An update on THORPEX-related research in data assimilation and observing strategies

The international programme "THORPEX: a World Weather Research Programme" provides a framework in which to tackle the challenge of improving the forecast skill of high-impact weather through international collaboration between academic institutions, operational forecast centres, and users of forecast products. The objectives of the THORPEX Data Assimilation and Observation Strategy Working Group (DAOS-WG) are two-fold. The primary goal is to assess the impact of observations and various targeting methods to provide guidance for observation campaigns and for the configuration of the Global Observing System. The secondary goal is to setup an optimal framework for data assimilation, including aspects such as targeted observations, satellite data, background error covariances and quality control. The Atlantic THORPEX Regional campaign, ATReC, in 2003, has been very successful technically and has provided valuable datasets to test targeting issues. Various data impact experiments have been performed, showing a small but very slightly positive impact of targeted observations. Projects of the DAOS-WG include working on the AMMA field experiment, in the context of IPY and to prepare the future THORPEX-PARC field campaign in the Pacific by comparing sensitivity of the forecasts to observations between several groups.

[1]  Jean-Noël Thépaut,et al.  The value of observations. III: Influence of weather regimes on targeting , 2007 .

[2]  C. Bishop,et al.  Resilience of Hybrid Ensemble/3DVAR Analysis Schemes to Model Error and Ensemble Covariance Error , 2004 .

[3]  Rolf H. Langland,et al.  Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system , 2004 .

[4]  Matthew Fisher,et al.  On the equivalence between Kalman smoothing and weak‐constraint four‐dimensional variational data assimilation , 2005, Quarterly Journal of the Royal Meteorological Society.

[5]  M. Buehner.  Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting , 2005 .

[6]  O. Talagrand,et al.  Diagnosis and tuning of observational error in a quasi‐operational data assimilation setting , 2006 .

[7]  Peter Bauer,et al.  Implementation of 1D+4D‐Var assimilation of precipitation‐affected microwave radiances at ECMWF. II: 4D‐Var , 2006 .

[8]  Florence Rabier,et al.  Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances , 2002 .

[9]  Carla Cardinali,et al.  Impact of airborne Doppler lidar observations on ECMWF forecasts , 2006 .

[10]  Jean-Noël Thépaut,et al.  A 4D‐Var re‐analysis of FASTEX , 2003 .

[11]  Lars Isaksen,et al.  Observing‐system impact assessment using a data assimilation ensemble technique: application to the ADM–Aeolus wind profiling mission , 2007 .

[12]  Watts,et al.  Impact of airborne Doppler lidar observations on ECMWF forecasts , .

[13]  T. Bergot,et al.  Sensitivity to observations applied to FASTEX cases , 2001 .

[14]  Rolf H. Langland,et al.  Observation Impact during the North Atlantic TReC—2003 , 2005 .

[15]  Istvan Szunyogh,et al.  The Effect of Targeted Dropsonde Observations during the 1999 Winter Storm Reconnaissance Program , 2000 .

[16]  Craig H. Bishop,et al.  Optimization of the Fixed Global Observing Network in a Simple Model , 2003 .

[17]  Huw C. Davies,et al.  Large-scale weather systems: A future research priority , 2006 .

[18]  D. P. DEE,et al.  Bias and data assimilation , 2005 .

[19]  F. Bouttier,et al.  Observing‐system experiments in the ECMWF 4D‐Var data assimilation system , 2001 .

[20]  H.-L. Huang,et al.  Estimating effective data density in a satellite retrieval or an objective analysis , 1993 .

[21]  F. Rabier,et al.  The potential of high‐density observations for numerical weather prediction: A study with simulated observations , 2003 .

[22]  R. Gelaro,et al.  Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System , 2008 .

[23]  Peter Bauer,et al.  488 Implementation of 1 D + 4 D-Var Assimilation of Precipitation Affected Microwave Radiances at ECMWF , Part II : 4 D-Var , 2006 .

[24]  Roberto Buizza,et al.  Forecast Skill of Targeted Observations: A Singular-Vector-Based Diagnostic , 2003 .

[25]  Jean-Noël Thépaut,et al.  The value of observations. I: Data denial experiments for the Atlantic and the Pacific , 2007 .

[26]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[27]  Michael Tsyrulnikov,et al.  Stochastic modelling of model errors: a simulation study , 2005 .

[28]  Roger Daley,et al.  Observation and background adjoint sensitivity in the adaptive observation‐targeting problem , 2007 .

[29]  Erik Andersson,et al.  Influence‐matrix diagnostic of a data assimilation system , 2004 .

[30]  Jean-Noël Thépaut,et al.  The value of observations. II: The value of observations located in singular‐vector‐based target areas , 2007 .

[31]  T. Hamill,et al.  Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations , 2002 .

[32]  Jeffrey L. Anderson,et al.  A methodology for fixed observational network design: theory and application to a simulated global prediction system , 2006 .

[33]  Tilo Ochotta,et al.  Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods , 2005 .

[34]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[35]  M. Fisher.  Estimation of Entropy Reduction and Degrees of Freedom for Signal for Large Variational Analysis Systems , 2003 .

[36]  F. Rabier,et al.  Microwave land emissivity and skin temperature for AMSU‐A and ‐B assimilation over land , 2006 .

[37]  Andrew C. Lorenc,et al.  The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var , 2003 .

[38]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[39]  Craig H. Bishop,et al.  A comparison of ensemble‐transform Kalman‐filter targeting guidance with ECMWF and NRL total‐energy singular‐vector guidance , 2002 .

[40]  Dick Dee,et al.  Adaptive bias correction for satellite data in a numerical weather prediction system , 2007 .

[41]  Florence Rabier,et al.  Impact study of the 2003 North Atlantic THORPEX Regional Campaign , 2006 .

[42]  Robert Atlas,et al.  Atmospheric Observations and Experiments to Assess Their Usefulness in Data Assimilation , 1997 .

[43]  Paul Poli,et al.  Diagnosis of observation, background and analysis‐error statistics in observation space , 2005 .

[44]  Jean-Noël Thépaut,et al.  The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data , 2003 .

[45]  Rolf H. Langland,et al.  Issues in targeted observing , 2005 .

[46]  P. L. Houtekamer,et al.  Ensemble Kalman filtering , 2005 .

[47]  M. Leutbecher.  A Reduced Rank Estimate of Forecast Error Variance Changes due to Intermittent Modifications of the Observing Network. , 2003 .

[48]  Alan J. Thorpe,et al.  The impact on weather forecasts of targeted observations during A‐TReC , 2007 .

[49]  Craig H. Bishop,et al.  A Comparison of Adaptive Observing Guidance for Atlantic Tropical Cyclones , 2006 .

[50]  Florence Rabier,et al.  Cloud characteristics and channel selection for IASI radiances in meteorologically sensitive areas , 2004 .

[51]  Rod Frehlich,et al.  Adaptive data assimilation including the effect of spatial variations in observation error , 2006 .