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A method for early detection of the systems that become tropical cyclones (TCs) in the Atlantic hurricane basin is developed using the SeaWinds scatterometer aboard the QuikSCAT satellite. The method is based on finding positive vorticity signals exceeding a threshold magnitude and horizontal extent within the swath of vector wind observations. The thresholds applied herein are subjectively derived from the TCs of the 1999 Atlantic hurricane season. The thresholds are applied to two sets of data for the 2000 season: research-quality data and near-real-time (< 3-h delay) data (available starting 18 August 2000). For the 2000 research-quality data, 7 of 18 TCs had signals that were detected an average of 27 h before the National Hurricane Center (NHC) classified them as tropical depressions. For the near-real-time data, 3 of 12 TCs had signals that were detected an average of 20 h before NHC classification. The SeaWinds scatterometer is a powerful new tool that, in addition to other conventional products (e.g., satellite images that determine if convection is organized and persistent), could help the NHC detect potential TCs earlier.
A method for early detection of the systems that become tropical cyclones (TCs) in the Atlantic hurricane basin is developed using the SeaWinds scatterometer aboard the QuikSCAT satellite. The method is based on finding positive vorticity signals exceeding a threshold magnitude and horizontal extent within the swath of vector wind observations. The thresholds applied herein are subjectively derived from the TCs of the 1999 Atlantic hurricane season. The thresholds are applied to two sets of data for the 2000 season: research-quality data and near-real-time (< 3-h delay) data (available starting 18 August 2000). For the 2000 research-quality data, 7 of 18 TCs had signals that were detected an average of 27 h before the National Hurricane Center (NHC) classified them as tropical depressions. For the near-real-time data, 3 of 12 TCs had signals that were detected an average of 20 h before NHC classification. The SeaWinds scatterometer is a powerful new tool that, in addition to other conventional products (e.g., satellite images that determine if convection is organized and persistent), could help the NHC detect potential TCs earlier.
Abstract
Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications.
Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship’s heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations.
Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 60°) for ship speeds less than 2.0 m s−1. The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (±10°) depends upon the ship, winds, and currents in the vessel’s region of operation.
Abstract
Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications.
Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship’s heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations.
Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 60°) for ship speeds less than 2.0 m s−1. The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (±10°) depends upon the ship, winds, and currents in the vessel’s region of operation.
