Use of PySTEPS As a Problem-Based Learning Methodology for Weather Radar and Nowcasting Courses in Southern Brazil

In this work, results of the use of PySTEPS as a teaching tool for graduation in meteorology at the Federal University of Pelotas in the disciplines of nowcasting and meteorological radar are shown. In order to motivate the students, the specialists on education have encouraged the use of practical and playful tools in teaching, providing an environment that is more connected with the real world, i.e., technological, innovative and fun. The use of the Problem-based Learning method has helped in deepening the theoretical knowledge and in the sedimentation of the basic concepts, instructing the student to have innovative solutions to real problems. Through questionnaires and interviews with student of the disciplines of radar, nowcasting and postgraduate studies, the advantages and disadvantages of PySTEPS between the years 2022 and 2023 were highlighted. The main advantages reported were: 1) use of the python language, which is widely used in the meteorological community; the student has been yield the fundamentals of python language at the beginning of the course and most of the students already have some level of knowledge, facilitating the installation process, data preparation and understanding of the methods ; 2) open-source; any student can use it in the classroom as well as at home, permitting more time time with the methodology and the studies 3) Feature detection methodologies - the method identifies cell patterns automatically; this is a great preoccupation into the scholar environmental; 4) Extrapolation methodology and optical flow for prediction 5) Z-R relationship: the output can be easily conducted by choosing different parameters a and b 6) Output in probabilities and precipitation intensity - it can be useful when the student want to apply in some hydrological issue. As disadvantages, it is not always possible to introduce the local radar data directly as an input, requiring some processing. Although this is initially considered a disadvantage, it can also be interpreted as an obstacle to be overcome with exercises in python and using methods such as PyArt, Wradlib and XArray improving programming and data manipulation skills. The second disadvantage is the difficulty of evaluating forecasts which is inherent to the type of forecast. Using points with verifications in pairs of pluviometers, the evaluation is punctual and does not consider spatial errors. Implementing other methods such as the Fraction Skill Score (FSS) can result in more realistic feedback. In general, the students' feedback on the use of PySTEPS was very promising. The results indicated that PySTEPS resolved the prediction up to 3 hours for systems with linear propagation when subjectively compared with observed radar images. As expected, the prediction of non-linear systems was not so good, requiring data updates to correct the propagation of the phenomena. For systems in the convective initiation phase, PySTEPS needed to be updated to detect the phenomenon and perform the corresponding prediction, something also expected for this type of methodology in nowcasting. Finally, the method will be used in smart city projects to assist in the quantitative forecast of precipitation for two cities in southern Brazil, Pelotas - RS and Curitiba -PR, in a joint innovation project between SIMEPAR and the universities UFPEL and UFSM.