate of galactic cosmic ray arrival to the Earth. The method is based on a combination of the Autoencoder neural network with wavelet transform. The use of non-linear activation functions and the ability to flexibly change the structure of the network provide the ability of the Autoencoder to approximate complex dependencies in the recorded variations of cosmic rays. The article describes the numerical operations of the method implementation. Verification of the adequacy of the neural network model is based on the use of Box–Ljung Q-statistics. On the basis of the wavelet transform constructions, data-adaptive operations for detecting complex singular structures are constructed. The parameters of the applied threshold functions are estimated with a given confidence probability based on the $\alpha$-quantiles of Student’s distribution. Using data from high-latitude neutron monitor stations, it is shown that the proposed method provides efficient detection of anomalies in cosmic rays during increased solar activity and magnetic storms. Using the example of a moderate magnetic storm on 10–11 May 2019, the necessity of applying different methods and approaches to the study of cosmic ray variations is confirmed, and the importance of taking them into account when making space weather forecast is shown. Full article

Lightning discharges are the electrical production in thunderclouds. They radiate the bulk of radio signals in the very low-frequency and low-frequency (VLF/LF) that can be detected by ground-based receivers. One kind of special intra-cloud lightning discharges known as narrow bipolar events (NBEs) have been shown to be rare but closely linked to the convective activity that leads to hazardous weather. However, there is still lack of understanding on the meteorological conditions for thunderstorm-producing NBEs, especially for those of negative polarity, due to their rare occurrence. In this work, we aim to investigate what meteorological and electrical conditions of thunderclouds favor the production of negative NBEs. Combining with the VLF/LF radio signal measured by Jianghuai Area Sferic Array (JASA), S-band Doppler radar observation and balloon sounding data, two mid-latitude thunderstorms with outbreaks of negative NBEs at midnight in East China were analyzed. The comparison with the vertical radar profile shows that the bursts of negative NBEs occurred near thunderclouds with overshooting tops higher than 18 km. Manifestation of negative NBEs is observed with a relatively low spectrum width near thundercloud tops. Our findings suggest that the detection of negative NBEs would provide a unique electrical means to remotely probe overshooting tops with implications for the exchange of troposphere and stratosphere. Full article

In recent decades, efforts to investigate atmospheric circulation patterns have predominantly relied on either semi-empirical datasets (i.e., reanalyses) or modeled output (i.e., global climate models, GCMs). While both approaches can provide important insights, there is a need for more empirical data to supplement these approaches. In this paper, we demonstrate how the application of relatively simple calculations to the basic measurements from a standard weather balloon radiosonde can provide a vertical profile of the horizontal atmospheric mass fluxes. These mass fluxes can be resolved into their meridional (north/south) and zonal (east/west) components. This provides a new useful empirical tool for analyzing atmospheric circulations. As a case study, we analyze the results for a selected five stations along a fairly constant meridian in the North Atlantic sector from 2015–2019. For each station, we find the atmospheric mass flux profiles from the lower troposphere to mid-stratosphere are surprisingly coherent, suggesting stronger interconnection between the troposphere and stratosphere than previously thought. Although our five stations span a region nominally covered by the classical polar, Ferrel and Hadley meridional circulation cells, the results are inconsistent with those expected for polar and Ferrel cells and only partially consistent with that of a Hadley cell. However, the region is marked by very strong prevailing westerly (west to east) mass fluxes for most of the atmosphere except for the equatorial surface easterlies (“trade winds”). We suggest that the extension of the techniques of this case study to other stations and time periods could improve our understanding of atmospheric circulation patterns and their time variations. 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

Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coefficients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coefficient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work. Full article