No abstract

Tropical cyclone wind-pressure relationships are reexamined using 15 yr of minimum sea level pressure estimates, numerical analysis fields, and best-track intensities. Minimum sea level pressure is estimated from aircraft reconnaissance or measured from dropwindsondes, and maximum wind speeds are interpolated from best-track maximum 1-min wind speed estimates. The aircraft data were collected primarily in the Atlantic but also include eastern and central North Pacific cases. Global numerical analyses were used to estimate tropical cyclone size and environmental pressure associated with each observation. Using this dataset (3801 points), the influences of latitude, tropical cyclone size, environmental pressure, and intensification trend on the tropical cyclone wind-pressure relationships were examined. Findings suggest that latitude, size, and environmental pressure, which all can be quantified in an operational and postanalysis setting, are related to predictable changes in the wind-pressure relationships. These factors can be combined into equations that estimate winds given pressure and estimate pressure given winds with greater accuracy than current methodologies. In independent testing during the 2005 hurricane season (524 cases), these new wind-pressure relationships resulted in mean absolute errors of 5.3 hPa and 6.2 kt compared with the 7.7 hPa and 9.0 kt that resulted from using the standard Atlantic Dvorak wind-pressure relationship. These new wind-pressure relationships are then used to evaluate several operational windpressure relationships. These intercomparisons have led to several recommendations for operational tropical cyclone centers and those interested in reanalyzing past tropical cyclone events.

The first Advanced Microwave Sounding Unit (AMSU) was launched aboard the NOAA-15 satellite on 13 May 1998. The AMSU is well suited for the observation of tropical cyclones because its measurements are not significantly affected by the ice clouds that cover tropical storms. In this paper, the following are presented: 1) upper-tropospheric thermal anomalies in tropical cyclones retrieved from AMSU data, 2) the correlation of maximum temperature anomalies with maximum wind speed and central pressure, 3) winds calculated from the temperature anomaly field, 4) comparison of AMSU data with GOES and AVHRR imagery, and 5) tropical cyclone rainfall potential. The AMSU data appear to offer substantial opportunities for improvement in tropical cyclone analysis and forecasting.

The standard method for estimating the intensity of tropical cyclones is based on satellite observations (Dvorak technique) and is utilized operationally by tropical analysis centers around the world. The technique relies on image pattern recognition along with analyst interpretation of empirically based rules regarding the vigor and organization of convection surrounding the storm center. While this method performs well enough in most cases to be employed operationally, there are situations when analyst judgment can lead to discrepancies between different analysis centers estimating the same storm.In an attempt to eliminate this subjectivity, a computer-based algorithm that operates objectively on digital infrared information has been developed. An original version of this algorithm (engineered primarily by the third author) has been significantly modified and advanced to include selected ''Dvorak rules,'' additional constraints, and a time-averaging scheme. This modified version, the Objective Dvorak Technique (ODT), is applicable to tropical cyclones that have attained tropical storm or hurricane strength.The performance of the ODT is evaluated on cases from the 1995 and 1996 Atlantic hurricane seasons. Reconnaissance aircraft measurements of minimum surface pressure are used to validate the satellite-based estimates. Statistical analysis indicates the technique to be competitive with, and in some cases superior to, the Dvorak-based intensity estimates produced operationally by satellite analysts from tropical analysis centers. Further analysis reveals situations where the algorithm needs improvement, and directions for future research and modifications are suggested.