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  • 1. Dersch, Juergen
    et al.
    Schroedter-Homscheidt, Marion
    Gairaa, Kacem
    Hanrieder, Natalie
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Mueller, Stefan C.
    Santigosa, Lourdes Ramirez
    Sirch, Tobias
    Wilbert, Stefan
    Impact of DNI nowcasting on annual revenues of CSP plants for a time of delivery based feed in tariff2019In: Meteorologische Zeitschrift, ISSN 0941-2948, E-ISSN 1610-1227, Vol. 28, no 3, p. 235-253Article in journal (Refereed)
  • 2.
    Gustafsson, Nils
    et al.
    SMHI, Research Department, Meteorology.
    Berre, Loik
    SMHI, Research Department, Atmospheric remote sensing.
    Hörnquist, Sara
    SMHI, Research Department, Atmospheric remote sensing.
    Huang, X Y
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Navascues, B
    Mogensen, K S
    Thorsteinsson, S
    Three-dimensional variational data assimilation for a limited area model Part I: General formulation and the background error constraint2001In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 53, no 4, p. 425-446Article in journal (Refereed)
    Abstract [en]

    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 minimization 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 the general formulation of the HIRLAM 3D-Var and with Jb. while the companion paper by Lindskog and co-workers is concerned with the handling of observations, including the J(o) term, and with validation of the 3D-Var through extended parallel assimilation and forecast experiments. The 3D-Var minimization requires a pre-conditioning that is achieved by a transformation of the minimization control variable. This change of variable is designed as an operator approximating an inverse square root of the forecast error covariance matrix in the model space. The main transformations are the Subtraction of the geostrophic wind increment, the bi-Fourier transform, and the projection on vertical eigenvectors. The spectral bi-Fourier approach allows one to derive non-separable structure functions in a limited area model. in the form of vertically dependent horizontal spectra and scale-dependent vertical correlations. Statistics have been accumulated from differences between +24 h and +48 h HIRLAM forecasts valid at the same time. Results from single observation impact studies as well as results from assimilation cycles using operational observations are presented. It is shown that the HIRLAM 3D-Var produces assimilation increments in accordance with the applied analysis structure functions, that the fit of the analysis to the observations is in agreement with the assumed error statistics. and that assimilation increments are well balanced. It is also shown that the particular problems associated with the limited area formulation have been solved. These results, together with the results of the companion paper, indicate that the 3D-Var scheme performs significantly better than the statistical interpolation scheme.

  • 3.
    Gustafsson, Nils
    et al.
    SMHI, Research Department, Meteorology.
    Huang, Xiang-Yu
    Yang, Xiaohua
    Mogensen, Kristian
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Vignes, Ole
    Wilhelmsson, Tomas
    Thorsteinsson, Sigurdur
    SMHI, Research Department, Meteorology.
    Four-dimensional variational data assimilation for a limited area model2012In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 64, article id 14985Article in journal (Refereed)
    Abstract [en]

    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.

  • 4.
    Gustafsson, Nils
    et al.
    SMHI, Research Department, Meteorology.
    Janjić, Tijana
    Schraff, Christoph
    Leuenberger, Daniel
    Weissmann, Martin
    Reich, Hendrik
    Brousseau, Pierre
    Montmerle, Thibaut
    Wattrelot, Eric
    Bučánek, Antonĺn
    Mile, Máté
    Hamdi, Rafiq
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Barkmeijer, Jan
    Dahlbom, Mats
    Macpherson, Bruce
    Ballard, Sue
    Inverarity, Gordon
    Carley, Jacob
    Alexander, Curtis
    Dowell, David
    Liu, Shun
    Ikuta, Yasutaka
    Fujita, Tadashi
    Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres2018In: Quarterly Journal of thte Royal Meteorology Society, ISSN 1350-4827, Vol. 144, no 711Article in journal (Other academic)
  • 5. Jarvinen, H.
    et al.
    Salonen, K.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Huuskonen, A.
    Niemela, S.
    Eresmaa, R.
    Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation2009In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 61, no 2, p. 278-287Article in journal (Refereed)
    Abstract [en]

    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.

  • 6.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Dee, Dick
    Tremolet, Yannick
    Andersson, Erik
    Radnoti, Gabor
    Fisher, Mike
    A weak-constraint four-dimensional variational analysis system in the stratosphere2009In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 135, no 640, p. 695-706Article in journal (Refereed)
    Abstract [en]

    A weak-constraint four-dimensional variational (4D-Var) analysis system designed to correct stratospheric model errors has been evaluated. Verifications against upper-level radiosonde temperature observations and Stratospheric Sounding Unit (SSU) radiance data show that the addition of a weak constraint in the stratosphere call greatly reduce analysis bias. Both single-observation analysis experiments and extended assimilations have been performed to help us understand the impact of the model error covariance specifications required for the weak-constraint formulation. It is found that the use of multivariate balance constraints similar to those implemented in background-error covariances can be problematic. Copyright (C) 2009 Royal Meteorological Society

  • 7.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Gustafsson, Nils
    SMHI, Research Department, Meteorology.
    Mogensen, Kristian S.
    Representation of background error standard deviations in a limited area model data assimilation system2006In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 58, no 4, p. 430-444Article in journal (Refereed)
    Abstract [en]

    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.

  • 8.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Gustafsson, Nils
    SMHI, Research Department, Meteorology.
    Navascues, B
    Mogensen, K S
    Huang, X Y
    Yang, X
    Andrae, Ulf
    SMHI, Research Department, Meteorology.
    Berre, Loik
    SMHI, Research Department, Atmospheric remote sensing.
    Thorsteinsson, S
    Rantakokko, J
    Three-dimensional variational data assimilation for a limited area model Part II: Observation handling and assimilation experiments2001In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 53, no 4, p. 447-468Article in journal (Refereed)
    Abstract [en]

    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.

  • 9.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Jarvinen, H
    Michelson, Daniel
    SMHI, Core Services.
    Assimilation of radar radial winds in the HIRLAM 3D-Var2000In: Physics and chemistry of the earth. Part B: Hydrology, oceans and atmosphere, ISSN 1464-1909, E-ISSN 1873-4677, Vol. 25, no 10-12, p. 1243-1249Article in journal (Refereed)
    Abstract [en]

    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. (C) 2000 Elsevier Science Ltd. All rights reserved.

  • 10.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Prognoser av Solstrålning2018In: Polarfront, no 168, p. 41-44Article in journal (Other academic)
  • 11.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Ridal, Martin
    SMHI, Research Department, Meteorology.
    Thorsteinsson, Sigurdur
    Icelandic Meteorological Office, Reykjavík, Iceland.
    Ning, Tong
    Lantmäteriet.
    Data assimilation of GNSS zenith total delays from a Nordic processing centre2017In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 17, no 22, p. 13983-13998Article in journal (Refereed)
  • 12.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Salonen, K
    Jarvinen, H
    Michelson, Daniel
    SMHI, Core Services.
    Doppler radar wind data assimilation with HIRLAM 3DVAR2004In: Monthly Weather Review, ISSN 0027-0644, E-ISSN 1520-0493, Vol. 132, no 5, p. 1081-1092Article in journal (Refereed)
    Abstract [en]

    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.

  • 13. Mueller, Malte
    et al.
    Homleid, Mariken
    Ivarsson, Karl-Ivar
    SMHI, Core Services.
    Koltzow, Morten A. O.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Midtbo, Knut Helge
    Andrae, Ulf
    SMHI, Research Department, Meteorology.
    Aspelien, Trygve
    Berggren, Lars
    SMHI, Core Services.
    Bjorge, Dag
    Dahlgren, Per
    SMHI, Research Department, Meteorology.
    Kristiansen, Jorn
    Randriamampianina, Roger
    Ridal, Martin
    SMHI, Research Department, Meteorology.
    Vignes, Ole
    AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model2017In: Weather and forecasting, ISSN 0882-8156, E-ISSN 1520-0434, Vol. 32, no 2, p. 609-627Article in journal (Refereed)
  • 14.
    Ridal, Martin
    et al.
    SMHI, Research Department, Meteorology.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Gustafsson, Nils
    SMHI, Research Department, Meteorology.
    Haase, Günther
    SMHI, Research Department, Atmospheric remote sensing.
    Optimized advection of radar reflectivities2011In: Atmospheric research, ISSN 0169-8095, E-ISSN 1873-2895, Vol. 100, no 2-3, p. 213-225Article in journal (Refereed)
    Abstract [en]

    A nowcasting system for generation of short-range precipitation forecasts has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). The methodology consists of utilising a time-series of radar reflectivity composites for deriving an advection field, which will give a better representation of the motion of the precipitation pattern compared to a model wind field. The advection field is derived applying a 4-dimensional variational data assimilation technique. The resulting field is then used for a semi-Lagrangian advection of the latest available reflectivity field forward in time. During the forecast, the advected field is gradually replaced by a numerical weather prediction forecast in order to include the onset of convection and advection into the radar coverage area. In an idealised example with simulated observations the functionality of the method is demonstrated. For a case study of a full scale example the resulting precipitation forecast shows large improvements compared to the operational numerical weather prediction model used at SMHI, especially for forecasts up to three hours, where the largest influence from the radar advection occurs. In an objective validation of the structure, amplitude and location of modelled precipitation, where the forecasts are compared to radar observations, these findings are confirmed. The same validation of model runs over a longer time period also clearly indicates that the amplitude, structure and location of the precipitation patterns are significantly improved as compared to a short-range forecast from the operational forecast model used at SMHI. (C) 2010 Elsevier B.V. All rights reserved.

  • 15. Sanchez Arriola, Jana
    et al.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Thorsteinsson, Sigurdur
    SMHI, Research Department, Meteorology.
    Bojarova, Jelena
    Variational Bias Correction of GNSS ZTD in the HARMONIE Modeling System2016In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 55, no 5, article id UNSP 1259Article in journal (Refereed)
  • 16. Stengel, M.
    et al.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Unden, Per
    SMHI, Research Department, Meteorology.
    Gustafsson, Nils
    SMHI, Research Department, Meteorology.
    The impact of cloud-affected IR radiances on forecast accuracy of a limited-area NWP model2013In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 139, no 677, p. 2081-2096Article in journal (Refereed)
    Abstract [en]

    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.

  • 17. Stengel, M.
    et al.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Unden, Per
    SMHI, Research Department, Meteorology.
    Gustafsson, Nils
    SMHI, Research Department, Meteorology.
    Bennartz, R.
    An extended observation operator in HIRLAM 4D-VAR for the assimilation of cloud-affected satellite radiances2010In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 136, no 649, p. 1064-1074Article in journal (Refereed)
    Abstract [en]

    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

  • 18. Stengel, M.
    et al.
    Undén, Per
    SMHI, Research Department, Meteorology.
    Lindskog, Magnus
    SMHI, Research Department, Meteorology.
    Dahlgren, Per
    SMHI, Research Department, Meteorology.
    Gustafsson, Nils
    SMHI, Research Department, Meteorology.
    Bennartz, R.
    Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var2009In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 135, no 645, p. 2100-2109Article in journal (Refereed)
    Abstract [en]

    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

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