Inter-satellite Calibration of HIRS OLR Time Series Hai-Tien Lee University of Maryland, CICS/ESSIC-NOAA Arnold Gruber Robert G. Ellingson Florida State University, Dept. of Meteorology Istvan Laszlo NOAA/NESDIS NOAA/NESDIS Cooperative Research Program 2nd Annual Science Symposium Madison, WI. July 13-14, 2005

Outline Cal/Val Issues for the OLR Climate Data Record (CDR) Inter-satellite Calibration of HIRS OLR Time Series Findings and Discussion

Cal/Val Issues for the OLR Climate Data Record What is CDR? “A time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change.” – Committee on Climate Data Records from NOAA Operational Satellites, NRC/NAS, 2004.

Satellite OLR Products

Equator Crossing Time for NOAA Polar Orbiters

Typical Satellite Track Crossing for a Morning and an Afternoon POES PM

Inter-satellite calibration for HIRS OLR Collocation: • 1°x1° lat/lon • ±30 minutes • n > 1 Homogeneity filter: • Std error of mean OLR < 1 Wm-2 Satellites Bias (Wm-2) TN 0.15 N06 1.80 N07 2.13 N08 2.03 N09 Reference N10 0.53 N11 -5.36 N12 -2.42 N14 -5.14 N15 -3.65 N16 -3.25 HIRS OLR retrievals were collocated for each of the overlapping pairs of POES over a target of 1°x1° within plus/minus 30 minutes. Minimum number of retrievals is 2. The black curve histogram shows the distribution of the OLR differences. This example is for TIROS-N and NOAA6. (we label TIROS-N as N05) A homogeneity filter was applied to improve the data quality - only mean OLR with a standard error of less than 1 Wm-2 is used. The red dashed curve histogram shows the distribution of OLR differences from homogeneous scenes, with corresponding scatter plot shown at the bottom. There are two approaches for the calibration. Method 1 determines mean biases referenced to NOAA9. Method 2 determines linear adjustments also referenced to NOAA9. Table on the right shows method 1 results.

Improvement with inter-satellite calibration The blended HIRS monthly mean OLR data with the adjustments determined by inter-satellite calibration agrees much better with the CERES. This is the time series of monthly mean OLR averaged over the tropics (20S-20N). The brown curves are the HIRS OLR monthly mean derived from individual satellites, including NOAA11, 12, 14, 15 and 16, for various periods. The differences in sampling time and the systematic biases caused the deviation within these time series. The black solid (dotted) line is the blended product of the HIRS OLR that the individual satellite retrievals were first adjusted according to the inter-satellite calibration method 1 (2) results and then temporally integrated. CERES OLR is from broadband measurements that is considered here as the “ground truth” reference. Apparently, the blended HIRS OLR with the inter-satellite adjustment agrees much better with the CERES than any of the individual ones. Tropical Mean Magenta - CERES (TRMM, Terra, Aqua) Black solid/dotted - HIRS, blended with calibration method 1/2 Brown - HIRS from individual satellites: NOAA11, 12, 14, 15, 16

Global Mean Magenta - CERES (TRMM, Terra, Aqua) Black solid/dotted - HIRS, blended with calibration method 1/2 Brown - HIRS from individual satellites: NOAA11, 12, 14, 15, 16

Instrument Continuity & Algorithm Consistency N10-N09 N11-N10 HIRS/2 vs. HIRS/2I HIRS/2 N14-N11 N15-N14 HIRS/2I vs. HIRS/3 HIRS/2I

A B C D N05 N06 Collocation occurs in the rhombus ABCD: AB & CD: ZA5 > ZA6 BC & AD: ZA6 > ZA5 N06 > N05 N06 < N05

Summary The HIRS OLR inter-satellite calibration method worked very well. It significantly reduced the discontinuity in the OLR time series that were introduced by various sources of inconsistency and errors. Identified inconsistent OLR retrievals caused by the changes in HIRS instruments and OLR algorithm. Found some large OLR differences that might point to the limb correction and/or modeling for extreme conditions. Call for revision of the Operational algorithm. This procedure may apply to the inter-satellite calibration of other thematic CDR products.

Backup Slide

Multi-spectral HIRS OLR Algorithm =local zenith angle ai=regression coefficients (Ellingson et al., 1989)

Validation of Multi-spectral OLR Algorithms Ellingson et al., 1994: Validation of a technique for estimating outgoing longwave radiation from HIRS radiance observations J. Atmos. Ocean. Technol., 11, 357-365. Ba et al., 2003: Validation of a technique for estimating OLR with the GOES sounder. J. Atmos. Ocean. Technol., 20, 79–89.

Channel selections NOAA16 HIRS/3 GOES8 Sounder GOES8 Imager GOES-R ABI OLR Spectrum Mid-lat Summer

Earth Radiation Budget Longwave Radiation Products Outgoing Longwave Radiation at the top of the atmosphere Atmospheric Layer Heating Rate Downward Longwave Radiation at the earth’s surface Kiehl and Trenberth, 1997. Bull. Amer. Meteor. Soc., 78, 197-208.