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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Abundance of fluorescent biological aerosol particles at temperatures conducive to the formation of mixed-phase and cirrus clouds

Some types of biological particles are known to nucleate ice at warmer temperatures than mineral dust, with the potential to influence cloud microphysical properties and climate. However, the prevalence of these particle types above the atmospheric boundary layer is not well known. Many types of biological particles fluoresce when exposed to ultraviolet light, and the Wideband Integrated Bioaerosol Sensor takes advantage of this characteristic to perform real-time measurements of fluorescent biological aerosol particles (FBAPs). This instrument was flown on the National Center for Atmospheric Research Gulfstream V aircraft to measure concentrations of fluorescent biological particles from different potential sources and at various altitudes over the US western plains in early autumn. Clear-air number concentrations of FBAPs between 0.8 and 12 µm diameter usually decreased with height and generally were about 10–100 L -1 in the continental boundary layer but always much lower at temperatures colder than 255 K in the free troposphere. At intermediate temperatures where biological ice-nucleating particles may influence mixed-phase cloud formation (255 K ≤ T ≤ 270 K), concentrations of fluorescent particles were the most variable and were occasionally near boundary-layer concentrations. Predicted vertical distributions of ice-nucleating particle concentrations based on FBAP measurements in this temperature regime sometimes reached typical concentrations of primary ice in clouds but were often much lower. If convection was assumed to lift boundary-layer FBAPs without losses to the free troposphere, better agreement between predicted ice-nucleating particle concentrations and typical ice crystal concentrations was achieved. Ice-nucleating particle concentrations were also measured during one flight and showed a decrease with height, and concentrations were consistent with a relationship to FBAPs established previously at the forested surface site below. The vertical distributions of FBAPs measured on five flights were also compared with those for bacteria, fungal spores, and pollen predicted from the EMAC global chemistry–climate model for the same geographic region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Toward understanding of differences in current cloud retrievals of ARM ground-based measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Effects of environment forcing on marine boundary layer cloud-drizzle processes: MBL Cloud-Drizzle Processes

Determining the factors affecting drizzle formation in marine boundary layer (MBL) clouds remains a challenge for both observation and modeling communities. To investigate the roles of vertical wind shear and buoyancy (static instability) in drizzle formation, ground-based observations from the Atmospheric Radiation Measurement (ARM) Program at the Azores are analyzed for two types of conditions. The type I clouds should last for at least five hours and more than 90% time must be non-drizzling, and then followed by at least two hours of drizzling periods while the type II clouds are characterized by mesoscale convection cellular (MCC) structures with drizzle occur every two to four hours. By analyzing the boundary layer wind profiles (direction and speed), it was found that either directional or speed shear is required to promote drizzle production in the type I clouds. Observations and a recent model study both suggest that vertical wind shear helps the production of turbulent kinetic energy (TKE), stimulates turbulence within cloud layer, and enhances drizzle formation near the cloud top. The type II clouds do not require strong wind shear to produce drizzle. The small values of lower-tropospheric stability (LTS) and negative Richardson number ( R i ) in the type II cases suggest that boundary layer instability plays an important role in TKE production and cloud-drizzle processes. As a result, by analyzing the relationships between LTS and wind shear for all cases and all time periods, a stronger connection was found between LTS and wind directional shear than that between LTS and wind speed shear.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The Araucaria project. The distance to the small Magellanic Cloud from late-type eclipsing binaries

We present a distance determination to the Small Magellanic Cloud (SMC) based on an analysis of four detached, long-period, late-type eclipsing binaries discovered by the Optical Gravitational Lensing Experiment (OGLE) survey. The components of the binaries show negligible intrinsic variability. A consistent set of stellar parameters was derived with low statistical and systematic uncertainty. The absolute dimensions of the stars are calculated with a precision of better than 3%. The surface brightness-infrared color relation was used to derive the distance to each binary. The four systems clump around a distance modulus of (m – M) = 18.99 with a dispersion of only 0.05 mag. Combining these results with the distance published by Graczyk et al. for the eclipsing binary OGLE SMC113.3 4007, we obtain a mean distance modulus to the SMC of 18.965 ± 0.025 (stat.) ± 0.048 (syst.) mag. This corresponds to a distance of 62.1 ± 1.9 kpc, where the error includes both uncertainties. Taking into account other recent published determinations of the SMC distance we calculated the distance modulus difference between the SMC and the Large Magellanic Cloud equal to 0.458 ± 0.068 mag. Finally, we advocate μ{sub SMC} = 18.95 ± 0.07 as a new 'canonical' value of the distance modulus to this galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗
DOE OSTI2019

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Final Technical Report: Moving ASR Cloud Microphysical Retrievals Beyond the Vertical Column

Clouds of all types play important roles in atmospheric heating, surface heating and emission of radiation, hence energy, to space. Clouds not only affect atmospheric heating via their impacts on solar and terrestrial radiation, they also release kinetic energy to the atmosphere when they form and they extract kinetic energy from the atmosphere when they evaporate. To understand the impacts of clouds on weather and climate, one must be able to make accurate observations of the amount of water associated with them and the impact of this water on atmospheric and surface heating rates. One important cloud type is liquid water clouds. Liquid clouds are often found in the boundary layers of Earth at altitudes from just above the surface to approximately 2000 m above it, at a variety of temperatures, including temperatures below the freezing point of liquid water (0 °C). As a result, they are important constituents over the world’s oceans as well as over cold, ice-covered surfaces such as in the Arctic. Observationally estimating the total amount of liquid contained in a column through a liquid water cloud can now be accomplished to acceptable accuracies (except for the thinnest, lowest liquid containing clouds) using instruments called passive microwave radiometers. However, observationally determining the spatial distribution of liquid within these columns has proven to be difficult. The focus of this project was on the application of two radars operating at two different frequencies to retrieve the distribution of liquid water within columns passing through liquid water clouds. Motivation in this project for using two different radars operating at different frequencies is that as overlapping (in space) beams from the two radars pass through the same column of liquid water cloud absorption of power from one beam (the 94-GHz beam) is greater than absorption of power from the other beam (the 35-GHz beam). Therefore, the difference in returned powers between the two radar beams with distance from the radars is proportional to the amount of liquid water that the two beams have passed through. Therefore, one can use the differences in power between the two beams with distance through a liquid water cloud to determine the distribution of liquid water throughout the cloud. The two radars used in this study are the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Ka-band Scanning ARM Cloud Radar (Ka-SACR) and the W-band Scanning ARM Cloud Radar (W-SACR). When this projected started in 2013, the Ka- and W-band SACRs were new technology, state-of-the-art sensors. To put together a sufficiently diverse and big set of test cases for the study, data from the SACRs deployed to NSA, SGP, TCAP and MAGIC were used, leading to a test set composed of 65 hours of suitable data spread over approximately 17 case study days. Using the Ka- and W-band SACR data from the case study periods, vertical distributions of liquid water content through liquid water clouds were retrieved. Because there are no other data sets with which to compare the retrievals, distributions of liquid water through a column were summed and compared to the total amounts of liquid water obtained by retrievals applied to passive microwave radiometer observations. The total amounts of liquid water (in units of millimeters, or the thickness in millimeters of the total liquid water in a vertical column if allowed to pool at the bottom of the column) between the two retrievals agreed to within 0.060 mm with a standard deviation of 0.31 mm, or to within 0.016 mm with a standard deviation of 0.12 mm once outliers were removed. The standard deviation of 0.12 mm was fairly independent of the total amount of liquid water in the clouds, indicating that retrieval errors were larger for clouds with smaller amounts of liquid in them. Errors in the retrieved total amounts of liquid were also found to be sensitive to the a priori information that was fed into the retrieval approach. Overall, standard deviations of 0.12 mm are sufficient for some cloud studies but for radiation studies standard deviations on the order of 0.02 mm need to be achieved for clouds with the lowest amounts of liquid water. Many modifications to the retrieval approach were tested subsequently in an attempt to reduce the standard deviations relative to the radiometer retrievals below 0.12 mm but none of them worked reliably. We decided to wait until the DOE ARM Program deployed its second generation SACR2s to its NSA and ENA sites for further retrieval evaluation. The SACR2s were engineered to better synchronize data between the Ka- and W-band radars, reducing potential sources of noise for radar retrieval of liquid water distributions within clouds. The SACR2s have improved in reliability recently and the system deployed to ENA is best positioned to provide the data to advance research on this path. This will be an ongoing research activity beyond the project because of the promise in SACR2 data from the DOE ARM ENA Facility.

58 GEOSCIENCES↗
DOE OSTI2019

Cloud Influence on ERA5 and AMPS Surface Downwelling Longwave Radiation Biases in West Antarctica

The surface downwelling longwave radiation component (LW?) is crucial for the determination of the surface energy budget and has significant implications for the resilience of ice surfaces in the polar regions. Accurate model evaluation of this radiation component requires knowledge about the phase, vertical distribution, and associated temperature of water in the atmosphere, all of which control the LW? signal measured at the surface. In this study, we examine the LW? model errors found in the Antarctic Mesoscale Prediction System (AMPS) operational forecast model and the ERA5 reanalysis model relative to observations from the AWARE campaign at McMurdo Station and the West Antarctic Ice Sheet (WAIS) Divide. The errors are calculated separately for observed clear sky conditions, ice cloud occurrences, and liquid-bearing cloud layer (LBCL) occurrences. The analysis results show a tendency (in both models at each site) to underestimate the LW? during clear sky conditions, high error variability (standard deviations > 20 W/m2) during any type of cloud occurrence, and negative LW? biases when LBCLs are observed (bias magnitudes > 15 W/m2 in tenuous LBCL cases; > 43 W/m2 in optically thick/opaque LBCLs instances). We suggest that a generally dry and liquid water-deficient atmosphere responsible for the identified LW? biases in both models is the result of excessive ice formation and growth, which could stem from model initial and lateral boundary conditions, microphysics scheme, aerosol representation, and/or limited vertical resolution.

Silber, Israel↗
DOE OSTI2019

ParaOpt: Automated Application Parameterization and Optimization for the Cloud

The variety of instance types available on cloud platforms offers enormous flexibility to match the requirements of applications with available resources. However, selecting the most suitable instance type and configuring an application to optimally execute on that instance type can be complicated and time-consuming. For example, application parallelism flags must match available cores and problem sizes must be tuned to match available memory. As the search space of application configurations can be enormous, we propose an automated approach, called ParaOpt, to automatically explore and tune application configurations on arbitrary cloud instances. ParaOpt supports arbitrary applications, enables use of custom optimization methods, and can be configured with different optimization targets such as runtime and cost. We evaluate ParaOpt by optimizing genomics, molecular dynamics, and machine learning applications with four types of optimizers. We show with as few as 15 parameterized executions of an application, representing between 1.2%-26.7% of the search space, that ParaOpt is able to identify the optimal configuration in 32.7% of experiments and a near-optimal configuration in 83.2% of cases. As a result of using near-optimal configurations, ParaOpt reduces overall execution time by up to 85.8% when compared with using the default configuration.

Wu, Chaofeng↗
DOE OSTI2016

In-Situ Data for Microphysical Retrievals: TC4, 2007

This data set is derived from measurements collected in situ by the NASA DC8 during the Tropical Cloud Climate Composition Coupling Experiment (TC4) that was conducted during July and August, 2007 (Toon et al., 2010). During this experiment the DC8 was based in San Jose, Costa Rica and sampled clouds in the maritime region of the Eastern Pacific and adjoining continental areas. The primary objective of the DC8 during this deployment was to sample ice clouds associated with convective activity. While the vast majority of the data are from ice-phase clouds that have recent association with convection, other types of clouds such as boundary layer clouds and active convection were also sampled and are represented in this data set. The derived data set, as compiled in this delivery, includes approximately 15,000 5-second averaged measurements collected by the NASA DC8.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Effects of Sea Spray on Microphysics and Intensity of Deep Convective Clouds Under Strong Winds

Deep convective clouds similar to those arising in the tropical cyclone eyewall are simulated using a parcel model and 2–D slab symmetric cloud model with spectral bin microphysics (the Hebrew University Cloud Model). The size distribution of sea spray particles (SSPs) at cloud base is calculated using the Lagrangian–Eulerian bin microphysics model. The model describes the SSP production, advection, and formation of the size distribution of SSP in the hurricane atmospheric boundary layer at different strong wind speeds. The SSP distributions calculated by the Lagrangian–Eulerian bin microphysics model are used in the parcel model and the Hebrew University Cloud Model to investigate the microphysical and dynamical effects of SSP on clouds. The SSPs ascending in cloud updrafts dramatically increase the number concentration of cloud drops within a wide range of drop sizes. As a result, sea spray creates clouds with unique property combinations of both maritime and continental types. These clouds have droplet size distributions characterized by a high drop concentration and a low effective radius, as in continental clouds. At the same time, the presence of SSP of a few hundred microns in radii triggers intense rain just above the cloud base, which is typical of extreme maritime clouds. In the presence of large sea spray drops, the smallest cloud condensational nuclei, including the smallest SSP, are activated, giving rise to the permanent in–cloud nucleation of small droplets, which produce a high concentration of small ice crystals above the level of homogeneous freezing. We showed that the SSP substantially increased the maximum vertical velocity, cloud water content, and mass contents of ice particles. Here, the results are compared with available observed data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

About the horizontal variability of effective radius in stratocumulus clouds

Abstract The role of turbulent mixing in formation of low horizontal variability of effective radius near the top of nondrizzling stratocumulus clouds is investigated in simulations of clouds observed during the Second Dynamics and Chemistry of Marine Stratocumulus field experiment. The clouds are simulated using a spectral bin microphysics Lagrangian‐Eulerian model consisting of ~2000 adjacent parcels moving in a turbulence‐like field with observed correlation properties. The parcels interact through drop sedimentation and turbulent mixing. It was found that the effective radius variability in the horizontal direction near cloud top does not exceed ~10% of the averaged value. Three different types of cloud parcels are revealed to be differently influenced by mixing: ascending slightly diluted parcels, cloudy parcels experiencing intense mixing with parcels from inversion, and initially dry parcels. The evolution of droplet size distributions in parcels belonging to these types is investigated. It is shown that in parcels of the first two types the values of effective radii do not change or change only slightly remaining close to the adiabatic value. In initially droplet‐free parcels effective radius rapidly reaches a value close to the adiabatic value, while liquid water content remains low. Therefore, turbulent mixing leads to establishing vertical profiles of effective radius, which are close to the adiabatic profile.

Magaritz‐Ronen, L.↗
DOE OSTI2013

QUANTITATIVELY ASSESSING THE ROLE OF CLOUDS IN THE TRANSMISSION SPECTRUM OF GJ 1214b

Recent observations of the super-Earth GJ 1214b show that it has a relatively featureless transmission spectrum. One suggestion is that these observations indicate that the planet's atmosphere is vertically compact, perhaps due to a water-rich composition that yields a large mean molecular weight. Another suggestion is that the atmosphere is hydrogen/helium-rich with clouds that obscure predicted absorption features. Previous models that incorporate clouds have included their effect without a strong physical motivation for their existence. Here, we present model atmospheres of GJ 1214b that include physically motivated clouds of two types. We model the clouds that are present in chemical equilibrium, as has been suggested to occur on brown dwarfs, which include KCl and ZnS for this planet. We also include clouds that form as a result of photochemistry, forming a hydrocarbon haze layer. We use a photochemical kinetics model to understand the vertical distribution and available mass of haze-forming molecules. We model both solar and enhanced-metallicity cloudy models and determine the cloud properties necessary to match observations. In enhanced-metallicity atmospheres, we find that the equilibrium clouds can match the observations of GJ 1214b if they are lofted high into the atmosphere and have a low sedimentation efficiency (f{sub sed} = 0.1). We find that models with a variety of hydrocarbon haze properties can match the observations. Particle sizes from 0.01 to 0.25 μm can match the transmission spectrum with haze-forming efficiencies as low as 1%-5%.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Inferring Netflow Data

Netflow data is a specific format for looking at network traffic. With this format, many security tests are easy to run. Cloud Service Providers produce logs that do not contain all of the content that Netflow data contains. There is a possibility that the fields that are in Netflow data but absent in Cloud Service Provider logs can be inferred from the fields that are present in the Cloud Service Provider logs. By inferring the missing data fields, the same test that can be run with Netflow data would be possible to run on the logs produced by Cloud Service Providers. Cloud Service Providers include Amazon Web Services and Box.com. There are multiple different types of Cloud Services, and each provider handles them differently and produces different logs. There are IaaS (meaning infrastructure as a service), Saas (meaning software as a service), and Paas (meaning platform as a service). Each provides a different use for the user.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

GIANT MOLECULAR CLOUDS IN THE EARLY-TYPE GALAXY NGC 4526

We present a high spatial resolution (≈20 pc) of {sup 12}CO(2 −1) observations of the lenticular galaxy NGC 4526. We identify 103 resolved giant molecular clouds (GMCs) and measure their properties: size R, velocity dispersion σ{sub v}, and luminosity L. This is the first GMC catalog of an early-type galaxy. We find that the GMC population in NGC 4526 is gravitationally bound, with a virial parameter α ∼ 1. The mass distribution, dN/dM ∝ M{sup −2.39±0.03}, is steeper than that for GMCs in the inner Milky Way, but comparable to that found in some late-type galaxies. We find no size–line width correlation for the NGC 4526 clouds, in contradiction to the expectation from Larson’s relation. In general, the GMCs in NGC 4526 are more luminous, denser, and have a higher velocity dispersion than equal-size GMCs in the Milky Way and other galaxies in the Local Group. These may be due to higher interstellar radiation field than in the Milky Way disk and weaker external pressure than in the Galactic center. In addition, a kinematic measurement of cloud rotation shows that the rotation is driven by the galactic shear. For the vast majority of the clouds, the rotational energy is less than the turbulent and gravitational energy, while the four innermost clouds are unbound and will likely be torn apart by the strong shear at the galactic center. We combine our data with the archival data of other galaxies to show that the surface density Σ of GMCs is not approximately constant, as previously believed, but varies by ∼3 orders of magnitude. We also show that the size and velocity dispersion of the GMC population across galaxies are related to the surface density, as expected from the gravitational and pressure equilibrium, i.e., σ{sub v} R{sup −1/2} ∝ Σ{sup 1/2}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Impacts of secondary ice production on Arctic mixed-phase clouds based on ARM observations and CAM6 single-column model simulations

For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice-nucleating particle number concentrations in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent SIP processes well, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice–ice collisional breakup, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We show the important role of SIP in four types of clouds during M-PACE (i.e., multilayer, single-layer stratus, transition, and frontal clouds), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentrations, and provides a better agreement with observations. The findings of this study highlight the importance of considering SIP in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Quantifying and Modelling the Effect of Cloud Shadows on the Surface Irradiance at Tropical and Midlatitude Forests

Cloud shadows lead to alternating light and dark periods at the surface, with the most abrupt changes occurring in the presence of low-level forced cumulus clouds. We examine multiyear irradiance time series observed at a research tower in a midlatitude mixed deciduous forest (Harvard Forest, Massachusetts, USA: 42.53°N, 72.17°W) and one made at a similar tower in a tropical rain forest (Tapajós National Forest, Pará, Brazil: 2.86°S, 54.96°W). Here, we link the durations of these periods statistically to conventional meteorological reports of sky type and cloud height at the two forests and present a method to synthesize the surface irradiance time series from sky-type information. Four classes of events describing distinct sequential irradiance changes at the transition from cloud shadow and direct sunlight are identified: sharp-to-sharp, slow-to-slow, sharp-to-slow, and slow-to-sharp. Lognormal and the Weibull statistical distributions distinguish among cloudy-sky types. Observers’ qualitative reports of ‘scattered’ and ‘broken’ clouds are quantitatively distinguished by a threshold value of the ratio of mean clear to cloudy period durations. Generated synthetic time series based on these statistics adequately simulate the temporal “radiative forcing” linked to sky type. Our results offer a quantitative way to connect the conventional meteorological sky type to the time series of irradiance experienced at the surface.

54 ENVIRONMENTAL SCIENCES↗