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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗
NASA NTRSDate not supplied

Actor-based Runtime Verification with MESA

This work presents a runtime verification approach implemented in the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in data parameterized temporal logic and state machines. The tool is implemented as an internal Scala DSL. We employ the actor programming model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper presents a case study in which MESA is used to effectively monitor a large number of flights from live US airspace data streams. We also perform an empirical study by conducting experiments using monitoring systems with different numbers of concurrent monitors and different layers of indexing on the data contained in events. The paper describes the experiments, evaluates the results, and discusses challenges faced during the study. The evaluation shows the value of combining concurrency with indexing to handle data rich events.

runtime verification↗
NASA NTRS2020

Actor-based Runtime Verification with MESA

This work presents a runtime verification approach implemented in the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in data parameterized temporal logic and state machines. The tool is implemented as an internal Scala DSL. We employ the actor programming model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper presents a case study in which MESA is used to effectively monitor a large number of flights from live US airspace data streams. We also perform an empirical study by conducting experiments using monitoring systems with different num- bers of concurrent monitors and different layers of indexing on the data contained in events. The paper describes the experiments, evaluates the results, and discusses challenges faced during the study. The evaluation shows the value of combining concurrency with indexing to handle data rich events.

Mehlitz, Peter↗
NASA NTRS2012

Intercomparison of Desert Dust Optical Depth from Satellite Measurements

This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify the differences between current datasets. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. These include multi-angle approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as assumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, it is important to note that differences in sampling, related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset can be an important issue.

desert dust↗
NASA NTRS2012

Desert Dust Satellite Retrieval Intercomparison

This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify and understand the differences between current algorithms, and hence improve future retrieval algorithms. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. These include multi-angle approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as as20 sumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, at least as significant as these differences are sampling issues related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset.

Carboni, E.↗
NASA NTRS1988

Ocean color observations of phytoplankton distributions and primary productivity

The primary goal of this activity is to develop the means to assess the mean and variability of phytoplankton biomass and primary productivity on global scales. There are three major approaches whose goals are to provide global scale observations. These are processing and analysis of the complete CZCS data set in a consistent manner; preparing science mission and project implementation plans for the SeaWiFS sensor to be launched on LANDSAT 6 in 1991; and providing guidance to EOS flight projects for ocean color observations using the MODIS sensor planned for the Polar Platform in the mid 1990's. This processing presents the first consistent view of phytoplankton pigments on global scales, and analysis of this temporally undersampled data set is proving very instructive in specifying mission requirements for SeaWiFS and future algorithm development.

Esaias, W.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31944-DND

The Plum Island Estuary (PIE) in Massachusetts is New England’s largest salt marsh. This dynamic ecosystem plays an important role in the surrounding communities by providing ecosystem and social services, acting as a center for education, research, and recreation. However, marshes around the world are threatened by sea level rise. As the equilibrium between sediment supply, erosion rates, and vegetation growth is unbalanced by rising waters, marshes are liable to recede, or even drown. Because sediment is so crucial, researchers use sediment budgets as a metric to assess Plum Island Estuary’s vulnerability to rising seas; however, established data collection methods are limited in scope. The project employed five years of imagery from Landsat 8 Operational Land Imager (OLI) and three years of imagery from Sentinel-2 Multispectral Instrument (MSI), in conjunction with in situ data, to generate and refine a local algorithm that derives suspended sediment concentration from remote sensing reflectance. This information was applied to a flux model to analyze transport patterns and possible sediment sources, particularly the Merrimack River. The use of remote sensing techniques will provide our partners at the US Geological Survey, US Fish and Wildlife Service, and Plum Island Ecosystems Long Term Ecological Research Network with higher spatial and temporal resolution data, which will allow for the development of more effective management practices.

Sydney Neugebauer↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Sensor-induced temporal variability of Landsat MSS data

Landsat-1 and Landsat-2 multispectral scanner (MSS) data were studied to determine the consistency of the calculated reflectance values over time. Data from six spectrally stable targets were collected over a 3-year period (1975-1977). Reflectance values calculated from the digital numbers were regressed against time to note any long term changes. Results indicate that, over a 1000-day period beginning 1 January 1975, MSS 2 reflectances were stable. MSS 1 reflectances over that same period decreased approximately 25-32 percent. The observed decreases are most likely due to a deterioration of the transmissive qualities of the Landsat-1 scanner's internal optical path. Although MSS temporal variation may be unique to MSS 1, time (days in orbit) should be considered as an independent variable when intersatellite calibration equations are computed. A case is made for a stable, monitored calibration system which would permit the calculation of true top-of-the-atmosphere reflectance measures.

Nelson, R. F.↗
NASA NTRS2000

BOREAS TGB-8 Photosynthetic Rate Data over the SSA-OBS and the SSA-OJP

The BOREAS TGB-8 team collected data to investigate the controls over NMHC fluxes from boreal forest tree species. This data set includes measurements of photosynthetic rates at mature jack pine and black spruce sites. The data were collected at the OJP and OBS tower flux locations in the BOREAS SSA. These areas contained mature stands of jack pine and black spruce and were the focal sites in the BOREAS program for studies of biosphere/atmosphere exchange from these two habitat types. The OBS site is situated in a black spruce/sphagnum bog with the largest trees 155 years old and 10-15 m tall. The OJP site is in a jack pine forest, 80 to 120 years old, which lies on a sandy bench of glacial outwash with the largest tree standing 15 m tall. Temporally, the data cover the period of 24-May-1994 to 19-Sep-1994. The data are stored in tabular ASCII files.

Hall, Forrest G.↗
NASA NTRS2000

BOREAS TGB-8 Starch Concentration Data Over the SSA-OBS and the SSA-OJP

The BOREAS TGB-8 team collected data to investigate the controls over NMHC fluxes from boreal forest tree species. This data set includes measurements of starch concentrations in foliar samples at mature jack pine and black spruce sites. The data were collected at the OJP and OBS tower flux locations in the BOREAS SSA. These areas contained mature stands of jack pine and black spruce and were the focal sites in the BOREAS program for studies of biosphere/atmosphere exchange from these two habitat types. The OBS site is situated in a black spruce/sphagnum bog with the largest trees 155 years old and 10-15 m tall. The OJP site is in a jack pine forest, 80 to 120 years old, which lies on a sandy bench of glacial outwash with the largest tree standing 15 m tall. Temporally, the data cover the period of 24-May-1994 to 19-Sep-1994. The data are stored in tabular ASCII files.

Lerdau, Manuel↗
NASA NTRS1984

Gravity-wave spectra in the atmosphere observed by MST radar, part 4.2B

A universal spectrum of atmospheric buoyancy waves is proposed based on data from radiosonde, Doppler navigation, not-wire anemometer and Jimsphere balloon. The possible existence of such a universal spectrum clearly will have significant impact on several areas in the study of the middle atmosphere dynamics such as the parameterization of sub-grid scale gravity waves in global circulation models; the transport of trace constituents and heat in the middle atmosphere, etc. Therefore, it is important to examine more global wind data with temporal and spatial resolutions suitable for the investigation of the wave spectra. Mesosphere-stratosphere-troposphere (MST) radar observations offer an excellent opportunity for such studies. It is important to realize that radar measures the line-of-sight velocity which, in general, contains the combination of the vertical and horizontal components of the wave-associated particle velocity. Starting from a general oblique radar observation configuration, applying the dispersion relation for the gravity waves, the spectrum for the observed fluctuations in the line-of-sight gravity-wave spectrum is investigated through a filter function. The consequence of the filter function on data analysis is discussed.

Scheffler, A. O.↗
NASA NTRS2000

BOREAS TGB-8 Monoterpene Concentration Data over the SSA-OBS

The BOREAS TGB-8 team collected data to investigate the controls over NMHC fluxes from boreal forest tree species. This data set contains measurements of monoterpene concentrations in collected foliar gas emissions and foliar samples. The data were collected at the OJP and OBS tower flux sites in the SSA and were the locus for the monoterpene emission measurements. These areas contained mature stands of jack pine and black spruce and were the focal sites in the BOREAS program for studies of biosphere/atmosphere exchange from these two habitat types. The OBS site is situated in a black spruce/sphagnum bog with the largest trees 155 years old and 10-15 m tall. The OJP site is in a jack pine forest, 80 to 120 years old, which lies on a sandy bench of glacial outwash with the largest tree standing 15 m tall. Temporally, the data cover the period of 24-May-1994 to 19-Sep-1994. The data are stored in tabular ASCII files.

Hall, Forrest G.↗
DOE OSTI2025

Discovering Physically Meaningful Structures from Climate Extreme Data

The original proposal described an interdisciplinary team spanning UC San Diego (lead), Columbia University, and UC Irvine, with Columbia investigators including Pierre Gentine, Elias Bareinboim, and Marcus van Lier-Walqui. The proposal further specified a leadership structure in which Columbia co-investigators contributed across the three aims, with Co-PI Gentine serving as a point of contact with science teams and with responsibilities distributed across aims.

42 ENGINEERING↗
NASA NTRS2012

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Tan, Bin↗
NASA NTRS2020

Overcoming Dimensionality Barriers in Satellite Data to Elucidate SPECTRAL-SPATIAL-TEMPORAL Trends in the Ocean

The weighted harmonic mean of spectral wavelengths can be used as an index of optical water types that share similar spectral signatures.This technique can be translated to relate spectral shape between sensors of varying spectral resolution.The output along a continuum of color values enables spatio-temporal analysis of subtle spectral shifts.Directional trends in absorption and backscatter data lend a hidden context to changes in water color. Conceptualization of multiple data dimensions is imperative as passive/active sensors grow increasingly sophisticated in nature.

Vandermeulen, Ryan↗
NASA NTRS2016

Formaldehyde Column Density Measurements as a Suitable Pathway to Estimate Near-Surface Ozone Tendencies from Space

In support of future satellite missions that aim to address the current shortcomings in measuring air quality from space, NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign was designed to enable exploration of relationships between column measurements of trace species relevant to air quality at high spatial and temporal resolution. In the DISCOVER-AQ data set, a modest correlation (r2 = 0.45) between ozone (O3) and formaldehyde (CH2O) column densities was observed. Further analysis revealed regional variability in the O3-CH2O relationship, with Maryland having a strong relationship when data were viewed temporally and Houston having a strong relationship when data were viewed spatially. These differences in regional behavior are attributed to differences in volatile organic compound (VOC) emissions. In Maryland, biogenic VOCs were responsible for approx.28% of CH2O formation within the boundary layer column, causing CH2O to, in general, increase monotonically throughout the day. In Houston, persistent anthropogenic emissions dominated the local hydrocarbon environment, and no discernable diurnal trend in CH2O was observed. Box model simulations suggested that ambient CH2O mixing ratios have a weak diurnal trend (+/-20% throughout the day) due to photochemical effects, and that larger diurnal trends are associated with changes in hydrocarbon precursors. Finally, mathematical relationships were developed from first principles and were able to replicate the different behaviors seen in Maryland and Houston. While studies would be necessary to validate these results and determine the regional applicability of the O3-CH2O relationship, the results presented here provide compelling insight into the ability of future satellite missions to aid in monitoring near-surface air quality.

Surface Conditions from Column and Vertically Reso↗