Comparison of Results Between the Miniature FASat-Bravo Ozone Mapping Detector (OMAD) and NASA s Total Ozone Mapping Spectrometer (TOMS)

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1 Comparison of Results Between the Miniature FASat-Bravo Ozone Mapping Detector (OMAD) and NASA s Total Ozone Mapping Spectrometer (TOMS) Juan A. Fernandez-Saldivar, Craig I. Underwood Surrey Space Centre, University of Surrey, UK 22nd Annual AIAA/USU Conference on Small Satellites August 11 14, 2008 Utah State University Logan Utah USA

2 Overview Introduction Ozone Mapping Detector (OMAD) Total Ozone Mapping Spectrometer Total Ozone Content Algorithm Backscattered UV Radiance Atmospheric Ozone Measurements Results Global total ozone Ozone depletion in Austral Srping Conclusions 2

3 Introduction Ozone Mapping Detector (OMAD) Total Ozone Mapping Spectrometer (TOMS)

4 OMAD and TOMS OMAD TOMS Channel [nm] Gain [V/A] Responsivity [A/W] Total Nominal Transmission factors Transmission at CW [%] Spectral Bandwidth [nm] E E E E

5 OMAD and TOMS OMAD Ozone Mapping Detector Micro-satellite UV radiometer 4-channel (289, 313, 334 and 380 nm) 10-nm resolution bands Ground res: 150 x 150 km. Nadir Looking only Silicon Photodiodes 2 kg mass 500 mw in operation TOMS Total Ozone Maping Spectrometer NASA s Earth Probe 6-channel radiometer: with 308, 312, 317, 325, 331 and 360 nm 1-nm resolution bands Ground res: 50 x 50 km Swath Photomultiplier 34 kg mass > 25 Watts in operation 5

6 OMAD Applications UV Reflectivity Ozone Monitoring South Hemisphere Ozone Depletion Volcanic Eruption *(Not Suitable) 6

7 Total Ozone Algorithm Backscattered Radiance Reflectance (Albedo) Algorithm

8 Reflectivity (Albedo) Scene reflectivity is determined using 360 or 380 nm channel These channels are affected by clouds, snow or ice and NOT by Ozone 8

9 Ozone Determination Ozone absorbs in UV range < 325 nm Channel ratios are used normally TOMS v.8.0: / nm OMAD v.2.0: 313 / 334 nm Require assumption on profile distribution O 3 Aerosols Reflectivity O 3 9

10 Total Ozone Algorithm Backscattered Radiance Reflectance (Albedo) Algorithm

11 Reflectivity Comparison Reflectivity is limited to <70 % to minimize errors in the empirical method Spatial correlation overall is high >90 Proof of radiometric agreement with model and cross-validation with TOMS Indicator for cloud fraction also for ice or snow 11

12 Obtaining Ozone from OMAD 1. The un-calibrated slant column amount is derived from the estimated radiance from two channels: uo3slant = Log ( L334 / L313) 2. Geometrical Air Mass Factor (GAMF) correction based on the solar zenith angle (θ): GAMF = 1 / cos ( θ ) θ 3. From (1) and (2), we obtain a representative vertical ozone content OMAD O3. OMAD 4. Real vertical column content is derived from (3) using an empirical linear function vertical Where, M zone Slope factor Intercept factor B zone O3 = uo3 zone Slant - Log(GAMF) O3 = M OMAD + B O3 12 zone

13 Empirical Parameters Daily Linear Fit are obtained between OMAD and TOMS M (slope) and B (intercept) empirical parameters accounting for profiles and geographical features 1-sigma errors of parameters are included to weight linear fits 13

14 Empirical Parameters 14

15 Results Reflectivity Total Ozone Content

16 Ozone Monitoring 16

17 Monthly Average from OMAD 17

18 Ozone Depletion Errors increase with latitude and Albedo Relative errors lower than previous version. Absolute errors consistent with typical O 3 below cloud 18

19 Ozone latitude dependence Wide range of ozone concentrations from ~ Dobson Units [DU] [1] Ozone concentration decrease for southern latitudes Errors increase for this region due to the large solar zenith angles Polar Stratospheric Clouds near the poles are normally developed, it is expected that the errors in these regions will be greater. [1] 1 DU = 10-3 atmosphere cm i.e. 100 DU = 1mm thickness of gas at standard temperature and pressure. 19

20 Relative Errors Errors are almost constant throughout the period under analysis Offset in the beginning of the depletion due to overestimation Areas with reflectivities < 70 % were analysed, errors are expected to decrease for lower reflectivities Histogram of errors is consistent over the period under analysis 20

21 Errors in Ozone Measurements Errors increase with latitude and Albedo Relative errors lower than previous version. Absolute errors consistent with typical O 3 below cloud Zones M B Absolute 1-Sigma Error in OMAD O3 [DU] Relative 1-Sigma Error in OMAD O3 [%] Reflectivity 20% 20% 50% 40% 30% 20% 50% 40% 30% 20% All Regions Average Errors % 3.67% 3.33% 2.90% 21

22 Nyamuragira Eruption Nyamuragira volcano Oct 1998 (Rep. Dem. Congo) Nadir only, long revisit time and inadequate spectral resolution preclude this application Ozone Anomaly found during the data processing Indicator of volcanic activity (release of SO 2 ) Only qualitative agreement 22

23 Conclusions Improved empirical algorithm is also valid in higher latitudes with oblique solar angles and extreme variations due to the seasonal Ozone depletion Total Ozone Content Relative errors <10% Atmospheric monitoring capabilities of microsatellites were greater than expected New potential for future instrumentation and retrieval algorithms on microsatellites Low-cost alternative to provide additional observations for atmospheric monitoring 23

24 Thank you for your attention Questions? 24

25 Reflectivity: Errors and Correlation 1-sigma error from ranges from (mean 14.5 ) Correlation > 0.9 for 90% of the period under evaluation Different pixel sizes, cloud cover and timing of overpasses Also explained by calibration errors and differences in spectral bands Aerosols are ignored for simplicity 25

26 OMAD vs TOMS The maximum ozone concentration decreases between November and December when depletion is at its minimum. Most of the scattered points are concentrated in the DU range in October as opposed to the DU in November and the DU range in early December. 26