arXiv subjects

Antje Weisheimer

Publications and source records attributed to Antje Weisheimer.

8 recordsLinked to original sources

arXiv2026

Improved rainfall forecasts in daily use over East Africa

Ensemble forecasting has proven to be a vital tool for predicting extreme, life-threatening or only partially predictable weather events. As well as providing probabilistic products, individual ensemble members provide context and realisations of possible extreme weather. However, many National Meteorological Services in East Africa do not have the computing resources to enable them to run their local area models in ensemble mode over the full period of the two-week medium range. In this paper we test the performance of a forecast system, comprising the global ECMWF ensemble forecast, post-processed using cGAN, a neural network model, and forecasts calibrated using IDR, a recent statistical method, against the probabilistic climatology. cGAN provides comparable levels of probabilistic skill as IDR applied to the ECMWF ensemble or IDR applied to the machine-learning models FuXi and GraphCast. These methods improve the raw ECMWF ensemble forecast substantially, which is itself an improvement on deterministic forecasts. The ability of cGAN to produce individual realisations of future rainfall was important when assessed in an operational context by the National Meteorological and Hydrological Services of Kenya and Ethiopia. Moreover, in common with IDR, cGAN is cheap to train/run and requires no additional post-processing. It is run on laptops to generate many thousands of ensemble members, making it suitable for Meteorological Services with limited computational facilities.

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arXiv2026

Sensitivity of the ECMWF seasonal forecast model to CO2 and anthropogenic aerosol forcings: Experimental design and impact on climate trends

Detection and attribution studies typically rely on free running climate models with modified anthropogenic forcings, yet they fail to reproduce key observed decadal trends. Despite reduced biases, higher predictive skill, large ensembles, and improved trends through frequent reinitialization, seasonal forecast models have not been used for this purpose. We introduce a new set of counterfactual seasonal hindcasts using the ECMWF coupled seasonal forecasting system based on alternative forcing scenarios by modifying atmospheric forcing and ocean and sea ice initial conditions. An observation based estimate of the forced ocean temperature signal is derived to amplify or remove this signal from the ocean initial conditions. Retrospective forecasts include a control configuration together with enhanced and reduced forcing experiments in which either CO2 increases together with doubled anthropogenic ocean warming or CO2 is fixed and the anthropogenic ocean-warming signal is removed. Additional experiments isolate aerosol forcing. The counterfactual hindcasts substantially alter long term temperature trends while largely preserving seasonal prediction skill, interannual variability, and model drift. Enhanced forcing strengthens several observed climate trends underestimated by the control, including top of atmosphere radiative fluxes and aspects of tropical Pacific atmospheric circulation. However, the coupled atmosphere ocean system fails to sustain the strengthened tropical Pacific temperature gradient through Bjerknes feedbacks, suggesting a fundamental model limitation. Aerosol forcing has little impact, likely because indirect aerosol effects are omitted. These results establish counterfactual seasonal hindcasts as a powerful tool for dynamic attribution and diagnosing model deficiencies in responses to anthropogenic climate forcing.

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arXiv2023

A Bayesian Approach to Atmospheric Circulation Regime Assignment

The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes Theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes Theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time $t$ can be used to inform the prior probabilities at time $t+1$, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.

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arXiv2022

The link between North Atlantic tropical cyclones and ENSO in seasonal forecasts

This study assesses the ability of six European seasonal forecast models to simulate the observed teleconnection between ENSO and tropical cyclones (TCs) over the North Atlantic. While the models generally capture the basin-wide observed link, its magnitude is overestimated in all forecast models compared to reanalysis. Furthermore, the ENSO - TC relationship in the Caribbean is poorly simulated. It is shown that incorrect forecasting of wind shear appears to affect the representation of the teleconnection in some models, however it is not a completely sufficient explanation for the overestimation of the link.

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arXiv2020

Revisiting the Identification of Wintertime Atmospheric Circulation Regimes in the Euro-Atlantic Sector

Atmospheric circulation is often clustered in so-called circulation regimes, which are persistent and recurrent patterns. For the Euro-Atlantic sector in winter, most studies identify four regimes: the Atlantic Ridge, the Scandinavian Blocking and the two phases of the North Atlantic Oscillation. These results are obtained by applying k-means clustering to the first several empirical orthogonal functions (EOFs) of geopotential height data. Studying the observed circulation in reanalysis data, it is found that when the full field data is used for the k-means cluster analysis instead of the EOFs, the optimal number of clusters is no longer four but six. The two extra regimes that are found are the opposites of the Atlantic Ridge and Scandinavian Blocking, meaning they have a low-pressure area roughly where the original regimes have a high-pressure area. This introduces an appealing symmetry in the clustering result. Incorporating a weak persistence constraint in the clustering procedure is found to lead to a longer duration of regimes, extending beyond the synoptic timescale, without changing their occurrence rates. This is in contrast to the commonly-used application of a time-filter to the data before the clustering is executed, which, while increasing the persistence, changes the occurrence rates of the regimes. We conclude that applying a persistence constraint within the clustering procedure is a superior way of stabilizing the clustering results than low-pass filtering the data.

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arXiv2020

Beyond skill scores: exploring sub-seasonal forecast value through a case study of French month-ahead energy prediction

We quantify the value of sub-seasonal forecasts for a real-world prediction problem: the forecasting of French month-ahead energy demand. Using surface temperature as a predictor, we construct a trading strategy and assess the financial value of using meteorological forecasts, based on actual energy demand and price data. We show that forecasts with lead times greater than 2 weeks can have value for this application, both on their own and in conjunction with shorter range forecasts, especially during boreal winter. We consider a cost/loss framework based on this example, and show that while it captures the performance of the short range forecasts well, it misses the marginal value present in the longer range forecasts. We also contrast our assessment of forecast value to that given by traditional skill scores, which we show could be misleading if used in isolation. We emphasise the importance of basing assessment of forecast skill on variables actually used by end-users.

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arXiv2017

Stochastic Parameterization: Towards a new view of Weather and Climate Models

The last decade has seen the success of stochastic parameterizations in short-term, medium-range and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing longstanding climate biases and relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups which show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface and cryosphere of comprehensive weather and climate models (a) gives rise to more reliable probabilistic forecasts of weather and climate and (b) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics and turbulence is reviewed, its relevance for the climate problem demonstrated, and future research directions outlined.

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arXiv2013

On the reliability of Seasonal Climate Forecasts

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: How good are seasonal climate forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal climate forecasts are made from ensembles of integrations of numerical models of climate. We argue that goodness should be assessed primarily in terms of the probabilistic reliability of these ensemble-based forecasts and that a 5 should be reserved for systems which are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as a world leading operational institute producing seasonal climate forecasts. A wide range of goodness rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching 5 across all regions and variables in 30 years time.

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