Cell identification and verification of QPF ensembles using shape analysis techniques

Micheas, Athanasios C.; Fox, Neil I.; Lack, Steven A.; Wikle, Christopher K.

doi:10.1016/j.jhydrol.2007.05.036

Cited by 39 publications

(41 citation statements)

References 13 publications

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“…The original Procrustes verification scheme described by Micheas et al (2007) was devised to fill the need for a near-real-time object-based verification method for multiple realizations of a radar-reflectivity-based nowcaster. Convective objects in the domain are defined as contiguous pixels of reflectivity greater than a predefined intensity threshold.…”

Section: Procrustes Verification Scheme a Original Procrustes Verifimentioning

confidence: 99%

“…Following Micheas et al (2007), the full Procrustes fit in (1) is then applied as a matching baseline between two objects, the jth truth and the kth forecast realization (z j and z kj ). The study herein matches one forecast field to one truth field instead of multiple realizations and, thus, k 5 1:ẑ j k 5b jk 1r jk e if jk z kj .…”

Section: Procrustes Verification Scheme a Original Procrustes Verifimentioning

confidence: 99%

“…There are some shortcomings to object-oriented approaches, namely, the possibility for counterintuitive matching to occur with rigid object identification schemes. The method presented herein describes a modified and robust Procrustes objectoriented verification scheme, originally described by Micheas et al (2007). Some of the notable modifications include multiple-scale object identification, cell-by-cell verification metrics, and summary statistics of objects in the forecast and observed domains.…”

Section: Introductionmentioning

confidence: 99%

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An Object-Oriented Multiscale Verification Scheme

Lack

Limpert

Fox

2010

Weather and Forecasting

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Object-oriented verification methodology is becoming more and more common in the evaluation of model performance on high-resolution grids. The research herein describes an advanced version of an object-oriented approach that involves a combination of object identification on multiple scales with Procrustes shape analysis techniques. The multiscale object identification technique relies heavily on a novel Fourier transform approach to associate the signals within convection to different spatial scales. Other features of this new verification scheme include using a weighted cost function that can be user defined for object matching using different criteria, delineating objects that are more linear in character from those that are more cellular, and tagging object matches as hits, misses, or false alarms. Although the scheme contains a multiscale approach for identifying convective objects, standard minimum intensity and minimum size thresholds can be set when desirable. The method was tested as part of a spatial verification intercomparison experiment utilizing a combination of synthetic data and real cases from the Storm Prediction Center (SPC)/NSSL Weather Research and Forecasting (WRF) model Spring Program 2005. The resulting metrics, including error measures from differences in matched objects due to displacement, dilation, rotation, and intensity, from these cases run through this new, robust verification scheme are shown.

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Section: Procrustes Verification Scheme a Original Procrustes Verifimentioning

confidence: 99%

Section: Procrustes Verification Scheme a Original Procrustes Verifimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Object-Oriented Multiscale Verification Scheme

Lack

Limpert

Fox

2010

Weather and Forecasting

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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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“…One subgroup of spatial verification methods is feature-based/object-based approaches, where the spatial properties of precipitation objects obtained from precipitation estimations are verified by those from reference fields or ground observations (e.g., [21][22][23][24][25][26][27][28][29][30][31][32][33][34]). Li et al [35] developed an object-based verification approach that allows the geometric and spatial evaluation of the separable local-scale precipitation areas.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Assessment of Satellite Precipitation Products

Hsu

AghaKouchak

et al. 2016

Remote Sensing

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An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction center MORPHing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.

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Cell identification and verification of QPF ensembles using shape analysis techniques

Cited by 39 publications

References 13 publications

An Object-Oriented Multiscale Verification Scheme

An Object-Oriented Multiscale Verification Scheme

On the effect of model parameters on forecast objects

Object-Based Assessment of Satellite Precipitation Products

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