Improved Meteosat First Generation Infrared Data Jörg Schulz, EUMETSAT

Content Plan on the improvement of Meteosat First Generation MVIRI Infrared and water Vapour Channel. Status of Meteosat First Generation from LMD Status of HIRS from NOAA-NCDC Plan Comments on common reference channel for GEO infrared imagers

Sustained Climate Information Flow Satellite & In Situ Observations Short and Medium Latency Long-term Information Preservation Observing system performance Monitoring and automated corrections Satellite data Climate Data Records Archived Satellite Data and Records Fundamental Climate Data Records Inter-calibration Re-calibration Inter-calibration Reprocessing Environmental Data Records Interim Climate Data Records Thematical Climate Data Records Data conversion User Services Sustained Coordinated Processing Climate Information Records SCOPE-CM The term Fundamental Climate Data Record (FCDR) is used to denote a long-term satellite data record, generally involving a series of instruments (all platforms), with potentially changing measurement approaches, but with overlaps, calibration and quality control sufficient to allow the generation of homogeneous products providing a measure of the intended variable that is accurate and stable enough for climate monitoring. FCDRs include the ancillary data used to calibrate them. For “one-off” research type measurements, the principles do not apply, but as many of the other principles as possible (e.g., those for rigorous instrument characterisation prior and during operations, complementarity of surface and satellite-based observations, etc.) should be followed. The term Product denotes, values of fields of Essential Climate Variables derived from FCDRs. Products may be generated by blending satellite observations and in situ data, or by blending multiple in situ or multiple satellite data sources. Some products are generated within model assimilation schemes. Some satellite-based products are created by using the laws of radiative transfer to retrieve ECVs from the FCDRs. Other documents use the term Thematic Climate Data Record (TCDR) for such products; in the GCOS Implementation Plan , the term Integrated Climate Product was used. The development of products may require strong collaboration between organisations responsible for the generation of datasets (e.g., meteorological services, oceanographic centres, environmental agencies, space agencies) and the separate research or operational groups that generate products, to ensure continuous refinement and extension. Adequate details of the product generation approach need to be documented and made available, along with the products, to ensure repeatability and incremental improvement of the products. For further discussion of the terms Fundamental Climate Records and Thematic Climate Data Records see e.g. National Research Council (2004): Climate Data records from Environmental Satellites, the National Academic press, Washington D.C., USA, 150pp. Major model-based Reanalysis Sustained Applications Short scale physical phenomena monitoring Operational Climate Monitoring supporting Climate Services Longer term climate variability & climate change analysis Adaptation + mitigation planning (decision making)

METEOSAT 1984-1996 Archive evaluation using radiosoundings 3 Original Upgrade of calibration technique (Schmetz, 1989) Upgrade of calibration technique (van de Berg, et al., 95) ISCCP DX Normalized Instead of nominal Two upgrades corrected according to the work of Picon et al., JGR, 2003 but with ERA40 instead of ERA15. The DX data change of implementation corrected thanks to interaction with ISCCP people (V. Golea) Courtesy of Helene Brogniez and Remy Roca, LMD

METEOSAT 1984-1996 Archive evaluation using radio soundings, 6 micron 3 MET4 Original MET5 Homogenized 0.5K standard deviation indicating an effective homogenization Negligible bias with respect to radiosondes archive as expected from the calibration reference period selected (Mars-April 1994) and calibration technique Normalization to HIRS-12/NOAA-12 following Bréon et al., (JGR, 2000) ~ -3K on the DX brings the difference RAOB-SAT in agreement with numbers in literature Courtesy of Remy Roca, LMD

METEOSAT 1984-1996 Archive evaluation using radiosoundings Comparisons between the METEOSAT BTs and the simulated BTs from radoisoundings: (+) represent the raw data, (◊) represent the homogeneised data. The histogram shows the nb of soundings used for comparison. Courtesy of Helene Brogniez and Remy Roca, LMD

HIRS monthly mean time series (30S – 30N) Courtesy of Lei Shi, NOAA-NCDC

HIRS Spectral Filter Functions Differences between HIRS/2 and HIRS/3 are expected due to different filter functions. In-orbit performance still has biases unexplained by filter functions. Thus empirical approach is considered. Courtesy of Lei Shi, NOAA-NCDC

Overlaps of zonal means for tropical and mid-latitude atmosphere cases NOAA-NCDC Approach –Two Datasets for Intersatellite Calibration to Cover Diverse Atmospheres Overlaps of zonal means for tropical and mid-latitude atmosphere cases Zonal means of channel 12 (UTWV) are computed for every 10-degree latitude belt. Differences are derived from overlapping satellites. Simultaneous nadir overpass (SNO) observations for polar cases SNOs occur when two satellites cross each other Data taken at the same location at the satellite nadir within a few seconds In the regions 70-80N and 70-80S Uses HIRS on NOAA-12 as reference instrument Courtesy of Lei Shi, NOAA-NCDC

HIRS Channel 12 Temperature-dependent Intersatellite Differences Lei Shi More than half of satellites have bias variations larger than 0.5 K. Courtesy of Lei Shi, NOAA-NCDC

HIRS - Intersatellite Calibrated Product After Intersatellite Calibration Cloud-cleared and limb-corrected HIRS channel brightness temperatures from November 1978 to March 2009. Technique also applied for other HIRS channels including channel 8 (IR window, 11 m) Intercalibrated and original TB available from NOAA-NCDC and downloaded to EUMETSAT. Unclear: Uncertainty estimates for the HIRS intercalibration. Monthly differences of intercalibrated time series are mostly within ±0.2 K. Courtesy of Lei Shi, NOAA-NCDC

Plan for MVIRI and SEVIRI Infrared Intercalibration Use intercalibrated HIRS from NOAA/NCDC (adjusted to HIRS on NOAA12) with GSICS collocation and intercalibration procedures taking HIRS as truth; Control intercalibration using ARSA (Analyzed Radio Soundings Archive) database from LMD; Control intercalibration in overlapping areas after IODC start, e.g., Met-5 vs. Met-7; Derive AMV and other parameters from intercalibrated radiance and document changes. Deliver both FCDR data and corrections to be applied by expert users (SAFs, ECMWF reanalysis); Need to solve cloud detection issues (need one for both); Implementation targeted for 2011/2012; Longer Term: Estimate potential and feasibility of further improving MVIRI level 1.5 by reanalysing the image archive. Implement intercalibration based on IASI (based on GSICS and collaboration with CIMMS (Project within the NOAA CDR program)).

Comments on common reference channel [1] From the viewpoint of creating a long time series combined with the potential application of trend analysis the most important feature is stability, i.e., a bias with respect to a reference shall not vary with time. The absolute difference to a reference does not matter that much as it is only an adjustment factor. [2] The need for stitching together satellites with different and sometimes unknown SRFs is a fact so having one (even artificial) reference seems attractive. The improvement of spatial matching shown on slide 4 of Tim's GSICS presentation would also apply for instruments with different SRFs overlapping in time as MVIRI and SEVIRI. Those have, e.g., for the 6.x micron channel rather different SRFs.

WV channel filter functions In order to provide homogeneity between the METEOSAT First Generation dataset (MFG-7 operations at 0° ended in June 2006) and the following METEOSAT Second Generation observations (MSG-2 operational at 0° since July 2006), it is required to adjust the filter functions and project one on the other. Figure 7‑1 shows differences between the filter functions of MFG-5 (centered at 6.3 µm, MVIRI radiometer), MSG-1 and MSG-2 (both centered at 6.2 µm, SEVIRI radiometer): the MSG filter functions are narrower than the one of MFG-5, therefore observing less radiation than MFG-5. Normalized spectral filter functions for the “WV” channel of MFG-5 (plain line), and the 1st “WV” channels of MSG-1 (mixed line) and MSG-2 (dotted line). To provide a homogeneous record over MFG and MSG a projection of one filter function onto the other is required.

Linear fit conversion Scatter plot of simulated BTs for the MSG-1 SEVIRI radiometer versus the simulated BTs for the MFG-5 MVIRI radiometer for the same scenes. The radiative transfer simulations are performed for a large sample of T and q profiles (N=675221) extracted from the ERA-40 model level database. The y=x line is in red and the linear fit is the blue dashed line. Also indicated on the plot are the correlation coefficient R and the RMS error. Computations performed using RTTOV-9. Bias= -1.6 K RTTOV 8.7 RTTOV 9.3 slope intercept MSG-1  MFG-5 1.0080 -0.377 1.0160 -2.3498 MSG-2 1.0103 -0.844 1.0174 -2.6033 MFG-5  0.9905 0.770 0.99849 2.6126 0.9877 1.337 0.98149 2.8967