Remote Sensing Applications at MeteoSwiss

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Related lectures (32)

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Meteorological Radar and Lidar Applications

Covers the principles and applications of meteorological radar and lidar technologies in environmental monitoring.

Radar Meteorology: Principles and Applications

Explores radar meteorology principles, microphysics analysis, scanning techniques, and lidar applications in atmospheric measurements.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Radar principles

Explores the principles of radar technology for remote sensing applications and meteorology.

Snowfall Microphysics: Radar Doppler Spectra Analysis

Explores radar Doppler spectra analysis for studying snowfall microphysics and secondary ice production mechanisms.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Ensemble Methods: Random Forests

Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.