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NASA NTRS2014

New Approach to Monitor Transboundary Particulate Pollution over Northeast Asia

A new approach to more accurately monitor and evaluate transboundary particulate matter (PM) pollution is introduced based on aerosol optical products from Korea's Geostationary Ocean Color Imager (GOCI). The area studied is Northeast Asia (including eastern parts of China, the Korean peninsula and Japan), where GOCI has been monitoring since June 2010. The hourly multi-spectral aerosol optical data that were retrieved from GOCI sensor onboard geostationary satellite COMS (Communication, Ocean, and Meteorology Satellite) through the Yonsei aerosol retrieval algorithm were first presented and used in this study. The GOCI-retrieved aerosol optical data are integrated with estimated aerosol distributions from US EPA Models-3/CMAQ (Community Multi-scale Air Quality) v4.5.1 model simulations via data assimilation technique, thereby making the aerosol data spatially continuous and available even for cloud contamination cells. The assimilated aerosol optical data are utilized to provide quantitative estimates of transboundary PM pollution from China to the Korean peninsula and Japan. For the period of 1 April to 31 May, 2011 this analysis yields estimates that AOD as a proxy for PM2.5 or PM10 during long-range transport events increased by 117-265% compared to background average AOD (aerosol optical depth) at the four AERONET sites in Korea, and average AOD increases of 121% were found when averaged over the entire Korean peninsula. This paper demonstrates that the use of multi-spectral AOD retrievals from geostationary satellites can improve estimates of transboundary PM pollution. Such data will become more widely available later this decade when new sensors such as the GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI-2 are scheduled to be launched.

Data Assimilation↗
NASA NTRS2017

An Overview of the Design and Development of the GOES R-Series Space Segment

The first of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite R-series (GOES-R) satellites was launched in November 2016. GOES-R has been developed by NOAA in partnership with the National Aeronautics and Space Administration (NASA). The satellite represents a quantum leap in the state of the art for geostationary weather satellites by providing data from a suite of six new instruments. All instruments were developed expressly for this mission, and include two Earth-observing instruments (the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM)), two solar-viewing instruments (Solar Ultraviolet Imager (SUVI) and Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)) and two in situ instruments (Space Environment In-Situ Suite (SEISS) and a magnetometer pair). In addition to hosting the instruments, GOES-R also accommodates several communication packages designed to collect and relay data for weather forecasting and emergency management. Accommodating the six instruments and four communication payloads imposed challenging and competing constraints on the satellite, including requirements for extremely stable earth and solar pointing, high-speed and nearly error-free instrument data transmission, and a very quiet electromagnetic background. To meet mission needs, GOES-R employed several technological innovations, including low-thrust rocket engines that allow instrument observations to continue during maneuvers, and the first civilian use of Global Positioning System-based orbit determination in geostationary orbit. This paper will provide a brief overview of the GOES-R satellite and its instruments as well as the developmental challenges involved in accommodating the instruments and communications payloads.

GOES-16↗
NASA NTRSDate not supplied

Data Fusion and Mining Techniques to Map Water Use and Drought across Spatial and Temporal Scales

As the world’s water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor (in this case, initially MODIS with a planned transition to VIIRS) rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. This presentation will focus on global applications for mapping water use and drought using data mining and data fusion across spatial scales extending from 5-km to 30-m “field-scale” estimates.

Christopher Hain↗
NASA NTRS2016

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO (Tropospheric Emissions: Monitoring of Pollution) was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (approximately 2.1 kilometers N/S by 4.4 kilometers E/W at 36.5 degrees N, 100 degrees W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

tropospheric measurement suite↗
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Atmospheric 3D winds in the horizontal and vertical directions are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we use a state-of-the-art convolutional neural network for optical flow (FlowNetS) in a physics-guided deep learning framework for predicting AMVs in the horizontal direction. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations (DYAMOND) for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

geostationary↗
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Observations of atmospheric 3D winds are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we compare state-of-the-art convolutional neural networks for optical flow in a physics-guided deep learning framework for predicting AMVs. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

Geostationary↗
NASA NTRSDate not supplied

Hourly Carbon Fluxes Estimation Using the GOES Advanced Baseline Imager (ABI) Data Over the Conterminous USA

Tremendous efforts by Fluxnet scientists over the past few decades have made thousands of site-years of carbon flux observations available for advancing our understanding of carbon cycling in terrestrial ecosystems. One of key Fluxnet measurements is net ecosystem exchange (NEE) as it is directly related to carbon budget of terrestrial ecosystems. However, carbon flux estimation studies using satellite remote sensing have focused mainly on daily Gross Primary Production (GPP). The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. Because daily NEE is close to zero value, the carbon flux models using the polar orbiting satellite data have not been well used for NEE estimation. The new generation of geostationary satellite sensors (e.g., GOES Advanced Baesline Imager (ABI) and Himawari Advanced Himawari Imager (AHI)) provide frequent observations, often less than every 10 minutes. Here, we use GOES ABI data to estimate hourly NEE over the conterminous USA. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and, in particular, solar radiation that is directly derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Fluxnet data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

Geostationary satellite↗
NASA NTRS1979

Potential applications of a high altitude powered platform in the ocean/coastal zone community

The results of a survey of the ocean/coastal zone community conducted for the NASA Wallops Flight Center to identify potential applications of a high altitude powered platform (HAPP) are presented. Such a platform would stationkeep at 70,000 feet for up to a year over a given location and make frequent high resolution observations, or serve as a regional communications link. The survey results indicate user interest among scientific researchers, operational agencies and private industry. It is felt that such a platform would combine the desirable characteristics of both geostationary satellites (wide area, frequent observation) and aircraft (high resolution). As a result a concept for an operational HAPP system in the form of a 'mesoscale geostationary satellite' system evolved. Such a system could employ many of the same technologies used in current NASA and NOAA geostationary satellite programs. A set of generalized instrument requirements for HAPP borne sensors is also presented.

Escoe, D.↗
NASA NTRS1979

Orbit transfer operations for the Space Shuttle era

Orbit transfer operations are reviewed relative to the objectives, operational factors, and crew model concepts of future mission requirements. The review is based on studies presently underway and on projected needs and goals of the Space Shuttle era. Numerous tradeoff studies and further analyses are needed before the best form of the manned geostationary vehicle becomes fixed. However, the Shuttle can provide the necessary low-orbit logistics service for dispatching manned geostationary missions on as frequent a schedule as will be needed to serve the advanced geostationary satellites of the near future.

Davis, H. P.↗
NASA NTRS1985

Direct measurements of severe spacecraft charging in auroral ionosphere

Questions are addressed concerning how large space structures in polar orbit will interact with auroral environments. Because spacecraft charging at ionospheric attitudes does not seriously threaten the operation of today's relatively small polar satellites the subject of environment interactions has not received the widespread attention given to it at geostationary altitude. As a matter of economics it is desirable to apply as much as possible of what was learned about spacecraft interactions at geostationary orbit to low Earth orbits. The environment at auroral latitudes in the ionosphere differs from that encountered at geostationary altitude in at least two major aspects. (1) There is a large reservoir of high-density, cold plasma which tends to mitigate charging effects by providing a large source of charged particles from which neutralizing currents maybe drawn. Significant wake effects behind large structures will introduce new problems with differential charging. (2) Between the magnetic equator and the ionosphere, auroral electrons frequently undergo field-aligned accelerations of several kilovolts. In such environments, fluxes of energetic protons are usually below the levels of instrumentation sensitivity.

Burke, W. J.↗
NASA NTRS1985

Satellite voice broadcast system study, volume 2

This study investigates the feasibility of providing Voice of America (VOA) broadcasts by satellite relay, rather than via terrestrial relay stations. Satellite voice broadcast systems are described for three different frequency bands: HF (26 MHz), VHF (68 MHz), and L-band (1.5 GHz). The geographical areas of interest at HF and L-band include all major land masses worldwide with the exception of the U.S., Canada, and Australia. Geostationary satellite configurations are considered for both frequency bands. In addition, a system of subsynchronous, circular satellites with an orbit period of 8 hours is developed for the HF band. VHF broadcasts, which are confined to the Soviet Union, are provied by a system of Molniya satellites. Satellites intended for HF or VHF broadcastinbg are extremely large and heavy. Satellite designs presented here are limited in size and weight to the capability of the STS/Centaur launch vehicle combination. Even so, at HF it would take 47 geostationary satellites or 20 satellites in 8-hour orbits to fully satisfy the voice-channel requirements of the broadcast schedule provided by VOA. On the other hand, three Molniya satellites suffice for the geographically restricted schedule at VHF. At L-band, only four geostationary satellites are needed to meet the requirements of the complete broadcast schedule. Moreover, these satellites are comparable in size and weight to current satellites designed for direct broadcast of video program material.

Horstein, M.↗
NASA NTRS1994

TRMM-related research: Tropical rainfall and energy analysis experiment

The overall science objective of the participation in TRMM is the determination of daily rainfall and latent heating in the tropical atmosphere using TRMM and complementary spacecraft observations. The major focus these first three years has been to extend, in space and time, the TRMM satellite observations of rainfall. Observations from TRMM active and passive microwave radiometers will provide the fundamental observations for understanding the hydrological cycle of the tropics. Due to the orbit of the TRMM satellite and the extreme variability of convective rain systems, the TRMM observations provide rainfall estimates representative of a one month period. Monthly mean rainfall rates provide valuable information; however, this time scale limitation neglects the great value of the data towards a better understanding of the physics of tropical convection. Many tropical periodicities will not be characterized by these monthly averages, e.g. diurnal cycles, the 4-6 day easterly waves, and the 30 to 60 day cycle. In the spatial domain, due to its orbit, the TRMM satellite will over-fly many convective systems only once. Indeed, some precipitating systems will not be sampled at all. Observations from geostationary satellites can be used to extend the TRMM observations to smaller time and space scales. Although geostationary satellites cannot probe the interiors of precipitating systems, they do observe their life cycles. To acquire information on cloud water content and rain rate, it is proposed to combine geostationary and other satellite observations with the TRMM satellite measurements.

Suomi, Verner E.↗
NASA NTRS1993

Geolocation applications of the Gonets LEO messaging satellites

Geostationary satellites carry a majority of the international telecommunications traffic not carried by transoceanic cable. However, because the radio path links to and from geostationary satellites total at least 70,000 km and because of inherent on-board spacecraft power limitations, earth stations used in conjunction with geostationary satellites are usually large and expensive. This limits their installation to areas with a well-developed industrial and economic infrastructure. This reality helps perpetuate a chicken egg dilemma for the developing countries and isolated regions. Economic integration with the developed world requires being 'networked'. But for many developing entities, even the initial price of entry exceeds their modest resources. Exclusion from the global information highways virtually assures retardation of economic growth for developing nations, remote and isolated areas. Very Small Aperture Terminal (VSAT) earth stations are often thought of as a solution for networking developing regions. But economic considerations often forecloses this option. If VSAT size and cost is to be minimized, powerful spot beams from the satellite need to be focused on relatively small regions. This is not often feasible because of the high cost of the satellite itself. To dedicate a high power spot beam to a small region is usually not economically feasible.

Vlasov, Vladimir N.↗
NASA NTRS1996

Precipitable Water Variability Using SSM/I and GOES VAS Pathfinder Data Sets

Determining moisture variability for all weather scenes is critical to understanding the earth's hydrologic cycle and global climate changes. Remote sensing from geostationary satellites provides the necessary temporal and spatial resolutions necessary for global change studies. Due to antenna size constraints imposed with the use of microwave radiometers, geostationary satellites have carried instruments passively measuring radiation at infrared wavelengths or shorter. The shortfall of using infrared instruments in moisture studies lies in its inability to sense terrestrial radiation through clouds. Microwave emissions, on the other hand, are mostly unaffected by cloudy atmospheres. Land surface emissivity at microwave frequencies exhibit both high temporal and spatial variability thus confining moisture retrievals at microwave frequencies to over marine atmospheres (a near uniform cold background). This study intercompares the total column integrated water content Precipitable Water, (PW) as derived from both the Special Sensor Microwave Imager (SSM/I) and the Geostationary Operational Environmental Satellite (GOES) VISSR Atmospheric Sounder (VAS) pathfinder data sets. PW is a bulk parameter often used to quantify moisture variability and is important to understanding the earth's hydrologic cycle and climate system. This research has been spawned in an effort to combine two different algorithms which together can lead to a more comprehensive quantification of global water vapor. The approach taken here is to intercompare two independent PW retrieval algorithms and to validate the resultant retrievals against an existing data set, namely the European Center for Medium range Weather Forecasts (ECMWF) model analysis data.

Lerner, Jeffrey A.↗
NASA NTRS2001

Intercalibration of Meteorological Satellite Imagers Using VIRS, ATSR-2 and MODIS

Global retrievals of surface, cloud and radiative properties from geostationary (GEO) and low-Earth-orbit (LEO) meteorological satellites require accurate calibration of their imagers. An accurate and consistent calibration increases the reliability and effectiveness of long-term monitoring of climate changes. More emphasis has been placed on calibrating the thermal infrared (IR) channel. The lack of on-board calibration in the visible (VIS) channel has prompted efforts to characterize the degradation of the VIS sensor using vicarious post-launch calibration techniques that measure bright stable desert targets from space and aircraft or using satellite-to-satellite normalizations. While such inter-calibrations are valuable and widely used, the lack of a well-characterized calibration reference source and the lengthy time delay between updates have minimized their effectiveness in climate monitoring. To address these shortcomings, this paper examines the use of research satellite imagers to provide stable calibration references for the visible (VIS, approximately 0.65 micrometers) channels and develops a method for rapid intercalibration of existing satellites. Calibration coefficients are determined for the Geostationary Operational Environmental Satellites (GOES-8 to GOES-10), Geostationary Meteorological Satellite (GMS-5), Meteosat-7, and the NOAA-14 Advanced Very High Resolution Radiometer (AVHRR). As a reference calibration source, this technique uses the self-calibrating sensors on the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Radiometers (VIRS) or the ERS-2 Along Track Scanning Radiometer (ATSR-2). GOES-8 is calibrated with VIRS and then its calibration is transferred to other GEO or LEO satellites. The absolute accuracy of this technique relies on the assumption that the on-board calibration is stable and well maintained. Minnis et al. assessed the VIRS calibration using comparisons with other self-calibrated satellite sensors including the broadband Clouds and Earth's Radiant Energy System (CERES) scanners, the ERS-2 Along Track Scanning Radiometer (ATSR-2), and the Terra Moderate-resolution Imaging Spectrometer (MODIS). Thus, the VIRS data can be confidently used as the initial reference source.

Nguyen, Louis↗
NASA NTRS2001

The Future of Satellite-based Lightning Detection

The future of satellite-based optical lightning detection, beyond the end of the current TRMM mission, is discussed. Opportunities for new low-earth orbit missions are reviewed. The potential for geostationary observations is significant; such observations provide order-of-magnitude gains in sampling and data efficiency over existing satellite convective observations. The feasibility and performance (resolution, sensitivity) of geostationary measurements using current technology is discussed. In addition to direct and continuous hemispheric observation of lighting, geostationary measurements have the potential (through data assimilation) to dramatically improve short and medium range forecasts, offering benefits to prediction of NOx productions and/or vertical transport.

Bocippio, Dennis J.↗
NASA NTRS2006

NAVIGATION PERFORMANCE IN HIGH EARTH ORBITS USING NAVIGATOR GPS RECEIVER

NASA GSFC has developed a GPS receiver that can acquire and track GPS signals with sensitivity significantly lower than conventional GPS receivers. This opens up the possibility of using GPS based navigation for missions in high altitude orbit, such as Geostationary Operational Environmental Satellites (GOES) in a geostationary orbit, and the Magnetospheric MultiScale (MMS) Mission, in highly eccentric orbits extending to 12 Earth radii and higher. Indeed much research has been performed to study the feasibility of using GPS navigation in high Earth orbits and the performance achievable. Recently, GSFC has conducted a series of hardware in-the-loop tests to assess the performance of this new GPS receiver in various high Earth orbits of interest. Tracking GPS signals to down to approximately 22-25 dB-Hz, including signals from the GPS transmitter side-lobes, steady-state navigation performance in a geostationary orbit is on the order of 10 meters. This paper presents the results of these tests, as well as sensitivity analysis to such factors as ionosphere masks, use of GPS side-lobe signals, and GPS receiver sensitivity.

Bamford, William↗
NASA NTRS2011

SPoRT's Participation in the GOES-R Proving Ground Activity

The next generation geostationary satellite, GOES-R, will carry two new instruments with unique atmospheric and surface observing capabilities, the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM), to study short-term weather processes. The ABI will bring enhanced multispectral observing capabilities with frequent refresh rates for regional and full disk coverage to geostationary orbit to address many existing and new forecast challenges. The GLM will, for the first time, provide the continuous monitoring of total lightning flashes over a hemispherical region from space. NOAA established the GOES-R Proving Ground activity several years ago to demonstrate the new capabilities of these instruments and to prepare forecasters for their day one use. Proving Ground partners work closely with algorithm developers and the end user community to develop and transition proxy data sets representing GOES-R observing capabilities. This close collaboration helps to maximize refine algorithms leading to the delivery of a product that effectively address a forecast challenge. The NASA Short-term Prediction Research and Transition (SPoRT) program has been a participant in the NOAA GOES-R Proving Ground activity by developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the SPoRT program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. Participation in the Proving Ground activities extends SPoRT s activities and taps its experience and expertise in diagnostic weather analysis, short-term weather forecasting, and the transition of research and experimental data to operational decision support systems like NAWIPS, AWIPS, AWIPS2, and Google Earth. Recent SPoRT Proving Ground activities supporting the development and use of a pseudo GLM total lightning product and the transition of the AWG s Convective Initiation (CI) product, both of which were available in AWIPS and AWIPS II environments, by forecasters during the Hazardous Weather Testbed (HWT) Spring Experiment. SPoRT is also providing a suite of SEVIRI and MODIS RGB image products, and a high resolution composite SST product to several National Centers for use in there ongoing demonstration activities. Additionally, SPoRT has involved numerous WFOs in the evaluation of a GOES-MODIS hybrid product which brings ABI-like data sets in front of the forecaster for everyday use. An overview of this activity will be presented at the conference.

Jedlovec, Gary↗