Processing and validation of refractivity from GRAS radio occultation data

Abstract. We discuss the processing of GRAS radio occultation (RO) data done at the GRAS Satellite Application Facility. The input data consists of operational near-real time bending angles from December 2010 from the Metop-A satellite operated by EUMETSAT. The data are processed by an Abel inversion algorithm in combination with statistical optimization based on a two-parameter fit to an MSIS climatology. We compare retrieved refractivity to analyses from ECMWF. It is found that for global averages, the mean differences to ECMWF analyses are smaller than 0.2% below 30 km (except near the surface), with standard deviations around 0.5% for altitudes between 8 and 25 km. The current processing is limited by several factors, which are discussed. In particular, the penetration depth for rising occultations is generally poor, which is related to the tracking of the L2 signal. Extrapolation of the difference between the L1 and L2 signals below the altitude where L2 is lost is possible and would generally allow deeper penetration of retrieved refractivity profiles into the lower troposphere.

[1]  Anders Berg,et al.  Metop-GRAS in-orbit instrument performance , 2009 .

[2]  Gottfried Kirchengast,et al.  Advancements of Global Navigation Satellite System radio occultation retrieval in the upper stratosphere for optimal climate monitoring utility , 2004 .

[3]  John A. Dykema,et al.  Testing climate models using GPS radio occultation: A sensitivity analysis , 2006 .

[4]  R. Anthes,et al.  Exploring earth's atmosphere with radio occultation: contributions to weather, climate and space weather , 2011 .

[5]  Jean-Noël Thépaut,et al.  Assimilation experiments with CHAMP GPS radio occultation measurements , 2006 .

[6]  S. Healy,et al.  The combined impact of future space‐based atmospheric sounding instruments on numerical weather‐prediction analysis fields: A simulation study , 2003 .

[7]  Gottfried Kirchengast,et al.  Local time influence in single‐satellite radio occultation climatologies from Sun‐synchronous and non‐Sun‐synchronous satellites , 2007 .

[8]  J. Schofield,et al.  Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System , 1997 .

[9]  M. E. Gorbunov,et al.  Ionospheric correction and statistical optimization of radio occultation data , 2002 .

[10]  M. Lohmann,et al.  Application of dynamical error estimation for statistical optimization of radio occultation bending angles , 2005 .

[11]  C. Marquardt,et al.  Forecast impact experiment with GPS radio occultation measurements , 2005 .

[12]  Hans Bonekamp,et al.  An Introduction to the EUMETSAT Polar system , 2007 .

[13]  J. R. Eyre,et al.  Retrieving temperature, water vapour and surface pressure information from refractive‐index profiles derived by radio occultation: A simulation study , 2000 .

[14]  M. Gorbunov,et al.  GRAS SAF project and products , 2008 .

[15]  G. B. Larsen,et al.  Processing of GRAS/METOP radio occultation data recorded in closed-loop and raw-sampling modes , 2011 .

[16]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[17]  Juha-Pekka Luntama,et al.  Prospects of the EPS GRAS Mission For Operational Atmospheric Applications , 2008 .

[18]  Christian Rocken,et al.  Inversion and error estimation of GPS radio occultation Data , 2004 .

[19]  Stig Syndergaard,et al.  Errors in GNSS radio occultation data: relevance of the measurement geometry and obliquity of profiles , 2010 .

[20]  A. Hedin.  Extension of the MSIS Thermosphere Model into the middle and lower atmosphere , 1991 .

[21]  A. von Engeln,et al.  Validation of operational GRAS radio occultation data , 2009 .