Magnus Lindskog

Three‐dimensional variational data assimilation for a limited area model

ABSTRACT A 4-dimensional variational data assimilation (4D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system is described in this article. The innovative approaches to the multi-incremental formulation, the weak digital filter constraint and the semi-Lagrangian time integration are highlighted with some details. The implicit dynamical structure functions are discussed using single observation experiments, and the sensitivity to various parameters of the 4D-Var formulation is illustrated. To assess the meteorological impact of HIRLAM 4D-Var, data assimilation experiments for five periods of 1 month each were performed, using HIRLAM 3D-Var as a reference. It is shown that the HIRLAM 4D-Var consistently out-performs the HIRLAM 3D-Var, in particular for cases with strong mesoscale storm developments. The computational performance of the HIRLAM 4D-Var is also discussed. The review process was handled by Subject Editor Abdel Hannachi

Doppler radar wind data assimilation with HIRLAM 3DVAR

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 (sb) in observation space for wind and radial wind have been estimated by the so-called randomization method. The derived values of sb are used in the quality control of observations and also in the assignment of radar wind observation error standard deviations (so). 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.

Assimilation of radar radial winds in the HIRLAM 3D-var

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

AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model

AbstractSince October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Meteo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite...

Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

Abstract 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 theHIRLAM 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.

Representation of background error standard deviations in a limited area model data assimilation system

Two different approaches for improving the representation of background error standard deviations have been developed and introduced into the HIRLAM high-resolution limited area model 3-D variational data assimilation scheme. One of the methods utilizes a horizontally varying climatological background error standard deviation field, estimated from a time-series of innovations. The second approach attempts to take temporal and spatial variations of the background error standard deviations into account by applying an Eady instability measure to the background field. The two approaches are described in detail and their functionality is demonstrated. Parallel data assimilation and forecasts experiments indicate a slightly positive impact on average verification scores, and in addition a positive impact is demonstrated for an individual synoptically active case.

Variational Bias Correction of GNSS ZTD in the HARMONIE Modeling System

AbstractTo fill the gap in the observation system for humidity, the HIRLAM–ALADIN Research on Mesoscale Operational NWP in Euromed (HARMONIE) limited-area high-resolution kilometer-scale model has been prepared for assimilation of Global Navigation Satellite System (GNSS) zenith total delay (ZTD) observations. The observation-processing system includes data selection, bias correction, quality control, and a GNSS observation operator for data assimilation. A large part of the bias between observations and model equivalents comes from the relatively low model top used in the HARMONIE experiments. The functionality of the different observation-processing components was investigated in detail as was the overall performance of the GNSS ZTD data assimilation. This paper contains an extensive description of the GNSS ZTD observation-processing system and a comparison of a newly introduced variational bias correction for GNSS ZTD data with an alternative static bias correction, as well as a detailed analysis of th...

Assimilation of Radar Data in Numerical Weather Prediction (NWP) Models

Radar data have exciting potential for improving forecasts from operational numerical weather prediction (NWP) models. This potential, already partially realised, arises from a combination of developments. NWP models of the European National Meteorological Services (NMS) are now running routinely at the 10 km grid scale and in a few years will be moving to resolutions of the order of 2 km. Such high resolution models require correspondingly high resolution wind and moisture data for initialisation, which radar networks are well placed to deliver. Secondly, NWP data assimilation techniques have advanced considerably in the 1990s, with the arrival of techniques capable of extracting information from time sequences of observations only indirectly related to model prognostic variables. The first decade of the twenty-first century is likely to see further improvements in computing power, microphysical parametrisation and assimilation methods which will enable better exploitation of the information available from weather radars. Thirdly, developments in radar networking and processing around Europe are beginning to reach a maturity which makes feasible the routine operational delivery of quality controlled radar information of an accuracy sufficient for worthwhile NWP assimilation.