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A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part II: Analogs Selection and Comparison of Techniques

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  • 1 J.S. Marshall Radar Observatory, Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
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Abstract

Nowcasting is the short-range forecast obtained from the latest observed state. Currently, heuristic techniques, such as Lagrangian extrapolation, are the most commonly used for rainfall forecasting. However, the Lagrangian extrapolation technique does not account for changes in the motion field or growth and decay of precipitation. These errors are difficult to analytically model and are normally introduced by stochastic processes. According to the chaos theory, similar states, also called analogs, evolve in a similar way plus an error related with the predictability of the situation. Consequently, finding these states in a historical dataset provides a way of forecasting that includes all the physical processes such as growth and decay, among others.

The difficulty of this approach lies in finding these analogs. In this study, recent radar observations are compared with a 15-yr radar dataset. Similar states within the dataset are selected according to their spatial rainfall patterns, temporal storm evolution, and synoptic patterns to generate ensembles. This ensemble of analog states is verified against observations for four different events. In addition, it is compared with the previously mentioned Lagrangian stochastic ensemble by means of different scores. This comparison shows the weaknesses and strengths of each technique. This could provide critical information for a future hybrid analog–stochastic nowcasting technique.

Corresponding author address: Aitor Atencia, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke St. W., Burnside Bulding, Office 828, Montreal QC H3A 2K6, Canada. E-mail: aitor.atenciaruizdegopegui@mail.mcgill.ca

Abstract

Nowcasting is the short-range forecast obtained from the latest observed state. Currently, heuristic techniques, such as Lagrangian extrapolation, are the most commonly used for rainfall forecasting. However, the Lagrangian extrapolation technique does not account for changes in the motion field or growth and decay of precipitation. These errors are difficult to analytically model and are normally introduced by stochastic processes. According to the chaos theory, similar states, also called analogs, evolve in a similar way plus an error related with the predictability of the situation. Consequently, finding these states in a historical dataset provides a way of forecasting that includes all the physical processes such as growth and decay, among others.

The difficulty of this approach lies in finding these analogs. In this study, recent radar observations are compared with a 15-yr radar dataset. Similar states within the dataset are selected according to their spatial rainfall patterns, temporal storm evolution, and synoptic patterns to generate ensembles. This ensemble of analog states is verified against observations for four different events. In addition, it is compared with the previously mentioned Lagrangian stochastic ensemble by means of different scores. This comparison shows the weaknesses and strengths of each technique. This could provide critical information for a future hybrid analog–stochastic nowcasting technique.

Corresponding author address: Aitor Atencia, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke St. W., Burnside Bulding, Office 828, Montreal QC H3A 2K6, Canada. E-mail: aitor.atenciaruizdegopegui@mail.mcgill.ca
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