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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Southern Great Plains (SGP) Three-Dimensional Observations of Fair-Weather Cumuli Field Campaign Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) Three-Dimensional (3D) Observations of Fair-Weather Cumuli (SGP3DOBS) experiment employed scanning Doppler lidar and the recently installed stereo camera ring to conduct a one-month intensive operational period (IOP) of fair-weather cumuli in a 3D domain at the ARM SGP Central Facility (CF). The SGP3DOBS is part of the Climate Model Development and Validation (CMDV) “Coupling Mechanistically the Convective Motions and Cloud Macrophysics in a Climate Model” (CM4) project and provides advanced observational analyses for improving physical understanding of shallow cumulus and supporting model parameterization evaluation. For the CM4 project, cloud macroscale and dynamical properties are determined using vertically pointing active remote-sensing measurements (e.g., lidar and radar), which provide a 2D view of fair-weather cumuli (FWC). It is critical to evaluate how well the 2D observations represent 3D cloud fields. The objectives of SGP3DOBS include: 1) evaluate the 3D gridded cloud field from stereo photogrammetry with scanning Doppler lidar measurements; 2) retrieve sub-cloud-layer turbulence (vertical velocity and mass flux) in a 3D domain; 3) estimate shallow cumulus lateral entrainment rate and its relation to environment conditions. The SGP3DOBS IOP was held at the SGP Central Facility from August 31 to September 30, 2017, as the fair-weather nonprecipitating shallow cumuli are prevalent during summertime over land (Lamer and Kollias, 2015). A weather forecast team conducted a short-range forecast of weather conditions in the early morning each day to determine whether it was favorable for FWC. For each IOP day, the Doppler lidar at the SGP Central Facility ran under the range height indicator (RHI) scanning mode in the cross-wind direction. Occasionally, plan position indicator (PPI) scanning mode was run for updated wind directions. During each RHI scanning, the Doppler lidar run continuously between 30- and 150-degree elevation angles. The scanning rate was approximately 10 degree/s, corresponding to a transverse resolution of 111 m at 2 km of a typical FWC height. The newly established three pairs of stereo cameras ran concurrently during the IOP.

Southern Great Plains, Doppler lidar, fair-weather↗
DOE OSTI2021

Q-Learning Based Impact Assessment of Propagating Extreme Weather on Distribution Grids: Preprint

Increasing number of power outage events due to extreme weather condition is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical and can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantify weather severity and it’s effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by the extreme weather events.

distribution system↗
DOE OSTI2024

Generation and validation of comprehensive synthetic weather histories using auto-regressive moving-average models

As energy system design moves to more complex methods of optimization including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilized to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilizing both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. Here, it is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.

42 ENGINEERING↗
DOE OSTI2019

Exploring the Effect of Aspect to Inform Future Earthcasts of Climate-Driven Changes in Weathering of Shale

Projections of future conditions within the critical zone—earthcasts—can be used to understand the potential effects of changes in climate on processes affecting landscapes. We are developing an approach to earthcast how weathering will change in the future using scenarios of climate change. As a first step here, we use the earthcasting approach to model aspect-related effects on soil water chemistry and weathering on hillsides in a well-studied east-west trending watershed (Shale Hills, Pennsylvania, USA). We completed model simulations of solute chemistry in soil water with and without the effect of aspect for comparison to catchment observations. With aspect included, aqueous weathering fluxes were higher on the sunny side of the catchment. But the effect of aspect on temperature (0.8 °C warmer soil on sunny side) and recharge (100 mm/year larger on shaded side) alone did not explain the magnitude of the observed higher weathering fluxes on the sunny side. We note modeled aspect-related differences in weathering fluxes only approach field observations when we incorporated the measured differences in clay content observed in augered soils on the two hillslopes. We also had to include a biolifting module to accurately describe cation concentrations in soil water versus depth. Biolifting lowered some mineral dissolution rates while accelerating kaolinite precipitation. These short-duration simulations also highlighted that the inherited differences in particle size on the two sides of the catchment might in themselves be explained by weathering under different microclimates caused by aspect—over longer durations than simulated with our models.

58 GEOSCIENCES↗
DOE OSTI2024

Weathering Incongruence in Mountainous Mediterranean Climates Recorded by Stream Lithium Isotope Ratios

Lithium isotope ratios (δ 7 Li) of rivers are increasingly serving as a diagnostic of the balance between chemical and physical weathering contributions to overall landscape denudation rates. Here, we show that intermediate weathering intensities and highly enriched stream δ 7 Li values typically associated with lowland floodplains can also describe small upland watersheds subject to cool, wet climates. This behavior is revealed by stream δ 7 Li between +22.4 and +23.5‰ within a Critical Zone observatory located in the Cévennes region of southern France, where dilute stream solute concentrations and significant atmospheric deposition otherwise mask evidence of incongruence. The water-rock reaction pathways underlying this behavior are quantified through a multicomponent, isotope-enabled reactive transport model. Using geochemical characterization of soil profiles, bedrock, and long-term stream samples as constraints, we evolve the simulation from an initially unweathered granite to a steady state weathering profile which reflects the balance between (a) fluid infiltration and drainage and (b) bedrock uplift and soil erosion. Enriched stream δ 7 Li occurs because Li is strongly incorporated into actively precipitating secondary clay phases beyond what prior laboratory experiments have suggested. Chemical weathering incongruence is maintained despite relatively slow reaction rates and moderate clay accumulation due to a combination of two factors. First, reactive primary mineral phases persist across the weathering profile and effectively “shield” the secondary clays from resolubilization due to their greater solubility. Second, the clays accumulating in the near-surface profile are relatively mature weathering byproducts. These factors promote characteristically low total dissolved solute export from the catchment despite significant input of exogenous dust.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗
DOE OSTI2023

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k -means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

58 GEOSCIENCES↗
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 OSTI2015

The Weatherization Assistant User's Manual (Version 8.9)

The Weatherization Assistant is a Windows-based energy audit software tool that was developed by Oak Ridge National Laboratory (ORNL) to help states and their local weatherization agencies implement the U.S. Department of Energy (DOE) Weatherization Assistance Program. The Weatherization Assistant is an umbrella program for two individual energy audits or measure selection programs: the National Energy Audit Tool (NEAT) for site-built single-family homes and the Manufactured Home Energy Audit (MHEA) for mobile homes. The Weatherization Assistant User's Manual documents the operation of the user interface for Version 8.9 of the software. This includes how to install and setup the software, navigate through the program, and initiate an energy audit. All of the user interface forms associated with the software and the data fields on these forms are described in detail. The manual is intended to be a training manual for new users of the Weatherization Assistant and as a reference manual for experienced users.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortages with uncertainty-aware and physics-informed machine learning

Focal Area(s): The focus area is predictive modeling through the use of AI techniques and AI-derived model components with a particular emphasis on extreme weather in Atmospheric Science and power outages and shortages in Energy Science. Science Challenge: Predicting weather extremes (e.g., heavy precipitation, strong wind, and large hailstones), and weather-related power system outages and shortages can mitigate economic losses, save lives, support renewables integration, and improve power system resiliency. However, currently, the poor reliability and large uncertainty associated with the weather extreme prediction in the current climate models make the problem intractable. The key challenges are: (1) physical factors like green-house gases (GHGs), aerosols, and land use and land cover (LULC) can significantly impact extreme storms, but the understanding of these impacts is limited, particularly globally; (2) the convective permitting resolutions needed to model severe convective storms and their impacts are computationally prohibitive with global climate models (GCMs); (3) interactions between weather extremes and power system outages are complex and subject to great uncertainty. Current outage prediction models are short lead (~ 3 days), which do not allow for long-time planning of energy production and distribution. Moreover, we have limited capacity to predict weather events leading to sustained shortages in a renewable-energy-dominated power system. These challenges drive motivation for mechanistic understanding and reliable and efficient predictive modeling of extremes and their impacts from the sub-seasonal to long term projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Exploring an ‘ideal hill': how lithology and transport mechanisms affect the possibility of a steady state during weathering and erosion

We introduce a model of chemical reaction within hills to explore how evolving porosity (and by inference, permeability) affects flow pathways and weathering. The model consists of hydrologic and reactive-transport equations that describe alteration of ferrous minerals and feldspar. These reactions were chosen because previous work emphasized that oxygen- and acid-driven weathering affects porosity differently in felsic and mafic rocks. A parameter controlling the order of the fronts is presented. In the absence of erosion, the two reaction fronts move at different velocities and the relative depths depend on geochemical conditions and starting composition. In turn, the fronts and associated changes in porosity drastically affect both the vertical and lateral velocities of water flow. For these instances, estimates of weathering advance rates based on simple models that posit unidirectional constant-velocity advection do not apply. In the model hills, weathering advance rates diminish with time as the Darcy velocities decrease with depth. The system can thus attain a dynamical steady state at any erosion rate where the regolith thickness is constant in time and velocities of both fronts become equal to one another and to the erosion rate. The slower the advection velocities in a system, the faster it attains a steady state. For example, a massive rock with relatively fast-dissolving minerals such as diabase -- where solute transport across the reaction front mainly occurs by diffusion -- can reach a steady state more quickly than granitoid rocks in which advection contributes to solute transport. The attainment of a steady state is controlled by coupling between weathering and hydrologic processes that force water to flow horizontally above reaction fronts where permeability changes rapidly with depth and acts as a partial barrier to fluid flow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Reconstruction of Central European daily weather types back to 1763

Weather type classifications (WTCs) are a simple tool to analyse variations in weather patterns. Long series of WTCs could be used to address decadal changes in weather as a basis for studying changes in variability or extremes or for addressing contributions of sea-surface temperature or external forcings using climate models. However, there is no long series of daily objective weather types (WTs). A new method (Shortest Mahalanobis Distance, SMD) using daily European weather data is developed to reconstruct WTCs back in time. Here the SMD method is applied on the Cluster Analysis of Principal Components (CAP9) classification used by MeteoSwiss. The CAP9 daily WT time series (computed with ERA-40) is used as reference over the 1958–1998 period. Daily data (temperature, mean sea level pressure and pressure tendency) from 13 European stations covering the period 1763–2009 are used for the reconstruction. The reference CAP9 is reduced from nine to seven types so the new daily WTC is called CAP7. As an assessment, CAP7 is compared to the original classification CAP9 and to the same WTs computed with the Twentieth Century Reanalysis (20CR and 20CRv2c). Over the reference period up to 90% of all the daily WTs can be correctly reproduced in the new WTC compared to the original series, with higher reliability in winter than in summer. In addition, the reliability of the classification is increasing from 1763 onward. The annual occurrence of each type reveals some trends, mostly a decrease in the number of cyclonic days and an increase of cyclonic days.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The impact of depth-dependent water content on steady state weathering and eroding systems

Models for weathering and regolith formation are normally built on the assumption of constant rates of water advection in the zone of water-saturated pores, and constant water content of those pores, but it is common that weathering occurs in the water-unsaturated zone where lateral flow occurs. Hence, water content in pores varies with depth. Here we model mineral weathering profiles while accounting for depth-dependent water content. Like previous models, a mineral equilibrates with water over a length ξ that depends on dissolution and advection rates, but a new lengthscale λ is introduced to describe the decrease of water content with depth. Steady states of the regolith thickness can be attained for any finite λ and non-zero velocity v E of erosion at the land surface. The type of mineral depletion profile developed over geological timescales depends on coupling between weathering and erosion: for slow erosion, a completely developed profile (CDP) is observed, in which the mineral-depleted zone at the top of the regolith has thickness of order λ ; as v E increases, there is a transition to an incompletely developed profile (IDP), in which partial mineral depletion at the land surface is constrained to a narrow range of velocities; when v E exceeds the advance rate of weathering under far from equilibrium conditions, the profile transitions to an unstable regime that exposes bedrock. In general, the reaction front (RF) thickness and the velocity where CPD transitions to IDP depend on the interplay of both water infiltration and chemical equilibration over the timescale of water residence in regolith. The RF thickness roughly equals a correlation length χ defined as half the harmonic average of ξ and λ . In cases of limited water infiltration, water-mineral equilibration is achieved within the length λ , so that the RF thickness is controlled by hydrological properties and is independent of dissolution and advection rates. In the opposite endmember, water infiltrates to large depths and the effects of physical and chemical parameters on RF thickness are the same as in previous geochemical models. The relaxation time for reaching a steady state is shown to be ~ χ / v E . We discuss the effects of physical and chemical parameters in CDPs and IDPs in those endmembers and show an application to a CDP in granitic regolith.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A review of future weather data for assessing climate change impacts on buildings and energy systems

The effectiveness of climate change impact assessments and the development of adaptation strategies depend on the availability of high-quality future weather data. However, significant gaps exist between the needs of the energy research community and the focus of the climate modeling community, primarily due to a historical lack of communication and collaboration between the two groups. Here, to address this issue, this work provides a comprehensive overview of the critical aspects involved in creating future weather data for building and energy system modeling, including emissions scenarios, general circulation models, downscaling methods, categories of future weather data, and uncertainties in climate simulations. Moreover, it critically evaluates the applicability and suitability of various types of future weather data in five key application scenarios: energy use analysis, resilience analysis, HVAC design, utility-scale analysis, and renewable energy analysis. Finally, this work presents recommendations for high-level actions and research directions to foster collaboration between the energy research and climate modeling communities and to promote the integration of future weather data into energy codes and the design practices of buildings and energy systems.

Climate change↗
DOE OSTI2015

Vulnerability and adaptation to severe weather events in the American southwest

Climate change can induce changes in the frequency of severe weather events representing a threat to socio-economic development. It is thus of uttermost importance to understand how the vulnerability to the weather of local communities is determined and how adaptation public policies can be effectively put in place. We focused our empirical analysis on the American Southwest. Results show that, consistently with the predictions of an investment model, economic characteristics signaling local economic growth in the near future decrease the level of vulnerability. We also show that federal governments transfers and grants neither work to support recovery from and adaptation to weather events nor to distribute their costs over a broader tax base. Finally, we show that communities relying on municipal bonds to finance adaptation and recovery policies can benefit from local acknowledgment of the need for such policies and that they do not have to pay lenders a premium for the risk induced by weather events. In conclusion, our findings suggest that determinants of economic growth support lower vulnerability to the weather and increase options for financing adaptation and recovery policies, but also that only some communities are likely to benefit from those processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2011

Strontium and cesium release mechanisms during unsaturated flow through waste-weathered Hanford sediments

Leaching behavior of Sr and Cs in the vadose zone of Hanford site (WA, USA) was studied with laboratory-weathered sediments mimicking realistic conditions beneath the leaking radioactive waste storage tanks. Unsaturated column leaching experiments were conducted using background Hanford pore water focused on first 200 pore volumes. The weathered sediments were prepared by 6 months reaction with a synthetic Hanford tank waste leachate containing Sr and Cs (10-5 and 10-3 molal representative of LO- and HI-sediment, respectively) as surrogates for 90Sr and 137Cs. The mineral composition of the weathered sediments showed that zeolite (chabazite-type) and feldspathoid (sodalite-type) were the major byproducts but different contents depending on the weathering conditions. Reactive transport modeling indicated that Cs leaching was controlled by ion-exchange, while Sr release was affected primarily by dissolution of the secondary minerals. The later release of K, Al, and Si from the HI-column indicated the additional dissolution of a more crystalline mineral (cancrinite-type). A two-site ion-exchange model successfully simulated the Cs release from the LO-column. However, a three-site ion-exchange model was needed for the HI-column. The study implied that the weathering conditions greatly impact the speciation of the secondary minerals and leaching behavior of sequestrated Sr and Cs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Detecting Sub-Micron Space Weathering Effects in Lunar Grains With Synchrotron Infrared Nanospectroscopy

Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1,500 –700 cm -1 (6.7–14.3 μm). Overall, our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space weathered bodies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Aerosols and their Impact on Radiation, Clouds, Precipitation & Severe Weather Events

Aerosols, the tiny particles suspended in the atmosphere, have been in the forefront of environmental and climate change sciences as the primary atmospheric pollutant and external force affecting Earth’s weather and climate. There are two dominant mechanisms by which aerosols affect weather and climate: aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI). ARI arises from aerosol scattering and absorption, which alters the radiation budgets of the atmosphere and surface, while ACI is rooted to the fact that aerosols serve as cloud condensation nuclei and ice nuclei. Both ARI and ACI are coupled with atmospheric dynamics to produce a chain of complex interactions with a large range of meteorological variables that influence both weather and climate. Elaborated here are the impacts of aerosols on the radiation budget, clouds (microphysics, structure, and lifetime), precipitation, and severe weather events (lightning, thunderstorms, hail, and tornados). Depending on environmental variables and aerosol properties, the effects can be both positive and negative, posing the largest uncertainties in the external forcing of the climate system. This has considerably hindered our ability in projecting future climate changes and in doing accurate numerical weather predictions.

aerosol↗