Aquarius Algorithm Workshop Santa Rosa, CA 9 March 2010 College of Engineering Department of Atmospheric, Oceanic & Space Sciences Chris Ruf University of Michigan RFI Detection and Mitigation

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 2 of 9 RFI ATBD (1) The RFI detection algorithm can be broken into five steps: 1) Identify the set of TA samples surrounding the sample under test which will be used to estimate the local mean value of TA. The interval of time that those samples must lie within is constant in order to keep constant the ground track distance covered by the antenna footprint. However, because the spacing between samples of TA is not uniform, the actual number of samples that fall in the time interval will vary. The time between samples follows the pattern listed in Table 1.

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 3 of 9 RFI ATBD (2) Table 1. Sequencing of radiometer antenna temperatures, TA 1 Sample #Subcycle #Step #Time (s) 2 NOTES 111 & 20.00Start time; two 10 ms samples averaged on board 213 & 40.02two 10 ms samples averaged on board Skip steps 8-12 of subcycle 1; not antenna samples 621 & 20.12two 10 ms samples averaged on board 723 & 40.14two 10 ms samples averaged on board Skip steps 8-12 of subcycle 2; not antenna samples 1131 & 20.24two 10 ms samples averaged on board Repeat this pattern for subcycles Last sample from subcycle 12; Skip steps 8-12 of subcycle 12; not antenna samples & Start all over again with the pattern as in Sample #1 1. Based on “Aquarius Instrument High-Rate-Data Structure Definition,” Project Doc. AQ , v. 15 May 06 and J. Piepmeier, personal communication, 8 Jun Relative (elapsed) sample time with respect to sample #1.

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 4 of 9 RFI ATBD (3) (The RFI detection algorithm can be broken into five steps) 2) The TA samples surrounding the sample under test are examined for the presence of RFI. They are considered to be contaminated by RFI if they deviate from the local mean by more than a specified threshold. 3) Samples that pass the test are averaged together to estimate the local mean value of the sample under test. The average should equally weight samples made before and after the sample under test, in order to best represent the gradient of TA along the ground track of the radiometer.

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 5 of 9 RFI ATBD (4) (The RFI detection algorithm can be broken into five steps) 4) The TANT sample under test is compared to the local mean. It is considered to be contaminated by RFI if it deviates from the local mean by more than a specified threshold. 5) If TANT is determined to be contaminated by RFI, a specified range of samples surrounding it is also flagged as contaminated. This range is determined based on the characteristic time scale with which signals can enter and leave the radiometer antenna beam vs. the time interval between raw samples.

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 6 of 9 RFI Implementation (1) Input Radiometer data: Samples of the raw (shortest integration time; either 0.01 s or 0.02 s) radiometer antenna temperatures, TA. The TA samples should be calibrated using only internal noise diode counts to avoid RFI contamination and not external (CND) noise diode counts. The internal noise diode counts are measured during steps 9-12 of each subcycle. (The external (CND) noise diode counts are measured during step 8 of each subcycle.) For each sample to be tested for RFI, the preceding and subsequent 30 TA samples from the same radiometer at the same polarization are also needed.

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 7 of 9 RFI Implementation (2) Input Dynamic auxiliary data: Time tag for each TA sample: For each sample, TAi, a time tag, ti, is needed from which the relative time between each of the 61 samples can be determined. The time can be referenced to any common point in the integration interval (e.g. any of the start time, the center time or the end time of the integrator is okay). Location (latitude, longitude) of the center of the radiometer antenna footprint for the TA sample under test.

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 8 of 9 RFI Implementation (3) Input Static auxiliary data: Local mean running average window, WM. Values for this parameter are assigned independently in 1 degree increments of latitude and longitude and for each of the three radiometers. (units 0.01 s samples; baseline WM = 20) Local mean running average glitch threshold, TM. Values for this parameter are assigned independently in 1 degree increments of latitude and longitude and for each of the three radiometers. (units STDTA; baseline TM = 1) RFI detection glitch threshold, TD. Values for this parameter are assigned independently in 1 degree increments of latitude and longitude and for each of the three radiometers. (units STDTA; baseline TD = 4) RFI detection neighborhood window, WD. Values for this parameter are assigned independently in 1 degree increments of latitude and longitude and for each of the three radiometers. (units 0.01 s samples; baseline WD = 5) Nominal standard deviation of radiometer antenna temperature, STDTA. Values for this parameter are assigned independently for each of the three radiometers. (units Kelvins; baseline STDTA= K)

9 Mar 2010Ruf, RFI, Aquarius Algo Workshoppg 9 of 9 RFI Implementation (4) Output data For each sample of the radiometer antenna temperature that is tested for RFI: Number of TA samples in the neighborhood of the sample under test (neighborhood as defined by WM) that were flagged with RFI. The number of standard deviations that the sample under test deviated from the local mean by. TA RFI detection word: =3 if RFI detected in sample under test and in neighbors; =2 if RFI detected in sample under test only; =1 if RFI detected in neighbors only; =0 if RFI not detected in sample under test or in neighbors. CND RFI detection bit: =1 if RFI detected in neighbor; =0 if RFI not detected in neighbors.