Short-Term Forecast Validation of Six Models

White, Bryan G.; Paegle, Jan; Steenburgh, W. James; Horel, John D.; Swanson, Robert T.; Cook, Louis K.; Onton, Daryl J.; Miles, John G.

doi:10.1175/1520-0434(1999)014<0084:stfvos>2.0.co;2

Cited by 61 publications

(40 citation statements)

References 42 publications

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“…No interpolation of the WRF model output is required as each model grid cell represents the average atmospheric conditions across it (Pielke 2002). However, there are limitations to this approach that can result in large apparent model errors at specific locations (White et al 1999;Davis and Carr 2000;Mass et al 2002). For example, small errors in the timing and spacing of otherwise accurately modelled weather features can result in large apparent model errors if the weather conditions vary considerably over short distances.…”

Section: Model Verificationsupporting

confidence: 90%

Verification of WRF modelled fire weather in the 2009–10 New Zealand fire season

Simpson

Pearce

Sturman

et al. 2014

Int. J. Wildland Fire

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Abstract. The Weather Research and Forecasting (WRF) mesoscale model was used to simulate the fire weather conditions for the 2009-10 wildland fire season in New Zealand. The suitability of WRF to simulate the high-end fire weather conditions for this period was assessed through direct comparison with observational data taken from 23 surface and two upper-air stations located across New Zealand. The weather variables and fire weather indices considered in the verification were the 1200 hours NZST air temperature, relative humidity, wind speed and direction, 24-h rainfall, New Zealand Fire Weather Index (FWI) and Continuous Haines Index (CHI). On observed high-end fire weather days, the model under-predicted the air temperatures and relative humidities, and over-predicted the wind speeds and 24-h rainfall at most weather stations. The results demonstrated that although WRF is suitable for modelling the air temperatures, there are issues with modelling the wind speeds and rainfall quantities. The model error in the wind speeds and 24-h rainfall contributed significantly towards the model under-prediction of the FWI on observed high-end fire weather days. In addition, the model was not suitable for predicting the number of high-end fire weather days at most weather stations, which represents a serious operational limitation of the WRF model for fire management applications. Finally, the modelled CHI values were only in moderate agreement with the observed values, principally due to the model error in the dew point depression at 850 hPa.

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Section: Model Verificationsupporting

confidence: 90%

Verification of WRF modelled fire weather in the 2009–10 New Zealand fire season

Simpson

Pearce

Sturman

et al. 2014

Int. J. Wildland Fire

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show abstract

“…The initial analysis and model forecast errors are well within the bounds of mesoscale models, and are similar to the RMS and bias errors shown in SHI et al (2004) andWHITE et al (1999) for the winter season. In the verification of the vertical profiles, bias and RMS errors do not grow significantly with time as compared to the values at the initial time (0-hour forecast).…”

Section: Discussionsupporting

confidence: 72%

“…At the 12-hour forecast below 200 hPa there was a negative temperature bias error of 0.2-0.7°C, a negative geopotential height bias of 3 m to 9 m, and a positive wind bias of 0.5 m s )1 , which are similar to the bias errors of the models listed in WHITE et al (1999). NACHAMKIN and HODUR (2000) found the wind and temperature fields to be underpredicted and relative humidity to be overpredicted, with magnitudes similar to the bias errors in SHI et al (2004) and WHITE et al (1999). This paper describes a statistical evaluation of 48-hour COAMPS â forecasts performed in the Arabian Gulf region during the summer and early fall, from 1 August to 5 October, 2004.…”

Section: Introductionsupporting

confidence: 72%

A Seasonal Statistical Evaluation of COAMPS® over the Arabian Gulf Region

Eager

Raman

Holt

et al. 2007

Pure appl. geophys.

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Abstract-A statistical evaluation of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS â ) was performed over the Arabian Gulf region for the period, 1 August to 5 October, 2004. Verification skill scores of bias and root-mean-square error were estimated for surface variables and for vertical profiles to investigate any diurnal variations. The model predictions of boundary-layer heights are compared with the observations at Abu Dhabi, United Arab Emirates. The Middle East presents challenges to numerical weather prediction due to complex land-ocean-land mesoscale processes. An independent data set of surface measurements from 50 stations in the UAE was available from the Department of Water Resources Studies, Abu Dhabi for model verification. The results indicate a diurnal variation in the model errors. The errors are small considering the magnitudes of the observed variables. Errors in the coastal region can be attributed to the differences in the timing of the onset of sea and land breeze circulations in the simulations as compared to the observations. Errors are relatively smaller in the offshore locations.

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“…White et al (1999), for example, who did a short-term forecast validation, comparing research and operational models over the western United States, found that on shorter timescales (up to 24 h) the MesoEta Model did the best, while at 36 h MRF provided the best forecasts. Their explanation for this result is that, ''the value added from mesoscale information in high resolution models is lost due to phase and amplitude errors in the individual mesoscale structures.''…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Error Evaluation of NCEP and LAHM Regional Model Daily Forecasts over Southern South America

Saulo¹,

Seluchi²,

Campetella³

et al. 2001

Wea. Forecasting

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NCEP short-range operational forecast and Limited Area HIBU (Federal Hydrometeorological Institute and Belgrade University) Model (LAHM) regional model performance during a 2-month period over the southern part of South America are evaluated through the analysis of bias and rmse's. While spatial structure of errors could be only examined using gridded operational analyses as the ''ground truth,'' observed data have been used at two radiosonde stations to have an independent control of forecast and analysis quality. LAHM precipitation forecast error has been also determined using observed 24-h accumulated precipitation over a subregion of interest.Bias and rmse are, in general, lower for Medium-Range Forecast Model (MRF) 24-h forecasts than for the regional model, though MRF errors appear to be larger than those reported by other studies carried out over the whole Southern Hemisphere, suggesting the necessity to perform regional verification analysis whenever gridded analyses and/or forecasts are being used. This recommendation particularly holds over data-void regions like South America.While geopotential and wind biases do not exhibit a particular pattern in either forecast, there is a clear tendency to cold biases over the whole troposphere, and for the MRF in particular, growing with height.The results obtained from LAHM evaluation suggest that continuous development is needed to keep this regional forecast system as a plausible counterpart of available global model products for fulfillment of local requirements.

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Short-Term Forecast Validation of Six Models

Cited by 61 publications

References 42 publications

Verification of WRF modelled fire weather in the 2009–10 New Zealand fire season

Verification of WRF modelled fire weather in the 2009–10 New Zealand fire season

A Seasonal Statistical Evaluation of COAMPS® over the Arabian Gulf Region

Error Evaluation of NCEP and LAHM Regional Model Daily Forecasts over Southern South America

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