## 1. Introduction

[2] In recent years, the Met Office in the UK and the Bureau of Meteorology in Australia have invested significant effort in quantifying the uncertainties inherent in hydro-meteorological nowcasts and forecasts and communicating these to users via large ensembles that are generated using stochastic models of forecast error. This strategy recognizes the impact of nonlinear error growth on the accuracy of high resolution forecasts and the needs of customers in relation to the management of risks associated with severe weather.

[3] In support of this strategy, the Short Term Ensemble Prediction System (STEPS) was implemented as an operational rainfall nowcaster in 2008 in both the UK and Australia [*Bowler et al*., 2006, hereafter BPS]. It was developed to generate ensembles of rainfall nowcasts using observations from weather radar and forecasts from a mesoscale Numerical Weather Prediction (NWP) model. Over the intervening years, STEPS has been revised and extended to account for the effects of radar observation errors, improve certain aspects of model design and performance, notably the noise generation, and extend the modeling framework to facilitate the combination of rainfall fields from multiple sources.

[4] STEPS now comprises a collection of nowcasting and NWP postprocessing algorithms formulated to produce seamless, composite rainfall forecasts for use in pluvial and fluvial flood forecasting. Its capabilities include the downscaling of coarse resolution NWP forecasts and the generation of large ensembles (with between tens and hundreds of members) incorporating either deterministic or ensemble NWP rainfall forecasts.

[5] In the Met Office, a STEPS-based short range (24 h), 24 member ensemble rainfall forecast incorporating an extrapolation nowcast and high resolution (∼1.5 km) NWP forecast from the Met Office's Unified Model [*Davies et al*., 2005] is now employed to drive an operational, distributed hydrological forecast model [*Price et al*., 2012] for the Flood Forecasting Centre. In the Australian Bureau of Meteorology, STEPS is exploited for a variety of applications including nowcasting, medium range forecasting, and design storm modeling. Products include a 1 km, 30 member rainfall nowcast ensemble with a range of 90 min and a 2 km, 50 member ensemble rainfall forecast with a range of 10 days. The latter is generated from a 50 km, global NWP forecast.

[6] Nowcasting techniques, involving the extrapolation (advection) of current observations of rainfall from weather radar and meteorological satellite remain superior to NWP-based rainfall forecasts over at least the first 2 h, partly because NWP models are too costly and time consuming to run on an update cycle shorter than several hours, but also because techniques for assimilating high resolution observations do not reliably replicate the distribution of rainfall in these observations. Consequently, an optimal, very short range forecast must evolve from an extrapolation-based solution to one dominated by a recent NWP forecast [*Browning*, 1980].

[7] The Met Office's first fully automated nowcasting system, Nimrod [*Golding*, 1998], used a simple weighted averaging technique to blend a 5 km resolution (grid length), radar and satellite-based extrapolation nowcast with a deterministic, 12 km resolution NWP forecast. The nowcast contribution decayed exponentially to zero over a 6 h period. Although more skilful on average than either component between 2 and 5 h ahead, the value of the resulting deterministic predictions was limited due to the hardwiring of the nowcast contribution, loss of spatial resolution beyond *T* + 3 h, and rapid error growth close to the grid scale [*Werner and Cranston*, 2009].

[8] During the late 1990s, the Australian Bureau of Meteorology implemented the Spectral Prognosis (S-PROG) nowcasting system [*Seed*, 2003] as part of the Sydney 2000 Forecast Demonstration Project [*Fox et al*., 2001]. S-PROG modeled the observed scaling behavior of rainfall with the aim of minimizing the root-mean-squared forecast error. This was achieved by smoothing the nowcast rainfall fields to remove small scale features at a rate consistent with their measured longevity. The resulting nowcasts were equivalent to an ensemble mean and were of limited value on their own due to the loss of detail (variance) with increasing forecast range. It was recognized that the S-PROG modeling framework could be extended to generate conditional simulations suitable for probabilistic hydro-meteorological forecasting. This work laid the foundations for STEPS, developed jointly by the Met Office and the Bureau shortly after the Sydney 2000 Olympic Games.

[9] With continuing improvements in the horizontal resolution of operational radar and satellite-based estimates of surface rainfall and the introduction of operational, convection resolving NWP models [*Lean et al*., 2008], the impact of nowcast and forecast errors on the utility of rainfall forecasts has now come to the fore because errors grow most rapidly at the smallest scales. This is not to dismiss the incremental benefits of resolution increases but to emphasize that the additional information content can only be fully exploited in a probabilistic context that conveys the associated uncertainties [*Pierce et al*., 2005]. High resolution NWP-based ensembles afford only a partial solution to this problem because they do not capture the very short range uncertainty adequately and their ensemble sizes are much too small to resolve the forecast uncertainties at the convective scales properly.

[10] STEPS provides a cost effective means of addressing the two issues outlined above: namely, the generation of skilful, very short range rainfall forecasts, and the more effective quantification of forecast uncertainty. The former challenge is addressed using a scale decomposition technique that allows multiple, time synchronous forecasts to be combined scale by scale, and weighted in proportion to estimates of their predictive skill at each scale. The latter issue is dealt with by generating time series of noise fields (pseudorandom numbers with the space-time characteristics of rainfall) with which to perturb the resulting combination of forecast components at each scale. This provides a cheap means of generating large ensembles.

[11] There were a number of weaknesses in the formulation of BPS. The most significant limitations were the use of a multiplicative cascade-based decomposition to combine and perturb rainfall fields incorporating wet and dry areas (i.e., a failure to treat the raining fraction of the field and dry areas as separate processes) and the use of a technique reliant on approximations of the spatial power spectra of rainfall fields to generate noise fields with which to perturb the nowcast and NWP forecast components. Other weaknesses included ignoring the impact of radar errors on the performance of the nowcast in the first hour and not accounting for the covariance between the nowcast and NWP forecast components when deriving weights subsequently employed to combine them. The focus of this paper is on the improvements made to STEPS to address some of the performance issues that have been outlined here.

[12] Section 2 of this paper provides a brief overview of the initial version of STEPS as described by BPS. A review of the space-time properties of rainfall fields in section 3 sets the scene for a description of STEPS's formulation enhancements in section 4. Section 5 presents summary performance statistics for both the STEPS control member and ensemble nowcasts. Conclusions are drawn and further work proposed in section 6.