Search Results

You are looking at 11 - 20 of 1,910 items for :

  • Forecasting techniques x
  • Weather and Forecasting x
  • All content x
Clear All
Phillip E. Shafer and Henry E. Fuelberg

: Multivariate regression techniques applied to thunderstorm forecasting at the Kennedy Space Center. Preprints, Int. Conf. on Aerospace and Aeronautical Meteorology, Washington, DC, Amer. Meteor. Soc., 6–13 . Orville, R. E. , and Silver A. C. , 1997 : Lightning ground flash density in the contiguous United States: 1992-95. Mon. Wea. Rev. , 125 , 631 – 638 . 10.1175/1520-0493(1997)125<0631:LGFDIT>2.0.CO;2 Orville, R. E. , Huffines G. R. , Burrows W. R. , Holle R. L. , and Cummins K. L

Full access
Ruixin Yang

does offer insights for future directions of RI investigation with data mining techniques. For example, the POD and FAR criteria for RI forecasting enforce a two-dimensional search from the perspective of data mining for optimal results. One technical improvement on the experimental design is to use the receiver operating characteristic (ROC), which combines the true positive rate and the false positive rate together to form a single performance measure ( Tan et al. 2006 ). With a single criterion

Full access
Buo-Fu Chen, Boyo Chen, Hsuan-Tien Lin, and Russell L. Elsberry

experts at TAFB and SAB differed by 20 kts in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 kt lower than either of them.” In summary, most of the current techniques for TC intensity estimation rely upon feature-engineering to transform low-level satellite imagery into high-level human-constructed features. Even for the most experienced meteorologists and forecasters, it is still hard to identify if a feature is suitable for intensity regression for all TCs in

Full access
Eric Metzger and Wendell A. Nuss

utilized operationally by the NWS Forecast Office (NWSFO) in Huntsville since 2003 ( Darden et al. 2010 ), where forecasters note a sudden increase in total lightning activity prior to the onset of severe weather. These lightning jumps occurred as much as 30 min prior to the occurrence of severe weather ( Darden et al. 2010 ), confirming earlier studies by Williams et al. (1999) and Goodman et al. (2005) . The observations from these prior studies and the Huntsville site led to the development of

Full access
Binbin Zhou and Jun Du

new diagnostic fog-forecasting method compared to a conventional method used in current practice; the second goal is to examine the forecast skill level of current operational NWP models in predicting fog with various approaches, including ensemble technique, multimodel approach, and the increase in ensemble size; and the last goal is to compare the performances of a single-model-based ensemble and multimodel-based ensembles, as well as to examine the impacts of ensemble size on probabilistic

Full access
J. V. Ratnam, Takeshi Doi, Yushi Morioka, Pascal Oettli, Masami Nonaka, and Swadhin K. Behera

ensemble may enhance the skill of the regional predictions of surface air temperatures (SAT). This technique of selectively averaging the members of a seasonal forecasting system to improve predictions is called the selective ensemble mean (SEM; Qi et al. 2014 ; Nishimura and Yamaguchi 2015 ). This technique is similar to the method often adopted by researchers in choosing a particular model from a large number of Coupled Model Intercomparison Project (CMIP) models for analysis ( Sabeerali et al

Restricted access
David R. Novak, Keith F. Brill, and Wallace A. Hogsett

public misinterpretations of probability of precipitation forecasts have been documented (e.g., Joslyn et al. 2009 ). This article proposes an objective technique using percentiles from a probability distribution function (PDF) to determine forecast snowfall ranges consistent with the risk tolerance of users. This technique is dynamic, with the resultant ranges varying based on the spread of ensemble forecasts. Furthermore, this technique allows users to choose the risk tolerance, quantified as the

Full access
Doug McCollor and Roland Stull

. Previous studies ( Stensrud and Skindlov 1996 ; Mao et al. 1999 ; Eckel and Mass 2005 ; Stensrud and Yussouf 2005 ) have shown how straightforward moving-average techniques can reduce systematic error in DMO. In the study presented in this paper, moving-average and other related postprocessing techniques are applied to daily maximum and minimum temperature forecasts and daily quantitative precipitation forecasts (QPFs). These sensible weather element forecasts of temperature and precipitation are

Full access
Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, and Alan F. Blumberg

predictions. Therefore, researchers have proposed different methodologies to improve the forecast models. Cheng and Steenburgh (2007) , Gel (2007) , Glahn and Lowry (1972) , Ott et al. (2004) , and Houtekamer and Mitchell (2001) present several postprocessing techniques such as model output statistics (MOS), running-mean bias removal, and Kalman filtering. MOS is a statistical method that generates a better forecast by using a multiple linear regression model. However, this methodology needs a long

Full access
William A. Gallus Jr.

have been shown to be inconsistent with the subjective impressions of forecasters ( Chapman et al. 2004 ). In an effort to provide more informative measures of forecast performance that better reflect the quality of these finer-grid forecasts, several new spatial verification techniques have been proposed including neighborhood or fuzzy verification, scale decomposition, object-based verification, and field verification approaches [see Casati et al. (2008) and Gilleland et al. (2009) for

Full access