A numerical compass for experiment design in chemical kinetics and molecular property estimation.

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure-activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.

Reappraising the appropriate calculation of a common meteorological quantity: Potential Temperature.

The potential temperature is a widely used quantity in atmospheric science since it is conserved for dry air's adiabatic changes of state. Its definition involves the specific heat capacity of dry air, which is traditionally assumed as constant. However, the literature provides different values of this allegedly constant parameter, which are reviewed and discussed in this study. Furthermore, we derive the potential temperature for a temperature-dependent parameterisation of the specific heat capacity of dry air, thus providing a new reference potential temperature with a more rigorous basis. This new reference shows different values and vertical gradients, in particular in the stratosphere and above, compared to the potential temperature that assumes constant heat capacity. The application of the new reference potential temperature is discussed for computations of the Brunt-Väisälä frequency, Ertel's potential vorticity, diabatic heating rates, and for the vertical sorting of observational data.

Upper tropospheric water vapour and its interaction with cirrus clouds as seen from IAGOS long-term routine in situ observations.

IAGOS (In-service Aircraft for a Global Observing System) performs long-term routine in situ observations of atmospheric chemical composition (O3, CO, NOx, NOy, CO2, CH4), water vapour, aerosols, clouds, and temperature on a global scale by operating compact instruments on board of passenger aircraft. The unique characteristics of the IAGOS data set originate from the global scale sampling on air traffic routes with similar instrumentation such that the observations are truly comparable and well suited for atmospheric research on a statistical basis. Here, we present the analysis of 15 months of simultaneous observations of relative humidity with respect to ice (RHice) and ice crystal number concentration in cirrus (Nice) from July 2014 to October 2015. The joint data set of 360 hours of RHice-Nice observations in the global upper troposphere and tropopause region is analysed with respect to the in-cloud distribution of RHice and related cirrus properties. The majority of the observed cirrus is thin with Nice < 0.1 cm-3. The respective fractions of all cloud observations range from 90% over the mid-latitude North Atlantic Ocean and the Eurasian Continent to 67% over the subtropical and tropical Pacific Ocean. The in-cloud RHice distributions do not depend on the geographical region of sampling. Types of cirrus origin (in situ origin, liquid origin) are inferred for different Nice regimes and geographical regions. Most importantly, we found that in-cloud RHice shows a strong correlation to Nice with slightly supersaturated dynamic equilibrium RHice associated with higher Nice values in stronger updrafts.