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

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).

Zhu, Jiang↗
NASA NTRS2016

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

MERRA↗
NASA NTRS2017

Atmospheric Waves and Dynamics Beneath Jupiters Clouds from Radio Wavelength Observations

We observed Jupiter at wavelengths near 2 cm with the Karl G. Jansky Very Large Array (VLA) in February 2015. These frequencies are mostly sensitive to variations in ammonia abundance and probe between approx. 0.5- 2.0 bars of pressure in Jupiters atmosphere; within and below the visible cloud deck which has its base near 0.7 bars. The resultant observed data were projected into a cylindrical map of the planet with spatial resolution of approx. 1500 km at the equator. We have examined the data for atmospheric waves and observed a prominent bright belt of radio hotspot features near 10 N, likely connected to the same equatorial wave associated with the 5-m hotspots. We conducted a passive tracer power spectral wave analysis for the entire map and latitude regions corresponding to eastward and westward jets and compare our results to previous studies. The power spectra analysis revealed that the atmosphere sampled in our observation (excluding the NEB region) is in a 2-D turbulent regime and its dynamics are predominately governed by the shallow water equations. The Great Red Spot (GRS) is also very prominent and has a noticeable meridional asymmetry and we compare it, and nearby storms, with optical images. We find that the meridional radio profile has a global north-south hemisphere distinction and find correlations of it to optical intensity banding and to shear zones of the zonal wind profile over select regions of latitude. Amateur optical images taken before and after our observation complemented the radio wave- length map to investigate dynamics of the equatorial region in Jupiters atmosphere. We find that two radio hotspots at 2 cm are well correlated with optical plumes in the NEB, additionally revealing they are not the same 5 m hotspot features correlated with optical dark patches between adjacent plumes. This analysis exploits the VLAs upgraded sensitivity and explores the opportunities now possible when studying gas giants, especially atmospheric dynamics of layers beneath upper level clouds.

radio observations↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS1993

The Mars Observer Camera ground data system

The Mars Observer Camera (MOC), launched on board the Mars Observer spacecraft in September 1992, is to be operated by a small team using large amounts of computer assistance. Here, we describe the architecture of the MOC Ground Data System, which is capable of taking imaging requests, generating imaging command sequences, receiving the acquired images, archiving them, and delivering them in several processed forms to the MOC user community, all with the need for only a limited amount of human interaction.

Caplinger, Michael↗
NASA NTRS2008

Exploiting Satellite Remote-Sensing Data in Fine Particulate Matter Characterization for Serving the Environmental Public Health Tracking Network (EPHTN): The HELIX-Atlanta Experience and NPOESS Implications

As part of the U.S. National Environmental Public Health Tracking Network (EPHTN), the National Center for Environmental Health (NCEH) at the U.S. Centers for Disease Control and Prevention (CDC) led a project in collaboration with the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) called Health and Environment Linked for Information Exchange (HELIX-Atlanta). Under HELIX-Atlanta, pilot projects were conducted to develop methods to better characterize exposure; link health and environmental datasets; and analyze spatial/temporal relationships. This paper describes and demonstrates different techniques for surfacing daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM(sub 2.5) for the purpose of integrating respiratory health and environmental data for the CDC's pilot study of HELIX-Atlanta. It describes a methodology for estimating ground-level continuous PM(sub 2.5) concentrations using spatial surfacing techniques and leveraging NASA Moderate Resolution Imaging Spectrometer (MODIS) data to complement the U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM(sub 2.5) from the EPA database for the year 2003 as well as PM(sub 2.5) estimates derived from NASA's MODIS data. Hazard data have been processed to derive the surrogate exposure PM(sub 2.5) estimates. The paper has shown that merging MODIS remote sensing data with surface observations of PM(sub 2.5), may provide a more complete daily representation of PM(sub 2.5), than either data set alone would allow, and can reduce the errors in the PM(sub 2.5) estimated surfaces. Future work in this area should focus on combining MODIS column measurements with profile information provided by satellites like the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Visible Infrared Imager/Radiometer Suite (VIIRS) and the Aerosol Polarimeter Sensor (APS) NPOESS sensors will provide first-order information on aerosol particle size and are anticipated to provide information on aerosol products at higher resolution and accuracy than MODIS. Use of the NPOESS remote sensing data should result in more robust remotely sensed data that can be coupled with the methods discussed in this paper to generate surface concentrations of PM(2.5) for linkage with health data in Environmental Public Health Tracking.

Al-Hamdan, Mohammad Z.↗
NASA NTRS1993

New progress of ranging technology at Wuhan Satellite Laser Ranging Station

A satellite laser ranging system with an accuracy of the level of centimeter has been successfully developed at the Institute of Seismology, State Seismological Bureau with the cooperation of the Institute of Geodesy and Geophysics, Chinese Academy of Science. With significant improvements on the base of the second generation SLR system developed in 1985, ranging accuracy of the new system has been upgraded from 15 cm to 3-4 cm. Measuring range has also been expanded, so that the ETALON satellite with an orbit height of 20,000 km launched by the former U.S.S.R. can now be tracked. Compared with the 2nd generation SLR system, the newly developed system has the following improvements. A Q modulated laser is replaced by a mode-locked YAG laser. The new device has a pulse width of 150 ps and a repetition rate of 1-4 pps. A quick response photomultiplier has been adopted as the receiver for echo; for example, the adoption of the MCP tube has obviously reduced the jitter error of the transit time and has improved the ranging accuracy. The whole system is controlled by an IBM PC/XT Computer to guide automatic tracking and measurement. It can carry out these functions for satellite orbit calculation, real-time tracking and adjusting, data acquisition and the preprocessed of observing data, etc. The automatization level and reliability of the observation have obviously improved.

Xia, Zhiz-Hong↗
NASA NTRS2005

Data Assimilation and Data Fusion for Planetary Atmospheres

The overarching goal of this Cooperative Agreement was to develop a model and procedures for the data assimilation of planetary spacecraft atmospheric observations. Data assimilation - in its application to weather analysis and prediction - is the process of finding an initial state of the meteorological variables (winds, temperatures, pressures, etc.) of an atmosphere, which, when propagated forward in time using a deterministic general circulation model, reproduces all of the available observations over that time to within the measurement and computational errors. With this definition, data assimilation is seen to be a natural extension of well-known least-squares minimization techniques. The primary complication arises from the scale of the problem: For the Martian atmosphere with the available nadir-viewing Thermal Emission Spectrometer data from Mars Global Surveyor, approximately 1,000,000 individual measurements of channel radiances (in the 15-micrometer region, where these radiances relate directly to the surface and atmospheric temperature) were made per day. A suitable general circulation model for dealing with this data set has on the order of 20,000 independent variables. After some spatial and temporal averaging of the data - which provides a necessary statistical estimate of the representativeness of the measurements, a crucial issue in data assimilation - the problem reduces in scale to the solution of approximately 50,000 equations for the 20,000 variables.

Houben, Howard↗
DOE OSTI2019

Constraining Temporal Oscillations of Cosmological Parameters Using SNe Ia

The existing set of SNe Ia is now sufficient to detect oscillatory deviations from the canonical ΛCDM cosmology. In this work we determine that the Fourier spectrum of the Pantheon data set of spectroscopically well-observed SNe Ia is consistent with the predictions of ΛCDM. We also develop and describe two complementary techniques for using SNe Ia to constrain those alternate cosmological models that predict deviations from ΛCDM that are oscillatory in conformal time. The first technique uses the reduced $χ^2$ statistic to determine the likelihood that the observed data would result from a given model. The second technique uses bootstrap analysis to determine the likelihood that the Fourier spectrum of a proposed model could result from statistical fluctuations around ΛCDM. We constrain three oscillatory alternate cosmological models: one in which the dark energy equation of state parameter oscillates around the canonical value of $w_Λ = -1$, one in which the energy density of dark energy oscillates around its ΛCDM value, and one in which gravity derives from a scalar field evolving under an oscillatory coupling. We further determine that any alternate cosmological model that produces distance modulus residuals with a Fourier amplitude of $\simeq$36 millimags is strongly ruled out, given the existing data, for frequencies between $\simeq$0.08 Gyr$^{-1} h_{100}$ and $\simeq$80 Gyr$^{-1} h_{100}$.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRSDate not supplied

Integrating Earth Observations and Socioeconomic Data to Address Health, Equity, and Environmental Justice

Access to reliable data about the characteristics of populations is a crucial component of decision making, policy development, and the assessment of progress towards strategic goals. Social determinants of health provide insight into the status of social and economic conditions within populations that have profound effects on public health, equity, and vulnerability. International frameworks such as the Sustainable Development Goals (SDGs) fundamentally focus on equality, but to efficiently address targets and indicators, socioeconomic data and Earth observations must be integrated to help identify populations most vulnerable to the impacts of climate change, natural disasters, health disparities, and poor policy planning. The ability to identify vulnerable populations with data analysis has the potential to empower community stakeholders with spatial awareness needed to inform decision-making that addresses equity and environmental justice issues within their communities. The EPA defines environmental justice (EJ) as “the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation and enforcement of environmental laws, regulations and policies.” NASA’s Earth Science Division (ESD) recognizes the benefits that Earth observations with NASA satellites create by equipping individuals with the knowledge to address community challenges. NASA’s Socioeconomic Data and Applications Center (SEDAC) supports the integration of socioeconomic and Earth science data as an “informational gateway.” This integration of data is advancing health, equity, and environmental justice initiatives.

Natasha Johnson-Griffin↗
NASA NTRS1972

Free adjustment of a geometric global satellite network (solution MPS-7)

The basic purpose of this experiment was to compute reduced normal equations from the observational data of several different systems described below to combine them eventually with the normal equations of the Wild BC-4 observations taken in the DOD/DOC cooperative worldwide geodetic satellite program and provide station coordinates from a single least squares adjustment. The solution described is a partial one obtained without the use of the BC-4 data. The observational systems combined were the Baker-Nunn simultaneous camera observational systems combined were the Baker-Nunn simultaneous camera observations from the SAO worldwide network, the MOTS and PC-1000 optical observations in North America, miscellaneous camera observations in Europe which were included in the SAO69 solution, and, lastly, a group of optical observations where Baker-Nunn cameras observed simultaneously with MOTS and/or PC-1000 cameras in the previously mentioned group.

Mueller, I. I.↗
NASA NTRS2018

The SPASE Data Model: A Metadata Standard for Registering, Finding, Accessing, and Using Heliophysics Data Obtained from Observations and Modeling

The Space Physics Archive Search and Extract Consortium has developed and implemented the SPASE Data Model that provides a common language for registering a wide range of Heliophysics data and other products. The Data Model enables discovery and access tools such that any researcher can obtain data easily, thereby facilitating research, including on space weather. The Data Model includes descriptions of Simulation Models and Numerical Output, pioneered by the Integrated Medium for Planetary Exploration (IMPEx) group in Europe, and subsequently adopted by the Community Coordinated Modeling Center (CCMC). The SPASE group intends to register all relevant Heliophysics data resources, including space-, ground-, and model-based. Substantial progress has been made, especially for space-based observational data and associated observatories, instruments, and display data. Legacy product registrations and access go back more than 50 years. Real-time data will be included. The National Aeronautics and Space Administration (NASA) portion of the SPASE group has funding that assures continuity in the upkeep of the Data Model and aids with adding new products. Tools are being developed for making and editing data descriptions. Digital Object Identifiers (DOIs) for Data Products can now be included in the descriptions. The data access that SPASE facilitates is becoming more uniform, and work is progressing on Web Service access via a standard Application Programming Interface. The SPASE Data Model is stable; changes over the past 9 years were additions of terms and capabilities that are backward compatible. This paper provides a summary of the history, structure, use, and future of the SPASE Data Model.

Roberts, D. Aaron↗
DOE OSTI2019

A metrics‐based analysis of seasonal daily precipitation and near‐surface temperature within seven Coordinated Regional Climate Downscaling Experiment domains

We compare ensemble mean daily precipitation and near‐surface temperatures from regional climate model simulations over seven Coordinated Regional Climate Downscaling Experiment domains for the winter and summer seasons. We use Taylor diagrams to show the domain‐wide pattern similarity between the model ensemble and the observational data sets. We use the Climatic Research Unit (CRU) and the University of Delaware gridded observations and ERA‐Interim reanalysis data as an additional observationally based estimate of historical climatology. Taylor diagrams determine the relative skill of the seven sets of simulations and quantify these results in terms of center pattern root‐mean square error and correlation coefficient. Results suggest that there is good agreement between the models and the CRU, in terms of their respective seasonal cycles, as shown in Taylor diagrams and bias plots. There is also good agreement between both gridded observation sets. In addition, downscaled ERA‐Interim precipitation is closer to observations than raw ERA‐Interim precipitation. Domains located in the low latitudes and those having high topography appear to have larger biases, especially precipitation.

Glisan, Justin M.↗
NASA NTRS2018

EOSDIS CMR: Shifting Data Discovery & Use into a Higher Gear

Earth observation data comes in many forms, formats, and from a multitude of sources; to make the best of a very large and diverse data catalog (data from a dozen different national Distributed Active Archive Centers (DAACs) as well as international sources), NASA has created a one-stop-shop for earth data consumers to view, find, and get the data they need regardless of its original source or format, which is powered by a Common Metadata Repository (CMR). CMR is the underpinning that allows for the visualization, search, discovery, manipulation, and acquisition of a variety of datasets, and as such it is constantly evolving to do more and serve our communities better; CMR has embraced community ownership by making itself an open-source API, being compatible with Catalog Services for the Web (CSW) and OpenSearch APIs, and by encouraging the user community to make and share improvements.

CMR↗
NASA NTRS2019

Harmony: Bringing Data Together by Bringing Services Together

As Earth observation data volume, velocity, and variety continues to accelerate, so do the challenges of working with those data. The computation, storage, and network transfer requirements for multi-petabyte datasets demand solutions that allow users to process, store, and move fewer bytes while still finding the valuable insights they seek. Our cloud-based data transformation framework, Harmony, tackles these challenges by providing consistent, standards-based interface to multiple disparate backend processors collocated with NASA EOSDIS data. It allows data scientists to perform data processing and harmonization activities across many datasets through familiar tooling. Simultaneously, it provides a clear, consistent interface for data providers to expose new or customized backend services for their data. This talk will discuss Harmony, its architecture and interface, and the path forward for cloud-based NASA EOSDIS data harmonization services.

Quinn, Patrick↗
DOE OSTI2022

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open-forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap-filled. These data are available to download from Bonner, Smyth, et al. at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process-based snow model. We demonstrate the utility of these data with a modeling experiment that explores open-forest differences and identifies opportunities for improvements in model representation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Coronal Reconstruction using LASCO and UVCS Observations

The main goal of the research described in the original proposal was to develop methods to quantify coronal and inner-heliospheric velocity fields of the 'quiet' solar wind. For this we planned to use several sources of observations:(1) SOHO/UVCS velocity information in the range 1.5-3 Solar Radii obtained from Doppler dimming observations; (2) projected solar wind velocities (into the plane of the sky) obtained from SOHO/ LASCO images (1.1-30 Solar Radii), primarily derived from two-dimensional correlation tracking techniques; (3) Interplanetary scintillation observations of the heliospheric (> 26 Solar Radii) solar wind velocity from the Solar-Terrestrial Environment Laboratory (STELab) in Nagoya, Japan; (4) Ecliptic in situ observations: data for the ecliptic solar wind are available from the MIT and Los Alamos plasma experiments on the Earth-orbiting IMP-8 spacecraft, from the Cellas instrument on SOHO near the LI Lagrange point, and from the WIND spacecraft; (5) Out-of-ecliptic in situ observations: these data are available primarily from the Los Alamos SWOOPS instrument on Ulysses, which passed over the solar north pole in August 1995, about one year prior to the Whole Sun Month period. Where ever possible we planned to use the first Whole Sun Month as the main time period for the analysis, since we expected that for this period it would be easiest to obtain adequate coverage over the extended period of time required to analyze 'quiet' solar wind patterns. Beyond the observations mentioned above (primarily SOHO data) we extended our selection of data to several events identified in the Yohkoh/SXT data base which directly promised to provide us with clues about the connection between the slow solar wind observed by IPS in the inner heliosphere and their sources in the low corona, in particular active regions. We also obtained valuable results using SWOOPS ill situ observations from the pole-to-pole passage of Ulysses in a comparison with solar wind velocities derived from a tomographic reconstruction of Nagoya IPS observations.

Hick, P. P.↗