An Object-Oriented Multiscale Verification Scheme

Lack, Steven A.; Limpert, George; Fox, Neil I.

doi:10.1175/2009waf2222245.1

Cited by 49 publications

(37 citation statements)

References 4 publications

(9 reference statements)

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“…One interesting finding in the verification was that the strength parameter was not as useful as expected in discerning between good and poor tracks. It has been suggested that, in addition to a cost function based on the distance from a forecast position, a strength parameter might be useful in matching objects to existing tracks within a tracking algorithm, such as that done by Lack et al (2010). If strength is of limited utility in discerning between good tracks and bad tracks, incorporating strength into a cost function may not significantly improve tracking.…”

Section: Discussionmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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ABSTRACT:The advanced algorithm for the tracking of objects (AALTO) constructs tracks from objects, such as thunderstorms or mesocyclones, detected by multiple weather radars at irregular time intervals. It is important to have high accuracy in tracking thunderstorms to generate skilful forecasts and high-quality climatologies and, fundamentally, to ensure that any derived product from tracks captures only that particular storm, and in its entirety. AALTO incorporates many of the best practices of existing tracking algorithms and techniques employed by meteorologists in constructing tracks. AALTO differs from existing algorithms designed to track meteorological phenomena that manifest in radar data in the following ways: (1) AALTO is designed to track objects from multiple radars, enabling analysis over a larger domain than if a single radar was used; (2) improved tracking is realized through improved initial motion estimates and directional thresholding and (3) AALTO looks both at the track history and at the subsequent possible positions along a track when constructing the best possible tracks, mimicking the approach that would be taken by a human meteorologist. Verification was done using metrics that were objectively determined to distinguish between good and degraded tracks; a description of the approach to determine the appropriate metrics is presented. An overview of the AALTO tracking procedure and an example case are presented in this study.

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Section: Discussionmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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“…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 EvaluationÁ (MODE; Davis et al, 2006), FSS (Roberts and Lean, 2008), Structure, Amplitude and Location (SAL; Wernli et al, 2008), and Procrustes Shape Analysis (Lack et al, 2010). Ahijevych et al (2009) carried out idealised and real test cases to gain a basic understanding of behaviour of each method.…”

Section: Brief Review Of Qpf Verification Methodsmentioning

confidence: 99%

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Duc

Saitō

Seko

2013

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Experiments with two ensemble systems of resolutions 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS) was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighbourhood in FSS to the time and ensemble dimension. The extension was carried out by recognising that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighbourhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems.The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called the ensemble FSS, which is a good representative of FSS for ensemble forecast in comparison with the FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rains, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells.

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“…For example, here the shape of an object is approximated by an ellipse. But it is possible to use more sophisticated methods of shape analysis (Bookstein, 1991;Lack et al, 2010;Micheas et al, 2007;Lakshmanan et al, 2009) to model more complex shapes.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

On the effect of model parameters on forecast objects

Marzban

Jones

et al. 2018

Geosci. Model Dev.

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Abstract. Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature "map". The field for some quantities generally consists of spatially coherent and disconnected "objects". Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final "output" of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

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An Object-Oriented Multiscale Verification Scheme

Cited by 49 publications

References 4 publications

The advanced algorithm for tracking objects (AALTO)

The advanced algorithm for tracking objects (AALTO)

Spatial-temporal fractions verification for high-resolution ensemble forecasts

On the effect of model parameters on forecast objects

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