Data assimilation techniques combine observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting assigned to each observation in the analysis is determined by the error associated with its measurement. Remote sensing data often have correlated errors, but the correlations are typically ignored in NWP. As operational centres move towards high-resolution forecasting, the assumption of uncorrelated errors becomes impractical. This thesis provides new evidence that including observation error correlations in data assimilation schemes is both feasible and beneficial. We study the dual problem of quantifying and modelling observation error correlation structure. Firstly, in original work using statistics from the Met Office 4D-Var assimilation system, we diagnose strong cross-channel error covariances for the IASI satellite instrument. We then see how in a 3D-Var framework, information content is degraded under the assumption of uncorrelated errors, while retention of an approximate correlation gives clear benefits. These novel results motivate further study. We conclude by modelling observation error correlation structure in the framework of a one-dimensional shallow water model. Using an incremental 4D-Var assimilation system we observe that analysis errors are smallest when correlated error covariance matrix approximations are used over diagonal approximations. The new results reinforce earlier conclusions on the benefits of including some error correlation structure.

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Agenda, terms of reference and membership of the committees 5 18.1 Science overview and update on progress, Julia Slingo 15 18.2 Progress, outputs and plans from across research (Foundation Science), Andy Brown 19 18.3 Progress, outputs and plans from across research (Weather Science), Gilbert Brunet 25 18.4 Progress, outputs and plans from across research (Climate Science), Stephen Belcher 35 18.6 Scientific framework for the ensemble prediction system for the UKV, Ken Mylne 39 18.7 Developments to high resolution regional models, Simon Vosper 51

A key component of the modern Earth observation system is the Mid-Infrared (MIR) hyper-spectral sounder. Operational instruments on polar orbiting satellites are collecting a continuous record of highly accurate infrared spectra with a wide variety of applications in geoscience. Al-though some research instrumentation early in the development of the meteorological satellite used Far-Infrared (FIR) sensitive detectors, the primary focus of infrared instrumentation has been the MIR region. Recent developments in FIR instrumentation and a renewed interest in the FIR for climate applications has brought attention to this underexplored part of Earth’s Infrared spectrum. The information content of Earth’s FIR spectrum is investigated within a modeling frame-work that simulates satellite observations of FIR and MIR spectra at hyperspectral resolution. The framework allows for direct comparison of the two spectral ranges, which can quantify the po-tential benefits of combining FIR spectral observations with the state of the art MIR observations. The framework is first applied to investigate the information content for retrieving the vertical tem-perature and water vapor profile in clear sky conditions. The FIR shows additional sensitivity to upper tropospheric and lower stratospheric water vapor, and a slight but consistent vertical resolu-tion advantage relative to the MIR. By extending the simulation framework to include layer clouds

Potential for the use of reconstructed IASI radiances in the detection of atmospheric trace gases

Kernel-based retrieval of atmospheric profiles from IASI data

www.atmos-chem-phys.net/14/9583/2014/ doi:10.5194/acp-14-9583-2014 © Author(s) 2014. CC Attribution 3.0 License. Comparison of IASI water vapor retrieval with H2O-Raman lidar in the framework of the Mediterranean HyMeX and ChArMEx programs