System, method and apparatus for tracking fallout of atmospheric sounding devices

Abstract

The present disclosure relates to a system, method, and apparatus for mapping the fallout of atmospheric sounding devices such as radiosondes. This technology provides a mechanism for receiving launch data, plotting launch sites, and displaying trajectory heatmaps through a graphical user interface. The system includes features for overlaying concentric range circles, offering interactive timelines, and distinguishing between radiosonde altitudes using color indicators. Additional functionalities enable the display of detailed fallout data, calculation of Circle Error Probable (CEP) for estimating landing sites, and generation of fallout density heatmaps. These capabilities facilitate a comprehensive understanding and management of radiosonde deployment and environmental impact, enhancing the efficiency of meteorological data collection and analysis.

Description

Agent Docket No. P12813PC00 SYSTEM, METHOD AND APPARATUS FOR TRACKING FALLOUT OF ATMOSPHERIC SOUNDING DEVICES CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from Canadian Patent Application No.3240014, filed May 29, 2024, which is also incorporated herein by reference in its entirety. FIELD

[0002] The present disclosure generally relates to radiosondes more particularly to tracking fallout of thereof. BACKGROUND

[0003] Meteorological data are used for weather forecasting for numerous sectors including agriculture, aviation, and disaster management. Radiosondes, airborne instruments typically carried by weather balloons, gather data such as atmospheric pressure, temperature, humidity, and wind profiles (speed and direction) during their flights. The vast amount of data collected by these devices forms the backbone of meteorological science, informing global models that predict weather patterns and climatic shifts with greater accuracy.

[0004] Despite their significant utility, radiosondes present a logistical dilemma after their operational cycle is complete. The devices are designed for a single ascent and when the weather balloon carrying them aloft bursts, they fall to the ground and often land in locations that are not easily accessible. The retrieval of radiosondes is thus impractical, leading to almost a complete loss of instruments across varied and often remote geographic landscapes. This loss not only represents a material and financial wastage but also raises concerns regarding the environmental impact of the non-recoverable devices that accumulate over time.Agent Docket No. P12813PC00

[0005] A platform like SondeHub Tracker (https: / / sondehub.org / ) serve the meteorological community by providing real-time data on radiosonde flights. Sondehub typically focuses on the tracking aspects of the radiosondes, offering visualizations of their journey through the atmosphere. SondeHub Tracker, for example, compiles data from a network of contributors and makes it accessible for hobbyists and professionals alike. Recovery of radiosondes, as presented on the site, caters to the incidental discovery and voluntary reporting by individuals who may come across the devices, allowing for unsystematic and random identification of fallen radiosondes should a contributor accidentally find one and choose to report it.

[0006] Still, even with tools like SondeHub Tracker, the increasing global deployment of radiosondes exacerbates the challenge of managing the end-of-life phase of these devices. Each unrecovered radiosonde contributes to the growing concern over the environmental stewardship of atmospheric data collection practices. The sheer number of radiosondes launched annually underscores the magnitude of this issue, highlighting the complexities associated with their post-flight treatment and the implications for the natural environments where they ultimately come to rest. SUMMARY

[0007] The present specification also provides methods, apparatuses and computer- readable media according to the foregoing.

[0008] An aspect of the specification provides a method for mapping fallout of atmospheric sounding devices, the method including: receiving, by one or more processors, data indicative of launches of radiosondes from at least one launch site; plotting, by the one or more processors on a graphical user interface (GUI), a launch site map indicating the geographic locations of the at least one launch site; upon receiving a selection of a particular one of the at least one launch sites on the GUI, displaying, by the one or more processors, a trajectory heatmap of radiosondes launched from the selected launch site; overlaying, by the one or more processors on the GUI, concentric range circles centered on the selected launch site for reference of distance from the launch point; providing, by the one or more processors on the GUI, an interactive timeline allowing selection of a time period for which fallout data is displayed; distinguishing, byAgent Docket No. P12813PC00 the one or more processors on the GUI, between final altitudes of the radiosondes using different indicators for data points on the trajectory heatmap; upon receiving a selection of a data point on the trajectory heatmap, displaying, by the one or more processors, detailed information including launch date and final apogee or last position of the corresponding radiosonde; calculating, by the one or more processors, a Circle Error Probable (CEP) for each radiosonde based on the altitude of the last known position, lateral speed and direction during descent, and descent time remaining to estimate a final resting place of the radiosonde; and generating, by the one or more processors, a fallout density heatmap based on the calculated CEP.

[0009] An aspect of the specification provides a method wherein each launch site is represented by an icon on the map, the icon color-coded based on a data reception status from the respective launch site.

[0010] An aspect of the specification provides a method wherein the heatmap represents aggregate flight paths and distances of the radiosondes.

[0011] An aspect of the specification provides a method wherein the map includes colour.

[0012] An aspect of the specification provides a method wherein the wherein the fallout density heatmap visually represents a likelihood distribution of fallout locations per square kilometer on the GUI.

[0013] An aspect of the specification provides a method wherein the GUI is on a device that is remote from the processors.

[0014] An aspect of the specification provides a method wherein the data is received from an engine associated with the at least one launch site.

[0015] An aspect of the specification provides a method wherein the CEP includes a machine learning algorithm that compares an actual final resting place with estimated final resting place, and the CEP is updated based on training of the machine learning algorithm.Agent Docket No. P12813PC00

[0016] An aspect of the specification provides a method further including generating a statistical analysis of the fallout data across multiple launch sites to identify patterns in the geographical distribution and environmental impact of the radiosondes.

[0017] An aspect of the specification provides a method, further including implementing an alert system that notifies relevant environmental authorities when a radiosonde is predicted to lands in a sensitive or protected area, based on the calculated CEP and predefined environmental protection criteria.

[0018] An aspect of the specification provides a method, further including correlating the collected telemetry data with existing meteorological data to enhance predictive models of radiosonde fallout based on weather patterns, seasonal variations, and geographical factors.

[0019] An aspect of the specification provides a mapping engine including mapping fallout of atmospheric sounding devices; the platform including a processor and a memory; the processor executing programming instructions for: receiving, by one or more processors, data indicative of launches of radiosondes from at least one launch site; plotting, by the one or more processors on a graphical user interface (GUI), a launch site map indicating the geographic locations of the at least one launch site; upon receiving a selection of a particular one of the at least one launch sites on the GUI, displaying, by the one or more processors, a trajectory heatmap of radiosondes launched from the selected launch site; overlaying, by the one or more processors on the GUI, concentric range circles centered on the selected launch site for reference of distance from the launch point; providing, by the one or more processors on the GUI, an interactive timeline allowing selection of a time period for which fallout data is displayed; distinguishing, by the one or more processors on the GUI, between final altitudes of the radiosondes using different indicators for data points on the trajectory heatmap; upon receiving a selection of a data point on the trajectory heatmap, displaying, by the one or more processors, detailed information including launch date and final apogee or last position of the corresponding radiosonde; calculating, by the one or more processors, a Circle Error Probable (CEP) for each radiosonde based on the altitude of the last known position, lateral speed and direction during descent, and descent time remaining to estimate a final resting place ofAgent Docket No. P12813PC00 the radiosonde; and generating, by the one or more processors, a fallout density heatmap based on the calculated CEP.

[0020] An aspect of the specification provides an engine wherein each launch site is represented by an icon on the map, the icon color-coded based on a data reception status from the respective launch site.

[0021] An aspect of the specification provides an engine wherein the heatmap represents aggregate flight paths and distances of the radiosondes.

[0022] An aspect of the specification provides an engine wherein the wherein the fallout density heatmap visually represents a likelihood distribution of fallout locations per square kilometer on the GUI.

[0023] An aspect of the specification provides an engine wherein the GUI is on a device that is remote from the processors.

[0024] An aspect of the specification provides an engine wherein the data is received from an engine associated with the at least one launch site.

[0025] An aspect of the specification provides an engine wherein the CEP includes a machine learning algorithm that compares an actual final resting place with estimated final resting place, and the CEP is updated based on training of the machine learning algorithm.

[0026] An aspect of the specification provides an engine further including generating a statistical analysis of the fallout data across multiple launch sites to identify patterns in the geographical distribution and environmental impact of the radiosondes.

[0027] An aspect of the specification provides an engine further including implementing an alert system that notifies relevant environmental authorities when a radiosonde is predicted to lands in a sensitive or protected area, based on the calculated CEP and predefined environmental protection criteria. BRIEF DESCRIPTION OF THE FIGURES

[0028] Certain embodiments will now be described, by way of example only, in which:Agent Docket No. P12813PC00

[0029] Figure 1 is a schematic diagram of a system for mapping fallout of atmospheric sounding devices.

[0030] Figure 2 shows a block diagram of an example internal components of the mapping engine of Figure 1.

[0031] Figure 3 shows a flowchart depicting a method for mapping fallout of atmospheric sounding devices.

[0032] Figure 4 shows a graphical user interface displaying a map with icons representing various launch sites.

[0033] Figure 5 shows an interactive graphical user interface with a trajectory heatmap and concentric range circles.

[0034] Figure 6 shows the display of detailed information for a selected radiosonde on the graphical user interface.

[0035] Figure 7 shows a fallout density heatmap visualizing the likelihood distribution of radiosonde landing locations over water and land.

[0036] Figure 8 shows the graphical user interface to allow detailed examination of individual radiosonde data points (e.g. such as estimated landing point).

[0037] Figure 9 shows the graphical user interface functionality to display a layered view combining fallout density heatmap with final position data.

[0038] Figure 10 introduces an interactive path overlay within the graphical user interface to trace the descent journey of individual radiosondes post bursting of the weather balloon at apogee. DETAILED DESCRIPTION

[0039] Figure 1 shows a system 100 for mapping fallout of atmospheric sounding devices such as radiosondes 102. (Note that radiosondes 102 are individually labelled as 102-1,Agent Docket No. P12813PC00 102-2 … 102-p. Collectively, they are referred to as radiosondes 102, and generically, as radiosonde 102. This nomenclature is used throughout the specification.)

[0040] For illustrative purposes, system 100 is a specific embodiment described in the context of a graphical interface as a presently preferred embodiment, but as will be explained further below, system 100 can be applicable to other aspects of mapping fallout of radiosondes and other types of atmospheric sounding devices such as ozone-sondes.

[0041] Thus, according to the illustrative embodiment, system 100 comprises a mapping engine 104 connected to a network 108 such as the Internet. Network 108 interconnects mapping engine 104 with: a) a plurality of receiving station engines 112; b) a plurality of client devices 116.

[0042] Receiving Station Engines 112, with equipment to track and collect atmospheric data from radiosondes 102, are managed by national meteorological services like the U.S. National Weather Service (NWS), Environment and Climate Change Canada (ECCC), or the UK's Met Office. In addition to these, certain educational institutions, research facilities, and military organizations might also operate their own receiving stations for specialized meteorological studies or for training purposes. International and regional bodies concerned with atmospheric research, like the World Meteorological Organization (WMO), may also coordinate the operation of these stations on a broader scale for global data collection efforts. Thus, engines 112 gather telemetry—including atmospheric pressure, temperature, humidity, and wind patterns—from the radiosondes 102 that are launched by their respective stations, supporting weather forecasting and research. Each engine 112 processes telemetry from radiosondes 102 launched by its station.

[0043] Engines 112 thus maintain data files 132 that log the telemetry of radiosondes 102 that have been launched. As will be discussed further below, data files 132 are made available to mapping engine 104.

[0044] Client devices 116 are operated by individual users 124, each of which use a separate account 128 to access system 100. The present specification contemplates scenarios where, from time to time, users 124 may wish to track radiosonde 102 fallout by accessing mapping engine 104. Mapping engine 104 thus performs a number ofAgent Docket No. P12813PC00 central processing functions to intermediate between devices 116 and engines 112. Mapping engine 104 will be discussed in greater detail below.

[0045] Client devices 116 can be any type of human-machine interface for interacting with platforms 104. For example, client devices 116 can include traditional laptop computers, desktop computers, mobile phones, tablet computers and any other device that can be used to send and receive communications over network 108 and its various nodes that complement the input and output hardware devices associated with a given client device 116. It is contemplated client devices 116 can include virtual or augmented reality gear complementary to virtual reality or augmented reality or “metaverse” environments that can be offered by variations of mapping engine 104.

[0046] Client devices 116 can include geocoding capability, such as a global position system (GPS) device, that allows the location of a device 116, and therefore its user 124, to be identified within system 100. Other means of implementing geocoding capabilities to ascertain the location of users 124 are contemplated, but in general system 100 can include the functionality to identify the location of each device 116 and / or its respective user 124. For example, the location of a device 116 or a user 124 can also be maintained within mapping engine 104 or other nodes in system 100.

[0047] Client devices 116 are operated by different users 124 that are associated with a respective account 128 that uniquely identifies a given user 124 accessing a given client device 116 in system 100. A person of skill in the art is to recognize that the electronic structure of each account 128 is not particularly limited, and in a simple example embodiment, can be a unique identifier comprising an alpha-numeric sequence that is entirely unique in relation to other accounts 128 in system 100. Accounts 128 can also be based on more complex structures that may include combinations of account credentials (e.g. user name, password, Two-factor authentication token, etc.) that further securely and uniquely identify a given user 124. Accounts 128 can also be associated with other information about the user 124 such as name, address, age, language preferences, and any other information about a user 124 relevant to the operation of system 100. Accounts 128 themselves may also point to additional accounts (not shown in the Figures) for each user 124, as a plurality of accounts may be uniquely provided for each user 124, with each account being associated with different nodes in system 100.Agent Docket No. P12813PC00 For simplicity of illustration, it will be assumed that one account 128 serves to uniquely identify each user 124 across system 100. Indeed, the salient point is that accounts 128 make each user 124 uniquely identifiable within system 100.

[0048] Users 124 can interact, via devices 116, with mapping engine 104 to obtain mapping and other information regarding radiosondes 102. As desired or required, each account 128 (or linked accounts respective to different nodes) can be used by other nodes in system 100, including engines 112 to authenticate and / or allow access to radiosondes 102 data.

[0049] At this point it is to be clarified and understood that the nodes in system 100 are scalable, to accommodate a large number of users 124, devices 116, and engines 112. Scaling may thus include additional mapping engines 104 and / or engines 112.

[0050] Having described an overview of system 100, it is useful to comment on the hardware infrastructure of system 100. Figure 2 shows a schematic diagram of a non- limiting example of internal components of mapping engine 104.

[0051] In this example, mapping engine 104 includes at least one input device 204. Input from device 204 is received at a processor 208 which in turn controls an output device 212. Input device 204 can be a traditional keyboard and / or mouse to provide physical input. Likewise output device 212 can be a display. In variants, additional and / or other input devices 204 or output devices 212 are contemplated or may be omitted altogether as the context requires.

[0052] Processor 208 may be implemented as a plurality of processors or one or more multi-core 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.

[0053] To fulfill its programming functions, processor 208 is configured to communicate with one or more memory units, including non-volatile memory 216 and volatile memory 220. Non-volatile 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.Agent Docket No. P12813PC00 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.

[0054] 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.

[0055] Processor 208 also connects to network 108 via a network interface 232. Network interface 232 can also be used to connect another computing device that has an input and output device, thereby obviating the need for input device 204 and / or output device 212 altogether.

[0056] 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. Various methods discussed herein can be coded as one or more applications 224. One or more tables or databases 228 are maintained in non-volatile memory 216 for use by applications 224.

[0057] The infrastructure of mapping engine 104, or a variant thereon, can be used to implement any of the computing nodes in system 100, node 120, or node 122 and / or engines 112. Furthermore, mapping engine 104, node 120, node 122, and / or engines 112 may also be implemented as virtual machines and / or with mirror images to provide load balancing. They may be combined into a single engine or a plurality of mirrored engines or distributed across a plurality of engines. Functions of mapping engine 104 may also be distributed amongst different nodes, such as within node 120, node 122 and / or engines 112, thereby obviating the need for a central mapping engine 104, having a mapping engine 104 with partial functionality while the remaining functionality is effected by other nodes in system 100. By the same token, a plurality of mapping engines 104 may be provided, especially when system 100 is scaled.

[0058] Furthermore, a person of skill in the art will recognize that the core elements of processor 208, input device 204, output device 212, non-volatile memory 216, volatile memory 220 and network interface 232, as described in relation to the server environmentAgent Docket No. P12813PC00 of mapping engine 104, have analogues in the different form factors of client machines such as those that can be used to implement client devices 116. Again, client devices 116 can be based on computer workstations, laptop computers, tablet computers, mobile telephony devices or the like.

[0059] Figure 3 shows a flowchart depicting a method for mapping fallout of atmospheric sounding devices indicated generally at 300. Method 300 can be implemented on system 100. Persons skilled in the art may choose to implement method 300 on system 100 or variants thereon, or with certain blocks omitted, performed in parallel or in a different order than shown. Method 300 can thus also be varied. However, for purposes of explanation, method 300 will be described in relation to its performance on system 100 with a specific focus on treating method 300 as, for example, application 224-2 maintained within mapping engine 104 and the use of application 224-2 to effect radiosondes 102 mapping. Method 300 contemplates mapping engine 104 receiving radiosondes 102 information from engines 112 and controlling one or more client devices 116 to display the mapping information.

[0060] Block 310 comprises receiving, by one or more processors, data indicative of launches of radiosondes from at least one launch sites. In system 100, mapping engine 104 effects block 310 as it receives telemetry about radiosondes 102 from at least one engine 112, where engines 112 is respective to each launch site.

[0061] Block 320 comprises plotting, by the one or more processors on a graphical user interface (GUI), a launch site map indicating the geographic locations of the at least one launch site, wherein each launch site is represented by an icon on the map. The icon can be color-coded based on a data reception status from the respective launch site. The graphical user interface can be hosted on one or more client devices 116, and thus mapping engine 104 can generate the GUI and transmit it via network 108 to any client devices 116 that intend to display the launch site map. Example performance of block 320 is shown in Figure 4.

[0062] Block 330 comprises upon selection of the at least one launch site on the GUI, displaying, by the one or more processors, a trajectory heatmap of radiosondes launched from the selected launch site, wherein the heatmap represents aggregate flight paths andAgent Docket No. P12813PC00 distances of the radiosondes. Block 330 can be effected by mapping engine 104 sending appropriate mapping information to one or more client devices 116.

[0063] Block 340 comprises overlaying, by the one or more processors on the GUI, concentric range circles centered on the selected launch site for reference of distance from the launch point. Block 340 again can be effected by mapping engine 104 sending mapping information to one or more client devices 116 and thereby control the display of the respective client device 116 to show the overlay.

[0064] Block 350 comprises providing, by the one or more processors on the GUI, an interactive timeline allowing selection of a time period for which fallout data is displayed. Block 350 again can be effected by mapping engine 104 sending mapping information to one or more client devices 116 and thereby control the display of the respective client device 116 to show the timeline.

[0065] Block 360 comprises distinguishing, by the one or more processors on the GUI, between final altitudes of the radiosondes using different indicators for data points on the trajectory heatmap. The indicators may be in colour. Block 360 in the context of system 100 contemplates an the trajectory heatmap being generated by mapping engine 104 on the display of one or more respective client devices 116, allowing selection of a time period for which fallout data is displayed. Block 360 again can be effected by mapping engine 104 sending mapping information to one or more client devices 116 and thereby control the display of the respective client device 116 to show the trajectory heatmap.

[0066] Example performance of block 330, block 340, block 350, and block 360 is shown in Figure 5.

[0067] Block 370 comprises upon selection of a data point on the trajectory heatmap, displaying, by the one or more processors, detailed information including launch date and final apogee or last position of the corresponding radiosonde. Block 370 in the context of system 100 contemplates the detailed information being generated by mapping engine 104 on the display of one or more respective client devices 116. Block 370 again can be effected by mapping engine 104 sending mapping information to one or more client devices 116 and thereby control the display of the respective client device 116 to show the timeline. Example performance of block 370 is shown in Figure 6.Agent Docket No. P12813PC00

[0068] Block 380 comprises calculating, by the one or more processors, a Circle Error Probable (CEP) of a final resting place for each radiosonde 102 based on a “dead reckoning” that considers the altitude of the last known position, lateral speed and direction during descent, and descent time remaining to estimate a final resting place of the radiosonde 102. As discussed below, block 380 may be augmented by machine learning techniques.

[0069] Variables that can be considered at block 380 include: 1. Altitude at Last Known Position (h): The height above ground from which the last signal was received from the radiosonde. 2. Lateral Speed (v): The horizontal speed of the radiosonde at the last known position. 3. Descent Rate (d): The rate at which the radiosonde descends, typically in meters per minute. 4. Wind Speed (ws): The average horizontal wind speed affecting the descent path. 5. Wind Direction (wd): The direction from which the wind is blowing, which affects the horizontal displacement of the radiosonde. 6. Descent Time Remaining (t): Estimated time from the last known position to the ground impact, based on descent rate and remaining altitude.

[0070] The following is a hypothetical example that illustrates the application of a hypothetical scenario to compute the Circle Error Probable (CEP) for the final landing position of a radiosonde, using realistic parameters:

[0071] Variables and Their Hypothetical Values:

[0072] Altitude at Last Known Position (h): 2000 meters

[0073] Lateral Speed (v): 15 meters per second (m / s)

[0074] Descent Rate (d): 5 meters per second (m / s)Agent Docket No. P12813PC00

[0075] Wind Speed (ws): 10 meters per second (m / s)

[0076] Wind Direction (wd): 90 degrees (eastward)

[0077] Descent Time Remaining (t): Calculated from the altitude and descent rate:

[0078] ^=ℎ / ^=2000^ / 5^ / ^=400^^^^^^^t=h / d=2000m / 5m / s=400seconds

[0079] ^ in minutes=400^^^^^^^ / 60≈6.67^^^^^^^t in minutes=400seconds / 60≈6.67minu tes

[0080] Calculation of Horizontal Displacements:

[0081] Horizontal Displacement Due to Wind (x_wind):

[0082] Assuming the wind is constant and directly eastward:

[0083] ^^^^^=^^×^=10^ / ^×400^^^^^^^=4000^^^^^^xwind =ws×t=10m / s×400seconds=4000meters

[0084] Horizontal Displacement Due to Lateral Speed (x_lat):

[0085] ^^^^=^×^=15^ / ^×400^^^^^^^=6000^^^^^^xlat =v×t=15m / s×400seconds=6000meters

[0086] Total Horizontal Displacement:

[0087] Combining the effects of wind and lateral movement, the total eastward displacement is:

[0088] Total Displacement=^^^^^+^^^^=4000^+6000^=10000^^^^^^Total Displaceme nt=xwind+xlat=4000m+6000m=10000meters

[0089] Circle Error Probable (CEP) Estimation:

[0090] Assuming statistical data from previous launches indicates a standard deviation (σ) of 500 meters in landing displacements due to variable atmospheric conditions:Agent Docket No. P12813PC00

[0091] The CEP, representing the radius within which the radiosonde is expected to land 50% of the time, can be centered around the mean displacement of 10000 meters with a radius of ±500 meters.

[0092] To be clear, this is one non-limiting example of how calculations may be performed. This example demonstrates how the system can calculate the landing position of radiosondes with consideration of dynamic atmospheric factors, and how the CEP can provide a statistically meaningful measure of landing accuracy. This method enhances predictive accuracy and operational efficiency in meteorological data collection and analysis.

[0093] The CEP can be calculated based on the variability or standard deviation of the total displacement from multiple such events, providing an estimate of the radius within which the radiosonde will land with a certain probability (e.g., 50%).

[0094] This is a simplified example, and in practice, more sophisticated meteorological and ballistic modeling tools to account for changing atmospheric conditions, non-linear descent paths, and varying wind speeds and directions at different altitudes.

[0095] Block 390 comprises generating, by the one or more processors, a fallout density heatmap based on the calculated CEP, wherein the fallout density heatmap visually represents a likelihood distribution of fallout locations per square kilometer on the GUI. Again mapping engine 104 can generate this information for one or more displays of client devices 116. Example performance of block 390 is shown in Figure 7.

[0096] Figure 8 illustrates an enhanced graphical user interface (GUI) capability, of block 390, where mapping engine 104 enables a detailed examination of individual radiosonde data points within the overall density heatmap initially presented in Figure 7. In this depiction, the GUI, as generated by mapping engine 104, presents a magnified overlay that provides users with specific data concerning a single radiosonde 102 when selected. The overlay offers information such as the date of the radiosonde’s launch (e.g., 2022- 04-24) and its last recorded altitude (e.g., 2217 meters), thus allowing users to discern the precise characteristics of a singular fallout event within the larger dataset visualized in the heatmap.Agent Docket No. P12813PC00

[0097] The interface includes interactive elements such as a close button (indicated by an ‘X’) allowing users to exit the detailed view and return to the broader heatmap perspective. Each individual data point is interactive and can provide real-time detailed information upon selection, facilitating a user-friendly experience for analyzing specific incidents or events captured within the system's fallout data. This detailed view serves as an important tool for users requiring granular data for research, environmental impact assessment, or operational planning.

[0098] Figure 9 shows how the mapping engine 104 can further generate a GUI that allows for a more detailed view of individual radiosondes 102 on the density heatmap of Figure 7. Thus Figure 9 is a variant on how block 370 and / or block 390 can be performed. Figure 9 displays an interactive graphical user interface (GUI), generated by the mapping engine 104, which facilitates the detailed visualization of fallout data for individual radiosondes 102 within the broader context of a density heatmap, as referenced in Figure 7. This GUI, generated by mapping engine 104, allows users to interact with the map to extract specific information. Here, the GUI provides a layered view combining a fallout density heatmap with an additional informational layer showcasing data about the final positions of radiosondes 102.

[0099] Included in the GUI are filter options that permit users to refine the displayed data based on categories such as the display type—here set to 'Last Position'—and a date range, allowing for temporal analysis of fallout patterns (e.g. seasonality). A pie chart is also presented, offering a visual representation of the proportion of radiosondes falling over water versus land, contributing to environmental impact assessments.

[0100] Such a GUI empowers users, via client devices 116, to dynamically interact with the data, toggling between views, adjusting date ranges, and selecting specific points to reveal detailed data such as date, time, and the final location coordinates of each radiosonde. This capability underscores adaptability in addressing a variety of user-driven queries and the enhancement of user experience in engaging with the collected meteorological data.

[0101] Figure 10 shows how mapping engine 104 can further generate a GUI that allows for a more detailed view of individual radiosondes 102 on the density heatmap ofAgent Docket No. P12813PC00 Figure 7. Thus Figure 9 is a variant on how block 370 and / or block 390 can be performed. Figure 10 presents a sophisticated graphical user interface (GUI), rendered by mapping engine 104, which conveys detailed path information for individual radiosondes 102 against the backdrop of a density heatmap, as shown in Figure 7. This GUI elucidates the fallout trajectory of each radiosonde, integrating a path overlay that traces the journey from launch to last known position. The interface, accessible via client device 116, allows for the selection of a particular path display, enhancing the analytical capabilities by including specific trajectory data such as horizontal distance traveled, vertical and horizontal speeds at descent, effectively showcasing the dynamics of each radiosonde's flight.

[0102] The GUI also offers customizable filter options, enabling the user to sort the data by various time frames—ranging from the last few days to an entire year—and even a custom date range. A pie chart reflects the statistical distribution of fallout locations, delineating the proportion of radiosondes landing over water as compared to land. Such detailing within the GUI demonstrates the utility of system 100 in performing thorough fallout analysis, which can be used for environmental monitoring and operational planning within meteorological services. The interactive elements of the GUI provide an intuitive platform to dissect comprehensive datasets, useful for decision-makers who rely on accurate atmospheric data.

[0103] In view of the above it will now be apparent that variations, combinations, and / or subsets of the foregoing embodiments are contemplated. For example, mapping engine 104 may be obviated or its function distributed throughout a variant on system 100, by incorporating mapping engine 104 one or more engines 112 and / or into client devices 116. In other words, one client devices 116 may be local to a one engines 112 whereby the mapping is performed entirely locally.

[0104] One or more of the applications 224 may include the machine learning studio platform with any desired related machine learning deep-learning based algorithms and / or neural networks, and the like, which are trained to improve method 300. (Hereafter machine learning applications 224). The machine learning applications 224 can be trained with any actual information bout where a radiosondes 102 landed in comparison to where a landing was estimated. Notably, this training applies to those radiosondes 102 that areAgent Docket No. P12813PC00 actually recovered. The machine learning can be used to improve performance of block 380. Furthermore, in these examples, the machine learning applications 224 may be operated by the processor 208 in a training mode to train the machine learning and / or deep-learning based algorithms and / or neural networks of the machine learning applications 224 in accordance with the teachings herein.

[0105] The one or more machine-learning algorithms and / or deep learning algorithms and / or neural networks of the machine learning applications 224 may include, but are not limited to: a generalized linear regression algorithm; a random forest algorithm; a support vector machine algorithm; a gradient boosting regression algorithm; a decision tree algorithm; a generalized additive model; neural network algorithms; deep learning algorithms; evolutionary programming algorithms; Bayesian inference algorithms; reinforcement learning algorithms, and the like. However, generalized linear regression algorithms, random forest algorithms, support vector machine algorithms, gradient boosting regression algorithms, decision tree algorithms, generalized additive models, and the like may be preferred over neural network algorithms, deep learning algorithms, evolutionary programming algorithms, and the like.

[0106] Other possible ways of calculating CEP include:

[0107] 1. Particle Filter Algorithm: A particle filter, or sequential Monte Carlo method, is ideal for predicting the state of a system where the state transitions and observations are probabilistic. In the context of Block 380, a particle filter could be used to estimate the trajectory and final landing position of radiosondes based on stochastic inputs like wind speed and direction changes during descent. This method would involve: Generating a set of particles representing possible states (positions and velocities) of the radiosonde; Weighing these particles according to the likelihood of each state, given observed data up to the current time; Using the weighted particles to predict the future state, thereby refining the estimate of the landing position over time.

[0108] 2. Kalman Filter: The Kalman Filter is a linear dynamic system model used in time series analysis, which could be adapted for the nonlinear context of atmospheric descent by employing an Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF). These filters would be suitable for continuously updating the estimated state of aAgent Docket No. P12813PC00 radiosonde's descent path as new data (e.g., wind speed and direction at different altitudes) becomes available. Steps include: Initializing the predicted state and uncertainty; Updating predictions as new data arrives, minimizing the estimate's error covariance;

[0109] 3. Regression Models: As noted, using machine learning regression models to predict the impact point based on historical data of radiosonde launches and their outcomes under similar atmospheric conditions could also be beneficial. Models like: Random Forest Regression or Gradient Boosting Regression: As noted, these techniques could analyze historical launch data and learn complex dependencies between launch conditions, weather patterns, and landing areas. These models can provide a probabilistic estimate of landing locations, which could be used to calculate the CEP.

[0110] 4. Genetic Algorithms: A genetic algorithm could optimize the trajectory prediction by simulating a population of possible descent paths, evolving them over generations to minimize the deviation from observed descent data. This method can involve: Defining a fitness function based on the proximity of the descent path to observed end points; Iteratively selecting, crossing, and mutating path parameters to find an optimal prediction.

[0111] 5. Neural Networks: A deep learning approach could utilize Recurrent Neural Networks (RNN) or LSTM (Long Short-Term Memory) networks to model the temporal dependencies of atmospheric variables on the radiosonde's flight path. Training such a model can involve: Feeding historical data of descent trajectories and their corresponding environmental conditions; Learning to predict the trajectory based on sequential data points.

[0112] Implementing one or a combination of these algorithms can enhance the accuracy of predicting where radiosondes will land, which is crucial for improving recovery rates and reducing environmental impact. Each algorithm offers a different strength, and the choice might depend on the availability and quality of historical data, computational resources, and specific requirements for accuracy and robustness in the estimation process.Agent Docket No. P12813PC00

[0113] A person skilled in the art will now appreciate that the teachings herein can improve the technological efficiency and computational and communication resource utilization across system 100 by making more efficient use of network and processing resources in system 100, as well as more efficient use of its nodes. In the context of the embodiments described herein, the system presents various technical advantages over existing technologies. The integration of machine learning algorithms within the mapping engine enhances the efficiency of data processing. This feature allows for more accurate predictive modeling, improving the precision of estimations regarding radiosonde landing sites, thereby reducing computational overhead and accelerating processing times which are critical for real-time applications.

[0114] Additionally, the implementation of advanced predictive algorithms such as gradient boosting and reinforcement learning refines the Circle Error Probable (CEP). This refinement enables continuous improvements in the accuracy of the system's predictions by adjusting based on discrepancies between actual and expected fallout locations.

[0115] The system's architecture also optimizes network and bandwidth usage through a distributed configuration of processing nodes. This setup provides balanced processing loads across the network, improving resource utilization and reducing data bottlenecks, thereby enhancing overall system responsiveness.

[0116] Moreover, the system can leverage centralized statistical analyses of fallout data to increase computational efficiency. By reducing redundant processing across multiple nodes, the system improves resource allocation and lowers operational costs.

[0117] The scalability of the system's modular design effectively supports an increase in the number of launch sites and client devices while maintaining performance. This scalability is essential for supporting global operations without compromising data integrity or system reliability.

[0118] The capability to integrate diverse telemetry data from various sources also enhances the comprehensiveness and reliability of the meteorological data collected. This integration is vital for conducting precise and detailed environmental impactAgent Docket No. P12813PC00 analyses, which are crucial for developing effective mitigation strategies in atmospheric research.

[0119] These technical enhancements improve the technological efficiency of systems designed for meteorological data collection and analysis, offering a scalable, accurate, and resource-efficient solution to address the challenges associated with radiosonde fallout mapping and environmental impact assessments.

[0120] It should be recognized that features and aspects of the various examples provided above can be combined into further examples that also fall within the scope of the present disclosure. In addition, the figures are not to scale and may have size and shape exaggerated for illustrative purposes.

Claims

Agent Docket No. P12813PC00 CLAIMS 1. A method for mapping fallout of atmospheric sounding devices, the method comprising: receiving, by one or more processors, data indicative of launches of radiosondes from at least one launch site; plotting, by the one or more processors on a graphical user interface (GUI), a launch site map indicating the geographic locations of the at least one launch site; upon receiving a selection of a particular one of the at least one launch sites on the GUI, displaying, by the one or more processors, a trajectory heatmap of radiosondes launched from the selected launch site; overlaying, by the one or more processors on the GUI, concentric range circles centered on the selected launch site for reference of distance from the launch point; providing, by the one or more processors on the GUI, an interactive timeline allowing selection of a time period for which fallout data is displayed; distinguishing, by the one or more processors on the GUI, between final altitudes of the radiosondes using different indicators for data points on the trajectory heatmap; upon receiving a selection of a data point on the trajectory heatmap, displaying, by the one or more processors, detailed information including launch date and final apogee or last position of the corresponding radiosonde; calculating, by the one or more processors, a Circle Error Probable (CEP) for each radiosonde based on the altitude of the last known position, lateral speed and direction during descent, and descent time remaining to estimate a final resting place of the radiosonde; andAgent Docket No. P12813PC00 generating, by the one or more processors, a fallout density heatmap based on the calculated CEP.

2. The method of claim 1 wherein each launch site is represented by an icon on the map, the icon color-coded based on a data reception status from the respective launch site.

3. The method of any preceding claim wherein the heatmap represents aggregate flight paths and distances of the radiosondes.

4. The method of any preceding claim wherein the map includes colour.

5. The method of any preceding claim wherein the wherein the fallout density heatmap visually represents a likelihood distribution of fallout locations per square kilometer on the GUI.

6. The method of any preceding claim wherein the GUI is on a device that is remote from the processors.

7. The method of any preceding claim wherein the data is received from an engine associated with the at least one launch site.

8. The method of any preceding claim wherein the CEP includes a machine learning algorithm that compares an actual final resting place with estimated final resting place, and the CEP is updated based on training of the machine learning algorithm.

9. The method of any preceding claim further comprising generating a statistical analysis of the fallout data across multiple launch sites to identify patterns in the geographical distribution and environmental impact of the radiosondes.

10. The method of any preceding claim further comprising implementing an alert system that notifies relevant environmental authorities when a radiosonde is predicted to lands in a sensitive or protected area, based on the calculated CEP and predefined environmental protection criteria.Agent Docket No. P12813PC00 11. The method of any preceding claim further comprising correlating the collected telemetry data with existing meteorological data to enhance predictive models of radiosonde fallout based on weather patterns, seasonal variations, and geographical factors.

12. A mapping engine including mapping fallout of atmospheric sounding devices; the platform including a processor and a memory; the processor executing programming instructions for: receiving, by one or more processors, data indicative of launches of radiosondes from at least one launch site; plotting, by the one or more processors on a graphical user interface (GUI), a launch site map indicating the geographic locations of the at least one launch site; upon receiving a selection of a particular one of the at least one launch sites on the GUI, displaying, by the one or more processors, a trajectory heatmap of radiosondes launched from the selected launch site; overlaying, by the one or more processors on the GUI, concentric range circles centered on the selected launch site for reference of distance from the launch point; providing, by the one or more processors on the GUI, an interactive timeline allowing selection of a time period for which fallout data is displayed; distinguishing, by the one or more processors on the GUI, between final altitudes of the radiosondes using different indicators for data points on the trajectory heatmap; upon receiving a selection of a data point on the trajectory heatmap, displaying, by the one or more processors, detailed information including launch date and final apogee or last position of the corresponding radiosonde; calculating, by the one or more processors, a Circle Error Probable (CEP) for each radiosonde based on the altitude of the last known position, lateral speed and direction during descent, and descent time remaining to estimate a final resting place of the radiosonde; andAgent Docket No. P12813PC00 generating, by the one or more processors, a fallout density heatmap based on the calculated CEP.

13. The engine of claim 12 wherein each launch site is represented by an icon on the map, the icon color-coded based on a data reception status from the respective launch site.

14. The engine of claim 12 or any claim thereafter wherein the heatmap represents aggregate flight paths and distances of the radiosondes.

15. The engine of claim 12 or any claim thereafter wherein the wherein the fallout density heatmap visually represents a likelihood distribution of fallout locations per square kilometer on the GUI.

16. The engine of claim 12 or any claim thereafter wherein the GUI is on a device that is remote from the processors.

17. The engine of claim 12 or any claim thereafter wherein the data is received from an engine associated with the at least one launch site.

18. The engine of claim 12 or any claim thereafter wherein the CEP includes a machine learning algorithm that compares an actual final resting place with estimated final resting place, and the CEP is updated based on training of the machine learning algorithm.

19. The engine of claim 12 or any claim thereafter further comprising generating a statistical analysis of the fallout data across multiple launch sites to identify patterns in the geographical distribution and environmental impact of the radiosondes.

20. The engine of claim 12 or any claim thereafter further comprising implementing an alert system that notifies relevant environmental authorities when a radiosonde is predicted to lands in a sensitive or protected area, based on the calculated CEP and predefined environmental protection criteria.

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