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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

High‐Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

Abstract The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary conditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal‐length time slices (2045–2054 and 2085–2094) are compared with a historical decade (1995–2004). Probability density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm‐side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm‐side shift with decreased variability. Using “business‐as‐usual” scenario, 5‐day heat waves are projected to occur at least 5–10 times per year in most CONUS and ≥95°F days will increase by 1–2 months by the end of the century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Climate-driven trends of biogenic volatile organic compound emissions and their impacts on summertime ozone and secondary organic aerosol in China in the 2050s

As a precursor of ozone (03) and secondaty organic aerosol (SOA), volatile organic compounds (VOCs) largely derive from natural sources, which may va1y with future climate change patterns. In this study, the emissions and impacts of biogenic VOCs (BVOCs) were projected into summertime of the 2050s (average for 2048-2052) for China using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Community Multiscale Air Quality Model (CMAQ) driven by future meteorological fields simulated with the Weather Research and Forecasting Model (WRF) for two future climate scenarios (RCP4.5 and RCP8.5). Sutface temperatures were projected to increase by 0.18 K yr 1 and 0.34 K yr 1 under RCP4.5 and RCP8.5, respectively. Such increases in future surface temperatures will increase BVOC emissions, as emissions of isoprene and monoterpene (ac­counting for 77.73% of all BVOCs in China at the current time) are projected to increase by 11.13% and 25.20% under the two RCP scenarios in the 2050s, respectively. Consequently, such increases in BVOCs will also have considerable impacts on concentrations of 03 and SOA. BVOCs contribute 10.11% of 03 and 62.59% of SOA concentrations in eastern China at current time. Under the RCP8.5 scenario, climate-driven BVOC changes will enhance 03 and SOA concentrations by 0.90% and 7.33% in eastern China from ClUTent to the 2050s and account for 31.83% and 52.80% of total 03 and SOA changes, respectively, on average across five typical regions. Such increases in 03 and SOA concentrations resulting from climate-driven BVOC changes decline considerably under the RCP4.5 scenario to less than 0.80% and 6.50%, respectively, implying that climate mitigation can facilitate air pollution control by alleviating an increase in BVOCs.

Liu, Song↗
DOE OSTI2023

Techno-economic life cycle assessment of CO 2 -EOR operations towards net negative emissions at farnsworth field unit

Optimizations of CO 2 Water Alternating Gas(WAG)- systems with multi-objectives of incremental recovery and maximization of CO 2 storage are challenging. Here, the incorporation of a total Greenhouse gas (GHG) life cycle assessment is mostly ignored leading to inaccurate estimation of overall net carbon emissions of their operations. In this study, the effect of a total GHG life cycle assessment on a multi-objective CO 2 -WAG optimization with integrated techno-economic assessment (TEA) which factors carbon tax credit is conducted. A life cycle assessment (LCA) was conducted utilizing a 20 -year optimized post history matched data from a high fidelity reservoir simulation model. Using data generated from the optimum result, a techno-economic life cycle analysis was further conducted. The first scenario classified as the base model had an estimated 81% of purchased CO 2 sequestered. The results through a comprehensive techno-economic LCA model yielded a net estimate of 73% of purchased CO 2 . The optimized forecasted model which considered key operational and reservoir factors such as WAG ratio, injection rates and periods, and well specification resulted in an improved sequestration of 92% of purchased CO 2 . However, this also dropped to 84% after taking it through LCA. These results clearly indicate a significant amount of net CO 2 is not accounted for when operations are not analyzed through LCA. From the LCA, direct flaring volumes of CO 2 , energy consumption and efficiency of unit equipment were noticed to be the major causes of these reductions. Considering ten main sources of energy as source of energy generation, a comparative techno-eco LCA was conducted. The results confirmed a lower net volume and NPV for energy sources with higher carbon footprints and vice versa. Thus, a total LCA of CO 2 -WAG greatly influences net storage factor of purchased CO 2 and hence project NPV where tax credit/incentives per ton of CO 2 sequestered is considered. Although operational conditions are optimized for best results, there are significant factors that leads to minimization of net storage factor. This study therefore provides an insightful information for optimizing CO 2 -WAG multi-objectives to achieve minimum GHG emission.

02 PETROLEUM↗
DOE OSTI2025

High-resolution climate model datasets for energy infrastructure planning in a renewable-dependent future

Electrification and renewables deployment efforts are amplifying the interdependence of the climate and energy systems. Increases in climate model resolution, which is now approaching that of reanalysis datasets and operational weather forecast models, present a unique opportunity to use future climate projections for energy infrastructure planning. In this Perspective, we review recent developments in high-resolution climate modeling, which have been driven by increased computing power and advanced software tools. We then look ahead to discuss how high-resolution climate data can be used to plan for a renewable-dependent future, and envision a unified climate-energy model framework that captures the two-way feedbacks between these interdependent systems.

climate change↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Regional Modeling of Dust Mass Balance and Radiative Forcing over East Asia using WRF-Chem

The Weather Research and Forecasting model with Chemistry (WRF-Chem) is used to investigate the seasonal and annual variations of mineral dust over East Asia during 2007-2011, with a focus on the dust mass balance and radiative forcing. A variety of measurements from in-stu and satellite observations have been used to evaluate simulation results. Generally, WRF-Chem reproduces not only the column variability but also the vertical profile and size distribution of mineral dust over and near the dust source regions of East Asia. We investigate the dust lifecycle and the factors that control the seasonal and spatial variations of dust mass balance and radiative forcing over the seven sub-regions of East Asia, i.e. source regions, the Tibetan Plateau, Northern China, Southern China, the ocean outflow region, and Korea-Japan regions. Results show that, over the source regions, transport and dry deposition are the two dominant sinks. Transport contributes to ~30% of the dust sink over the source regions. Dust results in a surface cooling of up to -14 and -10 W m-2, atmospheric warming of up to 20 and 15 W m-2, and TOA cooling of -5 and -8 W m-2 over the two major dust source regions of East Asia, respectively. Over the Tibetan Plateau, transport is the dominant source with a peak in summer. Over identified outflow regions, maximum dust mass loading in spring is contributed by the transport. Dry and wet depositions are the comparably dominant sinks, but wet deposition is larger than dry deposition over the Korea-Japan region, particularly in spring (70% versus 30%). The WRF-Chem simulations can generally capture the measured features of dust aerosols and its radaitve properties and dust mass balance over East Asia, which provides confidence for use in further investigation of dust impact on climate over East Asia.

balance↗
DOE OSTI2019

Relating Convection to GCM Grid-Scale Fields Using Cloud-Resolving Model Simulation of a Squall Line Observed during MC3E Field Experiment

In this study, a WRF (Weather Research and Forecasting) model is used as a cloud-resolving model to simulate a squall line observed on 20 May 2011 in the Southern Great Plains (SGP) of the United States. The model output is then used to examine the relationships between convective precipitation and coarse-grained variables averaged over a range of subdomain sizes equivalent to various global climate model horizontal resolutions. The objective is to determine to what extent convection within the subdomains can be related to these “large-scale” variables, thus that they can potentially serve as closure in convective parameterization. Results show that convective precipitation is well correlated with the vertical velocity at 500 hPa, column integrated moisture convergence and CAPE change due to large-scale advective forcing (dCAPE) for various subdomain sizes, but the correlation decreases with decreasing subdomain size. dCAPE leads convective precipitation for all subdomain sizes examined; however, the lead time decreases with decreasing subdomain size. Moisture convergence leads convective precipitation for subdomain sizes greater than 32 km but has no lead time for smaller subdomain sizes. Mid-tropospheric vertical velocity has no lead time or slightly lags convective precipitation. The lead/lag composite analysis with respect to maximum precipitation time indicates that peaks of large-scale variables increase with decreasing subdomain size. The peaks of 500 hPa vertical velocity and column integrated moisture convergence occur at the same time as maximum precipitation, but maximum dCAPE leads maximum precipitation by twelve minutes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A Reduced Resistive Wall Mode Kinetic Stability Model for Disruption Forecasting

Kinetic modification of ideal stability theory from stabilizing resonances of mode-particle interaction has had success in explaining resistive wall mode (RWM) stability limits in tokamaks. With the goal of real-time stability forecasting, a reduced kinetic stability model has been implemented in the new Disruption Event Characterization and Forecasting (DECAF) code, which has been written to analyze disruptions in tokamaks. The reduced model incorporates parameterized models for ideal limits on beta, a ratio of plasma pressure to magnetic pressure, which are shown to be in good agreement with DCON code calculations. Increased beta between these ideal limits causes a shift in the unstable region of delta W_K space, where delta W_K is the change in potential energy due to kinetic effects that is solved for by the reduced model, such that it is possible for plasmas to be unstable at intermediate beta but stable at higher beta. Gaussian functions for delta W_K are defined as functions of E cross B frequency and collisionality, with parameters reflecting the experience of the National Spherical Torus Experiment (NSTX). The reduced model was tested on a database of discharges from NSTX and experimentally stable and unstable discharges were separated noticeably on a stability map in E cross B frequency, collisionality space. The reduced model only failed to predict an unstable RWM in 15.6% of cases with an experimentally unstable RWM and performed well on predicting stability for experimentally stable discharges as well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Enhancing Short-Range Weather Forecasts through Temporal Variation Encoding: A Multiperiod Embedding Approach

Machine learning (ML) techniques have emerged as promising approaches to improve regional weather forecast accuracy and reliability through data-driven methods. We propose a novel ML-based weather forecasting model, the Multiperiod Embed Net (MPENet). A key distinguishing feature of MPENet is its explicit utilization of the inherent cyclic nature in weather dynamics, unlike the autoregressive strategies commonly used in other ML weather forecasting approaches. Critical cyclic structures are identified via Fourier analyses of dynamic time series. Cyclicity in the convolutional representation is achieved by transforming one-dimensional time series of meteorological variables into two-dimensional tensors based on identified periods. This approach enables the model to leverage intrinsic weather patterns, enhancing regional forecast performance. To demonstrate the effectiveness of MPENet, we conduct a comparative analysis with Nvidia’s FourCastNet. Both models are trained on High-Resolution Rapid Refresh (HRRR) data from 2015 to 2022, over a 192 km × 192 km region in Tennessee. The comparisons are performed locally at two specific locations known to have different weather dynamics due to orographic effects: Crossville, on the relatively flat Cumberland Plateau with fewer topographic airflow disruptions, and Oak Ridge, in the ridge-and-valley region, where airflow is heavily influenced by surrounding valleys and mountains. Our results indicate that FourCastNet achieves strong accuracy at very short lead times, while MPENet maintains competitive skill and shows advantages in capturing temporal evolution over longer periods. Cross-correlation analyses of MPENet and FourCastNet predictions with the HRRR data suggest that encoding critical cyclicity into the network architecture leads to improvements in the forecasting skill.

Artificial intelligence↗
DOE OSTI2018

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. We report our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Improving energy efficiency while reducing anthropogenic heat from buildings: how retrofits influence the building stock and urban microclimate in Los Angeles

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.

Anthropogenic heat↗
DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗
DOE OSTI2017

Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations

Abstract The bulk adaptive habit model (AHM) explicitly predicts ice particle aspect ratio, improving the representation of microphysical processes and properties, including ice–liquid-phase partitioning. With the unique ability to predict ice particle shape and density, the AHM is combined with an offline forward operator to produce fields of simulated polarimetric variables. An evaluation of AHM-forward-simulated dual-polarization radar signatures in an idealized Arctic mixed-phase cloud is presented. Interpretations of those signatures are provided through microphysical model output using the large-eddy simulation mode of the Weather Research and Forecasting Model. Vapor-grown ice properties are associated with distinct observable signatures in polarimetric radar variables, with clear sensitivities to the simulated ice particle properties, including ice number, size, and distribution shape. In contrast, the liquid droplet number has little influence on both polarimetric and microphysical variables in the case presented herein. Polarimetric quantities are sensitive to the dominating crystal habit type in a volume, with enhancements for aspect ratios much lower or higher than unity. This synthesis of a microphysical model and a polarimetric forward simulator is a first step in the evaluation of detailed AHM microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Surface Energy Balance Responses to Radiative Forcing in the Central Arctic From MOSAiC and Models

The Arctic surface energy budget (SEB) couples the atmosphere with the sea ice, making it useful for both studying surface processes as well as evaluating models. Improved understanding of atmosphere-ice interactions is required to improve models, requiring year-round observations to address seasonally dependent biases. This work uses novel observations from the MOSAiC expedition to quantify the responses of surface fluxes to radiative forcing over sea ice throughout a complete annual cycle. We identify two primary regimes of flux response: an ice growth regime in winter and an ice melt regime in summer. In the growth regime, changes in radiative forcing impact upwelling longwave, sensible heat, and subsurface heat fluxes, whereas in the melt regime changes in radiative forcing primarily alter the amount of melt and subsurface transmission because the surface temperature is fixed. These observed responses of surface fluxes to radiative forcing are used to evaluate seven weather forecast models during the ice growth regime. In most models, the responses of surface fluxes to radiative forcing do not match observations. Many models also have biased downwelling longwave. One model (the Coupled Arctic Forecast System; CAFS) adequately captures both the mean radiative forcing and the flux responses in winter. CAFS is further evaluated against observations spanning the full MOSAiC year, demonstrating sufficient agreement to provide a more generalized understanding of these SEB process relationships across the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

3D cloud detection and tracking system for solar forecast using multiple sky imagers

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A copula-based Bayesian method for probabilistic solar power forecasting

With increased penetration of solar energy sources, solar power forecasting has become more crucial and challenging. This paper proposes a copula-based Bayesian approach to improve probabilistic solar power forecasting by capturing the joint distribution between solar power and ambient temperature. A prior forecast distribution is first obtained using different underlying point forecasting models. Parametric and empirical copulas of solar power and temperature are then developed to update the prior distribution to the posterior forecast distribution. A public solar power database is used to demonstrate effectiveness of the proposed method. Numerical results show that the copula-based Bayesian method outperforms the forecasting method that directly uses temperature as a feature. The Bayesian method is also compared with persistent models and show improved performance. Furthermore, this article includes supplementary material (data and code) for reproducibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter

Abstract Hydraulic stimulation of low‐permeability rocks in enhanced geothermal systems, shale resources, and CO 2 storage aquifers can trigger microseismic events, also known as microearthquakes (MEQs). The distribution of microseismic source locations in the reservoir may reveal important information about the distribution of hydraulic and geomechanical rock properties. In this paper, we present a framework for conditioning heterogeneous rock permeability and geomechanical property distributions on microseismic data. To simulate the multiphysics processes in these systems, we combine a fully coupled flow and geomechanical model with the Mohr‐Coulomb type rock failure criterion. The resulting multiphysics simulation constitutes the forecast model that relates microseismic source locations to reservoir rock properties. We adopt this forward model in an ensemble Kalman filter (EnKF) data assimilation framework to jointly estimate reservoir permeability and geomechanical property distributions from injection‐induced microseismic response measurements. We show that integration of a large number of spatially correlated microseismic data with practical ensemble sizes can lead to severe underestimation of ensemble spread, and eventually ensemble collapse. To mitigate the variance underestimation issue, two low‐rank data representation schemes are presented and discussed. In the first approach, microseismic data are projected onto a low‐dimensional subspace defined by the left singular vectors of the perturbed observation matrix. The second method uses a coarser grid for representing the microseismic data. A series of numerical experiments is presented to evaluate the performance of the proposed methods and to illustrate their applicability for assimilating microseismic data into coupled flow and geomechanical forward models to estimate multiphysics rock properties.

Tarrahi, Mohammadali↗
DOE OSTI2017

Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions: Sensitivity Across Gray Zone Resolutions

Simulating summer precipitation is a significant challenge for climate models that rely on cumulus parameterizations to represent moist convection processes. Motivated by recent advances in computing that support very high-resolution modeling, this study aims to systematically evaluate the effects of model resolution and convective parameterizations across the gray zone resolutions. Simulations usingthe Weather Research and Forecasting model were conducted at grid spacings of 36 km, 12 km, and 4 km fortwo summers over the conterminous U.S. The convection-permitting simulations at 4 km grid spacing are most skillful in reproducing the observed precipitation spatial distributions and diurnal variability. Notable differences are found between simulations with the traditional Kain-Fritsch (KF) and the scale-aware Grell-Freitas (GF) convection schemes, with the latter more skillful in capturing the nocturnal timing in the Great Plains and North American monsoon regions. The GF scheme also simulates a smoother transition from convective to large-scale precipitation as resolution increases, resulting in reduced sensitivity to model resolution compared to the KF scheme. Nonhydrostatic dynamics has a positive impact on precipitation over complex terrain even at 12 km and 36 km grid spacings. With nudging of the winds toward observations, we show that the conspicuous warm biases in the Southern Great Plains are related to precipitation biases induced by large-scale circulation biases, which are insensitive to model resolution. Overall, notable improvements in simulating summer rainfall and its diurnal variability through convection-permitting modeling and scale-aware parameterizations suggest promising venues for improving climate simulations of water cycle processes.

54 ENVIRONMENTAL SCIENCES↗