1 Operational Calibration of Satellite Microwave Instruments for Weather and Climate Applications Fuzhong Weng and Tsan Mo Sensor Physics Branch NOAA/NESDIS/Office of Research and Applications Banghua Yan, QSS Group Inc. Ninghai Sun, IMSG and many others Achieving Satellite Instrument Calibration for Climate Change (ASIC3) Workshop, May 16-18, 2006

2 Outline Significance of satellite instrument calibration Microwave instrument calibration components Microwave sensor calibration for operational and research satellites Issues and Challenges Summary and Conclusions

3 Global Temperature Trend Depicted by NOAA MSU and AMSU 5-day and global-ocean-averaged time series for NOAA 10,11,12, and 14 obtained from MSU 1B data which uses NESDIS operational calibration algorithm Combined MSU and AMSU observations can be used to detect climate trend, however, different merging procedure in removing intersatellite biases causes different trend results

4 Calibration Accuracy in Relation to Climate Trend – Ocean Mean Wind Speed This is the case for SSM/I 37 GHz, V-Pol, surface wind > 12 m/s. The sensitivity of wind speed to brightness temperature is about 1. – 3 m/s/K. Tropical mean wind speed increases 0.5 m/s per decade. Is the recent increasing hurricane wind damage responding to this trend? How can we assure this trend not related to inter-satellite calibration and algorithms

5 Calibration in Support of Satellite Data Assimilation No radiance biases –Instrument –Forward model Known Errors –Observation –Forward model where x is a vector including all possible atmospheric and surface parameters. I is the radiance vector B is the error covariance matrix of background E is the observation error covariance matrix F is the radiative transfer model error matrix You can’t simply fudge the weights!

6 Comparison of Impact of Observing Sounding Data Global degradation From Roger Sounder, The Metoffice, UK Ten years ago? TOVS NESDIS retrievals, AMV, more but lower quality radiosondes

7 Microwave Instrument Calibration Components Energy sources entering feed for a reflector configuration 1.Earth scene Component, 2.Reflector emission 3.Sensor emission viewed through reflector, 4.Sensor reflection viewed through reflector, 5.Spacecraft emission viewed through reflector, 6.Spacecraft reflection viewed through reflector, 7.Spillover directly from space, 8.Spillover emission from sensor, 9.Spillover reflected off sensor from spacecraft, 10.Spillover reflected off sensor from space, 11.Spillover emission from spacecraft

Example: Emission by Antenna/Front End Component Emission by the antenna and front-end components can introduce a diurnal temperature variation. T = Physical temperature of antenna, feed horn, waveguide, etc., TbTb  a = Transmittance due to absorption of antenna, feed horn, etc., T’ b [1] [2] Emission & absorption by antenna & front-end. Two-point radiometer calibration : Combining (1) and (2) : [3a] T b = T bo +  T b [3b] [3c] Antenna Feed Horn Waveguide

Example: Spill-Over due to Antenna Side-Lobes A very small portion of the antenna side lobes “sees” radiation emanating from outside the Earth. An even smaller portion,  S ( antenna gain) results from the solar radiation, T SL, being reflected with reflectivity R from materials onboard the spacecraft. Earth The brightness temperature can also be written as where

Square Law Detector  VI V I Example: Non-Linear Calibration Time-averaged voltage : Nyquist theorem : Combining [1] and [2] : K=Boltzman’s constant G=Amplifier gain, B=Bandwidth T=Amplifier temperature, T e = Radiometric temperature Two-point radiometer calibration eliminates b o and b 1 from (output in counts) so that [1] [2] [3] Output Voltage Input Current At microwave region:

11 NESDIS/STAR Integrated Cal/Val System 1.Current Capabilities: Noise quantification (NEDT), Linear and non-linear calibration algorithms, Correction of sudden jumps and contamination associated with warm load and space view calibration counts, Monitoring instrument noise, gain, telemetry and PRT uniformity, Mitigation of radio frequency interference, Global bias analysis from forward calculations using NWP models, Time series of SNO/SCO matched data from a pair of operational satellites, Time series of updated calibration coefficients with digital access, Reference areas/site for vicarious calibration, Monitoring of key MW products sensitive to calibration 2.Future Capabilities: Validation of EDRs

12 NOAA AMSU Sensor Flown Since NOAA-15 (May 1998) Contains 20 channels: AMSU-A 15 channels 23 – 89 GHz AMSU-B (now MHS on NOAA-18) 5 channels 89 – 183 GHz 6-hour temporal sampling: 200, 730, 1400, 1930 LST

13 NOAA AMSU Calibration and Monitoring Pre-launch checkup –Noise quantification (NEDT) from EDU and PFM –Non-linearity –….. Update AMSU/MHS Calibration Parameters Input Data Set (CPIDS) in Level 1B, –Polynomial coefficients form converting PRT counts into temperature –Warm load correction at three instrument temperatures, –Cold spaces correction to the cosmic background temperatures –Error limits of warm and cold radiometric counts between the sample of the same scan line, –Non-linearity parameter –Temperature to radiance conversion factors –Min and max of RF shelf instrument temperature sensors –Analog data conversion coeff –Antenna position data in counts –Gross radiometric limits (max and min) on space and warm targets views –Antenna pattern parameters for lunar correction –Asymmetry correction On-board Monitoring –Correction of sudden jumps and contamination associated with warm load and space view calibration counts, –Monitoring instrument noise, gain, telemetry and PRT uniformity, –Detect the radio frequency interference from AMSU, –Global bias analysis from forward calculations using NWP models, –Time series of SNO/SCO matched data from a pair of operational satellites, –Reference areas/site for vicarious calibration, –key MW products sensitive to calibration (Cloud Liquid Water and Precip)

14 Pre- and Post-launch Noise Characterization NOAA-18 AMSU-A NOAA-18 MHS

15 NOAA-15 AMSU-A Asymmetry Correction, ∆T = A 0 exp{ -0.5[(θ - A 1 ) /A 2 ] 2 } + A 3 + A 4 θ + A 5 θ 2

16 Effects of Biases on Operational Products AMSU A2 model cross scan asymmetry was detected from the first NOAA-15 cloud liquid water Physical retrievals of cloud liquid water are directly subject to instrument biases If AMSU cloud liquid water is assimilated or used for QC others, it results in global false alarm clouds and rejection of many other useful information Bad consequence from AMSU xing scan radiance biases if not corrected because CLW is used for NWP QC NOAA-15 NOAA-16

17 DMSP Special Sensor Microwave Imager and Sounder (SSMIS) The Defense Meteorological Satellite Program (DMSP) successfully launched the first of five Special Sensor Microwave Imager/Sounder (SSMIS) on 18 October SSMIS is a joint United States Air Force/Navy multi-channel passive microwave sensor Combines and extends the current imaging and sounding capabilities of three separate DMSP microwave sensors, SSM/T, SSM/T-2 and SSM/I, with surface imaging, temperature and humidity sounding channels combined. The SSMIS measures partially polarized radiances in 24 channels covering a wide range of frequencies (19 – 183 GHz) –conical scan geometry at an earth incidence angle of 53 degrees –maintains uniform spatial resolution, polarization purity and common fields of view for all channels across the entire swath of 1700 km.

18 F DMSP LTANs F F F F NOAA LTANs N N N N N As of August 2005 N15 F14 F15 N17 N18 F16 N16 DMSP and NOAA Constellation

19 SSMIS vs. AMSU-A Weighting Functions Oxygen Band Channels SSMIS13 Channels Sfc – 80 km AMSU-A 13 Channels Sfc - 40 km SSMIS vs. AMSU Sounding

20 SSMIS Antenna System and Calibration Main-reflector conically scans the earth scene Sub-reflector views cold space to provide one of two-point calibration measurements Warm loads are directly viewed by feedhorn to provide other measurements in two-point calibration system The SSMIS main reflector emits radiation from its coating material –SiOx VDA (coated vapor-deposited aluminum) –SiOx and Al VDA Mixture –Graphite Epoxy Warm load calibration is contaminated by solar and stray Lights –Reflection Off of the Canister Top into Warm Load –Direct Illumination of the Warm Load Tines Lunar contamination on space view

21 SSMIS Anomaly Distribution Shown is the difference between simulated and observed SSMIS 54.4 GHz. The SSMIS is the first conical microwave sounding instrument, precursor of NPOESS CMIS. The calibration of this instrument remains unresolved after 2 years of the lunch of DMSP F16. The outstanding anomalies have been identified from three processes: 1) antenna emission after satellite out of the earth eclipse which contaminates the measurements in ascending node and small part in descending node, 2) solar heating to the warm calibration target and 3) solar reflection from canister tip, both of which affect most of parts of descending node.

22 SSMIS Anomalies and Their Mitigation Algorithms 1.Antenna is not a pure reflector. It emits radiation with a very small emissivity and its own temperature. This additional radiation is called as an antenna emission anomaly 2.Warm load is heated by intruded solar radiation. The energy received through feedhorn does not match with the warm load physical temperature measured by the platinum résistance thermisters (PRT). This is referred as a warm load anomaly 3.The radiance from space view by the sub- reflector does not correspond to the sum of cosmic background temperature (2.73K) and pre-calculated correction values for each channel due to antenna side-lobe effort. 1.Use the emissivity from NRL antenna model and the temperature measured from the thermister mounted on antenna arm as approximation 2.Analyze the time series of warm load counts together with PRT and define the anomaly locations in terms of the FFT harmonics 3.Analyze the time series of cold space view count and define the anomaly locations in terms of the FFT harmonics and cosmic temperature plus antenna correction Anomaly CausesAnomaly Mitigation Process

23 SSMIS Calibration Algorithms 1.Use the emissivity from NRL antenna model and the temperature measured from the thermister mounted on antenna arm as an approximation 2.Analyze the time series of warm load counts together with PRT and define the anomaly locations in terms of the FFT harmonics 3.Analyze the time series of cold space view count and define the anomaly locations in terms of the FFT harmonics and cosmic temperature plus antenna correction where T A is the antenna temperature corresponding to the earth scene’s radiance, and  R and T R is the reflector emissivity and Temperature, respectively

24 Theoretical SSMIS Reflector Surface Parameters (NRL Multilayer Antenna Model) Emissivity (V-pol/20deg) [ ∈ R ] Freq. (GHz)AlGrEpSiOx SiOx/Al

25 FFT Analyses of Warm Counts (54.4 GHz) Note: (1) C W F = FFT -1 ( FFT(C W ) * Filter(f L ) ) ), where f L is a cutoff frequency of the low pass filter, where T  102 minutes. (2) f 0 is sampling frequency = 1.0/T.

26 SSMIS Antenna Temperature Bias February 3, 2006 Before anomaly correctionAfter anomaly correction Temperature biases from TDR and SDR space are related through the slope coeff. for spill-over correction, Tb = a*Ta + b

27 SSMIS Bias Trending

28 SSMIS Assimilation Trials at ECMWF Graeme Kelly Pre-processed data: 40 % flagged limited coverage tuning ongoing T sounding chs only 0.5K obs errors NO SAT NO SAT + SSMIS NO SAT + N15 AMSU SH AC 500hPa height NH AC 500hPa height

29 SSMIS vs. SSM/I Products SSMIS-F16 SSM/I-F15 Cloud Liquid WaterTotal Precipitable Water

30 Other Microwave Instrument Calibration Windsat vicarious calibration –Amazon/Congo basins –Time series of averaged 3 rd and 4 th components Aqua AMSR-E radio frequency detection –Develop RFI index fro 6 V/H pol over land

31 Microwave Sensor Inter-calibration for Climate Applications DMSP Series SSM/I (F8 to F15) –Data rescue and archival –Metadata for re-calibration –Inter-calibration using simultaneous conical overpassing –Reproduce all SSM/I EDRs climatology NOAA MSU (N10-14) Time Series Analysis –Non-linearity parameter –Bias removal using SNO

32 The First SSM/I Monthly Products Generated from NOAA/NESDIS

33 Intersatellite Calibration Using Simultaneous Nadir/Conical Overpass (SNO/SCO) SNO – every pair of POES satellites with different altitudes make orbital intersections within a few seconds regularly in the polar regions (predictable w/ SGP4) Precise coincidental pixel-by-pixel match-up data from radiometer pairs provide reliable long-term monitoring of instrument performance The SNO method (Cao et al., 2005) is used for on-orbit long-term monitoring of imagers and sounders (AVHRR, HIRS, AMSU) and for retrospective intersatellite calibration from 1980 to 2003 to support climate studies The method has been expanded for SSM/I with Simultaneous Conical Overpasses (SCO) SNOs occur regularly in the +/- 70 to 80 latitude

34 NOAA-18 vs. Aqua AMSU SNO Matching

35 DMSP F-10 vs. F-13 SSM/I SCO Matching ( GHz Channels)

36 DMSP F-10 vs. F-13 SSM/I SCO Matching (19-22 GHz Channels)

37 Calibration Issues That Affect NOAA Uses of Microwave Data in Weather and Climate Research Microwave Radiometry System Major Postlaunch Calibration Problems Impacts on Weather & Climate Applications Mitigation strategies MSUNon-linearity Warm Load PRT anomaly Cross-sensor biases Controversy climate trendNon-linearity correction SNO derived biases NOAA AMSU/MHSCross-scan asymmetry AMSU-B RFI from STX transmission Lunar contamination Rejection of AMSU data in NWP Little uses of AMSU-B Asymmetry bias correction RFI correction LCC EOS Aqua AMSU Cross-scan asymmetryRejection of AMSU data in NWP DMSP SSM/I APC and spill-over correction Cross-instrument biases Uncertainty in derived emissivity spectra Long-term climatology SCO derived biases DMSP SSMIS Reflector emission Warm load anomaly Difficult to use of sounding channels in NWP Poor quality of sounding products Characterization of reflector emissivity/temperature FFT removals for warm load count and PRT anomalies WindSAT Biases at polarimetric channels RFI at low frequencies Wind direction biases Limited uses for soil moisture retrievals Vicarious calibration RFI detection/removal algorithms Aqua AMSR-E Warm load instability RFI at low frequencies Wind direction biases Limited uses for soil moisture retrievals Cross-sensor calibration with TMI RFI detection/removal algorithms

38 Summary and Conclusions Operational microwave instruments AMSU-A/B (MHS) on board NOAA POES have been well calibrated for weather applications. Major NWP centers have demonstrated the greatest impacts on weather forecasts from direct radiance assimilation, and they are pleased with the quality of the microwave calibration algorithms developed by NESDIS/STAR. DMSP SSMIS may soon become another major data source for NWP data assimilation. Currently, resolving its calibration uncertainty from antenna emission and contamination by solar/stray lights is of a highest priority. The NESDIS/STAR beta- version calibration algorithm has significantly eliminated most of anomalies. The biases in the polarimetric microwave instruments (e.g. WindSAT) can be characterized from vicarious sites where surface polarimetric properties are well understood from some field campaigns and advanced radiative transfer modeling. Intersatellite biases for microwave sounders or imagers can be quantified from simultaneous nadir/conical overpassing, but the bias characteristics from those surface sensitive channels could be quite significantly different from both poles. The differential biases may produce an inconsistent climate trending analysis. Thus, for climate studies, the current SNO/SCO algorithms may need some further constraints

39 Backup Slides: NOAA POES AMSU Calibration and Monitoring

40 Pre- and Post-launch Noise Characterization NOAA-18 AMSU-A NOAA-18 MHS

41 Trending over 65 days AMSU-A NEDT Trending

42 Trending over 65 days MHS Gain and NEDT Trending

43 Monitoring Uniformity of Warm Load PRT Temperatures  T =Max – Min T Spec:  T < 0.2 K

44 Digital Counts Radiance (Brightness Temp) (C c, 2.73K) (C w, R w ) (C e, R L ) (C e, R e ) Two Point Radiometer Linear Calibration: Two Point Radiometer with Nonlinear Calibration Correction: Linear and Non-linear Calibration where δR is the post-launch bias caused by factors other than non-linearity

45 NonlinearitySpec: Ch.1, 2, 15: 0.5 K Ch.3-14: K A1-1 Channels: Out of spec

46 Correction for Lunar Contamination on Cold Space Calibration

47 Possible Causes for AMSU Asymmetry A misalignment of AMSU polarization vector –Mostly noticeable at clean window channels Errors in Instrument pointing angle –It is unlikely because the cross-track pointing error (0.1 to 0.3 degree) is not large enough to produce this kind of asymmetry. Side lobe intrusion to the solar array –There should be some latitudinal dependence –The response would occur at multiple channels

48 Trending over 65 days AMSU-A Gain Trending

49 Offset Changed Trending over 65 days Trending for AMSU-A Calibration Counts

50 Libyan Desert July 2005 Vicarious Calibration Using Libyan Desert

51 Backup Slides: Windsat and AMSR-E Calibration and Monitoring

52 WindSat Applications Main Applications: ocean surface wind vector. Other applications at NOAA/NESDIS: Test the community radiative transfer model Possibility for directly assimilating radiances Microwave products such as CLW, TPW, land emissivity

53 WindSat Biases from Vicarious Calibration Monthly mean of 4 th Stokes components over Amazon rainforests should be zero because of surface roughness and heterogeneity relative to azimuthal direction. The residual of this mean is largely due to the instrument calibration biases. The bias (-0.5K) at 18.7 GHz will result in substantial bias in wind direction retrievals because of the actual wind induced signal is on the order of a couple of degrees in Kelvin (from Liu and Weng, 2005, Appl. Optics) 18.7 GHz 10.7 GHz and 37 GHz

54 EOS Aqua AMSR-E Team Algorithm Ocean products : SST,SSW,TPW,CLW, Rain rate, Sea ice concentration Land products: LST, Soil moisture,Rain rate,Snow cover, Snow/Ice Types, Snow equivalent water ParametersSMMR (Nimbus-7) SSM/I (DMSP- F08,F10,F11,F13,F15) AMSR (Aqua, ADEOS-II) Time Period1978 to to PresentBeginning 2001 Frequency (GHz)6.6, 10.7, 18, 21, , 22.3, 36.5, , 10.7, 18.7, 23.8, 36.5, 89.0 Sample Footprint Sizes (km) 148 x 95 (6.6 GHz) 27 x 18 (37 GHz) 37 x 28 (37 GHz) 15 x 13 (85.5 GHz) 74 x 43 (6.9 GHz) 14 x 8 (36.5 GHz) 6 x 4 (89.0 GHz)

55 AMSR-E Radio Frequency Detection Radio-frequency interference (RFI): Any man- made emissions from active microwave transmitters, usually generated by television, radio, antennas Location: mostly over highly populated urban areas, military fields. RFI (V/H) index = TV(H)6.9 - TV(H) ~ 10 K Weak 10 ~ 20 K Moderate > 20 K Strong

56 AMSR-E Radio Frequency Interference (March 2004)

57 AMSR-E Radio Frequency Interference (March 2004)

58 Time Series of RFI Indices in Chicago

59 Backup Slides: MSU Non-Linearity Calibration using SNO

60 k j For many pairs of SNO, multivariable linear regression will resolve  R (intersallite bias),  k and  j (non-linearity parameters for k, j satellites, respectively SNO Pairs We would like to have zero bias between two satellites, R k = R j SNO Time Series Used for Deriving Intersatellite Bias and Nonlinearity

61 Results-New Calibration Coefficients  R and  obtained by SNO  R and  obtained from pre-launch Calibration (Mo et al. 2001) Satellites  R   N N N N Calibration coefficients for different satellites obtained by sequential adjusting process using the SNO matchups when NOAA 10 is assumed to be the reference satellite. Units for  R and  are (mW) (sr m 2 cm -1 ) -1 and (sr m 2 cm -1 ) (mW) -1, respectively. (Courtesy of C. Zou)

62 Reconcile Tropospheric Climate Trend using SNO with MSU Past MSU Channel 2 Trend Results: Spencer and Christy (1992): C Decade-1, Christy et al. (2003): C Decade-1, Mears et al. (2003): C Decade-1, Vinnikov and Grody (2003): 0.220C Decade-1, Grody et al. (2004) 0.170C Decade-1,

63 Trends for linear calibration algorithm 0.32 K Decade -1 Trends for NESDIS operational calibration algorithm 0.22 K Decade -1 (Vinnikov and Grody, 2003) Trends for nonlinear calibration algorithm using SNO cross calibration 0.17 K Decade -1 SNO Derived Climate Trend from MSU Courtesy of C. Zou