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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗
DOE OSTI2022

Long Term Damage Testing (NREL)

DOE's Regional Test Center program has fielded several strings of PV modules as part of their Long Term Testing program. Some of these modules have been installed since 2016 and have been exposed to severe weather events. A key to developing long lifespans for PV modules (in excess of 30 years) is understanding how damage and defects develop, propagate and progress. Researchers at the NREL Regional Test Center have started developing testing procedures and analysis tools in order to characterize modules in a controlled manner with the goal of understanding how damage spreads under normal operations. Characterization methods include EL and IR imaging, outdoor and indoor IV curves, and long term exposure monitoring. Modules to be evaluated will be chosen based on severity of defect, ability to track changes in defect and space requirements. The samples will be loaded at maximum power using grid-tied inverters or other means. Gathering and reporting on this data may help engineers and scientists design for damage and improve long-term performance.

crack damage↗
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 OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

Xie, Yu↗
DOE OSTI2026

Outdoor Deployment Data for a Four-Terminal GaAs//Si Tandem Solar Mini-Module

This dataset contains the complete outdoor measurement and analysis data for a mechanically stacked, four-terminal (4T) gallium arsenide (GaAs)//silicon (Si) tandem solar mini-module deployed from October 2019 to January 2021 at the Solar Radiation Research Laboratory (SRRL) in Golden, Colorado, USA. The data support a performance modeling and degradation analysis framework for tandem photovoltaic devices, as described in the accompanying publication. The dataset includes: (1) current–voltage (J–V) characteristics of each sub-cell measured approximately every five minutes, with extracted performance parameters; (2) spectral irradiance from an EKO MS-710 WISER spectroradiometer, along with derived spectral mismatch ratios (SMR) and average photon energy (APE); (3) one-minute resolution meteorological data from the co-located SRRL weather station and GPS-derived precipitable water vapor (PWV); (4) pre-deployment laboratory characterization (external quantum efficiency, J–V curves, standard test conditions parameters); (5) outdoor-extracted temperature and PWV correction coefficients; and (6) PVcircuit equivalent-circuit simulation outputs used for model validation. Degradation rates of −4.1 ± 0.2 %/year (GaAs) and −2.5 ± 0.9 %/year (Si) were determined using a filtering and normalization methodology adapted for fixed-tilt tandem modules. All data are provided in open, portable formats (Apache Parquet, CSV, JSON) to enable full reproducibility of the published analysis.

14 SOLAR ENERGY↗
DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model: Preprint

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

DNI↗
DOE OSTI2023

New Space Companies Meet a “Normal” Solar Maximum

The monthly mean sunspot number has been larger in June–July 2023 than the double peak of solar cycle 24 (146 in February 2014 and 139 in November 2011) and brings us back to the sunspot level of solar cycle 23. However, the number of rocket launches, satellites in orbit and private space companies has increased dramatically in the past 20 years. Additionally, there is a growing interest for space exploration beyond Earth's orbit, to the Moon and beyond, which comes with higher risk of being affected by space weather. Here, we discuss some of these trends and the role of the journal to improve awareness of space weather impacts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A numerical simulation of CCN impacts on weather modification efficiency

Aerosols affect development of clouds and precipitation by serving as cloud condensation nuclei (CCN) and ice nuclei (IN). Considering the dramatically changing ambient aerosol concentration, it is important to examine the potential “side effect” of aerosol pollution on precipitation enhancement by weather modification. In this study, the cloud seeding was performed on a precipitation event in Beijing in the summer of 2008, which is simulated by the NSSL two-moment cloud scheme of the Weather Research and Forecasting (WRF) model. Sensitivity tests were conducted by modifying the ambient aerosol concentration and the ice crystal seeding amount to investigate the cloud seeding efficacy in different CCN concentration scenarios. There was a slight difference in the precipitation distribution between the simulations with two ambient CCN concentrations: the northern precipitation center in polluted scenario was weaker and the southern center was stronger. Compared with normal CCN scenario, the cloud liquid water mass and ice crystal mass in the severe pollution scenario is larger, and the total contents of snow and graupel were not sensitive to the CCN concentration. With the same amount of man-made ice crystals seeding, the precipitation enhancement was quite different under different CCN conditions. The higher the CCN concentration usually leads to stronger precipitation suppression. As CCN concentration increase, the deposition growth of snow, auto-conversion and accretion of ice crystals to snow were weakened, as well as the conversion of melting snow and graupel into rainwater.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling the Sources and Transport Processes During Extreme Ammonia Episodes in the U.S. Corn Belt

Abstract Atmospheric ammonia (NH 3 ) is the primary form of reactive nitrogen (N r ) and a precursor of ammonium (NH 4 + ) aerosols. Ammonia has been linked to adverse impacts on human health, the loss of ecosystem biodiversity, and plays a key role in aerosol radiative forcing. The midwestern United States is the major NH 3 source in North America because of dense livestock operations and the high use of synthetic nitrogen fertilizers. Here, we combine tall‐tower (100 m) observations in Minnesota and Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) modeling to investigate high and low NH 3 emission episodes within the U.S. Corn Belt to improve our understanding of the distribution of emission sources and transport processes. We examined observations and performed model simulations for cases in February through November of 2017 and 2018. The results showed the following: (1) Peak emissions in November 2017 were enhanced by above‐normal air temperatures, implying a Q 10 (i.e., the change in NH 3 emissions for a temperature increase of 10°C) of 2.5 for emissions. (2) The intensive livestock emissions rom northern Iowa, approximately 400 km away from the tall tower, accounted for 17.6% of the abundance in tall‐tower NH 3 mixing ratios. (3) Ammonia mixing ratios in the innermost domain 3 frequently (i.e., 336 hr, 48% of November 2017) exceeded 5.3 ppb, an important air quality health standard. (4) In November 2017, simulated NH 3 net ecosystem exchange (the difference between NH 3 emissions and dry deposition) accounted for 60–65% of gross NH 3 emissions for agricultural areas and was 2.8–3.1 times the emissions of forested areas. (5) We estimated a mean annual NH 3 net ecosystem exchange of 1.60 ± 0.06 nmol · m −2 · s −1 for agricultural lands and −0.07 ± 0.02 nmol · m −2 · s −1 for forested lands. These results imply that future warmer fall temperatures will enhance agricultural NH 3 emissions, increase the frequency of dangerous NH 3 episodes, and enhance dry NH 3 deposition in adjacent forested lands.

Hu, Cheng↗
DOE OSTI2025

Adiabatic and radiative cooling are both important causes of aerosol activation in simulated fog events in Europe

Aerosol–fog interactions affect the visibility in, and life cycle of, fog and are difficult to represent in weather and climate models. Here we explore processes that impact the simulation of fog droplet number concentrations (N d ) at sub-kilometer scale horizontal grid resolutions in the UK Met Office Unified Model. We modify the parameterization of aerosol activation to include droplet activation by radiative cooling in addition to adiabatic cooling and determine the relative importance of the two cooling mechanisms. We further test the sensitivity of simulated N d to: (a) interception of droplets by trees and buildings, (b) overestimation of updrafts in temperature inversions (which leads to artificially high N d values), and (c) potential mechanisms for droplet deactivation due to downward fluctuations in supersaturation. We evaluate our model against observations from the ParisFog and LANFEX field campaigns, building on evaluation described in the companion paper. Including radiative cooling in the activation mechanism improves how accurately we represent the liquid water path and the vertical structure of the fog in our LANFEX case study. However, with radiative cooling, the N d are overestimated for most of the ParisFog cases and for the LANFEX case. The time-averaged overestimate exceeds a factor of three (the normalized mean bias factor exceeds 2.0) in 4 out of 11 ParisFog cases. Our sensitivity studies demonstrate how these overestimates can be mitigated. Assuming the overestimate affects both radiative and adiabatic cooling, we find that although radiative cooling is more often the dominant source, both cooling sources can sometimes dominate activation.

Ghosh, Pratapaditya [Carnegie Mellon University, P↗
DOE OSTI2023

A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation

Abstract Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Natural Gas Community of the Future (Final Report)

Nicor Gas, a subsidiary of Southern Company Gas and its parent company Southern Company, is developing the concept of the Carbon-neutral, Affordable, Resilient, and Equitable Community ("CARE Community") to address the challenges of accessibility and affordability of grid-interactive efficient buildings in historically disadvantaged communities. The CARE Community is a new construction net-zero energy and carbon neutral residential neighborhood consisting of 50 homes with electricity and natural gas services in South Suburban Chicago, built as low-income affordable housing. The National Renewable Energy Laboratory (NREL) is assisting Southern Company to explore how energy efficiency, renewable technologies, and the reliability and resilience of natural gas can be combined and optimized in an islandable energy system as part of a broader effort to understand the role natural gas plays in future communities integrated with renewable energy and improved energy efficiency. The CARE Community will demonstrate how energy efficiency, distributed energy resources (DERs), and advanced controls can be combined with existing natural gas infrastructure to serve historically disadvantaged communities in a cold climate with low energy cost for affordable housing programs and enhanced resilience to withstand extreme weather. Homes will be a part of an islandable energy system, integrating multiple DER technologies such as solar panels, natural gas fuel cells, reciprocating natural gas engines, and battery storage. The CARE Community will provide a replicable template that affordable housing developers and energy companies can adopt throughout the United States. In this report, we present the design and modeling of the community and potential approaches for integrating DERs. We analyze and discuss annual simulations under normal operating conditions and week-long simulations under electric power grid outage conditions.

03 NATURAL GAS↗
DOE OSTI2022

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Occlusion-Perturbed Deep Learning for Probabilistic Solar Forecasting via Sky Images

Solar forecasting is shifting to the probabilistic paradigm due to the inherent uncertainty within the solar resource. Input uncertainty quantification is one of the widely used and best-performing ways to model solar uncertainty. However, compared to other sources of inputs, such as numerical weather prediction models, pure sky image-based probabilistic solar forecasting lags behind. In this research, an occlusion-perturbed convolutional neural network, named the PSolarNet, is developed. The PSolarNet provides very short-term deterministic forecasts, forecast scenarios, and probabilistic forecasts of the global horizontal irradiance from sky image sequences. Case studies based on 6 years of open-source data show that the developed PSolarNet is able to generate accurate 10-minute ahead deterministic forecasts with a 5.62% normalized root mean square error, realistic and diverse forecast scenarios with a 0.966 average correlation with the actual time series, and reliable and sharp probabilistic forecasts with a 2.77% normalized continuous ranked probability score.

Bayesian model averaging↗
DOE OSTI2022

Occlusion-Perturbed Deep Learning for Probabilistic Solar Forecasting via Sky Images: Preprint

Solar forecasting is shifting to the probabilistic paradigm due to the inherent uncertainty within the solar resource. Input uncertainty quantification is one of the widely used and best-performing ways to model solar uncertainty. However, compared to other sources of inputs, such as numerical weather prediction models, pure sky image-based probabilistic solar forecasting lags behind. In this research, an occlusion-perturbed convolutional neural network, named the PSolarNet, is developed. The PSolarNet provides very short-term deterministic forecasts, forecast scenarios, and probabilistic forecasts of the global horizontal irradiance from sky image sequences. Case studies based on 6 years of open-source data show that the developed PSolarNet is able to generate accurate 10-minute ahead deterministic forecasts with a 5.62% normalized root mean square error, realistic and diverse forecast scenarios with a 0.966 average correlation with the actual time series, and reliable and sharp probabilistic forecasts with a 2.77% normalized continuous ranked probability score.

Bayesian model averaging↗
DOE OSTI2023

Algorithm Theoretical Basis Document For PreMevE 2.0 Model

Man-made satellites operating in medium- and high-altitude Earth orbits are continuously exposed to hazardous space radiation originated from different sources. Among them, one major contributor is the relativistic electron population—with kinetic energies comparable to or larger than their rest energy of 0.511 Megaelectron-volt (MeV)—trapped inside Earth’s outer Van Allen belt. Owing to their high penetration capability, these MeV electrons are difficult to be fully stopped by normal shielding. Particularly, during MeV electron events when electron intensities across the outer belt are greatly elevated to sustaining high levels—up to 10 3 - 10 4 times as observed in the geosynchronous (GEO) orbit and low-Earth-orbits (LEOs), space-borne electronic systems with inadequate hardening are susceptible to deep-dielectric charging and discharging phenomenon caused by those electrons, and thus may suffer severe damages or even stop functioning. Therefore, protecting critical space infrastructures from harsh space weather conditions– including MeV electron events – has high priority for many space sectors, such as the aerospace industry, service providers and government agencies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Revisiting adiabatic fraction estimations in cumulus clouds: high-resolution simulations with a passive tracer

The process of mixing in warm convective clouds and its effects on microphysics are crucial for an accurate description of cloud fields, weather, and climate. Still, they remain open questions in the field of cloud physics. Adiabatic regions in the cloud could be considered non-mixed areas and therefore serve as an important reference to mixing. For this reason, the adiabatic fraction (AF) is an important parameter that estimates the mixing level in the cloud in a simple way. Here, we test different methods of AF calculations using high-resolution (10 m) simulations of isolated warm cumulus clouds. The calculated AFs are compared with a normalized concentration of a passive tracer, which is a measure of dilution by mixing. This comparison enables the examination of how well the AF parameter can determine mixing effects and the estimation of the accuracy of different approaches used to calculate it. Comparison of three different methods to derive AF, with the passive tracer, shows that one method is much more robust than the others. Moreover, this method's equation structure also allows for the isolation of different assumptions that are often practiced when calculating AF such as vertical profiles, cloud-base height, and the linearity of AF with height. The use of a detailed spectral bin microphysics scheme allows an accurate description of the supersaturation field and demonstrates that the accuracy of the saturation adjustment assumption depends on aerosol concentration, leading to an underestimation of AF in pristine environments.

54 ENVIRONMENTAL SCIENCES↗