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Event

Distribution of upper tropospheric and lower stratospheric (UTLS) water vapor influenced by deep convections

Wednesday, October 16, 2019 14:30to15:30

Burnside Hall Room 934, 805 rue Sherbrooke Ouest, Montreal, QC, H3A 0B9, CA

Student Seminar Series

Department of Atmospheric & Oceanic Sciences

presents

a talk by

Jing Feng
PhD candidate

Distribution of upper tropospheric and lower stratospheric (UTLS) water vapor influenced by deep convections

Despite its scarcity, water vapor in the upper troposphere and lower stratosphere (UTLS) is crucial to the climate due to both its radiative and chemical impacts. The distribution of water vapor in this region, however, remains poorly measured, partly due to deep convection processes that injected potential water sources through the cold trap. Researchers have found inconsistency among products from different satellite missions. This brings large uncertainty to stratospheric water vapor decadal variation and its contribution to climate feedback.

The focus of this study is to apply an innovative method to sounding UTLS water vapor profiles above opaque deep convective cloud layers, using nadir view satellite measurements. The retrieval algorithm is performed based on a widely used Optimal Estimation method that combines satellite observation and historical data to give an optimal estimation of the atmospheric state. Theoretical assessments show that this method largely eliminates major difficulties in a clear-sky retrieval, including the non-monotonic temperature around the tropopause, and the spectral signal of upper-level water vapor being weak relative to noise from lower atmosphere uncertainty.

Comparisons between the retrieval using radiance data from Atmospheric Infrared Sounder (AIRS, https://airs.jpl.nasa.gov/) and collocated aircraft measurements show that the retrieval can detect the water vapor anomalies caused by deep convective events. Using this approach in synergy with cloud properties retrieved by CloudSat, we further characterize the water vapor distribution pattern above deep convection events passed by A-train satellites in the past decades.