SearcharxivSearch

arXiv · 2602.19233

On Using Medium-Range Ensemble Forecasts for Storm Transposition of Synoptic-Scale Systems in Probable Maximum Precipitation Estimation

Abstract

Most methods for estimating probable maximum precipitation (PMP) -- the greatest depth of precipitation that is physically possible over a given area and duration -- rely on storm transposition (ST), the process of transporting a storm, either historically observed or simulated, from its original location to a target basin. Existing ST approaches, whether classical or physically based, involve assumptions and manipulations that can introduce inconsistencies, leaving the physical validity of the transposed storm uncertain. In this study, the internal variability leveraging (IVL) approach is used to transpose an atmospheric river cluster that affected the U.S. West Coast during 20-29 October 2021. Steering the storm toward the target basin and determining its transposition region are achieved by considering an ensemble of plausible storm evolutions and trajectories obtained from archived ECMWF medium-range forecasts. The Willamette River and Nass River watersheds, located approximately 6 deg N, 2 deg W and 16 deg N, 8 deg W, respectively, from the area most affected by the observed precipitation, were selected as target basins. For each basin, the IVL realization yielding the largest 24-h basin-average precipitation depth was identified, and the initial and boundary condition shifting method was subsequently applied to further enhance its impact, producing 24-h precipitation depths of 119 mm for the Willamette and 98 mm for the Nass.

Explore related subjects

Keep this discovery

BibTeXRIS

Mathieu Mure-Ravaud. 2026-02-22. On Using Medium-Range Ensemble Forecasts for Storm Transposition of Synoptic-Scale Systems in Probable Maximum Precipitation Estimation. https://arxiv.org/abs/2602.19233

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph