12 research outputs found
Progresos en el instrumental meteorológico: creación de nuevos tipos de instrumentos para las estaciones de radiación en Europa
An estimate of the surface heat fluxes transfer of the Persian Gulf with the overlying atmosphere
Effects of long-term elevated atmospheric carbon dioxide onLolium perenne andTrifolium repens, using a simple photosynthesis model
Application of the New Standards for the Evaluation of Daylight and Solar Availability Measurements
The Adaptable 4A Inversion (5AI): description and first <i>X</i><sub>CO<sub>2</sub></sub> retrievals from Orbiting Carbon Observatory-2 (OCO-2) observations
A better understanding of greenhouse gas surface sources
and sinks is required in order to address the global challenge of climate
change. Space-borne remote estimations of greenhouse gas atmospheric
concentrations can offer the global coverage that is necessary to improve
the constraint on their fluxes, thus enabling a better monitoring of
anthropogenic emissions. In this work, we introduce the Adaptable 4A
Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters
from any remote sensing observation. The algorithm is based on the Optimal
Estimation algorithm, relying on the Operational version of the Automatized
Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along
with the Gestion et Étude des Informations Spectroscopiques
Atmosphériques: Management and Study of Atmospheric Spectroscopic
Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied
to retrieve the column-averaged dry air mole fraction of carbon dioxide
(XCO2) from a sample of measurements performed by the Orbiting
Carbon Observatory-2 (OCO-2) mission. Those have been selected as a
compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal
cycle and long-term increasing trend. Comparison with ground-based
observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases.</p