1. New Australian SST Products from the Australian Bureau Of Meteorology and the Integrated Marine Observing System 1 Centre for Australian Weather and Climate Research 2 Australian Bureau of Meteorology, Melbourne George Paltoglou 1,2, Leon Majewski 2 and Helen Beggs 1,2

2. Introduction As part of its commitment to IMOS and GHRSST, the Australian Bureau of Meteorology has implemented a new real time HRPT AVHRR Sea Surface Temperature system for the Australian region. This new processing system implements updated methodologies in providing high resolution (to 1km), low error SSTs in near RT (  ~0.25ºC night-time, ≤0.4ºC daytime; bias  0.1ºC). Near Real Time processing (<3hrs) of L2P, L3U and L3C GDS V2.0 Reprocessing of archived data back to 1992

3. 6 Mainland Stations, 2 Antarctic Mainland archive to 1992; Antarctic to 1997 – not realtime Purely empirical, regression based processing, minimal use of NWP (winds and yesterday’s analysis SST). How crass! Cloud mask is a modified CLAVR-1 SSTs computed using modified transforms and regression coefficients based on regional drifting buoy SSES for each proximity confidence value calculated using +/-30 days of matches, collocated, and ± 2 hours Locations of X/L band stations BoM HRPT AVHRR Processing Overview

4. Typical stitched swath coverage

5. Improvements to the SST Processing Modified and tuned CLAVR-1 cloud clearing. Leads to improved spatial coverage and lower SST errors BT -> SST transforms modified to include latitude and higher order cross terms. –Day-time 2 channel ‘non linear’ (BT4 and BT5) § –Night-time 3 channel linear (BT3b, BT4 and BT5) § –Stable, noise resistant, coefficients derived from SVD solution Calibration with regionally based coefficients based on local in situ matchups with drifting buoys Validation with new high accuracy ship SST sensors Full swath utilized, not just low satellite view angles – now full 70° view angle..

6. Cloud Mask Based on original CLAVR from Stowe et. al. 1999 :- Modifications include :- Loosening the FMFT test threshold: (BT4-BT5) vs BT4 Removing the CIRT threshold: (BT3-BT5)/BT5 vs BT4 ULST essentially ineffective: (BT3-BT5) vs BT4 Fine tuning the spatial Uniformity Thresholds (pass ocean fronts): Gross Cloud Test = funct(latitude) but not season or climatology All thresholds done by eye (‘People who use “Chi By Eye” get what they deserve’, Numerical Recipes, Press et. al.) Additions :- Adding a night-time ‘2-colour’ TIR test – FMFT, ULST, CIRT not efficient Defining a day-time Normalised Reflectance ‘Excess’ = (NR1-baseline) *See these and other refs for advanced cloud clearing - Petrenko et al. 2010 American Meteorological Society; pp1609:1623 Merchant et al 2008 ; J of Atmospheric and Oceanic Technology Dybbroe et al; 2003; Americal Meteorological Society, pp39-54; pp55-71 Vermury et al, 2001; J of Atmospheric and Oceanic Technology; pp169-186

7. Night-time Two Colour Threshold (BT3-BT5) vs (BT4-BT5)

8. At low SST, even a saturated atmosphere has a low TCWV. Consequently, clear pixels cluster near (0,0) At high SST, TCWV can vary significantly. Consequently clear pixels populate along the water vapour line Night-time Two Colour Threshold

9. 2-Colour Threshold. Chi by Eye after examining 60 swaths over different seasons Atmospheric H 2 O Line

10. Day-time reflectance channel excess threshold Define NR1 excess

12. BT -> SST Transformation Typically § : – Daytime 2 channel 2 nd order transform something like - SST = a 0 + a 1 BT4 + a 2 (BT4-BT5) + a 3.T guess.(BT4-BT5) + a 4.S.(BT4-BT5) Or, alternatively SST = a 0 + a 1 BT4 + a 2 (BT4-BT5) + a 3.BT4.(BT4-BT5) + a 4.S.(BT4-BT5) + a 5.S.BT4.(BT4-BT5) + a 5.S.(BT4-BT5)^ 2 Nighttime 3 channel linear transform something like – SST = a 0 + a 1 BT3 + a 2 BT4 + a 3 BT5 + a 4 (BT3-BT5)S ‘airmass’ term: S = 1/Cos(SatZ) – 1 Everyone seems to have their own favourite functional form. But we were not happy with this. § eg. Barton 1995; Journal of Geophysical Research ; V100; pp8777-8790 McClain et al 1985; J of Geophsical Research ; 90; pp11783-11798 Kubota 1994; Journal of Oceanography; V50; pp31-41 Eastwood; 2002-2008; several publications in the Norwegian Met Society And many others

13. Latitude Bias for high view angle data – occurs for N17, N18 and N19

14. BT -> SST Transformation Daytime So, we require a correction factor that is a function of latitude and is only effective at large satellite view angles airmass term A = Cos(lat)/Cos(SatZ) Daytime transform is now SST = a 0 + a 1 BT4 + a 2 (BT4-BT5) + a 3.T guess.(BT4-BT5) + a 4.A.(BT4-BT5) + a 5.A OR SST = a 0 + a 1 BT4 + a 2 (BT4-BT5) + a 3.BT4.(BT4-BT5) + a 4.A.(BT4-BT5) + a 5.(BT4-BT5) 2 + a 6.A + a 7.A 2

15. Diminished latitude bias

17. BT -> SST Transformation Nighttime :- For Night-time we found no statistically significant improvement, so we used the standard 3 channel linear transform with S, but heavily weighting high latitude and high view angle data. No improvement? Why is this so? Once we settled on the transform equation we used SVD with heavy zeroing of the weighting matrix to ensure we were not fitting to the noise NB 1) Matchups restricted to wind speed > 6m/s daytime; > 2m/s night time. 2) Calibrated to in situ at 20-30cm depth, converted to SSTSkin with a constant ‘cool skin’ offset of -0.17K

20. What’s my bias?: +/- 1 month window

23. Quality Level = 5Quality Level = 4Quality Level = 3 BiasStdDevBiasStdDevBiasStdDev NOAA 17 Day (to April 2010) 0.060.35-0.030.45-0.020.46 NOAA 17 Night (to April 2010) 0.020.240.010.29-0.020.40 NOAA 18 Day0.040.35-0.040.460.010.47 NOAA 18 Night0.020.270.010.300.020.46 NOAA 19 Day0.020.340.030.430.000.54 NOAA 19 Night0.010.26-0.040.37-0.030.48 Matchup Stats: AVHRR SST – in situ SST Daytime: ≤ 0.1% matchups with  SST (AVHRR - in situ) < -3°C Nighttime: ≤ 0.03% matchups with  SST (AVHRR - in situ) < -3°C

25. Examples of L3S multi sensor composite products - Night-time 72 hr composite – NOAA 17, 18 & 19 January 1-3 2010

26. Bureau Legacy composite off the WA coast

27. New 3 day L3S composite N17, N18 & N19

28. Standard deviation in an L3S composite-N17 N18 & N19 9 swaths; 72 hours; NO bias correction; grid cells 0.02  x0.02  ; up to 18 swath pixels contribute to a grid cell

29. Standard deviation in an L3S composite-N17 N18 & N19 High temporal variability around Ocean Fronts Only 1 pixel contributes so we set  = SSES = 0.4 In areas of thermal stability we have a formal Gaussian  ~0.25

30. Comparing the Bureau’s Legacy and New AVHRR Mosaics Legacy 0.01  AVHRR Mosaic L3S SSTsubskin NOAA-17 & 18 for 28 Mar - 11 Apr 2010

31. Compared with our new composite, 0.02  AVHRR L3S SSTskin from NOAA-17, 18 and 19 for 7-11 Apr 2010 Comparing the Bureau’s Legacy and New AVHRR Mosaics

32. L3S Composite NOAA 17, 18 & 19, NSW-Qld coast Jan 7-9 2010Sept 1-3 2009 Nemo,found

33. Locally Received AVHRR SSTskin GHRSST L2P/L3C products from IMOS Nighttime NOAA-18 AVHRR SSTskin L3C Goal – achieved! Significantly improve accuracy and useability of 1 km HRPT AVHRR SST products over Australian region Accuracy at quality level = 5: 0.23 – 0.27  C cf drifting buoys (night) 0.34 – 0.37  C cf drifting buoys (day) Improved cloud detection, day-time and night-time SST algorithms Regional not global buoys for calibration 10 April 2009 Now Available Real-time and reprocessed 1 km HRPT AVHRR SSTskin L2P files from NOAA-17, 18 and 19 available from ftp://aodaac2-cbr.act.csiro.au/imos

34. Locally Received AVHRR SSTskin GHRSST L2P/L3C products from IMOS 10 April 2009 By June 2011 >15 years of 1 km AVHRR SST L2P and 0.02  x 0.02  L3U and L3C files over Australian region back to 1996 available via IMOS Ocean Portal and GHRSST Daytime HRPT AVHRR SSTskin 0.01° L3C

35. GHRSST Products from IMOS http://imos.org.au/srs_data.html Now: RT and reprocessed (to Jan 2010) HRPT AVHRR SSTskin L2P, L3U and L3C via FTP server Jun 2011: Reprocessed HRPT AVHRR SSTskin L2P and L3C back to 2000 (and shortly thereafter to 1992) Jun 2013: Antarctic (Casey and Davis) HRPT AVHRR SST data included in IMOS L2P and L3C products with improved calibration and cloud/ice masking over Southern Ocean

36. New SST products with quality level of 5 (best) exhibit roughly half the RMS error of the Bureau’s pre-existing HRPT AVHRR level 2 SST data from NOAA-17 and NOAA-18 satellites For Australian region : s = 0.25°C (night), 0.35°C (day) Achieved by –Implementing new cloud clearing algorithms –Implementing new and improved BT->SST transforms – New daytime terms include latitude and higher order –Using QC’d regional not global buoy obs for regression –available through ftp://aodaac2-cbr.act.csiro.au/imos/, http://www.marine.csiro.au/remotesensing/imos/aggregator.html and the IMOS Ocean Portal at http://imos.aodn.org.au/webportal/ Summary of new IMOS AVHRR SST

37. Dr George Paltoglou, Ocean Observations, Assessment and Prediction Programme, Centre for Australian Weather and Climate Research, Bureau of Meteorology, 700 Collins St, Melbourne. Email: g.paltoglou@bom.gov.au 03 9669 4824

38. EXTRA SLIDES

43. Nation-wide collaborative program designed to observe the oceans around Australia 27 Australian institutions mid-2007 to mid-2013 $AUS107M to install infrastructure for marine research All IMOS data publicly available See http://www.imos.org.au

44. BoM HRPT AVHRR Processing Overview Data received at 8 reception stations and sent to BoM (Melbourne) as raw data Reception stations operated in conjuction with a number of partners - Western Australian Satellite Technology and Applications Consortium, Australian Institute of Marine Science, and CSIRO Overlapping raw data is stitched using CSIRO code (Edward King) Locations of X/L band stations

46. L3 Composite NOAA 17,18 & 19 Sept 1-3 2009

47. Close-up of the WA coastline Leeuwin Current Jan 7-9 2010 Sept 1-3 2009

48. 48 New composite AVHRR L2P SST for 11 Apr 2010 Composite, 0.02  AVHRR L3S SSTskin from NOAA-17, 18 and 19 for 9-11 Apr 2010 (QL=5) Composite, 0.01  AVHRR Mosaic L3S SSTblend from NOAA-17 and 18 for 28 Mar - 11 Apr 2010 CSIRO 0.04  AVHRR L3S SSTblend from NOAA- 17 and 18 for 9-11 Apr 2010 Composite, 0.02  AVHRR L3S SSTskin from NOAA-17, 18 and 19 for 9-11 Apr 2010 (QL≥3)

50. L3S 9 swaths, N17, N18, N19

51. Formally derived Std Dev of contributing pixels (not SSES!)

52. Which depth SST? skin, sub-skin, “blend” or “foundation” Infrared SST measurements Microwave SST measurements Diurnal warming model Skin-subskin model Analysed SST product (light winds) (light winds + strong solar insolation)

53. IMOS 1 km AVHRR SSTfnd – In Situ SSTfnd 1 June 2008 – 23 May 2010 60  E – 190  E, 20  N – 70  S Nighttime,  2 hour/  1 km matchups, Quality level = 5 NOAA-17 NOAA-18 NOAA-19 Hull-contact SST sensors IMOS SST ships

54. IMOS 1 km AVHRR SSTfnd – In Situ SSTfnd 1 June 2008 – 23 May 2010 60  E – 190  E, 20  N – 70  S Nighttime,  2 hour/  1 km matchups, Quality level = 5 NOAA-17 NOAA-18 NOAA-19 Hull-contact SST sensors IMOS SST ships

55. \\oeb-sat-dev0.bom.gov.au\gpaltog\sst_19a-7.png

56. L3 Composite NOAA 17, 18 & 19 Sept 1-3 2009

57. 57 Different SST level files using AVHRR L2P SST for 11 Apr 2010 Composite, 0.02  AVHRR L3S SSTskin from NOAA-17, 18 and 19 (quality level 5) Composite, 0.02  AVHRR L3S SSTskin from NOAA- 17, 18 and 19 for 7-11 Apr 2010 (QL 5) RAMSSA 0.083  L4 SSTfnd OI Analysis OceanMAPS 0.1  OGCM SST5m Analysis

58. Which depth SST? skin, sub-skin, “blend” or “foundation” (light winds) (light winds + strong solar insolation)

59. International program: 2000 – present Set standards for satellite SST processing and formats ( CF-compliant netCDF) Share satellite SST level 2 ( “L2P” ) and level 3 (“L3U” and “L3C”) data products –For each pixel: Time, lat, lon, SST(depth), error estimates (bias, S.D.), quality level, wind speed, sea- ice fraction, land/ice/water flag, difference from SST climatology, etc Share global and regional SST analysis products (“L4”) See web page at: http://www.ghrsst.org

60. What’s the motivation? Climatology requires ever more precise and accurate data Need for well defined error statistics Calibration of some AVHRR SST sensors can be improved, particularly over Southern Ocean South of 45°S large bias separation between different satellite SST data streams Lack of buoy SST and ship-borne radiometer SST, in particular at high latitudes

61. AHVRR Spectral Response Curves 2 near infrared channels (0.7 & 0.9  m )– used for daytime obs only 1 infrared channel (3.7  m) - used night time only 2 thermal infrared channels (11 & 12  m) –day and night time

62. Water Vapour and CO 2 absorption

63. Lets have a look at the (AVHRR SST – in situ SST) matchups as a function of latitude

64. AVHRR- in situ as a function of satellite view angle – Note the poor fit at the edge of the swath! AVHRR- in situ as a function of latitude

65. Looking for that holiday home? Where are the most salutary waters? Sept 1-3 2009Jan 7-9 2010

66. AVHRR- in situ as a function of satellite view angle – This time done correctly! AVHRR- in situ as a function of latitude

67. Levels of SST products SST ProductsDescription L2P Geolocated SST from individual sensors over a single swath– irregular gird L3U, L3C, L3S Composite, gridded, SST products (no interpolation) – regular 0.02° grid L4 Gridded, gap-free, SST analyses created by statistically interpolating multiple L2/L3 products Ocean GCM SST Dynamically consistent SST outputs from ocean global circulation models that assimilate L2, L3 or L4 products

68. GHRSST – L2P Files For each IR satellite sensor: In situ SST used to create Matchup Database Quality level is assigned to each pixel based on multivariate table of distance to cloud, satellite zenith angle and day/night MDB used to compute SSES (bias and standard deviation) for each quality level for a time window (eg.  30 days) SSES bias and standard deviations are assigned to each pixel depending on that pixel’s quality level Quality Level‘Scientific’ Descriptor 5Excellent 4Good 3Satisfactory 2Poor 1Cloud

69. Lake Woods, NT Lake Argyle, WA L3 Composite Sept 1-3 2009