References

1. Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS) Alexander Ignatov1 and XingMing Liang1,2 1NOAA/NESDIS/STAR, 2Colorado State University/CIRA 4. Nighttime BT and SST Biases and Double Differences (DD) 1. Introduction Double Differences (DD) BT and SST Biases MICROS is a web-based Near-Real Time tool to monitor Model (Community Radiative Transfer Model, CRTM) minus Observation (AVHRR) biases in clear-sky brightness temperatures (BT) over oceans, produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). MICROS has been in routine use since July 2008 with AVHRR data from NOAA-16, -17, -18, -19 and MetOp-A. Input to MICROS is global analysis SST (Reynolds, OSTIA) and NCEP/GFS upper air fields. V1.00 2. Objectives V1.02 V1.10 V1.00 V1.02 V1.10 • Introduce the MICROS system (Liang et al., 2009, 2010). • Test the improvements in ACSPO versions. • Demonstrate utility of MICROS to monitor satellite radiances over clear-sky ocean for stability and cross-platform consistency. • Preliminarily evaluate CRTM daytime performance using MICROS. V1.10 V1.02 V1.00 3. ACSPO versions tested in MICROS since July 2008 • ACSPO v1.00: uses CRTM r577 in conjunction with Reynolds 1° weekly SST and NCEP/GFS 1° 6hr fields to produce model BTs; Fresnel’s surface emissivity model; Ordinary CRTM coefficients. • ACSPO v1.02: uses CRTM v.1.1 in conjunction with Reynolds 0.25° daily SST; Wu-Smith (1997) emissivity model; Planck-Weighted CRTM coefficients. • ACSPO v1.10: uses improved cloud mask (Petrenko et al., 2009). Also, day/night flag switches now at solar zenith angle of 90° (v1.00/1.02: 85°) V1.00 V1.02 V1.10 MICROS: www.star.nesdis.noaa.gov/sod/sst/micros 5. CRTM daytime performance in Ch3B • Inaccurate solar reflectance model used in CRTM v1 results in unrealistic cold M-O bias ~-20 K in the sun glint areas and a smaller warm bias ~+5 K outside glint in AVHRR Ch3B band centered at 3.7 μm. • Using a physical specular reflectance model (Breon, 1993; Gordon, 1997) dramatically improves the daytime M-O biases. V1.02 V1.10 V1.00 Using daily Reynolds instead of weekly SST in ACSPO v1.02 significantly improves global STDs. Note that NOAA-16 Ch3B remains out of family. V1.00 V1.02 V1.10 Weekly SST CRTM r577 Weekly SST V1.00 V1.02 V1.10 CRTM r577 AVHRR SST CRTM r577 Figure 2. Global M-O distribution and sun glint angle dependencies for NOAA18 Ch3B on 13 Dec 2008. (top) CRTM v1; (bottom) CRTM v2. V1.02 V1.10 V1.00 AVHRR SST • Warm M-O biases are due to a combined effect of not included aerosols; using bulk SST (instead of skin); using daily mean Reynolds to represent nighttime SST; residual cloud. • Cross-platform DDs (e.g, Strow et al, 2008; Wang and Wu, 2008) in BTs and SSTs cancel out many errors (CRTM errors, uncertainties in input SST, GFS, missing aerosol, etc). • DD patterns suggest that they are mainly due to errors in sensor calibration and spectral response functions. Largest systematic errors are in N-18 Ch4 and N-19 (all bands). • A simple diurnal variability and bulk-to-skin model is needed to adjust Reynolds SST at night, to help more accurately estimate the cross-sensor biases using the DD technique. CRTM v.1.1 Using CRTM v1.1 instead of rev577 improves the satellite view zenith angle dependencies, and brings NOAA-16 Ch3B in family (Liu et al., 2009). CRTM v.1.1 6. Conclusion and Future Work • Nighttime BT and SST biases are largely consistent (except for NOAA-16). Remaining short-term variability of several tenths kelvin is likely caused by residual instabilities in Reynolds SST. • Double-differencing (DD) technique can help establishing cross-sensor calibration links and contribute to the Global Satellite-based Inter-Calibration System (GSICS). • Cross-platform nighttime DDs are large and variable for NOAA-16 due to sensor calibration issues. For other platforms, DDs are typically within several hundredths kelvin, except for NOAA-18 Ch4 and NOAA-19 where DD~0.10-0.15K. • Work is underway to reconcile the observed nighttime cross-platform DDs by improving AVHRR calibration, and incorporating a simple diurnal variability (e.g., Kennedy et al., 2007) and bulk-to-skin model (e.g. Donlon et al., 2002). • The new reflectance model was proposed based on MICROS analyses and implemented in CRTM v2. References Figure 1. Global distributions, histograms and view zenith angle dependencies of the M-O biases for 24 hours of nighttime data on 14 July 2008 for MetOp-A Ch3B. Petrenko, B., et al, 2009: 38th AMS Annual Meeting, Phoenix, AZ, JP1.12. Strow, L. , et al, 2008: EUMETSAT, 15-17 Sep 2008. (http://www.ssec.wisc.edu/hsr/meetings/2008/presentations/ Larrabee_Strow.pdf ) Wang, L. and X. Wu, 2008: GSICS Quarterly, 2, 4, 4-4. Wu, X. and W. L. Smith, 1997: Appl. Opt., 36, 2609–2618. Breon, F.M., 1993, RSE, 43, 179-192. Donlon et al, 2002: JClim, 15, 353-369. Gordon, H. R., 1997, JGR, 102, 081-017. Kennedy, J.J., P. Brohan, and F.B. Tett, 2007: GRL, 34. Liang, X.-M., A. Ignatov, and Y. Kihai, 2009a: JGR, 114. Liang, X.-M and A. Ignatov, 2009b: JGR, to be submitted. Liu, Q, et al, 2009: JTech, doi:10.1175/2009JTECHA1259.1. Acknowledgement This work is conducted under the Algorithm Working Group funded by GOES-R Program Office, NPOESS Ocean Cal/Val funded by IPO, and Polar PSDI, NDE and ORS Programs funded by NOAA. CRTM is provided by NESDIS JCSDA. Thanks go to Yury Kihai, Boris Petrenko, Feng Xu, Yong Han, Mark Liu, Yong Chen, Paul Van Delst, Fuzhong Weng, John Sapper, Changyong Cao, Likun Wang, Fred Wu and Nick Nalli for advice and help. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U.S. Government position, policy, or decision. Center for Satellite Applications and Research (STAR) Review, 09-11 March 2010 Correspondence: Alex.Ignatov@noaa.gov, Tel: 301-763-8102 x190, Fax: 301-763-8572