Measuring Environmental Favorability for Tropical Cyclogenesis by Statistical Analysis of Threshold Parameters

2011 ◽  
Vol 24 (23) ◽  
pp. 5968-5997 ◽  
Author(s):  
Michael G. McGauley ◽  
David S. Nolan
Keyword(s):  
Tropical Cyclone ◽  
Threshold Value ◽  
New Method ◽  
Tropical Cyclogenesis ◽  
Tropical Cyclone Genesis ◽  
Threshold Values ◽  
Reanalysis Dataset ◽  
Statistical Probability ◽  
Saturation Deficit ◽  
Meteorological Features

Abstract As the climate changes, the ability to predict changes in the frequency of tropical cyclogenesis is becoming of increasing interest. A unique approach is proposed that utilizes threshold values in potential intensity, wind shear, vorticity, and normalized saturation deficit. Prior statistical methods generally involve creating an index or equation based on averages of important meteorological parameters for a given region. The new method assumes that threshold values exist for each important parameter for which cyclogenesis is unlikely to develop. This technique is distinct from previous approaches that seek to determine how each of these parameters interdependently favors cyclogenesis. To determine three of the individual threshold values (shear, potential intensity, and vorticity), an idealized climate is first established that represents the most advantageous but realistic (MABR) environment. An initial numerical simulation of tropical cyclone genesis in the MABR environment confirms that it is highly favorable for cyclogenesis. Subsequent numerical simulations vary each parameter individually until no tropical cyclone develops, thereby determining the three threshold values. The new method of point downscaling, whereby background meteorological features are represented by a single vertical profile, is used in the simulations to greatly simplify the approach. The remaining threshold parameter (normalized saturation deficit) is determined by analyzing the climatological record and choosing a value that is statistically observed to prevent cyclogenesis. Once each threshold value is determined, the fraction of time each is exceeded in the location of interest is computed from the reanalysis dataset. The product of each fraction for each of the relevant parameters then gives a statistical probability as to the likelihood of cyclogenesis. For predicting regional and monthly variations in frequency of genesis, this approach is shown to generally meet or exceed the predictive skills of earlier statistical attempts with some failure only during several off-season months. This method also provides a more intuitive rationale of the results.

scholarly journals Tropical Cyclone Genesis Factors in Simulations of the Last Glacial Maximum

2012 ◽  
Vol 25 (12) ◽  
pp. 4348-4365 ◽  
Author(s):  
Robert L. Korty ◽  
Suzana J. Camargo ◽  
Joseph Galewsky
Keyword(s):  
Tropical Cyclone ◽  
Last Glacial Maximum ◽  
Large Scale ◽  
Glacial Maximum ◽  
Tropical Cyclogenesis ◽  
Tropical Cyclone Genesis ◽  
Surface Cooling ◽  
Last Glacial ◽  
The Last Glacial Maximum ◽  
The Last Glacial

Abstract Large-scale environmental factors that favor tropical cyclogenesis are calculated and examined in simulations of the Last Glacial Maximum (LGM) from the Paleoclimate Modelling Intercomparison Project Phase 2 (PMIP2). Despite universally colder conditions at the LGM, values of tropical cyclone potential intensity, which both serves as an upper bound on thermodynamically achievable intensity and indicates regions supportive of the deep convection required, are broadly similar in magnitude to those in preindustrial era control simulation. Some regions, including large areas of the central and western North Pacific, feature higher potential intensities at the LGM than they do in the control runs, while other regions including much of the Atlantic and Indian Oceans are lower. Changes in potential intensity are strongly correlated with the degree of surface cooling during the LGM. Additionally, two thermodynamic parameters—one that measures midtropospheric entropy deficits relevant for tropical cyclogenesis and another related to the time required for genesis—are broadly more favorable in the LGM simulation than in the preindustrial era control. A genesis potential index yields higher values for the LGM in much of the western Pacific, a feature common to nearly all of the individual models examined.

scholarly journals An Intraseasonal Prediction Model of Atlantic and East Pacific Tropical Cyclone Genesis

2013 ◽  
Vol 141 (6) ◽  
pp. 1925-1942 ◽  
Author(s):  
Stephanie A. Slade ◽  
Eric D. Maloney
Keyword(s):  
Logistic Regression ◽  
Tropical Cyclone ◽  
Southern Oscillation ◽  
Selection Procedure ◽  
Ocean Basin ◽  
Tropical Cyclogenesis ◽  
Tropical Cyclone Genesis ◽  
East Pacific ◽  
Skill Scores ◽  
Cyclone Genesis

Abstract A real-time statistical model based on the work of Leroy and Wheeler is developed via multiple logistic regression to predict weekly tropical cyclone activity over the Atlantic and east Pacific basins. The predictors used in the model include a climatology of tropical cyclone genesis for each ocean basin, an El Niño–Southern Oscillation (ENSO) index, and two indices representing the propagating Madden–Julian oscillation (MJO). The Atlantic model also includes a predictor representing the variability of sea surface temperature (SST) in the Main Development Region (MDR). These predictors are suggested as useful for the prediction of tropical cyclogenesis based on previous work in the literature and are further confirmed in this study using basic statistics. Univariate logistic regression models are generated for each predictor in each region to ensure the choice of prediction scheme. Using all predictors, cross-validated hindcasts are developed out to a seven-week forecast lead. A formal stepwise predictor selection procedure is implemented to select the predictors used in each region at each forecast lead. Brier skill scores and reliability diagrams are used to assess the skill and dependability of the models. Results show an increase in model skill over the time-varying climatology at predicting tropical cyclogenesis by the inclusion of the MJO out to a three-week forecast lead for the east Pacific and a two-week forecast lead for the Atlantic. The importance of ENSO and MDR SST for Atlantic genesis prediction is highlighted, and the uncertain effects of ENSO on east Pacific tropical cyclogenesis are revisited.

scholarly journals Attributing Tropical Cyclogenesis to Equatorial Waves in the Western North Pacific

2011 ◽  
Vol 68 (2) ◽  
pp. 195-209 ◽  
Author(s):  
Carl J. Schreck ◽  
John Molinari ◽  
Karen I. Mohr
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Wave Spectrum ◽  
Vertical Wind Shear ◽  
Warm Season ◽  
Threshold Value ◽  
Rainfall Anomaly ◽  
Tropical Cyclogenesis ◽  
Positive Anomaly ◽  
Equatorial Wave

Abstract Tropical cyclogenesis is attributed to an equatorial wave when the filtered rainfall anomaly exceeds a threshold value at the genesis location. It is argued that 0 mm day−1 (simply requiring a positive anomaly) is too small a threshold because unrelated noise can produce a positive anomaly. A threshold of 6 mm day−1 is too large because two-thirds of storms would have no precursor disturbance. Between these extremes, consistent results are found for a range of thresholds from 2 to 4 mm day−1. Roughly twice as many tropical cyclones are attributed to tropical depression (TD)-type disturbances as to equatorial Rossby waves, mixed Rossby–gravity waves, or Kelvin waves. The influence of the Madden–Julian oscillation (MJO) is even smaller. The use of variables such as vorticity and vertical wind shear in other studies gives a larger contribution for the MJO. It is suggested that its direct influence on the rainfall in forming tropical cyclones is less than for other variables. The impacts of tropical cyclone–related precipitation anomalies are also presented. Tropical cyclones can contribute more than 20% of the warm-season rainfall and 50% of its total variance. The influence of tropical cyclones on the equatorial wave spectrum is generally small. The exception occurs in shorter-wavelength westward-propagating waves, for which tropical cyclones represent up to 27% of the variance. Tropical cyclones also significantly contaminate wave-filtered rainfall anomalies in their immediate vicinity. To mitigate this effect, the tropical cyclone–related anomalies were removed before filtering in this study.

scholarly journals Tropical cyclone genesis across palaeoclimates

2015 ◽  
Vol 11 (1) ◽  
pp. 181-220 ◽  
Author(s):  
J. H. Koh ◽  
C. M. Brierley
Keyword(s):  
Carbon Dioxide ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Large Scale ◽  
Climate Models ◽  
Tropical Cyclogenesis ◽  
Tropical Cyclone Genesis ◽  
Inter Tropical Convergence Zone ◽  
Potential Index ◽  
Cyclone Genesis

Abstract. Tropical cyclone genesis is investigated for the Pliocene, Last Glacial Maximum (LGM) and the mid-Holocene through analysis of five climate models. The genesis potential index is used to estimate this from large scale atmospheric properties. The mid-Pliocene and LGM characterise periods where carbon dioxide levels were higher and lower than pre-industrial respectively, while the mid-Holocene differed primarily in its orbital configuration. The number of tropical cyclones formed each year is found to be fairly consistent across the various palaeoclimates. Although there is some model uncertainty in the change of global annual tropical cyclone frequency, there are coherent changes in the spatial patterns of tropical cyclogenesis. During the Pliocene and LGM, changes in carbon dioxide led to sea surface temperature changes throughout the tropics, yet the potential intensity of tropical cyclones appears relatively insensitive to these variations. Changes in tropical cyclone genesis during the mid-Holocene are observed to be asymmetric about the Equator: genesis is reduced in the Northern Hemisphere, but enhanced in the Southern Hemisphere. This is clearly driven by the altered seasonal insolation. Nonetheless, the enhanced seasonality may have driven localised effects on tropical cyclone genesis, through changes to the strength of monsoons and shifting of the inter-tropical convergence zone. Trends in future tropical cyclone genesis are neither consistent between the five models studied, nor with the palaeoclimate results. It is not clear why this should be the case.

scholarly journals Testing the Performance of Tropical Cyclone Genesis Indices in Future Climates Using the HiRAM Model

2014 ◽  
Vol 27 (24) ◽  
pp. 9171-9196 ◽  
Author(s):  
Suzana J. Camargo ◽  
Michael K. Tippett ◽  
Adam H. Sobel ◽  
Gabriel A. Vecchi ◽  
Ming Zhao
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Atmospheric Model ◽  
The Other ◽  
Tropical Cyclone Genesis ◽  
Good Prediction ◽  
Present Climate ◽  
Environmental Predictors ◽  
Saturation Deficit ◽  
Cyclone Genesis

Abstract Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment that are designed to be proxies for the probability of tropical cyclone (TC) genesis. While the performance of TCGIs in the current climate can be assessed by direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here the authors examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer climate scenarios. They investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs that perform well in the present climate do not accurately reproduce the simulated future decrease in TC frequency. This decrease is captured when the humidity predictor is the column saturation deficit rather than relative humidity. Using saturation deficit with relative SST as the other thermodynamic predictor overpredicts the TC frequency decrease, while using potential intensity in place of relative SST as the other thermodynamic predictor gives a good prediction of the decrease’s magnitude. These positive results appear to depend on the spatial and seasonal patterns in the imposed SST changes; none of the indices capture correctly the frequency decrease in simulations with spatially uniform climate forcings, whether a globally uniform increase in SST of 2 K, or a doubling of CO2 with no change in SST.

scholarly journals Investigate the relationship between Storm Formation and Tropical Cyclone Genesis Potential Index in the Vietnam East Sea

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Nguyen Manh Linh ◽  
Jack Katzfey ◽  
John McGregor ◽  
Nguyen Kim Chi ◽  
Pham Quang Nam ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
El Nino ◽  
East Sea ◽  
Tropical Cyclogenesis ◽  
Tropical Cyclone Genesis ◽  
Genesis Potential Index ◽  
American Meteorological Society ◽  
La Nina ◽  
Potential Index ◽  
Cyclone Genesis

Abstract: In this paper, the relationship between Tropical Cyclone (TC) Genesis Potential Index (GPI) and the number of TC (NTC) associated with ENSO over the Vietnam East Sea (VES) was investigated. Observed TC data of the Regional Specialized Meteorological Center (RSMC) Tokyo Typhoon Center and ERA Interim reanalysis data for the period 1985-2015 were used. The results show a good agreement between GPI and NTC over the VES with the correlation coefficient is 0.84. There were more TCs formed over the VES during La Nina years and less TCs during El Nino years. There were positive anomalies of GPI, environmental factors (relative humidity, sea surface temperature, absolute vorticity, potential intensity)over the region where the highest densityof TCs genesis locatedduring La Nina years while there were negative anomalies found during El Nino years. Relative humidity has the largest contribution to the positive difference GPI between La Nina years and El Nino years, the less contribution comes from the potential intensity, absolute vorticity, and wind shear. Keywords: GPI, Tropical Cyclone Genesis, ENSO, Vietnam East Sea. References: [1] K.A. Emanuel, D.S. Nolan, Tropical cyclone activity and global climate, Reprints, 26th Conference on hurricane and Tropical Meteorology, American meteorological Society: Miami, (2004) 240–241.[2] D.S. Nolan, E.D. Rappin, K.A. Emanuel., Tropical cyclogenesis sensitivity to environmental parameters in radiative-convective equilibrium, Quarterly Journal of the Royal Meteorological Society. 133 (2007) 2085–2107.[3] S.J. Camargo, K.A. Emanuel, A.H. Sobel, Use of the Genesis Potential Index to Diagnose ENSO effected on Tropical Cyclone Genesis, American Meteorological Society.20 (2007) 4819-4834[4] C.L. Bruyere, G.J. Holland, E. Towler, Investigating the Used of a Genesis Potential Index for Tropical Cyclones in the North Atlatic Basin, American Meteorological Society..25 (2012) 8611-8626[5] Song Yuan, Wang Lei, Lei Xiaoyan and Wang Xidong, Tropical cyclone genesis potential index over western north Pacific simulated by CMIP5 models, (2015).[6] Lei Wang, Diagnostic of the ENSO modulation of Tropical cyclogenesis over the southern South China Sea using a genesis potential index, Acta Oceanol. Sin., Vol. 31, No. 5 (2012) 54-68.[7] Xin Kieu-Thi, Hang Vu-Thanh, Truong Nguyen-Minh, Duc Le, Linh Nguyen-Manh, Izuru Takayabu, Hidetaka Sasaki, Akio Kito, Rainfall and tropical cyclone activity over Vietnam simulated and projected by the Non-Hydrostatic Regional Climate Model – NHRCM, Journal of the Meteorological Society of Japan. 94A (2016) 135-150.[8] https://www.jma.go.jp/jma/jma-eng/jma-center/ rsmc-hp-pub-eg/trackarchives.html[9] Trần Quang Đức, Xu thế biến động của một số đặc trưng ENSO, Tạp chí Khoa học Đại học Quốc gia Hà Nội, Khoa học Tự nhiên và Công nghệ. 1S (2011) 29-36.[10] https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php[11] E. Palmen, Formation and development of tropical cyclones, Proceedings of tropical cyclone Symposium, Brisbane, Australian Bur. Meteorol., Melbourne, (1956) 213-231[12] M. DeMaria, The effect of vertical wind shear on tropical cyclone intensity change, Jounal of Atmospheric Sicences. 53 (1996) 2076-2087.[13] S.J. Camargo, Diagnosis of the MJO modulation of Tropical cyclogenesis using an empirical index. American Meteorological Society. 66 (2009) 3061-3074.[14] S.J. Camargo, A.H. Sobel, Anthony G. Barnston, K.A. Emanuel, Tropical cyclone genesis potential index in climate models, Tellus A: Dynamic Meteorology and Ocenaography. 59:4 (2007) 428-443. doi: 10.1111/j.1600-0870.2007. 00238.

scholarly journals An Objective Algorithm for Detecting and Tracking Tropical Cloud Clusters: Implications for Tropical Cyclogenesis Prediction

2011 ◽  
Vol 28 (8) ◽  
pp. 1007-1018 ◽  
Author(s):  
Christopher C. Hennon ◽  
Charles N. Helms ◽  
Kenneth R. Knapp ◽  
Amanda R. Bowen
Keyword(s):  
Tropical Cyclone ◽  
Numerical Models ◽  
Global Analysis ◽  
Ocean Basin ◽  
Necessary Condition ◽  
Tropical Cyclogenesis ◽  
Movement Vector ◽  
Cloud Clusters ◽  
Real Time Tracking ◽  
Do So

Abstract An algorithm to detect and track global tropical cloud clusters (TCCs) is presented. TCCs are organized large areas of convection that form over warm tropical waters. TCCs are important because they are the “seedlings” that can evolve into tropical cyclones. A TCC satisfies the necessary condition of a “preexisting disturbance,” which provides the required latent heat release to drive the development of tropical cyclone circulations. The operational prediction of tropical cyclogenesis is poor because of weaknesses in the observational network and numerical models; thus, past studies have focused on identifying differences between “developing” (evolving into a tropical cyclone) and “nondeveloping” (failing to do so) TCCs in the global analysis fields to produce statistical forecasts of these events. The algorithm presented here has been used to create a global dataset of all TCCs that formed from 1980 to 2008. Capitalizing on a global, Gridded Satellite (GridSat) infrared (IR) dataset, areas of persistent, intense convection are identified by analyzing characteristics of the IR brightness temperature (Tb) fields. Identified TCCs are tracked as they move around their ocean basin (or cross into others); variables such as TCC size, location, convective intensity, cloud-top height, development status (i.e., developing or nondeveloping), and a movement vector are recorded in Network Common Data Form (NetCDF). The algorithm can be adapted to near-real-time tracking of TCCs, which could be of great benefit to the tropical cyclone forecast community.

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