Search Results

You are looking at 1 - 10 of 14 items for :

  • Forecasting techniques x
  • Tropical Cyclone Intensity Experiment (TCI) x
  • User-accessible content x
Clear All
T. Ghosh and T. N. Krishnamurti

. (2007) have studied ocean sonic-layer depth estimation by applying ANN techniques. Surface parameters were taken as input in their study. Forecasts of ceiling and visibility using ANN from surface observations and model output were studied by Marzban et al. (2007) . Sharma and Ali (2013) applied ANN to achieve high-resolution tropospheric temperature profiles using geostationary satellite observations. Sharma et al. (2013) have shown that the use of ANN for the prediction of cyclone intensity

Full access
Robert G. Nystrom, Fuqing Zhang, Erin B. Munsell, Scott A. Braun, Jason A. Sippel, Yonghui Weng, and Kerry Emanuel

) are constructed separately, the effects of the IC perturbations from each of these regions can be isolated and their influence on the track and intensity forecast spread examined. The process of removing the IC differences in a specific region is referred to throughout this manuscript as relaxing. A strength of this technique is that it illustrates the effects of IC differences only within a specific region and can help to more clearly understand how IC differences within a specific region evolve

Full access
Russell L. Elsberry, Eric A. Hendricks, Christopher S. Velden, Michael M. Bell, Melinda Peng, Eleanor Casas, and Qingyun Zhao

utilize all of the forty 6-h AMV datasets to optimize the analysis technique that spreads the AMV information, and more fully demonstrates the impacts of the AMVs on the regional and global model forecasts. An important goal of these future studies will be to demonstrate that a global model forecast after 6 h can then provide the initial and lateral boundary conditions for the next dynamic initialization, and this cycling can be an end-to-end NWP application that fully utilizes the information content

Full access
Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

1. Introduction In contrast to the significant improvements in tropical cyclone (TC) track forecasts, only limited progress has been made in TC intensity forecasting in the last two decades ( Rogers et al. 2006 , 2013 ; Rappaport et al. 2009 ; Gall et al. 2013 ). Part of the difficulty in forecasting the intensity of TCs originates from deficiencies in the representation of the initial vortices in numerical weather prediction (NWP) models due to the general lack of high

Full access
Xu Lu and Xuguang Wang

speed (Vmax), and minimum sea level pressure (MSLP)] ( Thu and Krishnamurti 1992 ; Kurihara et al. 1995 , 1998 , Liu et al. 2000 , 2006 ; Pu and Braun 2001 ; Tallapragada et al. 2014 ). In the National Oceanic and Atmospheric Administration (NOAA) operational Hurricane Weather Research and Forecasting system (HWRF), vortex initialization (VI) contains two components: vortex relocation (VR) and vortex modification (VM), where VR corrects the storm location and VM modifies the storm intensity

Full access
Robert L. Creasey and Russell L. Elsberry

vortex tilt and the radial and tangential wind structure. It will be productive to compare the vortex tilt (if any) in the initial conditions and forecasts of numerical models of the TCI-15 tropical cyclones. It may be challenging to incorporate these high temporal and spatial resolution HDSS observations in the numerical models. Perhaps our technique of creating layer-average wind direction and speed from overlapping 1-km layers may be useful for initializing those computer models that also

Full access
Eric A. Hendricks, Russell L. Elsberry, Christopher S. Velden, Adam C. Jorgensen, Mary S. Jordan, and Robert L. Creasey

that among the Hendricks et al. (2010) environmental factors that best compared with the Joaquin intensity changes was the VWS from both the SHIPS and the CIMSS technique ( Gallina and Velden 2002 ; Velden and Sears 2014 ). Whereas the SHIPS simply takes the difference between the Global Forecast System (GFS) 200 and 850 hPa horizontal wind analyses, the CIMSS approach utilizes a local three-dimensional analysis of high-density, satellite-derived atmospheric motion vectors (AMVs) to calculate

Full access
David R. Ryglicki, Joshua H. Cossuth, Daniel Hodyss, and James D. Doyle

years to construct guidelines for forecasters by relating specific features with current intensity. It has since been refined several times to add objective methods for estimating TC strength ( Dvorak 1984 ; Zehr 1989 ; Guard et al. 1992 ; Velden et al. 1998 ; Olander et al. 2004 ; Olander and Velden 2007 ). A modern version of the Dvorak technique, the advanced Dvorak technique (ADT), has even been used as a reanalysis tool for historical TC studies ( Velden et al. 2017 ). In addition to the

Full access
Jonathan Martinez, Michael M. Bell, Robert F. Rogers, and James D. Doyle

to better understand the evolution of the strongest TC observed to date in the Western Hemisphere ( Rogers et al. 2017 ). The current state of forecasting TC rapid intensification (RI) events is largely dependent on probabilistic techniques, which aid the intensity guidance provided by deterministic models. For example, the rapid intensification index (RII; Kaplan et al. 2010 ) employs large-scale environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS

Full access
James D. Doyle, Jonathan R. Moskaitis, Joel W. Feldmeier, Ronald J. Ferek, Mark Beaubien, Michael M. Bell, Daniel L. Cecil, Robert L. Creasey, Patrick Duran, Russell L. Elsberry, William A. Komaromi, John Molinari, David R. Ryglicki, Daniel P. Stern, Christopher S. Velden, Xuguang Wang, Todd Allen, Bradford S. Barrett, Peter G. Black, Jason P. Dunion, Kerry A. Emanuel, Patrick A. Harr, Lee Harrison, Eric A. Hendricks, Derrick Herndon, William Q. Jeffries, Sharanya J. Majumdar, James A. Moore, Zhaoxia Pu, Robert F. Rogers, Elizabeth R. Sanabia, Gregory J. Tripoli, and Da-Lin Zhang

goals for the TCI program to be addressed using the observational dataset collected during the field campaign: understand the coupling of TC outflow with inner-core convection and its implications for intensity change; interpret observations of the finescale horizontal and vertical structure of the outflow layer and inner-core regions of the TC; assess the quantitative impact of assimilating observations in the TC inner core and outflow layer on model forecasts of TC track and intensity; and

Open access