Genera&on of the Daily OLR Climate Data Record
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1 Genera&on of the Daily OLR Climate Data Record Hai- Tien Lee 1, Carl J. Schreck 2,3, Kenneth R. Knapp 3 1 University of Maryland, College Park, Maryland, USA 2 North Carolina State University, Raleigh, North Carolina, USA 3 NOAA/NESDIS/NCDC, Asheville, North Carolina, USA EUMETSAT 2014 Meteorological Satellite Conference, Geneva, Switzerland, Sept 22-26, 2014
2 Outline Overview of NOAA Daily OLR CDR OLR Es6ma6on Methods LEO- Geo Blending Process Valida6ons/Evalua6ons Results Summary
3 Product Descrip&on DAILY OLR CLIMATE DATA RECORD: Daily mean OLR 1 x1 equal- angle grid in global coverage Jan 1, 1979 present Updated daily (two- day lag) OBSERVATIONAL DATA INPUT: HIRS Level- 1b data Imagers observa6ons via Gridsat CDR and NESDIS Geosta6onary Surface and Insola6on (GSIP) products APPLICATIONS Climate variability (Essen6al Climate Variable) Earth radia6on budget studies Numerical model verifica6on Precipita6on es6mate MJO and Monsoon diagnos6cs/forecast Tropical expansion studies MANAGEMENT AND SPONSORSHIP NOAA CDR Program at NCDC 3
4 OLR Es&ma&on Method 4
5 HIRS Observa&ons NOAA- 12 HIRS/2: N5-10, 12 HIRS/3: N15-17 HIRS/4: N18-21 Robbed Lower Tropospheric Water Vapor Channel 10 NOAA- 14 HIRS/2i: N11,14 New Ch10 Shi>ed Ch12 HIRS on- board NOAA TIROS- N Series and Eumetsat MetOp A/B (since late 1978) NOAA- 15 5
6 HIRS OLR Es&ma&on Algorithm Ellingson et al. (1989) Problems in v2.2 Regression Models V2.2 (old) OLR = a 0 (") + $ a i (")# N i (") HIRS- 2: Channels: 3, 7, 10, 12 HIRS- 2I/3/4: Channels: 3, 10, 11, 12 i OLR Biases N10 N09 N11 N09 La&tude V2.7 Improves Retrieval Consistency V2.2 V2.7 V2.7 (new) HIRS- 2/2I/3/4: Predictors: 3, 7, 8, 11, 8 2, , N11 vs. N09 N11 vs. N09 6
7 Gridsat Imager OLR Algorithm Gridsat CDR Product (v02r01) (Knapp et al, 2011) provides cross- calibrated brightness temperatures for the atmospheric window and ~6.7 µm water vapor channels, with limb correcqon, for (CalibraQon reference is NOAA- 14 HIRS channels 8 and 12). [Refer hdp:// Adapted from Wark et al (1962) [cf. AVHRR OLR algorithm] OLR =! T f4 T f4 = (a0 + a1twin ) "Twin + (b0 + b1twv ) "Twv Knapp et al,
8 LEO- Geo Blending Process RadiaPve NormalizaPon and Temporal IntegraPon Schemes with the Grid- based 7- day Boxcar 8
9 7- Day Boxcar (1995 day 180) HIRS OLR (Red asterisk) Gridsat OLR (Black asterisk) Normalized Gridsat OLR (Blue diamond) Temporal integraqon (Red curve) NOAA- 12/14
10 Inter- comparisons Daily OLR CDR v01r02 vs. CERES EBAF Ed2.6r & ERBS WFOV Ed3r1 (in monthly means) 10
11 OLR Anomalies (Mar2000- Jun2012) Global Tropical [20S- 20N] Daily OLR CDR v01r02 (Blue) EBAF Ed2.6r (Red) Daily OLR CDR minus EBAF (Green) Slope of OLR anomalies differences: Global: 0.03 ± 0.09 Wm - 2 /decade with 2- sigma Tropical: 0.28 ± 0.10 Wm - 2 /decade with 2- sigma 11
12 Tropical OLR Anomalies ( ) Daily OLR CDR v01r02 (Blue) ERBS WFOV Ed3r1 (Red) Daily OLR CDR minus ERBS (Green) divergent in mid 1990 s? Slope of OLR anomalies diff = ± 0.24 Wm - 2 /decade with 2- sigma
13 Inter- comparisons Daily OLR CDR v01r02 vs. CERES SYN1deg Ed3A (in daily means) 13
14 STD of Global Daily OLR Differences Time series shock due to CERES sampling change when Aqua entered in July Processing bug near the beginning of each month (red points) in SYN1deg Ed3A. 14
15 MJO & Tropical Waves 15
16 ESRL AVHRR OLR Wheeler- Kiladis Space- Time Spectra Spurious signals at wavenumber 14 with period 4.5 and 9 days are seen in the power spectra of ESRL AVHRR OLR. Daily OLR CDR v01r02 Inclusion of geostaqonary data in Daily OLR CDR eliminates aliasing from the orbital precession of the polar- orbiters. 16
17 Hovmöller Diagram for OLR Anomalies 7.5 S- 7.5 N ESRL AVHRR OLR Daily OLR CDR v01r02 Daily OLR CDR shows more dis6nct signals due to higher spa6al resolu6on and higher precision adributed to the beder OLR es6ma6on and the explicit accoun6ng of diurnal varia6ons. 17
18 Final Remarks New 1 x1 Daily OLR CDR product for 1979 to present Major enhancements & improvements over previous Monthly OLR CDR Full synergy of LEO and GEO taking advantages of both observing systems Opera6onal produc6on with a 2- day lag. Visit hfp://olr.umd.edu Quality Control evalua6ons Well compared to CERES EBAF, SYN1deg and ERBS WFOV OLR products Possible local ar6facts (around 1995). Missing days (~250) to be filled. Recommend replacement of AVHRR OLR (tropical dynamics) Future works: Use of IASI and CrIS for OLR 6me series extension (seeking supports and collabora6ons)
19 Acknowledgments NOAA Climate Data Record Program Data Centers: NOAA NCDC CLASS NASA LaRC ASDC Robert G. Ellingson, Arnold Gruber, Istvan Laszlo, Vladimir Kondratovich CERES ScienQst Team 19
20 Backup Slides 20
21 STD of Global Daily OLR Differences StdDev of CERES SSF minus CERES SYN OLR are about 12 Wm - 2 StdDev of Daily OLR CDR minus CERES SYN OLR are about 5 Wm - 2
22 Mean of Global Daily OLR Differences HIRS-SYN diff are well within CERES LW 1.5% uncertainty Global mean OLR diff vary seasonally in a range of about 1 Wm -2 in both HIRS and SSF data relative to SYN.
23 Global Monthly OLR Anomalies
24 HIRS- EBAF Global Monthly OLR Differences Incomplete monthly sampling in EBAF Causes unknown
25 OLR Rela&ve Bias for N11- N10 La&tudinal and Seasonal Varia&ons
26 Improvement in OLR Models Consistency in Residual Behavior NOAA- 11 vs. NOAA- 09 LZA=0 LZA=53 V2.2 V2.5 V2.7
27 7- Day Boxcar (1985 day 300) NOAA- 9
28 Abstract A long 6me series of the daily mean outgoing longwave radia6on (OLR) data set at one by one- degree resolu6on has been produced for the period of 1979 to 2012 as a new product to be released and opera6onally maintained by the US Climate Data Record (CDR) Program. This data set is an important contribu6on to the climate change studies and monitoring as well to the atmospheric dynamics applica6ons. This data set was generated with the radiance observa6ons from infrared sensors onboard mul6- na6onal opera6onal environmental satellites of both polar- orbi6ng and geosta6onary types. Accurate OLR is retrieved using the mul6- spectral algorithm (Ellingson et al, 1989) with the High- resolu6on Infrared Radia6on Sounder (HIRS) radiance observa6ons. The OLR diurnally resolving informa6on is derived from the Gridsat CDR product, which provides the inter- calibrated Imager observa6ons from a collec6on of geosta6onary satellites since A blending method is designed to calibrate the Imager- based OLR to the HIRS OLR on the regional and dynamical bases. The resul6ng daily mean OLR 6me series maintains the same radiometric level of the HIRS OLR retrievals with an internally 3- hourly resolving power for its diurnal varia6ons. This new data set brings significant enhancement and improvement over the exis6ng Monthly OLR CDR product. 28
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