A radar-based verification of precipitation forecast for local convective storms

Řezáčová, Daniela; Sokol, Zbyněk; Pešice, Petr

doi:10.1016/j.atmosres.2005.08.011

Cited by 49 publications

(31 citation statements)

References 15 publications

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“…In contrast, weather radar provides an alternative means of determining Quantitative Precipitation Estimates (QPE) with a fine spatial and temporal resolution over a large area (Morin and Gabella, 2007). Because of its large spatial coverage relative to raingauges, and area-averaged response, radar is a useful source of data for verification of QPF, provided that the errors in radar-based precipitation estimates are corrected (Ebert et al, 2007;Rezacova et al, 2007). Radar is an active sensor that emits short pulses of microwave energy, and measures the power scattered back by raindrops as a reflectivity factor (Z).…”

Section: Mcbride and Ebert (2000) Verified 24 H Precipitation Forecasmentioning

confidence: 94%

“…While there have been many efforts by researchers to use radar-based rainfall estimates for NWP forecast rainfall verification (Colle and Mass, 1996;Johnson and Olsen, 1998;Yu et al, 1998;Casati et al, 2004;Davis et al, 2006;Rezacova et al, 2007;Roberts, 2008;Roberts and Lean, 2008), this approach has not yet been conducted for evaluation of forecasts from Australian models. In addition, radar-based verifications have been mostly used for specific events or periods rather than long-time assessment.…”

Section: Mcbride and Ebert (2000) Verified 24 H Precipitation Forecasmentioning

confidence: 98%

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An evaluation of numerical weather prediction based rainfall forecasts

Shahrban

Walker

Wang

et al. 2016

Hydrological Sciences Journal

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A.Seed@bom.gov.au +61 39669 4591 (A. Seed), P.Steinle@bom.gov.au +61 39669 4848 (P. Steinle) Abstract Assessment of the forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in south-eastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) is evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It was shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the Relative Error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on hourly accumulations, while magnitude was the dominant error on daily time scale. Consequently, the precipitation output from ACCESS-A may not be useful for direct application in hydrological modelling, and pre-processing approaches such as bias correction or exceedance probability correction will likely be necessary for application of the Numerical Weather Prediction (NWP) outputs.

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Section: Mcbride and Ebert (2000) Verified 24 H Precipitation Forecasmentioning

confidence: 94%

Section: Mcbride and Ebert (2000) Verified 24 H Precipitation Forecasmentioning

confidence: 98%

An evaluation of numerical weather prediction based rainfall forecasts

Shahrban

Walker

Wang

et al. 2016

Hydrological Sciences Journal

View full text Add to dashboard Cite

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“…3.10. Area-related RMSE Rezacova et al (2007) compared the values of highresolution gridded forecasts and observations (rather than events) within neighbourhoods to get a scaledependent error E s . Using a decision model of, 'A useful forecast has a similar distribution of intensities as the observations,' this method computes an area-related RMSE.…”

Section: Intensity-scalementioning

confidence: 99%

“…Several techniques have been developed in recent years including those of Brooks et al (1998); Zepeda-Arce et al (2000); Atger (2001); Casati et al (2004); Damrath (2004); Germann and Zawadzki (2004); Weygandt et al (2004); Theis et al (2005); Roberts and Lean (2008); Rezacova et al (2007) and Segawa and Honda (2007). Although they were developed quite independently to look at particular aspects of forecast quality, these techniques have a great deal in common in the way they treat observations and forecasts.…”

Section: Introductionmentioning

confidence: 99%

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

Ebert

2008

Meteorological Applications

386

290

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High-resolution forecasts from numerical models can look quite realistic and provide the forecaster with very useful guidance. However, when verified using traditional metrics they often score quite poorly because of the difficulty of predicting an exact match to the observations at high resolution. 'Fuzzy' verification rewards closeness by relaxing the requirement for exact matches between forecasts and observations. The key to the fuzzy approach is the use of a spatial window or neighbourhood surrounding the forecast and/or observed points. The treatment of the data within the window may include averaging (upscaling), thresholding, or generation of a PDF, depending on the particular fuzzy method used and its implicit decision model concerning what makes a good forecast. The size of the neighbourhood can be varied to provide verification results at multiple scales, thus allowing the user to determine at which scales the forecast has useful skill.This article describes a framework for fuzzy verification that incorporates several fuzzy verification methods. It is demonstrated on a high-resolution precipitation forecast from the United Kingdom (UK) and the results interpreted to show the additional information that can be gleaned from this approach.

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“…Whilst it is possible that non-hydrostatic models with a grid resolution greater than 1 km can resolve convection if the storm is sufficiently organized (e.g. Rotunno et al, 1988), much more research needs to be done in this area (see discussions in Rezacova and Sokol, 2003;Rezacova et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

The usability of 250 m resolution data from the UK Meteorological Office Unified Model as input data for a hydrological model

Younger

Gadian

Wang

et al. 2008

Meteorological Applications

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This article examines whether very-high resolution (250 m) data provided by the United Kingdom Meteorological Office Unified Model (UM) could be used as an input to a hydrological model for hydrological forecasting purposes. A summer convective event and a winter stratiform event are both considered and it is found that in these two cases, despite errors in both the positioning and timing of storms the predictions were of a sufficient quality to provide beneficial inputs for hydrological modelling. There are encouraging indications that this technique may be valuable in improving flood forecasting generally.

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A radar-based verification of precipitation forecast for local convective storms

Cited by 49 publications

References 15 publications

An evaluation of numerical weather prediction based rainfall forecasts

An evaluation of numerical weather prediction based rainfall forecasts

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

The usability of 250 m resolution data from the UK Meteorological Office Unified Model as input data for a hydrological model

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