Atmospheric data collection device

Abstract

An atmospheric data collection device in the form of a recoverable drone includes a body with a retractable wing assembly, a plurality of input devices, a controller, a power supply, and a network interface. The input devices collect telemetry data such as temperature, pressure, humidity, wind speed, and radiation during flight. The retractable wing assembly dynamically adjusts during descent to refine the glide path and steer the device toward a predefined or dynamically updated target landing location based on real-time telemetry data. The controller can be trained on historical flight data to optimize ascent, descent, and landing trajectories under varying atmospheric conditions. The device transmits collected telemetry data to remote systems via wireless communication protocols and stores flight and environmental data for post-flight analysis.

Description

Atmospheric Data Collection DeviceFIELD

[0001] The present specification relates generally to atmospheric data collection and devices therefor.BACKGROUND

[0002] Weather balloons, also known as radiosondes, represent a cornerstone technology in the field of meteorology. These devices are essential for routine atmospheric monitoring and are used globally by meteorological organizations. The current practice and operational standards surrounding weather balloons include:

[0003] Frequent Launches: Weather balloons can be launched twice daily at many locations worldwide, usually at 00:00 and 12:00 UTC. This schedule is coordinated internationally to align with the global synoptic meteorological observation timetable.

[0004] Extensive Network: Over 800 radiosonde stations operate globally. Each station typically conducts at least one launch daily, with many executing two launches per day. This extensive network ensures a broad coverage of atmospheric data collection.

[0005] Adaptability to Weather Events: Additional launches often occur in response to specific meteorological events such as approaching hurricanes or other severe weather conditions, with the aim of gathering more detailed and timely data.

[0006] Annual Launch Volumes: An estimated total of over 580,000 launches occur annually, based on a conservative calculation of two daily launches from each of the 800+ stations. This figure does not account for extra launches during special weather situations.

[0007] Global Variability: The frequency and number of launches vary depending on the country's size, geographical diversity, and available resources for meteorological observation.I

[0008] While this method of atmospheric data collection is invaluable for accurate weather forecasting and climate research, it presents significant challenges:

[0009] Environmental Impact: The high frequency of balloon launches results in considerable material waste, as most weather balloons are not recovered after use.

[0010] Operational Costs: The need for regular launches entails ongoing costs for materials, labor, and logistics.

[0011] Data Gaps: Despite the extensive network, there are still geographical and temporal gaps in data collection, especially over oceans and remote areas.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 shows a perspective view an atmospheric data collection device with the wings in an extended position.

[0013] Figure 2 shows a top elevation view the atmospheric data collection device of Figure 1 .

[0014] Figure 3 shows a front elevation view the atmospheric data collection device of Figure 1 .

[0015] Figure 4 shows a right-side elevation view the atmospheric data collection device of Figure 1 .

[0016] Figure 5 shows a left-side elevation view the atmospheric data collection device of Figure 1.

[0017] Figure 6 shows a rear elevation view the atmospheric data collection device of Figure 1 .

[0018] Figure 7 shows a bottom elevation view of the atmospheric data collection device of Figure 1 .

[0019] Figure 8 shows a perspective view of the wings of the device of Figure 1 .

[0020] Figure 9 shows the retractable wing assembly of the device of Figure 1 .

[0021] Figure 10 shows the device of Figure 1 with the wings in a retracted position.

[0022] Figure 11 shows a block diagram of the controller of the device of Figure 1 .

[0023] Figure 12 shows a flowchart depicting a method of atmospheric data collection.

[0024] Figure 13 shows example flight paths of the device of Figure 1 .DESCRIPTION

[0025] An aspect of the specification provides a method of atmospheric data collection, including: preparing a drone-based atmospheric collection device for launch by verifying system components and orienting wings into a launch-ready position; defining a target landing location for the device, the target landing location being geographical area for recovery; ascending the device to an apex altitude; collecting telemetry data during flight using one or more sensors, the telemetry data including atmospheric conditions determining if the apex altitude has been reached by comparing the current altitude to a predefined apex target; descending the device from the apex altitude by orienting the wings into a glide-position and steering toward the target landing location ; monitoring the descent to determine whether the device has reached the target landing location or completed its descent; and ending the method upon confirmation that the descent is complete or the target location is reached.

[0026] An aspect of the specification provides a method of atmospheric data collection, including: preparing a drone-based atmospheric collection device for launch by verifying system components and orienting wings into a launch-ready position; defining a predefined primary target landing location as well as predefining alternate target landing locations for the device, the target landing locations being pre-surveyed geographical areas for recovery; ascending the device to an apex altitude; collecting telemetry data on ascent and during descent flight using one or more sensors, the telemetry data including atmospheric conditions determining which predefined landing location has the optimum probability of being reached and dynamically changing from the primary landing location to an alternate landing location should the extantatmospheric conditions determine that one of the alternate landing locations has a higher probability of being reached, if the apex altitude has been reached by comparing the current altitude to a predefined apex target; descending the device from the apex altitude by orienting the wings into a glide-position and steering toward the optimum target landing location with the highest probability of successfully reaching it; passively monitoring for any air traffic that may enter within a predetermined range ‘bubble’ of the drone at some point during its descent path, taking avoiding action to keep the traffic outside of its predefined ‘bubble’, dynamically reselecting its landing location based on the highest probability of achievement, monitoring the descent to determine whether the device has reached the target landing location or completed its descent; completing its flight by conducting a belly landing or parachute landing, and ending the method upon confirmation that the descent is complete or the target location is reached.

[0027] An aspect of the specification provides a method, wherein the telemetry data include one or more of temperature, pressure, humidity, wind speed and direction, solar radiation data, ozone, CO2, Methane or any other atmospheric gas.

[0028] An aspect of the specification provides a method or claim 2, wherein ascending the device to an apex altitude further includes adjusting the ascent trajectory using real-time wind speed and direction data.

[0029] An aspect of the specification provides a method, wherein the wings are retractable and are oriented into a launch-ready position by a wing sweep controller.

[0030] An aspect of the specification provides a method, further including transmitting telemetry data to a remote location during flight using a wireless network interface.

[0031] An aspect of the specification provides a method, wherein machine learning techniques are applied to select the optimum pre-defined landing location, optimize the flight path, the machine learning techniques including supervised learning, unsupervised learning, or reinforcement learning algorithms. This is particularly relevant to the dynamic selection of the optimum landing location should air traffic require modifications to the descent glide path.

[0032] An aspect of the specification provides a method, further including storing telemetry data and flight path information in non-volatile memory for post-flight analysis.

[0033] An aspect of the specification provides a method, wherein the pre-defined target landing locations are dynamically updated during ascent and flight based on real-time atmospheric conditions where the optimum landing location is dynamically selected.

[0034] An aspect of the specification provides a method, wherein the monitoring of the descent includes using a Global Positioning System (GPS) sensor to guide the device toward the target landing location.

[0035] An aspect of the specification provides a method, wherein the monitoring of the ascent and descent includes using an Automatic Dependent Surveillance-Broadcast (ADS-B) sensor to monitor air traffic within a specified radius of the drone and adjusting the descent flight path to maneuver to avoid said traffic if it is likely that said traffic will enter into the pre-defined avoidance bubble (based on a preset horizontal and vertical minimum point of approach closest point to guide the device toward the target landing location.

[0036] An aspect of the specification provides a method, further including performing a system self-check prior to launch to ensure operational readiness of input sensors and output components.

[0037] An aspect of the specification provides a method, further including adjusting the descent path by dynamically varying the sweep angle of the wings in response to real-time wind speed, wind direction, and atmospheric pressure data, thereby refining the glide trajectory and enhancing landing accuracy.

[0038] An aspect of the specification provides a method, wherein the processor applies a machine learning model trained on historical flight and extant sensed meteorological variables during the ascent and telemetry data to predict atmospheric conditions encountered during descent, and dynamically adjusts the wings' orientation or angle of attack based on thosepredictions to achieve a more efficient, controlled and stable approach to the target landing location.

[0039] An aspect of the specification provides a method, further including continuously updating a predicted landing footprint based on real-time GPS positioning and sensed meteorological variables, and adjusting the descent path by varying flight control surfaces to compensate for shifting wind patterns or thermal gradients.

[0040] An aspect of the specification provides a method, wherein the processor references previously stored descent profiles tailored to specific environmental conditions, selecting an appropriate profile and modifying the wing sweep configuration accordingly, thereby improving the reliability of guided landings under varying weather scenarios.

[0041] An aspect of the specification provides a method, further including communicating the inflight atmospheric data to a remote command station, receiving updated targeting commands or refined descent parameters from the station, and implementing those commands during descent, thereby allowing dynamic human-in-the-loop adjustments to the glide path when unexpected air traffic or atmospheric changes occur.

[0042] An aspect of the specification provides a device for atmospheric data collection, including: a body including a fuselage, a nose, and a tail, the tail including a plurality of fins for stabilizing the device during flight; a retractable wing assembly coupled to the body, the retractable wing assembly including: a first wing and a second wing moveable between an extended position and a retracted position, and a wing sweep mechanism to adjust the position of the wings; a plurality of input devices to collect telemetry data including atmospheric conditions during flight; a controller including a processor, a memory, and at least one output device, the processor to receive telemetry data from the input devices; control the wing sweep mechanism to orient the wings during flight for guiding the device toward a predefined target landing location; a power supply to provide electrical energy to the components of the device; and a network interface to transmit collected telemetry data and / or receive control commands during flight.

[0043] An aspect of the specification provides a device wherein the input devices include a GPS sensor for determining position and altitude as well as wind speed and direction; and at least one of a thermometer for measuring temperature; a barometric pressure sensor; a hygrometer for measuring humidity;, a radiation sensor for measuring solar radiation; and a pitot sensor for determining dynamic pressure.

[0044] An aspect of the specification provides a device, wherein the predefined target landing location is dynamically updated during flight in response to real-time wind speed, wind direction, and GPS data collected by the input devices.

[0045] An aspect of the specification provides a device, wherein the controller adjusts the flight path dynamically based on real-time atmospheric telemetry data collected during flight.

[0046] An aspect of the specification provides a device, wherein the controller is configured to employ a machine learning algorithm trained on historical flight data to predict atmospheric changes during descent and to adjust the wing orientation in real time to maintain a stable glide path toward the dynamically updated optimum landing location.

[0047] An aspect of the specification provides a device, further including one or more aerodynamic control surfaces on the wings, wherein the controller selectively actuates the aerodynamic control surfaces based on real-time wind shear and turbulence data to refine the descent trajectory.

[0048] An aspect of the specification provides a device, wherein the memory stores a plurality of predefined descent profiles, each associated with a set of atmospheric conditions, and the controller is configured to select and modify one of the predefined profiles during flight to achieve a more accurate approach to the target landing location.

[0049] An aspect of the specification provides a device, further including a wireless communication interface configured to receive updated guidance commands from a remote operations center during descent, wherein the controller responds to these commands todynamically alter the wing sweep angle and adjust flight path parameters as the device nears the target landing location.

[0050] An aspect of the specification provides a device, wherein the controller continuously compares predicted landing coordinates against actual GPS coordinates measured in-flight, iteratively refining the descent path by altering the angle of attack, thereby improving positional accuracy and repeatability of the landing procedure.

[0051] An aspect of the specification provides a device, wherein the network interface transmits telemetry data to a remote system in real time via a wireless communication protocol selected from Wi-Fi, Bluetooth, 4G, 5G, or satellite communication.

[0052] Certain embodiments will now be discussed to elaborate on the foregoing. Referring now to Figures 1 -10, an atmospheric data collection device is indicated generally at 100. A person skilled in the art will recognize that device 100 is generally in the form factor of a drone or unmanned aerial vehicle or UAV that can be recovered after flight. Device 100 includes a nose 104, a body 108 and a tail 112.

[0053] The materials used to construct device 100 are selected to balance durability, weight, and environmental considerations. In one embodiment, device 100 is constructed from a combination of carbon fiber-reinforced polymer and lightweight aluminum alloys, which provide high strength-to-weight ratios suitable for atmospheric flight. For applications prioritizing environmental sustainability, biodegradable composites or recycled polymers may be used for non-structural components. Additionally, certain parts of device 100, such as the fins 128 and wing box 120, may incorporate advanced polymers or thermoplastics to withstand aerodynamic stresses and varying atmospheric conditions. These materials are chosen to enhance the device's recoverability and reusability while maintaining efficient performance across a range of environmental conditions.

[0054] Body 108 includes a fuselage 116 proximal to tail 112 and a wing box 120 between fuselage 116 and nose 104. A first wing 124-1 and a second wing 124-2 depend from wing box 120. (Collectively, wing 124-1 and wing 124-2 are referred to as wings 124 and generically aswing 124. This nomenclature repeats.) Tail 1 12 tapers into a tail cone 132. Tail 112 includes a plurality of fins 128 for stabilizing device 100 during flight. A pitot tube 136 projects from wing box 120 towards nose 104 and the front of device 100.

[0055] Wing box 120 is mechanically structured to handle the stresses and strains associated with the movement and positioning of the wings 124, especially during high-speed flight and maneuvers. As best seen in Figure 9, the wing box 120 contains the mechanisms that control the wing sweep (represented by arrows “A” in Figure 9), including pivot points 140, actuators, and the linkage to control systems (not shown). The wing box 120 is designed to be aerodynamically efficient to reduce drag and maintain the overall performance device 100. In a present embodiment, the wing box 120 also houses a controller 200, however in other embodiments, controller 200 can be located elsewhere within the interior of device 100. The wing box 120 integrates with the rest of the systems of device 100, including electrical and flight control systems that are responsive to controller 200.

[0056] Figure 1 1 shows a schematic diagram of a non-limiting example of controller 200. Controller 200 includes at least one input device 204. Input from devices 204 is received at a processor 208 which in turn controls at least one output device 212. In the present embodiment, input devices 204 include one or more of:

[0057] A Global Positioning System (GPS) input device 204-1 to track the position, altitude, of device 100 and to help calculate wind speed and direction.

[0058] A thermometer input device 204-2 to measure the temperature of the air. Device 204-2 is usually shielded from direct sunlight to avoid heating that could distort the measurements.

[0059] A barometer input device 204-3 to measure atmospheric pressure. As device 100 ascends, the air pressure decreases. This data is used for understanding weather patterns and making forecasts.

[0060] A hygrometer input device 204-4 to measure the humidity of the air. This sensor can help determine the amount of water vapour present in the atmosphere for predicting precipitation and cloud formation.

[0061] A pitot sensor input device 204-5 which measures the dynamic pressure in pitot tube 136 to ascertain the speed of device 100 during flight.

[0062] A wind speed and / or direction input device 204-6 which can be independent of pitot sensor input device 204-5. Device 204-6 can measure the speed of the wind and its direction within the immediate vicinity of device 100.

[0063] A radiation sensor input device 204-7, which can measure solar radiation and other types of atmospheric radiation.

[0064] It is to be understood that not all of the specific input devices 204 mentioned herein may be necessary. Furthermore, any other desired input device 204-n (such as an ozone sensor, CO2 sensor, etc...) can also be included in addition to or in lieu of the other specifically mentioned input device 204. Another potential input device 204-n can be an Automatic Dependent Surveillance-Broadcast (ADS-B) input device to track the position, speed, direction and closest point of approach of any air traffic to help with air traffic deconfliction.

[0065] Likewise one or more output devices 212 under the control of processor 208 can be provided. In the present embodiment, output devices 212 include:

[0066] A wing sweep controller output device 212-1 , which can send command controls to actuators (not shown) move wings 124 between the extended position of Figure 1 or a retracted position of Figure 10.

[0067] A lighting output device 212-2, which can be provided if desired to include solid and / or flashing lights (not shown) mounted anywhere on the exterior of device 100 for visual locating and / or tracking.

[0068] Additional output devices 212-n can also be included, such as one or more speakers to audible signals to assist in locating device 100.

[0069] Processor 208 may be implemented as a plurality of processors or one or more multicore processors. The processor 208 may be configured to execute different programing instructions responsive to the input received via the one or more input devices 204 and to control one or more output devices 212 to generate output on those devices.

[0070] To fulfill its programming functions, the processor 208 is configured to communicate with one or more memory units, including non-volatile memory 216 and volatile memory 220. Nonvolatile memory 216 can be based on any persistent memory technology, such as an Erasable Electronic Programmable Read Only Memory (“EEPROM”), flash memory, solid-state hard disk (SSD), other type of hard-disk, or combinations of them. Non-volatile memory 216 may also be described as a non-transitory computer readable media. Also, more than one type of non-volatile memory 216 may be provided.

[0071] Volatile memory 220 is based on any random access memory (RAM) technology. For example, volatile memory 220 can be based on a Double Data Rate (DDR) Synchronous Dynamic Random-Access Memory (SDRAM). Other types of volatile memory 220 are contemplated.

[0072] Programming instructions in the form of applications 224 are typically maintained, persistently, in non-volatile memory 216 and used by the processor 208 which reads from and writes to volatile memory 220 during the execution of applications 224. One or more tables or databases 228 can also be maintained in non-volatile memory 216 for use by applications 224 and / or to store telemetry collected by input devices 204 and / or to store flight logs of device 100.

[0073] Processor 208 can also connect to a network 236 via a network interface 232 which includes a buffer and a modulator / demodulator or MODEM and / or a radio if wireless functions are included.

[0074] Network interface 232 can be wireless so that, in flight, device 100 can send telemetry gathered by input devices 204 and / or receive command controls. By the same token, interface 232 can also be configured to receive input control signals via network 236 while device 100 is in-flight, which can be used to activate various output devices 212, such as extending or retracting wings 124 in flight to guide the direction of device 100.

[0075] Network interface 232 can include a wired port (e.g. USB-C) for physical connection when device 100 is on the ground. Such wired connections can be used to access non-volatile memory 216, to download telemetry and / or flight logs from a previous flight. Non-volatile memory 216 may also be accessed in order to update applications 224.

[0076] When wireless, network 236 can thus be any network of transceivers within radio-distance of device 100. Such transceivers can be located on, for example, cell phone towers, satellites, surrounding aircraft or ground vehicles. Thus different radio protocols may be incorporated into network interface 232 used such as Wi-Fi, Bluetooth, 3G, 4G, 5G and any successor or variant standards. Network 236 can also be expansive to include the Internet, thereby allowing controller 200 to be accessed from a remote location and allow for program updates in nonvolatiles storage 216 to be updated remotely or data stored on non-volatile storage 216 to be downloaded from controller 200.

[0077] A power supply 240 is also provided to provide electrical energy to the components of device 100. Power supply 240 can be a rechargeable lithium battery, for example.

[0078] Figure 12 shows a flowchart depicting a method for atmospheric data collection indicated generally at 500. Method 500 can be implemented on a controller, such as controller 200 of device100. Method 500 can be stored as code within non-volatile memory 216 as one or more applications 224. Persons skilled in the art may choose to implement method 500 on device 100 or variants thereon, or with certain blocks omitted, performed in parallel or in a different order than shown. Method 500 can thus be varied. However, for purposes of explanation, method 500 as per the flow chart of Figure 5 and will be described in relation to its performance on device 100.

[0079] Block 504 comprises preparing for launch. Block 504 thus generally contemplates the beginning of a launch sequence for device 100. Block 504 can comprise using controller 200 to activate wing sweep controller output device 212-1 and place wings 124 in the retracted position of Figure 10, (if they are not already in the retracted position). Block 504 can also comprise doing a complete system check of input devices 204 and at least one output device 212 and other components of device 100, to otherwise verify that device 100 is functioning properly and is ready for flight.

[0080] Block 508 comprises determining a target location for landing device 100. A significant advantage of device 100 over traditional weather balloons lies in its designed recoverability and reusability. It can be pre-programmed before launch to return to its launch point, but dynamically updates its optimum landing location as it ascends and descends. Consequently, a target landing location for device 100 is established to a flight path that strategically positions device 100 to cover a designated geographical area for optimal collection of weather-related telemetry data through input devices 204. It is to be understood that target location typically includes a target area, such as a radius, that is defined to be practically possible for device 100 to actually land or parachute within, while also satisfying a flight path to gather the desired weather telemetry.

[0081] Block 512 comprises ascending. More specifically, block 512 contemplates launching or otherwise causing device 100 to leave the ground and begin an aerial climb. In some embodiments, variants of device 100 may include its own propulsion system such that device 100 can be launched from the ground. Wing sweep controller output device 212-1 may be used to help guide the ascent trajectory of device 100 towards an apex target location.

[0082] In other embodiments, block 512 may be effected by a launch vehicle such as an aircraft. Such a launch vehicle may be used to carry device 100 from the ground into an airspace that is of interest for gathering weather-related telemetry via input devices 204. In the latter example, an aircraft may have a plurality of device 100 placed into a launch bay before takeoff, and then, upon reaching a desired aerial location, release device 100 for atmospheric data collection and descent towards the target landing location of block 508.

[0083] Block 516 comprises determining if an apex has been reached. The desired apex itself can be explicit as part of block 508, or can be inherently calculated given known atmospheric conditions that would generally lead to an unpowered but guided descent of device 100 towards the target landing location. At the apex, device 100 can be released for a descent towards the target location defined at block 508.

[0084] Block 520 comprises collecting telemetry. As noted earlier, method 500 can be varied in several ways, including the order at which a given block is effected. Block 520 can thus be initiated at any time in method 500. In the present embodiment, telemetry collection of atmospheric conditions via input devices 204 is implemented both during the ascent and following the point when the apex is reached, with the consideration that the desired atmospheric readings will occur both along the pathway of the ascent and descent from the launch position to the apex to the target landing location.

[0085] Block 524 comprises descending and air traffic avoidance. The descent can be controlled by adjusting the sweep of wings 124 using wing sweep controller output device 212-1 , utilizing input devices 204 as feedback in order to direct device 100 towards the target landing location (or target area) of block 508. It is contemplated that GPS input device 204-1 is used heavily for guidance and likewise, ADS-B input device can be used for traffic avoidance, as well as using wind speed and direction to predict the fall or descent of device 100 and steer it towards the target location. It is contemplated that the drone is dynamically selecting the optimum alternate landing location to proceed to if it determines it cannot reach the primary landing location. It is also contemplated that the drone will adjust its flight path and selection of landing location based on air traffic avoidance.

[0086] During this descent, other input devices 204 are used to gather atmospheric data relevant to the meteorological functions of device 100, thus continuing the collection of telemetry from block 520.

[0087] Block 528 comprises determining if the target has been reached. Thus block 528 comprises utilizing GPS input device 204-1 and / or other input devices 204 such that block 524can be terminated. A “Yes” determination at block 528 may thus be reached when device 100 is on the ground.

[0088] It is to be understood that in an ideal scenario, device 100 will land or parachute land at the target location from block 508 or within a defined radius or other geographic range of that target location. At the same time, it is expected that exceptions will occur such that device 100 will land outside the target location. Thus a “Yes” determination also occurs at block 528 if device 100 has landed outside the target area. In the event device 100 lands outside the target location or area, flight path logs on non-volatile memory 216 can be used to analyze the flight path and correct for future flights. At this point, method 500 ends.

[0089] While the foregoing discusses certain embodiments, it is to be understood that variations, combinations, and / or subsets of those embodiments are contemplated. For example, machine learning can be used to assist in method 500 or otherwise control device 100. To elaborate, machine learning techniques can refine various aspects of the device's operations, such as determining an appropriate apex at block 512, predicting optimal descent pathways at block 524, and ensuring alignment with the desired target at block 508. These enhancements can be achieved by analyzing data collected from repeated flights under different atmospheric conditions, using telemetry from input devices 204 as training data.

[0090] In particular, machine learning applications 224 can incorporate environmental variables — such as wind speed, temperature, pressure, and humidity — captured during previous flights to build predictive models for specific weather patterns. For example, if a particular flight experienced unexpected turbulence at a given altitude, this information can inform future predictions, enabling the device to adjust its ascent trajectory or wing configurations proactively. Over time, as data accumulates, the system can improve accuracy in predicting the apex altitude at block 516 and optimizing descent paths at block 524 to consistently align with the designated target at block 508.

[0091] Machine learning applications 224 may include supervised, unsupervised, or reinforcement learning techniques. For instance: Supervised learning can utilize labeled flight data to create regression models predicting the impact of atmospheric variables on flight stability;Unsupervised learning can identify patterns or anomalies in weather data that correlate with unexpected flight behavior; Reinforcement learning can adapt the device's flight strategies in real time by rewarding successful landings near the target location or penalizing deviations.

[0092] The one or more machine learning algorithms and / or neural networks of the machine learning applications 224 may include, but are not limited to, generalized linear regression algorithms, random forest algorithms, support vector machines, gradient boosting algorithms, decision tree algorithms, and generalized additive models for simpler predictive tasks. For more complex scenarios requiring deeper contextual understanding or real-time decision-making, neural network-based approaches, such as convolutional or recurrent neural networks, or reinforcement learning methods, like policy-gradient algorithms, may be employed. For example: A random forest algorithm may be used to predict wind shear patterns based on historical meteorological data; A gradient boosting regression algorithm may optimize the calculation of an apex altitude, given varying atmospheric conditions; A reinforcement learning algorithm could adjust wing sweep configurations dynamically during flight, maximizing efficiency and precision of the descent.

[0093] To enable continuous improvement, machine learning applications 224 may operate in a training mode during ground operations, utilizing historical telemetry data stored in non-volatile memory 216 to refine their models. Updated models can then be deployed via network interface 232 before subsequent flights. In some embodiments, the training process may leverage cloudbased resources to analyze large datasets from multiple devices 100, enabling fleet-wide performance enhancements.

[0094] Furthermore, machine learning techniques may extend beyond flight path optimization to include predictive maintenance of device 100. For example, algorithms can analyze sensor performance data over time to identify components at risk of failure, reducing downtime and improving reliability.

[0095] As another example, wings 124 may or may not be retractable, and device 100 may also include aerodynamic control surfaces, which are movable surfaces on the wings 124 or tail 112 that can be adjusted in flight to influence attitude, direction, and speed. Common examplesinclude ailerons, elevators, rudders, and flaps. In the context of a drone or UAV, these surfaces can be manipulated to change lift, drag, and stability, thereby refining its flight path and descent trajectory.

[0096] The present specification provides certain advantages over the prior art, particularly in addressing the limitations of traditional weather balloons and similar atmospheric data collection devices. For example, prior art weather balloons are generally not recovered after flight, leading to staggering amounts of waste, including non-biodegradable materials such as latex, plastics, and metal components, which contribute to environmental pollution. The present specification mitigates that waste by designing a device 100 that is recoverable, reusable, and / or constructed from materials that can be sustainably sourced or recycled. This can reduce the environmental footprint associated with atmospheric data collection.

[0097] Beyond the environmental advantages, the present specification can also address certain operational challenges. Traditional weather balloons rely on passive drifting and are subject to unpredictable wind patterns, resulting in data gaps and limited control over geographic coverage. In contrast, device 100 employs active guidance mechanisms, such as retractable wings and on-board controllers, to navigate predetermined flight paths and land within a target area. This can enable more precise data collection and so that a broader range of regions, such as remote or oceanic areas, can be monitored with greater reliability.

[0098] Economically, the present specification can offers a more cost-effective solution compared to prior art. Traditional weather balloons must be launched in large volumes to achieve broad coverage, incurring ongoing material, labor, and logistical expenses. The recoverable and reusable nature of device 100 significantly reduces these recurring costs. Over time, the initial investment in device 100 is offset by its reusability and reduced need for frequent replacement, making it a more sustainable option for meteorological organizations and research institutions.

[0099] Furthermore, the present specification integrates advanced technology that enhances data quality and expands functionality. Device 100 can carry a diverse suite of sensors, including temperature, pressure, humidity, and radiation sensors, all of which provide high-resolution, realtime telemetry. By leveraging these capabilities, device 100 can at least match and may surpassthe data collection potential of the prior art. Additionally, the integration of machine learning algorithms allows device 100 to optimize flight performance over time, ensuring that subsequent flights yield even more accurate and valuable data.

[0100] Another advantage lies in the adaptability and scalability of device 100. While prior art systems are typically designed for specific use cases, device 100 can be customized for various applications, such as atmospheric research, climate monitoring, and disaster response. For example, in hurricane tracking scenarios, multiple devices 100 can be deployed simultaneously to provide comprehensive, high-resolution data on storm dynamics, enabling forecasts and better-informed disaster preparedness measures.

[0101] Finally, the use of modern communication technologies, such as wireless telemetry and network connectivity, allows for real-time data transmission and remote operation of device 100.

[0102] The scope of the monopoly of this specification is defined by the claims, properly construed in relation to the narrative and drawings. Any limiting phrases should not be viewed in isolation but in view of the broader context of the entire teachings and advantages afforded by the specification.

Claims

Claims1 . A method of atmospheric data collection, comprising: preparing a drone-based atmospheric collection device for launch by verifying system components and orienting wings into a launch-ready position; defining a target landing location for the device. , the target landing locations being predefined geographical area for recovery; ascending the device to an apex altitude; collecting telemetry data during flight using one or more sensors, the telemetry data comprising atmospheric conditions determining if the apex altitude has been reached by comparing the current altitude to a predefined apex target; descending the device from the apex altitude by orienting the wings into a glide-position and steering toward the target landing location; monitoring the descent to determine whether the device has reached the target landing location or completed its descent; and ending the method upon confirmation that the descent is complete or the target location is reached.2 The method of claim 1 , wherein the telemetry data include one or more of temperature, pressure, humidity, wind speed, solar radiation data and direction.3 The method of claim 1 or claim 2, wherein ascending the device to an apex altitude further comprises adjusting the ascent trajectory using real-time wind speed and direction data.

4. The method of any preceding claim, wherein the wings are retractable and are oriented into a launch-ready position by a wing sweep controller.5 The method of any preceding claim, further comprising transmitting telemetry data to a remote location during flight using a wireless network interface.6 The method of any preceding claim, wherein machine learning techniques are applied to optimize the flight path, the machine learning techniques comprising supervised learning, unsupervised learning, or reinforcement learning algorithms.7 The method of any preceding claim, further comprising storing telemetry data and flight path information in non-volatile memory for post-flight analysis.8 The method of any preceding claim, wherein the target landing location is dynamically adjusted during flight based on real-time atmospheric conditions and air traffic.9 The method of any preceding claim, wherein the monitoring of the descent comprises using a Global Positioning System (GPS) and Automatic Dependent Surveillance-Broadcast (ADS-B) sensors to guide the device toward the target landing location.10 The method of any preceding claim, further comprising performing a system self-check prior to launch to ensure operational readiness of input sensors and output components.11 The method of any preceding claim, further comprising adjusting the descent path by dynamically varying the sweep angle of the wings in response to real-time wind speed, winddirection, and atmospheric pressure data, thereby refining the glide trajectory and enhancing landing accuracy.

12. The method of any preceding claim, wherein the processor applies a machine learning model trained on historical flight and telemetry data to predict atmospheric conditions encountered during descent, and adjusts the wings’ orientation or angle of attack based on those predictions to achieve a more controlled and stable approach to the target landing location.

13. The method of any preceding claim, further comprising continuously updating a predicted landing footprint based on real-time GPS positioning and sensed meteorological variables, and adjusting the descent path by varying flight control surfaces to compensate for shifting wind patterns or thermal gradients.

14. The method of any preceding claim, wherein the processor references previously stored descent profiles tailored to specific environmental conditions, selecting an appropriate profile and modifying the wing sweep configuration accordingly, thereby improving the reliability of guided landings under varying weather scenarios.

15. The method of any preceding claim, further comprising communicating the in-flight atmospheric data to a remote command station, receiving updated targeting commands or refined descent parameters from the station, and implementing those commands during descent, thereby allowing dynamic human-in-the-loop adjustments to the glide path when unexpected atmospheric changes occur.

16. The method of any preceding claim further comprising collecting Automatic Dependent Surveillance-Broadcast (ADS-B) data during ascent and descent, and adjusting the descentflight path to keep air traffic outside the predefined closest point of approach ‘bubble’ around the drone:

17. A device for atmospheric data collection, comprising: a body comprising a fuselage, a nose, and a tail, the tail including a plurality of fins for stabilizing the device during flight; a retractable wing assembly coupled to the body, the retractable wing assembly including: a first wing and a second wing moveable between an extended position and a retracted position, and a wing sweep mechanism to adjust the position of the wings; a plurality of input devices to collect telemetry data including atmospheric conditions during flight; a controller comprising a processor, a memory, and at least one output device, the processor to receive telemetry data from the input devices; control the wing sweep mechanism to orient the wings during flight for guiding the device toward a predefined target landing location; a power supply to provide electrical energy to the components of the device; and a network interface to transmit collected telemetry data and / or receive control commands during flight.

18. The device of claim 17 wherein the input devices include a GPS sensor for determining position and altitude; and at least one of a thermometer for measuring temperature; a barometric pressure sensor; a hygrometer for measuring humidity; a wind speed and direction sensor, a radiation sensor for measuring solar radiation; and a pitot sensor for determining dynamic pressure.

19. The device of claim 17, wherein comprising an alternate target landing location; and wherein the predefined target landing locations are dynamically updated and the optimumlocation is selected during flight in response to real-time wind speed, wind direction, and GPS data collected by the input devices.

20. The device of claim 17, 18 or 19, wherein the controller adjusts the flight path dynamically based on one or more of real-time atmospheric telemetry data and ADS-B data collected during flight.21 . The device of claim 19 or 20, wherein the controller is configured to employ a machine learning algorithm trained on historical flight data to predict atmospheric changes during descent and to adjust the wing orientation in real time to maintain a stable glide path toward the dynamically updated target landing location.

22. The device of claim 19 or 20, further comprising one or more aerodynamic control surfaces on the wings, wherein the controller selectively actuates the aerodynamic control surfaces based on real-time wind shear and turbulence data to refine the descent trajectory.

23. The device of claim 19 or 20, wherein the memory stores a plurality of predefined descent profiles, each associated with a set of atmospheric conditions, and the controller is configured to select and modify one of the predefined profiles during flight to achieve a more accurate approach to the target landing location.

24. The device of claim 19 or 20, further comprising a wireless communication interface configured to receive updated guidance commands from a remote operations center during descent, wherein the controller responds to these commands to dynamically alter the wing sweep angle and adjust flight path parameters as the device nears the target landing location.

25. The device of claim 19 or 20, wherein the controller continuously compares predicted landing coordinates against actual GPS coordinates measured in-flight, iteratively refining the descent path by altering the angle of attack, thereby improving positional accuracy and repeatability of the landing procedure.

26. The device of any one of claims 15-25, wherein the network interface transmits telemetry data to a remote system in real time via a wireless communication protocol selected from WiFi, Bluetooth, 4G, 5G, or satellite communication.

27. The device of any one of claims 15-26, wherein the atmospheric conditions include atmospheric chemistry.

28. A method of atmospheric data collection, comprising: preparing a drone-based atmospheric collection device for launch by verifying system components and orienting wings into a launch-ready position; defining a primary and alternate target landing locations for the device; the primary landing location being normally the launch position; the target landing location being predefined geographical area for recovery; ascending the device to an apex altitude; collecting telemetry data during ascent and descent flight using one or more sensors, the telemetry data comprising atmospheric conditions ; collecting Automatic Dependent Surveillance-Broadcast (ADS-B) data during ascent and descent, and adjusting the descent flight path to keep air traffic outside the predefined closest point of approach ‘bubble’ around the drone:determining if the apex altitude has been reached by comparing the current altitude to a predefined apex target; descending the device from the apex altitude by orienting the wings into a glide-position and steering toward the optimum target landing location; monitoring the descent to determine whether the device has reached the target landing location or completed its descent; and ending the method upon confirmation that the descent is complete or the target location is reached.

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