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- Statistical Modelling of Climate Change - ScienceDirect
A more varied prior parameter ensemble, a less severe data compatibility weighting, or a broader simulator discrepancy distribution will all lead to less precise climate predictions but may do so in different ways One of the key roles of the statistician is to investigate the dominant causes of imprecision so as to identify the most effective
- A new statistical approach to climate change detection and . . .
Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings
- STATISTICAL METHODS FOR CLIMATE SCIENTISTS
Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis
- An introduction to the statistical modelling of climate . . .
In the second part of this article, we fit statistical models to these data to estimate the amount by which each variable has affected global temperatures during 1850-2020
- Climate Resilience Analysis: Statistical Modeling
Successful climate data analytics rely on data from multiple sources, including local weather stations, satellite imagery, sensor networks, and historical climate archives These varied sources feed into complex models that forecast climate impacts and assess the resilience of communities
- State-of-the-art global models underestimate impacts from . . .
Global impact models represent process-level understanding of how natural and human systems may be affected by climate change Their projections are used in integrated assessments of climate
- Climate Statistics | Climate Data User Guide - EPRI
Basic concepts of statistics covered include statistical distributions of climate variables, and statistical properties of climate data, including frequency and return period concepts, correlation, and regression
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