Kent B. Lauritsen & ROM SAF team Danish Meteorological Institute, Copenhagen, Denmark Contents

1. ROM SAF and Radio Occultation Products Kent B. Lauritsen & ROM SAF team Danish Meteorological Institute, Copenhagen, Denmark Contents - Short introduction to ROM SAF and Radio Occultations (RO) - Status of NRT operations - Offline processing and offline and climate data products - Overview of some development and climate related activities EUM 2012, 3-7 September 2012, Sopot, Poland

2. ROM Satellite Application Facility Consortium Occultation Antenna DMI x ECMWF x UKMO x EUMETSAT x IEEC x Leading Entity:Danish Meteorological Institute (Copenhagen, Denmark) Kent B. Lauritsen, Hans Gleisner, Johannes K. Nielsen, Frans Rubek, Helge Jønch- Sørensen, Stig Syndergaard, Hallgeir Wilhelmsen, Kristian Rune Larsen Partners:ECMWF (Reading, UK) Sean Healy Institute d’Estudis Espacials de Catalunya (IEEC, Barcelona, Spain) Estel Cardellach, Santi Oliveras Met Office (Exeter, UK) Dave Offiler, Chris Burrows, Ian Culverwell

3. Radio Occultation Meteorology (ROM) SAF objectives One of EUMETSAT’s 8 SAFs: Operational processing and archiving center for radio occultation (RO) data from Metop (GRAS) and other RO missions Data products and software deliverables: Near-real time (NRT) radio occultation products - operational products in NRT (refractivity, temperature, pressure, humidity); Offline radio occultation products - offline profiles (bending angle, refractivity, temperature, pressure, humidity); - climate products: gridded data of bending angle, refractivity, temperature, humidity, geopotential height; - reprocessed RO data sets; Radio Occultation Processing Package (ROPP) - routines for assimilation and processing of RO data;

4. Atmosphere profiling by radio occultation t2 Bending angle (BA) & vertical profiles of: - N (refractivity) - T (temperature) - P (pressure) - q (humidity) time: t1 Metop/GRAS (setting occ) t3 GPS 2 GPS signals: L1: ≈19 cm L2: ≈24 cm (rising occ) • While a GPS satellite ‘sets’ or ‘rises’ behind the horizon: • Additional bending of the GPS signal’s ray path due to refraction in the atmosphere • The GPS receiver measures the excess Doppler shift

5. General principle of RO data processing Bending angle:obtained from the measured phases and amplitudes and the positions and velocities of the two satellites using Doppler shift and geometric optics or wave optics inversion (canonical transform); Ionosphere corrected bending angle:obtained by linear combination of the bending angles corresponding to the two GPS frequencies L1 and L2; Refractivity: obtained from the bending angle as a function of height using the Abel Transform inversion (assuming spherical symmetry and statistical optimization); “Dry” temperature and pressure:obtained by using the ideal gas law and the assumption of hydrostatic equilibrium (set humidity  0); Pressure, temperature and specific humidity (water vapor):obtained using an ancillary temperature, humidity and, e.g., the 1D-Var algorithm;

6. Atmospheric sounding with RO L ~ 300 km Z ~ 0.1 – 1.5 km

7. Global distribution of RO profiles 1 day (Metop-A) 1 month (Metop-A)

8. ROM SAF NRT refractivity product (GRM-01) • Produced from geometric optics (GO) level 1b operational NRT bending angles from GRAS/Metop-A data from EUMETSAT CAF • Used by NWP centers worldwide for assimilation or as QC when assimilating EUMETSAT CAF level 1b bending angles • Limited information contents at low altitudes (due to GO and so far not using raw sampling data) Future plan • Production of improved product based on wave optics processing will increase the information content in the lower troposphere; to be done after EUMETSAT CAF NRT upgrade end of 2012 • Improve statistical optimization (SO) by using an enhanced background climatology

9. Monthly refractivity (GRM-01) statistics: 2011-2012 Feb 2011 Mar 2011 Jun 2011 Apr 2011 May 2011 Feb 2012 Mar 2012 Apr 2012 May 2012 Jun 2012 - Very similar bias above 30 km month for month between 2011 & 2012 statistics - Seasonal variation: Due to ECMWF model, measurements/stat. opt., or both?

10. BAROCLIM: Bending angle RO climatology ROM/GRAS SAF VS study with Ulrich Foelsche and Barbara Scherllin-Pirscher (U. Graz) Purpose: to derive a bending angle (BA) climatology from averaged RO data and being able to use the climatology in the statistical optimization initialization process BAROCLIM characteristics: • COSMIC data from 08/2006 to 07/2011 • Careful outlier rejection • Calculate monthly mean profiles for 10° zonal bands • Mean profiles are still noisy at high (impact) altitudes (>60 km) • Statistical optimization between 60 km and 80 km: no MSIS <60 km, no RO >80 km

11. BAROCLIM compared to ECMWF • Systematic differences are very small below 40 km (ECMWF assimilates RO bending angles) • Systematic positive difference (>0.5 %) at ~40 km to ~45 km at all latitudes • Positive difference (larger than 2%) above 50 km • Negative systematic difference (larger than –2 %) above 50 km at high southern latitudes • Differences are mainly attributable to ECMWF

12. Average-profile vs. single-profile processing Statistical optimization based on averaging of BA profiles directly. Relative difference between observations (average-profile and single-profile) and ECMWF.

13. Offline GRAS processing: BA & Refr. statistics Processing of April 2012 GRAS data from the EUMETSAT CAF offline prototype (ftp) BA Refr.

14. Refractivity differences relative to ECMWF:COSMIC RO data for 2007, 2008, 2009 Change in ECMWF cycle 32r3 in 2008: i) COSMIC RO assimilated to surface; ii) updated convection and entrainment physics; Ref: M. E. Gorbunov et al, J. Atm. Ocean. Tech. (2011)

15. ROM SAF offline gridded products: climate data ROM SAF climate data products are built upon the existing offline products. The climate products extend the range of such products offered to users. 1 A latitude-height grid where the height can be expressed in MSL altitude, geopotential height, or in terms of pressure. 2 The height resolution of the grid is determined by the height resolution of the profiles: 200 m

16. RO climate data intercomparison: ROtrends Fractional anomalies in the 12-20 km layer w.r.t. annual cycle; CHAMP RO data from 2001-2008; The slopes of trend lines agree between 6 RO processing centers

17. RO data in climate science • MSU/AMSU vs. RO temperatures: Steiner et al. (2007, 2009), Ho et al. (2008), Ladstädter et al. (2010) [ROM/GRAS VS study] MSU data show a stronger cooling trend than RO (particularly in the tropics). Possible explanation: strong tropical warming in the upper troposphere better resolved in RO (due to the high vertical resolution of RO data).

18. RO data and climate change detection • Feasibility of using RO data (bending angles) for trend detection:Ringer and Healy (2008) Analysis of zonal mean bending angles in a transient climate experiment: trends may be distinguished from natural variability after 10 to 16 years.

19. Concluding remarks Selected activities in ROM SAF CDOP-2 phase: • Metop-B: production of operational NRT and offline products and climate data • Reprocessing of Metop, COSMIC, CHAMP and other RO data with consistent algorithms and validation and production of offline climate data records • Continue to enhance the ROPP package with new routines for e.g. tropopause height and planetary boundary layer height calculations For more information: - Poster 56 by Johannes K. Nielsen on 1D-Var retrieval of temperature and humidity - Poster 37 by Hans Gleisner on level 3 offline climate data - ROM SAF website: http://www.romsaf.org