Search Results

You are looking at 41 - 50 of 18,783 items for :

  • Forecasting techniques x
  • User-accessible content x
Clear All
Lin Dong and Fuqing Zhang

models per se, but are instead combinations of forecasts from multiple models ( Cangialosi and Franklin 2014 ). Therefore, the performance of a consensus model is determined by two factors: the consensus technique and the consensus components. Consensus technique refers to how the weight of each component is assigned, and can be divided into two categories: equal weights and unequal weights. Equal weights are calculated by a simple arithmetic average, whereas unequal weights are determined using more

Full access
Sheng-Lun Tai, Yu-Chieng Liou, Juanzhen Sun, Shao-Fan Chang, and Min-Chao Kuo

, a cloud-scale model formulated using the Cartesian coordinate system and a warm rain microphysical process, is adopted as a forward forecast model ( Sun and Crook 1997 ). Using this prognostic model as a constraint and applying the 4DVAR technique, VDRAS is able to find an optimal initial state that minimizes a cost function ( J ), which measures the distances between the model predictions and the observations, and can be written as In (1) , the summation is conducted over space ( σ ) and time

Full access
Theodore W. Funk

548 WEATHER AND FORECASTING VOLUME6FORECASTING TECHNIQUESForecasting Techniques Utilized by the Forecast Branch of the National Meteorological Center During a Major Convective Rainfall Event THEODORE W. FUNK*NWS/NMC /Meteorological Operations Division/Forecast Branch, Washington, D. C.28 February 1991 and 10 July 1991 Meteorologists within the Forecast Branch (FB) of the

Full access
Wansuo Duan and Zhenhua Huo

mean of the forecasting members is often regarded as the result of a deterministic forecast. The ensemble mean may filter the unpredictable parts and leave the common parts of the forecasting members, ultimately decreasing the uncertainties of single forecast results ( Leith 1974 ; Leutbecher and Palmer 2008 ). With its benefit of producing probabilistic distribution information of forecast results, ensemble forecasting has become a major technique in numerical weather and climate forecasting

Full access
Anu Simon, Andrew B. Penny, Mark DeMaria, James L. Franklin, Richard J. Pasch, Edward N. Rappaport, and David A. Zelinsky

during HFIP ( Cangialosi and Franklin 2016 ). To extend these improvements in NWP skill, multimodel consensus postprocessing techniques can also be applied. Multimodel consensus forecast guidance is widely used in many operational weather forecasting centers. The National Hurricane Center (NHC) relies on various consensus aids to help improve TC track and intensity forecasts. Goerss (2000) found that a simple, equally weighted average of several models consistently outperforms each of the

Full access
Lynette van Schalkwyk and Liesl L. Dyson

Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data ( Kalnay et al. 1996 ) to investigate the synoptic circulation patterns that lead to fog over the northeastern part of the United States, focusing specifically on patterns resulting in fog in the New York region. After using clustering techniques to obtain more information about the nature of fog at three different airports in Finland, 40-yr European Centre for Medium-Range Weather Forecasts

Full access
Andrew J. Condon, Y. Peter Sheng, and Vladimir A. Paramygin

which can be important. As pointed out by Rego and Li (2009) and Jelesnianski (1972) , neglecting the forward speed and angle of approach may not be appropriate as there is a “critical motion relative to a coast that gives the highest possible surge.” Additionally the technique does not account for tides and wave setup, which can contribute significantly to the surge and inundation. This paper addresses the rapid generation of high-resolution probabilistic inundation forecasts. The optimal storm

Full access
Mana Inoue, Alexander D. Fraser, Neil Adams, Scott Carpentier, and Helen E. Phillips

here are not suitable for use with a simple conversion to RH ice , particularly during summertime (i.e., when most flight operations are conducted) when sea ice extent is low. Nevertheless, such a conversion should be investigated if these techniques are to be used farther inland. Inspection of the mean RH bias profile (RH NWP − RH sonde ) shows that the lower-troposphere NWP forecast RH values are drier than observed across all three stations, for times of ≥6 oktas ( Fig. 1 ). A dry bias of up to

Full access
Edward A. O’Lenic, David A. Unger, Michael S. Halpert, and Kenneth S. Pelman

. A method to objectively combine, or “consolidate,” four forecast tools for surface temperature and precipitation was implemented in late 2006. This technique resulted in a substantial increase in the skill of hindcasts, in comparison with official outlooks made operationally over the same 10-yr period (1995–2005). We now briefly describe the forecast tools. The climate forecast system (CFS, implemented 2004; see Table 1 ) is a “one tier”, fully coupled, dynamical model of the global oceans

Full access
Brian C. Ancell

-h forecasts and nearly as good as forecasts run after assimilating the observations themselves at 24-h forecast time. Madaus and Hakim (2015) extended this technique (termed ensemble forecast adjustment) to a more operational framework within ensemble systems using both European Center for Medium-Range Weather Forecasts (ECMWF) and Canadian Meteorological Centre (CMC) global models. By adjusting 12–30-h forecasts based on surface pressure observations at 6-h forecast time, they found

Full access