Data assimilation (DA) methods for convective‐scale numerical weather prediction at operational centres are surveyed. The operational methods include variational methods (3D‐Var and 4D‐Var), ensemble methods (LETKF) and hybrids between variational and ensemble methods (3DEnVar and 4DEnVar). At several operational centres, other assimilation algorithms, like latent heat nudging, are additionally applied to improve the model initial state, with emphasis on convective scales. It is demonstrated that the quality of forecasts based on initial data from convective‐scale DA is significantly better than the quality of forecasts from simple downscaling of larger‐scale initial data. However, the duration of positive impact depends on the weather situation, the size of the computational domain and the data that are assimilated. Furthermore it is shown that more advanced methods applied at convective scales provide improvements over simpler methods. This motivates continued research and development in convective‐scale DA. Challenges in research and development for improvements of convective‐scale DA are also reviewed and discussed. The difficulty of handling the wide range of spatial and temporal scales makes development of multi‐scale assimilation methods and space–time covariance localization techniques important. Improved utilization of observations is also important. In order to extract more information from existing observing systems of convective‐scale phenomena (e.g. weather radar data and satellite image data), it is necessary to provide improved statistical descriptions of the observation errors associated with these observations.

Abstract. In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the Northern Hemisphere (23.5–65∘ N) show strong variations of the Aeolus wind bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land–sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

Infrared satellite observations are strongly affected by clouds, which complicates their effective use in data assimilation. While observation minus first-guess (FG departure) statistics for cloud-free data are close to a normal (Gaussian) distribution, the occurrence of clouds leads to strongly increased uncertainty, systematic differences between observations and model forecasts and subsequently a clear deviation of the FG departures from the Gaussianity that is usually assumed in data assimilation. This study aims to classify the cloud impact on Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared brightness temperature observations and model equivalents to mitigate the issues of non-Gaussian FG departure statistics for data assimilation. A threshold brightness temperature is introduced that allows us to quantify the cloud impact and to derive an error estimate for FG departures as a function of the cloud impact. The use of the dynamic error estimate leads to substantially more Gaussian FG departure statistics. Based on the dynamic error estimate, an observation error model is derived for the assimilation of infrared brightness temperature observations in an all-sky approach. The proposed method allows us to treat cloud-free and cloud-affected observations in a uniform way, without the need for cloud screening.

Doppler lidar technology has advanced to the point where wind measurements can be made with confidence from space, thus filling a major gap in the global observing system.

This study investigates the possibilities and limitations of airborne Doppler lidar for adaptive observations over the Atlantic Ocean. For the first time, a scanning 2-m Doppler lidar was applied for targeted measurements during the Atlantic "The Observing System Research and Predictability Experiment" (THORPEX) Regional Campaign (A-TReC) in November and December 2003. The DLR lidar system was operated for 28.5 flight hours, and measured 1612 vertical profiles of wind direction and wind speed with a horizontal and vertical resolution of 5-10 km and 100 m, respectively. On average, there were 25 reliable wind values on every profile, which cover 2500 m in the vertical (about one-third of the mean vertical extent of the profiles). A statistical comparison of 33 dropsondes and collocated lidar winds profiles allowed individual estimates of the standard deviation to be assigned to every wind value and to determine threshold values for an objective quality control of the data. The standard deviation of the difference between dropsonde and lidar winds was correlated with the derived quality indices of the lidar data and was within a range of 0.6-1.8 m s Ϫ1. Comparisons of the lidar data to the operational analysis revealed differences of up to Ϯ15 m s Ϫ1. This emphasizes the need for more representative and higher resolved wind measurements in data-sparse regions above the Atlantic Ocean. The study constitutes the basis for the assimilation of the lidar data and impact studies at the European Centre for Medium-Range Weather Forecasts (ECMWF).