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The GOLD Science Data Center – Algorithm Heritage, Data Product Descriptions And User Services

J. D. Lumpe1, H. Foroosh2, R. Eastes3, A. Krywonos3, J. Scott Evans1, A. Burns4, D. Strickland1, R. Daniell5, S. England6, S. Solomon4, W. McClintock7, D. Anderson8 1 Computational Physics, Inc., 2University of Central Florida, 3 Florida Space Institute, UCF, 4 HAO/NCAR, 5 Ionospheric Physics, 3 Space Sciences Laboratory, UC Berkeley, 7 LASP/University of Colorado, 8 CIRES, University of Colorado

GOLD Science Data Processing

Table 2: GOLD science team members with primary and secondary responsibility for developing each data product algorithm.

GOLD Data Products

The Global-scale Observations of the Limb and Disk (GOLD) instrument is an imaging spectrograph to be launched onboard a commercial communications satellite in 2017. From its vantage point in geosynchronous orbit GOLD will image the Earth in the far-ultraviolet from 132 to 162 nm. The instrument consists of two independent optical channels, allowing for simultaneous implementation of multiple measurement sequences with different temporal sampling and spectral resolution. In addition to continuously scanning the disk of the Earth, GOLD will also perform routine limb scan and stellar occultation measurements. These measurements will be used to retrieve a variety of critical data products characterizing the temperature and composition of the thermosphere-ionosphere, and their response to geomagnetic storms and solar forcing. Primary data products include: daytime neutral temperatures near 160 km altitude; daytime O/N2 column density ratios; nighttime peak electron density; thermospheric O2 density profiles (day and night); daytime exospheric neutral temperature on the limb; atmospheric tides from temperature perturbations; and the location and evolution of ionospheric bubbles. GOLD data will be processed at the Science Data Center (SDC) located at the University of Central Florida. The SDC will also serve as the primary gateway for distribution of GOLD data products to end-users. In this poster we summarize the heritage and theoretical basis of the GOLD retrieval algorithms and describe the full range of GOLD data products that will be available at the SDC, including estimates of data latency and quality.

Thermospheric Temperature O/N2 Column Density Ratio & QEUV

Overview SES Mission Operations

Satellite operations, instrument command

uplink, low-ratetelemetry downlink

UCF GOLD Science Data CenterPrimary science data archive, genera-tion of science data products L1D-L5

Real-time Users* (e.g. NOAA, USAF)

Near-real-time (15 minutes) space weather nowcasting, input to forecast

models *data available, additional costs (data line,

etc.) to be covered by user

L1C data (15 min goal)

L0 data (2 days) Hsk Tlm (2 days)

High-rat

e

science

tlm

GOLD Science TeamData available Start of

Operations, in-flight data product verification

Science & Outreach Data available at start of operations + 2 months

Virtual Obervatory (e.g. VITMO)Data available at start of operations + 2 months

Low-rate hsk tlm

Uplink cmds

High-rate science tlm L0, L1

L0-L3 data for validation(2 days)

L1D data (2 days)

L2-L3 data (2 months)

GOLD Ground StationLocated at SES Facility

High-rate telemetry downlink

NSSDCData delivered at mission

closeout

L0, L2 and Higher data

(closeout)

LASP Science Operations Center Instrument operations:

planning, command generation, health/status monitoring, etc.

Primary housekeeping data archive. L0, L1 data products

L1D data (2 days)

L2-L3 data (2 months)

The boxes below provide a summary of the primary GOLD data products including a brief algorithm description, retrieval/data product heritage, references and plots illustrating the technique and/or results (to the extent available).

ALGORITHM The retrieval algorithm is an extension of those used previously with HITS data. Temperatures at ~160 km are retrieved by fitting the observed rotational structure of the N2 LBH bands, using an optimal estimation routine. Forward model includes the instrument lineshape, N2 and temperature variation along the slant-path line of sight, and photoabsorption by O2.

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Wavelength (Å)

Cou

nts/

Sam

ple

N2 LBH with NI 1493 removed

Simulated DataFit

1460 1465 1470 1475 14800

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250

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350

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Wavelength (Å)

Rel

ativ

e B

right

ness

T=400KT=600KT=1000K

(5,4)

(1,1)

REFERENCES Aksnes, A., R. Eastes, S. Budzien, and K. Dymond (2006), Neutral temperatures in the lower thermosphere from N2 Lyman-Birge-Hopfield (LBH) band profiles, Geophys. Res. Lett., 33, L15103, doi:10.1029/2006GL026255.   Krywonos, A., D. J. Murray, R. W. Eastes, A. Aksnes, S. A. Budzien, and R. E. Daniell (2012), Remote sensing of neutral temperatures in the Earth's thermosphere using the Lyman-Birge-Hopfield bands of N2: Comparisons with satellite drag data, J. Geophys. Res., 117, A09311, doi:10.1029/2011JA017226.

Dayside disk measurement Spatial Resolution: 250 x 250 km2

Cadence: 60 minutes Precision: 55 K

Dayside disk measurement Spatial Resolution: 250 x 250 km2

Cadence: 30 minutes Precision: 10%

REFERENCES Strickland, D. J., J. S. Evans, and L. J. Paxton, Satellite remote sensing of thermospheric O/N2 and solar EUV:  1. Theory, J. Geophys. Res., 100, 12,217, 1995. Strickland, D. J., et al., Quiet-time seasonal behavior of the thermosphere seen in the far ultraviolet dayglow, J. Geophys. Res., 109, A01302, doi:10.1029/2003JA010220, 2004. Strickland, D. J., et al., Constraining and validating Oct/Nov 2003 X-class EUV flare enhancements with observations of FUV dayglow and E-region electron densities, J. Geophys. Res., 112, A06313, doi:10.1029/2006JA012074, 2007.

ALGORITHMS The disk O/N2 retrieval algorithm has been developed for use with GUVI and SSUSI images, and will take advantage of GOLD’s ability to transmit the full spectrum to better eliminate some atomic emission lines that contaminate the N2 LBH bands. This algorithm has been extensively documented and applied. O/N2 column density is derived from the ratio of measured OI 135.6 nm and N2 LBH band brightness. The AURIC model is used to derive this relationship as a function of solar zenith angle and create look-up tables (LUT). The QEUV (solar irradiance proxy) product is also produced via a LUT approach, and depends on the observed OI 135.6 nm brightness, the solar zenith angle, and the derived O/N2 ratio.

O2 Density Profile

ALGORITHM O2 is derived from measurements of stellar occultation in the Shumann Runge continuum. The retrieval algorithm is based on POAM-heritage solar occultation algorithms, and has been previously used to retrieve thermospheric O2 profiles from solar occultation by SUSIM/UARS and stellar occultation by SOLSTICE/SORCE. Retrievals will utilize 132-162 nm measurements, using the spectral dependence of the O2 absorption cross-section to maximize the retrieval altitude range (~150-240 km).

REFERENCES Lumpe, J. D., L. E. Floyd, L. C. Herring, S. T. Gibson, and B. R. Lewis (2007), Measurements of thermospheric molecular oxygen from the Solar Ultraviolet Spectral Irradiance Monitor, J. Geophys. Res., 112, D16308, doi:10.1029/2006JD008076. Lumpe, J. D., L. Floyd, M. Snow, and T. Woods, “Thermospheric Remote Sensing by Occultation: Comparison of SUSIM and SOLSTICE O2 Measurements”, Presented at the Fall 2006 AGU meeting, San Francisco, CA, December 11-15 2006. Lumpe, J. D., R. M. Bevilacqua, K. W. Hoppel, & C. E. Randall (2002), POAM III retrieval algorithm and error analysis, J. Geophys. Res., 107(D21), 4575, doi:10.1029/2002JD002137.

Limb measurement – day & night Frequency: 10-12 occultations per day

Full local time sampling Latitudes to ±60 degrees at 2 fixed longitudes

Accuracy & Precision: ≤ 10 % Vertical Resolution: ≤ 10 km

0 5 10 15 20 25!60!40

!200

20

4060

Right Ascension (hours)

Latit

ude

(deg

)

SNR<20 20<SNR<50 50<SNR

!40 !20 0 20 40120140160180200220240260

MeanStd Dev

Nighttime

high SNRmid SNRlow SNR

!40 !20 0 20 40120140160180200220240260

MeanStd Dev

Daytime

Alti

tude

(km

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O/N2 Error (%)O2 Error (%)

Ionospheric Bubbles

REFERENCES Park, S. H., S. L. England, T. J. Immel, H. U. Frey, and S. B. Mende (2007), A method for determining the drift velocity of plasma depletions in the equatorial ionosphere using far-ultraviolet spacecraft observations, J. Geophys. Res., 112, A11314, doi:10.1029/2007JA012327.  England, S. L., and T. J. Immel (2012), An empirical model of the drift velocity of equatorial plasma depletions, J. Geophys. Res., 117, A12308, doi:10.1029/2012JA018091.  Guiping Liu, Scott L. England, Harald U. Frey, Thomas J. Immel, Chin S. Lin, Edgardo E. Pacheco, Kathrin Hausler, Eelco Doornbos, Comparison of drift velocities of nighttime equatorial plasma depletions with ambient plasma drifts and thermospheric neutral winds, J. Geophys. Res., in press. 

ALGORITHM The detection of bubble locations and drifts is based on the algorithm developed for IMAGE-FUV [Park et al., 2007]. This algorithm uses a simple image-processing system to detect discrete regions of reduced O+ recombination airglow. Coherence of these reduced regions over a range of both time and magnetic latitude is used as the detection criteria. In preparing this routine for GOLD, the existing algorithm will be reformulated for GOLD’s imaging resolution and temporal cadence. However, the most difficult part of this algorithm – that of mapping the emissions to geographic locations – which was challenging for IMAGE-FUV, is trivial for GOLD, given its geostationary viewing.

Incident Radiance 50 x 200 km2 binning COUNTS/WITH NOISE 100 x 100 km2 binning

Exospheric Temperature

Night side disk measurement Local time: 19 – 22 hrs

Resolution: 100 x 100 km2

Cadence: 20 min, single arc

Day side limb measurement Altitude range/res: 150-500/36.5 km

Latitudes: 10oS – 10oN Cadence: 30 min Precision: 40 K

ALGORITHM Exospheric temperatures (Texo) will be derived from the limb data by fitting the shape of the LBH radiance profile above 200 km. Texo has a direct relationship to this topside slope, which is also weakly dependent on O/N2 composition, and on the photoelectron model used. The GOLD algorithm will rely on CPI’s AURIC airglow model. A similar approach has been used in the analysis of .TIMED/GUVI data.  

Simulated LBH spectrum with fit. Sensitivity of the (1,1) and (5,4) LBH bands to temperature. The temperature dependence of the band is the basis of the temperature retrievals.

Global O/N2 response to Nov 20, 2003 storm. TIMED/GUVI data. Left panels: preflare and flare GUVI nadir data, QEUV, and ΣO/N2

versus latitude from 28 Oct. Right panels: similar to the left panels but for 4 Nov.

O2 density retrieval accuracy (blue lines) and precision (red lines).

Right Ascension and occultation latitude of GOLD target stars (26).

Simulated GOLD images at 1820 satellite local time..

Simulation of exospheric temperature retrieval from N2 LBH bands using model fits to the topside (>170 km) profile. Pluses: simulated data including Poisson noise, slit function, and jitter. Green dotted line: initial guess using model scaled to the simulated airglow peak intensity. Red dashed line: final fit after iterative adjustment of exospheric temperature in an MSIS model.

Peak Electron Density Night side disk measurement Measurements Vs. lat/lon/LT Cadence: 60 min

ALGORITHM The peak electron density, Nmax, will be deduced from the 135.6 nm nightglow emission. This method has been used successfully with GUVI. The 135.6 nm volume emission rate is assumed to be proportional to the square of the electron density. Although the observed radiance is, by definition, integrated over the entire column of emitters, it is most sensitive to the peak electron density and thus Nmax is derived directly from the square root of the radiance.

REFERENCES Chandra, S., E. I. Reed, R. R. Meier, C. B. Opal, and G. T. Hicks (1975), Remote Sensing of the Ionospheric F Layer by Use of OI 6300-Å and OI 1356-Å Observations, J. Geophys. Res., 80, 2327-2332.   Rajesh, P. K., J. Y. Liu, M. L. Hsu, C. H. Lin, K. I. Oyama, and L. J. Paxton (2011), Ionospheric electron content and NmF2 from nighttime OI 135.6 nm intensity, J. Geophys. Res., 116, A02313, doi:10.1029/2010JA015686.   Tinsley, B. A., and J. A. Bittencourt (1975), Determination of F Region Height and Peak Electron Density at Night Using Airglow Emissions From Atomic Oxygen, J. Geophys. Res., 80, 2333-2337.

Tides Day side disk measurement

(derived from disk temperature data) Resolution: 600 x 600 km2

Illustration of an example GOLD disk temperature map, showing the 600 km horizontal resolution (at nadir) and coverage of the data over all sunlit portions of the disk (here shown at 0800 LT for illustration). The axes show dimensionless pixel numbers at the spatial resolution of retrieved temperatures.

ALGORITHM The disk temperature observations from GOLD offer a fundamentally different viewpoint on atmospheric tides than low-earth-orbit (LEO) satellite data and as such, traditional analysis method developed for those LEO satellites are not applicable. The great advantage that GOLD’s observing scenario offers is that GOLD provides information on atmospheric tides over a wide range of latitudes and longitudes simultaneously. This observing geometry is illustrated in the figure below. Using data from all of these geographic locations simultaneously is a central part of the proposed GOLD tidal analysis method. Tidal information is derived from GOLD data via a 2-step analysis. Step one will establish the complete spectrum of tides present during each month using a 30-day average at 600x600 km2 resolution. Step 2 will then infer shorter-term variability using 5-day time intervals at a courser resolution (1000 km horizontal by 2 hours local time). The tidal spectrum will be found from the temperature data by fitting the spatial and temporal patterns of known atmospheric tides simultaneously. This well-established method uses data over a range of days to provide the spatial and temporal coverage needed to uniquely identify atmospheric tides. This method solves for latitudinal, longitudinal and temporal patterns simultaneously and as such makes the maximum use of the GOLD observational scenario.

Table 1: GOLD Science Data Levels. Processing of levels 1A, 1B, and 1C will be performed at the SOC and transferred to the SDC for higher-level processing.

GOLD ground data system: data received from the mission ground station is centralized through the Science Operations Center (SOC), and transformed into various levels of data products at the Science Data Center (SDC) for delivery to users.

INSTITUTIONAL ROLES AND RESPONSIBILITIES SES Government Services (SES-GS) SES-GS provides mission and instrument control, the ground station to receive the GOLD telemetry data, operation of the ground station, and transfer of telemetry data to the SOC. University of Colorado, Laboratory for Atmospheric and Space Physics (CU/LASP) LASP will house the SOC and will be responsible for constructing command sequences for the instrument, receiving the telemetry from SES-GS, monitoring the instrument status, creating Level 1 data products and archiving housekeeping data. University of Central Florida (UCF) UCF will house the GOLD SDC and maintain overall responsibility for accomplishing the GOLD Mission of Opportunity, which includes developing mission policies and procedures for data ingestion, processing, validation, and distribution. Computational Physics, Inc. (CPI) CPI will be responsible for overall management of the development, implementation, and validation of the science algorithms and data processing pipeline to generate Level 3 and higher Data Products.

 The SDC will make data products available to:

•  GOLD science team •  Scientific community •  NASA’s National Space Science Data Center (NSSDS)

  Primary distribution channels:

•  Web-based user interface •  VITMO (Virtual Ionosphere, Thermosphere, Mesosphere Observatory)

 Goal is easy and reliable access to data products for all users. •  Mirrored web servers for increased bandwidth & higher

reliability.

Data Dissemination UCF will be responsible for designing, building and maintaining storage and archive facilities and infrastructure for SDC operational tasks:  Measures for fault tolerance:

- Mirrored archival sites at UCF •  UCF main campus •  Institute for Simulation and Training (IST)

 Archival redundancy between SDC and SOC

-  LASP will archive all Level 0 data

Storage & Archiving

 Quick-look data products available within 15 minutes

 All level 3 data products available within 48-72 hours.

Data Latency

 UCF and CPI will provide full user support at the SDC:

•  CPI provides support on algorithms and data products. •  USF provides all other support.

  Support to GOLD science data users:

•  Online documentation. •  Offline from the science team.

User Support