Cathy Kessinger

The Identification and Verification of Hazardous Convective Cells Over Oceans Using Visible and Infrared Satellite Observations

Abstract Three algorithms based on geostationary visible and infrared (IR) observations are used to identify convective cells that do (or may) present a hazard to aviation over the oceans. The performance of these algorithms in detecting potentially hazardous cells is determined through verification with Tropical Rainfall Measuring Mission (TRMM) satellite observations of lightning and radar reflectivity, which provide internal information about the convective cells. The probability of detection of hazardous cells using the satellite algorithms can exceed 90% when lightning is used as a criterion for hazard, but the false-alarm ratio with all three algorithms is consistently large (∼40%), thereby exaggerating the presence of hazardous conditions. This shortcoming results in part from the algorithms’ dependence upon visible and IR observations, and can be traced to the widespread prevalence of deep cumulonimbi with weak updrafts but without lightning over tropical oceans, whose origin is attributed to sign...

Convection diagnosis and nowcasting for oceanic aviation applications

An oceanic convection diagnosis and nowcasting system is described whose domain of interest is the region between the southern continental United States and the northern extent of South America. In this system, geostationary satellite imagery are used to define the locations of deep convective clouds through the weighted combination of three independent algorithms. The resultant output, called the Convective Diagnosis Oceanic (CDO) product, is independently validated against space-borne radar and lighting products from the Tropical Rainfall Measuring Mission (TRMM) satellite to ascertain the ability of the CDO to discriminate hazardous convection. The CDO performed well in this preliminary investigation with some limitations noted. Short-term, 1-hr and 2-hr nowcasts of convection location are performed within the Convective Nowcasting Oceanic (CNO) system using a storm tracker. The CNO was found to have good statistical performance at extrapolating existing storm positions. Current work includes the development and implementation of additional atmospheric features for nowcasting convection initiation and to improve nowcasting of mature convection evolution.

Fuzzy Logic Applications

Fuzzy logic originated in the mid-1960s with the work of Lotfi Zadeh, as described in Chapter 6. However, it wasn’t until the 1990s that it was widely recognized as a valuable tool in the atmospheric and other environmental sciences. One of fuzzy logic’s first successful applications in the atmospheric sciences was the Machine Intelligence Gust Front Detection Algorithm (MIGFA) developed at the Massachusetts Institute of Technology’s Lincoln Laboratory (Delanoy and Troxel 1993). Since then, a wide range of environmental science problems have been successfully addressed using fuzzy data analysis and algorithm development techniques. The purpose of this chapter is to supplement the introduction to fuzzy logic presented in Chapter 6 by describing a few selected applications of fuzzy logic that provide a flavor of the power and flexibility of this approach.

Developing a Global Turbulence and Convection Nowcast and Forecast System

Turbulence is widely recognized as the leading cause of injuries to flight attendants and passengers on commercial air carriers. Turbulence encounters frequently occur in oceanic and remote regions where ground-based observations are sparse, making hazard characterization more difficult, and where current World Area Forecast System products provide only low temporal and spatial resolution depictions of potential hazards. This paper describes a new effort to develop a global diagnosis and forecast system that will augment and enhance international turbulence and convective SIGMETs and provide authoritative global turbulence data for the NextGen 4-D database. This fully automated system, modeled on the FAA’s Graphical Turbulence Guidance (GTG) and GTG Nowcast systems, will provide 3-D probabilistic turbulence nowcasts and forecasts globally above 10,000 feet MSL for 0-36 hour lead times, comprehensively addressing clear-air turbulence (CAT), mountain wave turbulence (MWT), and convectively-induced turbulence (CIT). The system will employ NCEP Global Forecast System model output and data from NASA and other national and international satellite assets to produce the CAT and MWT diagnoses based on both model-based turbulence diagnostics and satellite-based turbulence detection algorithms. The convective nowcast methodology makes use of GFS data and operational satellite data from GOES, Meteosat and MTSAT, and will be tuned and verified using data from NASA’s TRMM, Cloudsat and MODIS instruments. The convective nowcasts will be coupled with the GFS environmental information to assess the near-term likelihood of CIT. This paper presents an overview of the system elements and initial algorithm development results. Future work will perform additional development and verification using comparisons with automated quantitative in situ turbulence reports, AIREPs and AMDAR data. A real-time demonstration including a webbased display and cockpit uplinks is also planned.

Preliminary observational results from the NASA TCAD field program

On June 2-25, 1999, an observational field program in the vicinity of Greeley, CO was conducted by the NASA Aviation Safety Program (AvSP) with the goal of mapping turbulence location and intensities within convective clouds to features observable to Doppler radars. This field program was called the Turbulence Characterization and Detection (TCAD) field program. Three cloud penetrating aircraft (two equipped with onboard, forward-pointing Doppler radar), two groundbased Doppler radars, and a mobile’sounding van participated in the experiment. Preliminary analyses of the data are presented to show the utility of on-board Doppler radar to detect and classify in-cloud turbulence.