Krishnamurti T.N.,Florida State University |
Biswas M.K.,Florida State University |
Mackey B.P.,Predict Inc. |
Ellingson R.G.,Florida State University |
Ruscher P.H.,Florida State University
Tellus, Series A: Dynamic Meteorology and Oceanography | Year: 2011
This paper provides an account of the performance of a multimodel ensemble for real time forecasts of Atlantic tropical cyclones during 2004, 2005 and 2006. The Florida State University (FSU) superensemble is based on a suite of model forecasts and the interpolated official forecast that were received in real time at the National Hurricane Center. The FSU superensemble is a multimodel ensemble that utilizes forecasts from the member models by removing their individual biases based on a recent past history of their performances. This superensemble carries separate statistical weights for track and intensity forecasts for every 6 h of the member model forecasts. The real time results from 2004 show an improvement up to 15% for track forecasts and up to 11% for intensity forecasts for the superensemble compared to other models and consensus aids. During 2005, the superensemble intensity performance was best for most lead times. The consistency of the superensemble forecasts of track are also illustrated for several storms of 2004 season. The superensemble methodology produced impressive intensity forecasts for Rita and Wilma during 2005. The study shows the capability of the superensemble in predicting rapidly intensifying storms when most member models failed to capture their strengthening. ©2011 The Authors Tellus A©2011 John Wiley & Sons A/S.
Cakir S.,Technical University of Istanbul |
Kadioglu M.,Technical University of Istanbul |
Cubukcu N.,Predict Inc.
Theoretical and Applied Climatology | Year: 2013
The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts. © 2012 Springer-Verlag.
Lovicz R.J.,Predict Inc. |
Dalley R.J.,Predict Inc.
NLGI Spokesman | Year: 2010
Analysis of grease has always proven problematic because the grease soap matrix makes analysis difficult to run, reproduce, and trend data. The grease breakdown procedure is universal on all metal based and Teflon based greases. The procedure gives the consistency necessary for repeatable trending from sample to sample. A discussion covers the effective sampling of greases; solvents employed for grease breakdown; effective grease analysis; and two case histories, which outline the grease analysis of a grease lubricated swashplate on a military helicopter and a grease lubricated plain bearing.
Predict Inc. | Date: 2010-09-21
Computer software, namely, computer software for use with a personal media player, personal computer, digital audio player, MP3 player, MP4 player, and/or mobile phone to personalize the users experience by generating for the user suggested selections based on prior user selections; computer software for use with a personal media player, personal computer, digital audio player, MP3 player, MP4 player, and/or mobile phone for analyzing user selections to predict future user selections and for selectively implementing such future user selections; downloadable computer software for use with a personal media player, personal computer, digital audio player, MP3 player, MP4 player, and/or mobile phone for analyzing user selections to predict future user selections and for selectively implementing such future user selections; and computer search engine software.
Predict Inc. | Date: 2011-08-16
Accounting software for use in the construction industry for contract bidding and job accounting; Computer software for application and database integration; Computer software for computer system and application development, deployment and management; Computer software for providing an on-line database in the field of transaction processing to upload transactional data, provide statistical analysis, and produce notifications and reports; Computer software for the collection, editing, organizing, modifying, book marking, transmission, storage and sharing of data and information; Computer software for the field of warehousing and distribution, to manage transactional data, provide statistical analysis, and produce notifications and reports; Computer software for construction loan underwriting, monitoring, trending, analyzing, summarizing of loan portfolios, flagging of loans for non-performance and loan to value evaluation; Computer software that provides real-time, integrated business management intelligence by combining information from various databases and presenting it in an easy-to-understand user interface; Computer software to automate data warehousing.