On ALADIN precipitation modeling and validation in an Alpine watershed

Ahrens, Bodo; Jasper, Karsten; Gurtz, J.

doi:10.5194/angeo-21-627-2003

Cited by 11 publications

(14 citation statements)

References 39 publications

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“…Alternatively, it has been proposed to compare NWP and observed precipitation fields from the perspective of their spatial structure (Zepeda-Arce et al 2000;Harris et al 2001). This is in line with previous studies, attempting to use spatial spectral information to downscale NWP output to the high-resolution scales needed by impact models in hydrological (Schertzer et al 2007;Ahrens 2003;Ahrens et al 2003;Harris et al 2001;Tessier et al 1996) and now casting applications (Chumchean et al 2004;Bowler et al 2006). In fact, since the work of Wilson et al (1991), it has been clear that the study of the statistical variability of precipitation could be crucial, especially in view of its use in hydrological models.…”

Section: Introductionmentioning

confidence: 85%

“…In particular, it was suggested that building downscaling methodologies in combination with numerical models is important, so as to reproduce the variability of precipitation at the spatial and temporal scales resolved by impact models. For this purpose, a promising approach has used the scaling properties of the precipitation field, relating the small-and large-scale statistical structures (Ahrens 2003;Ahrens et al 2003;Waymire 1985;Lovejoy and Schertzer 1985). Using radar precipitation estimates at high space-time resolution, several studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of spectral characteristics of precipitation from radar estimates and COSMO-model predicted fields

Willeit

Amorati

Montani

et al. 2014

Meteorol Atmos Phys

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The spectral characteristics of hourly precipitation fields are studied for a number of meteorological events, classified depending on the precipitation process involved, namely stratiform, convective or mixed stratiform-convective. The study focuses on the comparison between spatial spectral characteristics of observed precipitation fields, obtained from hourly radar estimates, and modelled hourly predictions of the same field by the nonhydrostatic model COSMO. The results show that the power spectra of radar hourly precipitation are characterised by invariance within ranges of horizontal spatial scales that are different for the three classes of events. COSMO reproduces some basic characteristics of the radar spectra in all cases. Nevertheless, in stratiform events, COSMO spectra present a well-defined scale break at about 15 km with no counterpart in radar data. It is suggested that this model feature is related to the presence of spurious horizontal smoothing introduced by the semi-lagrangian advection scheme for precipitation. Smoothing affects all scales up to the maximum length scale of precipitation and horizontal advection by the wind. An analysis of wind intensity in the lower troposphere over the region supports this interpretation. Discrepancies in precipitation spectra between radar and COSMO data in convective events are interpreted as a consequence of the inadequacy of the model resolution for a correct representation of convection.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Comparison of spectral characteristics of precipitation from radar estimates and COSMO-model predicted fields

Willeit

Amorati

Montani

et al. 2014

Meteorol Atmos Phys

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“…Nevertheless, these uncertainties have minor impact on the conclusions drawn here about the importance of scaling and the performance of stochastic downscaling. WaSiM‐ETH simulations in the TVM watershed have already been used successfully as a validation tool of precipitation data generated by meteorological models [ Jasper et al , 2002; Ahrens et al , 2003]. Simulated runoff has been sensitive to amount and variability of precipitation input in these validation experiments.…”

Section: Evaluation By Hydrological Modelingmentioning

confidence: 99%

“…Therefore stochastic downscaling of rain is preferred in this study. The alternative, downscaling by nested deterministic meteorological simulations is applied, for example, in the work of Ahrens et al [2003].…”

Section: Introductionmentioning

confidence: 99%

Rainfall downscaling in an alpine watershed applying a multiresolution approach

Ahrens

2003

J. Geophys. Res.

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[1] Distributed hydrological modeling in alpine watersheds needs high-resolution ($1 km in space, 1 h in time) rainfall input. Spatial resolution of operational numerical weather forecast or regional climate models is coarser by at least one order of magnitude. Stochastic downscaling of meteorological model output is the appropriate way to bridge this scale gap. This study examines radar data in an orographically complex area in the southern European Alps by multiplicative multiresolution decomposition. Multiresolution rainfall fluctuations exhibit simple scaling characteristics. This result motivates the implementation of a spatial downscaling model, an example of an anisotropic microcanonical multiplicative random cascade, that is applied and evaluated. Necessity and skill of downscaling is illustrated by hydrological modeling of four heavy precipitation events in an Alpine watershed (total area of 2627 km 2 ) with the grid-based model WaSiM-ETH. Downscaling of rainfall fields with 16 km grid spacing to fields with 1 km grid spacing improves the simulated event-mean runoff by 5-10% and the peak runoff by up to 20%.

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“…This has been done in several studies (e.g. Bica et al, 2011;Valéry et al, 2009Valéry et al, , 2010Ahrens et al, 2003;Jasper and Kaufmann, 2003;Kunstmann and Stadler, 2005;Jasper et al, 2002). The common basis of these studies was to indirectly gain information on catchment rainfall by comparing simulated runoff results with observations.…”

Section: Catchment Precipitation From Runoff Observations Through Invmentioning

confidence: 99%

From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

Herrnegger

Nachtnebel

Schulz

2015

Hydrol. Earth Syst. Sci.

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Abstract. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall-runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall.

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On ALADIN precipitation modeling and validation in an Alpine watershed

Cited by 11 publications

References 39 publications

Comparison of spectral characteristics of precipitation from radar estimates and COSMO-model predicted fields

Comparison of spectral characteristics of precipitation from radar estimates and COSMO-model predicted fields

Rainfall downscaling in an alpine watershed applying a multiresolution approach

From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

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