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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Bayes classification of terrain cover using normalized polarimetric data

The normalized polarimetric classifier (NPC) which uses only the relative magnitudes and phases of the polarimetric data is proposed for discrimination of terrain elements. The probability density functions (PDFs) of polarimetric data are assumed to have a complex Gaussian distribution, and the marginal PDF of the normalized polarimetric data is derived by adopting the Euclidean norm as the normalization function. The general form of the distance measure for the NPC is also obtained. It is demonstrated that for polarimetric data with an arbitrary PDF, the distance measure of NPC will be independent of the normalization function selected even when the classifier is mistrained. A complex Gaussian distribution is assumed for the polarimetric data consisting of grass and tree regions. The probability of error for the NPC is compared with those of several other single-feature classifiers. The classification error of NPCs is shown to be independent of the normalization function.

Yueh, H. A.↗
NASA NTRS2003

Statistical Evaluation and Improvement of Methods for Combining Random and Harmonic Loads

Structures in many environments experience both random and harmonic excitation. A variety of closed-form techniques has been used in the aerospace industry to combine the loads resulting from the two sources. The resulting combined loads are then used to design for both yield/ultimate strength and high- cycle fatigue capability. This Technical Publication examines the cumulative distribution percentiles obtained using each method by integrating the joint probability density function of the sine and random components. A new Microsoft Excel spreadsheet macro that links with the software program Mathematica to calculate the combined value corresponding to any desired percentile is then presented along with a curve tit to this value. Another Excel macro that calculates the combination using Monte Carlo simulation is shown. Unlike the traditional techniques. these methods quantify the calculated load value with a consistent percentile. Using either of the presented methods can be extremely valuable in probabilistic design, which requires a statistical characterization of the loading. Additionally, since the CDF at high probability levels is very flat, the design value is extremely sensitive to the predetermined percentile; therefore, applying the new techniques can substantially lower the design loading without losing any of the identified structural reliability.

Brown, A. M.↗
NASA NTRS1984

Viking bistatic radar experiment - Summary of results in near-equatorial regions

Viking bistatic radar have been processed using Hagfors' scattering function to obtain estimates of rms surface roughness from ground tracks primarily in the equatorial region of Mars. Roughness varies from as little as 0.75 deg rms east of Solis and Sinai Plana to at least 8 deg on the western slope of Arsia Mons. Estimated roughness in cratered terrain is generally about 5 deg or less, implying the existence of smoothing processes on scales less than 100 m. There is good agreement between bistatic roughness estimates and earth-based results at ground track intersections near the equator in both cratered terrain and plains. Surface tilt probability density functions derived from these observations are usually more sharply peaked than the Hagfors function for similar values of rms surface roughness. Earlier results using the Gaussian model appear to have overestimated the surface roughness by 30-40 percent.

Simpson, R. A.↗
DOE OSTI2025

Bayesian quantification of observability and equation of state of twin stars

The possibility of discovering twin stars, two neutron stars (NSs) with the same mass but different radii, is usually studied in forward modelings by using a restricted number of NS matter equations of state (EOSs) encapsulating a first-order phase transition from hadronic to quark matter (QM). Informing our likelihood function with the NS radius data from GW170817 and using a metamodel with nine parameters capable of mimicking most NS EOSs available in the literature, we conduct a Bayesian quantification of the observability and underlying EOSs of twin stars. Of the accepted EOSs, between 12 and 18% yield twin stars, depending on the restrictions we place on the second branch. The possibility of twin stars remains robust even under recent observational constraints. Here, we show that many of these twin star scenarios are observable with currently available levels of accuracy in measuring NS radii. We also present the marginalized posterior probability density functions (PDFs) of every EOS parameter for each of four mass-radius correlation topologies. We find that the inferred EOS depends sensitively on not only whether twin stars are present, but also the category of twin stars, indicating that the observation of twin stars would provide a strong constraint on the underlying EOS. In particular, for two coexisting hybrid stars having QM cores at different densities, the PDF for QM speed of sound squared 𝑐$^2_{qm}$ has two peaks, one below and another above the conformal limit 𝑐$^2_{qm}$ = 1/3 predicted by perturbative QCD.

QCD in nuclear reactions↗
NASA NTRS2013

Galactic Cosmic Ray Event-Based Risk Model (GERM) Code

This software describes the transport and energy deposition of the passage of galactic cosmic rays in astronaut tissues during space travel, or heavy ion beams in patients in cancer therapy. Space radiation risk is a probability distribution, and time-dependent biological events must be accounted for physical description of space radiation transport in tissues and cells. A stochastic model can calculate the probability density directly without unverified assumptions about shape of probability density function. The prior art of transport codes calculates the average flux and dose of particles behind spacecraft and tissue shielding. Because of the signaling times for activation and relaxation in the cell and tissue, transport code must describe temporal and microspatial density of functions to correlate DNA and oxidative damage with non-targeted effects of signals, bystander, etc. These are absolutely ignored or impossible in the prior art. The GERM code provides scientists data interpretation of experiments; modeling of beam line, shielding of target samples, and sample holders; and estimation of basic physical and biological outputs of their experiments. For mono-energetic ion beams, basic physical and biological properties are calculated for a selected ion type, such as kinetic energy, mass, charge number, absorbed dose, or fluence. Evaluated quantities are linear energy transfer (LET), range (R), absorption and fragmentation cross-sections, and the probability of nuclear interactions after 1 or 5 cm of water equivalent material. In addition, a set of biophysical properties is evaluated, such as the Poisson distribution for a specified cellular area, cell survival curves, and DNA damage yields per cell. Also, the GERM code calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle in a selected material. The GERM code makes the numerical estimates of basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at the NASA Space Radiation Laboratory (NSRL) for the purpose of simulating space radiation biological effects. In the first option, properties of monoenergetic beams are treated. In the second option, the transport of beams in different materials is treated. Similar biophysical properties as in the first option are evaluated for the primary ion and its secondary particles. Additional properties related to the nuclear fragmentation of the beam are evaluated. The GERM code is a computationally efficient Monte-Carlo heavy-ion-beam model. It includes accurate models of LET, range, residual energy, and straggling, and the quantum multiple scattering fragmentation (QMSGRG) nuclear database.

Cucinotta, Francis A.↗
DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗
NASA NTRS2014

A Monte Carlo Approach to Modeling the Breakup of the Space Launch System EM-1 Core Stage with an Integrated Blast and Fragment Catalogue

The Liquid Propellant Fragment Overpressure Acceleration Model (L-FOAM) is a tool developed by Bangham Engineering Incorporated (BEi) that produces a representative debris cloud from an exploding liquid-propellant launch vehicle. Here it is applied to the Core Stage (CS) of the National Aeronautics and Space Administration (NASA) Space Launch System (SLS launch vehicle). A combination of Probability Density Functions (PDF) based on empirical data from rocket accidents and applicable tests, as well as SLS specific geometry are combined in a MATLAB script to create unique fragment catalogues each time L-FOAM is run-tailored for a Monte Carlo approach for risk analysis. By accelerating the debris catalogue with the BEi blast model for liquid hydrogen / liquid oxygen explosions, the result is a fully integrated code that models the destruction of the CS at a given point in its trajectory and generates hundreds of individual fragment catalogues with initial imparted velocities. The BEi blast model provides the blast size (radius) and strength (overpressure) as probabilities based on empirical data and anchored with analytical work. The coupling of the L-FOAM catalogue with the BEi blast model is validated with a simulation of the Project PYRO S-IV destruct test. When running a Monte Carlo simulation, L-FOAM can accelerate all catalogues with the same blast (mean blast, 2 σ blast, etc.), or vary the blast size and strength based on their respective probabilities. L-FOAM then propagates these fragments until impact with the earth. Results from L-FOAM include a description of each fragment (dimensions, weight, ballistic coefficient, type and initial location on the rocket), imparted velocity from the blast, and impact data depending on user desired application. LFOAM application is for both near-field (fragment impact to escaping crew capsule) and far-field (fragment ground impact footprint) safety considerations. The user is thus able to use statistics from a Monte Carlo set of L-FOAM catalogues to quantify risk for a multitude of potential CS destruct scenarios. Examples include the effect of warning time on the survivability of an escaping crew capsule or the maximum fragment velocities generated by the ignition of leaking propellants in internal cavities.

Richardson, Erin↗
DOE OSTI2020

Fluid and gyrokinetic turbulence in open field-line, helical plasmas

Two-fluid Braginskii codes have simulated open-field line turbulence for over a decade, and only recently, it has become possible to study these systems with continuum gyrokinetic codes. This work presents a first-of-its-kind comparison between fluid and (long-wavelength) gyrokinetic models in open field-lines, using the GDB and Gkeyll codes to simulate interchange turbulence in the Helimak device at the University of Texas [T. N. Bernard et al., Phys. Plasmas 26, 042301 (2019)]. Partial agreement is attained in a number of diagnostic channels when the GDB sources and sheath boundary conditions (BCs) are selected carefully, especially the heat-flux BCs that can drastically alter the temperature. The radial profile of the fluctuation levels is qualitatively similar and quantitatively comparable on the low-field side, although statistics such as moments of the probability density function and the high-frequency spectrum show greater differences. This comparison indicates areas for future improvement in both simulations, such as sheath BCs, and improvements in GDB like particle conservation and spatially varying thermal conductivity, in order to achieve better fluid-gyrokinetic agreement and increase fidelity when simulating experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties

This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). Here, the sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.

Aleatory uncertainty↗
NASA NTRS2016

Small vs. Large Convective Cloud Objects from CERES Aqua Observations: Where are the Intraseasonal Variation Signals?

During inactive phases of Madden-Julian oscillation (MJO), there are plenty of deep but small convective systems and far fewer deep and large ones. During active phases of MJO, a manifestation of an increase in the occurrence of large and deep cloud clusters results from an amplification of large-scale motions by stronger convective heating. This study is designed to quantitatively examine the roles of small and large cloud clusters during the MJO life cycle. We analyze the cloud object data from Aqua CERES observations for tropical deep convective (DC) and cirrostratus (CS) cloud object types according to the real-time multivariate MJO index. The cloud object is a contiguous region of the earth with a single dominant cloud-system type. The size distributions, defined as the footprint numbers as a function of cloud object diameters, for particular MJO phases depart greatly from the combined (8-phase) distribution at large cloud-object diameters due to the reduced/increased numbers of cloud objects related to changes in the large-scale environments. The medium diameter corresponding to the combined distribution is determined and used to partition all cloud objects into "small" and "large" groups of a particular phase. The two groups corresponding to the combined distribution have nearly equal numbers of footprints. The medium diameters are 502 km for DC and 310 km for cirrostratus. The range of the variation between two extreme phases (typically, the most active and depressed phases) for the small group is 6-11% in terms of the numbers of cloud objects and the total footprint numbers. The corresponding range for the large group is 19-44%. In terms of the probability density functions of radiative and cloud physical properties, there are virtually no differences between the MJO phases for the small group, but there are significant differences for the large groups for both DC and CS types. These results suggest that the intreseasonal variation signals reside at the large cloud clusters while the small cloud clusters represent the background noises resulting from various types of the tropical waves with different wavenumbers and propagation directions/speeds.

Xu, Kuan-Man↗
NASA NTRS2015

Background Noises Versus Intraseasonal Variation Signals: Small vs. Large Convective Cloud Objects From CERES Aqua Observations

During inactive phases of Madden-Julian Oscillation (MJO), there are plenty of deep but small convective systems and far fewer deep and large ones. During active phases of MJO, a manifestation of an increase in the occurrence of large and deep cloud clusters results from an amplification of large-scale motions by stronger convective heating. This study is designed to quantitatively examine the roles of small and large cloud clusters during the MJO life cycle. We analyze the cloud object data from Aqua CERES (Clouds and the Earth's Radiant Energy System) observations between July 2006 and June 2010 for tropical deep convective (DC) and cirrostratus (CS) cloud object types according to the real-time multivariate MJO index, which assigns the tropics to one of the eight MJO phases each day. The cloud object is a contiguous region of the earth with a single dominant cloud-system type. The criteria for defining these cloud types are overcast footprints and cloud top pressures less than 400 hPa, but DC has higher cloud optical depths (=10) than those of CS (<10). The size distributions, defined as the footprint numbers as a function of cloud object diameters, for particular MJO phases depart greatly from the combined (8-phase) distribution at large cloud-object diameters due to the reduced/increased numbers of cloud objects related to changes in the large-scale environments. The medium diameter corresponding to the combined distribution is determined and used to partition all cloud objects into "small" and "large" groups of a particular phase. The two groups corresponding to the combined distribution have nearly equal numbers of footprints. The medium diameters are 502 km for DC and 310 km for cirrostratus. The range of the variation between two extreme phases (typically, the most active and depressed phases) for the small group is 6-11% in terms of the numbers of cloud objects and the total footprint numbers. The corresponding range for the large group is 19-44%. In terms of the probability density functions of radiative and cloud physical properties, there are virtually no differences between the MJO phases for the small group, but there are significant differences for the large groups for both DC and CS types. These results suggest that the intreseasonal variation signals reside at the large cloud clusters while the small cloud clusters represent the background noises resulting from various types of the tropical waves with different wavenumbers and propagation speeds/directions.

Xu, Kuan-Man↗
DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Variations of Cloud and Radiative Properties of Boundary-layer and Deep Convective Systems with Sea Surface Temperature Anomalies

Gridded monthly-mean satellite data contain compositing information from different cloud system types and clear-sky environments. To isolate the variations of cloud physical properties of an individual cloud system type with its environment, orbital data are needed. In this study, we will analyze the variations of cloud and radiative properties of boundary-layer clouds and deep convective cloud systems with sea surface temperature (SST) anomalies. We use Terra-CERES (Clouds and the Earth s Radiant Energy System) Level 2 data to classify distinct cloud objects defined by cloud-system types (deep convection, boundary-layer cumulus, stratocumulus and overcast clouds), sizes, geographic locations, and matched large-scale environments. This analysis method identifies a cloud object as a contiguous region of the Earth with a single dominant cloud-system type. It determines the shape and size of the cloud object from the satellite data and the cloud-system selection criteria. The statistical properties of the identified cloud objects are analyzed in terms of probability density functions (PDFs) of a single property or joint PDFs between two properties. The SST anomalies are defined as the differences from five-year annual-cycle means. Individual cloud objects are sorted into one of five equal size subsets, with the matched SST anomalies ranging from the most negative to the most positive values, for a given size category of deep convective cloud objects, boundary-layer cumulus, stratocumulus and overcast cloud objects. The PDFs of cloud and radiative properties for deep convective cloud objects (between 30 S and 30 N) are found to largely similar among the five SST anomaly subsets except for the lowest SST anomaly subset. The different characteristics from this SST anomaly subset may be related to some cloud objects resulting from equatorward movement of extratropical cloud systems. This result holds true for all three different size categories (measured by equivalent diameters of 100-150, 150-300 and > 300 km). The PDFs of cloud and radiative properties for boundary-layer cloud types are more variable for macrophysical properties than for microphysical properties, due probably to different large-scale environments where these cloud objects are formed. Implications of these results for the cloud feedback will also be discussed.

Xu, Kuan-Man↗
DOE OSTI2025

Statistical evaluation of microscale stress conditions leading to void nucleation in the weak shock regime

Here, we investigate the heterogeneity of the stress state driven by anisotropic deformation response at the single crystal level through five statistical volume element (SVE) calculations of polycrystalline BCC tantalum. This work focuses on grain boundaries as a prominent material defect type prone to void nucleation based upon experimental observations of predominantly intergranular void nucleation in this material. The SVEs are constructed to be statistically representative of larger volumes of material and are meshed such that mean and standard deviation of grain size and orientation information is reconstructed. The computational meshes feature hexahedral (brick) elements and smooth conformal grain boundaries where significant stress concentration is known to occur, a tail effect of interest in the extreme events process of dynamic ductile damage. An existing micromechanical crystallographic plasticity model shown to capture the single crystal behavior of BCC tantalum well is used to perform the polycrystal calculations. The model includes representation of the non-Schmid effect of non-planar screw dislocation kinetics in tantalum. A three-dimensional stress state time profile predicted by damage modeling of a flyer plate impact experiment is applied as boundary conditions to each SVE. Resulting grain boundary stress state statistics are strongly non-Gaussian. Significant structural evolution is observed within the compressive hold before unloading into tension in the stress profile. Strong angular dependence of grain boundary traction magnitude with shock direction is observed. Non-Schmid effects continue to suggest their influence on propensity of microstructural defect types to nucleate voids. A general void nucleation criterion is proposed using probability theory. The general framework is specified to polycrystalline BCC tantalum in the weak shock regime to include the SVE calculations and literature molecular dynamics calculations of grain boundary void nucleation strength. Probability density functions (PDFs) are used to describe the interaction between the local stress state heterogeneity and the distributed grain boundary void nucleation strength state. A causation entropy maximization procedure removes the requirement for ad hoc selection of a PDF functional form and provides a rigorous procedure for data-based PDF determination. The resulting physically informed PDF describes the spatial appearance frequency of nucleated voids as a function of applied macroscale pressure. Lower length scale physics are thus packaged in a precise and computationally efficient way to provide computational plasticity insight to macroscale dynamic ductile damage models.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Numerical analysis of a time discretized method for nonlinear filtering problem with Lévy process observations

Abstract In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is an unnormalized probability density function of the filter solution. Then, we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equations, based on which we construct a splitting-up approximate solution and prove its half-order convergence. Furthermore, we apply a finite difference method to construct a time semi-discrete approximate solution to the splitting-up system and prove its half-order convergence to the exact solution of the Zakai equation. Finally, we present some numerical experiments to demonstrate the theoretical analysis.

Mathematics↗