Uncertainty and Error Management

Errors can occur at all levels in the modelling and simulation paradigm. Models themselves can have structural errors, parameters within the model can have errors, and data used to initialiize or to update the simulation computations can have errors. In every case, the main issue is to estimate the magnitude of the error and to stop the error from growing with each iterative time-step of the computational process.

Chaos at fifty

Model Imperfections

Paths out of uncertainty

The masters of uncertainty

Quantification  of uncertainty

Harnessing error prone chips

Quantifying uncertainty at scale

Intermediate error growth in the tropics

Uncertainty in weather and climate prediction

Assessing the uncertainty in climate sensitivity

On mismatches between models and observation

Statistics help clear fog for better climate change picture

Adding natural uncertainty improves mathematical models

Uncertainty in meteorology by the example of precipitation

Origin of probabilities and their application to the multiverse

Climate models disagree on why temperature 'wiggles' occur    

Communicating uncertainty about facts, numbers and science

Forecaster's dilemma: extreme events and forecast evaluation

We must do a better job of communicating forecast uncertainty

Error estimation in geophysical fluid dynamics through learning

Pop quiz: 20 percent chance of rain. Do you need an umbrella?

Confusion with a chance of clarity: your weather questions, answered

Error growth in CFS daily retrospective forecasts of South Asian monsoon

Cellular probabiiistic automota - a novel method for uncertainty propogation

Trapped between two tails: trading off scientific uncertainties via climate targets 

Calibration strategies: a source of additional uncertainty in climate change projections

Deriving the expected value of model improvement via specifying internal model discrepancies

Improving model fidelity and sensitivity for complex systems through empirical information theory

Using nonequilibrium fluctuation theorems to correct errors in discrete Langevin dynamics simulations

Errors in 1D satellite cloud water path retrievals with large eddy simulations & radiative transfer models

Environmental prediction, risk assessment & extreme events: adaptation strategies for developing world 

Modeling climate uncertainty with ensembles of region/global climate models & multiple observation data 

    
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