Many numerical weather prediction models are moving towards more prognostic schemes for the prediction of ice and liquid water contents within clouds. This paper describes a large-scale cloud and precipitation scheme developed for the UK Meteorological Office's Unified Model. It uses physically based transfer equations to predict ice as a prognostic variable. We review similar schemes and then describe our new scheme, giving examples of its performance in mesoscale forecasts compared with the current operational scheme and with observations. The microphysical processes occurring in a frontal cloud are well modelled. The prediction of supercooled stratocumulus cloud is much improved.

Convection-permitting models (CPMs) have provided weather forecasting centres with a step-change in capabilities for forecasting rainfall. They are now used operationally to forecast precipitation in many parts of the world, including the UK. CPMs are models in which the dynamics of atmospheric convection is treated with sufficient accuracy in order to make it viable to switch off convection parametrization. This review describes the current state-of-the-art in operational CPM-based numerical weather prediction (NWP), primarily within the UK, and the historical development of CPMs. The characteristics of CPM systems and forecasts are highlighted and placed in an international context to recognize similar trends and highlight some differences. It is shown that the realism of CPM-based forecasts can provide improved subjective guidance on convection, and, when measured on appropriate scales, can improve rainfall forecasting skill compared to coarser-resolution NWP. Data assimilation techniques used with operational CPMs are reviewed and given historical context. Examples of new types of observations that may increase the skill of forecasts from improved initial conditions are discussed. CPM-based nowcasting systems are shown to provide considerable improvements in short-range forecasts of rapidly developing, intense systems. As a result, these CPM-based systems provide a new forecasting capability. Finally, the development of CPMs has also required new techniques to verify forecasts and define their skill. These have revealed that the lack of predictability of the smallest scales involving convection means that ensemble techniques are required to represent forecast uncertainty, resulting in a new capability to provide objective forecast probabilities of local precipitation.

The Met. Office has developed a variational assimilation for its Unified Model forecast system, which contains a grid‐point mode) that is run operationally in global, mesoscale, and stratospheric configuration. Key characteristics of the design are: a development path from three‐dimensional to four‐dimensional variational assimilation; global and limited‐area configurations; variational analysis of perturbations; and a carefully designed, well conditioned background term. The background term is implemented using a sequence of variable transforms to independent balanced and unbalanced variables, to vertical modes, and to spectral coefficients. The coefficients used are based on statistics from differences of one‐ and two‐day forecasts valid at the same time. The covariance model represents many of the features seen in the covariances of forecast differences. The three‐dimensional variational data assimilation (3D‐Var) system was implemented in the operational global forecast system on 29 March 1999. In parallel trials, the 3D‐Var system gave a 2.7% improvement in a composite skill score (verified against observations and weighted according to the importance of each field).

With the development of convection-permitting numerical weather prediction the efficient use of high-resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Doppler radar radial winds (DRWs), is now common, although to avoid violating the assumption of uncorrelated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution U.K. model (UKV) using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam observation error statistics. The new results obtained show that the DRW error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly, the estimated observation error correlation length scales are longer than the operational thinning distance. They are dependent both on the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the background error covariance matrix used in the assimilation, although they are, in part, a result of using superobservations and a simplified observation operator. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and hence better use of the high-resolution observations.