Weather radar | EPFL Graph Search

Weather radar, also called weather surveillance radar (WSR) and Doppler weather radar, is a type of radar used to locate precipitation, calculate its motion, and estimate its type (rain, snow, hail etc.). Modern weather radars are mostly pulse-Doppler radars, capable of detecting the motion of rain droplets in addition to the intensity of the precipitation. Both types of data can be analyzed to determine the structure of storms and their potential to cause severe weather. During World War II, radar operators discovered that weather was causing echoes on their screens, masking potential enemy targets. Techniques were developed to filter them, but scientists began to study the phenomenon. Soon after the war, surplus radars were used to detect precipitation. Since then, weather radar has evolved and is used by national weather services, research departments in universities, and in television stations' weather departments. Raw images are routinely processed by specialized software to m

Toward Assessing the Sensitivity of Buildings to Changes in Climate

Sönke Frederik Horn

A building is designed for one set of typical climatic conditions. In the context of expected climate change, substantial numbers of existing and new buildings are expected to survive long enough to experience perceptible shifts in climate ‘normals’ (averages). This is problematic for predicting building performance, largely because the exact nature of climate change is hard to predict with certainty. Thus an optimal design is not only tuned to the original design conditions, but is also robust to changes in climatic conditions in the sense of a low sensitivity of its performance. To predict a building’s response to changes in typical weather, two inputs are required: weather data representing this change, and suitable metrics to compare building performance across different climate normals. This report presents initial work on a proposed method for assessing the sensitivity of new or existing buildings to climate change. This method begins with a selection of weather files to represent climate change, then quantifies a building’s passive performance in those climates using an enthalpy-based metric, and ends with a graphical analysis of the performance of the building in different climates to assess its robustness. In this report, we propose an objective performance metric based on the extent to which a building creates indoor conditions passively, i.e. without auxiliary systems. Initial work suggests that the performance assessment carried out here is reproducible and applicable for indoor environment design and evaluation in different ranges of climate change. This approach enables a comparison of building performance without the bias introduced by inherent differences in climatic conditions.

2013

Radar-rain gauge merging and discharge data assimilation for flood forecasting in Alpine catchments

Alain Tommy Foehn

Floods are responsible for one third of the economic losses induced by natural hazards throughout the world. To better protect the population and infrastructures, flood forecasting systems make us of weather forecasts to foresee floods several days in advance, providing more lead time for preventive measures. In the canton of Valais (Switzerland), an operational flood forecasting and management system is operational since 2013, as a result of the MINERVE project initiated in 1999. The present thesis aims at answering some of the challenges faced by this system. First, a new methodology for spatial interpolation of precipitation is implemented based on regression co-kriging using rain gauge and weather radar data. Two rain gauge networks equipped with instruments of different quality are considered. Compared to other precipitation interpolation methods, the quantitative precipitation estimates (QPE) obtained from the regression co-kriging provides the best performance over the studied area using cross-validation. The analysis highlights the need for further pre-processing of radar data, in particular to account for beam shielding by the complex topography. Integration of the above-mentioned QPE product in a snow model revealed a clear precipitation underestimation. A methodology to account for solid precipitation undercatch in QPE computation is therefore proposed. Four different QPE products are compared: the operational QPE product CombiPrecip of MeteoSwiss, the regression co-kriging QPE and two variants of it considering a correction factor for solid precipitation undercatch of 1.2 and 1.3, applied before the interpolation. The snow model is calibrated using satellite-based data from the MODIS spectroradiometer and validated using snow water equivalent measurements 11 snow monitoring sites. The best performance is obtained using the QPE product including a correction factor of 1.2. To evaluate the performance of the developed QPE products from a hydrological perspective, three sub-catchments of the MINERVE system were calibrated considering 5 different input. The GSM and SOCONT hydrological models are used to model respectively the glacial and non-glacial parts. A two-phase calibration of the model is explored, applying the MODIS-based calibration of snow-melt degree-day factors, before calibrating the other parameters using discharge data. Results suggest that the developed QPE product accounting for solid precipitation undercatch (factor 1.2) leads to the best performance over the catchment with a good radar visibility. In case of lower radar visibility, using station data provides equal or better performances. With the current implementation, the two-phase calibration did not allow to outperform the conventional calibration. Finally, an ensemble Kalman filter (EnKF) is implemented to improve initial conditions used for hydrological forecasts. Results are compared, for two high flow events, to the scenario without assimilation and to the simple assimilation scheme currently implemented in the MINERVE system, updating the soil saturation based on a discharge volume comparison over the preceding 24 hours. The Ensemble Kalman filter (EnKF) shows good performance during these events but also highlights difficulties over base flow, strengthened in presence of hydropower perturbations.

EPFL2019

A network of disdrometers to quantify the small-scale variability of the raindrop size distribution

Alexis Berne, Joël Jaffrain, Florian Pantillon, André Studzinski

Insight into the spatial variability of the (rain) drop size distribution (DSD), and hence rainfall, is of primary importance for various environmental applications like cloud/precipitation microphysical processes, numerical weather modeling, and estimation of rainfall using remote sensing techniques. In order to quantify the small-scale variability of the DSD, a network of 16 optical disdrometers has been designed and deployed over a typical operational weather radar pixel (about 1 x 1 km(2)) in Lausanne, Switzerland. This network is fully autonomous in terms of power supply as well as data transmission and storage. The combination of General Radio Packet Service and radio communication allows a real-time access to the DSD measurements. The network is sampling at a temporal resolution of 30 s. A period representative of frontal precipitation is analyzed to illustrate the measurement capabilities of the network. The spatial variability is quantified by the coefficient of variation of the total concentration of drops, the mass-weighted diameter, and the rain rate between the 16 stations of the network. The sampling uncertainty associated with disdrometer measurements is taken into account, and the analysis of a 1.5 month rainy period shows a significant variability of these quantities, which cannot be explained by the sampling uncertainty alone, even at such a small scale.

2009

Radar-rain gauge merging and discharge data assimilation for flood forecasting in Alpine catchments

Alain Tommy Foehn

EPFL2019