Abstract
Abstract. The subgrid spatial variability of water vapor is an important geophysical parameter for modeling tropical convention and cloud processes in atmospheric models. This study maps sub-kilometer spatial structures in total atmospheric column water vapor with visible to shortwave infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA's AVIRIS-NG spectrometer enable the highest-resolution measurement to date of water vapor's spatial variability and scaling properties. We find second-order structure function scaling exponents consistent with prior studies of convective atmospheres. Airborne lidar data show that this total column measurement provides information about variability in the lower troposphere. We conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.
Highlights
The complex spatial distribution of atmospheric water vapor surrounding clouds and precipitation structures has important consequences for parameterizing moist processes in atmospheric models
To the end of advancing remote observations of atmospheric water vapor, this paper focuses on a specific measurement that is independently useful and typifies the more general challenge of observing variability
We first assess the absolute accuracy of water vapor absorption measurements using airborne overflights of the AERONET robotic observation network (Holben et al, 1998)
Summary
The complex spatial distribution of atmospheric water vapor surrounding clouds and precipitation structures has important consequences for parameterizing moist processes in atmospheric models. The mean and variability of precipitation rate in the tropics are strongly dependent on the atmospheric water vapor (Peters and Neelin, 2006; Holloway and Neelin, 2010), a fact which has implications for parameterizing convection. Another ubiquitous property of convection is its tendency to aggregate (Bretherton et al, 2005). Over land surfaces with heterogeneous surface conditions, the variability in atmospheric water vapor can be larger and is seen as a critical component of the timing of deep convection (Stirling and Petch, 2004; Wulfmeyer et al, 2006). Water vapor variability, and its coupling to cloud types and multi-scale organization, is key for advancing the parameterization and simulation of cloud processes
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