Abstract ID: 3.10548 | Accepted as Poster | Poster | TBA | TBA

In high mountain regions with complex terrain. The precipitation types are poorly studied due to sparse observations and lack of robust instruments. This study focuses on evaluating precipitation types measured by a PARSIVEL2 disdrometer installed at 4,709 meters on the Huaytapallana tropical glacier in the Peruvian Andes. The instrument records the shadow of the precipitation particles passing through the optical laser to determine the diameter and calculate their falling velocity. Based on this information, it internally calculates the rainfall intensity (mm/h) and classifies the types of precipitation for each recorded minute. With one year of data collected, rain, drizzle, drizzle with rain, snow, hail, soft hail, and drizzle rain with snow were identified as precipitation types. According to this, the drizzle with rain type is highest at 30.6%, followed by snow at 26.2%. To analyze the precipitation amount (mm), a total of 114 precipitation events were identified, which were compared with the record from the Pluvio2 weighing rain gauge. In the presence of soft hail, snow, and hail for more than an hour, the total precipitation value doubled that the precipitation value registered by Pluvio2. This difference is due to the type classification and the high diameter of the particles detected by the disdrometer. To correct this, the types were reclassified according to empirical diameter-fall velocity relationships of particles. This method proved effective for solid events, as the root mean square error was reduced from 5.6 to 2.6 mm between the disdrometer and rain gauge values, in contrast to liquid events it increased from 0.51 to 1.3 mm. The initial precipitation values compared to the corrected values represent a 92% correlation, evidencing the similarity of the method to the instrument’s internal algorithm. However, unlike PARSIVEL2, the method considers types such as wet snow and graupel, which need to be validated with observational measurements in future research.