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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling Systems’ Disruption and Social Acceptance—A Proof-of-Concept Leveraging Reinforcement Learning

As the need for a just and equitable energy transition accelerates, disruptive clean energy technologies are becoming more visible to the public. Clean energy technologies, such as solar photovoltaics and wind power, can substantially contribute to a more sustainable world and have been around for decades. However, the fast pace at which they are projected to be deployed in the United States (US) and the world poses numerous technical and nontechnical challenges, such as in terms of their integration into the electricity grid, public opposition and competition for land use. For instance, as more land-based wind turbines are built across the US, contention risks may become more acute. This article presents a methodology based on reinforcement learning (RL) that minimizes contention risks and maximizes renewable energy production during siting decisions. As a proof-of-concept, the methodology is tested on a case study of wind turbine siting in Illinois during the 2022–2035 period. Results show that using RL halves potential delays due to contention compared to a random decision process. This approach could be further developed to study the acceptance of offshore wind projects or other clean energy technologies.

17 WIND ENERGY↗
NASA NTRS1995

The NAS Computational Aerosciences Archive

In order to further the state-of-the-art in computational aerosciences (CAS) technology, researchers must be able to gather and understand existing work in the field. One aspect of this information gathering is studying published work available in scientific journals and conference proceedings. However, current scientific publications are very limited in the type and amount of information that they can disseminate. Information is typically restricted to text, a few images, and a bibliography list. Additional information that might be useful to the researcher, such as additional visual results, referenced papers, and datasets, are not available. New forms of electronic publication, such as the World Wide Web (WWW), limit publication size only by available disk space and data transmission bandwidth, both of which are improving rapidly. The Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Ames Research Center is in the process of creating an archive of CAS information on the WWW. This archive will be based on the large amount of information produced by researchers associated with the NAS facility. The archive will contain technical summaries and reports of research performed on NAS supercomputers, visual results (images, animations, visualization system scripts), datasets, and any other supporting meta-information. This information will be available via the WWW through the NAS homepage, located at http://www.nas.nasa.gov/, fully indexed for searching. The main components of the archive are technical summaries and reports, visual results, and datasets. Technical summaries are gathered every year by researchers who have been allotted resources on NAS supercomputers. These summaries, together with supporting visual results and references, are browsable by interested researchers. Referenced papers made available by researchers can be accessed through hypertext links. Technical reports are in-depth accounts of tools and applications research projects performed by NAS staff members and collaborators. Visual results, which may be available in the form of images, animations, and/or visualization scripts, are generated by researchers with respect to a certain research project, depicting dataset features that were determined important by the investigating researcher. For example, script files for visualization systems (e.g. FAST, PLOT3D, AVS) are provided to create visualizations on the user's local workstation to elucidate the key points of the numerical study. Users can then interact with the data starting where the investigator left off. Datasets are intended to give researchers an opportunity to understand previous work, 'mine' solutions for new information (for example, have you ever read a paper thinking "I wonder what the helicity density looks like?"), compare new techniques with older results, collaborate with remote colleagues, and perform validation. Supporting meta-information associated with the research projects is also important to provide additional context for research projects. This may include information such as the software used in the simulation (e.g. grid generators, flow solvers, visualization). In addition to serving the CAS research community, the information archive will also be helpful to students, visualization system developers and researchers, and management. Students (of any age) can use the data to study fluid dynamics, compare results from different flow solvers, learn about meshing techniques, etc., leading to better informed individuals. For these users it is particularly important that visualization be integrated into dataset archives. Visualization researchers can use dataset archives to test algorithms and techniques, leading to better visualization systems, Management can use the data to figure what is really going on behind the viewgraphs. All users will benefit from fast, easy, and convenient access to CFD datasets. The CAS information archive hopes to serve as a useful resource to those interested in computational sciences. At present, only information that may be distributed internationally is made available via the archive. Studies are underway to determine security requirements and solutions to make additional information available. By providing access to the archive via the WWW, the process of information gathering can be more productive and fruitful due to ease of access and ability to manage many different types of information. As the archive grows, additional resources from outside NAS will be added, providing a dynamic source of research results.

Miceli, Kristina D.↗
DOE OSTI2021

Heat Based Power Augmentation for Modular Pumped Hydro Storage in Smart Buildings Operation

In the U.S., building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground-level. This unique design eliminates the geographical limitation associated with existing state of the art energy storage technologies. It is easy to be scaled for building level, community level and grid level applications. Using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g. from thermal storage, Combined Cooling, Heat and Power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Chen, Yang↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Sustainable urban transformations based on integrated microgrid designs

The impacts of natural hazards on infrastructure, enhanced by climate change, are increasingly more severe emphasizing the necessity of resilient energy grids. Microgrids, tailored energy systems for specific neighbourhoods and districts, play a pivotal role in sustaining energy supply during main grid outages. These solutions not only mitigate economic losses and well-being disruptions against escalating hazards but also enhance city resilience in alignment with Sustainable Development Goal (SDG) 11. However, disregarding socioeconomic factors in defining microgrid boundaries risks perpetuating inequalities and impeding progress towards other SDG 11 targets, including fair democratic participation. Our approach integrates social and technical indicators to bolster urban microgrid planning. Through a case study in a US county, we illustrate how integrated microgrid planning effectively intertwines urban resilience, well-being and equity while promoting sustainable development. This study underscores the importance of integrated microgrid planning for sustainable and resilient urban transformation amid environmental and societal challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Coupled Heat Power Operation of Smart Buildings via Modular Pumped Hydro Storage

In the United States, building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings’ energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low-grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high-efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground level. This unique design eliminates the geographical limitation associated with the existing state-of-the-art energy storage technologies. It is easy to be scaled for building level, community level, and grid level applications. By using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g., from thermal storage, combined cooling, heat and power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Here, several groups of comparison experiments have been conducted, and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Connected Thermostat Alternatives for Room Air Conditioners and Minisplit Heat Pumps

The availability of smart, connected thermostats has improved climate control, energy efficiency, and grid demand-response programs for central HVAC systems. However, a significant gap exists in addressing integrated control systems for point-source heating and cooling systems such as window air-conditioners (window ACs) and mini-split heat pumps (MSHPs). This report examines the emerging market of third-party connected thermostats tailored for these systems, focusing on their effectiveness, reliability, and potential barriers to adoption.This study evaluates several commercially available products designed for room ACs and MSHPs through a series of laboratory tests. While these infrared-based (IR-based) thermostats offer remote temperature control and scheduling via mobile apps, our findings reveal that none are seamless, with reliability of basic functions being a critical factor. Promising features include integration of indoor air quality metrics and time-of-use pricing, but the latter are not yet available in the U.S. Barriers to broad user acceptance include non-seamless setup processes, challenges in thermostat placement, and unclear product differentiation. There is a pressing need for research and development in enabling MSHPs and central thermostats to coordinate, enhancing energy savings and comfort in retrofit applications. This study underscores the importance of further innovation in connected thermostat technology to address the diverse needs of single-zone HVAC systems and promote efficient energy management in households.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2001

Designing Adaptive Low-Dissipative High Order Schemes for Long-Time Integrations

A general framework for the design of adaptive low-dissipative high order schemes is presented. It encompasses a rather complete treatment of the numerical approach based on four integrated design criteria: (1) For stability considerations, condition the governing equations before the application of the appropriate numerical scheme whenever it is possible; (2) For consistency, compatible schemes that possess stability properties, including physical and numerical boundary condition treatments, similar to those of the discrete analogue of the continuum are preferred; (3) For the minimization of numerical dissipation contamination, efficient and adaptive numerical dissipation control to further improve nonlinear stability and accuracy should be used; and (4) For practical considerations, the numerical approach should be efficient and applicable to general geometries, and an efficient and reliable dynamic grid adaptation should be used if necessary. These design criteria are, in general, very useful to a wide spectrum of flow simulations. However, the demand on the overall numerical approach for nonlinear stability and accuracy is much more stringent for long-time integration of complex multiscale viscous shock/shear/turbulence/acoustics interactions and numerical combustion. Robust classical numerical methods for less complex flow physics are not suitable or practical for such applications. The present approach is designed expressly to address such flow problems, especially unsteady flows. The minimization of employing very fine grids to overcome the production of spurious numerical solutions and/or instability due to under-resolved grids is also sought. The incremental studies to illustrate the performance of the approach are summarized. Extensive testing and full implementation of the approach is forthcoming. The results shown so far are very encouraging.

Yee, Helen C.↗
DOE OSTI2023

Simulation of Reverse Osmosis Membrane Compaction Using Material Point Method (MPM)

Access to fresh drinking water in the future requires effective management of industrial wastewater and water purification from available resources. In this study, we present the simulation methodology and the analysis of a Reverse Osmosis (RO) membrane under various pressure conditions using the Material Point Method (MPM). In contrast to other numerical methods, MPM solves the continuum governing equations on material points in a Lagrangian framework. The method does not require a grid connecting the material points hence making it suitable to simulate large deformations during membrane compaction. The time integration is carried out using the explicit Euler method, while the spatial discretization is performed using linear or cubic-spline shape functions. A series of planar images containing detailed pore structures obtained from X-ray tomography experiments is converted to a three-dimensional collection of material points to simulate the membrane. Compressive loads are applied to the top layer of the membrane to simulate the experiments. The membrane deformation and pore size distribution before and after load application are reported and compared with the experimental measurements. The presentation discusses the numerical methods used, the performance of the solver on high-performance computing machines, and the results of membrane compaction in detail.

ENGINEERING,MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system

Hydro-wind-photovoltaic (PV) hybrid system has the potential to increase the integration of renewable energy sources into an existing grid. For the long-term operation of the system, due to the non-storable nature of wind and PV power, it is essentially to decide the optimal long-term carryover storage of cascade reservoirs. However, it remains a challenge due to high uncertainties of long-term forecasts and complicated hydraulic/electrical relationships between cascade reservoirs. Here in this study, a forecast-driven decision-making model is proposed for the hybrid system, which converts the multi-stage long-term operation process into a two-stage operation problem (including current stage and carryover stage) to avoid using longer-horizon forecast information with lower accuracy. First, the carryover stage energy surfaces (CESs) considering the forecast uncertainties of wind, solar and hydro resources are proposed to characterize carryover stage benefit quantitatively. Then a CESs-based forward decision-making optimization model is developed to guide the long-term operation of a hydro-wind-photovoltaic hybrid system. The applications in a hydro-wind-PV hybrid system of Yalong River basin results show that: compared with conventional operation, 1) power generation increases 9.03%; 2) in terms of the carryover storages control, the reservoir impounding and drawdown timing are delayed, and the drawdown depth is increased, which can be used to formulate better reservoir operation rules.

13 HYDRO ENERGY↗
DOE OSTI2020

Preliminary Design Process for Networked Microgrids

Changes in economic, technology, and environmental policies are resulting in a re-evaluation of the dependence on large central generation facilities and their associated transmission networks. Emerging concepts of smart communities/cities are examining the potential to leverage cleaner sources of generation, and the potential to integrate electricity generation with other municipal functions. When grid-connected, these generation assets can supplement the existing interconnections with the bulk transmission system, and in the event of an extreme event, they can provide power via networks of microgrids. While the design process for a single stand-alone microgrid is relatively well understood, the process of designing the infrastructure for networked microgrid operations has not been well studied. Because of the wide range of potential operational goals for microgrids, it is typical to follow the engineering process of developing an initial conceptual design, a preliminary design, a detailed design, and then a final as-built design. The conceptual design is typically completed without detailed engineering analysis, and the preliminary design is the first stage that requires it. This paper presents a process for developing the preliminary design for networked microgrids, which can then be used as a basis for the final as-built design.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2002

Implementation of the NCAR Community Land Model (CLM) in the NASA/NCAR finite-volume Global Climate Model (fvGCM)

In this study, the NCAR CLM version 2.0 land-surface model was integrated into the NASA/NCAR fvGCM. The CLM was developed collaboratively by an open interagency/university group of scientists and based on well-proven physical parameterizations and numerical schemes that combine the best features of BATS, NCAR-LSM, and IAP94. The CLM design is a one-dimensional point model with 1 vegetation layer, along with sub-grid scale tiles. The features of the CLM include 10-uneven soil layers with water, ice, and temperature states in each soil layer, and five snow layers, with water flow, refreezing, compaction, and aging allowed. In addition, the CLM utilizes two-stream canopy radiative transfer, the Bonan lake model and topographic enhanced streamflow based on TOPMODEL. The DAO fvGCM uses a genuinely conservative Flux-Form Semi-Lagrangian transport algorithm along with terrain- following Lagrangian control-volume vertical coordinates. The physical parameterizations are based on the NCAR Community Atmosphere Model (CAM-2). For our purposes, the fvGCM was run at 2 deg x 2.5 deg horizontal resolution with 55 vertical levels. The 10-year climate from the fvGCM with CLM2 was intercompared with the climate from fvGCM with LSM, ECMWF and NCEP. We concluded that the incorporation of CLM2 did not significantly impact the fvGCM climate from that of LSM. The most striking difference was the warm bias in the CLM2 surface skin temperature over desert regions. We determined that the warm bias can be partially attributed to the value of the drag coefficient for the soil under the canopy, which was too small resulting in a decoupling between the ground surface and the canopy. We also discovered that the canopy interception was high compared to observations in the Amazon region. A number of experiments were then performed focused on implementing model improvements. In order to correct the warm bias, the drag coefficient for the soil under the canopy was considered a function of LAI (Leaf Area Index). Analysis of the results revealed that there was a substantial impact, and the warm and dry bias in the CLM2 was significantly reduced. For the interception scheme, the canopy throughfall was increased to allow for more infiltration of precipitation into the soil, resulting in increased low-level moisture and a decrease in the interception loss ratio (canopy evaporation to precipitation).

Radakovich, Jon D.↗
DOE OSTI2026

Summary of GFL Inverter Capability and Performance: Malawi [Slides]

This presentation provides a comprehensive comparison of inverter-based resource (IBR) technical requirements across multiple international grid codes - including IEEE 2800, ESO, EirGrid, and Malawi's evolving regulatory framework - to support capacity building for electricity-sector institutions in Malawi. It highlights key performance expectations for grid-following (GFL) inverters across critical domains such as primary and fast frequency response, ramping behavior, frequency and voltage ride-through capabilities, reactive power and voltage control, ROCOF withstand, phase jump tolerance, system strength considerations, protection schemes, and dynamic modeling requirements. Emphasizing a "Do no harm, do something, do more" philosophy, the document synthesizes best practices and operational benchmarks to guide the integration and stability of inverter-based resources within Malawi's power system. The presentation forms part of the SABESS Center of Excellence initiative supported by the Global Energy Alliance for People and the Planet.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1998

Exploration of the Moon with Remote Sensing, Ground-Penetrating Radar, and the Regolith-Evolved Gas Analyzer (REGA)

There are two important reasons to explore the Moon. First, we would like to know more about the Moon itself: its history, its geology, its chemistry, and its diversity. Second, we would like to apply this knowledge to a useful purpose. namely finding and using lunar resources. As a result of the recent Clementine and Lunar Prospector missions, we now have global data on the regional surface mineralogy of the Moon, and we have good reason to believe that water exists in the lunar polar regions. However, there is still very little information about the subsurface. If we wish to go to the lunar polar regions to extract water, or if we wish to go anywhere else on the Moon and extract (or learn) anything at all, we need information in three dimensions an understanding of what lies below the surface, both shallow and deep. The terrestrial mining industry provides an example of the logical steps that lead to an understanding of where resources are located and their economic significance. Surface maps are examined to determine likely locations for detailed study. Geochemical soil sample surveys, using broad or narrow grid patterns, are then used to gather additional data. Next, a detailed surface map is developed for a selected area, along with an interpretation of the subsurface structure that would give rise to the observed features. After that, further sampling and geophysical exploration are used to validate and refine the original interpretation, as well as to make further exploration/ mining decisions. Integrating remotely sensed, geophysical, and sample datasets gives the maximum likelihood of a correct interpretation of the subsurface geology and surface morphology. Apollo-era geophysical and automated sampling experiments sought to look beyond the upper few microns of the lunar surface. These experiments, including ground-penetrating radar and spectrometry, proved the usefulness of these methods for determining the best sites for lunar bases and lunar mining operations.

Cooper, B. L.↗
NASA NTRS2016

Investigation of Multiphase Flow in a Packed Bed Reactor Under Microgravity Conditions

In this paper we study the two-phase flow phenomena in a packed bed reactor using an integrated experimental and numerical method. The cylindrical bed is filled with uniformly sized spheres. In the experiment water and air are injected into the bed simultaneously. The pressure distribution along the bed will be measured. The numerical simulation is based on a two-phase flow solver which solves the Navier-Stokes equations on Cartesian grids. A novel coupled level set and moment of fluid method is used to construct the interface. A sequential method is used to position spheres in the cylinder. Preliminary experimental results showed that the tested flow rates resulted in pulse flow. The numerical simulation revealed that air bubbles could merge into larger bubbles and also could break up into smaller bubbles to pass through the pores in the bed. Preliminary results showed that flow passed through regions where the porosity is high. Comparison between the experimental and numerical results in terms of pressure distributions at different flow injection rates will be conducted. Comparison of flow phenomena under terrestrial gravity and microgravity will be made.

computational fluid dynamics↗
DOE OSTI2021

Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications

With building electric demand becoming increasingly dynamic, and a growing percentage of intermittent renewable power generation from solar photovoltaics and wind turbines, the power grid is facing increasing challenge to manage the real time balance between the supply and demand. With advancements in smart sensing and metering, smart appliances, electric vehicles, and energy storage technologies, demand side management of residential buildings can help the grid to improve stability by optimizing flexible loads. This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications. The reviewed studies showed four levels of applications: building level (45%), district or community level (29%), system level (19%), and building sector level (7%). Shifting loads is the dominant flexibility type in 60% of applications, followed by shedding (19%), generation (16%), and modulating (6%). Depending on the technology and application scope, flexible operations have a wide range of performance, with peak power reductions of 1%~65%, energy savings up to 60%, operational cost reduction of 1%~48%, and greenhouse gas emission reductions of up to29%. More than half (51%) of the studies employed control strategies to achieve flexibility; among those 72% used optimal controls, while 28% used rule-based controls. About 58% of the studies used mathematical formulation to quantify energy flexibility. Most studies were based on simulation, while less than 15% of the studies had measurements from experiments or field tests. The review reveals research opportunities to address significant gaps in the existing literature: (1) establishing a common definition and performance metrics for energy flexibility of buildings that are technology and application agnostic, (2) developing an ontology to standardize representation of flexibility resources for interoperability, (3) integrating occupant impacts into the quantification and optimization of energy flexibility, and (4) developing requirements and credits of energy flexibility in building energy codes and standards. Findings from the review can inform future research and development of energy flexible buildings which are essential to a reliable and resilient power grid.

Li, H↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to Using a Cloud-resolving Model to Study the Interactions Between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as l0,OOO km in two-dimensions, and 1,OOO x 1,OOO km2 in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗