Natural environmental drivers of seagrass degradation: A global systematic review with implications for Indonesia - Muhlis; Halmar Halide; Rohani Ambo-Rappe (Continental Shelf Research, 2026)

Analysis of the Impact of Weather Conditions on Flight Delays at Hartsfield–Jackson Atlanta International Airport ( ATL ) During 2013–2023 - Nur Rahmania Ramadhani; Halmar Halide (International Journal of Climatology, 2026)
This study examines the impact of meteorological variability on weather‐related flight delays at Hartsfield–Jackson Atlanta International Airport (ATL) during 2013–2023. Operational delay records from the Bureau of Transportation Statistics were integrated with atmospheric data from NASA POWER. Seasonal characteristics were evaluated using Analysis of Variance (ANOVA) and multivariate OLS regression. The results reveal a pronounced seasonal cycle, with delays peaking during summer (JJA), consistent with enhanced convective activity. Precipitation emerges as the dominant positive predictor of delays ( p p = 0.001); however, this inverse relationship reflects a seasonal phase mismatch—where peak summer delays coincide with climatologically weak wind shear—rather than a direct mitigating physical effect. The multivariate model explains 36.7% of the total delay variance (adjusted R 2 = 0.342). Overall, the findings indicate that flight delays at ATL are primarily governed by recurring seasonal atmospheric regimes, supporting the integration of meteorological indicators into airport operations and delay forecasting.

Complicated ENSO models do not significantly outperform very simple ENSO models - Halmar Halide; Peter Ridd (International Journal of Climatology, 2008)
An extremely simple univariate statistical model called ‘IndOzy’ was developed to predict El Niño‐Southern Oscillation (ENSO) events. The model uses five delayed‐time inputs of the Niño 3.4 sea surface temperature anomaly (SSTA) index to predict up to 12 months in advance. The prediction skill of the model was assessed using both short‐ and long‐term indices and compared with other operational dynamical and statistical models. Using ENSO‐CLIPER(climatology and persistence) as benchmark, only a few statistical models including IndOzy are considered skillful for short‐range prediction. All models, however, do not differ significantly from the benchmark model at seasonal Lead‐3–6. None of the models show any skill, even against a no‐skill random forecast, for seasonal Lead‐7. When using the Niño 3.4 SSTA index from 1856 to 2005, the ultra simple IndOzy shows a useful prediction up to 4 months lead, and is slightly less skillful than the best dynamical model LDEO5. That such a simple model such as IndOzy, which can be run in a few seconds on a standard office computer, can perform comparably with respect to the far more complicated models raises some philosophical questions about modelling extremely complicated systems such as ENSO. It seems evident that much of the complexity of many models does little to improve the accuracy of prediction. If larger and more complex models do not perform significantly better than an almost trivially simple model, then perhaps future models that use even larger data sets, and much greater computer power may not lead to significant improvements in both dynamical and statistical models. Investigating why simple models perform so well may help to point the way to improved models. For example, analysing dynamical models by successively stripping away their complexity can focus in on the most important parameters for a good prediction. Copyright © 2007 Royal Meteorological Society