Gaseous ammonia plays a crucial role in the earth’s atmosphere. Major sources of atmospheric ammonia include agriculture and fires. As the climate continues to change, the pattern of fires across the US will also change, leading to changes in ammonia emissions. This study examines four major science questions using satellite and in-situ data from 2010–2014: (1) How have concentrations of ammonia changed across the US? (2) How have the strength and frequency of fires changed? (3) How has this change in fires impacted ammonia emissions? (4) How does the US EPA NEI compare with the calculated emissions? Satellite and in-situ data were used to evaluate the annual concentrations of ammonia and to calculate the total ammonia emissions across the continental US. The results of this study showed that ammonia concentrations have slightly increased over the five-year period. The total fire number and the average fire radiative power have decreased, while the total yearly burn area has increased. The calculated ammonia emissions from fires on a national scale show an increasing trend and when compared with the US EPA NEI for ammonia emissions from fires, annual ammonia emissions are, on average, a factor of 0.49 higher than the NEI.

The values of the key atmospheric turbulence parameters (structure constants) for temperature and water vapor, i.e., C_T², and C_Q², are highly dependent upon the vertical height within the atmosphere thus making it necessary to specify profiles of these values along the atmospheric propagation path. The remote sensing method suggested and described in this work makes use of a rapidly integrating microwave profiling radiometer to capture profiles of temperature and humidity through the atmosphere. The integration times of currently available profiling radiometers are such that they are approaching the temporal intervals over which one can possibly make meaningful assessments of these key atmospheric parameters. These integration times, coupled with the boundary effects of the Earth’s surface are, however, unconventional for turbulence characterization; the classical Kolmogorov turbulence theory and related 2/3 law for structure functions prevalent in the inertial sub-range are no longer appropriate. An alternative to this classical approach is derived from first principles to account for the nuances of turbulent mechanics met with using radiometer sensing, i.e., the large-scale turbulence driven by the various possible boundary conditions within the buoyancy sub-range. Analytical expressions connecting the measured structure functions to the corresponding structure parameters are obtained. The theory is then applied to an experimental scenario involving radiometric profile measurements of temperature and shows very good results.

Exploratory analysis of time series (TS) data is an important approach in experimental studies, with a large range of applications in many different fields, including air pollution studies. To identify structures in single (univariate) TS, main clustering analyses are based on general-purpose clustering algorithms (e.g., k-means, hierarchical clustering methods) and made the assumption that the samples (data) of a TS are independent, ignoring the correlations in consecutive sample values in time. This is specially the case of air pollutant studies based on monitoring data. Air pollutants TS can be studied using TS clustering techniques and as a result, pollution profiles or concentration regimes detected as well as the dependency structure between consecutive data is preserved. Once TS clustering applied over the TS data stream, a set of clusters group the data according to their similar concentration values, and therefore, different pollution profiles can be defined and their estimated range of concentration values. Hidden Markov Models (HMMs) are flexible general-purpose models for univariate and multivariate TS. The TS data are assumed to have a Markov property, and may be viewed as the results of a probabilistic walk along a fixed set of (no directly observable) states. This class of approach considers that each TS is generated by a mixture of underlying probability distributions, typically the Gaussian ones. In this study, HMMs were applied to cluster daily average particulate matter with aerodynamic diameter of 10 μm or less (PM₁₀) TS collected at background monitoring stations from the Iberian Peninsula and Canarian Archipelago (Spain). As a result, PM₁₀ concentration regimes were studied and in particular, the contribution to PM₁₀ ambient concentration levels from the regimes associated to transport of air masses from North Africa deserts was estimated. Regarding this last contribution, we later compared to those obtained using the monthly moving 40th percentile (P40) method over the same TS and no significant quantitative differences were detected. However, the results obtained with HMMs seem to correct the net load of PM₁₀ given by the P40 method, and attributes less impact on areas suffering greater influence from African episodes. The method proposed in this work to estimate PM₁₀ from deserts could improve the P40 method in two ways since it avoids: (i) the smoothed effect which is implicit in the P40 methods after applying a mobile procedure in the TS treatment; and (ii) the empirical approach based on a correlation analysis applied in order to select this particular percentile (40th). Moreover, the use of statistical replicative techniques (bootstrap) together with HMMs has let to obtain an interval confidence in the PM₁₀ contribution estimates from North African deserts. This methodology may be used to estimate particulate matter contributions from any desert; however, a consensus among experts is required to give the regimes obtained with HMMs a definition.

Dry intrusion is an important mid-latitude atmosphere phenomenon within the upper troposphere and lower stratosphere. It is always found to be related to the cyclogenesis, rainstorm, as well as convection generation and precipitation enhancement. Since the atmosphere environment for any of these above-mentioned weather is terribly complicated, those preexisting popular schemes which takes no account of water vapor may not suitable for detecting the dry intrusion related to these weathers. With regard to the merits and demerits of the current preexisting schemes, a new scheme based on Fengyun-2E geo-stationary satellite data is presented in this study to detect the atmospheric dry intrusion. The scheme is set up based on the statistical relationship between water vapor at high level troposphere, the general moist potential vorticity, ozone concentration and upper-level jet. After using the total amount of ozone and ozone profile operational products retrieved by Fengyun-3 Polar Orbiting Meteorological Satellites and the potential vorticity calculated by ECMWF Interim data for validation, this scheme is applied to analyze two typical middle-latitude weather processes. One is the famous Beijing extreme rainfall of 21 July 2012 and the other is a hailstorm occurred on the eastern China during March 19, 2014. A good application effect in both cases suggests that our new method of detecting dry intrusion is feasible and can be helpful in middle-latitude disastrous weather monitoring and forecasting.

The ionosphere provides a channel able for long-haul and Non-Line-Of-Sight (NLOS) communications. Nonetheless, the amount of ionization depends on the Sun activity, whose diurnal and seasonal cycles transform the channel constantly. La Salle and the Observatori de l’Ebre have been sounding a 12,760 km ionospheric channel from Antarctica (62.7°S, 299.6°E) to Spain (41.0°N, 1.0°E) in order to find this evidence and to analyze the characteristics of this particular channel. The final goal of the project is to establish a stable communications link to be used as backup or for low rate data transmission. The aim of this paper is to prove the relation between the channel availability and the Sun phenomena affecting the ionization in four consecutive sounding campaigns.