ALBERT

Description: The raindrop size distribution (DSD) quantifies the micro-structure of rainfall and is critical to studying precipitation processes. We present a method to improve the accuracy of DSD measurements from Parsivel disdrometers, using a two-dimensional-video-disdrometer (2DVD) as a reference instrument. Parsivel disdrometers bin recorded raindrops into velocity and equivolume diameter classes, but may mis-estimate the number of drops per class. We define a filter for raw disdrometer measurements to remove particles that are unlikely to be plausible raindrops. In our correction method, drop velocities are corrected with reference to theoretical models of terminal drop velocity. Non-plausible measurements are removed. Lastly, drop concentrations are corrected such that on average the Parsivel concentrations match those recorded by a 2DVD. The correction can be trained on and applied to data from both generations of Parsivel disdrometers. The method was applied to data collected during field campaigns in Mediterranean France, for a network of first and second generation Parsivel disdrometers. We compared the moments of the resulting DSDs to those of a collocated 2DVD, and the resulting DSD-derived rain rates to collocated rain gauges. The correction vastly improved the accuracy of the moments of the Parsivel DSDs, and in the majority of cases the rain rate match with collocated rain gauges was improved.

Description: A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number nopt of hydrometeor classes that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a-priori, but they are learned from data. The proposed approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (totalling about 3000 h of precipitation). Seven hydrometeor classes have been found in the data set and they have been associated to drizzle (DZ), light rain (LR), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), rimed particles (RI).

Description: Accurate positioning of data collected by a weather radar is of primary importance for their appropriate georeferencing, which in turn makes it possible to combine those with additional sources of information (topography, land cover maps, meteorological simulations from numerical weather models to list a few). This issue is especially acute for mobile radar systems, for which accurate and stable levelling might be difficult to ensure. The sun is a source of microwave radiation, which can be detected by weather radars and used for the accurate positioning of the radar data. This paper presents a technique based on the sun echoes to quantify and hence correct for the instrumental errors which can affect the pointing accuracy of radar antenna. The proposed method is applied to data collected in the Swiss Alps using a mobile X-band radar system. The obtained instrumental bias values are evaluated by comparing the locations of the ground echoes predicted using these bias estimates with the observed ground echo locations. The very good agreement between the two confirms the good accuracy of the proposed method.

Description: A Markov switching algorithm is introduced to classify attenuation measurements from telecommunication microwave links into dry and rainy periods. It is based on a simple state-space model and has the advantage of not relying on empirically estimated threshold parameters. The algorithm is applied to data collected using a new and original experimental set-up in the vicinity of Zürich, Switzerland. The false dry and false rain detection rates of the algorithm are evaluated and compared to 3 other algorithms from the literature. The results show that, on average, the Markov switching model outperforms the other algorithms. It is also shown that the classification performance can be further improved if redundant information from multiple channels is used.

Description: This paper presents a hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using as input the statistical behavior of a set of particle descriptors, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over precipitation time steps labeled by visual inspection. In this way, 8 dominant hydrometeor classes could be discriminated. The algorithm achieves accurate classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.