Global, Multi-Year Analysis of Clouds and Earth's Radiant Energy System Terra Observations and Radiative Transfer Calculations

An extended record of the Terra Surface and Atmosphere Radiation Budget (SARB) computed by CERES (Clouds and Earth s Radiant Energy System) is produced in gridded form, facilitating an investigation of global scale direct aerosol forcing. The new gridded version (dubbed FSW) has a spacing of 1 at the Equator. A companion document (Rutan et al. 2005) focuses on advances to (and validation of) the ungridded, footprint scale calculations (dubbed CRS), primarily in clear-sky conditions. While mainly intended to provide observations of fluxes at the top of atmosphere (TOA), CERES (Wielicki et al. 1996) includes a program to also compute the fluxes at TOA, within the atmosphere and at the surface, and also to validate the results with independent ground based measurements (Charlock and Alberta 1996). ARM surface data has been a focus for this component of CERES. To permit the user to infer cloud forcing and direct aerosol forcing with the computed SARB, CERES includes surface and TOA fluxes that have been computed for cloud-free (clear) and aerosol free (pristine) footprints; this accounts for aerosol effects (SW scattering and absorption, and LW scattering, absorption and emission) to both clear and cloudy skies

Characterization of a high throughput approach for large scale retention measurement in liquid chromatography

Many contemporary challenges in liquid chromatography—such as the need for “smarter” method development tools, and deeper understanding of chromatographic phenomena—could be addressed more efficiently and effectively with larger volumes of experimental retention data than are available. The paucity of publicly accessible, high-quality measurements needed for the development of retention models and simulation tools has largely been due to the high cost in time and resources associated with traditional retention measurement approaches. Recently we described an approach to improve the throughput of such measurements by using very short columns (typically 5 mm), while maintaining measurement accuracy. In this paper we present a perspective on the characteristics of a dataset containing about 13,000 retention measurements obtained using this approach, and describe a different sample introduction method that is better suited to this application than the approach we used in prior work. The dataset comprises results for 35 different small molecules, nine different stationary phases, and several mobile phase compositions for each analyte/phase combination. During the acquisition of these data, we have interspersed repeated measurements of a small number of compounds for quality control purposes. The data from these measurements not only enable detection of outliers but also assessment of the repeatability and reproducibility of retention measurements over time. For retention factors greater than 1, the mean relative standard deviation (RSD) of replicate (typically n=5) measurements is 0.4%, and the standard deviation of RSDs is 0.4%. Most differences between selectivity values measured six months apart for 15 non-ionogenic compounds were in the range of +/- 1%, indicating good reproducibility. A critically important observation from these analyses is that selectivity defined as retention of a given analyte relative to the retention of a reference compound (kx/kref) is a much more consistent measure of retention over a time span of months compared to the retention factor alone. While this work and dataset also highlight the importance of stationary phase stability over time for achieving reliable retention measurements, we are nevertheless optimistic that this approach will enable the compilation of large databases (>> 10,000 measurements) of retention values over long time periods (years), which can in turn be leveraged to address some of the most important contemporary challenges in liquid chromatography. All the data discussed in the manuscript are provided as Supplemental Information

Planning, Preparation, And Implementation Of Turbomachinery Turnarounds.

Tutorialpg. 243-252Anyone involved in overhauling equipment during a scheduled turnaround knows the importance of having the right people and the right tools available in the right places at the right time. Obviously, the orchestration of the necessary events, which must occur, demands foresight and analytical thinking. The optimum amount of planning and preparation will depend on the complexity and size of the turnaround. Experience indicates that each turnaround is unique. While turnarounds may involve similar work items, constraints such as timing requirements and associated work interactions differ from one turnaround to the next. Nevertheless, prior work plans can serve as a basis for subsequent turnarounds. Generally, scheduled turnarounds are divided into three phases: • Pre-Turnaround Phase • Turnaround Implementation Phase • Post-Turnaround Phase This tutorial addresses the items to be considered and tasks to be accomplished during each of the critical phases, with emphasis on the pre-turnaround and implementation phases. Suggestions and examples are provided to improve task management

Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

The main point of this study was to use realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE satellite mission, was applied to CloudSat, CALIPSO, and MODIS satellite data thus producing 3D cloudy atmospheres measuring 60 km wide by 13,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances for each (1 km)2 column where then produced by a Monte Carlo photon transfer model run in both full 3D and independent column approximation mode (i.e., a 1D model)

Data for: Rational Design of Mixtures for Chromatographic Peak Tracking Applications via Multivariate Selectivity

Retention time and UV spectral data for 97 compounds for the purpose of rational design of mixtures

Matlab code for: Rational Design of Mixtures for Chromatographic Peak Tracking Applications via Multivariate Selectivity

These files are for a MATLAB GUI for the Rational Design of Mixtures for LC-DAD sample composition optimization. The calculations are based on multivariate selectivity metrics