[1] This paper presents the aerosol modeling now part of the ECMWF Integrated Forecasting System (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the forecast model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The forecastonly model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation system described in part 2 of this paper.

Because of careful quality control and relatively large observation errors, the all-sky system produces a weaker observational constraint on moisture analysis than the previous system. However, in single-observation experiments in precipitating areas, using the same observation errors as in the previous 1D-Var retrieval approach, the all-sky system is able to produce 4D-Var analyses that are slightly closer to the observations than before. Despite the nonlinearity of rain and cloud processes, 4D-Var minimizes successfully through the use of an incremental technique. Overall, the quality of the 4D-Var minimization, in terms of number of iterations and conditioning, is unaffected by the new approach.

SUMMARYThis paper presents the operational implementation of a 1D+4D-Var assimilation system of rain-affected satellite observations at the European Centre for Medium-Range Weather Forecasts. The first part describes the methodology and performance analysis of the 1D-Var retrieval scheme in clouds and precipitation that uses Special Sensor Microwave/Imager microwave radiance observations for the estimation of total-column water vapour. The second part shows the global and long-term impact of these observations on both model 4D-Var analyses and medium-range forecasts.The 1D-Var scheme employs a complex observation operator that consists of linearized moist physics parametrization schemes and a multiple-scattering radiative-transfer model. The observation operator shows rather linear behaviour in most situations except in the presence of very intense precipitation suggesting a possible use even for a direct assimilation of radiances in 4D-Var. A bias correction and observation-error estimation method were implemented and indicate stable error behaviour. The 1D-Var algorithm quality control shows the largest failure number in areas with mostly frozen precipitation where the Special Sensor Microwave/Imager channels have little sensitivity to changes in hydrometeor contents. From test analyses on a global scale, a small moisture increase was computed that was greatest in dry subtropical areas. Large-scale and convective precipitation were increased similarly but showed a significantly different geographical distribution. The large-scale precipitation scheme has a stronger sensitivity to moisture changes and therefore moisture increments mainly affect stratiform precipitation distributions. While the global mean moisture fields are only weakly affected by the assimilation of rain-affected observations, the impact on local systems may be quite large. The forecast of synoptic system development through the 4D-Var analysis can be significant.

Earth’s climate is a nonlinear dynamical system with scale-dependent Lyapunov exponents. As such, an important theoretical question for modeling weather and climate is how much real information is carried in a model’s physical variables as a function of scale and variable type. Answering this question is of crucial practical importance given that the development of weather and climate models is strongly constrained by available supercomputer power. As a starting point for answering this question, the impact of limiting almost all real-number variables in the forecasting mode of ECMWF Integrated Forecast System (IFS) from 64 to 32 bits is investigated. Results for annual integrations and medium-range ensemble forecasts indicate no noticeable reduction in accuracy, and an average gain in computational efficiency by approximately 40%. This study provides the motivation for more scale-selective reductions in numerical precision.

SUMMARYThis paper presents the operational implementation of a 1D+4D-Var assimilation system of rain-affected satellite observations at ECMWF performed on 28 June 2005. The first part describes the methodology and performance analysis of the 1D-Var retrieval scheme in clouds and precipitation that uses Special Sensor Microwave/Imager (SSM/I) microwave radiance observations for the estimation of total-column water vapour (TCWV). This part describes the technical implementation of the TCWV observations in 4D-Var as well as the impact analysis.The effect of the TCWV observations implied by precipitation on the 4D-Var analyses is significant and the total information content is comparable to that of the SSM/I, High-resolution InfraRed Sounder and Advanced Microwave Sounding Unit (AMSU-B) radiances. Regions with systematic drying in the analysis persist throughout the forecast while moistening is removed by precipitation after 1-2 days. The corresponding divergence increments reflect the feedback between moisture and dynamics. Forecast evaluation using model analyses exhibits mostly positive relative-humidity forecast scores, in particular at 700 hPa and in the Tropics. Some short-term negative forecast scores are observed for geopotential near 1000 hPa and in the southern hemisphere between days 2-4. Wind scores vary greatly between regions and different forecast lengths. Tropical cyclone tracking forecasts are only slightly affected by a reduced location error spread through the rain assimilation. Comparison to dropsonde observations of wind and temperature shows improvement as does TCWV analysis validation against independent observations from Jason radiometer data. The system has been implemented operationally in June 2005 and will be further developed towards a direct 4D-Var assimilation of radiances in clouds and precipitation.