Compressive Earth Observatory: An Insight from AIRS/AMSU Retrievals

TL;DR

This paper shows that atmospheric temperature, humidity, and geopotential height fields can be sparsely represented in the wavelet domain, enabling efficient data recovery from limited satellite measurements.

Contribution

It introduces a sparsity-based framework for reconstructing land-atmospheric states from space using AIRS/AMSU data, highlighting pressure-independent and pressure-dependent sparsity patterns.

Findings

Temperature fields are pressure-independent in sparsity.

Humidity and geopotential heights are sparser at specific pressure levels.

Land-atmospheric states can be accurately reconstructed from few measurements.

Abstract

We demonstrate that the global fields of temperature, humidity and geopotential heights admit a nearly sparse representation in the wavelet domain, offering a viable path forward to explore new paradigms of sparsity-promoting data assimilation and compressive recovery of land surface-atmospheric states from space. We illustrate this idea using retrieval products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) on board the Aqua satellite. The results reveal that the sparsity of the fields of temperature is relatively pressure-independent while atmospheric humidity and geopotential heights are typically sparser at lower and higher pressure levels, respectively. We provide evidence that these land-atmospheric states can be accurately estimated using a small set of measurements by taking advantage of their sparsity prior.