Search arXivSearch

arXiv · 1306.5649

First measurement of the small-scale spatial variability of the rain drop size distribution: Results from a crucial experiment and maximum entropy modeling

Abstract

The main challenges of measuring precipitation are related to the spatio-temporal variability of the drop-size distribution, to the uncertainties that condition the modeling of that distribution, and to the instrumental errors present in the in situ estimations. This PhD dissertation proposes advances in all these questions. The relevance of the spatial variability of the drop-size distribution for remote sensing measurements and hydro-meteorology field studies is asserted by analyzing the measurement of a set of disdrometers deployed on a network of 5 squared kilometers. This study comprises the spatial variability of integral rainfall parameters, the ZR relationships, and the variations within the one moment scaling method. The modeling of the drop-size distribution is analyzed by applying the MaxEnt method and comparing it with the methods of moments and the maximum likelihood. The instrumental errors are analyzed with a compressive comparison of sampling and binning uncertainties that affect actual devices. These analysis are further extended in several appendices where an error analysis is developed and new studies are proposed. The relevance of the pre-processing of disdrometric measurements is also assessed. The data-sets evaluated comprise experimental measurements of the GPM (NASA-JAXA) ground validation satellite mission and synthetic distributions generated computationally.

Explore related subjects

Keep this discovery

BibTeXRIS

Ramiro Checa-Garcia. 2013-06-21. First measurement of the small-scale spatial variability of the rain drop size distribution: Results from a crucial experiment and maximum entropy modeling. https://arxiv.org/abs/1306.5649

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