Markovian approach to the frequency of tropical cyclones and subsequent development of univariate prediction model

Abstract This study attempts, for the first time, to predict the annual number of tropical cyclones (TCs) affecting New York State (NYS), as part of the effort of the New York State Resiliency Institute for Storms and Emergencies (RISE). A pure statistical prediction model and a statistical–dynamical hybrid prediction model have been developed based on the understanding of the physical mechanism between NYS TCs and associated large-scale climate variability. During the cold phase of El Niño–Southern Oscillation, significant circulation anomalies in the Atlantic Ocean provide favorable conditions for more recurving TCs into NYS. The pure statistical prediction model uses the sea surface temperature (SST) over the equatorial Pacific Ocean from the previous months. Cross validation shows that the correlation between the observed and predicted numbers of NYS TCs is 0.56 for the June 1979–2013 forecasts. Forecasts of the probability of one or more TCs impacting NYS have a Brier skill score of 0.35 compared to climatology. The statistical–dynamical hybrid prediction model uses Climate Forecast System, version 2, SST predictions, which are statistically downscaled to forecast the number of NYS TCs based on a stepwise regression model. Results indicate that the initial seasonal prediction for NYS TCs can be issued in February using the hybrid model, with an update in June using the pure statistical prediction model. Based on the statistical model, for 2014, the predicted number of TCs passing through NYS is 0.33 and the probability of one or more tropical cyclones crossing NYS is 30%, which are both below average and in agreement with the actual activity (0 NYS TCs).

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Impact of horizontal resolution on prediction of tropical cyclones over Bay of Bengal using a regional weather prediction model

Journal of Earth System Science ◽

10.1007/bf02710045 ◽

2003 ◽

Vol 112 (1) ◽

pp. 79-93 ◽

Cited By ~ 1

Author(s):

M. Mandal ◽

U. C. Mohanty ◽

K. V. J. Potty ◽

A. Sarkar

Keyword(s):

Prediction Model ◽

Tropical Cyclones ◽

Bay Of Bengal ◽

Weather Prediction ◽

Horizontal Resolution

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Prediction of sea state under tropical cyclones in the UK Met Office operational global wave model

MAUSAM ◽

10.54302/mausam.v48i4.4351 ◽

2021 ◽

Vol 48 (4) ◽

pp. 621-628

Author(s):

M.W. HOLT ◽

J.C.R. HUNT

Keyword(s):

Tropical Cyclone ◽

Prediction Model ◽

Tropical Cyclones ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Wave Model ◽

Numerical Weather Prediction Model ◽

Model Surface ◽

Sea State ◽

Numerical Weather

The United Kingdom Meteorological Office (UKMO) routinely runs a global operational numerical weather prediction model. Surface winds from this model are used by a spectral wave model to forecast sea state. A brief description is given of the formulation of the wave model, and two cases of Tropical Cyclones in the Bay of Bengal are examined using the archived data generated in real time by the operational wave model. These are Tropical Cyclone 3B, 14-15 June 1996 and Tropical Cyclone 07B, 4-6 November 1996. At a resolution of 1.25° in longitude by 0.833° in latitude the numerical weather prediction model does not represent the dynamics of a tropical cyclone and the surface wind speeds are underestimated. Consequently, the extreme sea state generated by a Tropical Cyclone is not modelled. However, the wave model was able to generate a long period swell of over 3m height, which propagated away from the area of generation. Finally, work in progress to blend the operational numerical model surface winds with synthetically generated tropical cyclone surf winds, for use in the operational wave model, is outlined.

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Markovian Approach To The Frequency Of Tropical Cyclones And Subsequent Development Of Univariate Prediction Model

10.21203/rs.3.rs-641542/v1 ◽

2021 ◽

Author(s):

Shreya Bhowmick ◽

Surajit Chattopadhyay

Keyword(s):

Markov Chain ◽

Tropical Cyclones ◽

Goodness Of Fit ◽

Markov Chain Model ◽

Subsequent Development ◽

Autoregressive Models ◽

Bengal Basin ◽

Chain Model ◽

Wind Speeds ◽

Cyclonic Storms

Abstract Tropical cyclones is one of the most devastating meteorological events. In the recent years we faced some very severe cyclones to super cyclone successively that caused heavy damages to life and property during the helpless situations of the global pandemic. In this paper, westudied the frequency of cyclones from the year 1891 to 2019 i.e. for 129 years on the Arabian Sea Basin, Bay of Bengal Basin and land. We have categorised the cyclones according to their wind speeds: i) Cyclonic storms and Severe cyclonic storms(CS + SCS) and ii) Depressions, Cyclonic storms and Severe Cyclonic storms(D + CS + SCS) where Depressions, Cyclonic storms and Severe Cyclonic storms have wind speeds of more than equal to 17 knots, 34 knots and 48 knots respectively. We examined the Markovian dependence of the discretized time series of the two categories mentioned earlier for the first, second, third and fourth order of a two-state Markov chain model. It is found that CS + SCS represents the First Order Two State (FOTS) model of Markov chain and D + CS + SCS represents the Second Order Two State (SOTS) model of Markov chain. Thereafter we have developed autoregressive models for the two categories and checked its goodness of fit using Willmott’s indices of order 1 and 2. Its is found that CS + SCS best represents the autoregressive model of order 5 whereas D + CS + SCS could not be efficiently represented by the developed autoregressive models. So we further developed autoregressive neural networks for D + CS + SCS and obtained some significant hike in the prediction yield. Nevertheless, it is found that both the categories are clearly not serially independent.

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Micropatterning of Permalloy Films by Electron Lithography

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092207 ◽

1976 ◽

Vol 34 ◽

pp. 448-449

Author(s):

J. K. Maurin

Keyword(s):

Electron Beam ◽

Subsequent Development ◽

Electron Optical System ◽

Microelectronic Device ◽

Magnetic Bubble ◽

Control Beam ◽

Photosensitive Material ◽

Direct Exposure ◽

Electron Lithography ◽

Scanning Electron

Conductor, resistor, and dielectric patterns of microelectronic device are usually defined by exposure of a photosensitive material through a mask onto the device with subsequent development of the photoresist and chemical removal of the undesired materials. Standard optical techniques are limited and electron lithography provides several important advantages, including the ability to expose features as small as 1,000 Å, and direct exposure on the wafer with no intermediate mask. This presentation is intended to report how electron lithography was used to define the permalloy patterns which are used to manipulate domains in magnetic bubble memory devices.The electron optical system used in our experiment as shown in Fig. 1 consisted of a high resolution scanning electron microscope, a computer, and a high precision motorized specimen stage. The computer is appropriately interfaced to address the electron beam, control beam exposure, and move the specimen stage.

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