Microwave Radiometer (MWR) Counts to Tb (Brightness Temperature) Algorithm Development (Version 6.0) and On-Orbit Validation

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1 Microwave Radiometer (MWR) Counts to Tb (Brightness Temperature) Algorithm Development (Version 6.0) and On-Orbit Validation Zoubair Ghazi CFRSL Central Florida Remote Sensing Lab Dissertation Defense October 4, 014 1

2 To develop an improved counts to brightness temp (Tb) algorithm for the CONAE Microwave Radiometer on the Aquarius/SAC-D satellite Validation of Tb measurements using inter-satellite radiometric comparisons (X-CAL) Produce an Algorithm Theoretical Basis Document (ATBD) and deliver prototype MatLab code to CONAE

3 Post-launch CFRSL & CONAE evaluated MWR countsto-tb algorithm V5.0 Used 6 mo of MWR on-orbit collocation with WindSat Ocean Tb s exhibited small and acceptable Tb biases Land Tb s exhibited anomalous behavior Land/water Tb transitions were Smeared Step function changes of noise diode deflections Based upon on-orbit evaluation, it was concluded that: V5.0 was unacceptable for producing MWR science data An improved counts-to-tb algorithm must be developed to address the anomalous Tb effects Further, CONAE developed a revised Counts-to-Tb algo V5.0S that included a smear correction This was the starting point for my dissertation research

4 1. Evaluated the MWR counts-to-tb algorithm V5.0S On-orbit X-CAL with WindSat indicated that Smear effects at land/water boundaries were removed However, anomalous effect of noise diode deflections remained Determined that MWR system gain varied with scene Tb. Developed a forward model for MWR system Counts-to-Tb Empirically derived coefficients to match on-orbit observations, including deep space calibrations Characterized model coefficients versus scene Tb. Developed a gain non-linearity correction 4. Implemented a new inverse model Counts-to-Tb algorithm V Validated algorithm using X-CAL with WindSat 4

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Aquarius (AQ) is a mission of Original Exploration First NASA mission to measure Sea Surface Salinity (SSS) from space SAC-D was launched on June 10 th, 011 from Vandenberg Air Force

6 Aquarius (AQ) is a mission of Original Exploration First NASA mission to measure Sea Surface Salinity (SSS) from space SAC-D was launched on June 10 th, 011 from Vandenberg Air Force Base, California. Aquarius instrument - NASA MWR - CONAE (Argentinian Space Agency) MWR provides auxiliary environ measurements: water vapor, ocean surface wind speed, and oceanic rain rate 6

7 MWR supports AQ science by measuring simultaneous & collocated ocean brightness temperatures (Tb) channel push-broom Dicke radiometer: 6.5 GHz H- & V-Pol (forward-look).8 GHz H-Pol (aft-look) Earth Incidence angle 5 o for odd beams 58 o for even beams Matches the AQ swath width of 80 km 7

SW Matrix MWR Single Channel Block Diagram T in T N T o Counts On/off Three Dicke radiometer states: C C C a N o ( Tin Trecv ) Grecv Voff set ( Tin TN Trecv ) Grecv

8 SW Matrix MWR Single Channel Block Diagram T in T N T o Counts On/off Three Dicke radiometer states: C C C a N o ( Tin Trecv ) Grecv Voff set ( Tin TN Trecv ) Grecv Voff set ( To Trecv ) Grecv Voff set (1) () () Subtracting (1) from () yields the radiometer gain, which varies in time G recv C N C T N a T C C a o in * Cn Ca T N T o 8

9 Rad_counts, counts Dickie Sw in ant position + noise diode ON Tin + Tn Dickie Sw in ant position Tin To Dickie Sw in reference load position Slope is radiometer gain Radiometer Input to the antenna port of Dicke switch, T in, Kelvin 9

10 Our objective was to determine the MWR transfer function based upon on-orbit measurements However, under typical on-orbit condition, the radiometer system gain will vary cyclically (once/orbit) due to the receiver physical temperature changes Therefore, a procedure was developed to synthesize constant system gain from MWR measurements 10

11 Time variable gain was removed and all counts were normalized using the following equation: Co norm i Co i Gain Gain i Co ( To Trec ) * Gain i i i To i <Trec> Gain i <Gain> is the instantaneous reference load physical temperature, Kelvin is the orbit average receiver noise temperature is the instantaneous system gain is the orbit average gain 11

12 Reference Load Counts, counts Reference Load Temperature, K/100 Before Normalization After Normalization Co To * Gain Co _ norm To * Gain C o Orbital Time, Min Orbital Time, Min

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14 Noise Diode Deflection, counts Noise Diode Deflection, counts Scene Brightness Tb, K Before Count (gain) Normalization After Count (gain) Normalization Effects of variable gain Linear dependence on scene Tb Radiometer Input to the antenna port of Dicke switch, T in (Kelvin) 14

15 1 1 therefore, ) ( ) ( ) ( () ) ( () ) ( (1) ) ( recv recv recv recv ant recv ref recv N ant set off recv recv ref ref set off recv recv N ant N set off recv recv ant a G G G T T T T T T T V G T T C V G T T T C V G T T C 15

16 G G G recv _1 recv _ recv _ G o G G o o g( T g( T g( T ref ref ref ) h 1 ) h ) h ( T in ( T ( T in in ) ) ) G o is the mean long term gain g(t ref ) is the orbital gain change due to phy temp (T ref ) h(t in ) is the gain compression due to variable scene brightness temp (and injected noise diode) These parameters are estimated during a single orbit where a deep-space calibration is performed 16

17 Rad_counts Quadratic regression T in- = T ant + T N T in-1 = T ant T in- = T o Ref Load Full-dynamic range Radiometer Input to the antenna port of Dicke switch, T in (Kelvin) 17

18 Gain Compression, h(tin) T in-1 = T ant Linear regression T in- = T ant + T N Radiometer Input Brightness, T in (Kelvin) 18

19 Seven Deep Space Calibration (DSC) orbits that included, space, ocean, and land observations were used to cover wide range of scene Tb s After counts (gain) normalization, the radiometer transfer function was established Rad_counts = f(t in ) Quadratic regression for 7V channel yielded the following Rad _ counts 7.5x10 4 ( T in ) ( T in ) 70 19

20 Rad_counts, counts Quadratic Regression T in- = T ant + T N T in-1 = T ant T in- = T o Ref Load T in Full-dynamic range Radiometer Input to the antenna port of Dicke switch, T in (Kelvin) 0

21 Averaging nd order regression coeff s from 7 DSC orbits, the instantaneous counts linearization equation is: For 7 V _ linear x ( e-004)* T in C x C For 7 H For H _ linear x ( e - 004)* T in C x C _ linear x (-.1708e - 004)* T in C x C Where x = ant, N, and ref Tin is the input Tb to the Dicke switch, which is estimated using non-linear counts 1

22 Rad_counts (gain normalized) Rad _ count 8.x10 6 ( T in ) 16.6( T in ) 70 (V6.0) Rad _ count 7.5x10 4 ( T in ) 16.6( T in ) 70 (V5.0S) Radiometer Input to the antenna port of Dicke switch, T in (Kelvin)

23 Noise Diode deflection, counts/k Noise Diode deflection, counts/k Scene Brightness Tb, K V5.0S (Without counts linearization) V6.0 (With counts linearization) Space Linear Regression Ocean Ocean Land Land Brightness Temperature, K Brightness Temperature, K

24 Gain, counts/k Gain, counts/k Scene Brightness Tb, K Note: gain jumps V5.0S V6.0 Gain variation due to changing recvr phy temp Samples Samples 4

25 5

Characterization of injected noise diode temperature (T N ) over physical temperature Retrieve antenna switch matrix loss coefficients Empirical method

26 Characterization of injected noise diode temperature (T N ) over physical temperature Retrieve antenna switch matrix loss coefficients Empirical method (regression model) was applied Assumption: All transmission and reflection coeff s are constant and are NOT expected to change during MWR's mission life time 6

27 Distributed Temperature (Tguide) L o Th /Tc a d Warm / Cold Load waveguide Distributed Loss (L) Input to The Receiver T h /T c Receiver Calibration Ref Plane Counts 7

28 Noise Diode Deflection, counts Noise Diode Deflection, counts SAYAK BISWAS V.0 V6.0 Cold-load Hot-load Samples Samples 8

29 T ap =[T in -(b *T o +b *T 1 +b 4 *T +b 5 *T +b 6 *T 4 )]/b 1 where T ap is the scene brightness temp at horn aperture T in is the input brightness temperature to antenna port of Dicke switch T o, T 1, T, T,& T 4 are MWR physical temps b 1, b, b, b 4, b 5 and b 6 are antenna switch matrix loss coefficients derived using the regression model 9

30 MWR coffin The thermal vacuum (TV) test for MWR was performed in September 009. (09/06 09/09) Performance of the SW matrix losses coefficients 0

31 Temperatures (K) Temperatures (K) T ap =[T in -(b *T o +b *T 1 +b 4 *T +b 5 *T +b 6 *T 4 )]/b 1 SWM Model, RMS=.94 T in calc from rad_counts Regression Model,RMS= 0.75 Time, min T ap & T ap_regress Time, min 1

32 To = 8K To = 09K

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35 APC and residual bias correction were applied by inter-satellite XCAL MWR = target & WindSat = reference MWR and WindSat have different incident angles, therefore, Tbs were adjusted using theoretical radiative transfer model values for both satellites (MWR sim and WS sim ) WS adj = WS obs + (MWR sim WS sim ) Double Difference Technique DD = MWR obs WS adj 5

36 MWR Tb, K MWR Tb, K Before Correction After Correction Slope= 0.99 Offset = Slope= Offset = Land Linear Regression Linear Regression Space Ocean Space Ocean WS MWR Adjusted Tb, K Tb, K WS Adjusted Tb, K 6

37 Brightness Temperature. Tb, K Brightness Temperature. Tb, K Version 5.0S Version B B4 B6 B8 10 B B4 B6 B Samples Samples 7

38 Brightness Temperature. Tb, K Brightness Temperature. Tb, Brightness Temperature. Tb, K Version 5.0S Version B1 B B5 15 Samples B1 B B5 40 B7 10 B Samples Samples 8

39 Double Difference Radiometric Biases MWR/WindSat (Five Days Average) Jan 01, 01 Dec 1, 01 9

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41 DD, K 4 South Pole 10 0 North Pole South Pole Jan 01,01 - Dec 1,

DD, K 4 South Pole 10 0 North Pole 0 40 1 0 50-1 -

42 DD, K 4 South Pole 10 0 North Pole South Pole Jan 01,01 - Dec 1,

MWR Counts-to-Tb algorithm V6.0 has been developed and distributed to the AQ Cal/Val Team MWR transfer function non-linearity in V5.0S has been characterized and corrected in V6.

43 MWR Counts-to-Tb algorithm V6.0 has been developed and distributed to the AQ Cal/Val Team MWR transfer function non-linearity in V5.0S has been characterized and corrected in V6.0 Antenna switch matrix loss coefficients were derived using re-analysis of MWR pre-launch TV calib test Validation of V6.0 performed using years of onorbit measurements On-orbit X-CAL, between MWR and WindSat, have produced the antenna pattern correction (APC) and removed small Tb biases V6.0 Algorithm Theoretical Basis Document and MatLab code delivered to CONAE for science data processing 4

44 Conferences 1. Ghazi, Z.; Biswas, S.; Jones, L.; Hejazin, Y.; Jacob, M.M., "On-orbit signal processing procedure for determining Microwave Radiometer non-linearity," Southeastcon, 01 Proceedings of IEEE, vol., no., pp.1,5, 4-7 April 01 doi: /SECON Ghazi, Zoubair; Santos-Garcia, Andrea; Jacob, Maria Marta; Jones, Linwood, "CONAE Microwave Radiometer (MWR) counts to Tb algorithm and on-orbit validation," Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 014 1th Specialist Meeting on, vol., no., pp.07,10, 4-7 March 014 doi: /MicroRad Santos-Garcia, A; Biswas, S.; Jones, L.; Ghazi, Z.; "Aquarius/SAC-D Microwave Radiometer brightness temperature validation," Oceans, 01, vol., no., pp.1,4, Oct. 01 doi: /OCEANS

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48 Rad_counts V5.0S V6.0 Note: gain land/water boundaries Dynamic range = 60 counts 48

49 Brightness Temperature. Tb, K Brightness Temperature. Tb, K Version 5.0S Version B B B4 B6 B8 10 B4 B6 B Samples Samples

50 Brightness Temperature. Tb, K Brightness Temperature. Tb, K Version 5.0S Version B1 B1 B B5 B7 10 B B5 B Samples Samples

51 Brightness Temperature. Tb, K Brightness Temperature. Tb, K Version 5.0S Version 6.0 Samples Samples 51

52 Brightness Temperature. Tb, K Brightness Temperature. Tb, K Version 5.0S Version 6.0 Samples Samples 5

53 5 4 B1 5 4 B 5 4 B 5 4 B / 6/ 1 10/9 Sep 1 - feb B / 6/ 1 10/9 Sep 1 - feb B / 6/ 1 10/9 Sep 1 - feb B / 6/ 1 10/9 Sep 1 - feb B / 6/ 1 10/9 Sep 1 - feb / 6/ 1 10/9 Sep 1 - feb / 6/ 1 10/9 Sep 1 - feb / 6/ 1 10/9 Sep 1 - feb 8 5

54 5 4 B1 5 4 B 5 4 B 5 4 B / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/9 5 4 B5 5 4 B6 5 4 B7 5 4 B / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/9 54

55 5 4 B1 5 4 B 5 4 B 5 4 B / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/9 5 4 B5 5 4 B6 5 4 B7 5 4 B / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/ / 4/1 7/1 10/9 55

56 Scene Brightness Tb, K Before Gain Normalization After Gain Normalization Gain To Gain To Time (min) Time (min) 56

57 1 Secondary 57

58 New MWR Tb data set to be used for tuning and validation of the wind speed algorithms XCAL 5 day double difference (DD) biases calculated between WindSat & MWR DD = MWR-WS Applied triangular moving average on the 5 day DD time series to smooth the correction The new MWR Tb s V7.0 = V6.0 Tb biases These V7.0 Tb s will be normalized to match the WindSat Tb s in the mean i.e., have zero DD Tb-bias The new adjusted DD given in the following charts was derived as: DD adj = DD V6.0 -Tb biases

59 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01

60 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01 B /1 11/1 /1 7/1 11/1 July 01 - Nov 01

Roughness Correction for Aquarius (AQ) Brightness Temperature using MicroWave Radiometer (MWR)

Roughness Correction for Aquarius (AQ) Brightness Temperature using MicroWave Radiometer (MWR) Yazan Henry Hejazin Central FL Remote Sensing Lab (CFRSL) Department of Electrical Engineering College of