Search Results

You are looking at 1 - 4 of 4 items for :

  • Optical properties x
  • Spatial Forecast Verification Methods Inter-Comparison Project (ICP) x
  • All content x
Clear All
Christian Keil and George C. Craig

and observations and compare their properties. Object-oriented techniques are quite intuitive and effective when the features are well defined and can be associated between the forecast and observations. Examples are the techniques of Ebert and McBride (2000) and Davis et al. (2006) . (iv) Field verification techniques use optical flow algorithms to compare fields without decomposing them into separate elements or scales. The term optical flow stems from the image-processing community where

Full access
Eric Gilleland, David Ahijevych, Barbara G. Brown, Barbara Casati, and Elizabeth E. Ebert

changes in event frequency, spatial displacement, and bias. Among their findings, they showed that the behavior of several scores, including GSS, TSS, and odds ratio, were highly sensitive to event frequency. For more frequently occurring events, the scores were found to be more sensitive to displacement errors. In response to these undesirable properties of traditional verification methods when applied to high-resolution forecasts, researchers have proposed numerous new verification methods. Here

Full access
Eric Gilleland, Johan Lindström, and Finn Lindgren

. The method can also be used to verify forecasts and is similar in some respects to the procedure of Keil and Craig (2007 , 2009) , as well as the optical flow method in Marzban et al. (2009) , but with some important differences. In particular, the deformed forecast is obtained through a warping function as opposed to the hierarchical movement of points. Further, because the image-warping approach follows a stochastic model, there is a natural formulation for calculating uncertainty information

Full access
Caren Marzban, Scott Sandgathe, Hilary Lyons, and Nicholas Lederer

; Marzban et al. 2008 ), the variogram (VGM) method ( Marzban and Sandgathe 2009a ), and the optical flow (OF) method (Marzban and Sandgathe 2007, manuscript submitted to Wea. Forecasting , hereafter MSI; Marzban and Sandgathe 2009b, manuscript submitted to Wea. Forecasting , hereafter MSII). The three methods have little in common and, so, examine completely different facets of forecast quality. The CA method can be called object oriented in the sense described by Baldwin et al. (2002) , Brown et

Full access