Fuzzy verification of high‐resolution gridded forecasts: a review and proposed framework

By Elizabeth E. Ebert. 2008

484 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ad2cc93d200299eb8a29715368c879fe4ef83136

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Barbara Casati
2. Elizabeth E. Ebert
3. N. Roberts
4. Kazuo Saito
5. L. Duc
6. Hiromu Seko
7. B. Brown
8. Eric Gilleland

In Global and Planetary Change

1. Barbara Casati
2. N. Roberts
3. B. Brown
4. Elizabeth E. Ebert
5. Eric Gilleland
6. D. Ahijevych
7. Kazuo Saito
8. L. Duc

Citations in the corpus

• New Developments of the Intensity-Scale Technique within the Spatial Verification Methods Intercomparison Project (2010, background): “, the fraction skill score; see Mittermaier and Roberts (2010); Ebert (2009)] exhibit negative skill on small scales, and the skill becomes positive only when the neighborhood size is sufficiently large to encompass both the reference and the displaced ellipses.”
• Convection‐permitting models: a step‐change in rainfall forecasting (2016, methodology): “Numerous verification methods have now been developed and many have been documented in Ebert (2008, 2009) and Gilleland et al. (2010).”
• Intercomparison of Spatial Forecast Verification Methods (2009, methodology): “(2004); Casati (2009) IW Image warping Field deformation E. Gilleland, J. Lindström, and F. Lindgren (2009, unpublished manuscript); Lindström et al. (2009) MODE Method for Object-based Diagnostic Evaluation Features based Davis et al.”
• Can deep learning beat numerical weather prediction? (2021, methodology): “Evaluating spatiotemporal patterns, for example, precipitation forecasts, with the help of radar data is particularly challenging due to the double penalty problem (cf. [116,120]).”
• Spatial-temporal fractions verification for high-resolution ensemble forecasts (2013, methodology): “Some promising methods are listed here: Contiguous Rain Area (CRA; Ebert and McBride, 2000), Intensity Scale (Casati et al., 2004), Method for Object-based Diagnostic Evaluation (MODE; Davis et al., 2006), FSS (Roberts and Lean, 2008), Structure, Amplitude and Location (SAL; Wernli et al., 2008),…”
• Next-Day Convection-Allowing WRF Model Guidance: A Second Look at 2-km versus 4-km Grid Spacing (2009, background): “Thus, as horizontal grid spacing has decreased in recent years to the size of convective-scale features, a variety of methods that incorporate a neighborhood around each grid point have been created to allow for spatial and/or temporal error or uncertainty (reviewed in Ebert 2008).”
• Forecast verification: current status and future directions (2008, methodology): “Neighbourhood-based (fuzzy) verification approaches consider values nearby in space and time in the forecastobservation matching process, and so relax the requirements for perfect time-space matching (see Ebert, 2008, for a review of neighbourhood-based approaches).”
• Probabilistic weather forecasting with machine learning (2024, unknown): “Our approach is an instance of neighbourhood verification 39 , adapted to the surface of a sphere.”
• Probabilistic Forecast Guidance for Severe Thunderstorms Based on the Identification of Extreme Phenomena in Convection-Allowing Model Forecasts (2011, unknown): “Given the demonstration of skill of high-resolution forecasts provided by subjective assessments (e.g., Kain et al. 2006), there is a need for measures based on neighborhood verification techniques (e.g., Ebert 2008; Gilleland et al. 2009).”