Convection Parameters from Remote Sensing Observations over the Southern Great Plains

Area of Science:

• Atmospheric Science
• Remote Sensing
• Meteorology

Background:

• Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) are crucial for estimating atmospheric instability and convection potential.
• Traditional methods rely on infrequent radiosonde launches and model data, limiting temporal resolution.
• Continuous atmospheric profiling offers a potential improvement for operational forecasting.

Purpose of the Study:

• To evaluate the performance of passive and active remote sensing systems in deriving CAPE and CIN.
• To compare remote sensing-derived CAPE/CIN values against in situ radiosonde measurements.
• To assess the impact of different sensor types on the accuracy of CAPE and CIN calculations.

Main Methods:

• CAPE and CIN were calculated using data from Atmospheric Emitted Radiance Interferometer (AERI), Microwave Radiometer (MWR), Raman LiDAR, and Differential Absorption LiDAR (DIAL).
• Performance was evaluated by comparing remote sensing-derived values with radiosonde data.
• Water vapor profiles from active LiDAR systems were incorporated to assess their impact.

Main Results:

• Passive sensors showed AERI provided more accurate CAPE and CIN than MWR when compared to radiosondes.
• Active LiDAR systems, when combined with passive sensors, improved CAPE accuracy.
• The impact of active LiDAR on CIN accuracy was less significant than on CAPE.

Conclusions:

• Remote sensing offers a viable method for continuous derivation of atmospheric instability and inhibition metrics.
• AERI demonstrates superior performance among passive sensors for CAPE/CIN estimation.
• Integration of active LiDAR data enhances CAPE calculations, paving the way for improved understanding of moisture transport and cloud development.