Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

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Abstract

The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

Bebbington, D, Rae, S, Bech, J, Codina, B & Picanyol, M 2007,‘Modelling of weather radar echoes from anomalous propagation using a hybrid parabolic equation method and NWP model data’, Natural Hazards and Earth System Sciences, vol. 7, pp. 391–398.

Bech, J, Gjertsen, U & Haase, G 2007,‘Modelling weather radar beam propagation and topographical blockage at northern high latitudes’, Q. J. R. Meteorol. Soc., vol. 133, pp. 1191– 1204.

Chen, S, Cummings, J, Doyle, J, Hodur, RH, Holt, T, Liou, C, Liu, M, Mirin, A, Ridout, J, Schmidt, JM, Sugiyama, G & Thompson, WT 2003,‘COAMPS 3.0 model description – General theory and equations’, NRL Tech. Note NRL/ PUB/7500-0-3-448.

Einfalt, T, Szturc, J & O?ródka, K 2010,‘The quality index for radar precipitation data: a tower of Babel?’, Atmos. Sci. Let., vol. 11, pp. 139–144.

Gekat, F, Meischner, P, Friedrich, K, Hagen, M, Koistinen, J, Michelson, DB & Huuskonen, A 2004,‘The state of weather radar operations, networks and products’ in Weather Radar: Principles and Advanced Applications, ed P Meischner, Springer-Verlag, Berlin – Heidelberg, pp. 1–51.

Germann, U & Joss, J 2004,‘Operational measurement of precipitation in mountainous terrain’ in Weather Radar: Principles and Advanced Applications, ed P Meischner, Springer-Verlag, Berlin – Heidelberg, pp. 52–77.

Houtekamer, PL, Mitchell, HL, Pellerin, G, Buehner, M, Charron, M, Spacek, L & Hansen, B 2005,‘Atmospheric data assimilation with an ensemble Kalman flter: results with real observations’, Mon. Wea. Rev., vol. 133, pp. 604–620.

Meischner, P (ed.) 2004, Weather Radar: Principles and Advanced Applications. Springer-Verlag, Berlin – Heidelberg.

Michelson, D, Einfalt, T, Holleman, I, Gjertsen, U, Friedrich, K, Haase, G, Lindskog, M & Jurczyk, A 2005, Weather radar data quality in Europe – quality control and characterization. COST Action 717, Working document, Luxembourg.

Ośródka, K, Szturc, J & Jurczyk, A 2014,‘Chain of data quality algorithms for 3-D single-polarization radar refectivity (RADVOL-QC system)’, Meteorol. Appl. vol. 21, pp. 256–270.

Rezacova, D, Sokol, Z & Pesice, P 2007,‘A radar-based verifcation of precipitation forecast for local convective storms’, Atmospheric Research, vol. 83, pp. 211–224.

Šálek, M, Cheze, J-L, Handwerker, J, Delobbe, L & Uijlenhoet, R 2004, Radar techniques for identifying precipitation type and estimating quantity of precipitation. COST Action 717, Working Group 1 – A review. Luxembourg,.

Sun, J 2005,‘Convective-scale assimilation of radar data: Progress and challenges’, Q. J. R. Meteorol. Soc., vol. 131, pp. 3439–3463.

Villarini, G & Krajewski, WF 2010,‘Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall’, Surveys in Geophysics, vol. 31, pp. 107–129.

Xue, M, Jung, Y & Zhang, G 2010,‘State estimation of convective storms with a two-moment microphysics scheme and an ensemble Kalman flter: Experiments with simulated radar data’, Q. J. R. Meteorol. Soc., vol. 136, pp. 685–700.

Zappa, M, Beven, KJ, Bruen, M, Cofño, AS, Kok, K, Martin, E, Nurmi, P, Orfla, B, Roulin, E, Schröter, K, Seed, A. Szturc, J, Vehviläinen, B, Germann, U & Rossa, A 2010,‘Propagation of uncertainty from observing systems and NWP into hydrological models: COST-731 Working Group 2’, Atmos. Sci. Lett., vol. 11, pp. 83–91.

Miscellanea Geographica

Regional Studies on Development

Journal Information


CiteScore 2017: 0.73

SCImago Journal Rank (SJR) 2017: 0.404
Source Normalized Impact per Paper (SNIP) 2017: 0.759

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