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.

Dataset Overview: Flight Price Dataset of Bangladesh

Introduction

The "Bangladesh Flight Fare Dataset" is a synthetic dataset comprising 57,000 flight records tailored to represent air travel scenarios originating from Bangladesh. This dataset simulates realistic flight fare dynamics, capturing key factors such as airline operations, airport specifics, travel classes, booking behaviors, and seasonal variations specific to Bangladesh’s aviation market. It is designed for researchers, data scientists, and analysts interested in flight fare prediction, travel pattern analysis, or machine learning/deep learning applications. By combining real-world inspired statistical distributions and aviation industry standards, this dataset provides a robust foundation for exploring flight economics in a South Asian context.

Dataset Purpose

This dataset aims to: - Facilitate predictive modeling of flight fares, with "Total Fare (BDT)" as the primary target variable. - Enable analysis of travel trends, including the impact of cultural festivals (e.g., Eid, Hajj) and booking timings on pricing. - Serve as a training resource for machine learning (ML) and deep learning (DL) models, with sufficient sample size (50,000) and feature diversity for generalization. - Provide a realistic yet synthetic representation of Bangladesh’s air travel ecosystem, blending domestic and international flight scenarios.

Data Collection and Methodology

The dataset is synthetically generated using Python, with its methodology rooted in real-world aviation data and statistical principles. Below is a detailed breakdown of its construction:

1. Data Components

• Airlines:
  • Count: 25 airlines (21 international, 4 domestic).
  • Source: Compiled from Bangladesh Civil Aviation Authority and Airline History, including major carriers like Emirates, Qatar Airways, and Biman Bangladesh Airlines.
  • Selection: Random uniform choice per flight record to reflect operational diversity.
• Airports:
  • Source Airports: 8 domestic airports (e.g., DAC - Hazrat Shahjalal International Airport, Dhaka).
  • Destination Airports: 20 airports (8 domestic + 12 international, e.g., DXB - Dubai International Airport).
  • Coordinates: Sourced from World Airport Codes, used for distance calculations.
  • Full Names: Added for readability, mapped via a dictionary (e.g., "DAC" → "Hazrat Shahjalal International Airport, Dhaka").
• Travel Classes: Economy, Business, First Class, standard across the industry, randomly assigned with uniform distribution.
• Booking Sources: Direct Booking, Travel Agency, Online Website, reflecting common methods, per Statista, with uniform random selection.
• Aircraft Types: Boeing 777, Airbus A320, Boeing 737, Boeing 787, Airbus A350, assigned based on flight distance, sourced from Boeing and Airbus.

2. Key Calculations

• Distance:

  • Method: Haversine formula calculates great-circle distance: a = sin²(Δφ/2) + cos(φ₁) cos(φ₂) sin²(Δλ/2) c = 2 arctan2(√a, √(1-a)) d = R · c, R = 6371 km

• Purpose: Determines flight duration, aircraft type, and stopovers.

• Source: Wikipedia - Haversine Formula.

• Flight Duration:

• Formula: Duration = max(d/s · U(0.9, 1.1), 0.5), where s is speed (300 km/h for <500 km, 600 km/h for 500-2000 km, 900 km/h for >2000 km), and U is uniform random variation.

• Source: Speeds adjusted from World Atlas, ensuring realism (e.g., DAC to CGP ~45 minutes).

• Fares:

• Base Fares:

• Domestic: Economy (2000-5000 BDT), Business (5000-10000 BDT), First Class (10000-15000 BDT).

• International: Economy (5000-70000 BDT), Business (15000-150000 BDT), First Class (25000-300000 BDT).

• Source: Derived from Trip.com and Expedia, e.g., DAC to LHR ~$380-600 (~41800-66000 BDT at 1 USD = 110 BDT).

• Adjustments:

• Seasonal multipliers (Regular: 1.0, Eid: 1.3, Hajj: 1.5, Winter: 1.2), per demand trends from Timeanddate.com.

• Days Before Departure: 20% discount (60+ days), 10% discount (30-59 days), 20% surge (<5 days), per Skyscanner.

• Taxes: Domestic: 200 BDT; International: 2000-6000 BDT + 15% base fare, per [Bangladesh Civil Aviation Authority](https://www.dgca.g...

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.

As a result of the continued annual growth in global air traffic passenger demand, the number of airplanes that were involved in accidents is on the increase. Although the United States is ranked among the 20 countries with the highest quality of air infrastructure, the U.S. reports the highest number of civil airliner accidents worldwide. 2020 was the year with more plane crashes victims, despite fewer flights The number of people killed in accidents involving large commercial aircraft has risen globally in 2020, even though the number of commercial flights performed last year dropped by 57 percent to 16.4 million. More than half of the total number of deaths were recorded in January 2020, when an Ukrainian plane was shot down in Iranian airspace, a tragedy that killed 176 people. The second fatal incident took place in May, when a Pakistani airliner crashed, killing 97 people. Changes in aviation safety In terms of fatal accidents, it seems that aviation safety experienced some decline on a couple of parameters. For example, there were 0.37 jet hull losses per one million flights in 2016. In 2017, passenger flights recorded the safest year in world history, with only 0.11 jet hull losses per one million flights. In 2020, the region with the highest hull loss rate was the Commonwealth of Independent States. These figures do not take into account accidents involving military, training, private, cargo and helicopter flights.

This dataset contains daily and monthly oceanic precipitation analyses on a 2.5-degree global grid. The data were constructed from the Microwave Sounding Units of seven TIROS-N series satellites, as described in Spencer (1993, J. Climate). Data are available for the period between January 1979 and May 1994.

The daily data are considered to be not as reliable as the monthly data. Before using the daily data, it is highly recommended that you read the documentation associated with it.

The GRIP Campaign Reports dataset consists of various reports filed by scientists during the GRIP campaign which took place 8/15/2010 - 9/30/2010; however, several of the reports are from the planning and test flights. Reports included in this dataset contain information for the Tri Agency Mission Scientists; DC-8, Global Hawk, and WB-57 Platform Scientists; DC-8, Global Hawk, and WB-57 Flight Reports and WB-57 Flight Summary; GRIP Telecons; and TropicalGRIP Forecasts. The Tri Agency Mission Scientists reports, GRIP telecons and Forecast reports were primarily filed daily, while the Platform and Flight reports exist primarily for flight days.

(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.

Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023)

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023) 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 two CSV files:

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.

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.

Data Fields

Both files share the same set of fields, which are detailed below:

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).

day

Description: Date of the recorded data.

Format: YYYY-MM-DD (e.g., 2023-03-28).

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))).

flights

Description: Number of aircraft flights that passed through the grid on that day.

Format: Integer (e.g., 28).

GPS_jumps

Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.

Format: Integer (e.g., 1).

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).

gaps_density

Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.

Format: Decimal (e.g., 0).

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).

event_id_cnty

Description: ACLED event ID corresponding to political violence events in the grid on that day.

Format: String (e.g., BRA69267).

disorder_type

Description: Type of disorder as classified by ACLED (e.g., "Political violence").

Format: String.

event_type

Description: General category of the event according to ACLED (e.g., "Violence against civilians").

Format: String.

sub_event_type

Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").

Format: String.

acled_count

Description: Number of ACLED events in the grid on that day.

Format: Integer (e.g., 1).

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 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 analysis.

Usage and Applications

Security Analysis:

Assess potential correlations between GNSS anomalies and political violence events.

Identify regions with increased risk of GNSS spoofing or signal disruption.

Research and Development:

Develop models to predict socio-political events based on GNSS anomalies.

Study the impact of political instability on aviation safety.

Policy and Decision Making:

Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.

Support conflict analysis and monitoring efforts.

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.

ARGOS PTTs were fitted to 99 flying-foxes, n=49 males, 50 females.We managed data from deployed PTTs in a standardized format in Movebank (http://www.movebank.org/node/2). Prior to analysis, we examined the datasets for inconsistencies, and fixes with ARGOS code Z, along with fixes with longitudes >140 or latitudes <0, were removed. We used daytime fixes (between 10 am and 4 pm) to assign animals to a “roost site” (as mainland Australian flying-foxes do not forage during the day). If high resolution (ARGOS location code 3) daytime fixes occurred within 3.5 km of a “known colony”, we assumed animals were roosting at that site. Where accurate daytime fixes were more than 3.5 km from a known roost location, we manually assigned animals to a new “roost site” located at the center of the cluster of fixes. If multiple tracked individuals roosted at the same location, this new roost site was confidently considered to be a previously unidentified ‘colony’ of flying-foxes.

Data cons...

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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.

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.

TL2ATMTN_8 is the Tropospheric Emission Spectrometer (TES)/Aura Level 2 Atmospheric Temperatures Nadir Version 8 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.

Number of Flights: 7 Days Moving Average: France: Nice data was reported at 474.900 Unit in 17 May 2025. This records an increase from the previous number of 468.400 Unit for 16 May 2025. Number of Flights: 7 Days Moving Average: France: Nice data is updated daily, averaging 300.021 Unit from Jan 2020 (Median) to 17 May 2025, with 1964 observations. The data reached an all-time high of 627.700 Unit in 31 May 2022 and a record low of 11.429 Unit in 16 Apr 2020. Number of Flights: 7 Days Moving Average: France: Nice data remains active status in CEIC and is reported by European Organisation for the Safety of Air Navigation. The data is categorized under Global Database’s France – Table EUROCONTROL.FTV: Number of Flights: 7 Days Moving Average.