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Parametrization in Weather and Climate Models

Oxford Research Encyclopedias, Climate Science

Authors:

Hannah Christensen, Laure Zanna
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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

Authors:

Edward Groot
Advisors: Maurice Schmeits, Kirien Whan, Willem-Jan van de Berg

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.

Recovering valuations on Demushkin fields

Authors:

Jochen Koenigsmann, K Strommen

Abstract:

Let $K$ be a field with $G_K(2) \simeq G_{\mathbb{Q}_2}(2)$, where $G_F(2)$ denotes the maximal pro-2 quotient of the absolute Galois group of a field $F$. We prove that then $K$ admits a (non-trivial) valuation $v$ which is 2-henselian and has residue field $\mathbb{F}_2$. Furthermore, $v(2)$ is a minimal positive element in the value group $\Gamma_v$ and $[\Gamma_v:2\Gamma_v]=2$. This forms the first positive result on a more general conjecture about the structure of pro-$p$ Galois groups. As an application, we prove a strong version of the birational section conjecture for smooth, complete curves $X$ over $\mathbb{Q}_2$, as well as an analogue for varieties.
Details from ArXiV

Seasonal forecasting using the GenCast probabilistic machine learning model

Climate Dynamics Springer Nature

Authors:

Robert Antonio, Kristian Strommen, Hannah Christensen

Abstract:

Machine-learnt weather prediction (MLWP) models are now well established as being competitive with conventional numerical weather prediction (NWP) models in the medium range. However, there is still much uncertainty as to how this performance extends to longer timescales, where interactions with slower components of the earth system become important. We take GenCast, a state-of-the-art probabilistic MLWP model, and apply it to the task of seasonal forecasting with prescribed sea surface temperature (SST), by providing anomalies persisted over climatology (GenCast-Persisted) or forcing with observed SSTs (GenCastForced). The forecasts are compared to the European Centre for Medium-Range Weather Forecasts seasonal forecasting system, SEAS5. Our results indicate that, despite being trained at short timescales, GenCast-Persisted produces much of the correct precipitation patterns in response to El Ni˜no and La Ni˜na events, with several erroneous patterns in GenCast-Persisted corrected with GenCast-Forced. The uncertainty in precipitation response, as represented by the ensemble, compares favourably to SEAS5. Whilst SEAS5 achieves superior skill in the tropics for 2-metre temperature and mean sea level pressure (MSLP), GenCast-Persisted achieves higher skill in some areas in higher latitudes, including mountainous areas, with notable improvements for MSLP in particular; this is reflected in a slightly higher correlation with the observed NAO index. Reliability diagrams indicate that GenCast-Persisted has little skill relative to climatology, whilst GenCast-Forced produces forecasts with reliability comparable to SEAS5. These results provide an indication of the potential of MLWP models similar to GenCast for the ‘full’ seasonal forecasting problem, where the atmospheric model is coupled to ocean, land and cryosphere models.
Details from ORA
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Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model

Authors:

Raghul Parthipan, Hannah M Christensen, J Scott Hosking, Damon J Wischik
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