Presentation is loading. Please wait.

Presentation is loading. Please wait.

RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A. MARTINI (1), P. FEMENIAS RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A.

Similar presentations


Presentation on theme: "RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A. MARTINI (1), P. FEMENIAS RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A."— Presentation transcript:

1 RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A. MARTINI (1), P. FEMENIAS RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A. MARTINI (1), P. FEMENIAS (2), G. ALBERTI (3), M.P. MILAGRO-PEREZ (1) (1)SERCo S.p.a., Via Sciadonna, 24, Frascati, Rome, Italy (2) ESA/ESRIN, via G. Galilei, 1, Frascati, Rome, Italy (3) CO.RI.S.T.A., Viale Kennedy, 5, Naples, ItalyABSTRACT As widely known the RA-2 data are affected by the so-called "S-Band anomaly" discovered in the early days of the Commissioning Phase. As described in [R - 1] it consists in the accumulation of the S-Band echo waveforms that starts, apparently randomly, after an instrument Acquisition phase. Investigation is on going to try and find the instrumental cause of this behavior but in the meantime the data affected by this anomaly are completely unusable. For this reason the need has arisen for the users to be able to detect the anomalous data and eventually reconstruct a usable signal from them. This paper describes the algorithm developed for the L1b processor that allows to set a flag identifying the data affected by the "S- Band anomaly“ and reconstruct normal echo waveforms to be then ingested in the nominal re-tracking procedure at L2.It presents also the results obtained after the implementation in the L1b reference processor.REFERENCES [R – 1] ENVISAT RA-2: S-BAND PERFORMANCE, S. Laxon and M. Roca, Proceedings of the ENVISAT Calibration Workshop, Noordwijk, September 2002 [R – 2] Algorithm for Flag identification and waveforms reconstruction of RA- 2 data affected by “S-Band anomaly”, A. Martini, ENVI-GSEG-TN , Issue 1.4, September 2004 ALGORITHM DESCRIPTION Consider each Data Block acquired in Tracking or IF Cal. or Preset Loop Output or Preset Tracking Mode. For each couple of consecutive Data Blocks, subtract, bin per bin, the S-Band waveforms. For anomaly detection: –Consider a buffer with n_buffer Data Blocks of subtracted waveforms. –If the number of bins assuming a negative value is higher than n_count, then the S-Band anomaly exists for the first Data Block in the buffer. –Shift of one Data Block and repeat the pervious two steps. For waveforms reconstruction: –Consider one Data Block of subtracted waveforms –If some bin exists having a value lower than diff_threshold, then substitute it with the average of the two corresponding bins in the previous and following waveforms. –If for current Data Block, the S-Band anomaly had been detected, then substitute the S-Band waveform with the just evaluated one. –Shift of one Data Block and repeat the pervious steps. For details see [R – 2] RESULTS The results hereafter presented have been obtained by setting the n_buffer, n_count and diff_threshold respectively to 20 and 25 and 4*10 8 Power Units.DETECTION S-Band anomaly flag for cycle 27 This cycle presents the highest percentage of affected data during the whole ENVISAT mission. The choice of the three algorithm variables is the outcome of a big tuning exercise which has examined the performances of the algorithm with different combinations of the three parameters over four different surfaces (e.g. Ocean, Ice Sheet, Land and Sea Ice). The combination has been chosen which gave the lowest percentage of data characterized by wrongly detected flag; among cases with equivalent performances, the one has been selected producing the lowest percentage of false negatives (data affected by S-Band anomaly flagged as not affected). In this example the data wrongly flagged data represent about the 0.49% of the total and they are mostly located over Land and Sea Ice surfaces. In any case this amount can be considered a fair price to pay to detect and cure an anomaly, which affects, in average, between 2% and 6% of data per cycle. WAVEFORMS RECONSTRUCTION Examples of original and reconstructed waveforms over different surfaces: Ocean, Ice Sheet, Sea Ice and Land.CONCLUSIONS The poster presents an algorithm targeted to the detection of the so-called RA-2 “S-Band anomaly” from the L0 data products as well as an algorithm to reconstruct the affected waveforms in such a way that they can be directly ingested in the L2 re-tracking processing. The current algorithm with the chosen parameters allows the correct detection of the S-Band anomaly in 99.51% of the cases where the failing ones are mostly false negatives and localized over Land and Sea Ice. The operational implementation of the detection algorithm is almost completed while the waveforms reconstruction one will be completed by the end of the year. For j=0, 63 WF(i, j)-WF(i-1, j) For k=i, i-n_buffer-1 WF_S(k,j) If N < n_count Count number of elements for which WF_S(k,j) < 0 N WF(i) WF(i-1) WF_S(i, j) For j=0, 63 If WF_S(i, j) < diff_threshold YES WF_S(i, j)= (WF_S(i+1,j)+WF_S(i-1,j))/2 S Band anomaly flag (i) = 1 If S Band anomaly flag (i) = 1 NO For j=0, 63 WF(i,j) = WF_S(i,j) YES S-Band waveforms over OceanS-Band waveforms over Ice Sheet S-Band waveforms over Sea Ice S-Band waveforms over Land


Download ppt "RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A. MARTINI (1), P. FEMENIAS RA-2 S-BAND ANOMALY: DETECTION AND WAVEFORMS RECONSTRUCTION A."

Similar presentations


Ads by Google