Abstract

This study explores the inverse relationship between sea level pressure and tropical cyclones in the tropical Atlantic (TA). Upper-air observations, the National Centers for Environmental Prediction (formerly the National Meteorological Center)/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, and regional SSTs provide clues as to the physics of this relationship using composite and regression methods. Stratification of upper-air data by sea level pressure anomalies in the TA yields several interesting results, including anomalously high (low) pressure association with relatively dry (moist) middle levels, cooler (warmer) midlevel temperatures, and stronger (weaker) 200–850-mb vertical wind shears. The configuration of these composite wind differences suggests that higher summertime pressure in the TA is associated with an anomalously strong tropical upper tropospheric trough (TUTT) circulation. The observations show systematic association between the composite moisture, temperature, and wind differences. Studies of longwave sensitivity using a two stream model show that the moisture field dominates the longwave radiative cooling; hence, dry midlevels enhance cooling of the atmosphere. The effects of SST variations and tropical cyclones on TA pressure anomalies suggest that summertime pressure in this region is strongly influenced by additional (unresolved) climate forcings. These findings lead to a hypothesis that explains both the persistent nature of the summertime pressure (in the TA) as well as how variations of this pressure modulate the TUTT circulation strength. The hypothesis states that positive feedbacks operate between pressure/subsidence variations, midlevel moisture, and differential longwave radiative cooling that affects local baroclinicity (i.e., TUTT). When pressures are anomalously high, subsidence is greater and middle levels are dryer, resulting in increased atmospheric cooling to space and increased baroclinicity. Hence, pressure-related variations of both the midlevel moisture field and the TUTT circulation result in modulations of the upper-level winds and vertical wind shears in the TA. These, in turn, are found to be the primary cause of the observed pressure–tropical cyclone relationship; higher tropical Atlantic pressure results in an environment that is dryer and more sheared and, thus, less favorable for tropical cyclone formation and development.

Abstract

A method to predict the June–September (JJAS) Caribbean sea level pressure anomalies (SLPAs) using data available the previous April is described. The method involves the creation of a multiple linear regression equation that uses three predictors. These predictors are the January–March (JFM) North Atlantic (50°–60°N, 10°–50°W) sea surface temperature anomalies (SSTAs), JFM Niño 3.4 (5°N–5°S, 120°–170°W) SSTAs, and the strength of the eastern Atlantic subtropical pressure ridge measured in March between 20° and 30°W. The physical role of each of the predictors in determining the variations of Caribbean SLPAs is discussed. The forecast equation is developed using a training dataset covering the period 1950–95 (46 yr) and is tested upon independent data covering the period 1903–49 where the data availability permits (42 yr). Results suggest that skillful forecasts are possible. The method reduced the interannual variance of the JJAS Caribbean SLPAs by 50% in the developmental dataset and by 40% in the independent dataset. Separate forecasts for the June–July and August–September SLPAs are also developed, tested, and discussed.

Abstract

A large, low-level thunderstorm outflow boundary was observed as it exited from beneath the cirrus canopy of Hurricane Luis following a period of intense convection in the storm’s eyewall. A description of the feature and a short summary of its behavior are presented.

Abstract

The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts.

Abstract

This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within 5 h prior and 3 h after analysis time and makes use of prescribed methods to move observations to a common flight level (CFL; 700 hPa) for analysis and to reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the undersampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is <10 kt (<5 m s⁻¹).

Abstract

The Atlantic major hurricanes during the period of 1995–2005 are examined using best-track data, aircraft-based observations of central pressure, and infrared (IR) satellite images. There were 45 Atlantic major hurricanes (Saffir–Simpson category 3 or higher) during this 11-yr period, which is well above the long-term average. Descriptive statistics (e.g., average, variability, and range) of various characteristics are presented, including intensity, intensification rate, major hurricane duration, location, storm motion, size, and landfall observations. IR images are shown along with IR-derived quantities such as the digital Dvorak technique intensity and IR-defined cold cloud areas. In addition to the satellite intensity estimates, the associated component IR temperatures are documented. A pressure–wind relationship is evaluated, and the deviations of maximum intensity measurements from the pressure–wind relationship are discussed.

The Atlantic major hurricane activity of the 1995–2005 period distinctly exceeds the long-term average; however, the average location where major hurricanes reach maximum intensity has not changed. The maximum intensity for each 1995–2005 Atlantic major hurricane is given both as the highest maximum surface wind (Vmax) and the lowest minimum sea level pressure (MSLP). Comparisons are made to other Atlantic major hurricanes with low MSLP back to 1950. Maximum 24-h intensification rates average 21.1 m s⁻¹ day⁻¹ and range up to 48.8 m s⁻¹ day⁻¹ in terms of Vmax. The largest 24-h MSLP decreases average 34.2 hPa and range from 15 to 97 hPa. Major hurricane duration averages 2.7 days with a maximum of 10 days. Hurricane size, as given by the average radius of gale force wind at maximum intensity, averages 250.8 km and has an extremely large range from 92.5 to 427.4 km.

The very strong 1997–98 El Niño was the first major event in which numerous forecasting groups participated in its real-time prediction. A previously developed simple statistical tool—the El Niño–Southern Oscillation Climatology and Persistence (ENSO–CLIPER) model—is utilized as a baseline for determination of skill in forecasting this event. Twelve statistical and dynamical models were available in real time for evaluation. Some of the models were able to outperform ENSO–CLIPER in predicting either the onset or the decay of the 1997–98 El Niño, but none were successful at both for a medium-range two season (6–8 months) lead time. There were no models, including ENSO–CLIPER, able to anticipate even one-half of the actual amplitude of the El Niño's peak at medium-range (6–11 months) lead. In addition, none of the models showed skill (i.e., lower root-mean-square error than ENSO–CLIPER) at the zero season (0–2 months) through the two season (6–8 months) lead times. No dynamical model and only two of the statistical models [the canonical correlation analysis (CCA) and the constructed analog (ANALOG)] outperformed ENSO–CLIPER by more than 5% of the root-mean-square error at the three season (9–11 months) and four season (12–14 months) lead time. El Niño impacts were correctly anticipated by national meteorological centers one half-year in advance, because of the tendency for El Niño events to persist into and peak during the boreal winter. Despite this, the zero to two season (0–8 month) forecasts of the El Niño event itself were no better than ENSO–CLIPER and were, in that sense, not skillful—a conclusion that remains unclear to the general meteorological and oceanographic communities.

Abstract

A statistical prediction method, which is based entirely on the optimal combination of persistence, month-to-month trend of initial conditions, and climatology, is developed for the El Niño–Southern Oscillation (ENSO) phenomena. The selection of predictors is by design intended to avoid any pretense of predictive ability based on “model physics” and the like, but rather is to specify the optimal “no-skill” forecast as a baseline comparison for more sophisticated forecast methods. Multiple least squares regression using the method of leaps and bounds is employed to test a total of 14 possible predictors for the selection of the best predictors, based upon 1950–94 developmental data. A range of zero to four predictors were chosen in developing 12 separate regression models, developed separately for each initial calendar month. The predictands to be forecast include the Southern Oscillation (pressure) index (SOI) and the Niño 1+2, Niño 3, Niño 4, and Niño 3.4 SST indices for the equatorial eastern and central Pacific at lead times ranging from zero seasons (0–2 months) through seven seasons (21–23 months). Though hindcast ability is strongly seasonally dependent, substantial improvement is achieved over simple persistence wherein largest gains occur for two–seven-season (6–23 months) lead times. For example, expected maximum forecast ability for the Niño 3.4 SST region, depending on the initial date, reaches 92%, 85%, 64%, 41%, 36%, 24%, 24%, and 28% of variance for leads of zero to seven seasons. Comparable maxima of persistence only forecasts explain 92%, 77%, 50%, 17%, 6%, 14%, 21%, and 17%, respectively. More sophisticated statistical and dynamic forecasting models are encouraged to utilize this ENSO-CLIPER model in place of persistence when assessing whether they have achieved forecasting skill; to this end, real-time results for this model are made available via a Web site.

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 wind–pressure relationships. These intercomparisons have led to several recommendations for operational tropical cyclone centers and those interested in reanalyzing past tropical cyclone events.

Abstract

Veerasamy has made several comments concerning the results and methods presented in a recent article by the authors titled “Reexamination of Tropical Cyclone Wind–Pressure Relationships.” One comment concerns the terminology and definition of the environmental pressure. Another comment suggests the merits of a simpler approach developed by Veerasamy in 2005 that utilizes the radius of 1004 hPa to determine the “proper” wind–pressure relationship. The third comment concerns the performance of the Knaff and Zehr wind–pressure relationship [their Eq. (7)] during the well-observed North Atlantic Hurricanes Katrina, Rita, and Wilma during 2005. The final comment suggests that the techniques discussed in Knaff and Zehr are more difficult to apply than an operational method developed by Veerasamy and used in Mauritius. These comments are addressed individually along with some of the lessons learned since the publication of the Knaff and Zehr methodology that are important to the tropical cyclone community.