N: 54 S: -54 E: 180 W: -180
Description
The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.
The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.
The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.
The GEDI02_A data product contains 548 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.
Known Issues
- Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
- Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers.
- Known Issues: Section 8 of the User Guide provides additional information on known issues.
Version Description
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
Copy Citation
File Naming Convention
The file name begins with the Product Short Name (GEDI02_A), followed by the Julian Date and Time of Acquisition designated as YYYYDDDHHMMSS (2024333190440), the Orbit Number starting with the letter O (O33777), the Sub-Orbit Granule Number (03), Track Number (T03689), the Positioning and Pointing Determination System type where 00 is predict, 01 rapid, 02 and higher is final (02), the Product Generation Executables Version (004), the Granule Production Version (02), the Version Number (V002), and the Data Format (h5).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
Dataset Resources
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A novel light use efficiency model incorporating stand age to improve monitoring of mangrove productivity and biomass accumulation | Li, Shihua, Wei, Xu, Lin, Wei, Li, Peng | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| A 10 m High-precision Canopy Height Product for Nanping City, Fujian Province, China | Yi, Ling, Yao, Xiaojing, Yang, Aixia, Zhang, Liqiang, Wang, Dacheng, Jiao, Yue, Chen, Yaoliang, Liu, Shufu, Chen, Gang, Liu, Yalan | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Assessment of spaceborne and airborne lidar metrics using Fay-Herriot | Gonzalez Mesquida, Bernardo, Pascual, Adrian, Rodriguez-Puerta, Francisco, Guerra-Hernandez, Juan, Perroy, Ryan L., Garcia-Gomez, Rodrigo, Mauro, Francisco | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Comprehensive uncrewed aerial system data for Amazon rainforest at Tiputini Biodiversity Station, Ecuador | Jung, Minyoung, Chang, Anjin, Cannon, Charles H., Rivas-Torres, Gonzalo, Jung, Jinha | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Declining grassland canopy height in China under asymmetric biomass allocation | Li, Huaqiang, Hu, Xinmiao, Li, Fei, Zhang, Yingjun, Lin, Kejian, Wang, Jie, Wang, Jiating | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Reflectance, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Correcting Laser Footprint Geolocation Errors in Mountainous Forests by | Yang, Xiaomeng, Xie, Junfeng, Liu, Ren, Mo, Fan, Zhao, Huakai, Ai, Bo, Tang, Xinming | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Enhancing aboveground biomass estimation in tropical dry forests with GEDI, Sentinel-1/2 and national Forest inventory data | Reyes-Palomeque, Gabriela, Andres-Mauricio, Juan, Hernandez-Martinez, Luis A., Pena-Lara, Victor, Tun-Dzul, Fernando, Hernandez-Stefanoni, Jose Luis | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Integrating species distribution models (SDM) in spatially explicit GIS-tools to support nature-sensitive urban planning | Buhrs, Malte, Rienow, Andreas, Zepp, Harald | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Leveraging GEDI LiDAR and SRTM DSM for estimating temporal changes in tree canopy height | Ali, Yaqub, Rahman, M. Mahmudur, Hossain, Mohammad Mosharraf | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Mapping the structural diversity of Central African and Western US forests using GEDI | Schneider, Fabian D., Dean, Morgan, Ordway, Elsa M., Libalah, Moses B., Ferraz, Antonio | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Spaceborne LiDAR reveals 3D structural differences between natural forests and tree plantations in China | Bai, Hao, Yusup, Asadilla, Guo, Yanpei, Cheng, Kai, Chen, Xiuzhi, Liu, Jing, Tao, Shengli | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Unprecedented Amazonian rainforests damage during the 20232024 droughts | Bai, Hao, Liu, Xiangzhuo, Yang, Hui, Chave, Jerome, Ciais, Philippe, Wigneron, Jean-Pierre, Saatchi, Sassan, Xiao, Jingfeng, Le Toan, Thuy, Hu, Xiaomei, Yang, Ziyan, Wang, Lijun, Fan, Lei, Yao, Yitong, Chen, Xiuzhi, Liu, Yanxu, Xue, Baolin, Guo, Qinghua, Tang, Zhiyao, Liu, Hongyan, Fang, Jingyun, Tao, Shengli | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Reflectance, Anisotropy | |
| Water availability modulates maximum canopy heights of low-elevation Amazonian second-growth forests | Mohan, Midhun, Pastorello, Gilberto Z., Feng, Yanlei, Adrah, Esmaeel, Keller, Michael, Ewane, Ewane Basil, Longo, Marcos, Csillik, Ovidiu, Ferraz, Antonio, Dutta Roy, Abhilash, Meng, Lin, Chambers, Jeffrey Q. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Managed rainforests support higher carbon density and sequestration in the Congo Basin | Sagang, Le Bienfaiteur, Dalagnol, Ricardo, White, Lee, George-Chacon, Stephanie, Favrichon, Samuel, Li, Shuang, Wagner, Fabien, Liu, Zhihua, Zhang, Dafeng, Ngomanda, Alfred, Medjibe, Vincent, Sonke, Bonaventure, Barbier, Nicolas, Ordway, Elsa M., Saatchi, Sassan | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM, Terrestrial Ecosystems, Plant Characteristics, Terrain Elevation | |
| Baseline maps of US mature and old-growth forests for conservation and management | Bruening, Jamis M, May, Paul B, Dubayah, Ralph O, Wertis, Luke, Quinn, Colin, Pederson, Neil, Armstrong, Amanda H, Poulter, Ben | RADAR IMAGERY, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Biomass, Forest Composition/Vegetation Structure, Forests | |
| GlyphTrack: Detection of Anomalies Under Canopy Using Remote Sensing, Geomorphometry, and Environmental Niche Modeling | Ersoy, Pnar, Ersahin, Mustafa, Unal, Ece, Arslan, Dorukhan, Karamustafa, Ebubekir | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Phosphorus, Soil Chemistry, Forest Composition/Vegetation Structure, Digital Elevation/Terrain Model (DEM) | |
| Local prediction of temperate forest structure in Eastern North America using LiDAR, radar, and optical data | Wei, Chenyang, Sweeney, Colin, Roberts, Trevor, Keebler, Hikaru, Fink, Daniel, Zuckerberg, Benjamin, Jarzyna, Marta A, Zhao, Kaiguang | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Enhanced retrieval of forest parameters for vegetated areas in Guangxi province of China through synergistic integration of GEDI and Landsat 8 | Zhang, Bo, Zhang, Li, North, Peter, Huang, Cheng, Li, Mingzhe, Dian, Yuanyong, Zhang, Wangfei, Zhou, Jingjing, Yan, Min, Chen, Bowei | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Sensor fusion and downscaled climate projections reveal climate refugia in the California Channel Islands | Gallerani, Erica M., Ostermann-Kelm, Stacey, Williams, Cameron B., Fricker, G. Andrew, Cavanaugh, Kyle C., Gillespie, Thomas W. | Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Forest canopy cover estimation with machine learning using GEDI and | Seyrek, Eren Can, Narin, Omer Gokberk, Uysal, Murat | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| ForestCarbonNet: integrating terrain-corrected GEDI, Landsat, and PALSAR2 for enhanced forest aboveground carbon density estimation | Lyu, Guanting, Wang, Xiaoyi, Ding, Xiaoyu, Xu, Jinfeng, Wang, Jie, Zhong, Liheng, Huang, Huabing, Pang, Yong | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM | |
| gediDB: A toolbox for processing and providing Global Ecosystem Dynamics Investigation (GEDI) L2A-B and L4A-C data | Besnard, Simon, Dombrowski, Felix, Holcomb, Amelia | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM | |
| First demonstration of spaceborne L-band bistatic single-polarization | Lei, Yang, Li, Weiliang, Yu, Yanghai, Liu, Xiaotong, Xu, Jie, Fu, Anmin, Wan, Jie, Wang, Changcheng, Huang, Wenli, Qiu, Zixuan, Li, Tao, Fu, Haiqiang, Liu, Yu, Shi, Jiancheng | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, Terrain Elevation | |
| Evaluating GEDI for quantifying forest structure across a gradient of degradation in Amazonian rainforests | Doyle, Emily L, Graham, Hugh A, Boulton, Chris A, Lenton, Timothy M, Feldpausch, Ted R, Cunliffe, Andrew M | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Forest Composition/Vegetation Structure, LIDAR WAVEFORM, Evergreen Vegetation, Deciduous Vegetation, Biomass, Shrubland/Scrub, Forests, Grasslands | |
| Estimation of Forest Canopy Height from Spaceborne Full-Waveform LiDAR | Chen, Song, Gong, Ming, Sun, Hua, Chen, Ming, Wang, Binbin | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| /BEAM0000/ancillary/l2a_alg_count | Number of L2A algorithm runs present in file | N/A | uint8 | N/A | 0 to 10 | N/A | N/A |
| /BEAM0000/beam | Beam identifier | N/A | uint16 | N/A | 0 to 12 | N/A | N/A |
| /BEAM0000/channel | Channel identifier | N/A | uint8 | N/A | 0 to 6 | N/A | N/A |
| /BEAM0000/degrade_flag | Non-zero values indicate the shot occured during a degraded period. A non-zero tens digit indicates degraded attitude, a non-zero ones digit indicates a degraded trajectory. 3X=ADF CHU solution unavailable (ST-2); 4X=Platform attitude; 5X=Poor solution (filter covariance large); 6X=Data outage (platform attitude gap also); 7X=ST 1+2 unavailable (similar boresight FOV); 8X=ST 1+2+3 unavailable; 9X=ST 1+2+3 and ISS unavailable; X1=Maneuver; X2=GPS data gap; X3=ST blinding; X4=Other; X5=GPS receiver clock drift; X6=X5+X1; X7=X5+X2; X8=X5+X3; X9=X5+X4. | N/A | uint8 | N/A | 0 to 99 | N/A | N/A |
| /BEAM0000/delta_time | Transmit time of the shot, measured in seconds from the master_time_epoch. By adding the offset contained within /BEAMXXXX/ancillary/master_time_epoch to delta_time, the time in GPS seconds relative to the GPS epoch can be computed. Equivalent to /BEAMXXXX/master_int+/BEAMXXXX/master_frac. | seconds since 2018-01-01 | float64 | N/A | N/A | N/A | N/A |
| /BEAM0000/digital_elevation_model | TanDEM-X elevation at GEDI footprint location | m | float32 | N/A | N/A | N/A | N/A |
| /BEAM0000/digital_elevation_model_srtm | SRTM elevation at GEDI footprint location | m | float32 | N/A | N/A | N/A | N/A |
| /BEAM0000/elevation_bias_flag | Elevations potentially affected by 4bin (~60cm) ranging error | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/elevation_bin0_error | Error in elevation of bin 0 | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/elev_highestreturn | elevation of highest detected return relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/elev_lowestmode | elevation of center of lowest mode relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/energy_total | Integrated counts in the return waveform relative to the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/elevation_1gfit | Elevation corresponding to the center of a single gaussian fit to the waveform, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elevs_allmodes_a1 | Elevations of all modes detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elevs_allmodes_a2 | Elevations of all modes detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elevs_allmodes_a3 | Elevations of all modes detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elevs_allmodes_a4 | Elevations of all modes detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elevs_allmodes_a5 | Elevations of all modes detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elevs_allmodes_a6 | Elevations of all modes detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_highestreturn_a1 | Elevation of the highest return detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_highestreturn_a2 | Elevation of the highest return detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_highestreturn_a3 | Elevation of the highest return detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_highestreturn_a4 | Elevation of the highest return detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_highestreturn_a5 | Elevation of the highest return detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_highestreturn_a6 | Elevation of the highest return detected using algorithm N, relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestmode_a1 | Elevation of the center of the lowest mode detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestmode_a2 | Elevation of the center of the lowest mode detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestmode_a3 | Elevation of the center of the lowest mode detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestmode_a4 | Elevation of the center of the lowest mode detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestmode_a5 | Elevation of the center of the lowest mode detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestmode_a6 | Elevation of the center of the lowest mode detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestreturn_a1 | Elevation of lowest return detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestreturn_a2 | Elevation of lowest return detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestreturn_a3 | Elevation of lowest return detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestreturn_a4 | Elevation of lowest return detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestreturn_a5 | Elevation of lowest return detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/elev_lowestreturn_a6 | Elevation of lowest return detected using algorithm N,relative to reference ellipsoid | m | float32 | N/A | -1000 to 25000 | N/A | N/A |
| /BEAM0000/geolocation/energy_lowestmode_a1 | Energy of lowest mode, detected using algorithm N, in the waveform above the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/energy_lowestmode_a2 | Energy of lowest mode, detected using algorithm N, in the waveform above the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/energy_lowestmode_a3 | Energy of lowest mode, detected using algorithm N, in the waveform above the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/energy_lowestmode_a4 | Energy of lowest mode, detected using algorithm N, in the waveform above the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/energy_lowestmode_a5 | Energy of lowest mode, detected using algorithm N, in the waveform above the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/energy_lowestmode_a6 | Energy of lowest mode, detected using algorithm N, in the waveform above the mean noise level | counts | float32 | N/A | -5000 to 5000000 | N/A | N/A |
| /BEAM0000/geolocation/latitude_1gfit | Latitude corresponding to the center of a single gaussian fit to the waveform | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/geolocation/lats_allmodes_a1 | Latitudes of all modes detected using algorithm N | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/geolocation/lats_allmodes_a2 | Latitudes of all modes detected using algorithm N | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/geolocation/lats_allmodes_a3 | Latitudes of all modes detected using algorithm N | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/geolocation/lats_allmodes_a4 | Latitudes of all modes detected using algorithm N | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/geolocation/lats_allmodes_a5 | Latitudes of all modes detected using algorithm N | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/geolocation/lats_allmodes_a6 | Latitudes of all modes detected using algorithm N | degrees | float64 | N/A | -55 to 55 | N/A | N/A |