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A Method to Estimate the 3D–Time Structure of the Raindrop Size Distribution Using Radar and Disdrometer Data

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  • 1 Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
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Abstract

A geostatistical method to quantify the small-scale 3D–time structure of the drop size distribution (DSD) from the ground level up to the melting layer using radar and disdrometer data is presented. First, 3D–time radar reflectivity fields are used to estimate the large-scale properties of a rain event, such as the apparent motion, spatial anisotropy, and temporal innovation. The retrieved quantities are then combined with independent disdrometer time series to estimate the 3D–time variogram of each DSD parameter. A key point in the procedure is the use of a new metric for measuring distances in moving anisotropic rainfall fields. This metric has the property of being invariant with respect to the specific rainfall parameter being considered, that is, it is identical for the radar reflectivity, rain rate, mean drop diameter, drop concentration, or any other weighted moment of the DSD. Evidence is shown of this fact and some illustrations for a stratiform event in southern France and a convective case in the midwestern United States are provided. The proposed framework offers a series of new and interesting applications, including the possibility to compare the space–time structure of different rain events, to interpolate radar reflectivity fields in space–time and to simulate 3D–time DSD fields at high spatial and temporal resolutions.

Supplemental information related to this paper is available at the Journals Online website.

Corresponding author address: Marc Schleiss, Civil and Environmental Engineering, Princeton University, E-Quad E316, Olden Street, Princeton, NJ 08540. E-mail: schleiss.marc@gmail.com

Abstract

A geostatistical method to quantify the small-scale 3D–time structure of the drop size distribution (DSD) from the ground level up to the melting layer using radar and disdrometer data is presented. First, 3D–time radar reflectivity fields are used to estimate the large-scale properties of a rain event, such as the apparent motion, spatial anisotropy, and temporal innovation. The retrieved quantities are then combined with independent disdrometer time series to estimate the 3D–time variogram of each DSD parameter. A key point in the procedure is the use of a new metric for measuring distances in moving anisotropic rainfall fields. This metric has the property of being invariant with respect to the specific rainfall parameter being considered, that is, it is identical for the radar reflectivity, rain rate, mean drop diameter, drop concentration, or any other weighted moment of the DSD. Evidence is shown of this fact and some illustrations for a stratiform event in southern France and a convective case in the midwestern United States are provided. The proposed framework offers a series of new and interesting applications, including the possibility to compare the space–time structure of different rain events, to interpolate radar reflectivity fields in space–time and to simulate 3D–time DSD fields at high spatial and temporal resolutions.

Supplemental information related to this paper is available at the Journals Online website.

Corresponding author address: Marc Schleiss, Civil and Environmental Engineering, Princeton University, E-Quad E316, Olden Street, Princeton, NJ 08540. E-mail: schleiss.marc@gmail.com

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