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Jürg Schmidli, Christoph Frei, and Christoph Schär

Abstract

The feasibility of a statistical reconstruction of mesoscale precipitation fields over complex topography from a sparse rain gauge network is examined. Reconstructions of gridded monthly precipitation for the European Alps (resolution 25 km, 1202 grid points) are derived from rain gauge samples (70–200-km interstation distance, 25–150 stations). The statistical model is calibrated over a 15-yr period, and the reconstructed fields are evaluated for the remaining 5 yr of the period 1971–90. The experiments are used to define the statistical setup, to assess the data requirements, and to describe the error statistics of a centennial reconstruction to be used in a forthcoming study. Reduced-space optimal interpolation is employed as the reconstruction method, involving data reduction by empirical orthogonal functions (EOFs) and least squares optimal estimation of EOF coefficients. Also, a procedure to define covariance-guided station samples with a “representative” spatial distribution for the reconstruction is proposed.

Using a covariance-guided reference sample of 53 stations, the reconstruction accounts for 77% of the total variance. For individual grid points the relative reconstruction error (error variance divided by data variance) varies between 10% and 40%; this value drops to 2%–10% when considering subdomain means of 100 × 100 km2. The mesoscale patterns of the fields and multiyear precipitation anomalies are accurately reproduced. The EOF truncation is identified as the major limitation of the reconstruction skill but is necessary to avoid overfitting. Reconstructions from covariance-guided representative samples exhibit superior skill in comparison with those from randomly distributed stations. The skill of the reconstruction was found to depend marginally on the choice of the calibration period within the 20 yr, even when months with exclusively positive or negative values of the North Atlantic oscillation index were selected for calibration. This result indicates that the reconstruction model provides appreciable temporal stationarity.

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Peter R. Bannon, Jürg Schmidli, and Christoph Schär

Abstract

Dynamical, rather than kinematical, considerations indicate that a generalized potential vorticity in terms of the gradient of an arbitrary scalar function requires that the potential vorticity flux vector contain a contribution due to gravity and the pressure gradient force. It is shown that such a potential vorticity flux vector has a simpler definition in terms of the gradient of the kinetic energy rather than that of a Bernoulli function. This result is valid for multicomponent fluids. Flux vectors for a salty ocean and a moist atmosphere with hydrometeors are presented.

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Davide Panosetti, Steven Böing, Linda Schlemmer, and Jürg Schmidli

Abstract

On summertime fair-weather days, thermally driven wind systems play an important role in determining the initiation of convection and the occurrence of localized precipitation episodes over mountainous terrain. This study compares the mechanisms of convection initiation and precipitation development within a thermally driven flow over an idealized double-ridge system in large-eddy (LESs) and convection-resolving (CRM) simulations. First, LES at a horizontal grid spacing of 200 m is employed to analyze the developing circulations and associated clouds and precipitation. Second, CRM simulations at horizontal grid length of 1 km are conducted to evaluate the performance of a kilometer-scale model in reproducing the discussed mechanisms.

Mass convergence and a weaker inhibition over the two ridges flanking the valley combine with water vapor advection by upslope winds to initiate deep convection. In the CRM simulations, the spatial distribution of clouds and precipitation is generally well captured. However, if the mountains are high enough to force the thermally driven flow into an elevated mixed layer, the transition to deep convection occurs faster, precipitation is generated earlier, and surface rainfall rates are higher compared to the LES. Vertical turbulent fluxes remain largely unresolved in the CRM simulations and are underestimated by the model, leading to stronger upslope winds and increased horizontal moisture advection toward the mountain summits. The choice of the turbulence scheme and the employment of a shallow convection parameterization in the CRM simulations change the strength of the upslope winds, thereby influencing the simulated timing and intensity of convective precipitation.

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Linda Schlemmer, Cathy Hohenegger, Jürg Schmidli, Christopher S. Bretherton, and Christoph Schär

Abstract

This paper introduces an idealized cloud-resolving modeling (CRM) framework for the study of midlatitude diurnal convection over land. The framework is used to study the feedbacks among soil, boundary layer, and diurnal convection. It includes a setup with explicit convection and a full set of parameterizations. Predicted variables are constantly relaxed toward prescribed atmospheric profiles and soil conditions. The relaxation is weak in the lower troposphere and upper soil to allow the development of a realistic diurnal planetary boundary layer. The model is run to its own equilibrium (30 days).

The framework is able to produce a realistic timing of the diurnal cycle of convection. It also confirms the development of deeper convection in a more unstably stratified atmosphere.

With this relaxation method, the simulated “diurnal equilibrium convection” determines the humidity profile of the lower atmosphere, and the simulation becomes insensitive to the reference humidity profile. However, if a faster relaxation time is used in the lower troposphere, the convection and rainfall become much more sensitive to the reference humidity, consistent with previous studies.

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