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dc.contributor.author
Nerini, Daniele
dc.contributor.supervisor
Wernli, Heini
dc.contributor.supervisor
Foresti, Loris
dc.contributor.supervisor
Germann, Urs
dc.contributor.supervisor
Berenguer, Marc
dc.date.accessioned
2020-01-20T14:24:01Z
dc.date.available
2020-01-20T09:10:30Z
dc.date.available
2020-01-20T09:54:41Z
dc.date.available
2020-01-20T14:24:01Z
dc.date.issued
2019
dc.identifier.uri
http://hdl.handle.net/20.500.11850/391932
dc.identifier.doi
10.3929/ethz-b-000391932
dc.description.abstract
Nowcasting precipitation, that is, accurately predicting its location and intensity minutes to a few hours ahead, is a difficult task. Extreme rainfall events can be a threat to the population and challenge the limits of weather monitoring and prediction systems. For practical use, the forecasting techniques at our disposal range from physically-based numerical models to heuristic extrapolation procedures based on weather radars. Radar-based nowcasting models rely on the high spatial and temporal resolution of radar measurements and thus benefit from the best possible initial conditions, but the assumption of persistence leads to a rapid decay of predictive skill with increasing lead time and decreasing spatial scale. Numerical models provide physically consistent precipitation forecasts, but their practical relevance for nowcasting can be undermined by uncertain initial conditions, spinup issues, model approximations, or simply by their computational limits. Analyses presented in this study show that, on average, radar-based nowcasting outperforms numerical simulations during the first three hours and that it is particularly useful for forecasting precipitation patterns with a horizontal dimension below 60 kilometers, as state-of-the-art numerical models cannot provide useful skill at those small spatial scales. An interesting finding is that the predictive uncertainty of numerical predictions relatively to radar nowcasting improves during warm convective days, which is explained by the combined effect of shorter precipitation lifetimes and more effective model assimilation of locally triggered air mass convection. After a lead time of 4.5 hours, we observed that precipitation on all scales below 150 kilometers is poorly predictable by all forecasting means. Such serious limits to predictability determine the need to represent forecast uncertainty as accurately as possible. In radar nowcasting, ensemble methods use stochastic simulations to perturb a deterministic extrapolation and thus quantify forecast errors. We found that a more precise representation of the statistical properties of the precipitation field through localization can have a positive impact on the realism of the simulations as well as in terms of probabilistic forecast skill. We showed that localized nowcasts perform better in terms of ensemble reliability and resolution, as well as conditional bias. However, we also found that a too strong a localization can lead to lower skill as it implicitly relies on a stricter assumption of persistence. The quantitative estimation of forecast uncertainty provided by ensembles was finally used to design a seamless blending procedure that integrates all available sources of predictive skill. Implemented using a recursive formulation of the Bayesian update equations, the blending scheme involves a prediction step through a stochastic radar extrapolation, while a subsequent correction step updates the extrapolation using information from the most recent numerical model run. It is found that such an approach is able to capture the flow dependence of both the numerical forecast and the radar nowcast ensemble spreads resulting in an adaptive blending scheme that depends on the relative uncertainty of the individual forecasts. Despite the non-Gaussian nature of rainfall data, we were able to produce blended precipitation forecasts that are at least as skillful as the radar-only or the numerical model-only forecasts at any lead time.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Rainfall
en_US
dc.subject
Nowcasting
en_US
dc.subject
Radars/Radar observations
en_US
dc.subject
Forecasting and nowcasting
en_US
dc.subject
Scale dependence
en_US
dc.subject
Verification
en_US
dc.subject
Optical flow
en_US
dc.subject
STOCHASTIC MODELS + STOCHASTIC SIMULATION (PROBABILITY THEORY)
en_US
dc.subject
AUTOREGRESSION (MATHEMATICAL STATISTICS)
en_US
dc.subject
Kalman filters
en_US
dc.title
Ensemble precipitation nowcasting: limits to prediction, localization and seamless blending
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2020-01-20
ethz.size
150 p.
en_US
ethz.code.ddc
DDC - DDC::5 - Science::550 - Earth sciences
en_US
ethz.identifier.diss
26091
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03854 - Wernli, Johann Heinrich / Wernli, Johann Heinrich
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03854 - Wernli, Johann Heinrich / Wernli, Johann Heinrich
en_US
ethz.date.deposited
2020-01-20T09:10:37Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-20T09:54:55Z
ethz.rosetta.lastUpdated
2021-02-15T07:26:40Z
ethz.rosetta.versionExported
true
ethz.COinS
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