N: 54 S: -54 E: 180 W: -180
Error message
The submitted value 10 in the Items element is not allowed.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 156 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)
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Forest above-ground woody biomass estimation using multi-temporal space-borne LiDAR data in a managed forest at Haldwani, India | Musthafa, Mohamed, Singh, Gulab | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| GEDI launches a new era of biomass inference from space | Dubayah, Ralph, Armston, John, Healey, Sean P, Bruening, Jamis M, Patterson, Paul L, Kellner, James R, Duncanson, Laura, Saarela, Svetlana, Stahl, Goran, Yang, Zhiqiang, Tang, Hao, Blair, J Bryan, Fatoyinbo, Lola, Goetz, Scott, Hancock, Steven, Hansen, Matthew, Hofton, Michelle, Hurtt, George, Luthcke, Scott | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Evergreen Vegetation, Shrubland/Scrub, Grasslands, Forests, Deciduous Vegetation, Biomass, LIDAR WAVEFORM, Terrestrial Ecosystems | |
| Improving forest above-ground biomass retrieval using multi-sensor L- and C- Band SAR data and multi-temporal spaceborne LiDAR data | Musthafa, Mohamed, Singh, Gulab | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Global evaluation of the Ecosystem Demography model (ED v3. 0) | Ma, Lei, Hurtt, George, Ott, Lesley, Sahajpal, Ritvik, Fisk, Justin, Lamb, Rachel, Tang, Hao, Flanagan, Steve, Chini, Louise, Chatterjee, Abhishek, Sullivan, Joseph | Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Carbonaceous Aerosols, Nitrogen Oxides, Particulates, Hydrogen Cyanide, Emissions, Non-methane Hydrocarbons/Volatile Organic Compounds, Particulate Matter, Fire Occurrence, Nitrogen Oxides, Sulfur Dioxide, Carbon And Hydrocarbon Compounds, Carbon, Cation Exchange Capacity, Organic Matter | |
| Ecosystem extent mapping by integrating Landsat 8, PALSAR-2, and GEDI lidar | Geremew, Tenaw, Zewdie, Worku, Pellikka, Petri | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Consistency analysis of forest height retrievals between GEDI and ICESat-2 | Zhu, Xiaoxiao, Nie, Sheng, Wang, Cheng, Xi, Xiaohuan, Lao, Jieying, Li, Dong | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data | Liu, Xiaoqiang, Su, Yanjun, Hu, Tianyu, Yang, Qiuli, Liu, Bingbing, Deng, Yufei, Tang, Hao, Tang, Zhiyao, Fang, Jingyun, Guo, Qinghua | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data | Leite, Rodrigo Vieira, Silva, Carlos Alberto, Broadbent, Eben North, Amaral, Cibele Hummel do, Liesenberg, Veraldo, Almeida, Danilo Roberti Alves de, Mohan, Midhun, Godinho, Sergio, Cardil, Adrian, Hamamura, Caio, Faria, Bruno Lopes de, Brancalion, Pedro H.S., Hirsch, Andre, Marcatti, Gustavo Eduardo, Dalla Corte, Ana Paula, Zambrano, Angelica Maria Almeyda, Costa, Maira Beatriz Teixeira da, Matricardi, Eraldo Aparecido Trondoli, Silva, Anne Laura da, Goya, Lucas Ruggeri Re Y., Valbuena, Ruben, Mendonca, Bruno Araujo Furtado de, Silva Junior, Celso H.L., Aragao, Luiz E.O.C., Garcia, Mariano, Liang, Jingjing, Merrick, Trina, Hudak, Andrew T., Xiao, Jingfeng, Hancock, Steven, Duncason, Laura, Ferreira, Matheus Pinheiro, Valle, Denis, Saatchi, Sassan, Klauberg, Carine | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Comparative Analysis of GEDI's Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies | Fayad, Ibrahim, Baghdadi, Nicolas, Frappart, Frederic | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM | |
| Spaceborne LiDAR and animal-environment relationships: An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem | Smith, Austin B., Vogeler, Jody C., Bjornlie, Nichole L., Squires, John R., Swayze, Neal C., Holbrook, Joseph D. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| The use of GEDI canopy structure for explaining variation in tree | Marselis, Suzanne M, Keil, Petr, Chase, Jonathan M, Dubayah, Ralph | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms | Fayad, Ibrahim, Ienco, Dino, Baghdadi, Nicolas, Gaetano, Raffaele, Alvares, Clayton Alcarde, Stape, Jose Luiz, Ferraco Scolforo, Henrique, Le Maire, Guerric | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM | |
| Assessment of GEDI's LiDAR data for the estimation of canopy heights and wood volume of eucalyptus plantations in Brazil | Fayad, Ibrahim, Baghdadi, Nicolas N., Alvares, Clayton Alcarde, Stape, Jose Luiz, Bailly, Jean Stephane, Scolforo, Henrique Ferraco, Zribi, Mehrez, Maire, Guerric Le | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, VIEWING GEOMETRY, Terrain Elevation | |
| Assessing the accuracy of GEDI data for canopy height and aboveground biomass estimates in mediterranean forests | Dorado-Roda, Ivan, Pascual, Adrian, Godinho, Sergio, Silva, Carlos, Botequim, Brigite, Rodriguez-Gonzalvez, Pablo, Gonzalez-Ferreiro, Eduardo, Guerra-Hernandez, Juan | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA | Tang, Hao, Ma, Lei, Lister, Andrew, ONeill-Dunne, Jarlath, Lu, Jiaming, Lamb, Rachel L, Dubayah, Ralph, Hurtt, George | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Biomass | |
| Fusing Sentinel-1 and-2 to model GEDI-derived vegetation structure characteristics in GEE for the Paraguayan Chaco | Kacic, Patrick, Hirner, Andreas, Da Ponte, Emmanuel | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping | Silva, Carlos Alberto, Duncanson, Laura, Hancock, Steven, Neuenschwander, Amy, Thomas, Nathan, Hofton, Michelle, Fatoyinbo, Lola, Simard, Marc, Marshak, Charles Z., Armston, John, Lutchke, Scott, Dubayah, Ralph | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| GEDI elevation accuracy assessment: A case study of southwest Spain | Quiros, Elia, Polo, Maria-Eugenia, Fragoso-Campon, Laura | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, VIEWING GEOMETRY, Terrain Elevation | |
| Determination of structural characteristics of oldgrowth forest in ukraine using spaceborne lidar | Spracklen, Ben, Spracklen, Dominick V. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Evaluation of the performances of radar and lidar altimetry missions for water level retrievals in mountainous environment: The case of the swiss lakes | Frappart, Frederic, Blarel, Fabien, Fayad, Ibrahim, Berge-Nguyen, Muriel, Cretaux, Jean-Francois, Shu, Song, Schregenberger, Joel, Baghdadi, Nicolas | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM | |
| Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals | Liu, Aobo, Cheng, Xiao, Chen, Zhuoqi | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Mapping global forest canopy height through integration of GEDI and Landsat data | Potapov, Peter, Li, Xinyuan, Hernandez-Serna, Andres, Tyukavina, Alexandra, Hansen, Matthew C., Kommareddy, Anil, Pickens, Amy, Turubanova, Svetlana, Tang, Hao, Silva, Carlos Edibaldo, Armston, John, Dubayah, Ralph, Blair, J. Bryan, Hofton, Michelle | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Monitoring key forest structure attributes across the conterminous united states by integrating gedi lidar measurements and VIIRS data | Rishmawi, Khaldoun, Huang, Chengquan, Zhan, Xiwu | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Reflectance, Enhanced Vegetation Index (EVI) | |
| Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with | Bauer, Luise, Knapp, Nikolai, Fischer, Rico | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops | Di Tommaso, Stefania, Wang, Sherrie, Lobell, David B | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height |
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/rx_processing_a2/ancillary/botlocdist_limit3 | Final ground return pulse selection parameter | ns | float64 | N/A | N/A | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/cumulative_energy_minimum | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/cumulative_energy_thresh | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/enable_select_mode | Final ground return pulse selection parameter | N/A | int32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/energy_thresh | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 1000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/preprocessor_threshold | Initial search threshold multiplier to detect signal start and end | N/A | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/pulse_sep_thresh | Final ground return pulse selection parameter | ns | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_back_threshold | If use_fixed_threshold=0, this is the noise stddev multiplier used to detect the lowest elevation signal. If use_fixed_threshold is non_zero, use the value as the threshold | N/A | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_front_threshold | If use_fixed_threshold=0, this is the noise stddev multiplier used to detect the highest elevation signal. If use_fixed_threshold is non_zero, use the value as the threshold | N/A | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_max_mode_count | Maximum number of modes saved for each footprint | N/A | int64 | N/A | 0 to 40 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_searchsize | In combination with preprocessor_threshold, used to define area of waveform to be searched by algorithm | ns | float64 | N/A | 0 to 500 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_sentinel_location | location of sentinel pulse used by sensitivity algorithm | ns | float64 | N/A | 0 to 300 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_smoothing_width_locs | Width of guassian pulse convolved with waveform to reduce noise prior to toploc/botloc identification | ns | float64 | N/A | 0 to 30 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_smoothing_width_zcross | Width of guassian pulse convolved with waveform to reduce noise prior to pulse mode identification | ns | float64 | N/A | 0 to 30 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_subbin_resolution | Factor of increased vertical resolution relative to native waveform resolution (~0.15 m). | N/A | int64 | N/A | 0 to 10 | N/A | N/A |
| /BEAM0000/rx_processing_a2/ancillary/rx_use_fixed_thresholds | If =0, then use values in Front_Threshold and Back_Threshold as the noise stddev multipliers used to detect the highest and lowest elevation returns. If use_fixed_threshold is non_zero, use the value in Front_Threshold and Back_Threshold as the threshold value (not as a multiplier for stddev). | N/A | int32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a2/back_threshold | threshold used to detect lowest elevation return energy | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a2/botloc | waveform sample location of lowest detected return energy relative to bin0 of waveform | counts | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/botloc_amp | amplitude at lowest detected energy return | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a2/energy_sm | total energy of smoothed waveform | counts | float32 | N/A | -1000 to 100000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/front_threshold | threshold used to detect highest elevation return energy | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a2/lastmodeenergy | energy in lowest detected mode | counts | float32 | N/A | -1000 to 100000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/mean | mean noise level used in algorithm | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a2/mean_sm | mean noise level after smoothing | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a2/min_detection_energy | integrated area of the computed minimally-detectable gaussian | counts | float32 | N/A | -1000 to 100000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/min_detection_threshold | detection threshold used to compute the minimally detected gaussian | counts | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/peak | peak amplitude of raw waveform | counts | float32 | N/A | -100 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/pk_sm | peak ampltiude of smoothed waveform | counts | float32 | N/A | -100 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_algrunflag | Flag indicating signal was detected and algorithm ran successfully | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_cumulative | Waveform bin numbers of integer percents of integrated energy from cumulative waveform (botloc (0%) to toploc (100%)) | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_iwaveamps | Fraction of integrated waveform at location of each detected mode | counts | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modeamps | Amplitudes of each detected mode within waveform | counts | float32 | N/A | -100 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modeenergytobotloc | Total energy from the center of each detected waveform mode to botloc | counts | float32 | N/A | -1000 to 1000000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modelocalenergy | Energy between +- 8 samples of each detected mode, mean noise level removed | counts | float32 | N/A | -1000 to 1000000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modelocalenergyabovemean | Energy between +- 8 samples of each detected mode | counts | float32 | N/A | -1000 to 1000000 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modelocalslope | Signal trend within +- 8 samples of each detected mode | counts | float32 | N/A | -100 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modelocs | Sample numbers of each detected mode (relative to bin 0 of waveform) | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_modewidths | 1 sigma width estimates of each detected mode in waveform | ns | float32 | N/A | 0 to 700 | N/A | N/A |
| /BEAM0000/rx_processing_a2/rx_nummodes | Number of modes detected in waveform | ns | uint8 | N/A | 0 to 20 | N/A | N/A |
| /BEAM0000/rx_processing_a2/sd_sm | Noise standard deviation of the smoothed waveform | counts | float32 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a2/search_end | Sample number indicating end of signal search | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/search_start | Sample number indicating start of signal search | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/selected_mode | ID of mode selected as lowest non-noise mode | N/A | uint8 | N/A | 0 to 20 | N/A | N/A |
| /BEAM0000/rx_processing_a2/selected_mode_flag | Flag indicating status of selected_mode | N/A | uint8 | N/A | 0 to 4 | N/A | N/A |
| /BEAM0000/rx_processing_a2/shot_number | Shot number | N/A | uint64 | N/A | N/A | N/A | N/A |
| /BEAM0000/rx_processing_a2/smoothwidth | width of gaussian function used to smooth noise sections of waveforms | ns | float32 | N/A | 0 to 20 | N/A | N/A |
| /BEAM0000/rx_processing_a2/smoothwidth_zcross | width of gaussian function used to smooth waveform between botloc and toploc | ns | float32 | N/A | 0 to 20 | N/A | N/A |
| /BEAM0000/rx_processing_a2/stddev | noise standdard deviation used in algorithm | countd | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a2/toploc | Sample number of highest detected return | counts | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_processing_a2/toploc_miss | Flag indicating algorithm didn't detect valid toploc value | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |