Verification of Weather Forecasts

Brier, Glenn W.; Allen, Roger A.

doi:10.1007/978-1-940033-70-9_68

Cited by 137 publications

(75 citation statements)

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“…5). Skill was estimated using the Brier skill score (BSS; Brier and Allen, 1951;Sutherland et al, 2004), Willmott's (1981) index of agreement d 2 , and the squared correlation coefficient r 2 . The BSS was calculated as…”

Section: Sensitivity Of Model-data Comparison At B6 To Model Parametersmentioning

confidence: 99%

Sediment transport on the Palos Verdes shelf, California

Férré

Sherwood

Wiberg

2010

Continental Shelf Research

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a b s t r a c t Sediment transport and the potential for erosion or deposition have been investigated on the Palos Verdes (PV) and San Pedro shelves in southern California to help assess the fate of an effluent-affected deposit contaminated with DDT and PCBs. Bottom boundary layer measurements at two 60-m sites in spring 2004 were used to set model parameters and evaluate a one-dimensional (vertical) model of local, steady-state resuspension, and suspended-sediment transport. The model demonstrated skill (Brier scores up to 0.75) reproducing the magnitudes of bottom shear stress, current speeds, and suspended-sediment concentrations measured during an April transport event, but the model tended to underpredict observed rotation in the bottom-boundary layer, possibly because the model did not account for the effects of temperature-salinity stratification. The model was run with wave input estimated from a nearby buoy and current input from four to six years of measurements at thirteen sites on the 35-and 65-m isobaths on the PV and San Pedro shelves. Sediment characteristics and erodibility were based on gentle wet-sieve analysis and erosion-chamber measurements. Modeled flow and sediment transport were mostly alongshelf toward the northwest on the PV shelf with a significant offshore component. The 95th percentile of bottom shear stresses ranged from 0.09 to 0.16 Pa at the 65-m sites, and the lowest values were in the middle of the PV shelf, near the Whites Point sewage outfalls where the effluent-affected layer is thickest. Long-term mean transport rates varied from 0.9 to 4.8 metric tons m À 1 yr À 1 along the 65-m isobaths on the PV shelf, and were much higher at the 35-m sites. Gradients in modeled alongshore transport rates suggest that, in the absence of a supply of sediment from the outfalls or PV coast, erosion at rates of $ 0.2 mm yr À 1 might occur in the region southeast of the outfalls. These rates are small compared to some estimates of background natural sedimentation rates ( $ 5 mm yr À 1 ), but do not preclude higher localized rates near abrupt transitions in sediment characteristics. However, low particle settling velocities and strong currents result in transport length-scales that are long relative to the narrow width of the PV shelf, which combined with the significant offshore component in transport, means that transport of resuspended sediment towards deep water is as likely as transport along the axis of the effluent-affected deposit.Published by Elsevier Ltd.

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Section: Sensitivity Of Model-data Comparison At B6 To Model Parametersmentioning

confidence: 99%

Sediment transport on the Palos Verdes shelf, California

Férré

Sherwood

Wiberg

2010

Continental Shelf Research

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“…This study does not address the forecast value for a specific application of interest to a specific end-user. It assesses the overall forecast performance for the purpose of strategic planning (the administrative verification forecast; Mason and Weigel, 2009;Joliffe and Stephenson, 2003;Murphy, 1993;Brier and Allen, 1951). In particular: (a) cumulative statistics for the bias and root mean square errors (RMSE) are employed to assess the forecast error out to four days, and; (b) the skill of the RAMS forecast is compared by means of the Anomaly Correlation Coefficient (ACC) against those related to the persistence forecast, which is supposed to have good performance in summer because the Central Mediterranean is characterized by fair and stable weather during this season.…”

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confidence: 99%

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Federico

2011

Nat. Hazards Earth Syst. Sci.

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Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km).Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields.Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. ForCorrespondence to: S. Federico (s.federico@isac.cnr.it) the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 • C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 • C, respectively, for the fourth-day forecast.Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error.The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensiti...

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“…One is true positive ratio (TPR) and another one is false alarm ratio (FAR). Low FAR (equation (7)) and high TPR (equation (6)) is desirable for good result (Brier & Allen 1951):…”

Section: Methodology Adoptedmentioning

confidence: 99%

Study of soft classification approaches for identification of earthquake-induced liquefied soil

Sengar

Kumar

Wason

et al. 2013

Geomatics, Natural Hazards and Risk

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The existence of mixed pixels led to the development of several approaches for soft (or fuzzy) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. There exist many different potential techniques for sub-pixel mapping from remotely sensed imagery to identify specific class. The fuzzy-based possibilistic c-means (PCM), noise cluster (NC) and noise cluster with entropy (NCE) classifiers were applied to identify the Bhuj, India (2001), earthquake induced soil liquefaction and compared as soft computing approaches via supervised classification. The soil liquefaction identification was empirically investigated and compared with class-based sensor-independent (CBSI) spectral band ratio using Landsat-7 temporal images. It has been found that CBSI-based temporal indices yield the better results for identification of liquefied soil areas while it was easily separated with pre-earthquake existing water body in that area. The NCE classifier performed better for conventional temporal indices, while NC classifier performed better for soil liquefaction and PCM classifier performed better for water body identification with CBSI temporal indices.

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Verification of Weather Forecasts

Cited by 137 publications

References 1 publication

Sediment transport on the Palos Verdes shelf, California

Sediment transport on the Palos Verdes shelf, California

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Study of soft classification approaches for identification of earthquake-induced liquefied soil

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