Search NASA⌕ Search

SEARCH · Search NASA

Results for “timeseries”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Ozonesonde Quality Assurance: JOSIE-SHADOZ (2017) and SHALLOTS (2018)

Ozonesonde data constitute a mainstay of satellite calibration and are used for climatologies and analysis of trends, especially in the lower stratosphere where satellites are most uncertain. The electrochemical-concentration cell (ECC) ozonesonde has been deployed at ~100 stations worldwide since the 1960s, with changes over time in manufacture and procedures, including details of the cell chemical solution and data processing. As a consequence, there are biases among different stations and discontinuities in profile timeseries from individual site records. Since 1996 the Jülich [Germany] Ozone Sonde Intercomparison Experiment (JOSIE) has periodically tested ozonesondes in a simulation chamber designated the World Calibration Centre for Ozonesondes by WMO. In October- November 2017 a JOSIE campaign evaluated the sondes and procedures used in SHADOZ (Southern Hemisphere Additional Ozonesondes), a 14-station tropical and subtropical network. A distinctive feature of the 2017 JOSIE was that the tests were conducted by operators from eight SHADOZ stations; Nairobi, Natal, Irene, Costa Rica, Paramaribo, Reunion, Hanoi, Kuala Lumpur. Experimental protocols and preliminary results for the SHADOZ sonde configurations, which represent most of those in use today, are described. SHADOZ stations that follow WMO-recommended protocols record total ozone within 3% of the JOSIE reference instrument. Instrument biases noted in prior JOSIE and field tests like BESOS (2004) were noted in JOSIE-2017, with maximum effect in the stratosphere. In June 2018 we organized a series of dual launches during the OWLETS II campaign in the Maryland and Chesapeake Bay area (SHALLOTS = SHADOZ-OWLETS ParaLLel Ozonesonde Test Study). Instrument and solution types were varied as in JOSIE-2017 and three radiosonde-ozonesonde variants were tested. An example of a parallel sampling in SHALLOTS, from a Greenbelt EnSCI-iMet sonde combination flown with the Wallops SPCLMS package, is illustrated in the Figure. The result was a range of biases but in general the instrument combination (EnSCI-iMet) deployed at 11 SHADOZ stations recorded ~5-10% less ozone in the stratosphere than the SPC ECC sonde flown with a Vaisala or LMS system. These 2017 and 2018 results and prior JOSIEs demonstrate that regular testing is essential to maintain best practices in ozonesonde operations and to ensure high-quality data for the ozone assessment communities.

Quality assurance↗
NASA NTRS2020

Evaluating Multi-Sensor Agreement of Satellite Particulate Backscatter Retrievals by Validatin Against In-Water Measurement

Biogeochemical-Argo profiling floats have increased in situ data density across multiple water types, creating new opportunities to evaluate satellite instrument-to-instrument differences in particulate back scattering coefficient(bbp). Retrievals of bbp from identical GIOP algorithm configurations differ between satellite instruments due to1)algorithm input differences and 2) radiometric differences. 3.Instrument-to-instrument differences must be considered before creating a merged timeseries of satellite ocean color products,in order to distinguish real, environmental contributions from spurious algorithmic or radiometricones

Scott, Joel P.↗
NASA NTRSDate not supplied

Quantifying Post-fire Vegetation Recovery on the Colorado Front Range

Forest composition and structure in the Colorado Front Range has been altered by changing wildfire regimes, particularly in response to increased fire severity. Subsequent reductions in post-fire tree regeneration can result in chronic impacts to ecological function and water quality. This project partnered with the U.S. Forest Service to estimate tree canopy recovery from 15 to 21 years after four Colorado Front Range fires that burned between 1996 and 2002— the Bobcat, Buffalo Creek, Hayman, and High Meadows fires—using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI). First, we evaluated relationships between recovery metrics derived from spectral vegetation indices (i.e. post-fire vegetation, normalized burn ratio, percent unrecovered vegetation) and field-collected counts of post-fire tree seedling regeneration. Then, we used high-resolution imagery paired with Landsat timeseries spectral and geomorphometric data to map forest canopy cover 15-21 years post-fire. Finally, we assessed ecological drivers of recovery including topography, climate, soils, and fire severity using Random Forest. While we found that basic vegetation index metrics failed to distinguish field-measured tree regeneration, our data showed that both aspect and burn severity were important drivers of vegetation recovery. North-facing aspects tended to have higher pre-fire Normalized Difference Vegetation Index (NDVI) and thus higher burn severities. However, areas that burned at high severity experienced slower vegetation recovery, regardless of aspect suggesting that high severity fire can mute favorable site conditions on north-facing aspects (i.e. moister, more developed soils). While this work propels our understanding of variables that influence vegetative recovery, vegetation type, forest conversion, and watershed function, more work should focus on what vegetation indices identifying areas of recovery indicate across different forest types and burn severities.

NASA DEVELOP↗
NASA NTRS2021

Cities Exacerbate Climate Warming

Urbanization is a complex land transformation, not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize it within land surface models and assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and de-coupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15°C, with a warming of 0.24°C in cities built in vegetated areas and a cooling of 0.25°C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13°C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

Lahouari Bounoua↗
NASA NTRSDate not supplied

Calculator for Agricultural Water Use Fractions in California

Improved quantification of agricultural water use efficiency is a primary focus of California's Water Conservation Act. The California Department of Water Resources (CDWR), after extensive stakeholder consultation, has published methods and indicators to help identify opportunities for improved efficiency of irrigation management at various spatial scales. Additional 2017 legislation directs water suppliers to quantify water use efficiency using the CDWR metrics to support preparation of agricultural water management plans. The IrriQuest calculator derives the Crop Consumptive Use Fraction (CCUF), a foundational metric relating seasonal crop evapotranspiration (ET) to total applied water. Satellite normalized difference vegetation index (NDVI) data are used to develop fractional canopy cover timeseries. Additional key inputs include daily reference ET, irrigation volume, irrigation method, precipitation, soil texture and crop type. IrriQuest estimates of seasonal crop ET show less than 10% mean absolute error compared to ground-based measurements of several annual and perennial crops monitored by sensor deployment in commercial fields. Two additional indicators build on CCUF. The Agronomic Water Use Fraction adds consideration of water applied for beneficial purposes such as pre-plant soil preparation, soil salinity management, and climate control. Total Water Use Fraction additionally accounts for water applied for environmental purposes such as maintenance of wetland and riparian habitat. A convenient on-line portal is under development for CCUF estimation and a standalone spreadsheet is available for computation of all three use fractions.

Calculator↗
NASA NTRSDate not supplied

Wildfire Emergency Response Hazard Extraction and Analysis of Trends (HEAT) through Natural Language Processing and Time Series

A methodology for Hazard Extraction and Analysis of Trends (HEAT) is proposed and conducted on a data set of wildfire incident response forms, known as ICS-209-PLUS.The HEAT processes: (1) extract a set of hazards from a data set, (2) calculate hazard-relevant metrics in a primary analysis, (3) analyze trends over time in metrics using timeseries, and (4) examine potential explanations for metric trends using a secondary analysis. Hazards are extracted from narrative data in the ICS-209-PLUS based on a framework previously developed by the authors, using natural language processing. Metrics examined for each hazard include operational time to occurrence, rate of occurrence, frequency, and severity. Primary results include a taxonomy of hazards present in the data set with relevant quantitative metrics. The most frequent hazards identified are environmental and include hazardous terrain. Most hazards occur on average between 35-55% containment. Incidents with hazards tend to have a higher average severity score when compared to the average score for all incidents. Time series of the metrics and relevant predictors, including fire characteristics, fire intensity, and operations, are created to facilitate further analysis. Secondary results used to determine which factors best predict hazard frequency include a correlation matrix and regression analysis. These findings are relevant to safety for current, as well as emerging wildfire operations, and are an exploratory first step in developing historical data-driven risk assessment models.

Sequoia R. Andrade↗
NASA NTRSDate not supplied

Initial Evaluation of CropManage Decision-Support Model for Vineyard ET Estimation

The CropManage(CM) decision-support web application was originally developed by U.C. Cooperative Extension to support evapotranspiration (ET) based irrigation scheduling and nutrient management of cool-season vegetables. The model uses prescribed crop phenology curves to develop daily estimates of fractional green canopy cover (Fc) within the field. Periodic Fc observations acquired by ground-based methods or imported from NASA’s Satellite Irrigation Management Support (SIMS) can be used to adjust the prescribed timeseries for such factors as weather anomalies or non-standard agronomic practice, as needed. Fc is then converted to daily crop coefficient (fraction of reference ET) values. The crop coefficient is combined with reference evapotranspiration, collected by the California Irrigation Management Information System, to derive daily ET estimates for the given field. Irrigation runtime recommendations are issued for a given date based on total ET since the last irrigation event (less any rainfall), and corrected for distribution uniformity of the water delivery system. In this project, CM was adapted to vineyards by adding sub-models to account for early-season depletion of stored soil moisture, cover crop presence, and intentional water stress. An initial trial was performed on a Central Coast winegrape vineyard, where an eddy covariance tower measured daily ET during from May-Dec 2020. Total ET for the period showed strong agreement between the tower-based measurements (443 mm) and the CM model (431 mm). The model tended to overestimate cumulative ET during mid-June by up to 30 mm (about 15%) and later underestimated cumulative ET by as much as 65 mm (about 23%) in late September, suggesting that additional model calibration is needed to improve simulation of within-season variability. Results will be reported for trials on additional vineyard sites conducted during the 2021 season.

Evaluation↗
NASA NTRS2022

Midwest Water Resources: Developing an Evapotranspiration Climatology to Analyze Spatiotemporal Water Budget Patterns for Agriculture and Natural Resources Managers in the Midwest

Evapotranspiration (ET) is a climatic variable critical to the hydrologic cycle and is used to evaluate spatiotemporal trends in drought conditions. Although in-situ observations provide accurate ET information, these records are spatially sparse. The United States Department of Agriculture (USDA) Midwest Climate Hub, National Integrated Drought Information System, Minnesota Department of Agriculture, Michigan State University, and the USDA Foreign Agricultural Service have partnered with DEVELOP to gain new insights on spatiotemporal patterns of ET with NASA satellite data. This project evaluates the feasibility of using remotely sensed ET data products to understand trends from 2001 through 2020. Actual ET (aET) data were sourced from NASA’s Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and reference ET (refET) and precipitation data were sourced from the Gridded Surface Meteorological Dataset (gridMET). Spatial and temporal differences in the MODIS and gridMET data sets were resolved such that gridded data could be compared. These data sets were then used to produce monthly Normals maps of mm/8-day of precipitation, aET, and refET. Using precipitation and ET Normals maps, hydrologic state maps were produced by subtracting either refET or aET from precipitation. These hydrologic state maps provide a proxy water balance by summarizing the difference between water entering and leaving the ground surface. Additionally, the ET products were compared in timeseries plots. The ET products from this project provided partners with comparable datasets to assess potential drought and flooding conditions to support Midwest agricultural and natural resource managers in decision-making.

Emma Myrick↗
NASA NTRS2022

Mississippi Embayment Water Resources: Utilizing NASA Earth Observations to Understand Groundwater Recharge in the Mississippi Regional Aquifer System

The Memphis Sand Aquifer (MSA) is located in the Mississippi Embayment which extends 250,000 square kilometers across nine states. Groundwater recharge factors that influence the narrow recharge zone of the MSA include precipitation, potential evapotranspiration, and landcover changes. The unsustainable water practices and increasing landcover change from urban development in the MSA's narrow recharge zone threaten the aquifer’s groundwater storage. In partnership with Protect Our Aquifer, the team used data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM IMERG), National Land Cover Dataset (NLCD), and Gravity Recovery and Climate Experiment (GRACE). These datasets included annually-averaged precipitation, evapotranspiration, potential evapotranspiration, biannual landcover change, and monthly total water storage which were used to create groundwater recharge factors maps and timeseries. The evaporative stress index map, water balance map, and landcover change maps were used to identify thriving areas. The team found precipitation did not express a strong linear trend and showed high precipitation years in 2017 and 2018, and a drought year in 2011. The potential evapotranspiration showed a weak negative linear trend. The landcover change showed shifts in forested areas and urban development. The team identified four thriving areas in the western side of Tennessee that successfully contribute to aquifer recharge due to increased forest area, sufficient water use, low changes in total water storage, and lateral positioning to streams. These end products allowed our partners to make informed decisions about areas that are thriving in the Mississippi Embayment recharge zone for conservation efforts of the aquifer.

Lauren Mahoney↗
NASA NTRS2022

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the ‘long’ near infrared band (NIRL) used for atmospheric correction. For this band, the pre-launch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that MODIS/Aqua RRS time series are quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of VIIRS/SNPP RRS to NIRL calibration, and compared our results to previous MODIS/Aqua and SeaWiFS/OrbView2 analysis. In doing so, we note g(NIRL) sensitivities of mission-averaged RRS timeseries are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies, however, all sensors show prominent g(NIRL) sensitivity, with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations (e.g., using simultaneous same view measurements) toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS.

Brian B Barnes↗
NASA NTRS2023

Prediction and Verification of Parker Solar Probe Solar Wind Sources at 13.3 Rs

Drawing connections between heliospheric spacecraft and solar wind sources is a vital step in understanding the evolution of the solar corona into the solar wind and contextualizing in situ timeseries. Furthermore, making advanced predictions of this linkage for ongoing heliospheric missions, such as Parker Solar Probe (Parker), is necessary for achieving useful coordinated remote observations and maximizing scientific return. The general procedure for estimating such connectivity is straightforward (i.e., magnetic field line tracing in a coronal model) but validating the resulting estimates is difficult due to the lack of an independent ground truth and limited model constraints. In its most recent orbits, Parker has reached perihelia of 13.3R⊙ and moreover travels extremely fast prograde relative to the solar surface, covering over 120° longitude in 3 days. Here we present footpoint predictions and subsequent validation efforts for Parker Encounter 10, the first of the 13.3R⊙ orbits, which occurred in November 2021. We show that the longitudinal dependence of in situ plasma data from these novel orbits provides a powerful method of footpoint validation. With reference to other encounters, we also illustrate that the conditions under which source mapping is most accurate for near-ecliptic spacecraft (such as Parker) occur when solar activity is low, but also require that the heliospheric current sheet is strongly warped by mid-latitude or equatorial coronal holes. Lastly, we comment on the large-scale coronal structure implied by the Encounter 10 mapping, highlighting an empirical equatorial cut of the Alfvèn surface consisting of localized protrusions above unipolar magnetic separatrices.

S. T. Badman↗
NASA NTRS2023

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRS2022

Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case Over Bangladesh

Monitoring floods is a major issue in water resources management and risk mitigation, especially in the Global South. Optical and radar observations, even providing a fine spatial resolution, are still limited by cloud cover interaction or insufficient temporal resolution. On the other hand, passive microwave (PMW) sensors collect information on a daily frequency with minor cloud cover interaction, but they have been historically limited in terms of spatial resolution. Here, we evaluate the capability of an enhanced spatial resolution PMW dataset (3.125 km) in monitoring spatio-temporal evolution of flood events, focusing on a major flood event that occurred in October 2005 in Bangladesh. We apply an algorithm aimed to remove the seasonal variability of surface temperature from the PMW timeseries, exploiting the difference in emissivity between dry and water-covered pixels. We assess the capability of the algorithm in capturing flood evolution and extension through the comparison with quantities obtained from optical data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and water level measurements. We also compare the enhanced product with the historical coarser resolution dataset by means of a variogram-based analysis to evaluate the improvements in terms of spatial representation. Finally, we evaluate the possibility to extract the water fraction within a single pixel by using an Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) emissivity dataset and compare the estimates with MODIS-derived water fractions. Our results show that the enhanced PMW product outperforms the coarser one when compared to flood mapped from optical data based on information content, indicating that it is possible to integrate such a product into the mapping of floods at a global scale on a daily basis.

flood detection↗
NASA NTRSDate not supplied

Interactive Framework to Support Open Model-Data Validation Efforts at the CCMC

Validation of models using observation data has been a central activity at the Community Coordinated Modeling Center (CCMC) over more than a decade. The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework is a database-driven implementation of the interactive analysis of model-data comparisons that simultaneously provides a view across a multitude of locations and time periods. CAMEL thus extends the single-location or single-trajectory timeseries data comparison capabilities provided by the CCMC online visualization that formed the backbone of initial validation efforts. We demonstrate CAMEL capabilities for an example application to study Neutral Density in the upper atmosphere among comparisons in the heliosphere, the Earth’s radiation belt, magnetosphere, and ionosphere-thermosphere-mesosphere domains.

Lutz Rastaetter↗
NASA NTRSDate not supplied

Assessment of Long-term Trends in the Collection 4 Total Ozone Record from the Ozone Monitoring Instrument

Long-term changes in total ozone affect the amount of harmful UV radiation reaching Earth’s surface and reflect progress made towards recovery of stratospheric ozone. Satellite total ozone data are also used to estimate long-term trends in tropospheric ozone, a reactive and potent greenhouse gas, by subtracting the stratospheric column timeseries from that of total ozone. For these scientific applications, the long-term stability should be better than 1%. Left unchecked, instrument calibration drift can produce a trend of this magnitude or greater. NASA has produced nearly twenty years of total ozone data from the Ozone Monitoring Instrument (OMI) using the Total Ozone Mapping Spectrometer (TOMS) algorithm. The drift in the OMI instrument as monitored by ice radiance calibration has been relatively slow, but it has reached a level of ~3% over the mission lifetime. This drift was corrected in the recently released Collection 4 OMI calibrated radiances, updating the Collection 3 calibration released in 2006. We have reprocessed the OMI total ozone record using the Collection 4 calibration and updates to the TOMS algorithm. We summarize these changes and evaluate their impact by comparing to the previous Collection 3 OMI dataset, the Suomi NPP Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) and Solar Backscatter UV (SBUV) Merged Ozone Dataset (MOD) total ozone records and other independent satellite total ozone datasets. Climatological radiance residuals from OMI and OMPS-NM are calculated and compared to investigate differences in spectral calibration that can cause drifts in long-term total ozone trends. We also analyze the tropospheric ozone record produced using the Collection 4 OMI total ozone and stratospheric column ozone from MLS. Collection 3 OMI data processed with the TOMS algorithm show a positive drift relative to other satellite and ground-based data of 1-2 DU per decade. Initial results show that this drift is reduced in Collection 4 OMI, due to the updated OMI calibration. In this work we will quantify the improvements in Collection 4 OMI relative to independent data sources at both the ozone and radiance level

Collection 4 OMI Total Ozone↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRS2023

Predicting Patterns of Solar Energy Buildout to Identify Opportunities for Biodiversity Conservation

The construction of solar energy facilities can have positive or negative impacts on biodiversity depending on siting and associated land use transitions. We identified drivers of solar siting and quantified patterns of buildout in states surrounding the Chesapeake Bay watershed – a biodiversity hotspot with numerous ecosystem services. Using a convolutional neural network, we mapped the footprints of ground-mounted solar arrays present in satellite imagery annually from 2017 to 2021 in Delaware, Maryland, Pennsylvania, New York, Virginia, and West Virginia. As of 2021, we identified 958 solar arrays covering 52.3 km2 built primarily on previously cultivated land, while avoiding natural landcover. We fit a binomial-Weibull model to these solar timeseries data in a hierarchical, Bayesian framework to quantify the relationship between geospatial covariates and rate of solar development. Solar array construction rate increased in cultivated areas, areas of lower agricultural suitability, lower slope, lower forest cover, lower biodiversity protection, and greater distances from roads. We also estimated changes in the rate of solar construction over time and found differences among states: acceleration in Virginia and deceleration in New York. We used parameter estimates to map the relative likelihood of future solar development across the study area. This methodology can be used to anticipate where solar is likely to be built in different landscapes and how these patterns align with conservation goals. Around the Chesapeake Bay watershed, the selection of lower quality agricultural areas for solar energy minimizes removal of important habitat and provides opportunities for native plant and pollinator restoration.

Artificial Intelligence↗