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DOE OSTI2023

Interlaboratory Study: Testing Reproducibility of Solid Biofuels Component Identification Using Reflected Light Microscopy

Considering global market trends and concerns about climate change and sustainability, increased biomass use for energy is expected to continue. As more diverse materials are being utilized to manufacture solid biomass fuels, it is critical to implement quality assessment methods to analyze these fuels thoroughly. One such method is reflected light microscopy (RLM), which has the potential to complement and enhance current standard testing, leading to improving fuel quality assessment and, ultimately, preventing avoidable air pollution. An interlaboratory study (ILS) was conducted to test the reproducibility of biomass fuels component identification using a reflected light microscopy technique. The exercise was conducted on thirty photomicrographs showing biomass and various undesired components (like plastics or mineral matter), which were purposely added (by the ILS organizers) to contaminate wood pellets and charcoal-based grilling fuels. Forty-six participants had various levels of difficulty identifying the marked components, and as a result, the percentage of correct answers ranged from 52.2 to 94.4%. Among the most difficult components to distinguish were petroleum products and inorganic matter. Various reasons led to the misidentification, including insufficient morphological descriptions of the components provided to participants, ambiguities of the nomenclature, limitations of the analytical and exercise method, and insufficient experience of the participants. Overall, the results indicate that RLM has the potential to enhance the quality assessment of biomass fuels. However, they also demonstrate that the petrographic classification used in this exercise requires further refinement before it can be standardized. While a new simplified classification of solid biomass fuels components was created as an outcome of this study, future research is necessary to refine the nomenclature, develop a microscopic morphological description of the components, and verify the accuracy of component identification with a follow-up ILS.

biomass↗

DOE OSTI2023

Development and demonstration of a rolling ball viscometer for molten salts with near-minimum liquidus NaCl–KCl

The thermophysical characterization of molten chloride and fluoride salts is a key interest for the development of advanced molten salt nuclear reactors. Viscosity is one thermophysical property of interest; a high-accuracy understanding of this property is necessary for modeling the thermal energy transfer and mass flow rate for a given molten salt system. Only a few methods have consistently been used to measure the viscosity of molten chloride and fluoride salt systems, and large discrepancies have been observed in the literature between independent studies of nearly identical salt systems. As such, exploration of novel measurement methods or implementations may allow for further validation, improved measurement accuracy, and overall improved understanding about optimal approaches for measuring molten salt viscosity. Here, in this study, a novel implementation of the rolling ball viscometry method was used to measure the viscosity of a 44/56 mol % mixture of NaCl–KCl within a temperature range of 714–838 °C, with the intent to demonstrate the feasibility of this technique for molten salt viscosity measurement cross-validation. The purity of this salt was confirmed through laser-induced breakdown spectroscopy. Measured viscosities were approximately 1.3–1.0 cP within this temperature range, and the overall uncertainty was 10.8%, which was determined through Gaussian error propagation. The results agree reasonably well with measured viscosities of similar NaCl–KCl mixtures in the literature (within 2%–20% depending on the temperature and study), although the experimental error in some of these comparative studies may be significant.

47 OTHER INSTRUMENTATION↗

NASA NTRS1993

Tracking motions from satellite water vapor imagery: Quantitative applications to hurricane track forecasting

Water vapor imagery from GOES satellites has been available for over a decade. These data are used extensively, mainly in a qualitative mode, by forecasters in the United States (Weldon and Holmes, 1991). Some attempts have been made at quantifying the data by tracking features in time sequences of the imagery (Stewart et al., 1985; Hayden and Stewart, 1987). For a variety of reasons, applications of this approach have produced marginal results (Velden, 1990). Recently, METEOSAT-3 (M-3) was repositioned at 50W by the European Space Agency, in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the GOES satellite, the M-3 has a superior resolution and signal-to-noise ratio in its water vapor channel, which translates into improved automated tracking capabilities. During a period in 1992 which included the Atlantic hurricane season, water vapor tracking algorithms were applied to the M-3 data in order to evaluate the coverage, accuracy and model impact of the derived vectors. Data sets were produced during several tropical cyclone cases, including Hurricane Andrew. In this paper, the M-3 water vapor wind sets are assessed, and their impact on a hurricane track forecast model is examined.

Velden, Christopher↗

NASA NTRS2001

Evaluation of CFD Turbulent Heating Prediction Techniques and Comparison With Hypersonic Experimental Data

Results from a study to assess the accuracy of turbulent heating and skin friction prediction techniques for hypersonic applications are presented. The study uses the original and a modified Baldwin-Lomax turbulence model with a space marching code. Grid converged turbulent predictions using the wall damping formulation (original model) and local damping formulation (modified model) are compared with experimental data for several flat plates. The wall damping and local damping results are similar for hot wall conditions, but differ significantly for cold walls, i.e., T(sub w) / T(sub t) < 0.3, with the wall damping heating and skin friction 10-30% above the local damping results. Furthermore, the local damping predictions have reasonable or good agreement with the experimental heating data for all cases. The impact of the two formulations on the van Driest damping function and the turbulent eddy viscosity distribution for a cold wall case indicate the importance of including temperature gradient effects. Grid requirements for accurate turbulent heating predictions are also studied. These results indicate that a cell Reynolds number of 1 is required for grid converged heating predictions, but coarser grids with a y(sup +) less than 2 are adequate for design of hypersonic vehicles. Based on the results of this study, it is recommended that the local damping formulation be used with the Baldwin-Lomax and Cebeci-Smith turbulence models in design and analysis of Hyper-X and future hypersonic vehicles.

Dilley, Arthur D.↗

NASA NTRS2018

Dynamic Adjustment of Simulation Parameters for Efficient Vehicle Prognostics

Model-based diagnostics and prognostics rely on state estimation and uncertainty management algorithms to produce useful information for system operators and maintainers. This information enables more informed operational decisions, condition-based maintenance, and overall mission safety assurance. Typically, uncertainty is associated with vehicle state-of-health estimation and prediction results because of modeling errors, internal or external sources of noise, and sensor inaccuracy. Probabilistic uncertainty management methods including Sequential Monte Carlo simulation are commonly used to reason about state-of-health estimates and predictions in the presence of these sources of uncertainty. However, such algorithms can be computationally expensive as they require a very large number of samples to obtain a sufficiently accurate quantification of the end of life probability distribution. As a result, highly mobile autonomous systems that leverage the prognostic results for mission-level replanning are often constrained in their processing capability because of these computationally expensive simulation approaches. Therefore, in this paper, we investigate algorithmic methods for dynamically adjusting simulation time step as well as number of samples to achieve highly efficient prognostic results while maintaining results accuracy. Results obtained from simulated flight experiments of an electric unmanned aerial vehicle are presented to verify the efficacy of such algorithms.

Prognostics↗

NASA NTRS2000

Metrology and Alignment of Light Weight Grazing Incidence X-Ray Mirrors

Metrology and alignment of light weight X-ray optics have been a challenge for two reasons: (1) that the intrinsic mirror quality and distortions caused by handling can not be easily separated, and (2) the diffraction limits of the visible light become a severe problem at the order of one arc-minute. Traditional methods of using a normal incident pencil or small parallel beam which monitors a tiny fraction of the mirror in question at a given time can not adequately monitor those distortions. We are developing a normal incidence setup that monitors a large fraction, if not the whole, of the mirror at any given time. It will allow us to align thin X-ray mirrors to-an accuracy of a few arc seconds or to a limit dominated by the mirror intrinsic quality.

Zhang, William↗

NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗

NASA NTRS2016

Development of a Perfectly Matched Layer Technique for a Discontinuous-Galerkin Spectral-Element Method

The numerical simulation of many aerodynamic non-periodic flows of practical interest involves discretized computational domains that often must be artificially truncated. Appropriate boundary conditions are required at these truncated domain boundaries, and ideally, these boundary conditions should be perfectly "absorbing" or "nonreflecting" so that they do not contaminate the flow field in the interior of the domain. The proper specification of these boundaries is critical to the stability, accuracy, convergence, and quality of the numerical solution, and has been the topic of considerable research. The need for accurate boundary specification has been underscored in recent years with efforts to apply higher-fidelity methods (DNS, LES) in conjunction with high-order low-dissipation numerical schemes to realistic flow configurations. One of the most popular choices for specifying these boundaries is the characteristics-based boundary condition where the linearized flow field at the boundaries are decomposed into characteristic waves using either one-dimensional Riemann or other multi-dimensional Riemann approximations. The values of incoming characteristics are then suitably modified. The incoming characteristics are specified at the in flow boundaries, and at the out flow boundaries the variation of the incoming characteristic is zeroed out to ensure no reflection. This, however, makes the problem ill-posed requiring the use of an ad-hoc parameter to allow small reflections that make the solution stable. Generally speaking, such boundary conditions work reasonably well when the characteristic flow direction is normal to the boundary, but reflects spurious energy otherwise. An alternative to the characteristic-based boundary condition is to add additional "buffer" regions to the main computational domain near the artificial boundaries, and solve a different set of equations in the buffer region in order to minimize acoustic reflections. One approach that has been used involves modeling the pressure fluctuations as acoustic waves propagating in the far-field relative to a single noise-source inside the buffer region. This approach treats vorticity-induced pressure fluctuations the same as acoustic waves. Another popular approach, often referred to as the "sponge layer," attempts to dampen the flow perturbations by introducing artificial dissipation in the buffer region. Although the artificial dissipation removes all perturbations inside the sponge layer, incoming waves are still reflected from the interface boundary between the computational domain and the sponge layer. The effect of these refkections can be somewhat mitigated by appropriately selecting the artificial dissipation strength and the extent of the sponge layer. One of the most promising variants on the buffer region approach is the Perfectly Matched Layer (PML) technique. The PML technique mitigates spurious reflections from boundaries and interfaces by dampening the perturbation modes inside the buffer region such that their eigenfunctions remain unchanged. The technique was first developed by Berenger for application to problems involving electromagnetic wave propagation. It was later extended to the linearized Euler, Euler and Navier-Stokes equations by Hu and his coauthors. The PML technique ensures the no-reflection property for all waves, irrespective of incidence angle, wavelength, and propagation direction. Although the technique requires the solution of a set of auxiliary equations, the computational overhead is easily justified since it allows smaller domain sizes and can provide better accuracy, stability, and convergence of the numerical solution. In this paper, the PML technique is developed in the context of a high-order spectral-element Discontinuous Galerkin (DG) method. The technique is compared to other approaches to treating the in flow and out flow boundary, such as those based on using characteristic boundary conditions and sponge layers. The superiority of the current PML technique over other approaches is demonstrated for a range of test cases, viz., acoustic pulse propagation, convective vortex, shear layer flow, and low-pressure turbine cascade flow. The paper is structured as follows. We first derive the PML equations from the non{linear Euler equations. A short description of the higher-order DG method used is then described. Preliminary results for the four test cases considered are then presented and discussed. Details regarding current work that will be included in the final paper are also provided.

Spectral↗

DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Phase transition in two-dimensional monolayer (1L)-molybdenum disulfide induced by atomic S-basal plane gliding via synchrotron X-ray monochromatic beam radiation for superior electronic performance

Here, in this work, we report a novel approach to reduce the channel resistance by inducing a phase transition behavior from 2H to 1T in a monolayer MoS 2 (1L-MoS 2 ) by a synchrotron X-ray monochromatic beam (mono-beam) radiation. The effects of the biphase structure by the mono-beam on the 1L-MoS2 film were investigated using Raman spectra, photoluminescence (PL) spectra, scanning tunneling microscopy, and scanning tunneling spectroscopy, respectively. Through material characterization, we identified that the lateral sliding of S-vacancies along the S-plane in the 1L-MoS 2 is the key reason for the origin of unidirectional phase transition. The precise phase engineering triggered by the mono-beam radiation process allows the realization of field-effect transistors (FET) with 2X improvement in mobility toward a high on/off ratio (~10 8 ) and a near-ideal subthreshold swing of ~88 mV per decade. The validity of the phase engineering could be further extended for its application as a memory device, exhibiting a gate tunable conduction modulation behavior and a high resistance ratio of ~10 2 at a gate bias of 5 V with endurance of ~100 cycles. Furthermore, an artificial neural network using the synaptic weight update with accuracy of ~93 % was achieved.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Augmenting machine learning of Grad–Shafranov equilibrium reconstruction with Green's functions

This work presents a method for predicting plasma equilibria in tokamak fusion experiments and reactors. The approach involves representing the plasma current as a linear combination of basis functions using principal component analysis of plasma toroidal current densities (J t ) from the EFIT-AI equilibrium database. Then utilizing EFIT's Green's function tables, basis functions are created for the poloidal flux (ψ) and diagnostics generated from the toroidal current (J t ). Similar to the idea of a physics-informed neural network (NN), this physically enforces consistency between ψ, J t , and the synthetic diagnostics. First, the predictive capability of a least squares technique to minimize the error on the synthetic diagnostics is employed. The results show that the method achieves high accuracy in predicting ψ and moderate accuracy in predicting J t with median R 2 = 0.9993 and R 2 = 0.978, respectively. A comprehensive NN using a network architecture search is also employed to predict the coefficients of the basis functions. The NN demonstrates significantly better performance compared to the least squares method with median R 2 = 0.9997 and 0.9916 for J t and ψ, respectively. The robustness of the method is evaluated by handling missing or incorrect data through the least squares filling of missing data, which shows that the NN prediction remains strong even with a reduced number of diagnostics. Additionally, the method is tested on plasmas outside of the training range showing reasonable results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1991

AFE base flow computations

Hypersonic wake flows behind the Aeroassist Flight Experiment (AFE) geometry are analyzed using two Navier-Stokes flow solvers. Many of the AFE wake features observed in ballistic-range shadowgraphs are simulated using a simple, two-dimensional semicylinder geometry at moderate angles of attack. At free-stream conditions corresponding to a Hypersonic Free Flight Facility (HFFF) AFE experiment, the three-dimensional base flow for the AFE geometry is computed using an ideal-gas, Navier-Stokes solver. The computed results agree reasonably well with the shadowgraphs taken at the HFFF. An ideal-gas and a nonequilibrium Navier-Stokes solver have been coupled and applied to the complete flow around the AFE vehicle at the free-stream conditions corresponding to a nomial trajectory point. Limitations of the coupled ideal-gas and nonequilibrium solution are discussed. The nonequilibrium base flow solution is analyzed for the wake radiation and the radiation profiles along various lines of sight are compared. Finally, the wake unsteadiness is predicted using experimental correlations and the numerical solutions. An adaptive grid code, SAGE, has been used in all the simulations to enhance the solution accuracy. The grid adaptation is found to be necessary in obtaining base flow solutions with accurate flow features.

Venkatapathy, Ethiraj↗

NASA NTRS1998

Evaluation of the 29-km Eta Model: Objective Verification at Three Selected Stations - Part 1

This paper describes an objective verification of the National Centers for Environmental Prediction (NCEP) 29-km eta model from May 1996 through January 1998. The evaluation was designed to assess the model's surface and upper-air point forecast accuracy at three selected locations during separate warm (May - August) and cool (October - January) season periods. In order to enhance sample sizes available for statistical calculations, the objective verification includes two consecutive warm and cool season periods. Systematic model deficiencies comprise the larger portion of the total error in most of the surface forecast variables that were evaluated. The error characteristics for both surface and upper-air forecasts vary widely by parameter, season, and station location. At upper levels, a few characteristic biases are identified. Overall however, the upper-level errors are more nonsystematic in nature and could be explained partly by observational measurement uncertainty. With a few exceptions, the upper-air results also indicate that 24-h model error growth is not statistically significant. In February and August 1997, NCEP implemented upgrades to the eta model's physical parameterizations that were designed to change some of the model's error characteristics near the surface. The results shown in this paper indicate that these upgrades led to identifiable and statistically significant changes in forecast accuracy for selected surface parameters. While some of the changes were expected, others were not consistent with the intent of the model updates and further emphasize the need for ongoing sensitivity studies and localized statistical verification efforts. Objective verification of point forecasts is a stringent measure of model performance, but when used alone, is not enough to quantify the overall value that model guidance may add to the forecast process. Therefore, results from a subjective verification of the meso-eta model over the Florida peninsula are discussed in the companion paper by Manobianco and Nutter. Overall verification results presented here and in part two should establish a reasonable benchmark from which model users and developers may pursue the ongoing eta model verification strategies in the future.

Nutter, Paul A.↗

DOE OSTI2025

Automatic Generation of Chemical Mechanisms for Electrochemical Systems: Solid Electrolyte Interphase Formation in Lithium Batteries

Electrolytes in many lithium ion batteries decompose at the low potentials near the anode. The decomposition products form a layer termed the solid electrolyte interphase (SEI). The composition and growth of the SEI layer significantly affect both the capacity fade and safety of lithium ion batteries. However, SEI formation and growth kinetics are not well understood. In this work, we present an extension of the Reaction Mechanism Generator (RMG) software to automatically generate mechanisms for SEI formation. We extend RMG’s solvation correction framework to account for kinetic solvent effects and demonstrate the accuracy of our technique. We calculate thermochemical parameters for 252 species and rate coefficients for 69 reactions, most with associated solvation corrections. This and additional quantum chemistry data are used to extend RMG’s thermodynamic group additivity and solute parameter estimation schemes to handle lithiated species and add 14 new reaction families to RMG. RMG is additionally extended to simulate electrocatalytic systems. Lastly, we demonstrate RMG on the decomposition of acetonitrile and ethylene carbonate near a battery anode. Furthermore, while this framework does not yet resolve individual ions, as appropriate thermochemistry estimators are not available, and thus, cannot yet resolve more complex electrochemical pathways, RMG is able to generate reasonable pathways for SEI formation that match literature pathways and products. In particular, RMG identifies a new important reaction pathway that is not present in literature.

Chemical reactions↗

DOE OSTI2023

FORCE Integration with DRAFT and IDAES

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to produce multiple commodities and improve economic viability under uncertain market conditions. Technical and economic analysis of IES requires modeling of complex processes with software models having enough fidelity to capture real-world dynamics while still being capable of running using reasonable computing resources. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Recent developments for the FORCE toolset have centered on strengthening the flexibility and modularity to link with external models and other available software to enhance IES simulations. Adding versatility to the FORCE toolset improves capability resulting in better techno-economic simulation of nuclear and IES components (both in potential higher fidelity and accuracy to expected performance after deployment). It also helps leverage existing work in the IES field and improve efficiency in national code development. This report focuses on two such endeavors: integration of the Dynamic Reliability Analysis Framework Tool (DRAFT) and the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) software packages into FORCE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Hybrid Quantum-Classical Neural Networks

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers are soon expected to approach the physical limitations that will slow their performance improvements in the near future. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one of the most successful approaches to quantum deep learning on noisy intermediate scale quantum devices. We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. We empirically analyze the performance of this hybrid neural network on a series of binary classification data sets using a simulated IBM universal quantum computer and a state-of-the-art IBM universal quantum computer. On the simulated hardware, we observe that the hybrid neural network achieves around 10% higher classification accuracy and 20% better minimization of the cost function than an individual variational quantum circuit. On the quantum hardware, we observe that each model only performs well when the qubit and gate count is sufficiently small.

Arthur, Davis↗

DOE OSTI2021

Practices for Demonstrating Energy Savings from Commercial PACE Projects

Nearly three-fourths of U.S. states have authorized local governments to use voluntary special assessments on commercial properties to finance energy improvements that boost economic development, create jobs, increase property values and advance energy goals. Commercial Property Assessed Clean Energy (C-PACE) financing allows building owners to repay the borrowed capital — from private or public sources — over time using their property as security. Berkeley Lab is supporting the Department of Energy’s Commercial PACE Working Group by developing a series of C-PACE issue briefs. The second brief in this series, Practices for Demonstrating Energy Savings from Commercial PACE Projects, looks at common practices for demonstrating energy savings to support state and local governments that sponsor C-PACE programs and want to track their energy impacts. This brief reviews: -The value proposition and trade-offs of conducting energy impact assessments for C-PACE programs; -Methods to quantify energy savings impacts from energy efficiency building improvements; and -Available resources and tools to support energy impact assessments. C-PACE programs may benefit from energy impact assessments for many reasons, including: -Validating the public benefits of the programs -Demonstrating that C-PACE can deliver participant benefits -Illustrating program impacts on public policy goals -Generating data to help improve program performance Many C-PACE programs are collecting data on project energy savings impacts, and these data can be leveraged to further support decision making and program implementation. Potential drawbacks to energy impact assessments may include added cost and burdens on property owners (e.g., the need to collect building energy consumption data). Where these burdens are considerable, they might slow program uptake. State and local governments can balance the benefits of energy impact assessments with the range of costs and accuracy inherent to available assessment methodologies. Additionally, depending on the policy context in the state or local government, a C-PACE program may be able to leverage existing efforts (e.g., building energy benchmarking programs) to reduce impact assessment costs, align with building owner practices and expectations, and efficiently assess program impact.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1985

Optimum strategies and performance for the remote sensing of path-delay using ground-based microwave radiometers

Computer simulations were used to study the accuracy of remotely sensed microwave radiometer measurements of excess radio propagation path delay due to atmospheric water vapor. A number of strategies were investigated for remote sensing of path delay in order to define baseline parameters for the design of water vapor radiometers (WVRs) in geodetic applications. Strategies were judged according to their retrieval performance in a variety of climatological regions. An observing approach using the frequency 20.7/22.2/31.4 Gz was found to be close to optimum. A statistical retrieval approach using retrieval coefficients stratified for clear and cloudy weather was identified as a substantial improvement over conventional single-set all-weather retrieval strategies. It is shown that a reasonably well optimized WVR with an estimated calibration uncetainty of 0.5 K can achieve an overall retrieval performance of 0.27 cm in clear weather; and 0.51 cm in cloudy weather. The weather-averaged retrieval performance for individual locations was found to vary by no more than 14 percent from the average for all locations despite a mean path delay of 5 to 26 cm.

Gary, B. L.↗