The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

In prospective human exploration of outer space the need to maintain a species over several generations under changed gravity conditions may arise. This paper reports the analysis of the third generation of fruit fly Drosophila melanogaster obtained during the 44.5-day space flight (Foton-M4 satellite 2014 Russia) followed by the fourth generation on Earth and the fifth generation under conditions of a 12-day space flight (2014 in the Russian Segment of the ISS). The obtained results show that it is possible to obtain the third-fifth generations of a complex multicellular Earth organism under changed gravity conditions (in the cycle weightlessness - Earth - weightlessness) which preserves fertility and normal development. However there were a number of changes in the expression levels and content of cytoskeletal proteins that are the key components of the spindle apparatus and the contractile ring of cells.

Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from AirNav and Wingbits.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

The data set is updated daily.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/flights-2/

This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

Geospatial Dataset of GNSS Anomalies and Political Violence Events

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.

Dataset Files

The dataset consists of three CSV files:

1. Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv
• Description: Contains all grids and dates that had aircraft traffic during 2023.
• Number of Records: 6,777,228
• Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.
2. Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv
• Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.
• Number of Records: 718,237
• Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.
3. Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv
• Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.
• Number of Records: 25,770
• Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.

Data Fields: Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Format: String combining latitude and longitude (e.g., -10.0_-36.0).
2. day
• Description: Date of the recorded data.
• Format: YYYY-MM-DD (e.g., 2023-03-28).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).
4. flights
• Description: Number of aircraft flights that passed through the grid on that day.
• Format: Integer (e.g., 28).
5. GPS_jumps
• Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.
• Format: Integer (e.g., 1).
6. GPS_gaps
• Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.
• Format: Integer (e.g., 0).
7. gaps_density
• Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.
• Format: Decimal (e.g., 0).
8. jumps_density
• Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.
• Format: Decimal (e.g., 0.035714286).
9. event_id_cnty
• Description: ACLED event ID corresponding to political violence events in the grid on that day.
• Format: String (e.g., BRA69267).
10. disorder_type
• Description: Type of disorder as classified by ACLED (e.g., "Political violence").
• Format: String.
11. event_type
• Description: General category of the event according to ACLED (e.g., "Violence against civilians").
• Format: String.
12. sub_event_type
• Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").
• Format: String.
13. acled_count
• Description: Number of ACLED events in the grid on that day.
• Format: Integer (e.g., 1).
14. acled_flag
• Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).
• Format: Integer (0 or 1).

Data Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv

The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.
• Format: String combining latitude and longitude (e.g., -0.5_-79.0).
2. year_month
• Description: Month and year of the aggregated data.
• Format: String in Mon-YY format (e.g., Jan-23).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...))
  (e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).
4. flights
• Description: Total number of aircraft flights that passed through the grid cell during the month.
• Format: Integer (e.g., 1230).
5. GPS_jumps
• Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.
• Format: Integer (e.g., 13).
6. GPS_gaps
• Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.
• Format: Integer (e.g., 0).
7. event_id_cnty
• Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.
• Format: String (e.g., ECU3151;ECU3158;ECU3150).
8. disorder_type
• Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.
• Format: String.
9. event_type
• Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").
• Format: String.
10. sub_event_type

• Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").
• Format: String.

1. acled_count

• Description: Total number of ACLED conflict events in the grid cell during the month.
• Format: Integer (e.g., 2).

1. acled_flag

• Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.
• Format: Integer (0 or 1).

1. gaps_density

• Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.
• Format: Decimal (e.g., 0.0).

1. jumps_density

• Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.
• Format: Decimal (e.g., 0.0106).

Data Sources

• GNSS Anomalies Data:
• Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.
• GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).
• Political Violence Events Data:
• Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.

Temporal and Spatial Coverage

• Temporal Coverage:
• From January 1, 2023, to December 31, 2023.
• Daily records provide temporal granularity for time-series analysis.
• Spatial Coverage:
• Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Each grid cell represents an area on Earth's surface, facilitating spatial

China Air: Passenger Traffic: Domestic data was reported at 664.657 Person mn in 2024. This records an increase from the previous number of 590.516 Person mn for 2023. China Air: Passenger Traffic: Domestic data is updated yearly, averaging 95.618 Person mn from Dec 1970 (Median) to 2024, with 42 observations. The data reached an all-time high of 664.657 Person mn in 2024 and a record low of 0.210 Person mn in 1970. China Air: Passenger Traffic: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Passenger Traffic.

Description Welcome to the Drone-Based Malware Detection dataset! This dataset is designed to aid researchers and practitioners in exploring innovative cybersecurity solutions using drone-collected data. The dataset contains detailed information on network traffic, drone sensor readings, malware detection indicators, and environmental conditions. It offers a unique perspective by integrating data from drones with traditional network security metrics to enhance malware detection capabilities.

Dataset Overview The dataset comprises four main categories:

Network Traffic Data: Captures network traffic attributes including IP addresses, ports, protocols, packet sizes, and various derived metrics. Drone Sensor Data: Includes GPS coordinates, altitude, speed, heading, battery level, and other sensor readings from drones. Malware Detection Data: Contains indicators and scores relevant to detecting malware, such as anomaly scores, suspicious IP counts, reputation scores, and attack types. Environmental Data: Provides context through environmental conditions like location type, noise level, weather conditions, and more. Files and Features The dataset is divided into four separate CSV files:

network_traffic_data.csv

timestamp: Date and time of the traffic event. source_ip: Source IP address. destination_ip: Destination IP address. source_port: Source port number. destination_port: Destination port number. protocol: Network protocol (TCP, UDP, ICMP). packet_length: Length of the network packet. payload_data: Content of the packet payload. flag: Network flag (SYN, ACK, FIN, RST). traffic_volume: Volume of traffic in bytes. flow_duration: Duration of the network flow. flow_bytes_per_s: Bytes per second for the flow. flow_packets_per_s: Packets per second for the flow. packet_count: Number of packets in the flow. average_packet_size: Average size of packets. min_packet_size: Minimum packet size. max_packet_size: Maximum packet size. packet_size_variance: Variance in packet sizes. header_length: Length of the packet header. payload_length: Length of the packet payload. ip_ttl: Time to live for the IP packet. tcp_window_size: TCP window size. icmp_type: ICMP type (echo_request, echo_reply, destination_unreachable). dns_query_count: Number of DNS queries. dns_response_count: Number of DNS responses. http_method: HTTP method (GET, POST, PUT, DELETE). http_status_code: HTTP status code (200, 404, 500, 301). content_type: Content type (text/html, application/json, image/png). ssl_tls_version: SSL/TLS version. ssl_tls_cipher_suite: SSL/TLS cipher suite. drone_data.csv

latitude: Latitude of the drone. longitude: Longitude of the drone. altitude: Altitude of the drone. speed: Speed of the drone. heading: Heading of the drone. battery_level: Battery level of the drone. drone_id: Unique identifier for the drone. flight_time: Total flight time. signal_strength: Strength of the drone's signal. temperature: Temperature at the drone's location. humidity: Humidity at the drone's location. pressure: Atmospheric pressure at the drone's location. wind_speed: Wind speed at the drone's location. wind_direction: Wind direction at the drone's location. gps_accuracy: Accuracy of the GPS signal. malware_detection_data.csv

anomaly_score: Score indicating the level of anomaly detected. suspicious_ip_count: Number of suspicious IP addresses detected. malicious_payload_indicator: Indicator for malicious payload (0 or 1). reputation_score: Reputation score for the network entity. behavioral_score: Behavioral score indicating potential malicious activity. attack_type: Type of attack (DDoS, phishing, malware). signature_match: Indicator for signature match (0 or 1). sandbox_result: Result from sandbox analysis (clean, infected). heuristic_score: Heuristic score for potential threats. traffic_pattern: Pattern of the traffic (burst, steady). environmental_data.csv

location_type: Type of location (urban, rural). nearby_devices: Number of nearby devices. signal_interference: Level of signal interference. noise_level: Noise level in the environment. time_of_day: Time of day (morning, afternoon, evening, night). day_of_week: Day of the week. weather_conditions: Weather conditions (sunny, rainy, cloudy, stormy). Usage and Applications This dataset can be used for:

Cybersecurity Research: Developing and testing algorithms for malware detection using drone data. Machine Learning: Training models to identify malicious activity based on network traffic and drone sensor readings. Data Analysis: Exploring the relationships between environmental conditions, drone sensor data, and network traffic anomalies. Educational Purposes: Teaching data science, machine learning, and cybersecurity concepts using a comprehensive and multi-faceted dataset.

Acknowledgements This dataset is based on real-world data collected from drone sensors and network traffic monitoring s...

India Passenger Traffic: All Airports data was reported at 36,106,614.000 Person in Mar 2025. This records an increase from the previous number of 34,911,116.000 Person for Feb 2025. India Passenger Traffic: All Airports data is updated monthly, averaging 18,787,598.000 Person from Nov 2009 (Median) to Mar 2025, with 185 observations. The data reached an all-time high of 37,541,465.000 Person in Dec 2024 and a record low of 61,861.000 Person in Apr 2020. India Passenger Traffic: All Airports data remains active status in CEIC and is reported by Airports Authority of India. The data is categorized under Global Database’s India – Table IN.TA007: Airport Authority of India: Passenger Traffic. [COVID-19-IMPACT]

(UNCLASSIFIED) - In general, transportation infrastructure in Liberia is sub-par by most standards. Likewise, air transportation and modern infrastructure lags behind due to both conflict and a lack of capital investment. That being said, several major airlines operate out of the two international airports in Liberia including Astraeus, Bellview and SN Brussels Airlines as well as Slok Air International and Weasua Air Transport. Roberts International Airport is actually located outside of the capital of Monrovia, but remains the nation’s busiest aviation facility. Spriggs Payne Airport is centrally located in Monrovia but is a smaller facility with only a few arrivals per day. The remaining aviation facilities in the nation consist of unpaved runways in various cities. Some are finished, maintained runways of packed dirt while others are simply grass.Further complicating the travel situation has been the recent outbreak of the Ebola virus. Several airlines have suspended all flights to the country and currently it is unknown when or whether regular service will resume. Many other international airlines have begun considering suspending flights to and from Liberia as well.Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name ADM3_NAME - Administration level three identification / name NAME - Name of airfield TYPE - Classification in the geodatabase (Civil, Military, Dual) ICAO - International Civil Aviation Organization four letter airport location indicator IATA - International Air Transport Association three letter airport location indicator RUNWAY - Paved or unpaved runway N_RUNWAYS - Number of runways R1_SURFACE - Runway surface type (Asphalt, Dirt, Grass, Concrete) R2_SURFACE - Second runway surface type (Asphalt, Dirt, Grass, Concrete) R_LENGTH - Length of runway (meters) R_WIDTH - Runway width (meters) USE - Use description (Regional, Local, International) CUSTOMS - Presence of customs (Yes or No) SPA_ACC Spatial accuracy of site location (1- high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding the airfield SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from various air transportation websites as well as open source databases. DigitalGlobe imagery was used to assess and when necessary, improve the location of features. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aircraft Charter World, "Airports in Liberia." Last modified January 2009. Accessed September 29, 2014. http://www.aircraft-charter-world.com.DigitalGlobe, "DigitalGlobe Imagery Archive." Last updated September 2014. Accessed September 29, 2014. Falling Rain Global Gazetteer, "Directory of Airports in Liberia." Last modified 2010. Accessed September 29, 2014. http://www.fallingrain.com.Great Circle Mapper, "Liberia." Last modified January 2013. Accessed September 29, 2014. http://gc.kls2.com.GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. http://www.geonames.org.Google, "Liberia." Last modified September 2014. Accessed September 29, 2014. http://www.google.com.World Airport Codes, "Directory of Airports in Liberia." Last modified 2010. Accessed September 29, 2014. http://www.fallingrain.com.Sources (Metadata)"Transport in Liberia." The Lonely Planet. September 29, 2014. Accessed October 2, 2014. http://www.lonelyplanet.com.Zennie, Michael. "U.S. Airlines in Contact with Government about Ebola Concerns." The Daily Mail, October 2, 2014. Accessed October 2, 2014. http://www.dailymail.co.uk.

The volume of air-freight transport in the United Arab Emirates was forecast to decrease between 2024 and 2029 by in total 0.02 billion ton-kilometers. This overall decrease does not happen continuously, notably not in 2026 and 2027. The volume of air-freight transport is estimated to amount to 14 billion ton-kilometers in 2029. As defined by Worldbank, air freight refers to the summated volume of freight, express and diplomatic bags carried across the various flight stages (from takeoff to the next landing). The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the volume of air-freight transport in countries like Oman and Israel.

The airborne radiometric data are provided here as part of the data release, Airborne magnetic and radiometric survey, southeast Missouri and western Illinois, 2018-2019. The radiometric data include the processed aeroradiometric data provided by the contractor and geoTIFFs showing the potassium (K.tif), uranium (eU.tif), and thorium (eTh.tif) content. This data release provides digital flight line data for a high-resolution horizontal magnetic gradient and radiometric survey over an area of southeast Missouri and western Illinois. The survey represents the first airborne geophysical survey conducted as part of the U.S. Geological Survey (USGS) Earth Mapping Resource Initiative (Earth MRI) effort (Day, 2019). Earth MRI is a cooperative effort between the USGS, the Association of American State Geologists, and other Federal, State, and private sector organizations to improve our knowledge of the geologic framework of the United States. Data for this survey were collected by Terraquest, Ltd. under contract with the USGS using a fixed wing aircraft with magnetometers mounted in the tail stinger and each wing tip pod and a fully calibrated gamma ray spectrometer. The survey operated out of the Farmington, Missouri airport from December of 2018 to May of 2019. The survey covers a 146-kilometer x 154-kilometer area centered on the town of Ironton, Missouri. Data were collected along north-south flight lines spaced 300 meters (m) apart with east-west tie lines flown every 3000 m. A mean terrain clearance of 117 m was maintained except where safety dictated a higher elevation. A total of 68,375-line kilometers (km) of data were collected. Files that are available in this publication include flight line data for the magnetic gradient survey, flight line data for the radiometric survey and a report describing the survey parameters, field operations, quality control and data reduction procedures. A zip file is provided that contains the contractor's deliverable products that includes Geosoft databases and grids for the magnetic and radiometric survey and the report describing the survey and data reduction. The 2018-2019 survey was designed to augment and connect two previous USGS airborne geophysical surveys. Adjacent surveys include a magnetic and gravity gradiometry helicopter survey flown in 2014 (McCafferty, 2016a) centered on the Pea Ridge iron mine and a magnetic and radiometric survey flown in 2016 and centered on Ironton, Missouri (McCafferty, 2016b).

Realtime micro-scale weather simulations were performed to support UAV (Uncrewed Aerial Vehicle) flights during the ISARRA Lower Atmospheric Process Studies at Elevation a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field deployment. These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Details of the model configuration and data formats are given in Pinto et al. (2021).

LAPSE-RATE took place in the San Luis Valley of Colorado during July of 2018. Goals of LAPSE-RATE were to sample the finescale evolution of the boundary layer and associated sub-mesoscale flows across a sub-alpine desert valley using a combination of surface-based instrumentation and in situ data collected using numerous, low-flying small UAVs. The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. The simulation used for next-day planning was run using forcing data from NCEP's Global Forecast System (GFS) while the simulation available each morning of the experiment to support in flight operations was run using data from the NCEP High Resolution Rapid Refresh (HRRR), Version 3. Both simulations were valid between 04:00 and 16:00 MDT. The dataset consists of two sets of files: 3D grids and high temporal resolution time series and profiles for a select group of grid points. The 3D grids consist of all relevant basic state parameters (P, T, U, RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields, interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity. Point data were stored for select grid points coincident with 3 fixed observation sites set up during LAPSE-RATE (i.e., Saguache, Moffat and Leach Airfield). The 3D grid files are stored every 10 minutes, while grid point data have a time resolution of 0.666 and 6 seconds for the 111 m grid spacing domain and 1 km grid spacing domain, respectively.

; Please see the README files for more details describing the dataset.

This publication provides digital flight line data for a high-resolution horizontal magnetic gradient and radiometric survey over an area of southeast Missouri and western Illinois. The survey represents the first airborne geophysical survey conducted as part of the U.S. Geological Survey (USGS) Earth Mapping Resource Initiative (Earth MRI) effort (Day, 2019). Earth MRI is a cooperative effort between the USGS, the Association of American State Geologists, and other Federal, State, and private sector organizations to improve our knowledge of the geologic framework of the United States. Data for this survey were collected by Terraquest, Ltd. under contract with the USGS using a fixed wing aircraft with magnetometers mounted in the tail stinger and each wing tip pod and a fully calibrated gamma ray spectrometer. The survey operated out of the Farmington, Missouri airport from December of 2018 to May of 2019. The survey covers a 146-kilometer x 154-kilometer area centered on the town of Ironton, Missouri. Data were collected along north-south flight lines spaced 300 meters (m) apart with east-west tie lines flown every 3000 m. A mean terrain clearance of 117 m was maintained except where safety dictated a higher elevation. A total of 68,375-line kilometers (km) of data were collected. Files that are available in this publication include flight line data for the magnetic gradient survey, flight line data for the radiometric survey and a report describing the survey parameters, field operations, quality control and data reduction procedures. A zip file is provided that contains the contractor's deliverable products that includes Geosoft databases and grids for the magnetic and radiometric survey and the report describing the survey and data reduction. The 2018-2019 survey was designed to augment and connect two previous USGS airborne geophysical surveys. Adjacent surveys include a magnetic and gravity gradiometry helicopter survey flown in 2014 (McCafferty, 2016a) centered on the Pea Ridge iron mine and a magnetic and radiometric survey flown in 2016 and centered on Ironton, Missouri (McCafferty, 2016b).

The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.

This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters.

Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.

TL2ATMTN_7 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Nadir Version 7 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. TES Level 2 data contains retrieved species (or temperature) profiles at the observation targets and the estimated errors. The geolocation, quality, and other data (e.g., surface characteristics for nadir observations) were also provided. L2 modeled spectra were evaluated using radiative transfer modeling algorithms. The process, referred to as retrieval, compared observed spectra to the modeled spectra and iteratively updated the atmospheric parameters. L2 standard product files included information for one molecular species (or temperature) for an entire global survey or special observation run. A global survey consisted of a maximum of 16 consecutive orbits. Nadir and limb observations were added to separate L2 files, and a single ancillary file was composed of data that are common to both nadir and limb files. A Nadir sequence within the TES Global Survey was a fixed number of observations within an orbit for a Global Survey. Prior to April 24, 2005, it consisted of two low resolution scans over the same ground locations. After April 24, 2005, Global Survey data consisted of three low resolution scans. The Nadir standard product consists of four files, where each file is composed of the Global Survey Nadir observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Nadir observations only used a single set of filter mix. A Limb sequence within the TES Global Survey involved three high-resolution scans over the same limb locations. The Limb standard product consisted of four files, where each file was composed of the Global Survey Limb observations from one of four focal planes for a single orbit, i.e. 72 orbit sequences. The Global Survey Limb observations used a repeating sequence of filter wheel positions. Special Observations could only be scheduled during the 9 or 10 orbit gaps in the Global Surveys, and were conducted in any of three basic modes: stare, transect, step-and-stare. The mode used depended on the science requirement. A Global Survey consisted of observations along 16 consecutive orbits at the start of a two day cycle, over which 4,608 retrievals were performed (1,152 nadir retrievals and 1,152 retrievals in time ordered sequence for each limb observation). Each observation was the input for retrievals of species Volume Mixing Ratios (VMR), temperature profiles, surface temperature, and other data parameters with associated pressure levels, precision, total error, vertical resolution, total column density, and other diagnostic quantities. Each TES Level 2 standard product reported information in a swath format conforming to the HDF-EOS Aura File Format Guidelines. Each Swath object was bounded by the number of observations in a global survey and a predefined set of pressure levels, representing slices through the atmosphere. Each standard product could have had a variable number of observations depending upon the Global Survey configuration and whether averaging was employed. Also, missing or bad retrievals were not reported. Each limb observation Limb 1, Limb 2 and Limb 3, were processed independently. Thus, each limb standard product consisted of three sets where each set consisted of 1,152 observations. For TES, the swath object represented one of these sets. Thus, each limb standard product consisted of three swath objects, one for each observation, Limb 1, Limb 2, and Limb 3. The organization of data within the Swath object was based on a superset of Upper Atmosphere Research Satellite (UARS) pressure levels used to report concentrations of trace atmospheric gases. The reporting grid was the same pressure grid used for modeling. There were 67 reporting levels from 1211.53 hPa, which allowed for very high surface pressure conditions, to 0.1 hPa, about 65 km. In addition, the products reported values directly at the surface when possible or at the observed cloud top level. Thus, in the Standard Product files each observation could potentially contain estimates for the concentration of a particular molecule at 67 different pressure levels within the atmosphere. However, for most retrieved profiles, the highest pressure levels were not observed due to a surface at lower pressure or cloud obscuration. For pressure levels corresponding to altitudes below the cloud top or surface, where measurements were not possible, a fill value was be applied.To minimize the duplication of information between the individual species standard products, data fields common to each species (such as spacecraft coordinates, emissivity, and other data fields) have been collected into a separate standard product, termed the TES L2 Ancillary Data product (ESDT short name: TL2ANC). Users of this product should also obtain the Ancillary Data product.

The Convection And Moisture EXperiment (CAMEX)-4 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.

The Convection And Moisture EXperiment (CAMEX)-3 Mission Reports were filed every day that an aircraft flew in support of the experiment. The reports include a short description of the day's mission, its objective, and notes.