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DOE OSTI2025

Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030

The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

ARGO↗

DOE OSTI2014

Uncertainty Estimates for SIRS, SKYRAD, & GNDRAD Data and Reprocessing the Pyrgeometer Data (Presentation)

The National Renewable Energy Laboratory (NREL) and the Atmospheric Radiation Measurement (ARM) Climate Research Facility work together in providing data from strategically located in situ measurement observatories around the world. Both work together in improving and developing new technologies that assist in acquiring high quality radiometric data. In this presentation we summarize the uncertainty estimates of the ARM data collected at the ARM Solar Infrared Radiation Station (SIRS), Sky Radiometers on Stand for Downwelling Radiation (SKYRAD), and Ground Radiometers on Stand for Upwelling Radiation (GNDRAD), which ultimately improve the existing radiometric data. Three studies are also included to show the difference between calibrating pyrgeometers (e.g., Eppley PIR) using the manufacturer blackbody versus the interim World Infrared Standard Group (WISG), a pyrgeometer aging study, and the sampling rate effect of correcting historical data.

14 SOLAR ENERGY↗

DOE OSTI2018

Implications for conifer glacial refugia and postglacial climatic variation in western Beringia from lake sediments of the Upper Indigirka basin

A c. 13 800 cal. a BP palynological record from Tschuch'ye Lake (eastern Yakutia), when combined with other palaeobotanical records from western Beringia, supports previous conclusions that Larix and Pinus pumila spread from multiple cryptic refugia during the Lateglacial period and middle Holocene, respectively. The antiquity and spatial distribution of Larix in many of these records suggest that full-glacial landscapes probably had a small but important tree component. The Tschuch'ye record further indicates the absence of a Younger Dryas climatic reversal and is in accord with the preponderance of other data from western Beringia, which indicate warmer than present climates between c. 12 900 and 9100 cal. a BP. The subsequent spread of Pinus pumila, which has similar temperature requirements as Larix, denotes a shift to wetter but still warm climates during the middle Holocene, with palynological data suggesting optimal conditions in far western areas of western Beringia c. 7000–5000 cal. a BP. Lastly, the postglacial occurrence of relatively warm/dry and warm/wet intervals is consistent with results of a regional climate-model simulation that indicates warmer than present temperatures and decreased effective moisture at 11 000 cal. a BP and persistence of warm conditions but with greater moisture and longer growing season at 6000 cal. a BP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Energy Implications of Retrofitting Retail Sector Rooftop Units With Stepped Speed and Variable-Speed Functionality

Commercial retailers understand that retrofitting constant-speed RTU fan motors with stepped- or variable-speed alternatives could save significant energy in most U.S. climate zones. However, they lack supporting data, both real-world and simulation based, on the cost effectiveness and climate zone-specific energy savings associated with this measure. Thus, building managers and engineers havebeen unable to present a compelling business case for fan motor upgrades to upper management. This study uses whole-building energy simulation to estimate the energy impact of this type of measure so retailers can determine its economic feasibility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1979

Central atlantic regional ecological test site: A prototype regional environmental information system, volume 1

The Central Atlantic Regional Ecological Test Site (CARETS) project was a demonstration project for introducing data from Landsat and high-altitude aircraft sensors into regional land planning and management. This report summarizes the CARETS project results and describes its output of maps, reports, computer tapes, and other products. CARETS used a geographic information system model under which land use maps were prepared from sensor data, then digitized, processed, and linked to other environmental and social data sets, and to environmental consequences such as air pollution, stream runoff, local climatic factors, and coastal erosion. Landsat data showed the test region in 1972 to be nine percent urban and built-up land, 38 percent agriculture, 50 percent forest, three percent nonforested wetlands, and less than one percent barren land, exclusive of water-covered areas. User surveys, conferences, and workshops involving 65 agencies facilitated widespread distribution of data products and produced evaluations concerning their usefulness. We found a heterogeneous user community with diverse information needs largely preferring aerial photographs rather than satellite multispectral scanner data. Among project recommendations are establishment of a network of regional land resource information centers, working toward improved compatibility of Federal, State and local information programs supportive of land use decisions.

Land use↗

NASA NTRS2007

Reconstructed Historical Land Cover and Biophysical Parameters for Studies of Land-Atmosphere Interactions within the Eastern United States

The local environment where we live within the Earth's biosphere is often taken for granted. This environment can vary depending on whether the land cover is a forest, grassland, wetland, water body, bare soil, pastureland, agricultural field, village, residential suburb, or an urban complex with concrete, asphalt, and large buildings. In general, the type and characteristics of land cover influence surface temperatures, sunlight exposure and duration, relative humidity, wind speed and direction, soil moisture amount, plant life, birds, and other wildlife in our backyards. The physical and biological properties (biophysical characteristics) of land cover help to determine our surface environment because they directly affect surface radiation, heat, and soil moisture processes, and also feedback to regional weather and climate. Depending on the spatial scale and land use intensity, land cover changes can have profound impacts on our local and regional environment. Over the past 350 years, the eastern half of the United States, an area extending from the grassland prairies of the Great Plains to the Gulf and Atlantic coasts, has experienced extensive land cover and land use changes that began with land clearing in the 1600s, led to extensive deforestation and intensive land use practices by 1920, and then evolved to the present-day landscape. Determining the consequences of such land cover changes on regional and global climate is a major research issue. Such research requires detailed historical land cover data and modeling experiments simulating historical climates. Given the need to understand the effects of historical land cover changes in the eastern United States, some questions include: - What were the most important land cover transformations and how did they alter biophysical characteristics of the land cover at key points in time since the mid-1600s? - How have land cover and land use changes over the past 350 years affected the land surface environment including surface weather, hydrologic, and climatic variability? - How do the potential effects of regional human-induced land cover change on the environment compare to similar changes that are caused by the natural variations of the Earth's climate system? To help answer these questions, we reconstructed a fractional land cover and biophysical parameter dataset for the eastern United States at 1650, 1850, 1920, and 1992 time-slices. Each land cover fraction is associated with a biophysical parameter class, a suite of parameters defining the biophysical characteristics of that kind of land cover. This new dataset is designed for use in computer models of land-atmosphere interactions, to understand and quantify the effects of historical land cover changes on the water, energy, and carbon cycles

Steyaert, Louis T.↗

NASA NTRS2012

New and Improved GLDAS Data Sets and Data Services at NASA GES DISC

The goal of a Land Data Assimilation System (LDAS) is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast. With the motivation of creating more climatologically consistent data sets, NASA GSFC's Hydrological Sciences Laboratory has generated more than 60 years (Jan. 1948-- Dec. 2008) of Global LDAS Version 2 (GLDAS-2) data, by using the Princeton Forcing Data Set and upgraded versions of Land Surface Models (LSMs). GLDAS data and data services are provided at NASA GES DISC Hydrology Data and Information Services Center (HDISC), in collaboration with HSL and LDAS.

Rui, Hualan↗

DOE OSTI2018

Comparison of Daytime Low-Level Cloud Properties Derived From GOES and ARM SGP Measurements

Large-scale satellite data are critical for both verifying and improving general circulation model parameterizations of clouds and radiation for climate prediction. For reliable application of satellite data sets in cloud processes and climate models, it is important to have a reasonable estimate of the errors in the derived cloud properties. The daytime single-layered low-level cloud properties retrieved by the Geostationary Operational Environmental Satellite system (GOES) are compared with ground-based observations and retrievals over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility from June 1998 to December 2006. The GOES retrievals are made via the Visible-Infrared Solar-infrared Split-window Technique. They are spatially averaged within a 0.15° × 0.15° box centered on the ARM SGP site, and the ARM surface observations are temporally averaged ±15 min around the GOES scans to produce collocated pairs. Comparisons are made for monthly means, diurnal means, and one-to-one GOES and ARM collocated pairs. GOES T eff is highly correlated with ARM T top cloud temperature, having an R 2 value of 0.75, though GOES exhibits a cold bias. GOES-retrieved τ and liquid water path have very good agreement with ARM retrievals with R 2 s of 0.45 and 0.47, while r e (GOES), on average, is about 2 μm greater than ARM r e . An examination of solar and viewing geometry has shown that GOES-retrieved mean r e and τ values are impacted by solar zenith angle and especially scattering angle, which is not unexpected and needs to be accounted for by users.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Peru Health and Air Quality II: Leveraging Earth Observations and Health Data to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention

Peru's Madre de Dios region is a hotspot for dengue fever and leishmaniasis due to its tropical Amazonian climate. Though treatable, these zoonotic diseases are debilitating for under-resourced communities whose already close proximity to mosquito and sandfly vectors continues to increase via rapid urbanization and deforestation. Peru’s Ministries of Health (MINSA) and Environment (MINAM) are working to better understand the environmental factors amplifying the risk of dengue fever and leishmaniasis transmission. The first term of this project classified the land use and land cover of Madre de Dios’ 11 districts for 2010, 2015, and 2020 and identified a correlation between both diseases and urbanization. Our team expanded this analysis by creating urban-forest edge maps and incorporating climatic and topographic variables with data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), and the Shuttle Radar Topography Mission (SRTM). We determined these variables’ impacts on disease incidence by assessing existing literature and running regression models. Dengue fever correlated with urban-forest edge, urban area, slope, temperature, and precipitation. Leishmaniasis primarily correlated with forest-edge area and elevation, but lacking additional statistical significance prevented further work, a decision supported by the literature. Thus, the risk matrix and risk map which we scripted in R to visualize the risk of disease posed to districts alongside health post locations addresses only dengue fever. The results and products will inform MINSA and MINAM in public health interventions, resource distribution, and policy initiatives.

Jennifer Rogers↗

NASA NTRS1997

Applications of Data Assimilation to Analysis of the Ocean on Large Scales

It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height anomalies. Because of its potential for large scale synoptic coverage of the deep ocean, acoustic travel time data should be a natural complement to satellite altimetry. Satellite data give us vertical integrals associated with thermodynamic and dynamic processes.

Miller, Robert N.↗

NASA NTRS2014

Satellite Ocean Color Sensor Design Concepts and Performance Requirements

In late 1978, the National Aeronautics and Space Administration (NASA) launched the Nimbus-7 satellite with the Coastal Zone Color Scanner (CZCS) and several other sensors, all of which provided major advances in Earth remote sensing. The inspiration for the CZCS is usually attributed to an article in Science by Clarke et al. who demonstrated that large changes in open ocean spectral reflectance are correlated to chlorophyll-a concentrations. Chlorophyll-a is the primary photosynthetic pigment in green plants (marine and terrestrial) and is used in estimating primary production, i.e., the amount of carbon fixed into organic matter during photosynthesis. Thus, accurate estimates of global and regional primary production are key to studies of the earth's carbon cycle. Because the investigators used an airborne radiometer, they were able to demonstrate the increased radiance contribution of the atmosphere with altitude that would be a major issue for spaceborne measurements. Since 1978, there has been much progress in satellite ocean color remote sensing such that the technique is well established and is used for climate change science and routine operational environmental monitoring. Also, the science objectives and accompanying methodologies have expanded and evolved through a succession of global missions, e.g., the Ocean Color and Temperature Sensor (OCTS), the Seaviewing Wide Field-of-view Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS), and the Global Imager (GLI). With each advance in science objectives, new and more stringent requirements for sensor capabilities (e.g., spectral coverage) and performance (e.g., signal-to-noise ratio, SNR) are established. The CZCS had four bands for chlorophyll and aerosol corrections. The Ocean Color Imager (OCI) recommended for the NASA Pre-Aerosol, Cloud, and Ocean Ecosystems (PACE) mission includes 5 nanometers hyperspectral coverage from 350 to 800 nanometers with three additional discrete near infrared (NIR) and shortwave infrared (SWIR) ocean aerosol correction bands. Also, to avoid drift in sensor sensitivity from being interpreted as environmental change, climate change research requires rigorous monitoring of sensor stability. For SeaWiFS, monthly lunar imaging accurately tracked stability at an accuracy of approximately 0.1% that allowed the data to be used for climate studies [2]. It is now acknowledged by the international community that future missions and sensor designs need to accommodate lunar calibrations. An overview of ocean color remote sensing and a review of the progress made in ocean color remote sensing and the variety of research applications derived from global satellite ocean color data are provided. The purpose of this chapter is to discuss the design options for ocean color satellite radiometers, performance and testing criteria, and sensor components (optics, detectors, electronics, etc.) that must be integrated into an instrument concept. These ultimately dictate the quality and quantity of data that can be delivered as a trade against mission cost. Historically, science and sensor technology have advanced in a "leap-frog" manner in that sensor design requirements for a mission are defined many years before a sensor is launched and by the end of the mission, perhaps 15-20 years later, science applications and requirements are well beyond the capabilities of the sensor. Section 3 provides a summary of historical mission science objectives and sensor requirements. This progression is expected to continue in the future as long as sensor costs can be constrained to affordable levels and still allow the incorporation of new technologies without incurring unacceptable risk to mission success. The IOCCG Report Number 13 discusses future ocean biology mission Level-1 requirements in depth.

Ocean Color↗

NASA NTRS1981

NASA Global Atmospheric Sampling Program (GASP) data report for tape VL0015, VL0016, VL0017, VL0018, VL0019, and VL0020

This is the twelfth of a series of reports which describes the data currently available from GASP, including flight routes and dates, instrumentation, data processing procedures, and data tape specifications. In-situ measurements of atmospheric ozone, cabin ozone, carbon monoxide, water vapor, particles, clouds, condensation nuclei, filter samples and related meteorological and flight information obtained during 1732 flights of aircraft N533PA, N4711U, N655PA, and VH-EBE from January 5, 1978 through October 9, 1978 are reported. These data are now available from the National Climatic Center, Asheville, NC, 22801. In addition to the GASP data, tropopause pressures obtained from time ans space interpolation of National Meteorological Center archived data for the dates of the flights are included.

Papthakos, L. C.↗

NASA NTRS2023

A Surface Radiation Balance Data Set from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

A field campaign at Siple Dome in West Antarctica during the austral summer 2019-2020 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-poweredequipment, includes surface energy balance measurements, meteorology and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model's snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-micron atmospheric window reveal cloud phase transitions associated with cloud lifecycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

cloud remote sensing↗

NASA NTRS2017

The DeepMIP Contribution to PMIP4: Experimental Design for Model Simulations of the EECO, PETM, and pre-PETM (version 1.0)

Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( greater than 800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene (approximately 50 Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4(times) CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP - the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modeling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

climate models↗

DOE OSTI2018

Understanding the Evolution of Biomass Burning Particles in the Atmosphere: Analysis of BBOP Data, Laboratory Experiments, and Model Integration

Submicron particles in the atmosphere can have a profound influence on Earth’s climate [Stocker et al., 2013]. Particulate matter (PM) affects climate via both direct effects (scattering or absorption of radiation) and indirect effects (changes to the formation and properties of clouds). Biomass burning (BB) is the largest global source of submicron, carbonaceous particles [Wiedinmyer et al., 2011]. Field campaigns have shown that BB aerosol (BBA) can evolve through rapid transformations and removal processes in the atmosphere, with changing radiative and hygroscopic properties, but these processes are not well characterized. Better understanding of the evolution of biomass burning PM in the atmosphere is critical for improving the accuracy of regional to global climate models. In order to address uncertainties in the initial physical, chemical and radiative forcing of BBA and the evolution of these parameters, DOE ASR conducted an airborne field campaign, the Biomass Burning Observation Project (BBOP), during the summer and fall of 2013 to make measurements in the plumes of northwest U.S. wildfires and southeast U.S. agricultural burns. BBOP provided unique, extremely detailed trace gas and particle measurements in fresh biomass burning plumes and captured large, rapid downwind transformations within a few hours of the sources. These fast changes in properties have also been observed in laboratory-generated BB smoke in a smog chamber, but in general the controlling factors and most likely outcomes in the real atmosphere are still not clear. The goals of this project were to 1) analyze and interpret the BBOP measurements in detail, 2) conduct laboratory experiments to explore the formation of tar balls, and 3) to incorporate our findings into improved models of biomass burning emissions and aging. This research focuses on understanding the chemical, physical and optical properties and transformations of BB emissions in the near-field within hours downwind of the burn and on a more regional scale and, in turn, how BB emissions relate to climate forcing. Analysis of the BBOP data paid particular attention to relating SOA production, changes in size distribution, and mixing state to the environmental conditions (gas-phase precursors, relative humidity, radiation, oxidants, NO x , etc.). This project included subcontracts and unfunded collaborations with many of the key participants in the BBOP campaign. Professor Robert Yokelson at the University of Montana is an expert on biomass burning fuels and emissions and participated in BBOP as an unfunded collaborator. Professor Peter Buseck at Arizona State University collected particle samples during BBOP and analyzed some of them as part of this project. Unfunded collaborators include Dr. Arthur Sedlacek, III at Brookhaven National Laboratory (BNL) and Dr. Rahul Zaveri at the Pacific Northwest National Laboratory (PNNL) who were both principal investigators in the BBOP campaign, focusing on measurements and modeling, respectively. We are also working closely with Prof. Qi Zhang at UC Davis to coordinate analysis of BBOP flight data and data from the Mt. Bachelor Observatory (MBO), and with Dr. Kouji Adachi at the Meteorological Institute (Japan) to analyze laboratory-generated tar ball samples.

09 BIOMASS FUELS↗

NASA NTRS1999

Cloud Statistics for NASA Climate Change Studies

The Principal Investigator participated in two field experiments and developed a global data set on cirrus cloud frequency and optical depth to aid the development of numerical models of climate. Four papers were published under this grant. The accomplishments are summarized: (1) In SUCCESS (SUbsonic aircraft: Contrail & Cloud Effects Special Study) the Principal Investigator aided weather forecasters in the start of the field program. A paper also was published on the clouds studied in SUCCESS and the use of the satellite stereographic technique to distinguish cloud forms and heights of clouds. (2) In SHEBA (Surface Heat Budget in the Arctic) FIRE/ACE (Arctic Cloud Experiment) the Principal Investigator provided daily weather and cloud forecasts for four research aircraft crews, NASA's ER-2, UCAR's C-130, University of Washington's Convert 580, and the Canadian Atmospheric Environment Service's Convert 580. Approximately 105 forecasts were written. The Principal Investigator also made daily weather summaries with calculations of air trajectories for 54 flight days in the experiment. The trajectories show where the air sampled during the flights came from and will be used in future publications to discuss the origin and history of the air and clouds sampled by the aircraft. A paper discussing how well the FIRE/ACE data represent normal climatic conditions in the arctic is being prepared. (3) The Principal Investigator's web page became the source of information for weather forecasting by the scientists on the SHEBA ship. (4) Global Cirrus frequency and optical depth is a continuing analysis of global cloud cover and frequency distribution are being made from the NOAA polar orbiting weather satellites. This analysis is sensitive to cirrus clouds because of the radiative channels used. During this grant three papers were published which describe cloud frequencies, their optical properties and compare the Wisconsin FM Cloud Analysis to other global cloud data such as the International Satellite Cloud Climatology Program (ISCCP) and the Stratospheric Aerosol and Gas Experiment (SAGE). A summary of eight years of HIRS data will be published in late 1998. Important information from this study are: 1) cirrus clouds cover most of the earth, 2) they are found about 40% of the time globally, 3) in the tropics cirrus cloud frequencies are even higher, from 80-100%, 4) there is slight evidence that cirnis cloud cover is increasing in the northern hemisphere at about 0.5% per year, and 5) cirrus clouds have an average infrared transmittance of about 40% of the terrestrial radiation. (5) Global Cloud Frequency Statistics published on the Principal Investigator's web page have been used in the planning of the future CRYSTAL experiment and have been used for refinements of a global numerical model operated at the Colorado State University.

Wylie, Donald P.↗

NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗

NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

David M. Giles↗