Remote sensing is an important measurement technique when probing the atmosphere as it is rather flexible and allows for the measurement of numerous variables. For example, spectrometers are commonly used to quantify the concentrations of trace gases by recording spectra of direct or scattered sunlight. Due to scattering, the light paths between sun and detector can be rather complicated and radiative transfer models are necessary to retrieve the information contained in the spectra. Since these spectrometer measurements have to be made in spectral regions with strong absorption, it is necessary to model many wavelengths to reach a sufficiently accurate representation of the absorption lines and their effects on the light paths. Thus, 1D models are often implemented for a fast analysis of the recorded spectra. However, this approach assumes horizontal homogeneity and local sources cannot be resolved. In contrast, 3D Monte Carlo models are more realistic and are able to represent this inhomogeneity, but they are computationally expensive and are not suitable for operational use.We improve an existing Monte Carlo model by implementing efficient algorithms for the simultaneous calculation of several wavelengths to decrease the required computation time. Furthermore, we examine to which scatter order the 3D model provides more detailed results while maintaining a reasonable run time.This finally leads to a coupling of these two types of radiative transfer models via the scatter order into one efficient model which performs realistic simulations at a computational cost comparable to 1D models.So we are able to detect sources along the line of sight of ground-based measurements of scattered sunlight.Here, we present our objective and first results.

Severe hailstorms have the potential to damage buildings and crops. However, important processes for the prediction of hailstorms are insufficiently represented in operational weather forecast models. Therefore, our goal is to identify model input parameters describing environmental conditions and cloud microphysics, such as the vertical wind shear and strength of ice multiplication, which lead to large uncertainties in the prediction of deep convective clouds and precipitation. We conduct a comprehensive sensitivity analysis simulating deep convective clouds in an idealized setup of a cloud-resolving model. We use statistical emulation and variance-based sensitivity analysis to enable a Monte Carlo sampling of the model outputs across the multi-dimensional parameter space. The results show that the model dynamical and microphysical properties are sensitive to both the environmental and microphysical uncertainties in the model. The microphysical parameters lead to larger uncertainties in the output of integrated hydrometeor mass contents and precipitation variables. In particular, the uncertainty in the fall velocities of graupel and hail account for more than 65 % of the variance of all considered precipitation variables and for 30 %–90 % of the variance of the integrated hydrometeor mass contents. In contrast, variations in the environmental parameters – the range of which is limited to represent model uncertainty – mainly affect the vertical profiles of the diabatic heating rates.

Precise knowledge of sources and sinks in the carbon cycle is desired to understandits sensitivity to climate change and to account and verify man-made emissions. Animportant role herein play extended sources like urban areas. While in-situ measure-ments of carbon dioxide (CO2) and methane (CH4) are highly accurate but localized,satellites measure column-integrated concentrations over an extended footprint. Ourinnovative measurement technique aims at determining CO2 and CH4 concentrationsnear to the ground on the scale of a few kilometers and therefore fills the sensitivitygap between in-situ and satellite measurements.Using a modified EM27/SUN Fourier transform spectrometer we are able to recordspectra of surface scattered sunlight in the range of 4000 − 11000 cm−1 . To accuratelyretrieve CO2 and CH4 concentrations an advanced retrieval method is required thatincludes the simultaneous estimation of atmospheric scattering properties.Based on our radiative transfer and retrieval software RemoTeC, we built a simulationenvironment that includes atmospheric scattering processes. With this tool we cangenerate and retrieve synthetic scattered light observations. Here we present oursimulation environment, first results and ongoing developments.

We show that there is a strong sensitivity of cloud microphysics to model time step in idealized convection‐permitting simulations using the COnsortium for Small‐scale MOdeling model. Specifically, we found a 53% reduction in precipitation when the time step is increased from 1 to 15 s, changes to the location of precipitation and hail reaching the surface, and changes to the vertical distribution of hydrometeors. The effect of cloud condensation nuclei perturbations on precipitation also changes both magnitude and sign with the changing model time step. The sensitivity arises because of the numerical implementation of processes in the model, specifically the so‐called “splitting” of the dynamics (e.g., advection and diffusion) and the parameterized physics (e.g., microphysics scheme). Calculating one step at a time (sequential‐update splitting) gives a significant time step dependence because large supersaturation with respect to liquid is generated in updraft regions, which strongly affect parameterized microphysical process rates—in particular, ice nucleation. In comparison, calculating both dynamics and microphysics using the same inputs of temperature and water vapor (hybrid parallel splitting) or adding an additional saturation adjustment within the dynamics reduces the time step sensitivity of surface precipitation by limiting the supersaturation seen by the microphysics, although sensitivity to time step remains for some processes.

This study aims to identify model parameters describing atmospheric conditions such as wind shear and cloud condensation nuclei (CCN) concentration, which lead to large uncertainties in the prediction of deep convective clouds. In an idealized setup of a cloud‐resolving model including a two‐moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of six uncertain input parameters on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, and the size distribution of hail. Furthermore, we investigate whether the sensitivities are robust for different trigger mechanisms of convection. We find that the uncertainties of most cloud and precipitation outputs are dominated by the uncertainty in the temperature profile and the CCN concentration while the contributions of other input parameters to the uncertainties may vary. The temperature profile is also an important factor in determining the size distribution of surface hail. We also notice that the sensitivities of cloud water and hail to the CCN concentration depend on environmental conditions. Our results show that depending on the choice of the trigger mechanism, the contribution of the input parameters to the uncertainty varies, which means that studies with different trigger mechanisms might not be comparable. Overall, the emulator approach appears to be a powerful tool for the analysis of complex weather prediction models in an idealized setup.

In this presentation we show that there is a large and important sensitivity to the model timestep for convectionpermitting simulations. This sensitivity likely affects the skill and predictability of convective events. We have identified that the cause is the numerical and practical implementation of the dynamics and physics in our model – which is typical of NWP models and in particular affects the cloud microphysics parameterization. We have found a solution that removes almost all of the sensitivity to the timestep and requires only simple changes to the model code. The impact of this problem will be shown, the physical and numerical cause will be explained, and a solution will be presented.