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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Anion Data for the East River Watershed, Colorado (2014-2025)

The anion data for the East River Watershed, Colorado, consist of fluoride, chloride, sulfate, nitrate, and phosphate concentrations collected at multiple, long-term monitoring sites that include stream, groundwater, and spring sampling locations. These locations represent important and/or unique end-member locations for which solute concentrations can be diagnostic of the connection between terrestrial and aquatic systems. Such locations include drainages underlined entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of solute concentrations from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on the riverine export of multiple anionic species. Such data may be combined with stream gauging stations co-located at each monitoring site to directly quantify the seasonal and annual mass flux of these anionic species out of the watershed. This data package contains (1) a zip file (anion_data_2014_2025.zip) containing a total of 386 files: 387 data files of anion data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v7_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v7_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; and (4) a anion MDL fact sheet (anion_MDLs_202608 in PDF and docx formats). Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 47 locations containing anion data. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated anion data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, and (5) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2022-12-20: Updates were made to both the data files and reporting format specific files. Conversion issues affecting ER-PLM locations for anion data was resolved for the data files. Additionally, the flmd and dd files were updated to reflect the updated versions of these files. Available data was added up until 2022-03-14. Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-05-19. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-09-11. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until the end of WY2025 (September 30, 2025). An anion MDL document was included in this update.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Reflectance of metallic indium for solar energy applications

An investigation has been conducted in order to compile quantitative data on the reflective properties of metallic indium. The fabricated samples were of sufficiently high quality that differences from similar second-surface silvered mirrors were not apparent to the human eye. Three second-surface mirror samples were prepared by means of vacuum deposition techniques, yielding indium thicknesses of approximately 1000 A. Both hemispherical and specular measurements were made. It is concluded that metallic indium possesses a sufficiently high specular reflectance to be potentially useful in many solar energy applications.

Bouquet, F. L.↗
NASA NTRS2004

Satellite Observations of Spatial and Interannual Variability of Lightning and Radar Reflectivity

The authors use satellite data to examine the relationship between lightning and upper-level radar reflectivity. They find correlations between average flash rates and upper-level reflectivities over both land and ocean, although both flash rates and reflectivities are much lower over ocean than land. Analysis of the data using Empirical Orthogonal Functions (EOFs) shows similar EOFs for averaged lightning and reflectivity. In contrast, the EOFs of the anomalies of lightning and reflectivity have different spatial patterns; however, both have principal component time series that are correlated with the Southern Oscillation Index and, hence, El Nino. Differences in behavior of the lightning and reflectivity anomaly EOFs and principal components suggest that El Nino plays a smaller role in lightning anomaly than precipitation anomaly.

lightning↗
NASA NTRS1983

Slant path attenuation and cross-polarization prediction

The accuracy with which slant-path attenuation and cross-polarization are predicted using differential reflectivity (ZDR) radar data is discussed. Consideration is given to the use of generalized relationships between radar reflectivity (Z), ZDR, and rainfall rate (R); and between R, attenuation, and cross-polarization (XPD) on a slant path. The forward-scatter matrix which is used to calculate the slant path attenuation and XPD is described. It is shown that the use of a double exponential drop size distribution and forward-scatter matrices improves the accuracy of the prediction of slant-path attenuation and XPD. Data obtained with the VPI & SU S-band radar, using slow switching between orthorzonal polarizations is presented. The results are illustrated in graphs.

Pratt, T.↗
NASA NTRS1976

Earth-atmosphere system and surface reflectivities in arid regions from Landsat MSS data

Previously developed programs for computing atmospheric transmission and scattering of the solar radiation are used to compute the ratios of the earth-atmosphere system (space) directional reflectivities in the nadir direction to the surface Lambertian reflectivity, for the four bands of the Landsat multispectral scanner (MSS). These ratios are presented as graphs for two water vapor levels, as a function of the surface reflectivity, for various sun elevation angles. Space directional reflectivities in the vertical direction are reported for selected arid regions in Asia, Africa, and Central America from the spectral radiance levels measured by the Landsat MSS. From these space reflectivities, surface reflectivities are computed applying the pertinent graphs. These surface reflectivities are used to estimate the surface albedo for the entire solar spectrum. The estimated albedos are in the range 0.34-0.52, higher than the values reported by most previous researchers from space measurements, but are consistent with laboratory and in situ measurements.

Otterman, J.↗
NASA NTRS1988

Improved canopy reflectance modeling and scene inference through improved understanding of scene pattern

The Li-Strahler reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density within 20 percent of sampled values in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). Trees are treated as simply shaped objects, and multispectral reflectance of a pixel is assumed to be related only to the proportions of tree crown, shadow, and understory in the pixel. These, in turn, are a direct function of the number and size of trees, the solar illumination angle, and the spectral signatures of crown, shadow and understory. Given the variance in reflectance from pixel to pixel within a homogeneous area of woodland, caused by the variation in the number and size of trees, the model can be inverted to give estimates of average tree size and density. Because the inversion is sensitive to correct determination of component signatures, predictions are not accurate for small areas.

Franklin, Janet↗
NASA NTRS1985

Mars: Seasonally variable radar reflectivity

The 1971/1973 Mars data set acquired by the Goldstone Solar System Radar was analyzed. It was established that the seasonal variations in radar reflectivity thought to occur in only one locality on the planet (the Solis Lacus radar anomaly) occur, in fact, over the entire subequatorial belt observed by the Goldstone radar. Since liquid water appears to be the most likely cause of the reflectivity excursions, a permanent, year-round presence of subsurface water (frozen or thawed) in the Martian tropics can be inferred.

Roth, L. E.↗
NASA NTRS1994

Application of AI techniques to infer vegetation characteristics from directional reflectance(s)

Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.

Kimes, D. S.↗
NASA NTRS1988

Invertible canopy reflectance modeling of vegetation structure in semiarid woodland

The Li-Strahler canopy reflectance model, driven by Landsat Thematic Mapper (TM) data, provided regional estimates of tree size and density in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). The model was tested in sparse woodland and wooded grassland in the Sahelian and Sudanian bioclimatic zones in West Africa.

Franklin, Janet↗
DOE OSTI2023

HarDWR - Raw Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, along with the geospatial data describing the spatial units where water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. We collected water rights databases from 11 western United States states either by downloading them from publicly accessible web portals, or by contacting state water management representatives; detailed descriptions of where and when the data was collected is provided in the README.txt, as well as Lisk et al.(in review). This collection of data are those raw water rights. Each state formats their data differently, meaning that file types, field availability, and names vary from state to state. Note, the data provided here reflects the state of the water rights databases at the time we collected the data; updates have likely occurred in many states. Some pieces of information are common among all states. These are: priority date, volume or flow of water allowed by the right, stated water use of the right, and some means of identifying the geography and source of the water pertaining to the right - typically the coordinates of the Point of Diversion (PoD) of a waterbody or well. Arizona regulates water in a different way than the other 10 states. Outside of some relatively small critical agricultural areas called Active Management Areas (AMAs), Arizona does not maintain any water rights. However, the state does require registration of surface and groundwater pumping devices, which includes disclosing the mechanical specifics of the devices. We used these records as a proxy for water rights. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. In addition, the following states have requested specifically worded disclaimers to be included with their data. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." The available data is provided as a series of compressed files, which each containing the full data collected from each state. Some of the files have been renamed, to more easily know which state the data belongs to. The file renaming was also required as some files from different states had the same name. In other cases, the data for a state has been placed in a folder indicating which state it belongs to - as the state organized its data by selected subregions. Below is a brief description of the format of the collected data from each state. ArizonaRights_StatementOfClaimants: A folder containing a database of interconnected CSV files. The soc_erd.pdf file contains a visual flowchart of how the various files are connected, beginning with SOC_MAIN.csv in the center of the page. ArizonaRights_SurfaceWaterRightsData: A folder containing a database of a single Shapefile and 10 associated CSVs. SurfaceWater.pdf contains a visual flowchart of how the various files are connected, beginning with ADWR_SW_APPL_REGRY.csv. ArizonaRights_Well55Registry: A folder containing a database of a single Shapefile and 59 associated CSVs. Wells55.pdf contains a visual flowchart of how the various files are connected, beginning with WellRegistry.shp. CaliforniaRights_eWRIMS_directDatabase: A folder containing a collection of four "series" Microsoft Excel files, as either XLS or XLSX. The four "series": byCounty, byEntity (what type of legal entity holds the right), byUse (stated water use), and byWatershed, are various methods by which the California water rights are organized within the state's database. However, it was observed that by only collecting a single series, not all water rights were being provided. So, essentially, the majority of records within each "series" are copies of each other, with each "series" containing some unique records. ColoradoRights_NetAmounts: A folder containing 78 CSV files, with one file per Colorado Water District. IdahoRights_PointOfDiversion: A Shapefile containing the Points of Diversion for the entire state of Idaho. IdahoRights_PlaceOfUse: A Shapefile containing the Place of Use polygons for the entire state of Idaho. MontanaRights_WaterRights: A Geodatabase file containing the Points of Diversion and Places of Use for the entire state of Montana. The name of the Points of Diversion Feature Layer within the Geodatabase is "WRDIV", and the name of the Places of Use Feature Layer is "WRPOU". NevadaRights_POD_Sites: A Shapefile containing the Points of Diversion for the entire state of Nevada. NewMexicoRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of New Mexico. OregonRights_state_shp: A folder containing 36 Shapefiles and are split between "pod" (Point of Diversion) and "pou" (Place of Use) for each water management basin within Oregon. In other words, each basin has one "pod" file and one "pou" file. The "pod" files are point shapes, and the "pou" files are polygons. UtahRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of Utah. WashingtonRights_WaterDiversions_ECY_NHD: A Geodatabase file containing both the Points of Diversion for the entire state of Washington. The name of the Feature Layer within the Geodatabase is "WaterDiversions_ECY_NHD". WyomingRights: A folder containing four subdirectories, one for each Wyoming Water Division. Each Division directory includes a varying number of subdirectories for each Wyoming Water District. Each District folder contains two copies of the Point of Diversion records for that area, with one copying being in CSV and one copy in Microsoft Excel XLS format.

Lisk, Matthew↗
NASA NTRS1988

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Inflight radiometric calibration and the determination of surface reflectance

The inflight radiometric performance of AVIRIS is presented together with a comparison of methods of recovering surface spectral reflectance from the data. Performance is evaluated by comparing radiance predicted from AVIRIS with radiance generated from the LOWIRAN 6 atmospheric model and measured surface reflectance. Comparisons show apparent agreement to within a few percent between 1800 and 2450 nm. Between 600 and 1800 nm the response of AVIRIS is systematically low by as much as 70 percent, and between 400 and 600 nm it is higher than expected. These problems are traced to thermal distortions of the instrument, and to detachment during flight of optical fibers connecting foreoptics to two of four spectrometers in the instrument. Of three methods studied, an empirical one involving calibration curves constructed from field reflectance measurements returns accurate predictions of the surface reflectance independent of the actual radiometric significance of the flight data.

Conel, J. E.↗
NASA NTRS2012

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Rampe, E. B.↗
DOE OSTI2025

Nuclear data sensitivity and uncertainty study of copper-reflected integral experiments [Slides]

This presentation touches on reducing uncertainties in intermediate-energy actinide nuclear data and this continues to be a high priority for many applications. The goal of PARADIGM (PARallel Approach of Differential and InteGral Measurements): accelerate efforts to reduce biases and uncertainties in nuclear data through improvements to the nuclear data pipeline. This presentation includes integral experiments and a summarization of existing copper nuclear data.

Cu63↗
NASA NTRS2022

JPSS-2 VIIRS Pre-Launch Reflective Solar Band Testing and Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board the Suomi National Polar-orbiting Partnership (S-NPP) and Joint Polar Satellite System (JPSS) spacecrafts 1 and 2 provides calibrated sensor data record (SDR) reflectance, radiance, and brightness temperatures for use in environment data record (EDR) products. The SDRs and EDRs are used in weather forecasting models, weather imagery and climate applications such as ocean color, sea surface temperature and active fires. The VIIRS has 22 bands covering a spectral range 0.4–12.4 m with resolutions of 375 m and 750 m for imaging and moderate bands respectively on four focal planes. The bands are stratified into three different types based on the source of energy sensed by the bands. The reflective solar bands (RSBs) detect sunlight reflected from the Earth, thermal emissive bands (TEBs) sense emitted energy from the Earth and the day/night band (DNB) detects both solar and lunar reflected energy from the Earth. The SDR calibration uses a combination of pre-launch testing and the solar diffuser (SD), on-board calibrator blackbody (OBCBB) and space view (SV) on-orbit calibrator sources. The pre-launch testing transfers the National Institute of Standards and Technology (NIST) traceable calibration to the SD, for the RSB, and the OBCBB, for the TEB. Post-launch, the on-board calibrators track the changes in instrument response and adjust the SDR product as necessary to maintain the calibration. This paper will discuss the pre-launch radiometric calibration portion of the SDR calibration for the RSBs that includes the dynamic range, detector noise, calibration coefficients and radiometric uncertainties for JPSS-2 VIIRS.

JPSS↗
DOE OSTI2019

Energy and Water Sustainability in the U.S. Biofuel Industry

In 2018, Argonne National Laboratory, with support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, conducted a survey of biofuel producers in the United States. The survey covered a full range of plant operation parameters, including plant capacity, feedstock, product, production volume, coproducts, water resources, water treatment, water usage, wastewater management, and process fuel and electricity consumption, as reflected in 2017 plant operation data. This report presents the most up-to-date analysis of commercial-scale plants in the U.S., including facilities producing fuels from both starch and cellulosic materials. This is the first survey that includes the comprehensive recording of water resources, water use, and water and wastewater management for the U.S. biofuel industry at the facility level. Data presented in this study reflect primarily information on full-scale ethanol production from dry mills that was available at the time of survey. Results highlight the complexity of energy and water resource use in process steps and the role of water conservation, recycling, and reuse in advancing the production of biofuel and its contribution to the bioeconomy and the FEW nexus.

09 BIOMASS FUELS↗
NASA NTRS2005

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

Remotely sensed ground reflectance is the basis for many inter-sensor interoperability or change detection techniques. Satellite inter-comparisons and accurate vegetation indices such as the Normalized Difference Vegetation Index, which is used to describe or to imply a wide variety of biophysical parameters and is defined in terms of near-infrared and redband reflectance, require the generation of accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite geometry, and atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance, however, has been widely applied to only a few systems. In this study, we atmospherically corrected commercially available, high spatial resolution IKONOS and QuickBird imagery using several methods to determine the accuracy of the resulting reflectance maps. We used extensive ground measurement datasets for nine IKONOS and QuickBird scenes acquired over a two-year period to establish reflectance map accuracies. A correction approach using atmospheric products derived from Moderate Resolution Imaging Spectrometer data created excellent reflectance maps and demonstrated a reliable, effective method for reflectance map generation.

Pagnutti, Mary↗
NASA NTRS2020

An Algorithm for Hyperspectral Remote Sensing of Aerosols: 3. Application to the GEO-TASO Data in KORUS-AQ Field Campaign

This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from the data measured by GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TAOS has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries, and the constraints of surface reflectance parameters and aerosol scattering models respectively from k- means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) until the best match between simulated and GEOS-TASO measured spectra is achieved. The spectral AODs are compared between those retrieved from GEO-TASO (x) and 7 AERONETS (y) in 440, 550, 550, 675 nm, respectively. The comparison reveals that iterative updates of surface spectral PCs (and so surface reflectance) yield significant enhancement in AOD retrievals, improving the mean linear fitting equations from y = 0.735x + 0.088 (without update of PCs) to y = 1.055x+ 0.01, and the Pearson correlation coefficient (𝑅𝑅2) from 0.54 to 0.76, respectively. Move case studies are need to further evaluate the algorithm for its application to TEMPO that carries a enhanced version of GEO-TASO instrument.

Tropospheric Emissions: Monitoring of Pollution (T↗