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AIRS Version 6 is a physically based retrieval system Uses cloud cleared radiances Ri to determine the state vector XRi represents what AIRS would have seen in the absence of clouds.
This research is being done under the NPP Science Team Proposal: Analysis of CrISATMS Using an AIRS Version 6-like Retrieval Algorithm Objective: Generate a long term CrISATMS level-3 data set that is consistent with that of AIRSAMSU Approach: Adapt the currently operational AIRS Science Team Version-6 Retrieval Algorithm, or an improved version of it, for use with CrISATMS data. Metric: Generate monthly mean level-3 CrISATMS climate data sets and evaluate the results by comparison of monthly mean AIRSAMSU and CrISATMS products, and more significantly, their inter-annual differences and, eventually, anomaly time series. The goal is consistency between the AIRSAMSU and CrISATMS climate data sets.
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Additional changes in Version-6.19 include all previous updates made to the q(p) retrieval since Version-6: Modified Neural-Net q0(p) guess above the tropopause Linearly tapers the neural net guess to match climatology at 70 mb, not at the top of the atmosphereChanged the 11 trapezoid q(p) perturbation functions used in Version-6 so as to match the 24 functions used in T(p) retrieval step. These modifications resulted in improved water vapor profiles in Version-6.19 compared to Version-6.Version-6.19 is tested for all of August 2013 and August 2014, as well for select other days. Before finalized and operational in 2016, the V-6.19 can be acquired upon request for limited time intervals.
Combining the strengths of MPIProf and IOT, an efficient and systematic method is devised for I/O characterization at the per-job, per-rank, per-file and per-call levels of HPC programs running on the NASA Advanced Supercomputing Center. This method is applied to answer four I/O questions in this paper. A total of 13 MPI programs and 15 cases, ranging from 24 to 5968 ranks, are analyzed to establish the I/O landscape from answers to the four questions. Four of the 13 programs use MPI I/O and the behavior of their collective writes depends on the specific implementation of the MPI library used. The SGI MPT library, the prevailing MPI library for our systems, was found to gather small writes from a large number of ranks to perform larger writes by a small subset of collective buffering ranks. The number of collective buffering ranks invoked by MPT depends on the Lustre stripe count and the number of nodes used for the run. A demonstration of varying the stripe count to achieve double-digit speedup of one program's I/O was presented. Another program, which concurrently opens private files by all ranks and could potentially create a heavy load on the Lustre servers, was identified. The ability to systematically characterize I/O for a large number of programs running on a supercomputer, seek I/O optimization opportunity and identify programs that could cause a high load and instability on the filesystems is important for pursuing exascale in a real production environment.
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Most volcanic arcs and active rifts around the world host producing geothermal systems. Many undiscovered and/or undeveloped systems remain in each of these areas, including magmatic-heated systems, amagmatic extension-related systems, and blind resources lacking active surface manifestations in both types of settings. However, not all volcanic centers or extensional terranes have the same geothermal potential. For example, even when high heat flow is present, permeability is commonly a limiting factor to resource development. A tool recently adapted for the geothermal industry is play fairway analysis, which calculates the regional probability of power-capable resources based on the spatial coincidence of key geologic conditions. In a geothermal setting, these key geologic conditions are heat, permeability, and fluids. Higher enthalpy systems also benefit from suitable cap rock. These parameters can be assessed through multiple geologic, geochemical, and geophysical data sets and integrated quantitatively to produce a single geothermal play fairway map. Potential input data sets include but are not limited to: 1) tectonic setting; 2) structural setting; 3) faults attributed by recency of rupture and slip rate; 3) geodetic crustal stain models; 5) gravity; 6) earthquake distribution; 7) thermal models; 8) geochemistry from springs, wells, and fumaroles; 9) temperature from springs, wells, and fumaroles; 10) fumarole size and type; and 11) age of volcanism. Recent play fairway studies have been completed for volcanic arcs and the dominantly amagmatic, extensional Great Basin region, USA, but have yet to be applied to the East African Rift System (EARS). Previous estimates of total geothermal resource capacity in the EARS is ~20 GWe, but only three areas are currently in production as of 2015: Olkaria and Eburru in Kenya, and Aluto-Langano in Ethiopia for a total installed capacity of <0.7 GWe. Many more volcanic centers host potential resources. In addition, amagmatic and/or blind systems also have potential in the EARS. Play fairway analysis can highlight regions most prospective for geothermal resource development, help identify locations prospective for undiscovered resources, prioritize overall exploration and development efforts, and refine estimates for total undeveloped resource potential.
We produce fine-resolution, three-dimensional fields of meteorological and other variables for the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The Community Gridpoint Statistical Interpolation system is implemented in a multiscale data assimilation (MS-DA) framework that is used within the Weather Research and Forecasting model at a cloud-resolving resolution of 2 km. The MS-DA algorithm uses existing reanalysis products and constrains fine-scale atmospheric properties by assimilating high-resolution observations. A set of experiments show that the data assimilation analysis realistically reproduces the intensity, structure, and time evolution of clouds and precipitation associated with a mesoscale convective system. Evaluations also show that the large-scale forcing derived from the fine-resolution analysis has an overall accuracy comparable to the existing ARM operational product. For enhanced applications, the fine-resolution fields are used to characterize the contribution of subgrid variability to the large-scale forcing and to derive hydrometeor forcing, which are presented in companion papers.
Aerosol-cloud interaction processes can be represented more physically with bin cloud microphysics relative to bulk microphysical parameterizations. However, due to computational power limitations in the past, bin cloud microphysics was often run with very simple aerosol treatments. The purpose of this study is to represent better aerosol-cloud interaction processes in the Chemistry version of Weather Research and Forecast model (WRF-Chem) at convection-permitting scales by coupling spectral-bin cloud microphysics (SBM) with the MOSAIC sectional aerosol model. A flexible interface is built that exchanges cloud and aerosol information between them. The interface contains a new bin aerosol activation approach, which replaces the treatments in the original SBM. It also includes the modified aerosol resuspension and in-cloud wet removal processes with the droplet loss tendencies and precipitation fluxes from SBM. The newly coupled system is evaluated for two marine stratocumulus cases over the Southeast Pacific Ocean with either a simplified aerosol setup or full-chemistry. We compare the aerosol activation process in the newly-coupled SBM-MOSAIC against the SBM simulation without chemistry using a simplified aerosol setup, and the results show consistent activation rates. A longer time simulation reinforces that aerosol resuspension through cloud drop evaporation plays an important role in replenishing aerosols and impacts cloud and precipitation in marine stratocumulus clouds. Evaluation of the coupled SBM-MOSAIC with full-chemistry using aircraft measurements suggests that the new model works realistically for the marine stratocumulus clouds, and improves the simulation of cloud microphysical properties compared to a simulation using MOSAIC coupled with the Morrison two-moment microphysics.
Symbolic regression methods simultaneously determine the model functional form and the regression parameter values by generating expression trees. Symbolic regression can capture the complexity of real–world phenomena but the use of deterministic optimization for symbolic regression has been limited due to the complexity of the search space of existing formulations. Herein we present a novel deterministic mixed–integer nonlinear programming formulation for symbolic regression that incorporates derivative constraints through auxiliary expression trees. By applying the chain rule to mathematical operations, binary expression trees are capable of representing the calculation of first and second derivatives. We apply this formulation to illustrative examples using derivative information to show increased model discrimination capability. In addition, we perform a case study of a thermodynamic equation of state to gain insight on valid functional forms with thermodynamics–based constraints on the first and second derivatives.
A biomass fast pyrolysis model was developed for implementation in equation-oriented modeling software. Based on a previous framework of coupled 1-dimensional mass, momentum, and heat balance equations, this model includes updated reaction kinetics to provide a more detailed representation of biomass components, intermediates, and products. A recently published derivation of thermodynamic properties for the species in this model has allowed the energy balance around the pyrolysis reactor to be rigorously redefined. With these improvements, the optimum pyrolysis temperature for bio-oil production predicted by the model is increased by up to 50°C, bringing it in line with experimental data and increasing the overall agreement. More importantly, the reactor energy balance is strictly enforced. The resulting model can be used for the design and optimization of biomass fast pyrolysis processes, and comparisons with other options for biomass utilization.
Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.
Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.
The presence of free fatty acids interferes with the conversion of plant oils to biodiesel. In this work, four strong and weak base resins were evaluated for the removal of free fatty acids (FFA) from oil. Amberlite FPA 51 showed the highest adsorption capacity of FFA. A resin concentration above 3% could enable a higher percentage FFA adsorption. The adsorption process fitted a pseudo-first-order kinetic model and achieved equilibrium in approximately 8 h. A full factorial design was used to optimize the resin and FFA concentrations at a fixed temperature (40°C). A ratio of resin to fatty acid concentrations above 1.875 was sufficient for 70% adsorption and the amount adsorbed continued to increase with further added resin. A two-step washing of resin using hexane and ethanol recovered approximately 67.55%±4.05% of the initially added fatty acid. The resin that was used was regenerated with 5% NaOH and re-used for a minimum of three consecutive cycles. However, the adsorption capacity diminished to 75% of the initial cycle in cycles 2 and 3. Thus, the work presents a resin-based process for deacidification of oil to reduce fatty acid content of oil for biodiesel production.
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4‐year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio‐genomics‐based prognostic and predictive factors for HR+/HER− metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi‐omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as “maintaining effective communication” and “following good data science practices,” followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
Water oxidation is an important reaction studied as a way to generate electrons from water, to promote water splitting and the formation of green hydrogen. When using electrodes to drive homogeneous water oxidation catalysis, cyclic voltammograms are analyzed to provide catalytic rate constants. There are two main methods, foot-of-the-wave analysis (FOWA) and limiting current analysis. FOWA relies on approximations inherent to analyzing water oxidation catalysis, such as determining the formal potential of the catalytic intermediate, E 0 cat . Limiting current methods are the optimal way to analyze catalyst performance but rely on observable limiting current, which is virtually never seen in water oxidation. To avoid those issues, a method is proposed for analyzing nonideal cyclic voltammetry waveshapes in water oxidation: by analyzing rate data across a large range of potentials, an optimal potential, E 0 cat , can be obtained, where catalytic current, i cat , is nearly independent of scan rate and has a linear dependency on buffer concentration. Here, the method is applied to four homogeneous water oxidation catalysts with prior extensive electrochemical elucidation, all of which lack an ideal, purely kinetic waveshape in cyclic voltammetry. Application of the method avoids the biases of the other methods cited for the kinetic analyses of water oxidation catalysts.
Given the varying degrees of maturity among existing carbon capture (CC) technology alternatives, an understanding of the inherent technical and financial risk and uncertainty associated with these competing technologies is requisite to the success of carbon capture as a viable solution to the greenhouse gas emission challenge. The availability of tools and capabilities to conduct rigorous, risk–based technology comparisons is thus highly desirable for directing valuable resources toward the technology option(s) with a high return on investment, superior carbon capture performance, and minimum risk. To address this research need, we introduce a novel risk-based technology comparison method supported by an integrated multi-domain risk model set to estimate risks related to technological maturity, technical performance, and profitability. Through a comparison between solid sorbent and liquid solvent systems, we illustrate the feasibility of estimating risk and quantifying uncertainty in a single domain (modular analytical capability) as well as across multiple risk dimensions (coupled analytical capability) for comparison. This method brings technological maturity and performance to bear on profitability projections, and carries risk and uncertainty modeling across domains via inter-model sharing of parameters, distributions, and input/output. The integration of the models facilitates multidimensional technology comparisons within a common probabilistic risk analysis framework. This approach and model set can equip potential technology adopters with the necessary computational capabilities to make risk-informed decisions about CC technology investment. The method and modeling effort can also be extended to other industries where robust tools and analytical capabilities are currently lacking for evaluating nascent technologies.