Esteban Abellán

Post-processing in Numerical Weather Prediction (NWP) is essential for transforming raw model outputs into forecasts that are both accurate and actionable. This study investigates a dynamic weighting approach for multi-model blending, in which the contribution of each model is adjusted in real time based on recent performance, using root mean square error (RMSE) as a skill metric. We benchmark this approach against three baseline approaches: equal weighting, optimised static weighting and spatially static weighting derived from a training period. Experiments are conducted using deterministic and ensemble model outputs from two global NWP systems (Australian Community Climate and Earth System Simulator, ACCESS, and European Centre for Medium-Range Weather Forecasts, ECMWF) over two seasonal periods and are verified against both gridded analyses and surface station observations. Results show that dynamic weighting consistently improves forecast skill relative to both equal and static weighting approaches, particularly at longer lead times, and closely matches the performance of the operational Australian Bureau of Meteorology’s calibrated and bias-corrected IMPROVER products, even though the experiments presented here omit the bias-correction and calibration stages applied operationally. This suggests that adaptive performance-based weighting alone can recover a substantial fraction of the skill typically attributed to more complex post-processing.

Operational forecasting systems routinely blend outputs from multiple numerical weather prediction (NWP) models, yet these blends often rely on static weights that do not reflect how model skill fluctuates in time and space. This study explores a simple dynamic weighting approach that updates the contribution of two global NWP systems - ECMWF and ACCESS, using both their deterministic and ensemble variants - based on their recent performance. By assigning weights proportional to inverse RMSE over a rolling 30-day window, the method adapts automatically to evolving model behaviour across regions, lead times, and variables.The experiment focuses on 2-m temperature, 2-m dewpoint, and 10-m wind speed during two contrasting seasonal periods (December-February and June-August). Model fields are regridded to a common domain, and no bias correction or calibration is applied. This design isolates the effect of the weighting strategy itself, providing a clean assessment of how dynamic weighting compares to static, fixed-weight blends. Verification is performed against the Bureau of Meteorology's gridded Mesoscale Surface Analysis System (MSAS) and an extensive network of Automatic Weather Stations (AWS).The largest gains occur for temperature and dewpoint at longer lead times, where the dynamic blend outperforms both individual models and the static blends. A distinctive result from the MSAS-based evaluation is the reduced amplitude of the diurnal RMSE cycle for temperature and dewpoint. This behaviour reflects the method's ability to exploit complementary biases - such as opposing warm and cold tendencies across models at different valid times - resulting in partial cancellation of systematic errors without explicit bias correction.Wind speed forecasts also benefit, though improvements are more modest, reflecting the inherently higher variability and sensitivity of wind fields. Small regions of degradation emerge in areas with sparse observations or complex terrain, highlighting the limitations of retrospective RMSE when reference data are uncertain or unrepresentative.Overall, this work demonstrates that a transparent, performance-based dynamic weighting strategy can deliver meaningful forecast improvements using only raw model output. Its low operational cost and ability to adapt to evolving model skill make it a promising candidate for next-generation blending approaches and a foundation for future regime-dependent or probabilistic adaptive systems.

During the mature phase of El Niño–Southern Oscillation (ENSO) events there is a southward shift of anomalous zonal winds (SWS), which has been suggested to play a role in the seasonal phase locking of ENSO. Motivated by the fact that coupled climate models tend to underestimate this feature, this study examines the representation of the SWS in phase 5 of the Coupled Model Intercomparison Project (CMIP5). It is found that most models successfully reproduce the observed SWS, although the magnitude of the zonal wind stress anomaly is underestimated. Several significant differences between the models with and without the SWS are identified including biases in the magnitude and spatial distribution of precipitation and sea surface temperature (SST) anomalies during ENSO. Multiple-linear regression analysis suggests that the climatological meridional SST gradient as well as anomalous ENSO-driven convective activity over the northwest Pacific both might play a role in controlling the SWS. While the models that capture the SWS also simulate many more strong El Niño and La Niña events peaking at the correct time of year, the overall seasonal synchronization is still underestimated in these models. This is attributed to underestimated changes in warm water volume (WWV) during moderate El Niño events so that these events display relatively poor seasonal synchronization. Thus, while the SWS is an important metric, it is ultimately the magnitude and zonal extent of the wind changes that accompany this SWS that drive the changes in WWV and prime the system for termination.