Copernicus Arctic Regional Reanalysis covers the recent three decades for the European arctic at 2.5 km grid. The region is characterised by extensive areas with complex orography, cold surfaces and weather with strong local variability. It has a sparse conventional observation network. The reanalysis system is adapted from the HARMONIE-AROME NWP system, focusing on improved treatment of cold surfaces and enhancement on data input for physiographic databases, ocean and sea ice, snow and use of remote sensing data. A large amount of surface observations, including those over icecap, has been collected with quality control, resulting in significantly more use of surface data than in ERA5. High resolution, gap free albedo data over permafrost regions in the region have been used to improve physical realism. Efforts have been spent to enhance representation of the background error covariance model, and for optimal use of large scale information from the lateral boundary model in the analysis. From verification intercomparison, it appears that high resolution and the above mentioned enhancement in enhancement on data input, assimilation algorithm and treatment for cold surface have all contributed to the added values in the reanalysis. In this talk, we also discuss about weakness of assimilation system as found in some of the analysis bust cases. The work is part of the Copernicus Climate Change Service. ECMWF implements this Service on behalf of the European Commission.

The impact of cloud-affected satellite radiances on numerical weather prediction (NWP) accuracy is investigated. The NWP model used is the HIgh Resolution Limited Area Model (HIRLAM). Its four-dimensional variational data assimilation (4D-Var) system was used to assimilate cloud-affected infrared (IR) radiances from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Cloud parameters are modelled internally in the observation operator and used in the radiative transfer calculations. The interaction between the cloud parameters and the model control vector variables is incorporated in the adjoint version of the observation operator, which is used to derive cloud-affected Jacobians prior to the inner-loop minimization of the cost function. The developed framework supports an extensive usage of satellite observations with spatial coverage extended into cloudy regions, which therefore provides additional analysis increments and supports a more accurate description of the atmospheric state. In extended assimilation and forecast experiments the total number of assimilated satellite observations could be increased by approximately 10%. This was associated with a clear indication of a positive impact of cloud-affected radiances on the moisture and geopotential height fields of the NWP model analysis and forecast accuracy when used on top of clear-sky radiance observations. This is revealed by reduced analysis errors of the total integrated water vapour and by reduced forecast errors in the mid and upper troposphere.

An extended observation operator for the direct assimilation of cloud-affected infrared satellite radiances in the High Resolution Limited Area Model (HIRLAM) is examined. The operator includes a simplified moist-physics scheme, which enables the diagnosis of cloudiness in itself using background values of temperature, moisture and surface pressure. Subsequently, a radiative transfer model provides simulated cloud-affected radiances to be used as background equivalents to the satellite observations. The observation operator was evaluated by using infrared observations measured by the Spinning Enhanced Visible and Infrared Imager (SEVIRI). An observation-screening procedure, which incorporates SEVIRI cloud-retrieval products, supports an improved selection of usable cloudy scenes, leading to good agreement between the observations and background equivalents. The tangent-linear observation operator was verified against finite differences from its nonlinear formulation. The increments revealed a near-linear behaviour for the selected channels for a large number of cases. The adjoint observation operator was used to derive brightness-temperature sensitivities with respect to temperature and moisture changes in the presence of radiance-affecting clouds. Differences from the clear-sky sensitivities were found in and below clouds. In a four-dimensional variational data assimilation experiment, cloud-affected SEVIRI observations were assimilated, resulting in additional increments in both moisture and wind fields. The corresponding analysis fields revealed a reduced deviation from the observations for the majority of all cloudy scenes and a reduced bias for wind and temperature in the upper troposphere against independent radiosonde observations. Overall, our results highlight the capability of this observation operator in the HIRLAM assimilation system and encourage its application for the extended usage of cloudy satellite observations in numerical weather prediction. Copyright (C) 2010 Royal Meteorological Society

According to the action (DA/NWP-15) of the ITSC-16, single observation experiments were designed with the Met Office Unified (UM) global and different regional (UM, HIRLAM, and HARMONIE) data assimilation systems. Statistical objective analysis requires the explicit specification of the observationand background-error covariances. In this exercise, we tested the use of background error statistics, derived using different approaches: i) the largely-adopted NMC method, ii) global ensemble analyses projected forward to the short-range forecast time by the Limited Area Model itself, iii) limited-area ensemble variational assimilation and iv) limited-area ensemble variational assimilation with perturbed lateral boundary conditions. The UM (global and regional) and the HIRLAM assimilation systems were tested with background error statistics estimated using the NMC method, while all the four above cited approaches were tested with the HARMONIE regional model.

Four-dimensional variational data assimilation (4D-Var) systems are ideally suited to obtain the best possible initial model state by utilizing information about the dynamical evolution of the. atmospheric state from observations, such as satellite measurements, distributed over a certain period of time. In recent years, 4D-Var systems have been developed for several global and limited-area models. At the same time, spatially and temporally highly resolved satellite observations, as for example performed by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation satellites, have become available.Here we demonstrate the benefit of a regional NWP model's analyses and forecasts gained by the assimilation of those radiances. The 4D-Var system of the High Resolution Limited Area Model (HIRLAM) has been adjusted to utilize three of SEVIRI's infrared channels (located around 6.2 mu m, 7.3 mu m, and 13.4 mu m, respectively) under clear-sky and low-level cloud conditions. Extended assimilation and forecast experiments show that the main direct impact of assimilated SEVIRI radiances on the atmospheric analysis were additional tropospheric humidity and wind increments. Forecast verification reveals a positive impact for almost all upper-air variables throughout the troposphere. Largest improvements are found for humidity and geopotential height in the middle troposphere. The observations in regions of low-level clouds provide especially beneficial information to the NWP system, which highlights the importance of satellite observations in cloudy areas for further improvements in the accuracy of weather forecasts. Copyright (C) 2009 Royal Meteorological Society

An observation operator for Doppler radar radial wind measurements is developed further in this article, based on the earlier work and considerations of the measurement characteristic. The elementary observation operator treats radar observations as point measurements at pre-processed observation heights. Here, modelling of the radar pulse volume broadening in vertical and the radar pulse path bending due to refraction is included to improve the realism of the observation modelling.The operator is implemented into the High Resolution Limited Area Model (HIRLAM) limited area numerical weather prediction (NWP) system. A data set of circa 133 000 radial wind measurements is passively monitored against the HIRLAM six-hourly background values in a 1-month experiment. No data assimilation experiments are performed at this stage. A new finding is that the improved modelling reduces the mean observation minus background (OmB) vector wind difference at ranges below 55 km, and the standard deviation of the radial wind OmB difference at ranges over 25 km.In conclusion, a more accurate and still computationally feasible observation operator is developed. The companion paper (Part II) considers optimal super-observation processing of Doppler radar radial winds for HIRLAM, with general applicability in NWP.

A Doppler radar wind data assimilation system has been developed for the three-dimensional variational data assimilation (3DVAR) scheme of the High Resolution Limited Area Model (HIRLAM). Radar wind observations can be input for the multivariate HIRLAM 3DVAR either as radial wind superobservations (SOs) or as vertical profiles of horizontal wind obtained with the velocity-azimuth display (VAD) technique. The radar wind data handling system, including data processing, quality control, and observation operators for the 3DVAR, are described and evaluated. Background error standard deviation (sigma(b)) in observation space for wind and radial wind have been estimated by the so-called randomization method. The derived values of sigma(b) are used in the quality control of observations and also in the assignment of radar wind observation error standard deviations (sigma(o)). Parallel data assimilation and forecast experiments confirm reasonably tuned error statistics and indicate a small positive impact of radar wind data on the verification scores, for both inputs.

Interpretation of radar measurements is usually obtained through geophysical inversion, such as Z-R relationship or VAD technique. An alternative method to interpret and to quantitatively exploit radar measurements is data assimilation. It consists of modelling the radar measurement with the Numerical Weather Prediction (NWP) model variables. In data assimilation, the difference between the model counterpart and the observation is minimized. The solution is constrained by the NWP model background and by all available observational information and thus the problem is nearly always well-posed, unlike the geophysical inversion. Data assimilation enables exploitation of a wide variety of indirect observations of the NWP model variables through observation modelling. In this article we describe the observation modelling for the Doppler radar radial winds.

A 3-dimensional variational data assimilation (3D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system is described. The HIRLAM 3D-Var is based on the minimisation of a cost function that consists of one term, J(b), which measures the distance between the resulting analysis and a background field, in general a short-range forecast, and another term. J(o), which measures the distance between the analysis and the observations. This paper is concerned with J(o) and the handling of observations, while the companion Paper by Gustafsson et al. (2001) is concerned with the general 3D-Var formulation and with the J(b) term. Individual system components. such as the screening of observations and the observation operators, and other issues, such as the parallelisation strategy for the computer code, are described. The functionality of the observation quality control is investigated and the 3D-Var system is validated through data assimilation and forecast experiments. Results from assimilation and forecast experiments indicate that the 3D-Var assimilation system performs significantly better than two currently used HIRLAM systems. which are based on statistical interpolation. The use of all significant level data from multilevel observation reports is shown to be one factor contributing to the superiority of the 3D-Var system. Other contributing factors are most probably the formulation of the analysis as a single global problem, the use of non-separable structure functions and the variational quality control, which accounts for non-Gaussian observation errors.

During the last decade several attempts of assimilating radar wind data into atmospheric models have been reported by various research groups. Some of these are briefly reviewed here. A three-dimensional variational data assimilation (3D-Var) scheme for the High Resolution Limited Area Model (HIRLAM) forecasting system has been developed and prepared for assimilation of low elevation angle radar radial wind superobservations. The HIRLAM 3D-Var is based on a minimization of a cost function that consists of one term measuring the distance between the resulting analysis and a background field, which is a short-range forecast, and another term measuring the distance between the analysis and the observations. The development required for assimilating the radial wind data includes software for generating and managing the superobservations from polar volume data, a quality control algorithm and an observation operator for providing the model counterpart of the observation. The functionality of the components have been evaluated through assimilation experiments using data from Finnish and Swedish radars and further studies are underway.