Gauge-Radar network adjustment for the baltic sea experiment

Michelson, Daniel; Koistinen, Jarmo

doi:10.1016/s1464-1909(00)00125-8

Cited by 53 publications

(36 citation statements)

References 8 publications

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“…We used radar data from the Baltic Sea Experiment (BALTEX) Radar Data Center (BRDC; Michelson et al 2000), providing products for a Nordic network of 25 mostly C band weather radars from six countries. The data used for this study were gauge-adjusted radar reflectivities Z converted to rain rate R using a static Z-R relation (Thoss and Dybbroe 2001).…”

Section: Collocated Weather Radar and Satellite Datamentioning

confidence: 99%

NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation

Dybbroe

Karlsson

Thoss

2005

Journal of Applied Meteorology

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Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly.

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Section: Collocated Weather Radar and Satellite Datamentioning

confidence: 99%

NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation

Dybbroe

Karlsson

Thoss

2005

Journal of Applied Meteorology

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“…However, the method used is quite simple to implement and it has been used in different contexts, for example at ARPA-SMR, Meteorological Service of Emilia Romagna (Italy) (Alberoni et al, 1992) or in Michelson' (2000); moreover, it does not need great computational resources. The authors consider the proposed algorithm suitable for applications in a real-time operational framework, however, the introduction of different methodologies for radar gauge adjustment could be taken into consideration in future developments.…”

Section: Radar Rainfall Mapsmentioning

confidence: 99%

Towards a definition of a real-time forecasting network for rainfall induced shallow landslides

Segoni

Leoni

Benedetti

et al. 2009

Nat. Hazards Earth Syst. Sci.

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Abstract. PREVIEW is an European Commission FP6 Integrated Project with the aim of developing, at an European level, innovative geo-information services for atmospheric, geophysical and man-made risks. Within this framework, the Landslides Platform Service 2 (forecasting of shallow rapid slope movements) has developed an integrated procedure for the forecasting and warning of distributed shallow landsliding to be used for civil protection purposes.The Service consists of an automated end-to-end forecasting chain which uses data from a probabilistic downscaled short-term rainfall forecast, soil saturation estimates and meteorological radar outputs. The above data are entered into a hydro-geological model that makes use of an infinite slope approach to calculate the distributed Factor of Safety over the entire basin. All outputs, and much of the input data, are shown on a WebGIS system so that end-users can interactively access and download data. A distinctive feature of the service is the use of an innovative soil depth model for predicting the distributed thickness of the regolith cover within the basin, which is one of the most important parameters controlling shallow landslide triggering.The service was developed in a pilot test site in NE Italy, the Armea basin. Validation makes use of two rainfall events: one that occurred in 2000 and a smaller, more recent event (2006) that caused fewer landslides. Rainfall data have been used to compute a distributed factor-of-safety map that has been overlaid onto the landslide inventory. Instead of a traditional validation approach based on the number count of correctly identified landslides, we carried out an alternative procedure based on the landslides area that gave outcomes which, for this preliminary stage of the research, can be considered promising.

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“…A total of 35 radars are used to cover Sweden and the surrounding countries in the NORDRAD network ( Figure 2) with a spatial resolution of 2 £ 2 km 2 . The precipitation composites are quality controlled and gauge adjusted to be valid at ground level (Michelson 2006, Michelson andKoistinen 2000).…”

Section: Data Sourcesmentioning

confidence: 99%

Rainfall nowcasting: predictability of short-term extremes in Sweden

Olsson

Simonsson

Ridal

2014

Urban Water Journal

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Our current knowledge of the character of rainfall events in Sweden associated with extreme short-term accumulations and their predictability by forecasting, is very limited. In this study, observations from automatic stations and weather radars in Sweden were analysed to identify and characterise extreme short-term events. Often shorter-duration (1-6 h) extreme events were associated with small-scale structures, dominated by single cells, and longer-duration (12 -24 h) events with less variable, larger-scale fields. For lead time 3 h, ,20% of the events were forecasted at the correct place with an error of ,25% by the operational Swedish nowcasting system. If allowing for a 25 km displacement of the forecasted events, the hit rate increased by 10 -15 percentage points. Some predictability was found for lead time 8 h but not for 24 h. The results suggest a potential added gain of increasing the temporal resolution of the Swedish flood forecasting system to sub-daily steps.

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Gauge-Radar network adjustment for the baltic sea experiment

Cited by 53 publications

References 8 publications

NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation

NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation

Towards a definition of a real-time forecasting network for rainfall induced shallow landslides

Rainfall nowcasting: predictability of short-term extremes in Sweden

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