Löhnert, Ulrich | Search NASA

DOE OSTI2020

Characterizing Subsiding Shells in Shallow Cumulus Using Doppler Lidar and Large‐Eddy Simulation

Abstract The existence of subsiding shells on the periphery of shallow cumulus clouds has major implications concerning the parameterization of shallow convection, with the mass exchange between the shell and cloudy air representing a significant deviation from the commonly used bulk‐plume parameterization. We examine the structure and frequency of subsiding shells in shallow cumulus convection using Doppler lidars at the Atmospheric Radiation Measurement Southern Great Plains facility in the central United States and at the Jülich ObservatorY for Cloud Evolution in western Germany. Doppler lidar indicates that the vertical subsiding shell extent is asymmetric, while shell width is typically ~100 m. Large‐eddy simulation can reasonably simulate the observed shell structure using a grid spacing of 10 m and suggests that much of the observed asymmetry is not a result of transient cloud evolution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗