Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2
Geoscientific Model Development European Geosciences Union
Abstract:
Jet Latitude Regimes and the Predictability of the North Atlantic Oscillation
Abstract:
In recent years, numerical weather prediction models have begun to show notable levels of skill at predicting the average winter North Atlantic Oscillation (NAO) when initialised one month ahead. At the same time, these model predictions exhibit unusually low signal-to-noise ratios, in what has been dubbed a `signal-to-noise paradox'. We analyse both the skill and signal-to-noise ratio of the Integrated Forecast System (IFS), the European Center for Medium-range Weather Forecasts (ECMWF) model, in an ensemble hindcast experiment. Specifically, we examine the contribution to both from the regime dynamics of the North Atlantic eddy-driven jet. This is done by constructing a statistical model which captures the predictability inherent to to the trimodal jet latitude system, and fitting its parameters to reanalysis and IFS data. Predictability in this regime system is driven by interannual variations in the persistence of the jet latitude regimes, which determine the preferred state of the jet. We show that the IFS has skill at predicting such variations in persistence: because the position of the jet strongly influences the NAO, this automatically generates skill at predicting the NAO. We show that all of the skill the IFS has at predicting the winter NAO over the period 1980-2010 can be attributed to its skill at predicting regime persistence in this way. Similarly, the tendency of the IFS to underestimate regime persistence can account for the low signal-to-noise ratio, giving a possible explanation for the signal-to-noise paradox. Finally, we examine how external forcing drives variability in jet persistence, as well as highlight the role played by transient baroclinic eddy feedbacks to modulate regime persistence.
Probabilistic thunderstorm forecasts using statistical post-processing: Comparison of logistic regression and quantile regression forests and an investigation of physical predictors
Technical report published by KNMI and University of Utrecht
Abstract:
Probabilities of thunderstorm occurrence and conditional probabilities of lightning intensity over The Netherlands are forecast using statistical post-processing with predictors derived from the operational non-hydrostatic numerical weather prediction model Harmonie, at lead times up to 45 hours. Quantile regression forests (QRF) is compared with logistic regression (LR) for thunderstorm occurrence forecasts and with extended LR for lightning intensity forecasts. Using different sets of predictors that these statistical methods may select, it is demonstrated that pre-selection of predictors based on physical understanding and simultaneously exploiting QRF as machine learning tool can help improving statistical post-processing models. QRF is demonstrated to be beneficial for the predictions, with more skillful forecasts than LR for thunderstorm occurrence. Lightning intensity predictions are influenced by inhomogeneity of lightning detection datasets; despite inhomogeneity, skillful predictions can be made with both extended LR and QRF. The regional maximum of Modified Jefferson index and most unstable CAPE are found as best thunderstorm occurrence predictors and the regional minimum of Bradbury index and maximum of K-index emerge as best for lightning intensity. Neither most unstable CAPE nor microphysical predictors (graupel, snow) are essential for thunderstorm occurrence prediction.