Alexandre Guillaume

Cloud-State-Dependent Sampling in AIRS Observations Based onCloudSatCloud Classification

AbstractThe precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis data. The results show that AIRS provides a realistic representation of most meteorological regimes in most geographical regions, including those dominated by high thin cirrus and shallow boundary layer clouds. The mean AIRS observational biases relative to the ECMWF analysis between the surface and 200 hPa are within ±1 K in T and from −1 to +0.5 g kg−1 in q. Biases because of cloud-state-dependent sampling dominate the...

Using genetic algorithm to assess the robustness of project schedules with countable risks

By nature, space mission projects are ambitious endeavors with goals to expand the limits of our knowledge of the universe. Therefore, they involve the use, or development, of complex expertise as well as sophisticated technologies. This unique complexity coupled with large projects size make these missions prone to delays and overruns.

Heisenberg-limited measurements with superconducting circuits

We describe an assembly of N superconducting qubits contained in a single-mode cavity. In the dispersive regime, the correlation between the cavity field and each qubit results in an effective interaction between qubits that can be used to dynamically generate maximally entangled states. With only collective manipulations, we show how to create maximally entangled quantum states and how to use these states to reach the Heisenberg limit in the determination of the qubit bias control parameter (gate charge for charge qubits, external magnetic flux for rf-superconducting quantum interference devices)

Custom on-demand and data access services for news merged A-Train data

To simplify access to large and complex satellite data sets for climate analysis and model verification, we developed a web service tool that is used to study long-term and global-scale trends in climate, water and energy cycle, and weather variability. A related NASA Energy and Water Cycle Study (NEWS) task has created a merged NEWS Level 2 data from multiple instruments in NASAs A-Train constellation of satellites. We used this data to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for simple and well-designed distributed services so that users can perform analysis in their own familiar computing environments. A Federated OpenSearch capability with full text + space + time search of data products also facilitated interoperability with other data systems.

Web services for multiplatform exploratory analysis of level 2 and 3 NEWS merged A-Train data

To simplify access to large and complex satellite data sets for climate analysis and model verification, a service-oriented architecture-based tool was developed to help study long-term and global-scale trends in climate, water and energy cycle, and weather variability. NASAs A-Train satellite constellation set of Level 2 data can be used to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. However, the volume and inhomogeneity of Level 2 data have typically been difficult or time consuming to search and acquire. This tends to result in small-scale or short-term analysis. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for well-designed distributed services so that users can perform analysis in their own familiar computing environments. Voluminous merged Level 2 data containing the various instrument data from the A-Train have recently been generated. Scientists next want to efficiently access selected sets of this merged data and perform their analysis. Server-side capabilities were developed to off-load processing and reduce the amount of data to be transferred to the client. Correspondingly, client-side processing APIs were developed to enable scientists to perform analysis of voluminous server-side data from within their own familiar computing environment (Java, Python, Matlab, IDL, C/C++, and Fortran90).

Fine alignment of the James Webb Space Telescope with a genetic algorithm

The quality of the image recorded by the future James Webb Space Telescope or JWST will depend on the alignment of the multiple optical elements constituting the telescope. Several steps are necessary to align the optical system. In this paper, we describe a genetic algorithm (GA) that optimizes the last alignment stage or fine-phasing stage. We split the population of candidate solutions into several sub-populations in order to take into account the different parameter dependencies. Each optical element has several (seven) degrees of freedom. The worse ranked individuals undergo the GA variation operations on all their genes independently guarantying that solutions with independent genes will be searched. Conversely, since the symmetry of the problem imposes that most rotations, translations and curvatures must be related, we apply GA variation operations on only three (pseudo)-genes for better ranked solutions. Our method improves the current state of the art.