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Precipitation by PERUN
Zacharov, Petr, jr. ; Brožková, R. ; Řezáčová, Daniela
Weather reanalyses are a powerful tool for studying historical weather both at individual points and especially over an area. The detailed reanalysis produced by the PERUN project thus makes it possible to reveal various aspects of the atmosphere to a degree that we are unable to achieve with measurements. Since it is still a model approximation, it is of course necessary to detect systematic biases by verification before its use. Climate model runs, on the other hand, can uncover the future evolution of the atmosphere. Since these calculations cannot yet be verified, it is necessary to validate a historical run of the same model and subtract the revealed systematic errors from the future projections. In the PERUN project, both the historical run and two climate runs up to 2100 have been calculated. In this work, we present a basic verification and validation of the precipitation forecasts and an evaluation of the separation of precipitation into stratiform and convective precipitation and, in addition, into\nliquid and solid precipitation.
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Diagnostics of background error covariances in a connected global and regional data assimilation system
Bučánek, Antonín ; Brožková, Radmila (advisor) ; Sokol, Zbyněk (referee) ; Derková, Mária (referee)
The thesis deals with the preparation of initial conditions for nume- rical weather prediction in high resolution limited area models. It focuses on the problem of preserving the large-scale part of the global driving model analysis, which can not be determined in sufficient quality in limited-area models. For this purpose, the so-called BlendVar scheme is used. The scheme consists of the appli- cation of the Digital Filter (DF) Blending method, which assures the transmission of a large-scale part of the analysis of the driving model to the limited area model, and of the three-dimensional variational method (3D-Var) at high resolution. The thesis focuses on the appropriate background error specification, which is one of the key components of 3D-Var. Different approaches to modeling of background errors are examined, including the possibility of taking into account the flow- dependent character of background errors. Approaches are also evaluated from the point of view of practical implementation. Study of evolution of background errors during DF Blending and BlendVar assimilation cycles leads to a new pro- posal for the preparation of a background error covariance matrix suitable for the BlendVar assimilation scheme. The use of the new background error covariance matrix gives the required property...
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Non-conventional data assimilation in high resolution numerical weather prediction model with study of the slow manifold of the model
Benáček, Patrik ; Brožková, Radmila (advisor) ; Derková, Mária (referee) ; Randriamampianina, Roger (referee)
Satellite instruments currently provide the largest source of infor- mation to today's data assimilation (DA) systems for numerical weather predic- tion (NWP). With the development of high-resolution models, the efficient use of observations at high density is essential to improve small-scale information in the weather forecast. However, a large amount of satellite radiances has to be removed from DA by horizontal data thinning due to uncorrelated observation error assumptions. Moreover, satellite radiances include systematic errors (biases) that may be even larger than the observation signal itself, and must be properly removed prior to DA. Although the Variational Bias Correction (VarBC) scheme is widely used by global NWP centers, there are still open questions regarding its use in Limited-Area Models (LAMs). This thesis aims to tackle the obser- vation error difficulties in assimilating polar satellite radiances in the meso-scale ALADIN system. Firstly, we evaluate spatial- and inter-channel error correla- tions to enhance the positive effect of data thinning. Secondly, we study satellite radiance bias characteristics with the key aspects of the VarBC in LAMs, and we compare the different VarBC configurations with regards to forecast performance. This work is a step towards improving the...
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Broadband radiation scheme fully interacting with clouds
Mašek, Ján ; Brožková, Radmila (advisor) ; Bednář, Jan (referee) ; Kubátová, Brankica (referee)
The parameterization of radiative transfer is a part of numerical weather prediction and general circulation models that is both essential and computationally very expensive, and is therefore subject to neverending compromises between accuracy and computational cost. The present thesis offers an improvement to the existing broadband radiation scheme by revising its critical components - gaseous transmissions, cloud optical properties, and calculation of internal longwave exchanges. The accuracy of the fullspectrum broadband approach is thus raised to the level required for the short range numerical weather forecast. The intermittent update of broadband gaseous transmissions is introduced as a new component, reducing computational cost while preserving the full cloudradiation interaction. The scalability of longwave computations is ensured by adopting the net exchanged rate decomposition with bracketing, improved by an intermittently applied selflearning algorithm determining the interpolation weights. It has been demonstrated that under conditions of operational weather forecasting, this developed scheme is fully competitive with the mainstream approach, due to the improved error balance between the...
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Diagnostics of background error covariances in a connected global and regional data assimilation system
Bučánek, Antonín ; Brožková, Radmila (advisor)
The thesis deals with the preparation of initial conditions for nume- rical weather prediction in high resolution limited area models. It focuses on the problem of preserving the large-scale part of the global driving model analysis, which can not be determined in sufficient quality in limited-area models. For this purpose, the so-called BlendVar scheme is used. The scheme consists of the appli- cation of the Digital Filter (DF) Blending method, which assures the transmission of a large-scale part of the analysis of the driving model to the limited area model, and of the three-dimensional variational method (3D-Var) at high resolution. The thesis focuses on the appropriate background error specification, which is one of the key components of 3D-Var. Different approaches to modeling of background errors are examined, including the possibility of taking into account the flow- dependent character of background errors. Approaches are also evaluated from the point of view of practical implementation. Study of evolution of background errors during DF Blending and BlendVar assimilation cycles leads to a new pro- posal for the preparation of a background error covariance matrix suitable for the BlendVar assimilation scheme. The use of the new background error covariance matrix gives the required property...
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Non-conventional data assimilation in high resolution numerical weather prediction model with study of the slow manifold of the model
Benáček, Patrik ; Brožková, Radmila (advisor) ; Derková, Mária (referee) ; Randriamampianina, Roger (referee)
Satellite instruments currently provide the largest source of infor- mation to today's data assimilation (DA) systems for numerical weather predic- tion (NWP). With the development of high-resolution models, the efficient use of observations at high density is essential to improve small-scale information in the weather forecast. However, a large amount of satellite radiances has to be removed from DA by horizontal data thinning due to uncorrelated observation error assumptions. Moreover, satellite radiances include systematic errors (biases) that may be even larger than the observation signal itself, and must be properly removed prior to DA. Although the Variational Bias Correction (VarBC) scheme is widely used by global NWP centers, there are still open questions regarding its use in Limited-Area Models (LAMs). This thesis aims to tackle the obser- vation error difficulties in assimilating polar satellite radiances in the meso-scale ALADIN system. Firstly, we evaluate spatial- and inter-channel error correla- tions to enhance the positive effect of data thinning. Secondly, we study satellite radiance bias characteristics with the key aspects of the VarBC in LAMs, and we compare the different VarBC configurations with regards to forecast performance. This work is a step towards improving the...
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Diagnostics of background error covariances in a connected global and regional data assimilation system
Bučánek, Antonín ; Brožková, Radmila (advisor)
The thesis deals with the preparation of initial conditions for nume- rical weather prediction in high resolution limited area models. It focuses on the problem of preserving the large-scale part of the global driving model analysis, which can not be determined in sufficient quality in limited-area models. For this purpose, the so-called BlendVar scheme is used. The scheme consists of the appli- cation of the Digital Filter (DF) Blending method, which assures the transmission of a large-scale part of the analysis of the driving model to the limited area model, and of the three-dimensional variational method (3D-Var) at high resolution. The thesis focuses on the appropriate background error specification, which is one of the key components of 3D-Var. Different approaches to modeling of background errors are examined, including the possibility of taking into account the flow- dependent character of background errors. Approaches are also evaluated from the point of view of practical implementation. Study of evolution of background errors during DF Blending and BlendVar assimilation cycles leads to a new pro- posal for the preparation of a background error covariance matrix suitable for the BlendVar assimilation scheme. The use of the new background error covariance matrix gives the required property...
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Diagnostics of background error covariances in a connected global and regional data assimilation system
Bučánek, Antonín ; Brožková, Radmila (advisor) ; Sokol, Zbyněk (referee) ; Derková, Mária (referee)
The thesis deals with the preparation of initial conditions for nume- rical weather prediction in high resolution limited area models. It focuses on the problem of preserving the large-scale part of the global driving model analysis, which can not be determined in sufficient quality in limited-area models. For this purpose, the so-called BlendVar scheme is used. The scheme consists of the appli- cation of the Digital Filter (DF) Blending method, which assures the transmission of a large-scale part of the analysis of the driving model to the limited area model, and of the three-dimensional variational method (3D-Var) at high resolution. The thesis focuses on the appropriate background error specification, which is one of the key components of 3D-Var. Different approaches to modeling of background errors are examined, including the possibility of taking into account the flow- dependent character of background errors. Approaches are also evaluated from the point of view of practical implementation. Study of evolution of background errors during DF Blending and BlendVar assimilation cycles leads to a new pro- posal for the preparation of a background error covariance matrix suitable for the BlendVar assimilation scheme. The use of the new background error covariance matrix gives the required property...
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Multimodel weather forecast comparison
Žáček, Ondřej ; Žák, Michal (advisor) ; Brožková, Radmila (referee)
This thesis analyses comparison and verification of three global numeric weather models, GFS, ECMWF, NEMS. The research subjects are make comparison of their 48-hour forecast with, for this thesis created, index correspondence of models and evaluate predictability of weather. Next, introduce basic verification methods and their application to forecast verification, from previously mentioned models, against surface observations with resolution 2 ř x 2 ř lat/lon between 1. 6. 2017-28. 2. 2018. Results show, that the worst predictability is at areas with continental glaciers, extensive world mountain ranges and at ITCZ area. The best predictability is observed in subtropical anticyclones over the oceans. Verification of temperature we find out significant smoothing of diurnal cycle in all three models. Biases of relative humidity are strongly negative corelated with temperature bias, skill score for relative humidity is worse than for temperature. Performance of mean sea level pressure is the best for all verification metrics from all analysed quantities. Wind speed is for most world overestimated. Results of 3-hour precipitation depends on treshold. Models overestimate frequency of low intensity precipitation, opposite results are observed for high intensity precipitation, break occur at interval...
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