The vehicle behavior on the Hanshin Expressway, the urban expressway in Japan, in 0.1 second intervals, for all vehicles driving in target sections that span multiple kilometers are turned into trajectory data over a long period of time. The vehicle trajectory database is intended to realize a more safe, secure and comfortable drive for all highway users. The data is made targeting congestion bottlenecks using image sensing technology. The data is obtained by cameras installed on some of the light poles of the Hanshin Expressway to observe all vehicles in target sections at a 0.1 second interval. On top of that, other data such as vehicle length and other vehicle attributes together with location data at 0.1 second intervals and corresponding road surface information (longitudinal and cross slopes, road curvature, etc.) are combined.
The data covers roughly 100% of each vehicle’s trajectory for all vehicles in the target sections. Thus, the trajectories for each vehicle are generally continuous in the target section. The data can be used for various analyses and studies such as estimation of traffic conditions, understanding the influence of vehicle behavior, creation of scenarios for the various actual traffic conditions, visualization of actual traffic conditions, reinterpretation and rationalization of installed sensors and so on. The data can also be expected to contribute to improve road traffic services. As of September 2018, five one-hour traffic datasets obtained at the about 2 km section of the Hanshin Expressway, can be utilized.

TRACE-P_Trajectory_DC8_Data is the trajectory data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia.  In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.

We processed a unified trajectory dataset for automated vehicles' longitudinal behavior from 14 distinct sources. The extraction and cleaning of the dataset contains the following three steps - 1. extraction of longitudinal trajectory data, 2. general data cleaning, and 3. data-specific cleaning. The dataset obtained from step 2 and step 3 are named as the longitudinal trajectory data and car-following trajectory data. We also analyzed and validated the data by multiple methods. The obtained datasets are provided in this repo. The Python code used to analyze the datasets can be found at https://github.com/CATS-Lab/Filed-Experiment-Data-ULTra-AV. We hope this dataset can benefit the study of microscopic longitudinal AV behaviors.

The ATDM Trajectory Validation project developed a validation framework and a trajectory computational engine to compare and validate simulated and observed vehicle trajectories and dynamics. The field data were used to demonstrate how on-site instrumented vehicle data can be used to validate simulated vehicle dynamics using the validation framework. The vehicle trajectory data were collected in a separate task of the Active Transportation Demand Management (ATDM) Trajectory Level Validation project. The primary project objective was to develop a methodology to validate simulated vehicle dynamics at the trajectory level. Microscopic and macroscopic performance measures were calculated from the trajectory data and used in a number of validation tests related to safety, vehicle limits, driver comfort levels, and traffic flow

Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

All methods are described in the paper "Generation of Synthetic Urban Vehicle Trajectories", IEEE BigData 2022.

Shaanxi Province

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments. I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pi

Heliocentric trajectories for Voyager 2 in Heliographic, HG, Heliographic Inertial, HGI, and Solar Ecliptic, SE, Coordinates The original trajectory data are taken from http://ssd.jpl.nasa.gov/horizons.cgi where users can find many more objects. In the case of orbit data for planets, the orbit data can be used as a proxy for spacecraft ephemeris that are in orbit about the planets. On a heliospheric scale, differences between the planet orbital tarjectory and that of the spacecraft are very small. For instance, the heliocentric longitudes differ by only 0.25° for a spacecraft stationed near the L1 Lagrange point at approximately 100 Earth radii upstream of the Earth. The production of the HG, HGI, and SE trajectory data requires a values for the "Equinox Epoch", which is defined as the epoch time when the direction from the Earth to the sun at the time of the vernal equinox when the sun seems to cross equatorial plane of the Earth from below. This direction is called the First Point of Aries, FPA and it is not a fixed direction but drifts by about 1.4° per century or 50.26" per year. In addition, there are tiny irregularities in FPA drift that are on the order of 1" per year or less. The Equinox Epoch can be determined by using a variety of methods for calculating the instantaneous FPA longitudinal direction and whether the tiny irregularities have been smoothed or averaged out. Four methods for determining the Equinox Epoch are in common usage: +---------------------------------------------------------------------+ Method Name FPA Longitude Definition --------------------------------------------------------------------- B1950.0 the actual FPA at 22:09 UT on December 31, 1949 J2000.0 the smoothed FPA at 12:00 UT on January 1, 2000 True of Date the actual FPA at 00:00 UT on the date of interest Mean of Date the smoothed FPA at 00:00 UT on the date of interest +---------------------------------------------------------------------+ The heliocentric trajectory data included in this data product have been calculated by using the Equinox Epoch: defined via the "Mean of Date" method. More precise coordinates, and some planet-centered coordinates, are found in the "traj" subdirectories of spacecraft specific directories at https://spdf.gsfc.nasa.gov/pub/data/ and http://ssd.jpl.nasa.gov/horizons.cgi.

A spatial cluster of trajectories refers to objects that follow similar paths, revealing shared movement trends and aiding in anomaly detection. However, detecting clusters in trajectory data becomes challenging when the cluster-to-noise density ratio (CNDR) is low. For example, clusters in free-range sheep movements are easily seen due to their group behaviour, whereas the diversity of human movement introduces significant noise, making clustering difficult. The L-function, widely used for clustering detection in various data types (e.g., point or OD flow data), captures aggregation changes across scales without relying on predefined thresholds, offering potential for low CNDR trajectory data. Thus, we define a trajectory space to derive the Trajectory L (TL)-function for multipoint trajectories. Then we use the second derivative of the TL-function and the local TL-function to identify cluster sizes and extract clusters. Inflection points in the second derivatives enable the detection of subtle changes in aggregation, allowing for precise and sensitive cluster identification. Simulation experiments show that our method outperforms four state-of-the-art approaches in detecting clusters under low CNDR conditions while avoiding parameter dependency. We validated the generality and robustness of our method using both taxi GPS trajectories and mobile phone signalling trajectories.

The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day. As part of this dataset, the following files were provided: I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent

The city pair data set consists of one full year of 3536 trajectories flying from Paris--Orly (LFPO) to Toulouse--Blagnac (LFBO) airports between January and December 2017. This data set has been requested based on a set of 28 callsigns commonly attributed to trajectories serving this route.The airspace data set consists of seven months of trajectories flying through the LFBBPT airspace of the French Bordeaux Area Control Centre (ACC) between January 1st and August 6th 2017. The data set is limited to 14,461 trajectories crossing the airspace during the time intervals when the sector was operationally deployed according to the Sector Configurations Plans (SCP), also known as opening schemes. The goal of using the SCP is for the traffic under analysis to be representative of operational situations with a level of workload deemed acceptable by controllers.The landing data set consists of 19,480 trajectories landing at Zurich airport (LSZH) between October 1st and November 30rd 2019. We relied on The OpenSky Network database to properly label trajectories landing at LSZH.

Goddard’s LiDAR, Hyperspectral, and Thermal Imagery (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.

The purpose of G-LiHT’s Trajectory data product (GLTRAJECTORY) is to provide aircraft location and orientation to support and supplement other G-LiHT data products.

GLTRAJECTORY data are processed as a Google Earth overlay Keyhole Markup Language (KML) file over the extent of an entire flight path. A low resolution browse is also provided to show the flight path.

Data to be found in the data fileset, metadata for each experiment will also be produced and posted, see links. This is intended to become a repository for all data analysed with CeTrAn, an open source centroid trajetory analysis tool written in R (third link). The data will be uploaded while analysed (automatically), making it open by default. I am looking for people interested in developing the tool and the database structure. I am no specialist, not in R, not in database structures, but I so far managed to find help in the R community. Come and comment, your feedback is needed !!

Particle trajectory data from MD simulations utilizing the TIP4P/2005 water model and the Madrid-2019 force field. The simulation systems contain 4000 water molecules and different number of solute ion group (NaCl, KCl or CaCl2) to achieve concentration ranging from 0M to about 5.0M. The simulations were commenced within an NVT ensemble for 1ns and were then carried out in an NPT ensemble for 11ns with a time step of 1fs at a temperature ranging from 223.15K to 373.15K and a pressure of 1bar. Particle trajectories of a total of 101 moments were recorded at a uniform interval of 0.1ns in the last 10ns simulation.

This dataset was used to support our work and provided to the review for reference.

Larvae example trajectories

This is 2x 10.000 example trajectories from biophysical experiments performed with Parcels.

The trajectories are a tiny subset of a much bigger collection of trajectories that have been simulated with the aim of learning about the fate of larvae of different species of fish. Note that we cannot give a lot of background on the biological study here, because we are still working on this publication.

Also note, that these trajectories should not be used for biological or physical science but merely serve as study objects for developing, testing, or benchmarking (stasticical) methods and algorithms.

Different physics

The trajectories are taken from two classes of biophysical simulations which differ in the physical processes they take into account when estimating horizontal movement of the larvae at the surface.

One experiment only took into account ocean currents from a state estimate of the true ocean circulation in the Mediterranean Sea. The other experiment took into account ocean currents and the horizontal movement due to Stokes Drift which was estimated based on a wave state estimate for the Mediterranean Sea.

Data files

There are two data files:

• df_nostokes_first_10000_traj.csv.gz contains trajectories of particles moving only according to the ocean currents.
• df_stokes_first_10000_traj.csv.gz contains trajectories of particles moving according to the ocean currents and according to Stokes Drift.

There are also two non-compressed sample data files each of which contains the first 10 lines of the compressed files: df_nostokes_first_10_lines.csv and df_stokes_first_10_lines.csv.

Columns and their meaning

• "obs" contains the time step since the larva started to exist. Each trajectory covers 40 days of hourly positions.

• "traj" indicates the trajectory ID.

• "distance" contains the distance traveled through the water since the start of the trajectory in kilometers.

• "land" is measuring the land influence at the given position. The velocity and possibly wave data used in the simulation comes on a regular horizontal grid. Velocity on land is set to zero. At runtime of the simulation, the particle is moved according to a velocity that is linearly interpolated to the position of the particle. (This amounts to simulating trajectories with a No-slip condition.) Values land==0 indicate that no data from land points was used in the interpolation. Values land==1 indicates that all four interpolation base points were on land.

• "lat" and "lon" contain the horizontal positions in degrees Latitude and Longitude.

• "temp" contains the ambient temperature in degrees Celsius the simulated larva would have felt at the given time and position. Note that the temperature at positions under land influence (land > 0) is wrong, because there is a temperature of 0 degrees Celsius assumed for each land point.

• "time" contains time stamps.

• "z" contains the vertical positions of the simulated larva in meters counted downwards.

Using the data

This data set is licensed under a Creative Commons Attribution 4.0 International License.

If you use the data, we'd love to get a notice to wrath@geomar.de. This is, however, not required.

WNA-FLEXPART-BackTraj-2002 is the 2002 Western North America Back Trajectory data using the FLEXible PARTicle (FLEXPART) dispersion model. Data collection for this product is complete.Backward simulations of airmass transport using a Lagrangian Particle Dispersion Model (LPDM) framework can establish source-receptor relationships (SRRs), supporting analysis of source contributions from various geospatial regions and atmospheric layers to downwind observations. In this study, we selected receptor locations to match gridded ozone observations over Western North America (WNA) from ozonesonde, lidar, commercial aircraft sampling, and aircraft campaigns (1994-2021). For each receptor, we used the FLEXible PARTicle (FLEXPART) dispersion model, driven by ERA5 reanalysis data, to achieve 15-day backwards SRR calculations, providing global simulations at high temporal (hourly) and spatial (1° x 1°) resolution, from the surface up to 20 km above ground level. This product retains detailed information for each receptor, including the gridded ozone value product, allowing the user to illustrate and identify source contributions to various subsets of ozone observations in the troposphere above WNA over nearly 3 decades at different vertical layers and temporal scales, such as diurnal, daily, seasonal, intra-annual, and decadal. This model product can also support source contribution analyses for other atmospheric components observed over WNA, if other co-located observations have been made at the spatial and temporal scales defined for some or all of the gridded ozone receptors used here.

WNA-FLEXPART-BackTraj-2018 is the 2018 Western North America Back Trajectory data using the FLEXible PARTicle (FLEXPART) dispersion model. Data collection for this product is complete.Backward simulations of airmass transport using a Lagrangian Particle Dispersion Model (LPDM) framework can establish source-receptor relationships (SRRs), supporting analysis of source contributions from various geospatial regions and atmospheric layers to downwind observations. In this study, we selected receptor locations to match gridded ozone observations over Western North America (WNA) from ozonesonde, lidar, commercial aircraft sampling, and aircraft campaigns (1994-2021). For each receptor, we used the FLEXible PARTicle (FLEXPART) dispersion model, driven by ERA5 reanalysis data, to achieve 15-day backwards SRR calculations, providing global simulations at high temporal (hourly) and spatial (1° x 1°) resolution, from the surface up to 20 km above ground level. This product retains detailed information for each receptor, including the gridded ozone value product, allowing the user to illustrate and identify source contributions to various subsets of ozone observations in the troposphere above WNA over nearly 3 decades at different vertical layers and temporal scales, such as diurnal, daily, seasonal, intra-annual, and decadal. This model product can also support source contribution analyses for other atmospheric components observed over WNA, if other co-located observations have been made at the spatial and temporal scales defined for some or all of the gridded ozone receptors used here.