Impact of GPS radio occultation measurements in the ECMWF system using adjoint‐based diagnostics

In this article, a comprehensive assessment of the impact of radio occultation (RO) observations in the operational ECMWF assimilation and forecast system is presented using different diagnostic tools. In particular, the observations' influence in the assimilation process and the related contri...

Full description

Saved in:

Bibliographic Details
Published in:	Quarterly journal of the Royal Meteorological Society 2014-10, Vol.140 (684), p.2315-2320
Main Authors:	Cardinali, Carla, Healy, Sean
Format:	Article
Language:	eng
Subjects:	Assimilation Deflation Diagnostic software diagnostic tools Earth, ocean, space Error detection Errors Exact sciences and technology External geophysics Mathematical models Meteorology observation error variance observation impact Radio occultation radio occultation observations Radios Satellites Variance Weather forecasting
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	In this article, a comprehensive assessment of the impact of radio occultation (RO) observations in the operational ECMWF assimilation and forecast system is presented using different diagnostic tools. In particular, the observations' influence in the assimilation process and the related contribution to the short‐range forecast error of radio occultation observations is evaluated with recently developed diagnostic tools based on the adjoint version of the assimilation and forecast model. The sensitivity with respect to observation error variances is also evaluated for the assimilated observations. GPS‐RO is found to have the largest mean influence among satellite observations in the analysis. It is the fourth best satellite system for analysis information content and the second largest satellite contributor together with IASI and AIRS to decreasing the 24 h forecast error. For the whole observing system, with the exception of radiosondes and polar atmospheric motion vectors, the forecast error sensitivity to the observation error variance indicates that a deflation of the assumed observation errors would improve the forecast skill. For RO observations at all vertical levels, but predominantly between 10 and 20 km, a deflation of the observation error variance is suggested. Interestingly, the sensitivity computation recommends reducing the assumed errors mostly in layers where the weight given to GPS‐RO data is quite large.
ISSN:	0035-9009 1477-870X