Saharan Dust Corrections for the ENVISAT AATSR SST Product

1. Saharan Dust Corrections for the ENVISAT AATSR SST Product Xin Kong, Gary Corlett, Lizzie Noyes, John Remedios and David Llewellyn-Jones Chris Merchant and Owen Embury ENVISAT Symposium 2007

2. Contents • Background • (A)ATSR Re-analysis for Climate - (A)RC project • Saharan dust corrections on SST retrieval • Datasets & Methodology • AATSR data & SST retrieval • SEVIRI Saharan Dust Index (SDI) • Results • Empirical Comparisons • Radiative Transfer (RT) Modelling • Conclusions & Future studies

3. Background • This work is a part of (A)ATSR Re-analysis for Climate - (A)RC project (re-analysis 16 years (A)ATSR SST records). • The propose of (A)RC project is to reduce regional biases in retrieved (A)ATSR SSTs to less than 0.1K for all global oceans and to create a very homogeneous SST record • The re-analysis dataset will be extremely useful for climate change and NWP (* Dr Chris Merchant will give a talk on Section 5C1) • Saharan dust can cause biases up to 3K on SST retrieval • The aim of this work is to reduce AATSR SST biases caused by Saharan dust

4. (A)ATSR Sea Surface Temperature (SST) Retrieval • SST Retrieval N2: n11, n12 D2: n11, n12, f11, f12 N3: n3.7, n11, n12 D3: n3.7, n11, n12, f3.7, f11, f12 (*N-Nadir and D-dual view) • AATSR design target accuracy ~0.3K Error sources are cloud screening, water vapour and aerosols etc. • Our previous results show that Saharan dust caused a warm bias in the AATSR dual-view SSTs (D2&D3) and a cold bias in the nadir-view SSTs (N2&N3). Day/Night Night Only

5. SEVIRI Saharan Dust Index (SDI) • SEVIRI SDI is a dust indicator, derived from four SEVIRI thermal channels at 3.9, 8.7, 10.8 and 12μm at nighttime (Merchant et al., 2006). 3.9 μm channel is contaminated during the day. A composite technique using the most recent value over the last 24hr to estimate SDI during the day.

6. Datasets and Methodology Methodology also repeated at some small AOIs to reduce the atmospheric effects on the BTs. Datasets:AATSR and SEVIRI SDI matchups (20 at nighttime and 18 at daytime) Image Processing:extracted to AOI: Lats[0,30], longs[-50,0], cloud screened (by 1 extra pixel) and spatial averaged at 0.1 degree. • Empirical Comparisons: • AATSR Brightness Temperature (BTs) vs. SEVI SDI (∆BT/∆SDI) • AATSR BTs Difference (BTDs; e.g. n11-n37) vs. SEVI SDI (∆BTD/∆SDI) • AATSR dual minus nadir (D-N) SST vs. SEVI SDI ((∆D-N/∆SDI) Radiative Transfer Modelling(Rttov-Disort wrapper developed at UE): Input data are aerosol properties (OPAC & Haywood) and ECMWF atmospheric profile, model can predict TOA BTs for both clear-sky and aerosol conditions. Dust detection and corrections

7. Model Simulation Results – BTs vs. SDI(Haywood Aerosol Properties) Simulated AATSR ΔBT also vs. water vapour, atmospheric temperature and found that these two parameters are also contribute to ΔBT AATSR BTs depression increase with SDI. Effects are most significant in the 11 and 12μm channels than 3.7 μm channel and more in the forward views

8. Model Simulation Results – SSTs vs. SDI(Haywood Aerosol Properties) Aerosol effects are most significant N2 retrievals and least significant in D3 retrievals. With increasing SDI: N2 & N3 decrease, D3 increase, D2 decrease using Haywood and not unclear using OPAC, which is not consistent with empirical results

9. Model Simulation Results – indices vs. SDI (Haywood Aerosol Properties) The presence of dust can be detected in AATSR data, where certain BT relationships and dual-nadir SST differences correlate well with SDI

10. Model Simulation Results – indices for dust corrections * D3 – N3 applied for nighttime only

11. Empirical Results (Nighttime) over Large Areas: AATSR D-N SST vs. SEVI SDI AATSR D-N SST is correlated well with SEVI SDI for the dust events, which consistent with Noyes et al., 2006

12. di Empirical Results (Nighttime) over Large Areas - example images on 200507210000 (b) AATSR BTDs vs. SEVI SDI (a) AATSR D-N SST vs. SEVI SDI negative effect ↓ r2 =0.73 positive effect ↑ DUST EVENTS! (c) AATSR BT vs. SEVI SDI is not clear over large areas due to other effect factors (eg., water vapour and temperature), thus a further studies over some small AOIs. The perpendicular distance to the clear-sky line – di is highly correlated with SEVI SDI, r=0.94

13. Empirical and RT modelling Results at nighttime over a small area (AATSR vs. SEVIRI SDI) BTs BTDs D-N Empirical and RT modelling results are generally consistent and Haywood simulation is more close to the empirical results which is consistent with Merchant et al., 2006

14. r2 =0.77 Empirical Results (daytime) over large areas –example images on 200506181300 Daytime results are similar as nighttime results

15. SST (K) ∆SST (K) Preliminary Dust corrections – Theoretical- An example on 200507210000 over a small area AATSR D3-N3 indices for dust corrections derived from RT simulations (Haywood) are used in here SEVI SDI ATSR D-N Threshold: SDI > 0.2 Empirical instead!

16. Conclusions • Saharan dust has significant effects on the AATSR SST retrievals • Saharan dust has significant effects on the AATSR BTs and aerosol extinctions can be observed over some small areas. However, it is difficult to use single channel BTs for dust detection due to water vapour or temperature effects • Dust can be detected using AATSR D-N SSTs or AATSR BTDs (e.g., n11-n12 vs. n37-n11) • Our preliminary dust correction results suggest that Saharan dust could be corrected using the AATSR dual view or multi-channels capabilities. The regional biases on the SSTs caused by Saharan dust can be reduced • Further work will focus on improve the dust correction technique and validations. The correction technique can be transferred to ATSR-1 and ATSR-2