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

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 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

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

The development of an infrared (IR; specifically near 11 μm) eye probability forecast scheme for tropical cyclones is described. The scheme was developed from an eye detection algorithm that used a linear discriminant analysis technique to determine the probability of an eye existing in any given IR image given information about the storm center, motion, and latitude. Logistic regression is used for the model development and predictors were selected from routine information about the current storm (e.g., current intensity), forecast environmental factors (e.g., wind shear, oceanic heat content), and patterns/information (e.g., convective organization, tropical cyclone size) extracted from the current IR image. Forecasts were created for 6-, 12-, 18-, 24-, and 36-h forecast leads. Forecasts were developed using eye existence probabilities from North Atlantic tropical cyclone cases (1996–2014) and a combined North Atlantic and North Pacific (i.e., Northern Hemisphere) sample. The performance of North Atlantic–based forecasts, tested using independent eastern Pacific tropical cyclone cases (1996–2014), shows that the forecasts are skillful versus persistence at 12–36 h, and skillful versus climatology at 6–36 h. Examining the reliability and calibration of those forecasts shows that calibration and reliability of the forecasts is good for 6–18 h, but forecasts become a little overconfident at longer lead times. The forecasts also appear unbiased. The small differences between the Atlantic and Northern Hemisphere formulations are discussed. Finally, and remarkably, there are indications that smaller TCs are more prone to form eye features in all of the TC areas examined.

Abstract

The National Hurricane Center (NHC) has been forecasting gale force wind radii for many years, and more recently (starting in 2004) began routine postanalysis or “best tracking” of the maximum radial extent of gale [34 knots (kt; 1 kt = 0.514 m s⁻¹)] force winds in compass quadrants surrounding the tropical cyclone (wind radii). At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that wind radii forecasts could be evaluated for skill. If the best-track gale radii are used as ground truth (even accounting for random errors in the analyses), the skill of the NHC forecasts appears to be improving at 2- and 3-day lead times, suggesting that the guidance has also improved. In this paper several NWP models are evaluated for their skill, an equally weighted average or “consensus” of the model forecasts is constructed, and finally the consensus skill is evaluated. The results are similar to what is found with tropical cyclone track and intensity in that the consensus skill is comparable to or better than that of the individual models. Furthermore, the consensus skill is high enough to be of potential use as forecast guidance or as a proxy for official gale force wind radii forecasts at the longer lead times.

Abstract

The National Hurricane Center (NHC) has a long history of forecasting the radial extent of gale force or 34-knot (kt; where 1 kt = 0.51 m s⁻¹) winds for tropical cyclones in their area of responsibility. These are referred to collectively as gale force wind radii forecasts. These forecasts are generated as part of the 6-hourly advisory messages made available to the public. In 2004, NHC began a routine of postanalysis or “best tracking” of gale force wind radii that continues to this day. At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that NHC all-wind radii forecasts could be evaluated for skill. This statistical wind radii baseline forecast is also currently used in several applications as a substitute for or to augment NHC wind radii forecasts. This investigation examines the performance of NHC gale force wind radii forecasts in the North Atlantic over the last decade. Results presented within indicate that NHC’s gale force wind radii forecasts have increased in skill relative to the best tracks by several measures, and now significantly outperform statistical wind radii baseline forecasts. These results indicate that it may be time to reinvestigate whether applications that depend on wind radii forecast information can be improved through better use of NHC wind radii forecast information.

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

The radius of maximum wind (R _max) in a tropical cyclone governs the footprint of hazards, including damaging wind, surge, and rainfall. However, R _max is an inconstant quantity that is difficult to observe directly and is poorly resolved in reanalyses and climate models. In contrast, outer wind radii are much less sensitive to such issues. Here we present a simple empirical model for predicting R _max from the radius of 34-kt (1 kt ≈ 0.51 m s⁻¹) wind (R _17.5 ms). The model only requires as input quantities that are routinely estimated operationally: maximum wind speed, R _17.5 ms, and latitude. The form of the empirical model takes advantage of our physical understanding of tropical cyclone radial structure and is trained on the Extended Best Track database from the North Atlantic 2004–20. Results are similar for the TC-OBS database. The physics reduces the relationship between the two radii to a dependence on two physical parameters, while the observational data enables an optimal estimate of the quantitative dependence on those parameters. The model performs substantially better than existing operational methods for estimating R _max. The model reproduces the observed statistical increase in R _max with latitude and demonstrates that this increase is driven by the increase in R _17.5 ms with latitude. Overall, the model offers a simple and fast first-order prediction of R _max that can be used operationally and in risk models.