25 research outputs found
Statistical estimation of tropospheric temperatures and relative humidities from remote radiometric measurements
Statistical estimation of tropospheric temperature and relative humidities from remote radiometric measurement
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A nonlinear Fredholm integral equation as encountered in radiation through an atmosphere
On geophysical inverse problems and constraints
Mathematical methods for linearized geophysical inverse problems are reviewed, in cases with and in cases without constraints. The role of constraints receives particular attention, both in linear convex problems and in an exactly solvable non-linear example.
ARK: https://n2t.net/ark:/88439/y081128
Permalink: https://geophysicsjournal.com/article/178
 
Inversion Methods in Atmospheric Remote Sounding
The mathematical theory of inversion methods is applied to the remote sounding of atmospheric temperature, humidity, and aerosol constituents
Partial Identification with Proxy of Latent Confoundings via Sum-of-ratios Fractional Programming
Due to the unobservability of confoundings, there has been widespread concern
about how to compute causality quantitatively. To address this challenge,
proxy-based negative control approaches have been commonly adopted, where
auxiliary outcome variables are introduced as the proxy of
confoundings . However, these approaches rely on strong assumptions
such as reversibility, completeness, or bridge functions. These assumptions
lack intuitive empirical interpretation and solid verification techniques,
hence their applications in the real world are limited. For instance, these
approaches are inapplicable when the transition matrix
is irreversible. In this paper, we focus on a weaker assumption called the
partial observability of . We develop a more general
single-proxy negative control method called Partial Identification via
Sum-of-ratios Fractional Programming (PI-SFP). It is a global optimization
algorithm based on the branch-and-bound strategy, aiming to provide the valid
bound of the causal effect. In the simulation, PI-SFP provides promising
numerical results and fills in the blank spots that can not be handled in the
previous literature, such as we have partial information of
Retrieving global aerosol sources from satellites using inverse modeling
International audienceUnderstanding aerosol effects on global climate requires knowing the global distribution of tropospheric aerosols. By accounting for aerosol sources, transports, and removal processes, chemical transport models simulate the global aerosol distribution using archived meteorological fields. We develop an algorithm for retrieving global aerosol sources from satellite observations of aerosol distribution by inverting the GOCART aerosol transport model. The inversion is based on a generalized, multi-term least-squares-type fitting, allowing flexible selection and refinement of a priori algorithm constraints. For example, limitations can be placed on retrieved quantity partial derivatives, to constrain global aerosol emission space and time variability in the results. Similarities and differences between commonly used inverse modeling and remote sensing techniques are analyzed. To retain the high space and time resolution of long-period, global observational records, the algorithm is expressed using adjoint operators. Successful global aerosol emission retrievals at 2°×2.5 resolution were obtained by inverting GOCART aerosol transport model output, assuming constant emissions over the diurnal cycle, and neglecting aerosol compositional differences. In addition, fine and coarse mode aerosol emission sources were inverted separately from MODIS fine and coarse mode aerosol optical thickness data, respectively. These assumptions are justified, based on observational coverage and accuracy limitations, producing valuable aerosol source locations and emission strengths. From two weeks of daily MODIS observations during August 2000, the global placement of fine mode aerosol sources agreed with available independent knowledge, even though the inverse method did not use any a priori information about aerosol sources, and was initialized with a "zero aerosol emission" assumption. Retrieving coarse mode aerosol emissions was less successful, mainly because MODIS aerosol data over highly reflecting desert dust sources is lacking. The broader implications of applying our approach are also discussed