The Operational Weather Radar Network in Europe

Huuskonen, A.; Saltikoff, Elena; Holleman, Iwan

doi:10.1175/bams-d-12-00216.1

Cited by 153 publications

(139 citation statements)

References 6 publications

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“…The latter two regions were chosen specifically for their difference in topography and because high spatial and temporal resolution radar data were readily available. A similar approach can be performed in the future on a larger spatial scale based for instance on the radar composite imagery produced by the Eumetnet Operational Programme for the Exchange of Weather Radar Information (OPERA, Huuskonen et al, 2014) and related EUCLID domain.…”

Section: Discussionmentioning

confidence: 99%

Analysis of lightning outliers in the EUCLID network

et al. 2017

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Abstract. Lightning data as observed by the European Cooperation for Lightning Detection (EUCLID) network are used in combination with radar data to retrieve the temporal and spatial behavior of lightning outliers, i.e., discharges located in a wrong place, over a 5-year period from 2011 to 2016. Cloud-to-ground (CG) stroke and intracloud (IC) pulse data are superimposed on corresponding 5 min radar precipitation fields in two topographically different areas, Belgium and Austria, in order to extract lightning outliers based on the distance between each lightning event and the nearest precipitation. It is shown that the percentage of outliers is sensitive to changes in the network and to the location algorithm itself. The total percentage of outliers for both regions varies over the years between 0.8 and 1.7 % for a distance to the nearest precipitation of 2 km, with an average of approximately 1.2 % in Belgium and Austria. Outside the European summer thunderstorm season, the percentage of outliers tends to increase somewhat. The majority of all the outliers are low peak current events with absolute values falling between 0 and 10 kA. More specifically, positive cloud-toground strokes are more likely to be classified as outliers compared to all other types of discharges. Furthermore, it turns out that the number of sensors participating in locating a lightning discharge is different for outliers versus correctly located events, with outliers having the lowest amount of sensors participating. In addition, it is shown that in most cases the semi-major axis (SMA) assigned to a lightning discharge as a confidence indicator in the location accuracy (LA) is smaller for correctly located events compared to the semimajor axis of outliers.

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Section: Discussionmentioning

confidence: 99%

Analysis of lightning outliers in the EUCLID network

et al. 2017

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“…OPERA provides a European-scale radar composite, which is available every 15 min [4,59]. We employed OPERA surface rain rates re-projected on the MSG full disk service grid as a ground-based radar reference for the satellite rainfall retrieval.…”

Section: Ground-based Radar Referencementioning

confidence: 99%

“…Accurate rainfall measurements are essential for real-time nowcasting of severe weather and related hydrological applications, climatological precipitation studies, and to improve our understanding of the hydrological cycle, to name a few. Quantitative precipitation estimation (QPE) can be derived for a variety of temporal and spatial resolutions from rain gauges (e.g., [1,2]), ground-based weather radars (e.g., [3,4]), spaceborne weather radars (e.g., [5]), and satellite radiometers (e.g., [6][7][8]). Often, different data sources are combined to enhance the QPE accuracy, e.g., ground-based radar and gauges (e.g., [9,10]), ground-based radar and satellite radiometer (e.g., [11]), satellite radiometer and gauges (e.g.…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

et al. 2018

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Abstract:In this study, we develop and compare satellite rainfall retrievals based on generalized linear models and artificial neural networks. Both approaches are used in classification mode in a first step to identify the precipitating areas (precipitation detection) and in regression mode in a second step to estimate the rainfall intensity at the ground (rain rate). The input predictors are geostationary satellite infrared (IR) brightness temperatures and Satellite Application Facility (SAF) nowcasting products which consist of cloud properties, such as cloud top height and cloud type. Additionally, a set of auxiliary location-describing input variables is employed. The output predictand is the ground-based instantaneous rain rate provided by the European-scale radar composite OPERA, that was additionally quality-controlled. We compare our results to a precipitation product which uses a single infrared (IR) channel for the rainfall retrieval. Specifically, we choose the operational PR-OBS-3 hydrology SAF product as a representative example for this type of approach.With generalized linear models, we show that we are able to substantially improve in terms of hits by considering more IR channels and cloud property predictors. Furthermore, we demonstrate the added value of using artificial neural networks to further improve prediction skill by additionally reducing false alarms. In the rain rate estimation, the indirect relationship between surface rain rates and the cloud properties measurable with geostationary satellites limit the skill of all models, which leads to smooth predictions close to the mean rainfall intensity. Probability matching is explored as a tool to recover higher order statistics to obtain a more realistic rain rate distribution.

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“…The Operational Program for Exchange of Weather Radar Information (OPERA) was launched for improving the harmonization of radars and their measurements [6]. The program provides three composite products, such as instantaneous surface rain rate, instantaneous maximum reflectivity and one-hour rainfall accumulation; gathered data includes three-dimensional volumes of reflectivity and radial wind.…”

Section: Introductionmentioning

confidence: 99%

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

Ivanov¹,

Michaelides²,

Ruban³

2018

Preprint

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This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model.The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5-3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

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The Operational Weather Radar Network in Europe

Abstract: has worked to improve harmonization of radar systems and measurements since 1999 and has recently started production of network-wide radar mosaics.

Cited by 153 publications

References 6 publications

Analysis of lightning outliers in the EUCLID network

Analysis of lightning outliers in the EUCLID network

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

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