Premium
The local wavelet‐based organization index – Quantification, localization and classification of convective organization from radar and satellite data
Author(s) -
Brune Sebastian,
Buschow Sebastian,
Friederichs Petra
Publication year - 2021
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3998
Subject(s) - wavelet , thresholding , brightness temperature , radar , meteorology , convection , scale (ratio) , precipitation , brightness , remote sensing , geology , computer science , geography , physics , artificial intelligence , optics , cartography , telecommunications , image (mathematics)
We present a revised local wavelet‐based organization index (LW), which is applied to both synthetic fields and meteorological fields of precipitation and brightness temperature for different types of convective organization. The LW consists of three components that describe the scale (LW sc ), quantify the intensity (LW in ), and analyze the anisotropy (LW ai ) of the convection. It is based solely on 2D wavelet decomposition and does not require a clustering algorithm or thresholding. The great advantages of LW are that it localizes the organization in space, is a universally applicable measure of organization and can be calculated with little effort. A comparison with other organizational metrics shows that LW better describes the structure and organization of convection. We analyze the LW components in the vicinity of severe weather reports related to single‐cell storms, supercells or multi‐cell storms. Composites thereof reveal that large hail and heavy precipitation are particularly local events that lead to small‐scale and very intense precipitation. Especially in the case of hail, LW ai shows the structure of a hook echo, which is expected, since hail events are usually associated with supercells. In relation to wind gusts, precipitation is rather large‐scale and linearly oriented, since strong wind gusts are often a consequence of linearly organized systems such as squall lines or derechos. The brightness temperature analysis provides similar results to those of the rain rates. Structures like the typical radar hook echo are not visible in the satellite data, but overshooting tops caused by strong updraughts are recognized as small‐scale, intense regions. The three LW components are used to classify showers, thunderstorms and precipitation/hail events with the help of a neural network, where showers are mainly classified by their scale characteristics, and precipitation/hail events by their pronounced intensity.