Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning

Numerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted. In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge. Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi automated approach for the assessment of precipitation forecast ensemble members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions

The DYMECS project: a statistical approach for the evaluation of convective storms in high-resolution NWP models

A new frontier in weather forecasting is emerging by operational forecast models now being run at convection-permitting resolutions at many national weather services. However, this is not a panacea; significant systematic errors remain in the character of convective storms and rainfall distributions. The DYMECS project (Dynamical and Microphysical Evolution of Convective Storms) is taking a fundamentally new approach to evaluate and improve such models: rather than relying on a limited number of cases, which may not be representative, we have gathered a large database of 3D storm structures on 40 convective days using the Chilbolton radar in southern England. We have related these structures to storm life-cycles derived by tracking features in the rainfall from the UK radar network, and compared them statistically to storm structures in the Met Office model, which we ran at horizontal grid length between 1.5 km and 100 m, including simulations with different subgrid mixing length. We also evaluated the scale and intensity of convective updrafts using a new radar technique. We find that the horizontal size of simulated convective storms and the updrafts within them is much too large at 1.5-km resolution, such that the convective mass flux of individual updrafts can be too large by an order of magnitude. The scale of precipitation cores and updrafts decreases steadily with decreasing grid lengths, as does the typical storm lifetime. The 200-m grid-length simulation with standard mixing length performs best over all diagnostics, although a greater mixing length improves the representation of deep convective storms

Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future. Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE

Basic characteristics of post-frontal shower precipitation rates

For the post-frontal precipitation field, a rain rate analysis was carried out based on the radar composite RZ of the German Weather Service. Two different approaches were followed: an Eulerian- and a Lagrangian-type analysis. Rain rate distributions and their diurnal cycle were investigated and the instantaneous rain rates per individual cell, embedded in an enclosed rain area, were determined. The rain amount per individual cell within a rain area increases with the total cell number. A comparison of the tracks of the rain areas with the 925 hPa wind field revealed a movement with the mean wind direction. Furthermore, the life cycle of the rain areas was investigated with respect to related rain amounts as well as to the area. For single-cell-tracks the mean temporal development of the area integrated rain rate (AIRR) shows a parabola shape, while the area time series is better represented by a sine function. The resulting functions only depend on the life time of the track. This result reveals a simple underlying law for an apparently chaotic precipitation process

Rechtsextremismus, Jugendgewalt und Politikdistanz

SIGLEFES Bonn(Bo133)-A92-3592 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman