1. Earth Radiation Budget Observations Hai-Tien Lee Arnold Gruber University of Maryland College Park, CICS/ESSIC-NOAA Robert G. Ellingson Florida State University, Dept. of Meteorology Istvan Laszlo NOAA/NESDIS NOAA/NESDIS Cooperative Research Program 3rd Annual Science SymposiumFort Collins, CO, August 15-16, 2006

2. 2 Earth Radiation Budget Kiehl and Trenberth, 1997. Bull. Amer. Meteor. Soc., 78, 197-208.

3. 3 History First measurements of OLR as early as 1959 from Explorer-7 Experiments - ERB, ERBE, ScaRaB, CERES, GERB First routine measurements of OLR from operational satellites began in 1974 –NOAA scanning radiometer ( SR)- window channel ( 10-12 microns) –Linear algorithm between window radiances and total OLR – based on radiative calculations with model atmospheres –Evolved a few years later to a non linear algorithm which is still in use today - adjusted for different spectral interval SR data 1974-1978, AVHRR 1979-onward

4. AVHRR and HIRS OLR Algorithms and Products

5. Tropical AVHRR OLR Anomaly Time/Longitude plot 1984-2003 [5S-5N] Contour level 10Wm -2 Pastel yellow are within ±10Wm -2 El Nino

6. 6 ERBE-AVHRR Daytime OLR July 1985

7. 7 Clear-sky OLR Anomaly (Jan 1998) AVHRR OLR lacks sensitivity to water vapor variation, especially the upper tropo. humidity (UTH).

8. 8 Multi-spectral HIRS OLR Algorithm a i =regression coefficients  =local zenith angle Ellingson et al. (1989)

9. 9 Regression Model Channels and spectral intervals – stepwise regression based on 1600 Phillips soundings and radiation transfer model HIRS ChannelWavelength (μm)Atmos Sensitivity H713.1-13.6Near Sfc temp H107.8 – 8.5Lower trop water vapor H126.6-6.9Upper trop water vapor H314.3-14.7Air temp- at 100mb

10. 10 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. HIRS OLR is Operational since 1998.

11. HIRS OLR Climate Data Record

12. 12 Equator Crossing Times for NOAA Polar Orbiters

13. 13 Pingping Xie, 2006 AVHRR OLR PC3 and Satellite Observation Time  PC seems related to changes in satellite observations time

14. 14 Inter-satellite Calibration SatellitesBias (Wm -2 ) TN0.15 N061.80 N072.13 N082.03 N09Reference N100.53 N11-5.36 N12-2.42 N14-5.14 N15-3.65 N16-3.25 Collocation: 1°x1° lat/lon ±30 minutes Homogeneity filter: Std error of mean OLR < 1 Wm -2

15. 15 OLR Climatological Diurnal Model Tsaidam BasinWestern Pacific 25 Years of Monthly Mean OLR Local Time Composite

16. HIRS product is as stable as ERBS-NS. ERBS NS vs. HIRS Best-fit line slope = 0.998 STD = 0.97 Wm -2 r = 0.86 Tropical 20 NS ERBS Non-scanner and HIRS 1985-1999

17. 17 Tropical Mean OLR 1984-2003 Relative to HIRS ERBE SC (ERBS) -2.9±0.1 n=60 CERES TRMM -0.1±0.2 n=8 CERES Terra X -0.5±0.1 n=56 CERES Aqua X -1.0±0.2 n=25 ERBE NS-4.4±0.1 n=170 Tropical 20 NS HIRS OLR is a reliable and traceable Transfer Standard

18. 18 HIRS-CERES 2000-2004 STD diff Global = 2.8 Wm -2 * CERES ES4 from Terra-Xtrack Ed.2 Mean diff Global avg = 1.5 Wm -2 RMS diff Global = 4.0 Wm -2 Global RMS Diff = 4.0 Wm -2

19. Outlook

20. Synergy between Operational Polar-Orbiting and Geostationary Satellite OLR Products NOAA/MetOp/NPOESS - HIRS, IASI, CrIS, ERBS/CERES GOES-E GERB Met-8/9 Geostationary GMS FY-2C MTSATGOES-W Met-5

21. The End

22. 22 Florida Amazo n Nevada Storm track Andes Sierra Madre Occidental Yucatan Gulf of Mexico Subtropical Oceans Blue: HIRS Red: GOES GOES and GERB OLR data provide detailed diurnal variation information that we can use it to construct and examine the diurnal models. This figure shows the phase information of the OLR diurnal variation for the GOES-E full disk domain with some typical patterns at selected sites. The HIRS-based diurnal model was compared against that of the GOES, which acts as a reference for error analysis. 0 12 6 18 Nevada Validate HIRS OLR Diurnal Model using GOES Observations