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AGI Using EOP and Space Weather Data for Satellite Operations David A. Vallado and T. S. Kelso Analytical Graphics Inc. Center for Space Standards and Innovation Paper USR S7.3, Presented at the 6 th US Russian Space Surveillance Workshop, St. Petersburg, Russia, August 22-26, 2005

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Pg 2 of 33 AGI Outline Introduction –Definitions –Problem –Objectives –Motivation Analysis –EOP –Space Weather STK Files Summary

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Pg 3 of 33 AGI Definitions Earth Orientation Parameters (EOP) –Assist transformation between Celestial and Terrestrial Coordinate Frames Celestial – GCRF (generic ECI) Terrestrial – ITRF (generic ECEF) – UT1, LOD, x p, y p, AT Space Weather Data –Incoming solar radiation effects the atmosphere Geomagnetic –k p, a p »Eight 3-hourly and daily averages Solar radiation –F 10.7 »Daily values –Affects upper atmospheric temperature and density Large factor in determining atmospheric drag effect on satellite orbits

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Pg 4 of 33 AGI Problem EOP and space weather data needed to support tasks –Mission Design Fuel budgets Lifetime maneuver / orbit box planning –Real-time Operations Few days into the future Mission planning a few months ahead Anomaly resolution a few months in the past Available data is chaotic at best –Mix of predicted and observed values –Post processing data often takes a month or more –No synchronized update schedule for all parameters Need seamless file of data to accomplish each mission task

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Pg 5 of 33 AGI Objectives Analyze currently available data Recommend best option for splicing data Discuss setup of data files –Available on CelesTrak website STK compatible

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Pg 6 of 33 AGI Summary of EOP Data Files Current Time, T 00:00 Sunday 14:28 23:59 Saturday IERS EOPC04.62-now ( UT1, x p, y p,, LOD,,, X, Y) NGA EOPPyddd.txt ( UT1, x p, y p ) USNO Finals.daily, Finals2000a.daily ( UT1, x p, y p,,,, X, Y) + 90 days USNO Finals.all, Finals2000a.all ( UT1, x p, y p ) + 1 year months :00 FUTUREPAST

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Pg 7 of 33 AGI Summary of Space Weather Data Files Current Time, T 00:00 -1 month 14:28 00:00 1 month Quar_dgd.txt (3 hrly k p, daily a p ) Quar_dsd.txt (daily F 10.7 ) Predict.txt (monthly F 10.7 ) + 6 year + 45 days 45df.txt (daily F 10.7, a p ) yyyy.vm (daily F 10.7, daily and 3 hrly k p, a p ) Current Year - 3 months - 6 months 00:00 FUTURE PAST

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Pg 8 of 33 AGI I. EOP Data

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Pg 9 of 33 AGI Analyses Series are recomputed –IERS Twice weekly Some smoothing due to use of Vondrak algorithm –Removes high-frequency noise –USNO Weekly ? Comparisons Useful –Within Organizations USNO –Bulletin A and B », » X, Y –Between Organizations USNO, IERS, NGA –Note that axes scales and units are not constant

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Pg 10 of 33 AGI Analysis USNO –Bltn A and Bltn B values, Bull B values only from Jan 1, 1989 –Reasonably consistent Bltn B dpsi some anomalies –Sep 1999

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Pg 11 of 33 AGI Analysis USNO –Bltn A and Bltn B values X, Y Values from 1973 –Values before 1990 Additional variations –Bltn B some anomalies Sep-Oct 1999

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Pg 12 of 33 AGI Comparisons Between Organizations –USNO (Bltn A and Bltn B) and IERS (EOPC04) Before 1984 –Larger variations –FK5 instituted Before 1997 –Smaller variations –Update to Equation of the equinoxes Note last few values –See next slide

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Pg 13 of 33 AGI Comparisons (Cont) Between Organizations –USNO (Bltn A and Bltn B) and IERS (EOPC04) Last few days appear to differ (IERS and USNO) On Feb 22 for x p – IERS – USNO On May 5 for x p – IERS – USNO IERS Delta = USNO delta =

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Pg 14 of 33 AGI Comparisons (FK5 Corrections) Between Organizations –USNO (Bltn A and Bltn B) and IERS (EOPC04) Data availability –Bltn B values only after Jan 1, 1989 Each appear to have noise

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Pg 15 of 33 AGI Comparisons (IAU 2000 Corrections) Between Organizations –USNO (Bltn A and Bltn B) and IERS (EOPC04) Data availability –EOPCO4 Values after Jan 1, 1984 Bltn B appears to better follow EOPC04

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Pg 16 of 33 AGI Coefficient Approach NGA provides coefficients –Continuous representation of UT1, x p, y p –Lacks LOD,,, X, Y Generally smaller order effects Long-term behavior of EOP coefficients –x p, y p reasonable to use past the end of a data file – UT1 not recommended past about a month

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Pg 17 of 33 AGI Comparisons – Long Term EOPC04 to NGA Coefficients –One year different epochs are shown –Notice variability –Runoff for UT1, x p –Current week is valid (highlighted) –Note 0.04 s 280m at 7 km/s

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Pg 18 of 33 AGI EOP – How to Splice Together EOPC04 –Use 62-now file Up to current day Recomputed frequently USNO Predicted Bulletin A –Use.daily file Daily values (t-3 months to t+3 months) –Updated daily USNO Predicted Bulletin A –Use.all file 1 year predictions Data availability –Use last known file of each if any are not available –Values are acceptable for short periods of time (~week)

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Pg 19 of 33 AGI II. Space Weather Data

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Pg 20 of 33 AGI Space Weather Tracked for many years –Data to the 1930s Older data –Has numerous missing dates Physically a zero means little here! Corrected in our files –Quality flag set to 4 as an indicator Includes seasonal/solar cycle variations –Observed and adjusted to 1.0 AU values DRAO and Lenhart values –Atmospheric models use both

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Pg 21 of 33 AGI Sensitivity Results Atmospheric Drag (see Vallado AAS ) –Large variations Changing the atmospheric model Changing how the input data is interpreted –F 10.7 at 2000 UTC –Last 81-day average F 10.7 vs. the central 81-day average –Using step functions for the atmospheric parameters vs interpolation –Many others –Point to take away: km ephemeris differences are possible Unable to determine if from data interpretation or model differences

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Pg 22 of 33 AGI Space Weather – Predictions Lots of Variability –Constant F 10.7 Not very accurate Never use 0.0! –Schatten Varies with each solar cycle –Polynomial Trend (Vallado 2004, 535) Matches several solar cycles F 10.7 = *COS{ t – 0.35*SIN(π t )} –t is the number of days from Jan 1, 1981

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Pg 23 of 33 AGI Historical and Polynomial Trend

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Pg 24 of 33 AGI Schatten Predictions

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Pg 25 of 33 AGI Statistics Comparisons Combined Results Individual Results Avg Deviation Standard Deviation Ctr 81-day – Daily F Last 81-day – Daily F Trend 81-day – Daily F Monthly Trend – Monthly F Variability same between –81-day averages and daily values –Monthly trend and monthly averages –Predicted (3-day, 45-day and 2 year) solar flux values

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Pg 26 of 33 AGI Observed / Adjusted values

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Pg 27 of 33 AGI k p – a p Conversions Rigorously defined –1940 – Chapman and Bartels – Geomagnetism –Discrete values Values exist that are not in the discrete values –Finding 3-hourly a p values for the last month –Finding k p values from predicted a p values Some Interpolation clearly required –Approaches Linear Interpolation Iteration Spline

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Pg 28 of 33 AGI Comparison of Conversions Techniques – Interpolate, Iterate, Splines –Between approaches (left) –To exact values (right)

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Pg 29 of 33 AGI III. File Setup

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Pg 30 of 33 AGI File Setup Proceeding can be complex and time consuming –CSSI (Dr. Kelso) has done the work for you! –http://celestrak.com/SpaceData Files updated every 3 hours STK compatible Naming indicates start date Schedule –EOP and Space weather files ready today –Operational in next STK release

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Pg 31 of 33 AGI Additional Information Common changes for both files –All ASCII text –Start dates can be set to a time before the current date Naming convention permits quick determination –Set an end date –Observed and predicted sections consistent –Spacing minimized (file size) Maintain free-field read capability –Indicate number of data points

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Pg 32 of 33 AGI Additional Information Specifics –EOP Add day, month, year to data Switch to predominantly IERS EOP-C04 data from USNO Bltn A data –Data now available from 1962 instead of 1973 Removed data for errors in x p, y p, and UT1 Added LOD,, Added AT, X, Y into one file Last values set to zero Include automatic leap second introduction –Space Weather Data from 1957 to date (more is possible) Added centered 81 day in addition to last 81 day average values, observed and adjusted Spliced past, current, and predicted data New structure for predicted data –Monthly and Daily sections Trend values for long term prediction possible

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Pg 33 of 33 AGI Conclusions EOP and Space Weather data –Not high visibility However, large variations in numerical results –Ap/kp conversion Recommend using Cubic Spline approach –Predicted solar flux values Polynomial trend available for long term use Reasonable statistics to existing Schatten files –Your single source for consolidated values available:

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