Distance in spatial interpolation of daily rain gauge data

Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. The spatial density of available data sites is often changing with time. This paper investigates the application of statistical distance, like one minus common variance of time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of an actually available coarse observation network yields more robust interpolation results at sites of a denser network with actually lacking observations. The performance enhancement is in or close to mountainous terrain. This has the potential to parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain

A modification of the mixed form of Richards equation and its application in vertically inhomogeneous soils

Recently, new soil data maps were developed, which include vertical soil properties like soil type. Implementing those into a multilayer Soil-Vegetation-Atmosphere-Transfer (SVAT) scheme, discontinuities in the water content occur at the interface between dissimilar soils. Therefore, care must be taken in solving the Richards equation for calculating vertical soil water fluxes. We solve a modified form of the mixed (soil water and soil matric potential based) Richards equation by subtracting the equilibrium state of soil matrix potential ψE from the hydraulic potential ψh. The sensitivity of the modified equation is tested under idealized conditions. The paper will show that the modified equation can handle with discontinuities in soil water content at the interface of layered soils

Evaluation of a probabilistic hydrometeorological forecast system

Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread

On daily interpolation of precipitation backed with secondary information

This paper investigates the potential impact of secondary information on rainfall mapping applying Ordinary Kriging. Secondary information tested is a natural area indicator, which is a combination of topographic features and weather conditions. Cross validation shows that secondary information only marginally improves the final mapping, indicating that a one-day accumulation time is possibly too short

Die globale Zukunft der Gewalt : führt der Klimawandel des 21. Jahrhunderts zu einer bisher unbekannten Eskalation der Gewalt?

Rezension zu: Harald Welzer : Klimakriege. Wofür im 21. Jahrhundert getötet wird, S. Fischer Verlag, Frankfurt am Main 2008 ; ISBN-10 3100894332 ; 335 Seiten, 19,90 Euro

Soil moisture initialization effects in the Indian monsoon system

Towards the goal to understand the role of land-surface processes over the Indian sub-continent, a series of soil-moisture sensitivity simulations have been performed using a non-hydrostatic regional climate model COSMO-CLM. The experiments were driven by the lateral boundary conditions provided by the ERA-Interim (ECMWF) reanalysis. The simulation results show that the pre-monsoonal soil moisture has a significant influence on the monsoonal precipitation. Both, positive and negative soil-moisture precipitation (S-P) feedback processes are of importance. The negative S-P feedback process is especially influential in the western and the northern parts of India

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain. Thus, downscaling of global to regionally resolved projections is necessary to provide input to integrated water resources management approaches for river basins like the Upper Danube River Basin (UDRB) and the Upper Brahmaputra River Basin (UBRB). This paper discusses the application of the regional climate model COSMO-CLM as a dynamical downscaling tool. To provide accurate data the COSMO-CLM model output was post-processed by statistical means. This downscaling chain performs well in the baseline period 1971 to 2000. However, COSMO-CLM performs better in the UDRB than in the UBRB because of a longer application experience and a less complex climate in Europe. Different climate change scenarios were downscaled for the time period 1960–2100. The projections show an increase of temperature in both basins and for all seasons. The values are generally higher in the UBRB with the highest values occurring in the region of the Tibetan Plateau. Annual precipitation shows no substantial change. However, seasonal amounts show clear trends, for instance an increasing amount of spring precipitation in the UDRB. Again, the largest trends for different precipitation statistics are projected in the region of the Tibetan Plateau. Here, the projections show up to 50% longer dry periods in the months June to September with a simultaneous increase of about 10% for the maximum amount of precipitation on five consecutive days. For the Assam region in India, the projections also show an increase of 25% in the number of consecutive dry days during the monsoon season leading to prolonged monsoon breaks

The smoother extension of the nonlinear ensemble transform filter

The recently-proposed nonlinear ensemble transform filter (NETF) is extended to a fixed lag smoother. The NETF approximates Bayes' theorem by applying a square root update. The smoother (NETS) is derived and formulated in a joint framework with the filter. The new smoother method is evaluated using the low-dimensional, highly nonlinear Lorenz-96 model and a square-box configuration of the NEMO ocean model, which is nonlinear and has a higher dimensionality. The new smoother is evaluated within the same assimilation framework against the local error subspace transform Kalman filter (LESTKF) and its smoother extension (LESTKS), which are state of the art ensemble square-root Kalman techniques. In the case of the Lorenz-96 model, both the filter NETF and its smoother extension NETS provide lower errors than the LESTKF and LESTKS for sufficiently large ensembles. In addition, the NETS shows a distinct dependence on the smoother lag, which results in a stronger error increase beyond the optimal lag of minimum error. For the experiment using NEMO, the smoothing in the NETS effectively reduces the errors in the state estimates, compared to the filter. For different state variables very similar optimal smoothing lags are found, which allows for a simultaneous tuning of the lag. In comparison to the LESTKS, the smoothing with the NETS yields a smaller relative error reduction with respect to the filter result, and the optimal lag of the NETS is shorter in both experiments. This is explained by the distinct update mechanisms of both filters. The comparison of both experiments shows that the NETS can provide better state estimates with similar smoother lags if the model exhibits a sufficiently high degree of nonlinearity or if the observations are not restricted to be Gaussian with a linear observation operator

Blockierende Wetterlagen: Klimatologien für Wetterextreme aus quasi-stationären atmosphärischen Zuständen für Sachsen (Blockierende Wetterlagen): Endbericht: Projektlaufzeit: 13.3.2020 - 15.11.2023

Der Bericht informiert über Wetterextreme in Sachsen, deren Ursache quasistationäre atmosphärische Zustände sind (blockierende Wetterlagen). Die Ergebnisse zu Hitzeperioden, Starkregenereignissen (Vb) und flauten-reichen Perioden helfen, Anpassungsstrategien zu evaluieren und fortzuschreiben. Die Veröffentlichung richtet sich an die mit dem Thema befassten Fachleute. Redaktionsschluss: 10.06.202