text:
3. Results: Long-term trends in season length
4. Results: Seasonal influences upon season length
a. Early-starting seasons
b. Late-ending seasons
c. Representativeness of linear regression analyses
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Formation location, as assessed utilizing National Hurricane Center "best track" data, of all Atlantic TCs forming on or prior to the 10th-percentile formation date for each Atlantic TC season. For TC formation events between 1979 and 2010, the highest-probability genesis pathway determined following McTaggart-Cowan et al. (2013) is illustrated by the appropriate symbol. The genesis date within 2-week bins is referenced by the color given to each symbol.
As in Fig. 1, but for Atlantic TCs forming on or after the 90th-percentile formation date for each Atlantic TC season.
Trend, in days per year, in the 5th–95th-percentile Atlantic TC formation dates between (a) 1979 and 2007, (b) 1979 and 2006, (c) 1979 and 2005, (d) 1979 and 2004, (e) 1979 and 2003, (f) 1979 and 2002, (g) 1979 and 2001, and (h) 1979 and 2014 for TCs that formed within the Kossin (2008) subset of the full Atlantic basin. The blue line indicates the 90% confidence interval.
As in Fig. 3, but for TCs that formed anywhere within the North Atlantic basin for (a) 1979–2007 and (b) 1979–2014.
Slope of the best-fit regression line between the 10th-percentile Atlantic TC formation date and June monthly mean (a) SST (°C), (b) 600-hPa relative humidity (%), (c) 500-hPa geopotential height (m), (d) 850-hPa relative vorticity (×10−5 s−1), and (e) 850–200-hPa vertical wind shear magnitude (m s−1) over the domain 10°S–70°N, 150°W–0°. A one standard deviation increase in a given monthly mean field is associated with an n-day change (shaded; positive = earlier) in the 10th-percentile Atlantic TC formation date. The June monthly mean field is contoured in each panel. Cross-hatching is utilized to identify regions where the slope of the best-fit regression line is nonzero to at least 90% (light gray), 95% (dark gray), and 99% (black) confidence.
Standard deviation of the June monthly mean (a) SST (°C), (b) 600-hPa relative humidity (%), (c) 500-hPa geopotential height (m), (d) 850-hPa relative vorticity (×10−5 s−1), and (e) 850–200-hPa vertical wind shear magnitude (m s−1).
As in Fig. 5, but globally.
Slope of the best-fit regression line between the 90th-percentile Atlantic TC formation date and November monthly mean (a) SST (°C), (b) 600-hPa relative humidity (%), (c) 500-hPa geopotential height (m), (d) 850-hPa relative vorticity (×10−5 s−1), and (e) 850–200 hPa vertical wind shear magnitude (m s−1) over the domain 10°S–70°N, 150°W–0°. A one standard deviation increase in a given monthly mean field is associated with an n-day change (shaded; positive = later) in the 90th-percentile Atlantic TC formation date. The November monthly mean field is contoured in each panel. Cross-hatching is utilized to identify regions where the slope of the best-fit regression line is nonzero to at least 90% (light gray), 95% (dark gray), and 99% (black) confidence.
As in Fig. 6, but for November monthly mean fields.
Seasonal Influences upon and Long-Term Trends in the Length of the Atlantic Hurricane Season
Juliana M. Karloski 1 and Clark Evans 1
Considering a subset of the North Atlantic Ocean south of 30°N and east of 75°W, Kossin found that the Atlantic tropical cyclone (TC) season increased in length, at 80%–90% confidence, by about 2 days per year between 1980 and 2007. It is uncertain, however, whether the same is true over the entire Atlantic basin or when the analysis is extended to 2014. Separately, it is unclear whether meaningful subseasonal variability in the environmental factors necessary for TC formation exists between early- and late-starting (ending) seasons. Quantile regression is used to evaluate long-term trends in Atlantic TC season length. No statistically significant trend in season length exists for the period 1979–2007 when considering the entire Atlantic basin or for the period 1979–2014 independent of the portion of the basin considered. Linear regression applied to June and November monthly mean reanalysis data is used to examine subseasonal environmental variability between early- and late-starting (ending) seasons. Within an otherwise favorable environment for genesis, early-starting seasons are associated with increased lower-tropospheric relative vorticity where most early-season TCs form. Late-ending seasons are associated with La Niña, negative-phase Pacific decadal oscillation events, and environmental conditions that promote an increased likelihood of TC development along the preferred genesis pathways for late-season TCs. While confidence in these results is relatively high, they explain only a small portion of the total variation in Atlantic TC season length. More research is needed to understand how variability on all time scales influences Atlantic TC season length and its predictability.
Corresponding author address: Dr. Clark Evans, Atmospheric Science Program, Dept. of Mathematical Sciences, University of Wisconsin–Milwaukee, P.O. Box 413, Milwaukee, WI 53201. E-mail: evans36@uwm.edu
Every year, multiple meteorological organizations, such as NOAA; research groups, such as that at Colorado State University (http://hurricane.atmos.colostate.edu/forecasts/); and private firms release predictions of the number of tropical storms, hurricanes, and major hurricanes they expect to form that year, particularly within the North Atlantic basin. Other attributes of tropical cyclone (TC) activity, however, such as when the first TC will form or how long the season will last, are typically not forecast. Officially, the Atlantic TC season begins on 1 June and ends on 30 November. Over 97% of TC activity within the Atlantic basin occurs between these two dates (Hurricane Research Division 2015). However, as assessed utilizing the National Hurricane Center hurricane database (HURDAT2; Landsea and Franklin 2013), the average first TC formation for the period 1979–2014 occurred on 30 June and the average last TC formation occurred on 4 November. The question thus remains, what causes some seasons to be significantly shorter or longer than average?
It is worthwhile to consider whether there could be a relationship between the period over which the conditions necessary for TC development are present at one or more locations and the length of the TC season. Gray (1968, 1979) identified six necessary but not sufficient criteria for TC development: sea surface temperatures (SSTs) greater than or equal to 26.5°C, abundant lower- to midtropospheric relative humidity, conditional instability through a deep tropospheric layer, large lower-tropospheric cyclonic relative vorticity, low vertical wind shear of the horizontal winds (roughly less than 10 m s−1 between the surface and tropopause), and displacement by at least 5° latitude poleward from the equator. These six conditions may be condensed [e.g., as done by Frank (1987) and others] into four: sufficiently large cyclonic vertical vorticity, weak vertical wind shear of the horizontal winds, SSTs greater than or equal to 26°C, and abundant lower- to midtropospheric relative humidity. The SST criterion may alternatively be expressed in terms of upper-oceanic heat content and/or potential intensity, the latter of which is influenced by both SST and outflow-layer temperatures (e.g., Emanuel 1986). Likewise, the relative humidity and vertical wind shear criteria may alternatively be represented by ventilation, which reflects the extent to which environmental, midtropospheric low-entropy air may be imported into the circulation of a TC or predecessor disturbance (e.g., Tang and Emanuel 2012; Tang and Camargo 2014).
Subsequent investigators (e.g., Bruyère et al. 2012; Camargo et al. 2007; Emanuel 2010; Emanuel and Nolan 2004; McGauley and Nolan 2011; Schumacher et al. 2009; Tang and Camargo 2014; Tippett et al. 2011) have utilized these criteria to develop, apply, and evaluate genesis potential indices. Such indices are designed to highlight whether conditions favorable for TC development exist in observations or climate model outputs at one or more locations and times. For example, applying the ventilation index of Tang and Emanuel (2012) to eight model outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) representative concentration pathway 8.5 (RCP8.5) experiment, Tang and Camargo (2014) identified a potential decrease in the average annual number of days that the ventilation index is favorable for TC genesis in the North Atlantic Ocean of up to 2 weeks by the end of the twenty-first century. Alternatively, downscaling methods may be applied to climate model outputs to assess projected future changes in TC season length (e.g., Dwyer et al. 2015).
In the context of genesis potential indices, greater lower- to midtropospheric moisture content, higher upper-oceanic heat content and/or potential intensity, lower vertical wind shear, and sufficiently large cyclonic vertical vorticity indicate a greater potential for tropical cyclogenesis (e.g., McGauley and Nolan 2011). Presumably, TC seasons that start earlier or end later than normal are characterized by the presence of the necessary ingredients for TC formation at times when they are not typically present. The inverse is presumably true for seasons that begin later and/or end earlier than normal. Herein, we seek to examine the extent to which this is true on subseasonal time scales, particularly in the context of the genesis pathways that early- and late-season North Atlantic TCs typically follow (McTaggart-Cowan et al. 2008, 2013). Note that the genesis pathways identified by McTaggart-Cowan et al. (2008, 2013) describe the environments within which the physical processes believed to be responsible for tropical cyclogenesis (e.g., wind-induced surface heat exchange; Emanuel 1986) are triggered so as to lead to the formation of a tropical cyclone.
In addition to understanding the synoptic- to climate-scale factors that control the length of an individual TC season, it is worthwhile to consider whether the length of the Atlantic TC season has increased or decreased in recent decades. Focusing on an extended North Atlantic main development region (MDR) south of 30°N and east of 75°W where an increase of 0.5°–1.5°C in mean June–November SST was observed between 1950 and 2007, Kossin (2008) utilized quantile regression to identify changes in the annual distribution of Atlantic TC formation dates. Their findings (Fig. 4 of Kossin 2008) suggest that the Atlantic TC season has increased in length by 10 or more days per decade since 1950, reflective of both earlier starts and later ends to the season, with a level of confidence between 80% and 90%. This was attributed, albeit also with low confidence, to warmer May SSTs across the extended MDR, with an increase of the area-averaged SST by 1°C corresponding to as much as a 20-day shift in the start and/or end of the season.
The present research has multiple aims. First, the historical record indicates that Atlantic TC seasons since 2008 have not been associated with anomalously active early- and late-season TC activity (Table 1), in contrast to that in the preceding years referenced by Kossin (2008). Quantile regression is utilized to identify whether a statistically significant trend in Atlantic TC season length exists for the period 1979–2014 when considering only TCs that formed within Kossin's (2008) extended MDR. Second, the historical record also indicates that when the entire Atlantic basin is considered, Atlantic TC seasons prior to 2008 appear to feature the same anomalously active early- and late-season TC activity as was found over the extended MDR subset thereof (Kossin 2008; Table 2). This is noteworthy given that many of the earliest- and latest-forming TCs undergo genesis outside of Kossin's (2008) extended MDR (Figs. 1 and 2). Quantile regression is utilized to identify whether a statistically significant trend in Atlantic TC season length exists for the period 1979–2007 when considering TCs that formed anywhere within the Atlantic basin. This analysis is repeated for the period 1979–2014 given the apparent termination of anomalously active early- and late-season TC activity since 2008 (Table 2). In contrast to Kossin (2008), however, these analyses all are exclusively statistical; no physically based hypothesis is advanced or tested to attempt to explain the existence, or lack thereof, of a statistically significant trend in season length over any region or temporal extent considered. Third, we separately investigate how the synoptic- to planetary-scale distributions of proxies for the necessary conditions for tropical cyclogenesis vary on subseasonal to interannual time scales between early- and late-starting (ending) Atlantic TC seasons, particularly in light of the different genesis pathways followed by early- and late-season TCs.
The 5th- and 95th-percentile TC formation dates for TCs forming within the extended MDR of Kossin (2008) for the years 2001–14. For reference, the 1979–2014 average 5th- and 95th-percentile TC formation dates are provided in the bottom row.
As in Table 1, but for TCs forming within the full Atlantic basin.
The remainder of this work is structured as follows. Section 2 outlines the data utilized and methodology followed in this study. Section 3 presents results regarding long-term trends in Atlantic TC season length for the period 1979–2014. Section 4 presents results regarding the synoptic- to planetary-scale conditions that characterize early- and late-starting (ending) Atlantic TC seasons. A summary of the key findings of the work and a discussion of their implications are presented in section 5.
In this study, the National Hurricane Center HURDAT2 database (Landsea and Franklin 2013) is used to identify Atlantic TC formations, excluding tropical depressions and subtropical cyclones, between 1979 and 2014. A total of 431 TC formation events are identified across the entire basin, with 228 of these being located within the extended MDR considered by Kossin (2008). The year 1979 is chosen as the start of our analysis period for several reasons. First, this is the first year for which ERA-Interim data (Dee et al. 2011), the use of which is described below, are available. Second, substantial improvements in the quantity and quality of satellite clear-sky radiance and cloud-track wind observations were realized beginning in 1979, resulting in more robust atmospheric reanalysis products from 1979 onward (e.g., Uppala et al. 2005). Third, so as to facilitate direct comparison between our results and those of Kossin (2008), we desire that the start of our period of record closely match that of Kossin (2008). We note that the sensitivity to a starting year of 1979 versus 1980 in our results is negligible (not shown). Finally, as routine geostationary satellite surveillance of TCs began in the mid-1960s (e.g., Neumann et al. 1999), it is unlikely that TCs were systematically missed over part or all of the period of record considered herein (e.g., Landsea 2007). Landsea (2007) also notes that advances in observational and analysis capabilities appear to be contributing to one additional Atlantic TC event per year since 2002, with many of their cited examples occurring at the start or end of the TC season. While it is possible that this could influence both the results presented herein as well as those of Kossin (2008), no attempt is made to account for this possible change in classification practices.
To quantify long-term trends in Atlantic TC season length, we apply the method of quantile regression. In contrast to linear (or least squares) regression, which provides a method by which the nature of an assumed linear relationship between the means of grouped data can be evaluated, quantile (or percentile) regression (Koenker and Bassett 1978; Koenker and Hallock 2001) provides a method for evaluating the nature of an assumed linear relationship for conditional quantile functions. One common application of quantile regression is median regression, though quantile regression need not be limited exclusively to the median (or 50th percentile) of a dataset. Quantile regression has been applied in the atmospheric sciences to examine long-term trends in Atlantic TC season length (Kossin 2008) and the intensity of the most-intense Atlantic TCs (Elsner et al. 2008). Quantile regression considers the topology of the full dataset of TC events rather than just the first and last TCs for each season, thereby increasing the degrees of freedom of the statistical analysis; furthermore, unlike linear regression, it is relatively insensitive to individual outliers within the data (Kossin 2008).
Herein, quantile regression is first utilized to replicate the findings of Kossin (2008), considering only TCs that formed within their extended MDR between 1980 and 2007. Quantiles between the 5th and 95th percentile, at 5% intervals, are considered. This analysis is then replicated for the period beginning in 1979; as noted earlier, the results are found to be insensitive to small differences in the start year. Next, to evaluate whether the positive but not statistically significant trend in Atlantic TC season length identified by Kossin (2008) is maintained when the period of record is extended to the present, the analysis is repeated for the period ending in 2014. Subsequently, to evaluate whether the positive but not statistically significant trend in Atlantic TC season length identified by Kossin (2008) is maintained when the set of TCs is extended to the full Atlantic basin, the analysis is repeated for the periods of 1979–2007 and 1979–2014 when considering all Atlantic basin TCs. Finally, the sensitivity in how changing the end of the period of record by one year at a time is examined by repeating the analysis, over both Kossin's (2008) extended MDR and the full basin, for end years between 2001 and 2014. The 90th-percentile confidence interval is computed for each analysis utilizing bootstrapping (e.g., Efron and Tibshirani 1993) with 200 surrogates.
To assess how the synoptic- to planetary-scale distributions of the necessary conditions for TC formation vary on subseasonal to interannual time scales between early- and late-starting (ending) Atlantic TC seasons, linear regression is utilized. The 10%, 25%, 75%, and 90% TC formation date for each Atlantic TC season is first identified. These values are then linearly regressed against monthly mean fields of SST, 850-hPa relative vorticity, 600-hPa relative humidity, 500-hPa geopotential height, and 850–200-hPa vertical wind shear magnitude. Monthly mean SST data are obtained from the NOAA Extended Reconstructed Sea Surface Temperature, version 3b (ERSST.v3b), dataset on a 2° latitude × 2° longitude grid (Smith et al. 2008). It should be noted that this dataset differs slightly from that described by Smith et al. (2008) in that satellite-derived SST data are not used in its composition so as to remove a cold SST bias introduced by nonclear-sky satellite SST retrievals. All monthly mean atmospheric fields are obtained from ERA-Interim (Dee et al. 2011) on an approximate 0.7° latitude × 0.7° longitude grid. The results are insensitive to one-isobaric-level (50 hPa) shifts in the isobaric level(s) over which atmospheric fields are considered (not shown). Sensitivity in the results to the choice of SST or atmospheric reanalysis dataset is expected to be small but is not explicitly examined.
Herein, linear regression takes the following form:
This notation follows that of Torn and Hakim (2008), where the left-hand side of (1) represents the slope of the linear regression line between J, here defined as the nth percentile date, and x, here defined as the relevant oceanic or atmospheric field. Note that in (1), the long-term (1979–2014) means for each field are removed from both J and x. Thus, (1) enables the identification in changes in the nth percentile date as a function of changes in a given forcing. In (1), cov refers to the covariance between J and x, while var refers to the variance of x. In the analyses to follow, the right-hand side of (1) is multiplied by the standard deviation of x, such that a one standard deviation change in x is responsible for an N-day change in the nth percentile date. A Student's t test is utilized to test whether the slope of the linear regression between J and x is nonzero to 90%, 95%, and 99% confidence.
Four sets of linear regression analyses between monthly mean values of each field and nth percentile formation date are conducted: 1) June monthly mean to 10th-percentile formation date, 2) June and July monthly means to 25th-percentile formation date, 3) October and November monthly means to 75th-percentile formation date, and 4) November monthly mean to 90th-percentile formation date. The results are found to be qualitatively similar between linear regressions conducted against the 10th- and 25th-percentile formation dates and between linear regressions conducted against the 75th- and 90th-percentile formation dates (not shown). Consequently, the results presented in section 4 consider only linear regression computed between June monthly mean fields and the 10th-percentile formation date and between November monthly mean fields and the 90th-percentile formation date. As presented herein, positive values of (1) indicate an earlier start (as measured by the 10th-percentile formation date) or later end (as measured by the 90th-percentile formation date) of the TC season as a given oceanic or atmospheric field increases in value. All linear regression analyses are conducted globally with particular focus given to describing the results across the North Atlantic Ocean, North America, and equatorial Pacific Ocean. This area extends into the equatorial Pacific Ocean so as to reflect the potential influence of El Niño–Southern Oscillation (ENSO; e.g., Bjerknes 1969) upon Atlantic basin TC activity, particularly late in the Atlantic TC season (Dunion 2011; Klotzbach 2011a,b).
To complement the spatial linear regression analyses described above, we compute the linear correlation coefficient between the 10th- (90th-) percentile formation date and June (November) monthly mean values of selected teleconnection indices. Teleconnection indices considered include the Arctic Oscillation (AO; e.g., Thompson and Wallace 1998), Atlantic meridional mode (AMM; e.g., Moura and Shukla 1981), Atlantic multidecadal oscillation (AMO; e.g., Kerr 2000), North Atlantic Oscillation (NAO; e.g., Barnston and Livezey 1987), oceanic Niño (ONI; e.g., L'Heureux et al. 2013), Pacific decadal oscillation (PDO; e.g., Mantua et al. 1997), Pacific–North American (PNA; e.g., Wallace and Gutzler 1981), quasi-biennial oscillation (QBO; e.g., Baldwin et al. 2001), and Sahel precipitation (SPI; e.g., Haywood et al. 2013) indices. Many of these teleconnection indices have been shown to exert a control on North Atlantic TC activity on subseasonal to interannual scales, particularly for that which occurs at lower latitudes (e.g., Gray 1984; Kossin and Vimont 2007; Kossin et al. 2010; Villarini et al. 2010; Klotzbach 2011a; Villarini et al. 2012; to cite but a few examples). Only linear relationships that are statistically significant to ≥90% confidence, as assessed utilizing a Student's t test, are reported upon in the results that follow.
Over the period 1979–2007, when considering only TCs that formed within the extended MDR of Kossin (2008), the Atlantic TC season increased in length by approximately 1.5 days per year, as primarily associated with a later end to the season (Fig. 3a). This trend is not statistically significant to ≥90% confidence, however. These results are nearly identical to those obtained for the period 1980–2007 considered by Kossin (2008, their Fig. 4c), with subtle differences resulting from the inclusion of 1979 within our analysis. For the periods 1979–2001 and 1979–2002, no trend—statistically significant or otherwise—in Atlantic TC season length can be identified (Figs. 3f,g). There is some indication of a broadening of the peak of the season—here defined as formation events occurring between the 25th and 75th percentiles—but this result is also not statistically significant to ≥90% confidence. Introducing subsequent seasons into the analysis (Figs. 3b–e), particularly the active 2005 season (Beven et al. 2008), results in the gradual development of the positive but not statistically significant trend identified over the period 1979–2007 (Fig. 3a). This trend primarily results from four seasons (2001, 2003, 2005, and 2007) with TC season end dates—here represented by the 95th-percentile TC formation date to increase the degrees of freedom of the analysis—from 2 to 7 weeks later than the 1979–2014 average (Table 1).
Extending the end of the analysis through 2014 eliminates any trend in Atlantic TC season length (Fig. 3h). The below-average 2009 season (Berg and Avila 2011) contributes particularly strongly to the elimination of this trend, as manifest by a season end date approximately 2.5 weeks earlier than the long-term average (Table 2). This implies that the positive trend identified by Kossin (2008) to between 80% and 90% confidence is primarily a function of the several abnormally long TC seasons seen during the early to mid-2000s. It should be noted that extending the analysis through 2014 also decreases uncertainty in the results, as manifest by a narrower 90% confidence interval at both the start and end of the season.
When considering TC formation events across the entire Atlantic basin, no statistically significant trend in season length is identified for the period 1979–2007 (Fig. 4a) despite numerous late-ending TC seasons in the early to mid-2000s (Table 2). For the periods 1979–2001 and 1979–2002, a not statistically significant trend toward seasons that are shorter by one day per year is identified (not shown). The comparatively longer seasons of 2003– (Table 2) act to eliminate this trend but are not sufficient to result in an identifiable trend toward longer seasons. Furthermore, no trend—statistically significant or otherwise—in Atlantic TC season length is identified for the period 1979–2014 (Fig. 4b). As for the Kossin (2008) extended MDR, the uncertainty in this result is somewhat less than that associated with the period 1979–2007 (cf. Figs. 4a,b). Consequently, it is argued that the positive but not statistically significant trend toward longer Atlantic TC seasons identified by Kossin (2008) is not indicative of a robust long-term trend but is primarily the result of both the subset of TC formation events considered and the end date of the analysis. Fluctuations in season length therefore appear to be primarily controlled by subseasonal to interannual variability in the necessary conditions for TC formation, particularly early and late in the season.
Tropical cyclones that form before the 10th-percentile date for each season preferentially do so in the Gulf of Mexico, the northwestern Caribbean Sea, and near the southeastern United States coastline (Fig. 1). Note that the TCs within this composite that form in the MDR are almost exclusively associated with later-starting Atlantic TC seasons. The preferred genesis pathway for TCs that form in the preferred regions for early-season development is weak tropical transition, as evaluated utilizing the genesis pathway classification database of McTaggart-Cowan et al. (2013). For reference, tropical transition (TT; Davis and Bosart 2003, 2004) occurs in environments in which organized deep, moist convection upshear of an extratropical cyclone, through the horizontal and vertical redistribution of potential vorticity, acts to weaken initially strong vertical wind shear atop the surface low. If the cyclone is located over sufficiently warm waters (SST ≥ 24°–26°C) for a sufficiently long period of time (≥24 h) and is of sufficient intensity so as to foster wind-induced surface heat exchange (e.g., Emanuel 1986), an initially cold-core extratropical cyclone may transform into a warm-core TC. In the preferred genesis locations for early-season Atlantic TCs, June monthly mean SST is between 24° and 28°C (Fig. 5a), 600-hPa relative humidity is between 40% and 60% (Fig. 5b), 850-hPa relative vorticity is approximately zero (Fig. 5d), and the 850–200-hPa vertical wind shear magnitude is less than 10 m s−1 (Fig. 5e). In the June monthly mean, as compared to known TC-genesis-supporting environments in the deep tropics (e.g., Gray 1968, 1979; Frank 1987) and subtropical to midlatitudes (e.g., Mauk and Hobgood 2012), the SST and deep-layer vertical wind shear criteria for tropical cyclogenesis generally are met, whereas the precursor disturbance and midtropospheric relative humidity criteria for tropical cyclogenesis generally are not met.
Earlier-starting Atlantic TC seasons are associated with a statistically significant (to ≥90% confidence, as assessed utilizing a Student's t test) increase in June monthly mean 850-hPa relative vorticity in the eastern Gulf of Mexico (Fig. 5d). A one standard deviation increase in 850-hPa relative vorticity (0.5 × 10−5 s−1; Fig. 6d) is associated with a 6–9-day earlier start to the Atlantic TC season relative to its mean. As the June climatological mean 850-hPa relative vorticity is anticyclonic across the Gulf of Mexico (Fig. 5d), the presence of increased 850-hPa relative vorticity in the eastern Gulf of Mexico in June indicates a greater likelihood that a seedling disturbance for TC formation exists in the Gulf of Mexico in earlier-starting Atlantic TC seasons. Given that most early-forming Atlantic TCs do so via tropical transition (Fig. 1), it is believed that a stationary frontal boundary along and ahead of the June climatological mean 500-hPa trough in the far western Atlantic (Fig. 5c) is the likely source of such a disturbance. However, it is not immediately clear what the driving force is behind increased 850-hPa relative vorticity in the eastern Gulf of Mexico during earlier-starting Atlantic TC seasons. Though 500-hPa geopotential height is typically lower across the Gulf of Mexico and northwestern Caribbean Sea during earlier-starting Atlantic TC seasons, this correlation is not statistically significant to ≥90% confidence (Fig. 5c). It is possible that this result is a reflection of the earlier-developing TCs themselves. However, this is unlikely given a typically short duration of TCs near land compared to the duration of June and the lack of a similar relationship between increased 600-hPa relative humidity and earlier-starting Atlantic TC seasons (Fig. 5b).
Though few coherent, statistically significant (to ≥90% confidence) linear correlations between subseasonal variability and the 10th-percentile Atlantic TC formation date exist within the North Atlantic basin, the 10th-percentile Atlantic TC formation date is associated with statistically significant linear correlations to large-scale variability in the Southern Hemisphere (Fig. 7). This is particularly manifest by variability in the 500-hPa longwave pattern (Fig. 7c) and its impacts upon and relationship with atmospheric and oceanic variability in the remaining fields considered (Figs. 7a,b,d,e). It is unclear, however, as to whether the relationship between such variability, whether in whole or in part, and the 10th-percentile Atlantic TC formation date is causal or merely associative in nature. The cause of this variability is also uncertain, as it does not closely resemble that associated with any predominant planetary-scale modes of climatic variability known to the authors. Therefore, further research is necessary to identify what is responsible for the synoptic- to planetary-scale variability associated with early-starting Atlantic TC seasons.
Tropical cyclones that form after the 90th-percentile formation date for each season preferentially do so in the western Caribbean Sea and subtropical western Atlantic (Fig. 2). Late-season TCs that form in the subtropical western North Atlantic almost exclusively are the result of tropical transition, whereas TCs that form in the western Caribbean Sea either result from tropical transition or form in nonbaroclinic environments. In the preferred genesis locations for late-season Atlantic TCs, November monthly mean SST is between 24° and 28°C (Fig. 8a), 600-hPa relative humidity is between 30% and 60% (Fig. 8b), 850-hPa relative vorticity is approximately zero (Fig. 8d), and the 850–200-hPa vertical wind shear magnitude is between 10 and 20 m s−1 (Fig. 8e). In the November monthly mean, as compared to known TC-genesis-supporting environments, SST generally is sufficiently warm, deep-layer vertical wind shear is marginally favorable, and both midtropospheric relative humidity and lower-tropospheric relative vorticity are insufficiently large to promote tropical cyclogenesis.
To first order, late-ending Atlantic TC seasons preferentially occur during cool-phase (La Niña) ENSO events. This is manifest by a strengthened Walker circulation, as reflected in both November monthly mean SST (Figs. 8a and 9a) and 600-hPa relative humidity (Figs. 8b and 9b) fields across the equatorial Pacific. This is also reflected by a statistically significant (to ≥90% confidence, as assessed utilizing a Student's t test) inverse linear relationship between the 90th-percentile formation date and the November mean value of the ONI (R = −0.33). In the context of the linear regression analyses presented in Figs. 8 and 9, a one standard deviation reduction in SST (1°–2°C; Fig. 10a) and 600-hPa relative humidity (7.5%–12.5%; Fig. 10b) in the central to eastern equatorial Pacific is associated with a 6–9-day extension of the Atlantic TC season relative to its mean.
The increased likelihood of late-season TC formation in the western Caribbean Sea during La Niña events is consistent with Dunion (2011) and Klotzbach (2011a,b). Together, these studies found that atmospheric conditions known to be hostile to TC development—namely, increased vertical wind shear, increased static stability, and decreased midtropospheric relative humidity—occur in the western Caribbean Sea during October and November more (less) frequently during El Niño (La Niña) events. To that end, increased November monthly mean 600-hPa relative humidity (Fig. 8b), increased November monthly mean 850-hPa relative vorticity (Fig. 8d), and decreased November monthly mean 850–200-hPa vertical wind shear (Fig. 8e), relative to their respective climatologies, in the Caribbean Sea during later-ending Atlantic TC seasons are consistent with the influence of La Niña. It is uncertain, however, the extent to which an increased (decreased) likelihood of late-season TC formation at higher latitudes is directly attributable to La Niña (El Niño) and its influence upon the global circulation during boreal fall (e.g., Horel and Wallace 1981).
Late-ending Atlantic TC seasons also preferentially occur during negative-phase PDO events. This is manifest by abnormally cold November monthly mean SST along the west coast of North America and abnormally warm November monthly mean SST in the central North Pacific Ocean (Figs. 8a and 9a). This is also reflected by a statistically significant (to ≥90% confidence, as assessed utilizing a Student's t test) inverse linear relationship between the 90th-percentile formation date and the November mean value of the PDO index (R = −0.34). In the context of the linear regression analyses presented herein, a one standard deviation increase in SST across the central North Pacific (0.5°–1°C; Fig. 10a) and decrease in SST along the west coast of the United States (~0.5°C; Fig. 10a) are associated with an approximate 6-day increase in the length of the Atlantic TC season relative to its mean. The relationship between late-season Atlantic TC activity and the PDO has, to the authors' knowledge, not yet been documented within the refereed literature, and further identification of the physical underpinnings behind this relationship is planned for further study. It should be noted, however, that there exists a statistically significant (to ≥95% confidence, as assessed utilizing a Student's t test) direct linear relationship between the November ONI and PDO indices (R = 0.38), such that negative-phase PDO events during boreal fall have a tendency to occur during La Niña events.
In closer proximity to where late-season Atlantic TCs form, late-ending Atlantic TC seasons are associated with an eastward shift of the November monthly mean 500-hPa longwave pattern across the United States and subtropical western North Atlantic Ocean (Fig. 8c). A one standard deviation (30–40 m; Fig. 10c) increase (decrease) in 500-hPa geopotential height across the central United States (subtropical western North Atlantic Ocean) is associated with a 3–9-day extension of the Atlantic TC season relative to its mean. Though not statistically significant to ≥90% confidence, late-ending Atlantic TC seasons are also associated with increased 500-hPa geopotential heights near Greenland (Fig. 8c), wherein a one standard deviation (≥50 m; Fig. 10c) increase in 500-hPa geopotential height is associated with an approximate 3-day extension of the Atlantic TC season relative to its mean. The occurrence of below-normal 500-hPa geopotential heights in the subtropical western North Atlantic Ocean with above-normal 500-hPa geopotential heights near Greenland is consistent with an elevated likelihood of cutoff extratropical cyclones in the subtropical western North Atlantic. Further, given sufficiently warm SSTs, this is consistent with an increased likelihood of late-season TT occurrence in the subtropical western Atlantic so long as deep, moist convection associated with a given extratropical cyclone is able to locally reduce the 850–200-hPa vertical wind shear from meso- to synoptic time scales (Davis and Bosart 2003, 2004).
Spatial linear correlation is used to quantify the extent to which the linear regression analyses presented in Figs. 5, 7, 8, and 9 are representative of the variability in monthly mean fields observed with both early- and late-starting (ending) seasons. Herein, spatial linear correlation is computed over the region 10°S–70°N, 150°–0°W, consistent with the region depicted in Figs. 5 and 8. The statistically significant linear correlations between the 90th-percentile Atlantic TC formation date and boreal autumn atmospheric and oceanic variability associated with both the ENSO and PDO (section 4b) motivate the selection of the southern and western extents of this domain. The genesis locations for many of the earliest- and latest-forming Atlantic TCs (Figs. 1 and 2), many of which originate from precursor disturbances of midlatitude origin, motivate the selection of the northern extent of this domain. Similar qualitative insight to that described below is obtained when spatial linear correlation is computed over only the tropical North Atlantic (0°–30°N, 95°–10°W) or over a global domain (not shown). As a result, only the results for the region 10°S–70°N, 150°–0°W are presented below.
The results of the spatial linear correlation analyses are presented in Tables 3 and 4. To first order, the linear regression analyses are to some extent representative of variability in monthly mean fields observed with both early- and late-starting (ending) seasons. Anomalies in the monthly mean fields for the five earliest-starting and latest-ending Atlantic TC seasons are generally positively correlated with the linear regression fields, while anomalies in the monthly mean fields for the five latest-starting and earliest-ending Atlantic TC seasons are generally negatively correlated with the linear regression fields. However, correlations are generally weak, highly variable between individual Atlantic TC seasons within a given five-member composite, and stronger for some variables than for others. This implies a limit upon the predictive ability of the linear regression analyses.
Linear correlation coefficient, computed over the region 10°S–70°N, 150°W–0°, between the departure in the June monthly mean field from the 1979–2014 mean June monthly mean field and the slope of the best-fit linear regressions between the 10th-percentile Atlantic TC formation date and June monthly mean fields for the Atlantic TC seasons with the five earliest and five latest 10th-percentile formation dates. Positive (negative) linear correlation coefficients indicate that the anomaly field in question is of like (opposite) sense to the regression field.
As in Table 3, but for November monthly mean fields and the 90th-percentile Atlantic TC formation date.
For example, consider the Atlantic TC seasons with the five latest 90th-percentile formation dates, as listed in Table 4. Previously, La Niña and negative-phase PDO events were demonstrated to be associated with later-ending Atlantic TC seasons. Of the five latest-ending Atlantic TC seasons, as classified using the ONI, four were associated with neutral ENSO conditions and one (1994) was associated with El Niño conditions. Likewise, as classified by the PDO index, two of the five latest-ending Atlantic TC seasons were associated with weak positive-phase PDO conditions. Thus, while La Niña and negative-phase PDO events appear to increase the likelihood of a later-than-normal end to the Atlantic TC season, they do not represent the only pathways to a later-than-normal Atlantic TC season end. Further, slower-evolving fields with significant synoptic-scale structure in the linear regression analyses—particularly SST, but also 600-hPa relative humidity, 500-hPa geopotential height, and 850–200-hPa vertical wind shear magnitude—are generally associated with stronger correlations. The more rapidly evolving 850-hPa relative vorticity field, with primarily mesoscale structure in its linear regression analyses, is generally associated with weaker correlations. This implies that meso- to synoptic-scale variability not captured by the linear regression analyses, particularly with respect to whether or not a predecessor disturbance from which a TC can originate exists, contributes to variability in Atlantic TC season length.
The length of the Atlantic TC season varies from year to year with some seasons significantly shorter or longer than the average. Albeit only to 80%–90% confidence, Kossin (2008) suggested that the duration of the year over which TCs formed within an extended main development region of the Atlantic basin has increased by 1–2 days per year over the course of the last several decades. Herein, it was first determined whether this result is maintained when TC genesis events occurring over the full Atlantic basin are considered or when the period of record is extended to the present. Subsequently, monthly mean synoptic- to planetary-scale conditions associated with early- and late-starting (ending) Atlantic TC seasons were examined.
Quantile regression was applied to National Hurricane Center HURDAT2 data (Landsea and Franklin 2013) for the 36-yr period between 1979 and 2014 to first replicate and subsequently expand upon Kossin's (2008) findings. Extending the end of the analysis through 2014 eliminated the positive but not statistically significant trend toward longer Atlantic TC seasons identified by Kossin (2008). Rather, this trend appears to be a function of several abnormally long Atlantic TC seasons—2001, 2003, 2005, and 2007—that occurred during the early to mid-2000s (Tables 1 and 2). Of these particular seasons, all but 2003 were associated with weak to moderate La Niña and negative-phase PDO conditions, each of which were subsequently identified to be associated with later ends to the Atlantic TC season. However, the precise reasons as to why these seasons were considerably longer than normal remains to some extent unclear. Furthermore, when TC formation events across the entire Atlantic basin were considered, no statistically significant trend in Atlantic TC season length was identified for the period 1979–2007 and no trend whatsoever in Atlantic TC season length was identified for the period 1979–2014. Variations in season length therefore appear to be primarily controlled by interannual variability in the necessary conditions for TC formation, particularly early and late in the Atlantic TC season.
Utilizing linear regression applied to June and November monthly mean reanalysis data, synoptic- to planetary-scale variability associated with early- and late-starting (ending) Atlantic TC seasons was identified. Earlier-starting Atlantic TC seasons are associated with increased June monthly mean 850-hPa relative vorticity across the eastern Gulf of Mexico, indicative of a greater likelihood that a precursor disturbance for TC formation exists in the Gulf of Mexico in June in earlier-starting Atlantic TC seasons. Given that most early-forming Atlantic TCs do so via tropical transition, it is believed that a stationary frontal boundary along and ahead of the June climatological mean 500-hPa trough in the far western Atlantic is the likely source of such a disturbance. There also exist statistically significant correlations between the 10th-percentile Atlantic TC formation date and large-scale variability within the Southern Hemisphere. However, whether these correlations are causal or merely associative is uncertain, as are the causes of such variability.
Late-ending Atlantic TC seasons primarily occur during La Niña and negative-phase PDO events. The former is consistent with several previous studies (e.g., Dunion 2011; Klotzbach 2011a,b) and is believed to be the primary influence upon late-forming TCs (independent of genesis pathway) in the western Caribbean Sea. Further investigation into the physical connection(s) associated with the latter is planned for further study. A later end to the Atlantic TC season is also associated with an eastward shift in the November monthly mean 500-hPa longwave pattern across the United States and subtropical western North Atlantic Ocean. It is this modulation of the atmospheric pattern that is hypothesized to increase the likelihood of late-season TT events across the subtropical Atlantic Ocean, although it is uncertain whether this result can also be attributed to modulation of the midlatitude atmospheric pattern by the ENSO and/or PDO.
While the linear regression analyses are to some extent representative of variability in monthly mean fields observed with both early- and late-starting (ending) seasons, there exist multiple pathways to an early- or late-starting and/or -ending Atlantic TC season. Consequently, while associated with statistically significant (to ≥90% confidence) linear relationships, these results explain only a small portion of the total variation in Atlantic TC season length. For instance, in order for TCs to form, environmental conditions must locally be favorable on shorter time scales from several hours to a few days, such as may be associated with a transient Madden–Julian oscillation event (e.g., Klotzbach 2010) and/or a convectively coupled atmospheric Kelvin wave (e.g., Schreck 2015). Thus, while the results elucidate variability in the large-scale conditions necessary to support TC formation associated with early- and late-starting (ending) Atlantic TC seasons, variability on smaller spatiotemporal scales must be known in order to more completely quantify variability in Atlantic TC season length.
The results presented in this manuscript motivate a number of future studies aimed both at better understanding variability in TC season length and at quantifying large-scale conditions that promote early-starting and late-ending TC seasons. For example, what results in multiple successive short or long Atlantic TC seasons, as was observed in the early 2000s? Given that late- (early-) ending Atlantic TC seasons primarily occur during La Niña (El Niño) and negative- (positive-) phase PDO events, the persistence of such conditions between seasons may increase the likelihood of successive Atlantic TC seasons of atypical length. Further, the 90th-minus-10th-percentile formation date (R = 0.43) is strongly linearly correlated to annual Atlantic TC count. Thus, to some extent, more (less) active Atlantic TC seasons are also longer (shorter) Atlantic TC seasons, consistent with the hypothesis of Dwyer et al. (2015). This is also consistent with the influence of ENSO upon Atlantic TC activity (e.g., Gray 1984). Further research is planned to study the viability of these inferences. The methods utilized in this study can also be readily adapted to other oceanic basins that feature well-defined TC seasons or to subsets of cyclones (e.g., only TCs with maximum sustained surface winds of ≥33 m s−1) for any basin in which TCs occur. Finally, given appropriate downscaling and statistical sampling methods, these methods may be applied to climate model outputs to evaluate potential future changes in both TC season length and early- and late-season genesis pathways under a wide range of emissions scenarios.
The results presented herein also motivate an investigation into the predictability—or lack thereof—of the large-scale conditions that promote early- or late-starting and -ending TC seasons, whether in the Atlantic basin or elsewhere. In other words, are shorter- and longer-than-normal TC seasons primarily driven by synoptic-scale variability? Or are such events primarily driven by subseasonal to climate-scale variability that evolves more slowly (and is thus more predictable) than that on the synoptic scale? The strong linear correlations of Atlantic TC season length with the ENSO, PDO, and seasonal Atlantic basin TC count argue in favor of subseasonal to climate-scale controls upon Atlantic TC season length. Likewise, we note that linear regression analyses conducted between the 10th-percentile formation date and May monthly mean fields and between the 90th-percentile formation date and October monthly mean fields bear some resemblance to those presented in Figs. 5 and 7 and Figs. 8 and 9, respectively, particularly for the slower-evolving SST and 600-hPa relative humidity fields (not shown). However, the limited overall extent to which the linear regression analyses are representative of observed variability in both early- and late-starting (ending) Atlantic TC seasons argues against dominant climate-scale controls, though it remains possible that such relationships may exist if combinations of multiple modes of climate variability are considered. Further investigation is planned to address the intrinsic predictability of comparatively short and long Atlantic TC seasons.
We acknowledge fruitful discussions with Rob Hodges, Sergey Kravtsov, and Kyle Swanson during the course of this research. We are indebted to Ron McTaggart-Cowan for making available to us the genesis pathway classification database described in McTaggart-Cowan et al. (2013). This manuscript benefitted greatly from constructive review comments provided by Phil Klotzbach and two anonymous reviewers. Monthly mean ERA-Interim data were obtained from the ECMWF. NOAA ERSST.v3b SST data were obtained from the NOAA/OAR/ESRL Physical Sciences Division. Teleconnection index data were obtained from NOAA/NCEP/Climate Prediction Center (ONI, NAO, AO, PNA, and QBO), the University of Washington (PDO and SPI), and NOAA/OAR/ESRL Physical Sciences Division (AMM and AMO). Quantile regression computations were carried out utilizing MATLAB code provided by Aslak Grinsted.


Participant responses proved to be consistent throughout our research, even when provided with alternatives for preferred verbiage in new home marketing materials. The rankings, however, swapped places for concepts related to the two top-ranked words, affordable and quality, when used together in complete phrases.
Respondents chose We offer top-quality construction (75.9%) as the phrase they would prefer home builders use in describing their products, winning out over financial considerations. We offer more home for your money ranked third at 65%, among 12 available options.
"Spending money wisely is another way of saving money or getting the most you can out of your hard-earned dollar," another study respondent said of their preference for the phrase We o er more home for your money.
Other phrases respondents prefer most include We offer energy-efficient homes at 67.9%, and We build homes to fit your lifestyle at 62.1%.
Whatever their prospects fancy, cater to specific needs within specific audiences to help them choose the most appropriate home in a community designed just for them. All it takes is a little insight, a lot of heart and a heap of professional preparation.


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Need help justifying your conference attendance? Explore our tips to convince your boss page for ideas on how to get started building your case for attendance, making the most out of your conference experience, and how to share what you've learned after the event. We have a variety of useful tools available including a justification letter and conference summary report template. Let us help you make this your easiest approval process yet.
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Our Family DTS at YWAM Tyler is a unique expression of our traditional DTS and Crossroads DTS with an emphasis on discipleship for the entire family. During your DTS, parents will experience the heart of God by living in community with other families, hearing from seasoned missionaries and international teachers, and engaging in worship and intercession for the nations. Meanwhile, your school age children will be learning the same topics in the Family Sync School. They will be participating in their own times of intercession and worship, gain understanding about their role in the family and in the Great Commission, all the while learning to hear and obey God's voice. At various times throughout the week, your family will have the opportunity to worship together, have family devotionals, engage in interactive intercession, etc.
Throughout the week parents and children will spend intentional time together processing the week's topics. Your family will also be strengthened with team-building activities and local ministry opportunities. Our goal is to not only synchronize the parent's and children's DTS experience, but also to prepare each family member for a lifetime of service to the Kingdom of God.
The outreach phase of DTS will provide your family the opportunity for a family mission trip: to put into practice all the things you've learned in lecture phase. Outreaches are unique to each school and the location(s) will be determined after your arrival to DTS.
If you are interested in finding out more about how to be a mission minded family through our Family DTS, please fill out the form on this page and we will be in contact with you soon!
The lecture phase of the DTS focuses on – "To Know God." It is an intentional time that emphasizes heart transformation and character development. During the Family DTS lecture phase, parents and children alike are awakened to discover the heart of God and learn His ways.
In DTS, knowing God personally and intimately is the bottom line.
The outreach phase focuses on the practical application of – "To Make Him Known". This time of serving others provides an immediate opportunity for parents and children to put into practice the principles they have learned during their lecture phase.
Your family will be able to deliver God's message of love, life and hope through Christ that brings healing to the nations! Through evangelism, stories, dramas, church ministry and practical help for the poor and needy, your family will be able to see the transforming power of the Gospel.
Relationship is the essential message of YWAM. First with God, then with others. Small groups are a place where lifelong relationships with your classmates begin. As you walk through this eye-opening and inspiring time, join others to discuss what God is showing you.
*Lecture phase tuition only. Outreach phase airfare and grounds fees are in addition to the tuition cost.The price of the outreach phase can range from $1,500 to $3,500, depending on location. $100 discount per adult if tuition is paid in full one month before the start of school.
There is an enrollment fee of $50 for singles and $70 for couples due upon arrival.
There is also an additional cost for each child enrolled in the Family Sync School.
Applications should be submitted at least one month prior to the start of school.


To mark the 80th birthday of the dynamic Japanese conductor Seiji Ozawa in September 2015, Warner Classics has released a sumptuous 25-CD box containing his complete recordings for the label, which were made between 1968 and 1997.
The set places an emphasis on Seiji Ozawa�s mastery of the colourful, large-scale orchestral scores of the 19th and 20th centuries and on his collaboration with such soloists as Itzhak Perlman, Anne-Sophie Mutter and Mstislav Rostropovich.
The scope of the 25 CDs is wide � from mainstays of the Romantic repertoire to Modernist masterpieces and works premiered as recently as 1997 � and the recordings reflect Ozawa�s status as a global maestro and his relationship with a number of the world�s finest orchestras, including the Boston Symphony Orchestra (of which he was Music Director for 29 seasons), Chicago Symphony Orchestra and the Berliner Philharmoniker.
Seiji Ozawa was born in 1935 into a Japanese family living in China and trained at the Toho School of Music in Tokyo. He came to international attention when he won the Besan�on Conducting Competition in France in 1959, after which he was invited to Boston, where he studied with Charles Munch and Pierre Monteux, winning the Koussevitzky Prize in 1960. He subsequently received a scholarship to study in Berlin with no less a maestro than Herbert von Karajan. In 1961, another luminary of comparable brilliance, Leonard Bernstein, recruited Ozawa to be Assistant Conductor of the New York Philharmonic (1961), and he made his professional debut in 1962 with the San Francisco Symphony.
Over his career, Ozawa, who is known for his very physical conducting style and his preference for conducting without a score, has also been Music Director of the Vienna State Opera, Toronto Symphony Orchestra and San Francisco Symphony, Artistic Director of the Tanglewood Festival and Music Director of the Chicago Symphony Orchestra�s Ravinia Festival. He has also enjoyed close associations with such institutions as the Vienna Philharmonic Orchestra and the Paris Op�ra. In Japan, he is Artistic Director of the Saito Kinen Orchestra, which he formed in 1984, and of the Saito Kinen Festival.
Ozawa was diagnosed with cancer of the oesophagus in 2010 but, following successful surgery, he has returned to conducting and continues to mentor young musicians.
- Variations on "I Got Rhythm"


On the third day of SolidWorks World 2011 Graebert has announced an Apple AppStore style market for DraftSight at www.graebertmarket.com. DraftSight is the free 2D CAD offering from Dassault Systemes which has been downloaded 300,000 times in the last six months. DraftSight is based on the ARES CAD engine from Graebert and as part of the deal with Dassault Systemes it was agreed that Graebert will own and run the DraftSight market. This should not be confused with the Premium version of DraftSight which Dassault Systemes has been talking about.
This needs some explanation. There will be two versions of DraftSight, the free version that can be downloaded today and a DraftSight Premium version that will have a LISP API. As you may be aware the free version does not have any API and hence cannot be extended using plug-ins or LISP. Dassault Systemes will give away DraftSight for free and charge $250 per year for DraftSight Premium which will include support. I believe that for starters DraftSight Premium will have a LISP API and other API's like COM and .NET maybe added later. Dassault Systemes hasn't been forthcoming with details on this. From what I am told these API's and other DraftSight Premium features are being developed internally by Dassault Systemes and not Graebert.
So if the free Draftsight has no API how can developers built applications for it and sell them on Graebert's DraftSight Market. Here is the thing. ARES is built over the ODA Platform due to which it can be extended by means of DRX plug-ins. All my eleven ARES plug-ins are DRX DLL's built using a SDK supplied by Graebert. However, Graebert has blocked normal DRX DLL's from running inside DraftSight. To build a plug-in for DraftSight you need to get a special DraftSight SDK from Graebert and compile your plug-in with it.
Graebert has borrowed a page from Apple's AppStore book and will take a 25% cut on each license sold on the DraftSight Market. 30% in case the plug-in needs Spatial's ACIS solid modeling kernel to run. However, if a developer gets the special SDK from Graebert he will be able to build and sell DraftSight plug-ins left, right and center. To have a tighter control Graebert has instituted a system wherein every developer needs to submit to them his DraftSight plug-in (object code only) to be digitally signed. Only then will it load into DraftSight. After digitally signing the plug-in Graebert then puts it on the DraftSight Market and also sends it back to the developer to distribute it himself. However, the agreement between Graebert and the developer allows the developer to distribute his plug-in but restricts him from selling it. All sales need to happen through the DraftSight Market only. So basically, just like how users are sent to iTunes to purchase apps, the developer needs to send his customers to the DraftSight market to place their orders. The developer also needs to remove any software locking mechanism from his plug-in because that is handled by Graebert at the DraftSight end.
I know all this because I have been working closely with Graebert for some time to port my DRX plug-ins over to DraftSight. The current version of DraftSight is a Beta which does not have the DraftSight Market architecture built into it. I am given to understand that Dassault Systemes is going to end the DraftSight Beta and will release the first version in a week or so. I am not sure whether this first version will be DraftSight Market enabled or not. It doesn't matter since the DraftSight Store is due to open only in April 2011. This announcement is basically a call to developers to sign up and start developing plug-ins.
To help developers debug their DraftSight plug-ins, Graebert gives them a special version of DraftSight called Graebert Developer Studio which loads DRX plug-ins that need not be signed by Graebert. The image below shows a couple of DraftSight plug-ins that I have been working on.
As you can see the application name in the title bar reads "Graebert Developer Studio". The "TerrainCAD" and "CAD Converter" menus have been added by my plug-ins. You can expect to find SYCODE products in the DraftSight Market on the day it opens.
I hope to share more information on developing DraftSight plug-ins on this blog. If you have questions about the DraftSight Market or need clarifications please leave a comment. I'll do my best to answer or get an answer for you from Graebert or Dassault Systemes.


Grind the cheeses together quite fine, add 2 spoons olive oil, pinch of salt and break some basil leaves with your fingers.
Mix well and fill the flowers (if necessary use a piping bag). Steam for a few minutes.
Serve on a plate with some olive oil dribbled around and decorate with basil leaves.


text:
Q: Popular inputs de acordo com o valor do option selecionado Tenho 3 arrays, cidades, capitais e população. Como posso fazer ao clicar em um determinado estado aparecer nos inputs os respectivos valores, ou seja, capital e população do estado selecionado.

var cidades = new Array("Acre", "Bahia", "Minas Gerais", "Pernambuco", "Rio de Janeiro", "São Paulo")
var capital = new Array("Rio Branco", "Salvador", "Belo Horizonte", "Recife", "Rio de Janeiro", "São Paulo")
var populacao = new Array("829.619", "15.344.447", "21.119.536", "9.473.266", "16.718.956", "45.094.866" )

function getData() {
   var estado = document.getElementById("estados");
   document.getElementById("capital").value = ?????
   document.getElementById("populacao").value = ????
}

getData();
Selecione o estado:
<select id="estados" onChange="getData(this.form)">
   <option>Acre</option>
   <option>Bahia</option>
   <option>Minas Gerais</option>
   <option>Pernambuco</option>
   <option>Rio de Janeiro</option>
   <option>São Paulo</option>
</select>

A capital é:
<input type="text" id="capital" size=13>

A população é:
<input type="text" id="populacao" size=6>

A: Na função getData() é passado um parâmetro, o valor do índice de cada <option>, depois ele pega o parâmetro por JS e utiliza como índice do array:

var cidades = new Array("Acre", "Bahia", "Minas Gerais", "Pernambuco", "Rio de Janeiro", "São Paulo");
var capital = new Array("Rio Branco", "Salvador", "Belo Horizonte", "Recife", "Rio de Janeiro", "São Paulo");
var populacao = new Array("829.619", "15.344.447", "21.119.536", "9.473.266", "16.718.956", "45.094.866" );
function getData(valor){
   document.getElementById("capital").value = capital[valor];
   document.getElementById("populacao").value = populacao[valor];
}
<!DOCTYPE html>

   <title>teste</title>

   Selecione o estado:
   <select id="estados" onChange="getData(this.value)">
      <option disabled="disabled" selected="selected">Selecione o Estado</option>
      <option value="0">Acre</option>
      <option value="1">Bahia</option>
      <option value="2">Minas Gerais</option>
      <option value="3">Pernambuco</option>
      <option value="4">Rio de Janeiro</option>
      <option value="5">São Paulo</option>
   </select>

   A capital é:
   <input type="text" id="capital" disabled="disabled" size=13>

   A população é:
   <input type="text" id="populacao" disabled="disabled" size=6>

Obs: Coloque mais uma opção no <option> para que o Acre pudesse ser selecionado também (como está disabled não passa valor).

A: Trata-se de arrays paralelos. Dizem-se paralelos se os seus elementos identificados pelo mesmo indice estão relacionados.
Como os valores de indice das opções dentro do elemento SELECT combinam com os dos valores de indice de array paralelo, a propriedade selectedindex do elemento SELECT cria um modo conveniente de se chegar diretamente aos dados correspondentes em outros arrays
Na função crie uma variavel index,
Var index = document.getElementById("estados").selectedindex

Utilize essa variavel 'index' para retornar os respectivos itens  dos outros arrays, substitua os pontos de interrogação  por capital[index] e populacao[index]

redpajama_set_name: RedPajamaStackExchange


text:
News / UK
Hydropower deployed to protect centuries-old Welsh bible from damp damage
The bible is being protected with hydropower (National Trust Images/Iolo Penri Photography/PA)
A "hugely important" Welsh bible which is more than 400 years old is being protected from damage with the help of hydropower, the National Trust said.
The first bible translated into Welsh has been increasingly at risk of deterioration from flooding and damp at the 16th century farmhouse Ty Mawr Wybrnant, near Betws-y-Coed, Snowdonia, where it is on display.
But now the Trust, which cares for the property, says it has found a sustainable solution by installing a "pico" or small scale hydroelectric turbine to use water from the nearby stream to help power the heating system to prevent damp.
The bible is on display at Ty Mawr Wybrnant in Snowdonia (National Trust Images/Iolo Penri Photography/PA)
It means the bible is being protected by the very thing, water, that is also putting it at risk through increasingly heavy and persistent rainfall, flooding and damp, as the climate changes.
The book, on display with more than 200 other bibles in different languages, is one of only 24 known copies of the first bible translated into Welsh.
The translation was undertaken by Bishop William Morgan, who was born at Ty Mawr Wybrnant, and printed in 1588.
Experts say it helped standardise the Welsh language and is considered to be the single most significant step in ensuring the survival of the language today.
The bible dates back to the 16th century (National Trust Images/Iolo Penri Photography/PA)
But the bible, along with the rest of the collection, is susceptible to moisture in the air.
Now the hydroelectric turbine will generate power for the electric radiators that control levels of humidity in the Grade II listed farmhouse, with the switch to renewable energy protecting the books in a sustainable way, the Trust said.
It will only take a proportion of water from the stream to drive the turbine when water levels rise to a certain point – likely to be just when the electricity is most needed, as the air is full of moisture after rainfall.
The turbine, discreetly housed in a larch-clad shed, is expected to cut the property's carbon dioxide emissions by 5.2 tonnes a year.
Keith Jones, the National Trust's climate change adviser, said: "Earlier this year we experienced the worst flood at Ty Mawr Wybrnant in living memory and that extra moisture meant we needed to use more heating to ensure the humidity levels didn't get too high.
"Climate predictions indicate likely increases in the severity and frequency of rainfall in the area.
"This small-scale technology is allowing us to adapt to future changes more sustainably."
He added: "The energy is consumed directly on-site, solely for the conservation of this priceless bible collection.
"We must reduce our impact on the climate, but we can harness the tools nature gives us to adapt to the challenges we are facing."
The remote 16th century farmhouse was the birthplace of Bishop William Morgan (National Trust Images/Iolo Penri Photography/PA)
It is the first time the National Trust has used hydroelectric power for the primary aim of protecting a historic collection.
Mr Jones said the organisation would be looking at how the approach, developed with the help of the Dwr Uisce Project which includes researchers from Bangor University and Trinity College, Dublin, could be used where other collections may also be at risk.
Tim Pye, the National Trust's libraries curator, said: "Extreme weather is one of the threats to our collections, with sensitive and fragile objects like books, manuscripts and other documents especially susceptible to conditions such as damp.
"The energy-efficient and sustainable pico hydro solution for Ty Mawr Wybrnant will help greatly in its efforts to safeguard the Bible collection for today's and future generations to enjoy."
Ty Mawr Wybrnant
People are not aware of the changes that will be needed to cut the UK's greenhouse gas emissions to net zero by 2050, polling suggests.
A levy on airlines or fossil fuel companies could be introduced to help pay for a huge boost to woodland planting, Government climate advisers have urged.
BP has set itself a challenge of creating five "unicorn" companies – valued at more than $1 billion – in the next five years to address the "dual challenge" of supplying more energy with fewer emissions.
Hundreds of north-east school pupils have been exploring future careers in the energy industry at an inspirational event organised by TechFest in partnership with Equinor.
Experienced due diligence expert and partner at big four firm Deloitte, Shaun Reynolds, will chair a discussion on Mergers and Acquisitions (M&A) at a P&J business breakfast next month.
No magic wand to deal with off-payroll working rules
Aberdeen-based consultancy Hunter Adams recently convened 40 senior HR leaders from the oil and gas sector to debate the practicalities around the implementation of the IR35 reforms, due on April 6, 2020.
Chrysaor hands in decom plan for southern North Sea platform
Chrysaor has submitted its decommissioning plans for a southern North Sea platform to the UK Government.
Scottish Government refute fresh offshore wind jobs decline figures
The Scottish Government has rejected new offshore wind jobs figures published by the Office of National Statistics (ONS) that claim job numbers have declined.

redpajama_set_name: RedPajamaCommonCrawl


There is no peer critique, s's only self-critique.
Topic is same as speech #1, but of course you'll have to expand/improve/show eye contact, gestures, voice and have a very short ppt or prezi to go with it.
1.Finish ALL of the exercises for the unit.
2.Then, go to the "markbook" and take a picture of your unit score.
3.You must upload this picture to me by the homework deadline.
Tell me about what you enjoy doing in your free time. You can use the study guide on page 29 to give you some ideas.
This is a recorded homework assignment. Your answer must be a video recording. It should be between 1-2 minutes long.
You must record your answer (you can use your phone or your computer) and email me the link.
You should double-check that your file works correctly. If I cannot hear or see the file you will not get points for the assignment.
Describe the chores you have to do at home. Also, tell me about the kinds of complaints that your parents have of you?
You must record your answer (you can use your phone or your computer) and upload it directly to youtube and add a link by midnight.
What do you usually do on the weekends? What types of things you like and dislike doing on the weekends? You can use the vocabulary and conversations on page 11 to give you some ideas.
You must record your answer (you can use your phone or your computer) and upload it directly to youtube and send me the link by midnight.
I will watch your video and will reply to you (comments or feedback or just a chat) via email.
Original Student Writing Page 29.
You will write a paragraph, about 12 sentences, complete with a title, indent, topic sentence, and conclusion.
Pge 25 of the textbook shows some examples of both good and not so good titles. You need to write a title for your own paragraph.
Original Student Writing (page 29) gives you the instructions to follow.
Introduce yourself and talk about your daily life. You can use the study guide on page 9 to give you some ideas.
Video Recording HW (2) Here is a list of the weekly video assignments.
You do not need to download the app to your phone. Simply use the FLASH method and download it to your PC.