DIAMET wp C.2/C.3

1. DIAMET wpC.2/C.3 • Ross Bannister • General • New postdocs (Laura Baker and Ali Rudd). • Renewed proposal for MONSooN computer time. • C.2: Sources of forecast error (leader Stefano Migliorini) • To investigate and simulate sources of model error that most affect the forecast skill at meso/convective scale, and compare forecast error predictions with observations. • Choices of case studies. • Preliminary tests. • Choice of strategy to simulate model error. • Initial aims (run a 24-member ensemble with ‘model error’ with realistic forecast spread). • ‘Analysis of innovation’ tests with CSIP sonde data. • C.3: The changing nature of multivariate relations in high-resolution models (leader Ross Bannister) • To use ensembles of forecasts to assess the changing nature of multivariate relations at high-resolution and to understand their sensitivity to different sources of error, and study the feasibility of incorporating these relations in operational data assimilation systems. • Development of forecast error covariance ideas valid at high-resolution. • C.4S: Next generation data assimilation methods for the convective scale (leader Peter-Jan van Leeuwen) • Difficulty recruiting student.

2. Analysis of innovations • Forecast error covariances are essential in data assimilation. • Huge amount of data, cumbersome to deal with, needs modelling. • Most studied in large-scale systems. • Little known about their character in high-resolution systems. • Can estimate them using ensemble analysis (but sampling error due to Nens_mems small) • Want to derive an estimate of forecast error covariances that is independent of ensemble analysis. • Large amount of observational data useful (analysis of innovation procedure). • Innovation = observation – model’s version of observation 23/07/05 T-T vertcorrs Anal of innovs 26/07/07θ-θcorrsvs lat 24-member ensemble CSIP IOP 23/07/05 50 sonde ascents MetO 4km forecasts