Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · physics/0501106

Instrumentation for atmospheric ion measurements

K. L. Aplin
View original source ↗Read source document ↗Export metadata ↓

Abstract

Small ions are part of the atmospheric aerosol spectrum, and study of ion-aerosol interactions is fundamental in atmospheric physics. Air ion physics and instrumentation are reviewed, including the historical context. A miniaturised Gerdien condenser for ion measurement, operating in situ to minimise inlet errors, is described. Two operating modes using independent current and voltage decay measurements are employed. A more sophisticated self-calibrating and fully programmable ion mobility spectrometer (PIMS) based on the same principles, is also discussed. Detailed analysis of error terms and application of new technology is demonstrated to greatly improve its capability. Self-consistent experimental approaches, including ionisation and ion concentration instruments, are used for calibration. In developing and characterising the individual components of the PIMS, favourable and unfavourable operating regimes are identified: this approach can also be applied to other aspiration ion counting techniques. Use of a sophisticated programmable electrometer permits compensation for leakage terms. Electrically-charged aerosol particles have been found to complicate the ion measurements. Consequently, conventional ion-aerosol theory, which neglects the particulate concentration, is thought to be incomplete. The polymodal ion mobility spectrum is also found to influence the instrument's operation. Development of the novel programmable ion instrumentation, in conjunction with consideration of the ion mobility spectrum yields an improved and flexible approach to in situ atmospheric ion measurements.

Explore related subjects

physics.ao-phphysics.geo-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K. L. Aplin. 2005-01-19. Instrumentation for atmospheric ion measurements. https://arxiv.org/abs/physics/0501106

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗