Forecast verification: current status and future directions

Casati, Barbara; Wilson, Laurence J.; Stephenson, David B.; Nurmi, Pertti; Ghelli, Anna; Pocernich, Matthew; Damrath, U.; Ebert, Elizabeth E.; Brown, Barbara G.; Mason, Simon J.

doi:10.1002/met.52

Cited by 259 publications

(183 citation statements)

References 101 publications

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“…In addition to the traditional statistical scores, precipitation forecasts are verified by spatial verification methods, which not only consider the exact match of forecast and verification values at individual points but also take into account the matching of forecasts and observations in terms of objects or spatial scales (Casati et al, 2008;Ahijevych et al, 2009;Gilleland et al, 2010). This is necessary as precipitation fields exhibit high spatial variability and discontinuity.…”

Section: T Schellander-gorgas Et Al: On the Forecast Skill Of A Conmentioning

confidence: 99%

On the forecast skill of a convection-permitting ensemble

Schellander-Gorgas¹,

Wang²,

Meier³

et al. 2017

Geosci. Model Dev.

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Abstract. The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale -Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational -Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convectionpermitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

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Section: T Schellander-gorgas Et Al: On the Forecast Skill Of A Conmentioning

confidence: 99%

On the forecast skill of a convection-permitting ensemble

Schellander-Gorgas¹,

Wang²,

Meier³

et al. 2017

Geosci. Model Dev.

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show abstract

“…Therefore several other verification methods have been developed for this task. Casati et al (2008) provides an overview of a majority of these methods. These verification methods differ in the features of the forecast that they evaluate and in their complexity.…”

Section: Evaluation Of the Precipitation Forecasts And Discussion Of mentioning

confidence: 99%

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

Sokol

Zacharov

2011

Quart J Royal Meteoro Soc

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Two methods for assimilating radar reflectivity into the COSMO numerical weather prediction (NWP) model were compared to precipitation forecasts. The first method assimilated observed radar reflectivity, and the second one assimilated observed and extrapolated radar reflectivity. The assimilation technique was based on the correction of the model's water vapour mixing ratio. The extrapolation was performed by the COTREC method and was 1 hour long. The model's horizontal resolution was 2.8 km.The comparison of methods was based on verification of the observed and forecast hourly precipitation. The comparison was performed for the 1 st , 2 nd and 3 rd hours of each forecast. On the whole, 45 forecasts from nine days of convective precipitation were evaluated for each hour. The evaluation included subjective verification and the following objective skill scores: Fractions Skill Scores, SAL and a measure based on a categorical-probabilistic approach.The results confirmed that assimilation complemented by the extrapolated data improves the accuracy of precipitation forecasts. The improvement was obvious in a majority of the single forecasts studied, and it is confirmed by all evaluation techniques. COSMO forecasts that used the extrapolation showed reasonable competence in forecasting for the first and the second hours.

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“…Various methods have been developed (Murphy, et.al., 1988;Casati, 2008;Nurmy, 2003;Rajeevan, et.al., 2005;Stanski, et.al., 1989) to compare the distribution of forecasts against the observations. In this paper some important verification methods are used to assess the performance of VARSHA GCM for the summer monsoon seasons of 2009 and 2010.…”

Section: Methodsmentioning

confidence: 99%

"Verification of VARSHA Rainfall Forecasts for Summer Monsoon Seasons of 2009 and 2010"

2017

IJCRR

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Objective: The monsoon year 2009 is a severe drought with 21.8% deficit rainfall from the long period average according to India Meteorological Department (IMD) over India since the past decade and 2010 is a normal monsoon year. Therefore, the verification skill of the VARSHA model has been computed for one normal monsoon and one drought year. Method:The statistical parameters like mean error, root mean square error and correlation coefficient has been computed at every model grid point and verified against the rainfall observations from India Meteorological Department and Tropical Rainfall Measuring Mission (TRMM). The analysis is also extended to dichotomous forecasts, i.e. yes/no forecasts, by using contingency tables. The four regions namely, South peninsula, Central India, North-East and North-West regions of Indian land mass have been considered for the verification analysis.Result: India receives a major portion of its annual rainfall during the summer monsoon season (June to September) and thus accurate prediction of summer monsoon rainfall is very crucial. Rainfall is a discrete parameter which shows large variations spatially as well as temporally. VARSHA, a hydrostatic global circulation model, developed at Flosolver unit, National Aerospace Laboratories (NAL), Bangalore, is used to predict summer monsoon rainfall from the year 2005 onwards. Forecast verification serves to assess the state of the art of forecasting. Therefore, verification of the model forecast is an important task for any numerical weather prediction centre. In this study, an attempt has been made to examine the skill of VARSHA model in predicting the rainfall during summer monsoon period of 2009 and 2010. Conclusion:The results show that model forecasts are useful at medium range and the performance of VARSHA is better in 2010 compared to 2009. The statistical skill scores show high predictability for light rain events with a decreasing tendency with an increase in intensity.

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Forecast verification: current status and future directions

Cited by 259 publications

References 101 publications

On the forecast skill of a convection-permitting ensemble

On the forecast skill of a convection-permitting ensemble

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

"Verification of VARSHA Rainfall Forecasts for Summer Monsoon Seasons of 2009 and 2010"

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