This paper describes how operational radar, satellite and lightning data may be used in conjunction with numerical
weather model data to provide remote detection and diagnosis of atmospheric turbulence in and around thunderstorms.
In-cloud turbulence is measured with the NEXRAD Turbulence Detection Algorithm (NTDA) using extensively qualitycontrolled,
ground-based Doppler radar data. A real-time demonstration of the NTDA includes generation of a 3-D
turbulence mosaic covering the CONUS east of the Rocky Mountains, a web-based display, and experimental uplinks of
turbulence maps to en-route commercial aircraft. Near-cloud turbulence is inferred from thunderstorm morphology,
intensity, growth rate and environment data provided by (1) satellite radiance measurements, rates of change, winds, and
other derived features, (2) lightning strike measurements, (3) radar reflectivity measurements and (4) weather model
data. These are combined via a machine learning technique trained using a database of in situ turbulence measurements
from commercial aircraft to create a predictive model. This new capability is being developed under FAA and NASA
funding to enhance current U.S. and international turbulence decision support systems, allowing rapid-update, highresolution,
comprehensive assessments of atmospheric turbulence hazards for use by pilots, dispatchers, and air traffic
controllers. It will also contribute to the comprehensive 4-D weather information database for NextGen.
This paper describes the use of a machine learning data fusion methodology to support development of an automated
short-term thunderstorm forecasting system for aviation users. Information on current environmental conditions is
combined with observations of current storms and derived indications of the onset of rapid change. Predictor data
include satellite radiances and rates of change, satellite-derived cloud type, ground weather station measurements, land
surface and climatology data, numerical weather prediction model fields, and radar-derived storm intensity and
morphology. The machine learning methodology creates an ensemble of decision trees that can serve as a forecast logic
to provide both deterministic and probabilistic estimates of thunderstorm intensity. It also provides evaluation of
predictor importance, facilitating selection of a minimal skillful set of predictor variables and providing a tool to help
determine what weather regimes may require specialized forecast logic. This work is sponsored by the Federal Aviation
Administration's Aviation Weather Research Program. Its aim is to contribute to the development of the Consolidated
Storm Prediction for Aviation (CoSPA) system, which is being developed in collaboration with the MIT Lincoln
Laboratory and the NOAA Earth System Research Laboratory's Global Systems Division. CoSPA is scheduled to
become part of the NextGen Initial Operating Capability by 2012.
Conference Committee Involvement (1)
Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support
13 August 2008 | San Diego, California, United States
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