Comparing Loon Superpressure Balloon Observations of Gravity Waves in the Tropics With Global Storm‐Resolving Models

Abstract

Superpressure balloons, which drift approximately on isopycnal surfaces, get displaced by gravity waves and are thus capable of detecting gravity wave signatures. The project Loon provides superpressure balloon data in the upper troposphere and lower stratosphere from 2011 to 2021. We compare Loon data from the 6 years of best data coverage with output of global storm‐resolving models from the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains winter initiative in the tropics. We study the variance of the vertical velocity and, for the models, the gravity wave momentum flux as function of distance to closest convection. The models show large differences in the variance of the vertical wind velocity, which is crucial for calculating vertical gravity wave momentum fluxes. We find large differences between the models with respect to simulated convection, lateral propagation, and the wave background away from sources. We then sample balloons as models by optimizing the match of vertical wind distributions using a temporal low pass filter. The average distance the balloons travel during the optimum low pass filtering time turns out to correspond approximately to four times the model grid spacing. The functional dependence of the vertical velocity variance on distance to closest convection is similar between the models and the observations sampled as models. The robustness of this result across all models suggests that storm‐resolving models provide a useful resource for machine learning some characteristics of convectively generated gravity waves.

Reconstructing Balloon‐Observed Gravity Wave Momentum Fluxes Using Machine Learning and Input From ERA5

“…Ways forward include: Obtaining more observations to use as the target, keeping the same framework for the ML. Additional observations would come from the second Strateole 2 campaign (in 2021) and from Loon balloons (Köhler et al., 2023; Schoeberl et al., 2017). The additional Strateole data would enhance the data by less than a factor 2 and is therefore not expected to suffice to make a dramatic change.…”

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

“…A library of such high-resolution simulations, notably convectively generated GWs using the Weather Research and Forecasting (WRF) model, is now established (Y. Sun et al, 2023), alongside additional global high-resolution simulations (Köhler et al, 2023;Polichtchouk et al, 2023;Wedi et al, 2020).…”

Ice Supersaturation Variability in Cirrus Clouds: Role of Vertical Wind Speeds and Deposition Coefficients

“…High resolution global models pinpoint effects on precipitation and cloud radiative effects and life cycles, at the same time revealing how these impact larger-scale processes and circulations. However, such models currently fail to fully capture observed momentum fluxes and associated vertical wind speed variance due to high frequency GWs (Atlas & Bretherton, 2022;Köhler et al, 2023). At scales below the buoyancy length scale (<10-100 m in the upper troposphere), the impact of turbulent motions on ice supersaturation and cirrus formation remains to be investigated.…”