Uncertainty estimation is a research area that focuses on quantifying and understanding the uncertainties associated with measurement data, model predictions, or other types of data. This field involves the development of mathematical and statistical methods to assess the accuracy and reliability of results, as well as to provide measures of the uncertainty inherent in any scientific or engineering analysis. Uncertainty estimation is crucial in many scientific fields, including climate science, economics, and engineering, where decisions are often based on models and measurements that are subject to various sources of uncertainty. By quantifying and characterizing these uncertainties, researchers can make more informed decisions and draw more reliable conclusions from their data. Some common methods used in uncertainty estimation include Monte Carlo simulations, Bayesian statistics, and sensitivity analysis. These techniques help researchers to identify the sources of uncertainty in their data, assess the impact of these uncertainties on their results, and ultimately improve the reliability of their findings.