Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA-Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA-Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3-hourly at half the horizontal resolution). This paper describes the general setup of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re-forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA-Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global-mean correlation with monthly-mean GPCP data is increased from 67% This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

In model cycle 35r3 (Cy35r3) of the ECMWF Integrated Forecast System (IFS), the momentum deposition from small-scale nonorographic gravity waves is parameterized by the Scinocca scheme, which uses hydrostatic nonrotational wave dynamics to describe the vertical evolution of a broad, constant, and isotropic spectrum of gravity waves emanating from the troposphere. The Cy35r3 middle atmosphere climate shows the following: (i) an improved representation of the zonal-mean circulation and temperature structure; (ii) a realistic parameterized gravity wave drag; (iii) a reasonable stationary planetary wave structure and stationary wave driving in July and an underestimate of the generation of stationary wave activity in the troposphere and stationary wave driving in January; (iv) an improved representation of the tropical variability of the stratospheric circulation, although the westerly phase of the semiannual oscillation is missing; and (v) a realistic horizontal distribution of momentum flux in the stratosphere. By contrast, the middle atmosphere climate is much too close to radiative equilibrium when the Scinocca scheme is replaced by Rayleigh friction, which was the standard method of parameterizing the effects of nonorographic gravity waves in the IFS prior to Cy35r3. Finally, there is a reduction in Cy35r3 short-range high-resolution forecast error in the upper stratosphere.

ECMWF's preparations for cloud and rain assimilation encompass development of linearized physics, improved satellite data utilization, a new humidity analysis, and another look at the "spindown" problem. European, American, and Japanese satellite agencies have a number of Earth-observation missions with the objective of providing improved measurements of components of the global hydrological cycle-clouds, precipitation, soil moisture, and water vapor-from a range of operational platforms in both polar and geostationary orbits. Significant development of data assimilation methods will be necessary to make full use of both the existing and new types of observations of the water cycle. The small-scale

SUMMARYA new cloud scheme has been developed specifically for the purposes of variational data assimilation, a task complicated by the inherent nonlinearity of many cloud processes. The aim was to retain the most important features of the ECMWF nonlinear cloud scheme, while removing much of the complexity and as many of the discrete transitions as possible. The scheme thus retains a simplified link to convective detrainment, and uses a similar formulation for the production of precipitation. A flexible statistical cloud scheme approach is used, where the diagnosis of stratiform cloud fraction and condensate amount depend on assumptions concerning subgrid-scale fluctuations. A novel aspect of the new scheme is the treatment of precipitation evaporation that specifically takes this subgrid distribution of humidity variability into account.The scheme is tested by comparing physical tendencies of thermodynamical quantities, calculated for a series of input temperature and humidity profiles, to those produced by the complex prognostic cloud scheme used in the forecast model. Additionally, a series of model integrations are performed, with the full prognostic scheme used in operational forecasts replaced by the new scheme described here. In both cases, using the prognostic scheme as a metric, the new scheme improves on the current assimilation cloud scheme for many attributes such as cloud cover and ice water content, both in the tropics and midlatitudes. In particular the new diagnostic scheme addresses the overriding weakness of the current diagnostic scheme which produces almost no precipitation in the tropics.Tangent-linear (TL) and adjoint versions of the new scheme have been constructed and it is demonstrated that the scheme can successfully and robustly perform TL integrations for a 12-hour window, and significantly improves the TL approximation of the simplified linearized model to finite difference calculations using the full nonlinear forecast model.

SUMMARYFour-dimensional variational (4D-Var) assimilation schemes assume the linearity of their forward model in the vicinity of prior information and usually do not properly handle variables that have finer temporal and spatial scales in the real world than in the forward model. Hence cloud-affected satellite infrared radiances are discarded from numerical weather-prediction 4D-Var systems, despite the critical need of observations within the cloudy regions. This paper suggests the reappraisal of that choice, subject to achieving improvements in the numerical simulation of cloudiness.A new observation operator, that computes cloud-affected infrared radiances from 4D-Var control variables, namely atmospheric temperature, humidity, ozone, surface temperature and surface pressure, is presented. The vertical distributions of cloud cover and of cloud condensate are diagnosed in the operator itself. The goal of this paper is to assess the feasibility of using it to assimilate cloud-affected infrared radiances, such as those from the narrow-band Advanced Infrared Sounder on-board the Aqua platform or those from the broad-band Meteosat Visible and Infrared Imager. It is shown that there is a potential benefit in assimilating directly in 4D-Var some of the upper-tropospheric channels at 4.5, 6.3 and 14.3 µm in the presence of clouds, for instance the 6.3 µm channel on board all the geostationary satellites. The approach is illustrated with one-dimensional variational retrievals collocated with radiosonde observations.

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This article explores the uncertainties associated with evaluating a global atmospheric model with radar reflectivity observations. A forward operator for radar reflectivity (ZmVar) is described and used for the comparison of the ECMWF global numerical weather prediction model short-range forecasts with radar data from CloudSat. A sensitivity study is performed to determine which differences can be attributed to either specific radar forward operator assumptions or to deficiencies in the global model. The results show that model-derived reflectivities are particularly sensitive to the definition of subgrid precipitation fraction, as precipitation dominates the radar reflectivity signal, but also to the choice of particle size distribution and scattering properties of the different hydrometeor categories. However, there are a number of consistent differences in the reflectivity comparison that are significantly larger than can be explained by the sensitivity tests. This suggests that these discrepancies are due to deficiencies in the model cloud and precipitation frequency of occurrence and hydrometeor water contents. These include too frequent occurrence at high altitudes, too low occurrence in the Southern Hemisphere storm track and an overestimate of rain in warm-phase low cloud. The study shows the value of CloudSat for evaluating the model in terms of radar reflectivity and highlights the importance of taking into account forward operator uncertainties for both model evaluation and data assimilation applications.

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