SearcharxivSearch

arXiv · 1806.00833

On the potential for reconstructing the moisture field using commercial microwave network data

Abstract

The atmospheric greenhouse effect and the hydrological cycle of Earth are key components enabling the planet to support life. Water vapor is a central element in both of these, accounting for approximately half of the present day greenhouse effect, and comprising the most important gaseous source of atmospheric infrared opacity. Specifically, it functions as the fuel for the development of convective storm clouds. This parameter, however, is considered one of the least studied due to the limitations of conventional monitoring instruments. The current predominate monitoring tools are humidity gauges and satellites which suffer from a lack of spatial representativeness and difficulties in measuring at ground level altitudes, respectively. This study demonstrates for the first time the potential to reconstruct the 2-Dimensional humidity field using commercial microwave links which form the infrastructure for data transmission in cellular networks. Water vapor attenuates the waves transmitted by the system and thus these microwave links can potentially form as a virtual network for monitoring the humidity field. The results show a correlation of between 0.6 and 0.92 with root mean square differences ranging from 1.9 to 4.15 gr/m^3 between conventional humidity gauges and the humidity estimates calculated for the same points in space by the proposed technology. The results obtained are the first to point out the improved performance of humidity measurements when using data from multiple microwave links. These results indicate the tremendous potential of this novel approach for improving the initialization of meteorological forecasting models thus potentially improving the ability to cope with the dangers associated with extreme weather.

Explore related subjects

Keep this discovery

BibTeXRIS

Noam David, Omry Sendik, Hagit Messer, Huaizhu Oliver Gao, Yoav Rubin, Dorita Rostkier-Edelstein, Pinhas Alpert. 2018-06-03. On the potential for reconstructing the moisture field using commercial microwave network data. https://doi.org/10.1016/j.atmosres.2018.12.02

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