A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part I: Lagrangian Ensemble Technique

Aitor Atencia J. S. Marshall Radar Observatory, Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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Isztar Zawadzki J. S. Marshall Radar Observatory, Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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Abstract

Lagrangian extrapolation of recent radar observations is a widely used deterministic nowcasting technique in operational and research centers. However, this technique does not account for errors due to changes in precipitation motion and to growth and decay, thus limiting forecasting skill. In this work these uncertainties have been introduced in the Lagrangian forecasts to generate different realistic future realizations (ensembles). The developed technique benefits from the well-known predictable large scales (low pass) and introduces stochastic noise in the small scales (high pass). The existence of observed predictable properties in the small scales is introduced in the generation of the stochastic noise. These properties provide realistic ensembles of different meteorological situations, narrowing the spread among members. Finally, some statistical spatial and temporal properties of the final set of ensembles have been verified to determine if the technique developed introduced enough uncertainty while keeping the properties of the original field.

Corresponding author address: Aitor Atencia, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke St. W., Montreal QC H3A 2K6, Canada. E-mail: aitor.atenciaruizdegopegui@mail.mcgill.ca

Abstract

Lagrangian extrapolation of recent radar observations is a widely used deterministic nowcasting technique in operational and research centers. However, this technique does not account for errors due to changes in precipitation motion and to growth and decay, thus limiting forecasting skill. In this work these uncertainties have been introduced in the Lagrangian forecasts to generate different realistic future realizations (ensembles). The developed technique benefits from the well-known predictable large scales (low pass) and introduces stochastic noise in the small scales (high pass). The existence of observed predictable properties in the small scales is introduced in the generation of the stochastic noise. These properties provide realistic ensembles of different meteorological situations, narrowing the spread among members. Finally, some statistical spatial and temporal properties of the final set of ensembles have been verified to determine if the technique developed introduced enough uncertainty while keeping the properties of the original field.

Corresponding author address: Aitor Atencia, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke St. W., Montreal QC H3A 2K6, Canada. E-mail: aitor.atenciaruizdegopegui@mail.mcgill.ca
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