Evaluating multi-scale precipitation forecasts using high resolution analysis

Wittmann, Christoph; Haiden, Thomas; Kann, Alexander

doi:10.5194/asr-4-89-2010

Cited by 13 publications

(14 citation statements)

References 12 publications

(14 reference statements)

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“…However, characteristic differences are revealed by the investigation of the precipitation forecasts. A known drawback of models using deep convection schemes proves true, which is the premature onset of precipitation in the daily cycle by ALADIN-LAEF (see, e.g., Wittmann et al, 2010;Weusthoff et al, 2010). On the other hand, an overestimation of precipitation intensities at the peak of convection activities by AROME-EPS is also confirmed, which has been assumed in previous validations.…”

Section: Discussionmentioning

confidence: 52%

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: Discussionmentioning

confidence: 52%

On the forecast skill of a convection-permitting ensemble

Schellander-Gorgas¹,

Wang²,

Meier³

et al. 2017

Geosci. Model Dev.

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“…The model overestimation of convective precipitation over the entire alpine region has been already described by Wittmann et al (2010). The model is not able to reproduce the observed precipitation patterns, which are selective and clustered into specific regions and not as widespread as forecast by the model.…”

Section: A Precipitation Forecasts Of Opermentioning

confidence: 78%

“…The location score L is quite similar for all regions, which is a typical behavior for this parameter when averaging it for several events (Wittmann et al 2010). Therefore, this component is more useful for the investigation of single events.…”

Section: A Precipitation Forecasts Of Opermentioning

confidence: 93%

“…For mean precipitation, analyses of the observed precipitation are provided by the high-resolution Integrated Nowcasting System (INCA; Haiden et al 2011;Wittmann et al 2010), run operationally at ZAMG. This system combines station measurements and radar data to provide an optimal spatiotemporal analysis of precipitation, which is defined as the best available representation of the true rainfall distribution for verification.…”

Section: Verification Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Impact of ASCAT Soil Moisture Assimilation on Regional Precipitation Forecasts: A Case Study for Austria

Schneider

Wang

Wagner

et al. 2014

Monthly Weather Review

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In this study, remotely sensed soil moisture data from the Advanced Scatterometer (ASCAT) on board the Meteorological Operational (MetOp) series of satellites are assimilated in the regional forecasting model, Aire Limit ee Adaptation Dynamique D eveloppement International (ALADIN-Austria), using a simplified extended Kalman filter. A pointwise bias correction method is applied to the ASCAT data as well as quality flags prepared by the data provider. The ASCAT assimilation case study is performed over central Europe during a 1-month period in July 2009. Forecasts of those assimilation experiments are compared to the control run provided by the operational ALADIN version of the Austrian Met Service, Zentralanstalt f€ ur Meteorologie und Geodynamik (ZAMG). Forecasts are furthermore verified versus in situ data. For a single-day case study the ability of the approach to improve precipitation forecast quality in the presence of high impact weather is demonstrated. Results show that 1) based on a one station in situ data evaluation, soil moisture analysis is improved, compared to the operational analysis, when ASCAT soil moisture data is assimilated; 2) pointwise bias correction of the satellite data is beneficial for forecast quality; 3) screen level parameter forecasts can be slightly improved as a result of this approach; and 4) convective precipitation forecast is improved over flatland for the investigation period while over mountainous regions the impact is neutral.

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“…These novel verification methods require gridded precipitation analyses, preferably model-independent, of high quality as a reference. Wittmann et al (2010) have used highresolution precipitation analyses to validate the skill of different limited area models (LAM) during a convective season. Similarly, Sattler and Feddersen (2005) have applied daily precipitation analyses to evaluate the quality of a limited area and a global ensemble system during heavy precipitation events.…”

Section: A Kann Et Al: Evaluation Of High-resolution Precipitation mentioning

confidence: 99%

Evaluation of high-resolution precipitation analyses using a dense station network

Kann

Meirold-Mautner

Schmid

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. The ability of radar-rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling, thunderstorm warnings, and, as a reference, to spatially validate numerical weather prediction models. However, due to drawbacks of methods like crossvalidation and due to the limited availability of reference data sets on high temporal and spatial scales, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high-resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense weather station network (WegenerNet), operated in a limited domain of the southeastern parts of Austria (Styria). Based on case studies and a longer-term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown by both continuous and categorical verification measures, although the temporal and spatial variability of the errors is -by convective nature -high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

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Evaluating multi-scale precipitation forecasts using high resolution analysis

Cited by 13 publications

References 12 publications

On the forecast skill of a convection-permitting ensemble

On the forecast skill of a convection-permitting ensemble

Impact of ASCAT Soil Moisture Assimilation on Regional Precipitation Forecasts: A Case Study for Austria

Evaluation of high-resolution precipitation analyses using a dense station network

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