GRAS SAF Climate Products

1. GRAS SAF Climate Products Hans Gleisner & Kent B. Lauritsen Danish Meteorological Institute ----- Contents • GRAS SAF offline profiles and climate gridded data • Status of products • Monitoring and a priori assessment • 1D-Var diagnostics • Prototype data from Metop NRT data

2. Offline Climate gridded data products – an enhancement of GRAS offline data. Overview of GRAS SAF climate gridded data 1 A latitude-height grid where the height can be expressed in MSL height, geopotential height, or in terms of pressure. 2 The maximum resolution in height is determined by the height resolution of the profiles.

3. Some recent achievements • Delivered a complete RO climate data set based on CHAMP data Sept 2001 - Sept 2008 for the international ROtrends comparison study, November 2010; • Participated in the Mid-term meeting of the ESA DUE GlobVapour project, ESRIN, Frascati, Italy, 7 March 2011; • Participated in the GEWEX/ESA DUE GlobVapour workshop on long term water vapour data sets and their quality assessment, ESRIN, Frascati, Italy, 8-10 March 2011;

4. NRT, Offline, and Climate processing overview Phase, amplitude, ground station observations, NRT/offline data Produced by EUMETSAT Re-processed data (zero/single/double diff.): Phase, amplitude Phase, amplitude, ground station observations, near-real time orbits Other RO data (COSMIC, CHAMP, ...): Phase, amplitude Level 1a CT2 algorithm CT2 algorithm Geometric optics inversion algorithm Bending angle profiles (L1, L2, LC) Bending angle profiles (ionosphere corrected and statistically optimized) Bending angle profiles (ionosphere corrected and statistically optimized) Level 1b Bending angle profiles (statistically optimized) Abel transform algorithm Abel transform algorithm Abel transform algorithm Climate algorithms Level 2 Refractivity profiles Refractivity profiles Refractivity profiles Ancillary temperature, pressure, and humidity, from ECMWF forecasts 1D-Var algorithm 1D-Var algorithm 1D-Var algorithm Bending angle, refractivity, temperature, humidity, and geopotential height grids Temperature, pressure, and humidity profiles Temperature, pressure, and humidity profiles Temperature, pressure, and humidity profiles Level 2 GRAS SAF Climate/Gridded Data GRAS SAF NRT Products GRAS SAF Offline Products Level 2

5. Status of offline profile and gridded products Offline products from Metop: offline profiles and gridded climate data • first version based on prototype offline GRAS/Metop-A data via ftp from EUMETSAT CAF planned from: June 2011 • consolidation of new format, netCDF-3 or 4: June - August 2011 • demonstration product may be made available for download: Q1, 2012 Offline products from COSMIC: offline gridded climate data • offline processing based on ROPP_PP has started • internal validation planned for Sept 2011 • review for offline gridded data: end 2011 • operational product made available for download and monitoring: Q1, 2012

6. Status of offline climate data products - During 2010 (following the PCR-2 review) we have implemented algorithms to: - monitor noise on the bending angles - monitor stability of observed errors in refractivity - monitor stability of the errors the 1D-Var a priori - quantify the relative importance of a priori in the refractivity - quantify the relative importance of a priori in 1DVar temperature & humidity - The climate algorithms are now described in the Algorithm Theoretical Baseline Document (ATBD): Climate Algorithms, ver. 2.1.

7. Monitoring noise on bending angle: Algorithm The neutral-atmosphere bending angle is contaminated with noise that increases exponentially with altitude. The noise is of both instrumental and ionospheric origin and varies considerably from occultation to occultation. We estimate the upper-level bending angle noise by the smallest standard deviation of the bending angle difference aobs-aclim found over a scale height (here, taken to be 7.5 kilo- meters) in the interval 60 to 80 kilometer: Here, n is the number of data points within a sliding window of 7.5 kilometer width. aobs is the observed bending angle. aclim is the corresponding bending angle from the MSIS climatology.

8. Monitoring noise on bending angle: Example Biases (left panel) and standard deviations (right panel) of the bending angle differences aobs-aclim in CHAMP data during the year 2004.

9. Relative importance of a priori in refractivity: Algorithm For each grid-box and month we compute the quantity: where sobs and sbg are estimates of the errors in the observed and background bending angles, and index j loops over the Mi data points in grid box i. This quantity provides a measure of the observational information in the optimized Bending angles. As a consequence of the error characteristics, it goes from 0 (no background) at low altitudes to 1 (no observational information) at high altitudes.

10. Relative importance of a priori in refractivity: Example

11. Relative importance of a priori in 1DVar T, q: Algorithm We quantify the relative importance of the a priori information in the retrieved temperature and humidity by the error standard deviations used in the 1DVar retrieval: where index sol denotes solution and index bg denotes background. The factor 100 normalizes the ratio to percent.

12. Relative importance of a priori in 1DVar T and q: Example

13. 1D-Var minimization: Diagnostics The state xs that minimizes J(x) is a valid estimate of the true atmosphere only if the error covariance matrixes O and B provide good enough descriptions of the actual errors. These errors are not known perfectly known. Desroziers et al. [2005] described how information on the errors can be gained from the statistics of the differences between observation, background, and solution. In observation space:

14. Consistency criteria for 1D-Var errors If the errors are unbiased, Gaussian, and accurately describe the true errors, then the following set of relations should hold: The diagnosed error covariances are on the left hand side. The error covariances assumed in the 1D-Var retrieval are on the right hand side. H is the Jacobian of H(x).

15. Diagnosing 1D-Var errors The consistency criteria provide a means to monitor the stability of errors, e.g. by a regular diagnosis of the mean error covariance diagonal elements. Here, index i denotes a latitude band and index j loops over the Mj observations in latitude band i. If the observed quantity is refractivity, which falls off exponentially with height, the diagnosed errors are more conveniently expressed in relative terms:

16. Diagnosing 1D-Var errors Diagnosed background and observational errors for COSMIC-FM4 in March 2008.

17. Metop EUM-BA O-B/B

18. Metop EUM-BA O-B/B

19. Metop REF O-B/B

20. Metop REF & T: Polar bias

21. Metop REF & T: Polar bias May 2011

22. Reprocessing and construction of climate data sets • Major activity in the next phase of the GRAS SAF: Reprocessing of all RO data planned in CDOP-2 in 2014 and 2016 with new algorithms and improved input data: • GRAS data from EUMETSAT CAF • ERA-Clim RO data from EUMETSAT CAF (CHAMP, COSMIC, …) • RO data from CDAAC • Applications and data: • - Include QC info, error estimate and usable range; comparisons of reprocessed datasets within a reanalysis system; • - Produce RO datasets for testing forecast and climate models (this is more difficult for radiances because they are bias corrected to the models; RO data is assimilated without bias correction); • - Produce RO datasets for climate monitoring (RO information content is highest in the upper troposphere/lower-mid stratosphere);

23. Overview of reprocessing and validation Overview of GRAS SAF reprocessing, interfaces, and validation: EUMETSAT CAF Intercomparison: - BA (level 1b) Phases, amplitudes, orbits (GRAS, ERA-Clim) Offline and climate data GRAS SAF Website Users Reprocessing Intercomparison: - BA, REF, T, P, q (profiles) - Climate data (grids) Phases, amplitudes, orbits RO Processing Centers ECMWF NWP/Reanalysis ROtrends intercomparison SAF’s (CM SAF) ESA DUE GlobVapour GEWEX Radiation Panel RO Data providers

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