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NASA NTRS2023

Lifetimes of Overshooting Convective Events using High-Frequency Gridded Radar Composites

Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-min intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to form tracks. This process is performed for 12 four-day sample windows in the months May–August of 2017–19. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2–3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 1600–1700 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.

Atmosphere↗

NASA NTRS2023

Statistical Study of the Non-Thermal Continuum Radiation Beaming Angle Measured By the High Frequency Receiver on Van Allen Probes-A

The nonthermal continuum (NTC) radiation beaming angle is computed over the entire Van Allen Probes-A mission when the spacecraft was in the dawn sector. The conditions in the dawn sector are favorable for the wave vector to lie near/in the spacecraft's spin plane allowing a favorable estimate of the beaming angle, and the dawn sector is also advantageous in that previous studies show NTC occurrence to peak in this sector. We found that scatter plots, over the entire mission, of beaming angle versus magnetic latitude form a distinct inverted-V pattern, with the apex at/near the magnetic equator. This pattern was sharpest for frequencies (f) ≲ 100 kHz. Using the NTC beaming formula from the linear mode conversion theory (LMCT), we show that such an inverted-V pattern is expected due to the large variation in the plasmapause location over the entire mission. The theoretical derived pattern qualitatively reproduces the observed pattern but not quantitatively. The lack of quantitative agreement is discussed and is attributed to several factors, one factor is off-centered emissions from the radio window. The qualitative agreement strongly supports LMCT as being the dominant mechanism generating NTC for f ≲ 100 kHz. For f ≳ 100 kHz, the inverted-V pattern becomes less distinct, and strong near-equatorial beaming is observed. After considering contamination of our selections by left-handed polarized AKR, our study suggests that besides LMCT another unidentified NTC generation mechanism becomes important for f ≳ 100 kHz.

Linear Mode Conversion Theory↗

NASA NTRS2024

Accurate Assessment of Land-Atmosphere Coupling in Climate Models Requires High Frequency Data Output

Land-atmosphere (L-A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centred climate anomalies. Local L-A coupling (LoCo) metrics capture relevant L-A processes, highlighting the impact of soil and vegetation states on surface flux partitioning, and the impact of surface fluxes on boundary layer (BL) growth, development, and entrainment of air above the BL. A primary goal of the Climate Process Team on Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L-A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land-atmosphere interactions span time scales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability of behavioural regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyse, and better understand L-A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behaviour. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant time scales, (ii) enable demonstration of inter-model spread in L-A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

Kirsten L. Findell↗

NASA NTRSDate not supplied