Joint Typhoon Warning Center Research Articles

Modulation of Cyclone tracks in the Bay of Bengal by QBO

The possible influence of stratospheric quasi–biennial oscillation on cyclone passages in the Bay of Bengal (BOB) is examined using Indian Ocean cyclone tracking data obtained from Joint Typhoon Warning Center (JTWC) during the period 1991–2008. During this period 52 cyclones traversed the BOB. The numbers of cyclones are more during east phase (29) of QBO as compared to west phase (23) of QBO. The QBO influence is observed to be significant during easterly phase than westerly phase. The numbers of cyclone are more during post-monsoon season as compared to pre-monsoon season. In post-monsoon season, during east phase, cyclones move westward/north westward while during westerly phase they move northward/north eastward. During pre-monsoon season, cyclones move northward/north eastward irrespective of phases of QBO. The possible interaction between the stratospheric QBO and cyclone during post-monsoon season is investigated from variation of winds, geopotential height, tropopause pressure, OLR and SST.

A new wind retrieval algorithm for Jason-1 at high wind speeds

The altimeter wind speed algorithm at high wind speeds remains unsolved because of lack of observed data. In this study data at high wind speeds were generated using Yin's typhoon model, which consists of the Rankine vortex model and the angular momentum model with typhoon parameters, provided by the Joint Typhoon Warning Centre (JTWC). The accuracy of Yin's typhoon model can be validated by comparing it with recorded data from a weather station. By comparing the normalized radar backscatter cross‐section (NRCS) detected by the Jason‐1 altimeter with wind speed data inferred by Yin's typhoon model, an empirical algorithm valid for a range of wind speeds between 10 and 40 m s–1 is developed and proposed. The proposed algorithm is compared with the Jason‐1 operational algorithm and Young's altimeter wind retrieval algorithm. The study shows that, for the proposed algorithm and the operational algorithm for Jason‐1, the root mean square (RMS) errors are 3.38 and 3.60 m s–1, respectively, and the average relative errors are 18% and 19%, respectively, for wind speeds less than 27 m s–1. Hence, the proposed algorithm is in agreement with the operational algorithm for the Jason‐1 altimeter for wind speeds in the range 10–27 m s–1. However, the Jason‐1 operational algorithm is inaccurate for wind speeds above 27 m s–1 because the wind speeds used in the algorithm training process came from scatterometer wind products, and are significantly lower than those in strong wind and heavy rain conditions. Comparison of the proposed algorithm and Young's algorithm shows that the RMS errors are 6.27 and 15.18 m s–1, respectively, and the average relative errors are 16% and 59%, respectively, for wind speeds greater than 20 m s–1. The Holland typhoon model cannot accurately determine the outer wind field of typhoons since it extends cyclonic wind speeds to infinity. Young's altimeter wind retrieval algorithm depends on the Holland typhoon model, and the latter results in some errors. Compared with Young's altimeter wind retrieval algorithm, the proposed algorithm retrieves wind speeds with better accuracy. Therefore, the proposed algorithm, suitable for retrieving sea surface wind speeds in typhoons and other strong wind conditions, can be considered as supplementary to the Jason‐1 operational algorithm.

Comparison of Three Western North Pacific Tropical Cyclone Best Track Datasets in a Seasonal Context

The quality of tropical cyclone (TC) best track data has increasingly received more attention in climate research in recent years because of the rising interest in the possible effects of global warming on TC activity. In this paper, three best track datasets for the western North Pacific TCs were compared in a seasonal context. Firstly, the statistical characteristics were examined based on their distributions. Then, the potential impact of particular features within the datasets was evaluated by using contrastive cluster analyses. The mean amplitude of the annual cycle in the Japan Meteorological Agency (JMA) data is largest among the three datasets. The smallest interannual variability helped the Shanghai Typhoon Institute of China Meteorological Administration (CMA-STI) dataset to resist the negative effect of outliers. An inhomogeneity around 2003–2004 was identified in the Joint Typhoon Warning Center (JTWC) data by the outliers and was compared with the JMA and CMA-STI data. By using contrastive cluster analyses, the differences among the datasets and some effects related to particular issues, including the representation of the annual cycle, the outliers, and wind conversion were evaluated. The JTWC dataset is affected by outliers and wind conversion among the different average times, whereas the representation of the annual cycle was important for the CMA-STI data. After excluding all the outliers, the results indicated more consistent classifications of the TC annual cycle between the JMA and JTWC data.

Mesoscale Simulation of Tropical Cyclones in the South Pacific: Climatology and Interannual Variability

Abstract The Weather Research and Forecast model at ⅓° resolution is used to simulate the statistics of tropical cyclone (TC) activity in the present climate of the South Pacific. In addition to the large-scale conditions, the model is shown to reproduce a wide range of mesoscale convective systems. Tropical cyclones grow from the most intense of these systems formed along the South Pacific convergence zone (SPCZ) and sometimes develop into hurricanes. The three-dimensional structure of simulated tropical cyclones is in excellent agreement with dropsondes and satellite observations. The mean seasonal and spatial distributions of TC genesis and occurrence are also in good agreement with the Joint Typhoon Warning Center (JTWC) data. It is noted, however, that the spatial pattern of TC activity is shifted to the northeast because of a similar bias in the environmental forcing. Over the whole genesis area, 8.2 ± 3.5 cyclones are produced seasonally in the model, compared with 6.6 ± 3.0 in the JTWC data. Part of the interannual variability is associated with El Niño–Southern Oscillation (ENSO). ENSO-driven displacement of the SPCZ position produces a dipole pattern of correlation and results in a weaker correlation when the opposing correlations of the dipole are amalgamated over the entire South Pacific region. As a result, environmentally forced variability at the regional scale is relatively weak, that is, of comparable order to stochastic variability (±1.7 cyclones yr−1), which is estimated from a 10-yr climatological simulation. Stochastic variability appears essentially related to mesoscale interactions, which also affect TC tracks and the resulting occurrence.

Modeling storm surges associated with super typhoon durian in South China Sea

Super typhoon Durian struck the central Philippines on November 30, 2006 and southern coast of Vietnam on December 5, 2006. The reported maximum wind exceeded 250 km/h, and the central pressure was 904 hPa during the peak of the system. The typhoon brought colossal damage, both in terms of lives and in terms of properties to the Philippines and Vietnam while Thailand and Malaysia were slightly affected. The energy from the high-velocity wind and central pressure drop resulted in the generation of storm surges along the coastal region of the Philippines including its surrounding islands as well as parts of southern Vietnam. In this paper, a numerical 2D model is used to study the oceanic response to the atmospheric forcing by 2006 super typhoon Durian in the coastal regions of the Philippines and Vietnam. The initial study of this model aims to provide some useful insights before it could be used as a coastal disaster prediction system in the region of South China Sea (SCS). The atmospheric forcing for the 2D model, which includes the pressure gradient and the wind field, is generated by an empirical asymmetrical storm model. The simulated results of storm surges due to typhoon Durian at two locations lie in the range of observed data/estimates published by the Joint Typhoon Warning Centre (JTWC).

Near-equatorial typhoon development: Climatology and numerical simulations

The climatology of near-equatorial typhoons over the western-north Pacific are fully investigated using the JTWC (Joint Typhoon Warning Center) typhoon record from 1951 to 2006. The result shows that there are seasonal and decadal variations, as well as a distinctive spatial distribution, of such events. Among them, Typhoon Vamei is an example of a near-equatorial typhoon that occurred near Singapore in December of 2001. Using the WRF (Weather and Research Forecast) model, we attempt to find out how the well known “wind surge” of this event contributes to the development of Typhoon Vamei. It is found that the strong wind surge not only helps to provide advection of positive vorticity to Vamei between 800 and 500 hPa, but also increases the convective instability of the lower troposphere, and thus helps to induce convective outbreaks and rapid intensification. Furthermore, sensitivity experiments show that terrain and the land-sea distribution have very limited effects on the formation of Typhoon Vamei in the simulation, but an adequate Coriolis parameter (f) is still needed for the development of Vamei.

Consistent Tropical Cyclone Wind and Wave Forecasts for the U.S. Navy

Abstract A new algorithm to generate wave heights consistent with tropical cyclone official forecasts from the Joint Typhoon Warning Center (JTWC) has been developed. The process involves generating synthetic observations from the forecast track and the 34-, 50-, and 64-kt wind radii. The JTWC estimate of the radius of maximum winds is used in the algorithm to generate observations for the forecast intensity (wind), and the JTWC-estimated radius of the outermost closed isobar is used to assign observations at the outermost extent of the tropical cyclone circulation. These observations are then interpolated to a high-resolution latitude–longitude grid covering the entire extent of the circulation. Finally, numerical weather prediction (NWP) model fields are obtained for each forecast time, the NWP model forecast tropical cyclone is removed from these fields, and the new JTWC vortex is inserted without blending zones between the vortex and the background. These modified fields are then used as input into a wave model to generate waves consistent with the JTWC forecasts. The algorithm is applied to Typhoon Yagi (2006), in anticipation of which U.S. Navy ships were moved from Tokyo Bay to an area off the southeastern coast of Kyushu. The decision to move (sortie) the ships was based on NWP model-driven long-range wave forecasts that indicated high seas impacting the coast in the vicinity of Tokyo Bay. The sortie decision was made approximately 84 h in advance of the high seas in order to give ships time to steam the approximately 500 n mi to safety. Results from the new algorithm indicate that the high seas would not affect the coast near Tokyo Bay within 84 h. This specific forecast verifies, but altimeter observations show that it does not outperform, the NWP model-driven wave analysis and forecasts for this particular case. Overall, the performance of the new algorithm is dependent on the JTWC tropical cyclone forecast performance, which has generally outperformed those of the NWP model over the last several years.

Climatology and Landfall of Tropical Cyclones in the South- West Indian Ocean

—The climatology of cyclone formation and behaviour in the South-West Indian Ocean, including landfall in Mozambique and Madagascar, has been investigated. The records used were obtained by merging track data from the Joint Typhoon Warning Centre with data from La Reunion – Regional Specialised Meteorological Centre. During the period 1980-2007, 12.5 cyclones per year were formed; 85% of which in November-April (the cyclone season). The number of intense tropical cyclones increased from 36 during 1980-1993 to 56 during 1994-2007, parallel to a simultaneous but smaller decrease in the number of tropical storms. This increase in intense tropical cyclones occurred at the same time as an increase in the mean sea surface temperature of 0.12˚C. This temperature increase seems insufficient to explain the increased activity. In addition, investigating a longer record (1952-2007) from the same sources indicates a long-term decrease in cyclone frequency as well as in landfall, although this was simultaneous with a substantial increase (about 0.3˚C) in sea surface temperature. However, it is recognised that records before 1980 suffer from too few wind intensity data. From 1980-2007, the number of land-falling cyclones was 64 (compared to 88 from 1952-1979), 16 of which came ashore in Mozambique and 48 in Madagascar. Seasonal variations in areas of genesis and preferred tracks are presented and discussed in relation to a number of indices. Minimum and mean sea  surface temperatures for cyclone genesis are also estimated.Keywords: Tropical cyclones, Indian Ocean, Mozambique Channel, global change, sea surface temperatures, ENSO, landfall

Trend discrepancies among three best track data sets of western North Pacific tropical cyclones

The hot debate over the influence of global warming on tropical cyclone (TC) activity in the western North Pacific over the past several decades is partly due to the diversity of TC data sets used in recent publications. This study investigates differences of track, intensity, frequency, and the associated long‐term trends for those TCs that were simultaneously recorded by the best track data sets of the Joint Typhoon Warning Center (JTWC), the Regional Specialized Meteorological Center (RSMC) Tokyo, and the Shanghai Typhoon Institute (STI). Though the differences in TC tracks among these data sets are negligibly small, the JTWC data set tends to classify TCs of category 2–3 as category 4–5, leading to an upward trend in the annual frequency of category 4–5 TCs and the annual accumulated power dissipation index, as reported by Webster et al. (2005) and Emanuel (2005). This trend and potential destructiveness over the period 1977–2007 are found only with the JTWC data set, but downward trends are apparent in the RSMC and STI data sets. It is concluded that the different algorithms used in determining TC intensity may cause the trend discrepancies of TC activity in the western North Pacific.

Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model

The objective of this study is to provide evidence of predictability on intraseasonal time scales (10–30 days) for western North Pacific tropical cyclone formation and subsequent tracks using the 51-member ECMWF 32-day forecasts made once a week from 5 June through 25 December 2008. Ensemble storms are defined by grouping ensemble member vortices whose positions are within a specified separation distance that is equal to 180 n mi at the initial forecast time t and increases linearly to 420 n mi at Day 14 and then is constant. The 12-h track segments are calculated with a Weighted-Mean Vector Motion technique in which the weighting factor is inversely proportional to the distance from the endpoint of the previous 12-h motion vector. Seventy-six percent of the ensemble storms had five or fewer member vortices. On average, the ensemble storms begin 2.5 days before the first entry of the Joint Typhoon Warning Center (JTWC) best-track file, tend to translate too slowly in the deep tropics, and persist for longer periods over land. A strict objective matching technique with the JTWC storms is combined with a second subjective procedure that is then applied to identify nearby ensemble storms that would indicate a greater likelihood of a tropical cyclone developing in that region with that track orientation. The ensemble storms identified in the ECMWF 32-day forecasts provided guidance on intraseasonal timescales of the formations and tracks of the three strongest typhoons and two other typhoons, but not for two early season typhoons and the late season Dolphin. Four strong tropical storms were predicted consistently over Week-1 through Week-4, as was one weak tropical storm. Two other weak tropical storms, three tropical cyclones that developed from precursor baroclinic systems, and three other tropical depressions were not predicted on intraseasonal timescales. At least for the strongest tropical cyclones during the peak season, the ECMWF 32-day ensemble provides guidance of formation and tracks on 10–30 day timescales.

MEETING SUMMARIES

What: Representatives, data managers, and tropical cyclone researchers from agencies and organizations around the globe that provide tropical cyclone best-track data met to discuss and improve the historical record of tropical cyclones. When: 5–7 May 2009 Where: Asheville, North Carolina despite the numerous articles in peer-reviewed literature using global tropical cyclone (TC) best-track datasets, until recently there existed no central repository of these data. Many researchers simply used best-track data from two sources to obtain global coverage: National Oceanic and Atmospheric Administration’s (NOAA) Hurricane Database (HURDAT) for the North Atlantic and eastern North Pacific basins, and the U.S. Navy/Air Force Joint Typhoon Warning Center (JTWC) for all other basins. Aside from the many issues in combining data from just these two sources, doing so excludes data from most of the World Meteorological Organization (WMO) Regional Specialized Meteorological Centres (RSMC) and TC Warning Centres (TCWC), which also officially forecast and monitor TCs. Furthermore, there are similar best-track datasets from other sources. However, the two primary issues that have hindered scientists from using all available global data are 1) easily accessing the best-track data and 2) accurately combining these disparate datasets. The International Best Track Archive for Climate Stewardship (IBTrACS) project was formed under the auspices of the World Data Center for Meteorology– Asheville (WDC–Asheville) to address the best-track data accessibility issue by combining TC best-track data from all agencies into an integrated dataset readily available to the user community. Figure 1 denotes the number of agencies tracking TCs by basin. [Also see page 363 in this issue.] Note that there are numerous sources of TC intensity in the Southern Hemisphere and western North Pacific, which complicates data access and analysis. Therefore, the new IBTrACS dataset (Knapp et al. 2010; Kruk et al. 2010) provides the original TC intensities in a uniform format and catalogs the full range of reported values for pressure, wind speed, and position for each 6-h time step from each agency. The final dataset is available in a variety of formats in use by the TC community, which includes the original unedited data as received from each of the RSMCs. AFFILIATIONS: Levinson, DiamonD, anD Knapp—NOAA/ National Climatic Data Center, Asheville, North Carolina; KruK—STG, Inc., Asheville, North Carolina; Gibney—IMSG, Inc., Asheville, North Carolina CORRESPONDING AUTHOR: David H. Levinson, NOAA/ National Climatic Data Center, 151 Patton Avenue, Asheville, NC 28801 E-mail: david.levinson@noaa.gov The abstract for this article can be found in this issue, following the table of contents.

Upper-Ocean Thermal Structure and the Western North Pacific Category 5 Typhoons. Part II: Dependence on Translation Speed

Abstract Using new in situ ocean subsurface observations from the Argo floats, best-track typhoon data from the U.S. Joint Typhoon Warning Center, an ocean mixed layer model, and other supporting datasets, this work systematically explores the interrelationships between translation speed, the ocean’s subsurface condition [characterized by the depth of the 26°C isotherm (D26) and upper-ocean heat content (UOHC)], a cyclone’s self-induced ocean cooling negative feedback, and air–sea enthalpy fluxes for the intensification of the western North Pacific category 5 typhoons. Based on a 10-yr analysis, it is found that for intensification to category 5, in addition to the warm sea surface temperature generally around 29°C, the required subsurface D26 and UOHC depend greatly on a cyclone’s translation speed. It is observed that even over a relatively shallow subsurface warm layer of D26 ∼ 60–70 m and UOHC ∼ 65–70 kJ cm−2, it is still possible to have a sufficient enthalpy flux to intensify the storm to category 5, provided that the storm can be fast moving (typically Uh ∼ 7–8 m s−1). On the contrary, a much deeper subsurface layer is needed for slow-moving typhoons. For example at Uh ∼ 2–3 m s−1, D26 and UOHC are typically ∼115–140 m and ∼115–125 kJ cm−2, respectively. A new concept named the affordable minimum translation speed Uh_min is proposed. This is the minimum required speed a storm needs to travel for its intensification to category 5, given the observed D26 and UOHC. Using more than 3000 Argo in situ profiles, a series of mixed layer numerical experiments are conducted to quantify the relationship between D26, UOHC, and Uh_min. Clear negative linear relationships with correlation coefficients R = −0.87 (−0.71) are obtained as Uh_min = −0.065 × D26 + 11.1, and Uh_min = −0.05 × UOHC + 9.4, respectively. These relationships can thus be used as a guide to predict the minimum speed a storm has to travel at for intensification to category 5, given the observed D26 and UOHC.

A Conceptual Model for the Influence of TUTT Cells on Tropical Cyclone Motion in the Northwest Pacific Ocean

Abstract Eleven (10 Pacific, 1 Atlantic) tropical cyclones (TCs), which include typhoons/hurricanes and tropical storms, are examined using the latest 40-yr ECMWF Re-Analysis (ERA-40) dataset and Joint Typhoon Warning Center (JTWC) best-track data to determine if and how tropical upper-tropospheric trough (TUTT) cells influence TC tracks. This type of interaction has led to rather large TC track forecast errors at 72 h (2000+ km) in the northwest Pacific and is often ignored or poorly forecast due to inadequate numerical model TUTT cell predictions. Ten selected cases out of the initial 25 potential Pacific cases exhibited a “nonstandard” TC track; a TUTT cell was the sole large-scale transient feature within 2000 km of the TC’s center, and the TC intensity was >17 m s−1. The circulations’ separation distance, orientation, intensity, and TUTT cell’s closed circulation size are critical characteristics in determining the likelihood of a TUTT cell influencing a TC track. Interactions occur at distances greater than 1700 km, continue for periods from 24 to 48 h, and occur 2–3 times per year in the NW Pacific. Examination of the TC’s tropospheric winds’ deep layer mean (100–1000 hPa), and upper (100–500 hPa), middle (300–850 hPa), and lower (500–1000 hPa) layers, along with various quadrants of the upper layer, demonstrate a link between the TUTT cell’s wind field and the nonstandard TC tracks. A conceptual model of how a TUTT cell can influence TC track is presented. The model provides decision-grade operational guidance for TC forecasters using pattern recognition scenarios. Application of the conceptual model at the JTWC is currently under way.

Southern hemisphere tropical cyclone intensity forecast methods used at the Joint Typhoon Warning Center, Part I: control forecasts based on climatology and persistence

The development of a simple statistical tropical cyclone (TC) intensity forecast model is described. The primary purpose of this model, called southern hemisphere five-day statistical typhoon intensity forecast scheme (SH ST5D), is to provide a skill/no-skill control forecast for verifying other TC intensity forecasts. However, it also provides useful and always-available forecasts of TC intensity in the southern hemisphere. The model is created by fitting an optimal combination of factors related to climatology and persistence (or CLIPER) using multiple linear regression. These CLIPER factors are determined from the best track tropical cyclone dataset produced by the United States of America’s Joint Typhoon Warning Center (JTWC) in the years 1980-2002. In 2004 the SH ST5D model became part of the operational suite of tropical cyclone intensity guidance run at JTWC. The forecasts from the model since that time have outperformed both climatology (i.e. a constant 65 knots or 33 ms -1 forecast) and the persistence of initial conditions in a statistically significant manner in independent testing during 2004-2007. This documentation is provided to promote the use of this model’s output and provide adequate background for the development of similar models.

Southern hemisphere tropical cyclone intensity forecast methods used at the Joint Typhoon Warning Center, Part III: forecasts based on a multi-model consensus approach

Consensus forecasts aids, those derived from forecasts from several models, and ensemble forecasts aids, those derived from several forecast created by a single model, have become commonplace. Consensus forecast aids are now an integral part of operational tropical cyclone forecasting at the Joint Typhoon Warning Center (JTWC). These consensus aids generally have lower average errors than individual forecast aids and benefit from the skill and independence of their mem bers. This paper discusses the performance of one ensemble forecast aid and one consensus forecast aid run in real-time and made available to JTWC during the 2007 and 2008 southern hemisphere tropical cyclone seasons. The ensemble forecast aid is shown to be as skillful as the top performing ensemble member at each forecast time. The consensus forecast aid is shown to be the most skillful aid available to JTWC during the 2007 and 2008 seasons. Further experiments indicate that adding more forecast aids to the intensity consensus may marginally improve both skill and forecast availability of the consensus forecast aid.

Southern hemisphere tropical cyclone intensity forecast methods used at the Joint Typhoon Warning Center, Part II: statistical-dynamical forecasts

The development and performance of a statistical - dynamical tropical cyclone intensity forecast model, which was developed for the United States of America’s Joint Typhoon Warning Center (JTWC), is described. This model, called the Southern Hemisphere Statistical Typhoon Intensity Prediction Scheme (SH STIPS), mirrors similar capabilities created for use in the western North Pacific and North Indian Ocean tropical cyclone basins. The model is created by fitting an optimal combination of factors related to climatology and persistence, intensification potential, vertical wind shear, dynamic size/intensity forecasts and atmospheric stability. All of these factors except the climatology and persistence information are derived from global forecast model analyses and forecasts. In July 2005 the SH STIPS model began a real-time evaluation period. The forecasts from the SH STIPS model have outperformed the combined climatology and persistence based forecast and thus are skillful in independent testing since that time. Since October 2006, SH STIPS has been the primary member in an operational consensus forecast of tropical cyclone intensity change provided to the JTWC. Documentation is provided for potential users of forecasts based on this methodology and for researchers interested in developing similar capabilities in the future.

Upper-Ocean Thermal Structure and the Western North Pacific Category 5 Typhoons. Part I: Ocean Features and the Category 5 Typhoons’ Intensification

Abstract Category 5 cyclones are the most intense and devastating cyclones on earth. With increasing observations of category 5 cyclones, such as Hurricane Katrina (2005), Rita (2005), Mitch (1998), and Supertyphoon Maemi (2003) found to intensify on warm ocean features (i.e., regions of positive sea surface height anomalies detected by satellite altimeters), there is great interest in investigating the role ocean features play in the intensification of category 5 cyclones. Based on 13 yr of satellite altimetry data, in situ and climatological upper-ocean thermal structure data, best-track typhoon data of the U.S. Joint Typhoon Warning Center, together with an ocean mixed layer model, 30 western North Pacific category 5 typhoons that occurred during the typhoon season from 1993 to 2005 are systematically examined in this study. Two different types of situations are found. The first type is the situation found in the western North Pacific south eddy zone (SEZ; 21°–26°N, 127°–170°E) and the Kuroshio (21°–30°N, 127°–170°E) region. In these regions, the background climatological warm layer is relatively shallow (typically the depth of the 26°C isotherm is around 60 m and the upper-ocean heat content is ∼50 kJ cm−2). Therefore passing over positive features is critical to meet the ocean’s part of necessary conditions in intensification because the features can effectively deepen the warm layer (depth of the 26°C isotherm reaching 100 m and upper-ocean heat content is ∼110 kJ cm−2) to restrain the typhoon’s self-induced ocean cooling. In the past 13 yr, 8 out of the 30 category 5 typhoons (i.e., 27%) belong to this situation. The second type is the situation found in the gyre central region (10°–21°N, 121°–170°E) where the background climatological warm layer is deep (typically the depth of the 26°C isotherm is ∼105–120 m and the upper-ocean heat content is ∼80–120 kJ cm−2). In this deep, warm background, passing over positive features is not critical since the background itself is already sufficient to restrain the self-induced cooling negative feedback during intensification.

Influences of tropical cyclones on China during 1965–2004

Using the Joint Typhoon Warning Center (JTWC) and China Meteorological Administration (CMA) tropical cyclone track datasets, variations in frequency and intensity of the affecting-China tropical cyclones (ACTCs) are studied for the period of 1965–2004. First, the differences between the two tropical cyclone datasets are examined. The annual frequencies of tropical cyclones in the western North Pacific basin are reasonably consistent to each other, while the intensity records are less reliable. The annual numbers of ACTCs based on different datasets are close to each other with similar interdecadal and interannual variations. However, the maximum intensity and the annual frequency of ACTCs for strong categories show great dependence on datasets. Tropical cyclone impacts on China show the same variations as the annual number of ACTCs and also show dependence on datasets. Differences in tropical cyclone impacts on China are mainly caused by datasets used. The annual frequency of ACTCs, especially the length of lifetime of ones that make landfall, and the intensity estimates all have effects on the value of impacts on China.

Assessment of Western North Pacific 96- and 120-h Track Guidance and Present Forecastability

Abstract Because the Joint Typhoon Warning Center (JTWC) has only four dynamical models for guidance in making 96- and 120-h track forecasts, an opportunity exists for improving the consensus forecast by the proper removal of a likely erroneous forecast to form a selective consensus (SCON). Forecast fields from all four models [the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS), the U.S. Navy version of the Geophysical Fluid Dynamics Laboratory model (GFDN), the Met Office (UKMO) model, and the Global Forecast System (GFS)] were available during the 2005 western North Pacific season to evaluate for the first time the error mechanisms leading to large track errors. As shown previously for the NOGAPS and GFDN models during the 2004 season, error sources related to the midlatitude circulations accounted for about 90% of all large 120-h track errors by all four models during the 2005 season. This dominance of midlatitude-related error source is a major shift from the 72-h errors, which include more errors related to tropical circulations. In the GFS model, 95% of the large errors occurred because of an incorrect depiction of the vertical structure of the tropical cyclone. A systematic error in the GFDN model was identified in which a false anticyclogenesis was predicted downstream of the Tibetan Plateau, which accounted for over 50% of the large GFDN track errors. The consensus spread versus consensus error relationship is examined to isolate those 20%–25% of cases with large spreads and large errors that are candidates for forming an SCON. If the model tracks that contributed to the large errors are eliminated, the average improvement of the SCON forecasts relative to the nonselective consensus is 222 (239) n mi during 2005 (2004), and the corresponding average improvement relative to the JTWC forecasts is 382 (203) n mi. This application of SCON is considered the potential “forecastability” in that it represents the optimum use of the present numerical guidance for consensus forecasting.

Dynamical Tropical Cyclone 96- and 120-h Track Forecast Errors in the Western North Pacific

Abstract The Joint Typhoon Warning Center has been issuing 96- and 120-h track forecasts since May 2003. It uses four dynamical models that provide guidance at these forecast intervals and relies heavily on a consensus of these four models in producing the official forecast. Whereas each of the models has skill, each occasionally has large errors. The objective of this study is to provide a characterization of these errors in the western North Pacific during 2004 for two of the four models: the Navy Operational Global Atmospheric Prediction System (NOGAPS) and the U.S. Navy’s version of the Geophysical Fluid Dynamics Laboratory model (GFDN). All 96- and 120-h track errors greater than 400 and 500 n mi, respectively, are examined following the approach developed recently by Carr and Elsberry. All of these large-error cases can be attributed to the models not properly representing the physical processes known to control tropical cyclone motion, which were classified in a series of conceptual models by Carr and Elsberry for either tropical-related or midlatitude-related mechanisms. For those large-error cases where an error mechanism could be established, midlatitude influences caused 83% (85%) of the NOGAPS (GFDN) errors. The most common tropical influence is an excessive direct cyclone interaction in which the tropical cyclone track is erroneously affected by an adjacent cyclone. The most common midlatitude-related errors in the NOGAPS tracks arise from an erroneous prediction of the environmental flow dominated by a ridge in the midlatitudes. Errors in the GFDN tracks are caused by both ridge-dominated and trough-dominated environmental flows in the midlatitudes. Case studies illustrating the key error mechanisms are provided. An ability to confidently identify these error mechanisms and thereby eliminate likely erroneous tracks from the consensus would improve the accuracy of 96- and 120-h track forecasts.