5. ACSPO Cloud Mask

1. Towards Improved ACSPO SST Imagery Marouan Bouali1,2 and Alexander Ignatov1 1NOAA/NESDIS/STAR; 2Colorado State University-CIRA; 5. Destriping Algorithm Performance 4. SST Image Quality • 1. Introduction • Detector radiometric calibration errors in level 1B clear-sky Top-Of-Atmosphere (TOA) radiances lead to pronounced striping in level 2 SST products. This affects: • Quality of SST imagery • Accuracy of SST retrieval at pixel level • Spatial consistency of cloud mask • Detection of ocean submesoscale thermal fronts • As a result, reduction of stripe noise in clear-sky brightness temperatures (BTs) is required to improve the usefulness of native resolution SST fields (≤1km) from whiskbroom scanners (MODIS and VIIRS). • Three days of global data was used to evaluate the temporal stability of the algorithm • A scene-based metric, the Normalized Improvement Factor (NIF) was computed for each ACSPO SST granule • Results indicate systematic positive improvement in SST imagery from +5 to +25% Suomi NPP, January 20, 2013, 11:30, Mediterranean Sea SST from original SDRs SST from destriped SDRs • 2. Adaptive Destriping Algorithm • We consider the following image degradation model: • where and are the true and observed images respectively and is the stripe noise. • We exploit the anisotropy of striping within an iterative quadratic unidirectional variational decomposition scheme as: • The observed image can be decomposed as: • with stripe noise isolated in • The low-frequency signal remaining in can be retrieved using a nonlocal filter YNF that constrains reconstruction errors within pre-launch NEdT • The destriped image is then obtained with: Fig. 1. Impact of BT destriping on ACSPO VIIRS SST imagery (0.75 km) 5. ACSPO Cloud Mask Suomi NPP, January 31, 2013, 06:00, Indian Ocean Cloud mask from destriped SDRs Cloud mask from standard SDRs Fig. 3. The Normalized Improvement Factor (NIF) was computed for each ACSPO SST granule for a three day period (January 20-22, 2013) to evaluate the improvement of ACSPO SST image quality resulting from BT destriping. • 6. Summary and Future Work • On board calibration of individual detectors is expected to keep artifacts in clear-sky BTs within specified requirements. However, it does not fully mitigate striping. • Adaptive destriping with advanced image processing techniques leads to improved quality of level 1 BTs, derived level 2 SST imagery, and downstream SST applications such as detection of ocean thermal fronts • In future work, SST coefficients will be derived from destriped BTs to determine potential impact on SST global statistics. • 3. Data and Processing • Terra and Aqua MODIS level 1B, B20 (3.7 µm), B31 (11 µm), B32 (12 µm) • S-NPP VIIRS, M12 (3.7 µm), M15 (11 µm), M16 (12 µm) • TOA calibrated radiances converted to brightness temperatures (BTs) • Destriping algorithm applied to BT in each band before ACSPO processing • Output of ACSPO with original and pre-processed BTs is then compared • References • Bouali, 2010, A simple and robust destriping algorithm for imaging spectrometers: application to MODIS data, ASPRS annual conference, San Diego, CA, 2010 • Bouali & Ladjal, 2010: A variational approach for the destriping of MODIS data, IGARSS, Hawaii, HI, 2010, 2194-2197 • Bouali & Ladjal, 2011; Toward Optimal Destriping of MODIS Data using a Unidirectional Variational Model, IEEE TGRS, 2011, 49 (8), 2924-2935 • Bouali & Ignatov, 2013: Adaptive reduction of striping for improved SST imagery from the S-NPP VIIRS , JTech, (under review) • Bouali & Ignatov, 2013: Destriping of MODIS/VIIRS SST imagery, Météo France Workshop ‘ SST from polar orbiters’, CMS Météo France, Lannion, March 5-7, 2013 Fig. 2. Impact of BT destriping on ACSPO VIIRS cloud mask. Note the improved spatial consistency of the cloud mask from destriping of BTs Acknowledgment This work was conducted under JPSS SST Project funded by the JPSS Program Office. We thank John Sapper, John Stroup, Boris Petrenko, Yury Kihai, Korak Saha, Prasanjit Dash and Xingming Liang for helpful discussions. The views and findings are those of the authors and should not be construed as an official NOAA or US Government position, policy, or decision. NOAA Satellite Conference, 08-12 April 2013, College Park, MD.marouan.bouali@noaa.gov