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Satellite inter-calibration activities and methods for window channel radiometers

Satellite inter-calibration activities and methods for window channel radiometers. Matt Sapiano 1 and Wes Berg 2 1 University of Maryland 2 Colorado State University. msapiano@umd.edu. 1. The Global Precipitation Mission. CORE SATELLITE Dual frequency radar Multifrequency radiometer

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Satellite inter-calibration activities and methods for window channel radiometers

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  1. Satellite inter-calibration activities and methods for window channel radiometers Matt Sapiano1and Wes Berg2 1University of Maryland 2Colorado State University msapiano@umd.edu 1

  2. The Global Precipitation Mission • CORE SATELLITE • Dual frequency radar • Multifrequency radiometer • Non-sun synchronous orbit • ~70 deg inclination • ~400km altitude • ~4km horizontal resolution • 250m vertical resolution • CONSTELLATION SATELLITES • ~8 satellites • Radiometer only • Rely on existing radiometers • Multifrequency radiometer • ~3 hour average revisit time MISSION: Provide enough sampling to reduce uncertainty in short-term rainfall accumulations. Extend scientific and societal applications. MISSION: Understand the horizontal and vertical structure of rainfall and its microphysical elements. Provide training for constellation radiometers

  3. Outline • Introduction • Overview of NOAA CDR Project • XCAL and other intercalibration activities • Cross-track biases • Ephemeris and geolocation errors • Diurnal variability • Monitoring of changes in TBs over time • Approaches to intercalibration • Summary

  4. Sample products from passive microwave radiometers

  5. Currently Available Radiometer Data

  6. Differences in Sensor Characteristics Sensors for GPM a V-Polonly; b H-Polonly; c V, H, +/45º, L, R

  7. Effect on precip: SSM/I Calibration

  8. FCDR for SSM/I & SSMIS at CSU • Produce two sets of NetCDF4 files • Basefiles include granularized raw data with duplicates removed • FCDR files include intercalibrated TBs with new geolocation, QC, etc. • Produce well documented software package (“Stewardship code”) that ingests basefiles, applies corrections and outputs the FCDR • Generate a transparent and documented FCDR of SSM/I and SSMIS brightness temperatures (Tb) from 1987 – Present • Tb will be Intercalibrated for SSM/I and SSMIS • Tb will be physically consistent: time-of-day bias and EIA dependence not removed

  9. SSMI(S) FCDR Intercalibration • Sensor data must be physically consistent • Sensor still different due to differences in frequencies, view angles, resolution, observation time etc. • Geophysical retrieval algorithms must take into account sensor differences in order to create TCDR (eg: precipitation, wind, humidity, etc). • Investigate calibration differences using multiple approaches [described later]. • Consistency/inconsistency between approaches will provide insight into methods as well as estimate of the uncertainties. • Goal is to understand any differences and use sensor information to select correct solution

  10. Known issues/risks • Inconsistency between Calibration Methods • Using multiple approaches will help to identify problems and estimate uncertainties. • Ultimately stewardship must allow for future investigators to look into discrepancies and develop new/improved techniques/approaches. • Geolocation Errors • Difficulty in separating effects of spacecraft orientation from other issues. • Limitations in determining spacecraft attitude • Other sensor Issues • Warm versus cold end calibration, nonlinearities etc. • Unknown error sources that can be difficult/impossible to quantify/correct.

  11. Xcal and other intercalibration activities • XCAL is a working group of the NASA Precipitation Measurement Missions (PMM) project whose goal is to intercalibrateavailable radiometers for GPM. • Meets ~2-3 times per year • Comprised of multiple institutions/groups who have developed different techniques for intercalibration. • Working group consists of experts in both engineering and science application aspects of radiometers • Responsible for developing intercalibration adjustments for Level 1C Data (Intercalibrated TB dataset for GPM). • GSICS – Global Space-based Inter-Calibration System • Effort is focused on operational weather satellites and funded by operational agencies • http://www.wmo.int/pages/prog/sat/GSICS/

  12. Cross Track Biases • Caused by partial beam blockage or other (unknown?) issues

  13. Spacecraft ephemeris and pixel geolocation: SSMI/SSMIS FCDR • Many operational satellites such as SSM/I on board the DMSP spacecraft use predicted ephemeris. • Spacecraft ephemeris are recomputed using 2-line element data and SGDP4 code. • Pixel geolocation depends on accurate spacecraft ephemeris and pointing information (spacecraft attitude and sensor alignment). • Retrieval algorithms are often sensitive to the EIA or view angle of the sensor.

  14. Predicted vs computed spacecraft ephemeris Computed values derived from SGP4 NASA/NORAD code.

  15. Estimation of Satellite Attitude • Satellite attitude offsets required to accurately calculate geolocation • Roll, Pitch, Yaw • Earth Incidence Angle (EIA) was not included in original data • EIA is needed for intercalibration and geophysical retrievals • Estimates of EIA are grossly inaccurate without satellite attitude estimates

  16. Geolocation and solar angles • Original data source (TDR files) did not include Earth Incidence Angle (EIA) or Sun glint • Used Two-Line Elements (TLEs) with NORAD SGP4 code to add Spacecraft Position and Velocity vectors to Basefiles • Stewardship code uses SCpos and SCvel to calculate ephemeris (SClat, SClon, SCaltitude) for every scan, as well as pixel lat/lon, EIA and sun glint angle for every pixel • Stewardship code can also be set to output satellite azimuth, solar zenith/azimuth and time since eclipse

  17. Methodology: Coastline analysis • Used mean of ascending TBs minus mean descending to assess Geolocation error • Used 11 months of 85H TBs mapped onto 1/20th degree grid • Before corrections: coastlines are visible as red and blue bands because A&D do not line up • Differences in A-D due meteorological events unaffected by small changes in geolocation • Root mean square error (RMSE) of the A-D used as a metric for geolocation accuracy • Fast iterative technique developed to find optimal roll, pitch and yaw • Involves estimating RMSE associated with many RPY combinations

  18. SSM/I Satellite Attitude Estimates • Attitude is relatively stable over the life of each spacecraft • F08 and F10 are less stable than F11-F15 • Technique works well • Yields Time varying attitude with estimated accuracy within ~0.1 degrees. • Determination of high-frequency changes not possible with this approach • Expected impact for climate is generally negligible

  19. Impact on Geolocation • Trends in EIA or differences between sensors can introduce biases if not properly accounted for • EIA variability over orbit (driven by altitude) may also be important • Particularly true for F10 due to its more eccentric orbit • Significant differences in EIA for each sensor will impact intercalibration • Probably also important for some geophysical retrieval algorithms

  20. EIA effect on retrievals SSM/I Precip (GPROF2010) SSM/I TPW (OE) Constant nominal EIA (53.1) Calculated EIA

  21. TRMM V7 Correction and Angle IssuesPresentation material for GPM inter-calibration working group meeting, May 19, 2009 SayakBiswas, Dr. Linwood Jones UCF, CFRSL Dr. KaushikGopalan UMD, ESSIC Steve Bilanow Wyle, PPS

  22. Space Craft Aft-Structure Shadowing Earth Shadow Exit TB Bias (K)

  23. Eclipse = 31min Eclipse = 26.4 min

  24. Time of Day Dependence

  25. Diurnal Impacts • It is important to properly account for these differences when comparing TBs from different satellites • The intercalibrated brightness temperatures (i.e. FCDR) should retain these differences.

  26. Window Channel Radiometer Record

  27. Monitoring TBs SSM/I F15 RADCAL issue: beacon was switched on in 2006, lead to severe interference • Important to monitor satellites for calibration issues 19 V 22 V 85 H RadCal Beacons Turned on

  28. Intercalibration • Satellite providers deal with the absolute calibration of satellite observations • Errors can be large (1-2K) • Providers do not use same technique or standard so absolute cals differ • Projects such as NOAA SDS and NASA GPM need consistent estimates from several satellites • Seek to intercalibrate the satellites to within ~.1-.2K • Goal is to estimate adjustments to TBs for each channel so as to make observations consistent with some calibration standard • Calibration standard choice not easy and might be arbitrary/political • Aim to make sensors physically consistent (not correct channel diffs, etc) • Intercalibration often takes the form of simple offsets, although TB dependent adjustments may be more appropriate • How do we calculate TB-dependent adjustment? Need to use techniques that cover a large portion of TB space • Need at least 2 points: low and high TB

  29. Intercalibration techniques • Different satellites have different sampling characteristics, so intercalibration has to use a common target • May need to account for Incidence angle, channel differences • The following is not an exhaustive list, but does encompass the techniques used by XCal and for the CSU SDS project • Direct comparison of satellites being intercompared • For polar orbiters, this takes the form of polar matchups • Matchups are available elsewhere if one of the satellites is not in a non-Sun-synchronous orbit (TRMM, GPM core) • Comparison of each satellite with a non-Sun synchronous satellite • Comparison with a fixed target • Target could be an in situ observation related to TBs by a geophysical retrieval • Target could be a strictly homogeneous area (not many of these exist) • Calculation of some fixed reference point with known properties • Only example of this is from Vicarious calibration

  30. SSM/I Intercalibration • As part of creation of SSM/I FCDR, require intercalibration estimates for F8-F15 • Applied 5 different techniques to SSM/I • Model double difference • Polar Crossovers • TMI matchups • Vicarious Cold Calibration • Amazon Warm Calibration • Strategy is to seek agreement between methods and use to estimate uncertainty of intercalibration

  31. 1. Model Double Difference • Simulate TBs using model (reanalysis) data at 1 degree as an input to radiative transfer model • Match with gridded observed TBs • Screening used to remove land/coast • Also screen rain and clouds based on model (liquid water) and satellite (SD85: standard deviation of 85Ghz in 1 degree box) • Two implementations based on • NASA MERRA • ECMWF ERA-I • In this case, the model is used as a transfer standard from satellite to satellite • Satellites are not co-located in time or space in this technique

  32. 2. Polar Crossovers • Gridded SSM/I data onto equal area polar grid (~100km grid box size) • Extracted coincident overpasses (within 30 mins) for two SSM/I • Matchups occur only at the poles • Used standard RT model with MERRA atmosphere to correct EIA bias over open ocean Open Ocean (1day) F13 F15

  33. 3. TMI Matchups SSMI and TMI Ground tracks for 2-3am on 20 Nov 2004 • Gridded to 1 degree and extracted coincident overpasses of SSMI and TRMM TMI • Occur only in the tropics • Estimated sensor differences between TMI and SSM/I (EIA, channel diffs) using three methods • Optimal Estimation (OE): estimate geophysical profile from TMI; simulate TBs from TMI and SSMI; use this to estimate sensor diffs • MERRA: use model profile to simulate TBs from TMI and SSMI; use this to estimate sensor diffs • ERA-I: use ERA-I instead of MERRA • All three implementations use the same RT model

  34. 4. Vicarious Cold Cal • For each frequency and polarization, there is a fixed surface temperature at which the minimum TB occurs • TB minimum is affected by water vapor, so finding minimum can be challenging in practice • Vicarious Cold Cal technique involves finding this minimum • Build histogram of TBs from large amount of data (~1 year) • Screen high water vapor pixels (cloud, rain, etc) • Estimate minimum TB from histogram • Use OE or Merra/ERA-I data to correct for EIA of each sensor since TB minimum is EIA dependent Ruf, 2000; IEEE Trans. Geosci. Rem. Sens., 38(1), 44-52 Vicarious Cold Calibration technique developed at U. Michigan by Prof Chris Ruf

  35. 5. Warm Calibration • Other techniques give estimates at cold end of TBs – really need an estimate at warm end • This would allow estimation of gain (ie: slope) • Warm Calibration technique developed by UMich group uses Amazon rainforest • This is a warm, (near) black-body (= unpolarized) target • First, estimate surface (temperature and emissivity) and atmospheric (water vapor, cloud liquid water) parameters • Forward RT model used with non-linear least squares fit • Forward model then used to remove EIA differences • Diurnal cycle correction also necessary • Technique applied to SSM/I by Darren McKague (U. Michigan) Sample Amazon Targets Brown and Ruf, 2005; J. Atmos. Ocean. Technol., 22(9), 1340–1352

  36. Combining aproaches • Have estimates from five techniques, most with several realizations of sensor differences

  37. Combined analysis F13-F15 • Plot shows intercal estimates for F13-F15 as a function of F13 Tb • Vicarious Cold cal and Amazon warm cal are valid only for single Tb values • Agreement is extremely good • Spread amongst colder techniques ~0.5K for most channels • Amazon Warm cal in less good agreement, but has 0.57K error • Larger (~1K) for F08 and F10 (not shown)

  38. Beta (B6) Calibration (April 2012) • Combination of techniques to get Beta Intercal requires some care • TMI only available for F11, F13, F14 and F15 • Amazon Warm Cal technique not available for all satellites (requires TMI to remove diurnal effects) • F08 is a special challenge due to lack of overlaps: use VCC and Model • Since variations of each technique are not independent, average these first, then take mean and SD of four techniques to make Beta Cal • Amazon warm results not used in order to be consistent amongst sensors Sapiano et al., Towards an Intercalibrated Fundamental Climate Data Record of the SSM/I Sensors, Trans. Geosci. And Rem. Sens., in revision.

  39. SSMIS Beta Version (April 2012) • SSMIS has several major, known issues • Worse for F16 than for F17, F18 • Preoperational data not used currently • Applied corrections for • Solar/lunar Intrusions • Emissive antenna • Estimated attitude for EIA calculation • SSMIS has 6 feedhorns: use common satellite attitude with a fixed sensor alignment offset for each feedhorn • Spillover and cross-pol corrections from operational code used, but not scene-dependent correction. • SSMIS beta intercalibration • Same approach as used for SSM/I. Only applied to the 7 SSM/I equivalent channels

  40. Intercalibration Issues • Non-physical trends can exist in satellite estimates • Can be difficult to quantify these given sampling requirements of some techniques • No techniques calibrate in rainy part of TB spectrum • Difficult to get idea of how calibration should change with TB • Vicarious warm calibration helps here, but the middle of the spectrum is not sampled

  41. Summary of key issues • A number of specific calibration-related issues need to be addressed before sensors can be effectively intercalibrated (cross-track biases, geolocation and pointing errors etc.) • Although conical-scanning radiometers designed to have same view angle across the scan, differences of as little as 0.1 degrees can impact calibration as well as geophysical retrievals, particularly over oceans. • Calibration needs to be ongoing as sensors change/degrade, orbits drift, and new sensors are launched • As demonstrated by biases in TMI due to heating of the antenna (Jones et al.), calibration errors often change over time (as with spacecraft heating) and can mimic real physical signals (i.e. diurnal cycle). • Using multiple approaches to intercalibration helps to confirm results, identify issues, and determine residual errors. • As a result, calibration should be considered an ongoing, continually evolving endeavor that involves collaboration by multiple groups with expertise in both the engineering and scientific aspects of the sensors.

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