Ellrod Index

Ellrod Index

Description

The Ellrod Index is designed to detect regions of Clear-Air-Turbulence (CAT) in the atmosphere. It is computed based on horizontal deformation of air parcels and vertical wind shear. Other methods of forecasting CAT rely on subjective analyses of weather situations.

Application

The Ellrod index is calculated based on the product of horizontal deformation and vertical wind shear derived from numerical model forecast winds aloft. Vertical wind shear generates turbulence. Horizontal deformation refers to the shape of the wind field. If the wind field is such that contrasting air masses are brought together along an “axis of dilation”, then frontogenesis occurs along this axis. Frontogenesis will increase wind speeds and wind shear. The Ellrod Index is especially suitable for detecting turbulence near the jet stream (at upper level troughs and ridges, and the exit region of a jet) but does not account for low level turbulence due to convective activity (e.g. thunderstorms), mountain ranges (mountain waves) etc.

Charts displaying the Ellrod Index at different pressure levels in the atmosphere can provide forecasters and pilots with a visualisation of the location and severity of CAT. Forecasts derived from the Ellrod Index can be found on the U.S. National Oceanic and Atmospheric Administration's (NOAA)  Aviation Weather Center website.

Pilot reports (PIREPs) of CAT are subjective in nature and, therefore, uncalibrated since they depend upon the pilot's perception of the degree of turbulence, which in turn depends upon the weight, speed, and aerodynamic characteristics of the aircraft being flown.

The following table provides a rough guide to the turbulence conditions and the associated Ellrod Index values:

Pilot reported turbulence condition Ellrod Index range

Smooth conditions

0-2
Light turbulence 2-9
Moderate turbulence 9-20
Severe turbulence 20-36
Extreme turbulence >36

 

 

 

 

 

 

Related Articles

 

Categories

SKYbrary Partners:

Safety knowledge contributed by: