Development of a stochastic precipitation nowcast scheme for flood forecasting and warning

1. Development of a stochastic precipitation nowcast scheme for flood forecasting and warning Clive Pierce1 , Alan Seed2 , Neill Bowler3 1. Met Office, Joint Centre for Hydro-Meteorological Research, Wallingford, Oxfordshire, UK, OX10 8BB 2. Cooperative Research Centre for Catchment Hydrology, Bureau of Meteorology, Melbourne, Australia 3. Met Office, FitzRoy Road, Exeter, Devon, UK, EX1 3PB

2. Overview • A stochastic QPN scheme - STEPS • Overview of the Short Term Ensemble Prediction System • Cascade modelling framework • STEPS cascade model • Uncertainties in advection & Lagrangian temporal evolution • Formulation of STEPS • Towards stochastic fluvial forecasting • Propagating uncertainty in QPNs through a rainfall-run-off model • Plans

3. Short Term Ensemble Prediction System • Model design • Cascade framework (Lovejoy et al., 1996; Seed, 2003) to model dynamic scaling behaviour • merging extrapolation nowcasts with NWP forecast • Sources of uncertainty / error • diagnosed velocity fields (Bowler et al., 2004) • Lagrangian temporal evolution • NWP forecast • initial state • Forecast evolution • blends extrapolation, NWP and noise cascades • stochastic noise • replaces extrapolated features beyond their life times • introduces features unresolved by NWP • ensemble produced

4. STEPS cascade model • Radar based precipitation field • 2-D FFT • Bandpass filterper pixel, k=1,8 • Inverse transform • Additive cascade • Normalise Xk(t) • Based upon S-PROG cascade - Seed (2003)

5. 256-128 km 128-64 km 64-32 km 32-16 km 16-8 km 8-4 km 4-2 km Cascade decomposition courtesy of Alan Seed, Bureau of Meteorology, Australia

6. Uncertainty in the extrapolation nowcast • Uncertainty in field evolution • Modelled in Lagrangian reference frame • Noise replaces extrapolated features beyond predicted life time • k,i,j = temporally independent noise cascade • Uncertainty in advection velocities • Add perturbation to velocities

7. Formulation of STEPS • A blend of three cascades • Extrapolation • Noise • NWP • Weights assigned according to skill of extrapolation and NWP components • Advection velocities • blend perturbed velocity, e with NWP diagnosed velocity, m

8. STEPS - products • Ensemble members - T+15 minutes

9. STEPS - products • Probability of precipitation

10. Towards stochastic fluvial flood forecasting and warning • Uncertainty in rainfall input dominates (Moore, 2002) • Ignore errors in rainfall-runoff model • PDF of river flow from PDF of rain accumulation • Underestimates total uncertainty (Krzyztofowicz, 2001) • Cost-loss decision making model (Mylne, 2002)

11. Flow forecast ensembles courtesy of Bob Moore, Centre for Ecology and Hydrology, UK

12. Plans • STEPS operational trial in the UK and Australia • starts autumn 2005 • pdfs of rain accumulation and river flow (PDM – Moore, 1985) • cost-loss model (Mylne, 2002) for pluvial & fluvial flood warning • verification of deterministic and probabilistic forecasts

13. Thank you