Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

Hempelmann, Nils; Ehbrecht, Carsten; Alvarez-Castro, M. Carmen; Brockmann, Patrick; Falk, Wolfgang; Hoffmann, Jörg; Kindermann, Stephan; Koziol, Ben; Nangini, Cathy; Radanovics, Sabine; Vautard, Robert; Yiou, Pascal

doi:10.1016/j.cageo.2017.10.004

Cited by 16 publications

(18 citation statements)

References 32 publications

(30 reference statements)

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“…Their absolute values are larger than for the NAO index, and need to be normalized by the variance of temperature (or the CRPS value itself), as the units of TG are tenths of degrees. The average relative reliability values for lead times lower than 10 days are also similar to what is reported by Hersbach (2000). The reliability values seem to decrease with lead times in winter.…”

Section: European Temperaturessupporting

confidence: 87%

“…The computation of analogues with other parameters (geographical zone, atmospheric predictor, type of reanalysis, climate model output, etc.) can be easily performed with a web processing service (Hempelmann et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The reliability Reli term measures whether events that are forecast with a certain probability p did occur with the same fraction p from the observations (Hersbach, 2000). The remaining terms of the right-hand side of Eq.…”

Section: Forecast Skillmentioning

confidence: 99%

“…The CRPS reliability values range from 5×10 −3 (10 days) to 0.01 (40 days). If they are normalized to the CRPS value (or the variance of the NAO index), this is in the same range as the results of Hersbach (2000) for the ECMWF forecast system up to 10 days.…”

Section: Nao Indexmentioning

confidence: 99%

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Stochastic ensemble climate forecast with an analogue model

Yiou

Déandreis²

2019

Geosci. Model Dev.

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Abstract. This paper presents a system to perform large-ensemble climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast a North Atlantic Oscillation (NAO) index and the daily average temperature in five European stations. We simulated 100-member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e., from a meteorological to a seasonal forecast. We tested the hindcast simulations with the usual forecast skill scores (CRPS or correlation) against persistence and climatology. We find significantly positive skill scores for all timescales. Although this model cannot outperform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast.

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Section: European Temperaturessupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Forecast Skillmentioning

confidence: 99%

Section: Nao Indexmentioning

confidence: 99%

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Stochastic ensemble climate forecast with an analogue model

Yiou

Déandreis²

2019

Geosci. Model Dev.

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“…Out of the history of Birdhouse the deployed algorithms are to provide services for the climate model and climate impact community. The core functions are focusing on Climate change assessment and climate impact studies (Hempelmann et al, 2018). Climate impact in particular is supported by the Atmospheric flow Analogues for Climate Change (A2C2) project 2 which aims to study rare and extreme weather events.…”

Section: Thematics Being Coveredmentioning

confidence: 99%

Projects Based on the Web Processing Service Framework Birdhouse

Ehbrecht

Landry

Hempelmann

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Birdhouse is a collaborative project open for the community to participate. It is a software framework containing a collection of Web Processing Services (WPS). The deployed algorithms are focusing on Earth Systems and environmental data processing with the philosophy of streamlining the software development and deployment. By supporting climate, earth observation and biodiversity data and processes, Birdhouse can be used in a wide array of Earth sciences projects and workflows. The core benefit of this project is to allow the seamless use of climate services developed by a diverse network of national meteorological offices, regional climate service providers, academics, not-for-profit research centers and private industry. As governments move toward open-data policies, there will be a need for analytical services that extract value out of the deluge of information. Using an interoperable software architecture, institutions can provide both data and services allowing users to process the data remotely from a laptop, instead of having to acquire and maintain large storage infrastructures.</p>

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On the weather types that shape the precipitation patterns across the U.S. Midwest

Zhang

Villarini

2019

Clim Dyn

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The U.S. Midwest is an area that has been plagued by heavy and persistent precipitation leading to frequent flood events. The improved understanding of the types of weather conditions and settings associated with heavy precipitation can provide basic information to improve our preparation for and response to these events. Here we identify five weather types from daily 500-hPa geopotential height using the k-means cluster analysis. Consistent with their distinct large-scale atmospheric patterns, these weather types exert different effects on precipitation in the Midwest. Weather type 1 (WT1) features a zonally-aligned wave train propagating from the North Pacific to North America. Overall, WT2 is characterized by a wave train pattern with high (low) pressure in the western (eastern) United States. WT3 features a unique pattern with a high pressure system over the continental United States except for the northwestern United States, similar to the La-Niña forced responses. WT4 is characterized by a wave train moving from the Pacific Northwest to the North Atlantic with a strong ridge over the western United States, while WT5 features a positive geopotential height anomaly originating from the Arctic, probably influenced by the Arctic Amplification. Because of the strong moisture transport, strengthened low-level jet stream and wavy upperlevel polar jet stream located in the western United States, among the five weather types WT1 exerts the strongest impacts on precipitation, accounting for up to 40% of the total precipitation over the Midwest, followed by WT5. Moreover, we detect a significant upward trend in the number of WT1 and WT5 events for 1948-2017 and their persistency, suggesting a rising risk of heavy and long-lasting precipitation across the Midwest. Overall, the weather types during summer and winter are consistent with those obtained from the analysis of the entire year, although the weather types during winter have a larger magnitude in the geopotential height anomaly. WT1 accounts for the largest contribution to total precipitation in the Midwest during summer and winter.

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Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

Cited by 16 publications

References 32 publications

Stochastic ensemble climate forecast with an analogue model

Stochastic ensemble climate forecast with an analogue model

Projects Based on the Web Processing Service Framework Birdhouse

On the weather types that shape the precipitation patterns across the U.S. Midwest

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