ALBERT

Description: This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 〈 0.30, ORSS 〉 0.70). The methodology is available as a Python language program called RaProM at the public github repository.

Description: The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository.

Description: This collection of datasets contains data measured by various CTD instruments which have been deployed at the OBSEA platform since May, 2009. OBSEA is a permanent underwater cabled seabed observatory located in front of the Catalan's coast near Barcelona, at 20 m depth which is used as a test site for different experiments related with marine sensors. It has a sea station and a ground station, which include a surface buoy equipped with weather station and camera and a coastal cabled system (seabed platform). OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure and participates as a Coastal Observatory data provider and Test Site on the JERICO-RI. For a comfortable handling of these CTD data they were summarized monthly to one data set each. A complete list of the datasets belonging to this collection can be found at the end of this page.

Description: This collection of datasets contains data measured by two weather stations related to the OBSEA platform since Feb, 2010. The first weather station is deployed at the OBSEA buoy, the second is located at the OBSEA shore station on top of the UPC building. OBSEA is a permanent underwater cabled seabed observatory located in front of the Catalan's coast near Barcelona, at 20 m depth which is used as a test site for different experiments related with marine sensors. It has a sea station and a ground station, which include a surface buoy equipped with weather station and camera and a coastal cabled system (seabed platform). OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure and participates as a Coastal Observatory data provider and Test Site on the JERICO-RI. For a comfortable handling of these weather station data they were summarized monthly to one data set each. A complete list of the datasets belonging to this collection can be found at the end of this page.

Description: This collection of datasets contains current and wave data measured by an ADCP instrument related to the OBSEA platform since Mar, 2010. OBSEA is a permanent underwater cabled seabed observatory located in front of the Catalan's coast near Barcelona, at 20 m depth which is used as a test site for different experiments related with marine sensors. It has a sea station and a ground station, which include a surface buoy equipped with weather station and camera and a coastal cabled system (seabed platform). OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure and participates as a Coastal Observatory data provider and Test Site on the JERICO-RI. For a comfortable handling of these weather station data they were summarized monthly to one data set each. A complete list of the datasets belonging to this collection can be found at the end of this page.