Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

Haas, Rabea; Born, K.

doi:10.5194/npg-18-223-2011

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…Future investigations will focus on model output statistics to calibrate the downscaled wind signatures to observations. The probability distributions of simulations and observations may be related by probability mapping [e.g., Haas and Born , 2011]. This would enable to correct the footprints and apply the approach on pre‐adjusted gust speeds.…”

Section: Discussionmentioning

confidence: 99%

A combined statistical and dynamical approach for downscaling large‐scale footprints of European windstorms

Haas

Pinto

2012

Geophysical Research Letters

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.[1] The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical downscaling approach in combination with dynamical downscaling is introduced to derive storm related gust speeds on a highresolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical downscaling. This new tool can be easily applied to large ensembles of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections. Citation: Haas, R., and J. G. Pinto (2012), A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms,

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Section: Discussionmentioning

confidence: 99%

A combined statistical and dynamical approach for downscaling large‐scale footprints of European windstorms

Haas

Pinto

2012

Geophysical Research Letters

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“…(5a) states that the shape parameters of both measured variables are also linear. Knowing the distribution parameters of gust velocities, synthetic gusts can be derived for particular wind speeds through quantileÁ quantile mapping (Zhang et al, 2006;Haas and Born, 2011).…”

Section: Theorymentioning

confidence: 99%

Development of a wind gust model to estimate gust speeds and their return periods

Seregina¹,

Haas²,

Born³

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Spatially dense observations of gust speeds are necessary for various applications, but their availability is limited in space and time. This work presents an approach to help to overcome this problem. The main objective is the generation of synthetic wind gust velocities. With this aim, theoretical wind and gust distributions are estimated from 10 yr of hourly observations collected at 123 synoptic weather stations provided by the German Weather Service. As pre-processing, an exposure correction is applied on measurements of the mean wind velocity to reduce the influence of local urban and topographic effects. The wind gust model is built as a transfer function between distribution parameters of wind and gust velocities. The aim of this procedure is to estimate the parameters of gusts at stations where only wind speed data is available. These parameters can be used to generate synthetic gusts, which can improve the accuracy of return periods at test sites with a lack of observations. The second objective is to determine return periods much longer than the nominal length of the original time series by considering extreme value statistics. Estimates for both local maximum return periods and average return periods for single historical events are provided. The comparison of maximum and average return periods shows that even storms with short average return periods may lead to local wind gusts with return periods of several decades. Despite uncertainties caused by the short length of the observational records, the method leads to consistent results, enabling a wide range of possible applications.

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“…The downscaled data exhibited significant modeling improvement. Haas and Born (2011) introduced a twostep probabilistic downscaling method for disaggregating ERA-Interim using ground-based gauge data over a complex terrain in southeastern Morocco. The technique is a combination of cumulative distribution function (CDF) transformation based on probability mapping and a multi linear regression model to extrapolate observation data to a highresolution grid using digital elevation model data.…”

Section: Introductionmentioning

confidence: 99%

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

Seyyedi

Anagnostou

Beighley

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.

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Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

Cited by 10 publications

References 26 publications

A combined statistical and dynamical approach for downscaling large‐scale footprints of European windstorms

A combined statistical and dynamical approach for downscaling large‐scale footprints of European windstorms

Development of a wind gust model to estimate gust speeds and their return periods

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

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