SST ALGORITHMS IN ACSPO REANALYSIS OF AVHRR GAC DATA FROM 2002-2013

1. SST ALGORITHMS IN ACSPO REANALYSIS OF AVHRR GAC DATA FROM 2002-2013 B. Petrenko1,2, A. Ignatov1 , Y. Kihai1,2, X. Zhou1,3, J. Stroup1,4 1NOAA/NESDIS/STAR, 2GST, Inc., 3CIRA, 4STG, Inc. Reprocessing AVHRR GAC

2. Objectives • Following a request from the NOAA Coral Reef Watch Program, 2002-2013 AVHRR GAC data from NOAA-16, -17, -18, -19, MetOp-A and –B are being reprocessed at NESDIS with the Advanced Clear-Sky Processor for Oceans (ACSPO) • The objective is to create multiyear L2P SST data sets which would meet the following requirements: • Minimum regional SST biases • Close reproduction of true SST variations • Temporal stability • Cross-platform consistency • This presentation discusses the selection of SST algorithms for reprocessing. Reprocessing AVHRR GAC

3. Stages of reanalysis • First stage (RAN1): • AVHRR data from 2002-2013 were reprocessed with ACSPO v. 2.20 (heritage regression SST algorithm + ACSPO Clear-Sky Mask (ACSM)) • Matchups were created from clear-sky AVHRR BTs drifting and moored buoys’ SSTs from the In situ SST Quality monitor (iQuam) • SST algorithms were evaluated using these multiyear time series of matchups. • Second stage (RAN2): • Selected SST algorithms are implemented within ACSPO v.2.31 • The corresponding L2P SSTs products are regenerated with all algorithms • The L2 SST products are evaluated with MICROS and SQUAM Reprocessing AVHRR GAC

4. Explored SST algorithms • Regression • Currently, regression is the operational algorithm in ACSPO • OSI-SAF equations were shown to minimize regional SST biases and to balance precision and sensitivity (Petrenko et al., JGR, 2014) • In ACSPO versions 2.30 (operational) and 2.31 (RAN), the heritage SST equations were replaced with ones developed at EUMETSAT OSI-SAF • Incremental Regression (IncR) • RTM-based algorithm, developed for GOES-R ABI (Petrenko et al., RSE, 2011) and shown to perform at least comparably with other RTM-based algorithms (Merchant et al., RSE, 2008, 2009; Le Borgne et al., RSE, 2011) • IncR correlates SST increments (SST minus L4 SST) with BT increments (observed BTs minus simulated BTs), rather than absolute SSTs and BTs. Reprocessing AVHRR GAC

5. Regression (OSI-SAF) Day: TS = a0+ (a 1 + a 2 S θ) T11 + [a 3 + a 4 TS0 + a 5 S θ] (T11- T 12) + a6Sθ Night: TS = b0 +( b 1 + b 2 S θ) T3.7 + (b 3 + b 4S θ) (T11- T 12) + b5Sθ T11,T 12, T3.7 observed BTs S θ=1/cos(θ)θis satellite view zenith angle TS0 first guess SST (in C) a’s and b’s regression coefficients • OSI-SAF equations introduce angular dependencies of all regression coefficients Reprocessing AVHRR GAC

6. Incremental Regression • Day: TS = TS0 + c0 + α[c1ΔT11 + c2(ΔT11 - ΔT12)TS0]+ + c3Sθ+ c4θ + c5W + c6W2 + c7φ + c8φ2 • Night: TS = TS0+ d0+ α[d1 ΔT3.7 + d2ΔT12]+ + d3Sθ+ d4θ + d5W + d6φ + d7φ2 ΔT11,ΔT12, ΔT3.7 BT increments (OBS – CRTM) Wtotal water vapor content in the atmosphere φlatitude c’s and d’s “least squares” regression coefficients α>1scaling factor • IncR coefficients are derived from matchups of in situ SST increments with BT increments • Since the BT increments are small (~0.5 – 1 K) the least-squares method underestimates coefficients; scaling is required • The terms independent from BTs are needed to correct SST for biases caused by biases in BT increments Reprocessing AVHRR GAC

7. Scaling of IncR coefficients • Sensitivity μ of IncR SST to true SST is: • Day: μ=α[c1D11 + c2(D11 - D12)TS0], • Night: μ=α[d1D3.7 + d2D12] • D3.7 , D11, D12 are derivatives of BT wrt SST (from CRTM) • The histograms of μare shown for 2005 NOAA-17 matchups • The IncR with the least-squares coefficients (with α=1)has low sensitivity • Scaling brings the mean IncR sensitivity closer to 1 (on average, higher than for regression). DAY NIGHT Reprocessing AVHRR GAC

8. IncR coefficients with and without bias correction (Day) TS = TS0 + c0 + c1ΔT11 + c2(ΔT11 - ΔT12)TS0+ c3S θ+ c4θ +c5W+c6W2+ c7φ + c8φ2 • S θ, θ, W and φ arestatistically independent from BT increments. • Inclusion of the BC terms insignificantly changes coefficients at BT increments Reprocessing AVHRR GAC

9. The effect of bias correction terms in IncR (day) TS = TS0 + c0 + c1ΔT11 + c2(ΔT11 - ΔT12)TS0+ c3S θ+ c4θ +c5W+c6W2+ c7φ + c8φ2 • Statistics are presented for 2005 NOAA-17 matchups • IncR without BC produces unacceptably large biases • IncR with BC minimizes SST biases and SD, but does not change the sensitivity 5/6/2014 5/6/2014 Reprocessing AVHRR GAC Reprocessing AVHRR GAC Reprocessing AVHRR GAC 9 9

10. Using variable coefficients • The operational regression coefficients are calculated once and used for a long period of time. • Global biases in operational SSTs may change due to calibration trends in AVHRR BTs. • In the reprocessing, this problem is addressed by recalculation of regression and IncR coefficients on a daily basis using matchups within 3 months running window (current day ±45 days), • Here, we compare three SST algorithms: • Regression with constant coefficients (RCC) - a benchmark to explore the effect of variable coefficients • Regression with variable coefficients (RVC) • Incremental regression with variable coefficients (IncR) Reprocessing AVHRR GAC

11. NOAA-17: Time series of global bias, SD and mean sensitivity (Day) Bias wrt in situ SST Bias wrt CMC Mean sensitivity SD wrt in situ SST SD wrt CMC CMC is L4 SST by Canadian Meteorological Centre • RVC and IncR biases wrt in situ SST reduced from ~0.2 K to < 0.05 K • SD wrt in situ SST is smaller for IncR SST than for RCC and RVC • SD wrt CMC is greater for IncR than for regression algorithms • Mean sensitivity for IncR is ~.95, and <0.9 for regression algorithms Reprocessing AVHRR GAC

12. NOAA-17: Time series of nighttime bias, SD and mean sensitivity (Night) Bias wrt in situ SST Bias wrt CMC Mean sensitivity SD wrt in situ SST SD wrt CMC • Same features as for day, but with lesser difference between RVC and IncR • For night, RVC and IncR statistics are very close • Mean sensitivity is ~ 1 for IncR and ~0.98 for RCC and RVC Reprocessing AVHRR GAC

13. NOAA-17: Mean annual SST biases as functions of VZA, TPW and LAT (Day) RCC RVC IncR • Dependencies are shown for 7 full years of NOAA-17 operation • RCC biases are most variable and non-uniform • RVC biases are less variable but still non-uniform • IncR biases are most uniform and least variable Reprocessing AVHRR GAC

14. NOAA-17: Mean annual SST biases as functions of VZA, TPW and LAT (Night) RCC RVC IncR • RCC biases are non-uniform and most variable • RVC biases are less variable but still non-uniform • IncR biases are most uniform and least variable • The nighttime difference between RVC and IncR is smaller than for day Reprocessing AVHRR GAC

15. NOAA-17, -18 and MetOp-A: Multiyear mean biases as functions of VZA, TPW and LAT (Day) RCC RVC IncR • The biases are averaged over the following years: • 2003-2009 for NOAA-17 • 2006-2013 for NOAA-18 • 2007-2013 for MetOp-A • RCC biases are most variable between the satellites • RVC biases are less variable between the satellites but relatively non-uniform • IncR biases are most uniform, for all three satellites Reprocessing AVHRR GAC

16. NOAA-17, -18 and MetOp-A: Multiyear mean biases as functions of VZA, TPW and LAT (Night) RCC RVC IncR • The biases are averaged over the following years: • 2003-2009 for NOAA-17 • 2006-2013 for NOAA-18 • 2007-2013 for MetOp-A • RCC biases are most variable between the satellites • RVC biases are less variable between the satellites but relatively non-uniform • IncR biases are most uniform, for all three satellites Reprocessing AVHRR GAC

17. NOAA-17: Median annual regional biases for 2005 BA=median(| TS - Tsinsitu|), within 10⁰× 10⁰ lat/lon box RCC, DAY RCC, NIGHT RVC, DAY RVC, NIGHT IncR, DAY IncR, NIGHT • The IncR biases appear to be most uniform, both for day and night • Saharan dust is the problem for all algorithms Reprocessing AVHRR GAC

18. NOAA-17, NOAA-18, MetOp-A: Multiyear median regional biases (Day) BM=median(BA), within each 10⁰× 10⁰ lat/lon box • Regional biases are least uniform for RCC and most uniform for IncR Reprocessing AVHRR GAC

19. NOAA-17, NOAA-18, MetOp-A: Multiyear median regional biases (Night) BIA=median(BIA), within each 10⁰× 10⁰ lat/lon box • Regional biases are least uniform for RCC and most uniform for IncR • The difference between RVC and IncR biases is smaller than for day Reprocessing AVHRR GAC

20. Spatial variability of interannual biases V1=median(|BIA-median(BIA)|), BIAis a set of interannual biases within all 10⁰× 10⁰ lat/lon boxes • For day, variability of regional biases is highest for RCC and the lowest for IncR • For night, variability of regional biases for RVC and IncR is close. Reprocessing AVHRR GAC

21. Multiyear median variability of annual biases V2=median(VIA), VIA=median(|BA-BIA|) VIAis multiyear variability of annual biases in each 10⁰× 10⁰ lat/lon box • Regional biases are most variable for RCC and least variable for IncR, both for day and night Reprocessing AVHRR GAC

22. Summary and future work • The Regression and Incremental Regression SST algorithms were explored using NOAA-17, -18 and MetOp-A matchups collected during RAN1 • Recalculation of coefficients on a daily basis has reduced variations in global and regional SST biases for both types of algorithms • The Incremental regression made the dependencies of SST biases from such variables as VZA, TPW and latitude more uniform and reduced regional SST biases compared with conventional regression with variable coefficients • At the second stage of reprocessing (RAN2), the multiyear L2 datasets will be produced with regression and IncR algorithms and evaluated with SQUAM and MICROS • The set of metrics used to evaluate SST products will be further elaborated. In particular, the assessment methodology proposed in GHRSST Climate Data Assessment Framework (Merchant et al., 2013) will be explored • We also plan to explore possible improvements in the AVHRR calibration algorithms. This will further improve temporal and regional stability of SST biases, especially, for such platforms as NOAA-15, -16 (since mid-2004) and NOAA-18 (since mid-2011). Reprocessing AVHRR GAC