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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Data for: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial-Aquatic Interfaces

The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes and generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including a large-scale flood manipulation experiment, collectively generating over three million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi-institution projects. This dataset shows example data generated by the sensor network described above. Data output for data loggers connected to groundwater water quality sondes measuring dissolved oxygen, pH, oxidative redox potential (ORP), groundwater elevation, groundwater salinity and temperature, and groundwater elevation; replicate soil moisture and conductivity probes installed at 10 and 30 cm below the ground surface; rainfall and solar radiation; and mean sap flow from 8 replicate probes. Sensors are installed at upland forest (UP), wetlands (W), and transitional locations between these ecosystems (TR). Example datasets are from Goodwin Islands in the Chesapeake Bay and Portage River along the Lake Erie coastline.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Long-Term Ozone Variability and Trends from Reanalyses

Stratospheric ozone has a profound impact on radiation and chemistry over various spatial and temporal scales. The evolution of stratospheric ozone over the 21st century, however, is not well understood, especially in the lower stratosphere. Highly vertically resolved ozone data from satellite-borne limb sounders have proved to be invaluable for studying ozone in the middle and upper stratosphere but it was not until recently that these measurements were successfully incorporated in atmospheric reanalyses. Validation and comparison studies have demonstrated that the addition of observations from the Microwave Limb Sounder (MLS) on EOS (Earth Observing System) Aura greatly improved the quality of ozone fields in MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) making these assimilated data sets more useful for scientific research. In this presentation we demonstrate that multidecadal lower-stratospheric ozone variability and trends can be derived from NASA's MERRA-2 reanalysis ozone. In particular, the reanalysis ozone bias-corrected using a chemistry model simulation as a transfer function agrees very well with recently reprocessed long ozonesonde records. Ozone trends in the lower stratosphere will be discussed in the context of recent findings (Ball et al., 2018) and interpreted in connection with long-term circulation changes in the lower stratosphere. Next, we show that the use of ozone data retrieved from the next generation OMPS (Ozone Mapping Profiler Suite) instruments, including the OMPS Limb Profiler, can successfully extend the reanalyses into the future allowing comprehensive monitoring of global ozone and interpretation of its evolution during the critical period of expected ozone recovery and climate change from increasing concentration of greenhouse gases.

ozone↗
DOE OSTI2024

Diagnosing and overcoming recombination and resistive losses in non-silicon solar cells using a silicon-inspired characterization platform

This project aimed to generate the characterization tools needed for accurate and systematic loss analysis in non-silicon photovoltaic solar cell technologies and, through the use of these tools and analysis techniques, contribute to the development of a novel class of hetero-contacts to II-VI absorbers, with the final goal of demonstrating record-breaking CdSeTe devices. CdSeTe solar cells provide a prime example of the potential impact of the techniques we proposed to develop and implement: record poly-CdSeTe cells have bandgap-voltage deficits (W oc ) of approximately 550 mV, as compared with below 400 mV for all other mature PV technologies. Similarly, these record CdSeTe devices have FFs below 80%, when other mature cells are near or above 85%. Frustratingly, a systematic identification of the origin of these sub-par performances—for example recombination or resistive losses—has been lacking, thus slowing down the development of these technologies. Similarly, it is often asserted that CdSeTe cells need a better back (hole) contact. Although most believe this is true, no one knew—at the start of this project—how high the V oc and FF could be for a given cell if it had a perfect back contact. Such characterization techniques and loss analysis methods exist and are routinely performed on c-Si solar cells (e.g. injection-dependent lifetime, Suns-V oc , transfer length method, etc). Over the years, they have been instrumental in the development of silicon devices that operate at 91% of their theoretical (Auger) limit. Lifetime testing, and the associated reconstruction of the implied-J-V curve, can moreover be performed at every cell-processing step, thus allowing a direct peek into the impact of that step on cell performance. Therefore, adapting these techniques and tools to non-Si devices would greatly improve their learning rate. In this project, we developed a Suns-ERE technique—the equipment, methodology, and know-how—to measure the implied-J-V curve, the pseudo-J-V curve, and the actual J-V curve of a thin-film solar cell, allowing an accurate assessment of the quality of the bulk material and its surface passivation, the selectivity of the contact, and its resistivity. We used this technique to show that the absorber of present CdSeTe solar cells is capable of achieving 1 V Voc,, that passivation layers exist (e.g., Al2O3) that can support such high voltages, and that the barrier is identifying contact layers that are both passivating and carrier-selective. The characterization platform created in this project and the understanding generated using it will accelerate the progress of non-silicon PV technologies. In particular, the project will contribute to CdSeTe solar cells with Voc > 1 V and cell efficiency > 24%. Such cells provide a pathway to module-level efficiencies >23%. As CdSeTe presently competes with silicon on module cost (in $\$ $/W) and yet has significantly more room for efficiency gains, the potential for LCOE reduction is particularly large. For example, CdTe modules with an efficiency of 21% would allow an LCOE below $\$ $0.04kWh -1 in average US climates.

14 SOLAR ENERGY↗
DOE OSTI2024

Bridging gaps in permafrost-shrub understanding

Permafrost, permanently frozen ground which underlies much of the Arctic and sub-Arctic, is influenced by deciduous shrubs, yet our understanding of permafrost-shrub interactions is limited. This is largely due to a lack of widespread, long-term, high-quality observations across Arctic and sub-Arctic systems, which are difficult to study due to their remote locations. Shrubs are rapidly expanding in many areas of Arctic tundra and can either amplify or inhibit permafrost thaw, making it crucial that we can understand and predict permafrost-shrub interactions. As we have limited time and resources to make observations, we must design our field campaigns to be as impactful as possible. Below we outline the current state of knowledge and give suggestions about how we can maximize our fieldwork’s impact. From field studies conducted around the Arctic, we know that shrubs generally have a cooling effect on permafrost in the summer, largely caused by the shrub canopy shading the soil. However, summertime cooling can be counteracted by a warming effect in winter, caused by the trapping of blowing snow by shrubs, which leads to increased snow depths. In addition to these commonly observed effects, both observations and models show that other local factors such as climate, soil, snow, and disturbances can interact to cause contrasting dominant effects on permafrost temperatures. For example, shrubs that protrude through the snowpack have been observed to cool permafrost in winter via thermal bridging between the air above the snow and the soil below. However, at other research sites, protruding shrubs lower the surface albedo and cause earlier snowmelt in spring, amplifying permafrost thaw by exposing the soil to thawing earlier. Protruding shrubs have also been observed to induce snow melt in autumn, creating ice layers at the snow surface that prevent further snow accumulation and eliminate the ability of shrubs to trap blowing snow, thereby reducing the winter warming effect. While we understand the physical processes that lead to these effects, we cannot explain why we observe different effects between sites.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Airborne Measurements and Emission Estimates of Greenhouse Gases and Other Trace Constituents From the 2013 California Yosemite Rim Wildfire

This paper presents airborne measurements of multiple atmospheric trace constituents including greenhouse gases (such as CO2, CH4, O3) and biomass burning tracers (such as CO, CH3CN) downwind of an exceptionally large wildfire. In summer 2013, the Rim wildfire, ignited just west of the Yosemite National Park, California, and burned over 250,000 acres of the forest during the 2-month period (17 August to 24 October) before it was extinguished. The Rim wildfire plume was intercepted by flights carried out by the NASA Ames Alpha Jet Atmospheric eXperiment (AJAX) on 29 August and the NASA DC-8, as part of SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys), on 26 and 27 August during its intense, primary burning period. AJAX revisited the wildfire on 10 September when the conditions were increasingly smoldering, with slower growth. The more extensive payload of the DC-8 helped to bridge key measurements that were not available as part of AJAX (e. g. CO). Data analyses are presented in terms of emission ratios (ER), emission factors (EF) and combustion efficiency and are compared with previous wildfire studies. ERs were 8.0 ppb CH4/(ppm CO2) on 26 August, 6.5 ppb CH4 (ppm CO2)1 on 29 August and 18.3 ppb CH4 (ppm CO2)1 on 10 September 2013. The increase in CH4 ER from 6.5 to 8.0 ppb CH4/(ppm CO2) during the primary burning period to 18.3 ppb CH4/(ppm CO2) during the fire's slower growth period likely indicates enhanced CH4 emissions from increased smoldering combustion relative to flaming combustion. Given the magnitude of the Rim wildfire, the impacts it had on regional air quality and the limited sampling of wildfire emissions in the western United States to date, this study provides a valuable dataset to support forestry and regional air quality management, including observations of ERs of a wide number of species from the Rim wildfire.

Yates, E. L.↗
NASA NTRSDate not supplied

Biomass burning declines lead to large net reductions in NO2 concentrations over north equatorial Africa in spite of growing fossil fuel emissions

Socio-economic development in low and middle-income countries has been accompanied by increased emissions of air pollutants such as nitrogen oxides (NOx: nitrogen dioxide (NO2) + nitric oxide (NO)), which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, biomass burning—another important NOx source—has declined in Africa’s northern biomass burning region, attributed to changes in climate and anthropogenic fire management associated with agricultural development. Here we use satellite observations of tropospheric NO2 vertical column densities (VCDs) and burned area to identify NO2 trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs—home to over 350 million people—mean annual tropospheric NO2 VCDs decreased by 4.5% from 2005 through 2017 during the biomass burning season of November through February. Reductions in burned area explained the majority of these change in NO2 VCDs, but there were also weaker relationships between changes in NO2 VCDs and fossil fuel emissions over parts of West Africa, which were stronger during rainy season. Over Africa’s biomass burning regions, NO2 VCDs tended to decrease with increasing population density up to a threshold of approximately 180 people per km2, suggesting that anthropogenic activity causes a net reduction in NO2 emissions across roughly 90% of the continent’s biomass burning regions. In contrast to the widely-held perception that socio-economic development worsens air quality in low and middle-income nations, our results suggest that countries in Africa’s northern biomass burning region are following a different pathway, resulting in regional air quality benefits. However, these benefits may be lost with increasing fossil fuel use.

Biomass burning↗
NASA NTRSDate not supplied

Global SO 2 Data Record from OMPS Instruments on the JPSS Constellation

NASA’s Earth Observing System (EOS) SO 2 climate data record (CDR) started in 2004, with the launch of the Aura/Ozone Monitoring Instrument (OMI) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. An advantage of the data-driven PCA retrieval technique is that it enables highly consistent retrievals from different instruments, by inherently accounting for various instrumental factors. To further extend the EOS SO 2 CDR, we are implementing the PCA SO 2 retrieval algorithm with the L1B measurements from OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation. In this presentation, we will provide an update on our progress in NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022) PCA SO2 retrievals. We will focus on our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will present statistical analyses on the quality of NOAA-20 PCA SO 2 product, including retrieval noise, biases over background areas, and long-term stability. We will compare our PCA SO 2 retrievals from NOAA-20 with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument) for anthropogenic sources as well as large volcanic plumes. We will also discuss the application of a new machine learning technique that helps to further reduce the noise of NOAA-20 SO 2 retrievals. In addition, we will present preliminary PCA SO 2 retrievals from NOAA-21/OMPS, including those from direct readout implementation for aviation disaster avoidance. Finally, we will share some first results applying the PCA algorithm to NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument to obtain hourly, high resolution SO 2 data over North America.

SO2↗
DOE OSTI2025

Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region

U.S. Department of Energy (DOE), Office of Science, Award DE-SC0023215, Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region, supported capacity and partnership building between researchers at Central Michigan University (CMU) and scientists at DOE laboratories. The work involved identifying potential collaborations both internal to CMU and between CMU researchers and DOE staff, developing collaborative research proposals, and partnering on pilot-scale research projects focused on enhancing resilience of Great Lakes communities, ecosystems, and infrastructure to global change and environmental stressors. DOE funds supported travel, time to develop collaborations, investigate new research pathways, and draft grant proposals, and a loan of DOE equipment to collect pilot data in support of future work. The project achieved its goal of strengthening research collaborations between interdisciplinary researchers at CMU and establishing new collaborations and partnerships between CMU and DOE scientists. These collaborations resulted in 5 pre-applications and 4 full applications for funding to DOE BER Funding Opportunities, a successfully funded DOE BER award (DE-SC0025260), a new pilot project that leveraged DOE resources (AquaBOT) to improve understanding of water quality conditions in Michigan rivers, and two presentations of preliminary results at national scientific conferences. This project’s outcomes will benefit the Great Lakes Region via new lines of collaborative research focused on improving the resilience of Michigan communities, ecosystems, and infrastructure.

58 GEOSCIENCES↗
NASA NTRS2021

Flood Monitoring and Crop Damage Assessment

In recent years, the occurrence and impact of inland and coastal flood events have become more frequent and damaging, especially within agricultural fields, due to the global climate change and consistent sea level rise. Monitoring and measuring the magnitude of flood events in a timely manner and assessing the subsequent crop damages accurately are precursors in minimizing detrimental consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are capable of monitoring flood events precisely but limited within the smaller spatial extent, time-consuming, and costly. In recent decades, advancement in airborne- and satellite-based remote sensing technologies offering products at a daily global spatial extent with various spectral resolution helps address the shortcomings of the traditional in situ approaches in flood monitoring. Furthermore, the methods such as classification and band ratioing using remote sensing products are simple and effective in assessing flood-induced agricultural damages. The combination of remote sensing products and geographic information systems along with the current development in web mapping, users now can get near real-time flood monitoring and crop damage assessments, albeit dependent upon the quality of available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield across Nebraska and Missouri, and the overall accuracy in estimating corn yield was about 90%. The method implemented in this localized case study could be extended at a larger geographical scale.

Flood monitoring↗
NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗
NASA NTRS2002

Global Aerosol Remote Sensing from MODIS

The physical characteristics, composition, abundance, spatial distribution and dynamics of global aerosols are still very poorly known, and new data from satellite sensors have long been awaited to improve current understanding and to give a boost to the effort in future climate predictions. The derivation of aerosol parameters from the MODerate resolution Imaging Spectro-radiometer (MODIS) sensors aboard the Earth Observing System (EOS) Terra and Aqua polar-orbiting satellites ushers in a new era in aerosol remote sensing from space. Terra and Aqua were launched on December 18, 1999 and May 4, 2002 respectively, with daytime equator crossing times of approximately 10:30 am and 1:30 pm respectively. Several aerosol parameters are retrieved at 10-km spatial resolution (level 2) from MODIS daytime data. The MODIS aerosol algorithm employs different approaches to retrieve parameters over land and ocean surfaces, because of the inherent differences in the solar spectral radiance interaction with these surfaces. The parameters retrieved include: aerosol optical thickness (AOT) at 0.47, 0.55 and 0.66 micron wavelengths over land, and at 0.47, 0.55, 0.66, 0.87, 1.2, 1.6, and 2.1 micron over ocean; Angstrom exponent over land and ocean; and effective radii, and the proportion of AOT contributed by the small mode aerosols over ocean. To ensure the quality of these parameters, a substantial part of the Terra-MODIS aerosol products were validated globally and regionally, based on cross correlation with corresponding parameters derived from ground-based measurements from AERONET (AErosol RObotic NETwork) sun photometers. Similar validation efforts are planned for the Aqua-MODIS aerosol products. The MODIS level 2 aerosol products are operationally aggregated to generate global daily, eight-day (weekly), and monthly products at one-degree spatial resolution (level 3). MODIS aerosol data are used for the detailed study of local, regional, and global aerosol concentration, distribution, and temporal dynamics, as well as for radiative forcing calculations. We show several examples of these results and comparisons with model output.

Ichoku, Charles↗
DOE OSTI2023

Development of a hardware-in-the-loop testbed for laboratory performance verification of flexible building equipment in typical commercial buildings

The goals of reducing energy costs, shifting electricity peaks, increasing the use of renewable energy, and enhancing the stability of the electric grid can be met in part by fully exploiting the energy flexibility potential of buildings and building equipment. The development of strategies that exploit these flexibilities could be facilitated by publicly available high-resolution datasets illustrating how control of HVAC systems in commercial buildings can be used in different climate zones to shape the energy use profile of a building for grid needs. This article presents the development and integration of a Hardware-In-the-Loop Flexible load Testbed (HILFT) that integrates physical HVAC systems with a simulated building model and simulated occupants with the goal of generating datasets to verify load flexibility of typical commercial buildings. Compared to simulation-only experiments, the hardware-in-the-loop approach captures the dynamics of the physical systems while also allowing efficient testing of various boundary conditions. The HILFT integration in this article is achieved through the co-simulation among various software environments including LabVIEW, MATLAB, and EnergyPlus. Although theoretically viable, such integration has encountered many real-world challenges, such as: 1) how to design the overall data infrastructure to ensure effective, robust, and efficient integration; 2) how to avoid closed-loop hunting between simulated and emulated variables; 3) how to quantify system response times and minimize system delays; and 4) how to assess the overall integration quality. Lessons-learned using the examples of an AHU-VAV system, an air-source heat pump system, and a water-source heat pump system are presented.

Chen, zhelun↗
NASA NTRS2021

Reductions in NO2 Burden Over North Equatorial Africa from Decline in Biomass Burning in Spite of Growing Fossil Fuel Use, 2005 to 2017

Socioeconomic development in low- and middle-income countries has been accompanied by increased emissions of air pollutants, such as nitrogen oxides [NOx: nitrogen dioxide (NO2) + nitric oxide (NO)], which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, landscape biomass burning—another important NOx source—has declined in north equatorial Africa, attributed to changes in climate and anthropogenic fire management. Here, we use satellite observations of tropospheric NO2 vertical column densities (VCDs) and burned area to identify NO2 trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs—home to hundreds of millions of people—mean annual tropospheric NO2 VCDs decreased by 4.5% from 2005 through 2017 during the dry season of November through February. Reductions in burned area explained the majority of variation in NO2 VCDs, though changes in fossil fuel emissions also explained some variation. Over Africa’s biomass burning regions, raising mean GDP density (USD·km−2) above its lowest levels is associated with lower NO2 VCDs during the dry season, suggesting that economic development mitigates net NO2 emissions during these highly polluted months. In contrast to the traditional notion that socioeconomic development increases air pollutant concentrations in low- and middle-income nations, our results suggest that countries in Africa’s northern biomass-burning region are following a different pathway during the fire season, resulting in potential air quality benefits. However, these benefits may be lost with increasing fossil fuel use and are absent during the rainy season.

air pollution↗
NASA NTRS2025

On the Characterization and Mitigation of Noise in Space-borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

calibration↗
NASA NTRS2004

The Development of Two Science Investigator-led Processing Systems (SIPS) for NASA's Earth Observation System (EOS)

In 2001, NASA Goddard Space Flight Center's Laboratory for Terrestrial Physics started the construction of a science Investigator-led Processing System (SIPS) for processing data from the Ozone Monitoring Instrument (OMI) which will launch on the Aura platform in mid 2004. The Ozone Monitoring Instrument (OMI) is a contribution of the Netherlands Agency for Aerospace Programs (NIVR) in collaboration with the Finnish Meteorological Institute (FMI) to the Earth Observing System (EOS) Aura mission. It will continue the Total Ozone Monitoring System (TOMS) record for total ozone and other atmospheric parameters related to ozone chemistry and climate. OMI measurements will be highly synergistic with the other instruments on the EOS Aura platform. The LTP previously developed the Moderate Resolution Imaging Spectrometer (MODIS) Data Processing System (MODAPS), which has been in full operations since the launches of the Terra and Aqua spacecrafts in December, 1999 and May, 2002 respectively. During that time, it has continually evolved to better support the needs of the MODIS team. We now run multiple instances of the system managing faster than real time reprocessings of the data as well as continuing forward processing. The new OMI Data Processing System (OMIDAPS) was adapted from the MODAPS. It will ingest raw data from the satellite ground station and process it to produce calibrated, geolocated higher level data products. These data products will be transmitted to the Goddard Distributed Active Archive Center (GDAAC) instance of the Earth Observing System (EOS) Data and Information System (EOSDIS) for long term archive and distribution to the public. The OMIDAPS will also provide data distribution to the OMI Science Team for quality assessment, algorithm improvement, calibration, etc. We have taken advantage of lessons learned from the MODIS experience and software already developed for MODIS. We made some changes in the hardware system organization, database and software to adapt the system for OMI. We replaced the fundamental database system, Sybase, with an Open Source RDBMS called PostgreSQL, and based the entire OMIDAPS on a cluster of Linux based commodity computers rather than the large SGI servers that MODAPS uses. Rather than relying on a central I/O server host, the new system distributes its data archive among multiple server hosts in the cluster. OMI is also customizing the graphical user interfaces and reporting structure to more closely meet the needs of the OMI Science Team. Prior to 2003, simulated OMI data and the science algorithms were not ready for production testing. We initially constructed a prototype system and tested using a 25 year dataset of Total Ozone Mapping Spectrometer (TOMS) and Solar Backscatter Ultraviolet Instrument (SBUV) data. This prototype system provided a platform to support the adaptation of the algorithms for OMI, and provided reprocessing of the historical data aiding in its analysis. In a recent reanalysis of the TOMS data, the OMIDAPS processed 108,000 full orbits of data through 4 processing steps per orbit, producing about 800,000 files (400 GiB) of level 2 and greater data files. More recently we have installed two instances of the OMIDAPS for integration and testing of OM1 science processes as they get delivered from the Science Team. A Test instance of the OMIDAPS has also supported a series of "Interface Confidence Tests" (ICTs) and End-to-End Ground System tests to ensure the launch readiness of the system. This paper will discuss the high-level hardware, software, and database organization of the OMIDAPS and how it builds on the MODAPS heritage system. It will also provide an overview of the testing and implementation of the production OMIDAPS.

Tilmes, Curt↗
DOE OSTI2026

Comparative life cycle assessment of bioenergy in Japan from residual biomass-based wood pellets produced in the US Pacific Northwest

The US Pacific Northwest (PNW) faces an increase in wildfires due to forest overcrowding and climate change, posing significant environmental and public health risks. Traditional methods of managing surplus biomass, including prescribed burning, have increased air pollution and global warming. While in the PNW, residual woody biomass is being treated as waste, Japan’s growing demand for bio-based energy presents an opportunity to export value-added biomass as energy pellets. This study investigates whether producing wood pellets from the residual woody biomass from forest operations and sawmills for electricity generation in Japan is truly environmentally beneficial. Accordingly, we conducted a cradle-to-grave Life Cycle Assessment (LCA) to evaluate the environmental impact of residual pellets vs. coal for electricity generation. The assessment covered feedstock production, pellet processing, transportation, and combustion phases. Our findings indicate that replacing coal-based electricity in Japan with PNW residual pellet-based electricity can lower the Global Warming Potential (GWP) by about 90% for every unit of electricity displaced. Furthermore, the results show that repurposing the otherwise burnt harvest slash residues for pellet production would improve local air quality by reducing PM 2.5 and smog in the PNW. However, substituting coal with residual pellets marginally increased carcinogenic and ecotoxicity-related emissions. Based on these results, we conclude that substituting coal with residual wood pellets for electricity generation, particularly harvest-slash residual pellets, is environmentally beneficial across most impact categories, including the GWP. This research underscores how an export‑oriented pellet industry can help address the environmental challenges within the regional US wood products industry and global renewable energy supply.

Velappan, Hemalatha [Univ. of Washington, Seattle,↗
NASA NTRS2001

Results from Global Land-Surface Data Assimilation Methods

Realistic representation of the land surface is crucial in global climate modeling (GCM). Recently, the Mosaic land-surface Model (LSM) has been driven off-line using GEOS DAS (Goddard Earth Observing System Data Assimilation System) atmospheric forcing, forming the Off-line Land-surface Global Assimilation (OLGA) system. This system provides a computationally efficient test bed for land surface data assimilation. Here, we validate the OLGA simulation of surface processes and the assimilation of ISCCP surface temperatures. Another component of this study as the incorporation of the Physical-space Statistical Analysis System (PSAS) into OLGA, in order to assimilate surface temperature observations from the International Satellite Cloud Climatology Project (ISCCP). To counteract the subsequent forcing of the analyzed skin temperature back to the initial state following the analysis. incremental bias correction (IBC) was included in the assimilation. The IBC scheme effectively removed the time mean bias, but did not remove him in the mean diurnal cycle. Therefore, a diurnal him correction (DBC) scheme was developed, where the time-dependent bias was modeled with a sine wave parameterization. In addition, quality control of the ISCCP data and anisotropic temperature correction were implemented in PSAS. Preliminary results showed a substantial impact from the inclusion of PSAS and DBC that was visible in the surface meteorology fields and energy budget. Also, the monthly mean diurnal cycle from the experiment closely matched the diurnal cycle from the observations.

Radakovich, Jon D.↗
NASA NTRS2004

Regional Assimilation of NASA Atmospheric Infrared Sounder (AIRS) Data

The NASA Short-term Prediction Research and Transition (SPORT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NW S forecast operations and decision-making. The Atmospheric Infrared Sounder (AIRS), is expected to advance climate research and weather prediction into the 21 st century. It is one of six instruments onboard Aqua, a satellite that is part of NASA s Earth Observing System. AIRS, along with two partner microwave sounding instruments, represents the most advanced atmospheric sounding system ever deployed in space. The system is capable of measuring the atmospheric temperature in the troposphere with radiosonde accuracies of 1 K over 1 km-thick layers under both clear and cloudy conditions, while the accuracy of the derived moisture profiles will exceed that obtained by radiosondes. It is imperative that the scientific community is prepared to take full advantage of next-generation satellite data that will become available within the next decade. The purpose of this paper is to describe a procedure designed to optimally assimilate AIRS data at high spatial resolution over both land and ocean. The assimilation system used in this study is the Local Analysis and Prediction System (LAPS) developed at the Forecast System Laboratory used extensively around the globe. Results will focus on quality control issues associated with AIRS, optimal assimilation strategies, and the impact of the AIRS data on subsequent numerical forecasts at 12 km produced by the next generation Weather Research and Forecast (WRF) model.

Chou, Shih-Hung↗