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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

ExINP NSA Ice Nucleating Particle Concentrations

This data set comprises cumulative ambient ice-nucleating particle (INP) concentrations measured at the National Oceanic and Atmospheric Administration's (NOAA’s) Barrow Atmospheric Baseline Observatory (71.3230° N, 156.6114° W, “BRW” hereafter), next to the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site and ~ 6 km northeast of the town of Utqiaġvik. Our INP abundance data were generated using a combination of online instrument, the Portable Ice Nucleation Experiment chamber ver. 3 (PINE-03), and an offline cold stage, the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT). Our online INP data are all from the Examining the Ice-Nucleating Particles from NSA (ExINP-NSA) campaign conducted from October 19, 2021 to May 24, 2024. The offline INP concentration analysis was performed at West Texas A&M University for aerosol particle samples collected on polycarbonate filters (with 0.2-micron diameter pores). The PINE-03 measurements, as well as sampling activities for offline INP measurements, were conducted using the BRW site. An inset laminar sampling stack was mounted to the instrument platform, allowing PINE-03 to intake particle-laden air. For most of the campaign period, the semi-autonomous PINE-03 chamber was remotely controlled from West Texas A&M University using the LabView interface through the BeyondTrust remote-access console. PINE-03 was set to conduct an immersion freezing expansion experiment (i.e., simulated adiabatic cooling along with RHw at or above 100%). Except during the scheduled maintenance periods, the time resolution of each expansion experiment was approximately 12 minutes. PINE-03 continuously measured INP concentrations during the entire campaign without any substantial breaks. For most of the campaign period, PINE scanned its set-point vessel air temperatures from -14 °C to -31 °C and back to -14 °C about every 120 minutes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Differences in radar return from ice-covered North Slope Lakes

Comparisons are made between L and X band synthetic aperture radar images of frozen lakes on the North Slope of Alaska and ground truth observations of the nature of their ice covers. It is shown that the differences in radar backscatter observed on different areas of a lake can be correlated with whether or not the lake is frozen completely to the bottom at the site in question. This explanation is reasonable inasmuch as the reflection coefficient associated with the high-dielectric contrast ice/water interface is significantly higher than that associated with a low-contrast ice/soil interface. However, the presence of the ice/water interface cannot be the only condition required for the higher backscatter because the ice/water interface per se would be specular at X and L band frequencies, causing the energy returned from the interface to be reflected away from the radar receiver. The other principal factor contributing to the return of energy from the ice/water interface to the receiver is believed to be the presence in the ice of numerous vertically elongated air bubbles which would act as scatters.

Weeks, W. F.↗
NASA NTRS1979

Wetland classification on the Alaskan North Slope

An interactive supervised wetland classification was performed on Landsat digital data for three sites on the North Slope of Alaska. Color-coded classification maps identifying 10 wetland subcategories were produced. Field observations, topographic maps, and aerial photographs were employed as collateral data in classifying and verifying the Landsat information.

Morrow, J. W.↗
NASA NTRS1994

The growth of zeolites A, X and mordenite in space

Zeolites are a class of crystalline aluminosilicate materials that form the backbone of the chemical process industry worldwide. They are used primarily as adsorbents and catalysts and support to a significant extent the positive balance of trade realized by the chemical industry in the United States (around $19 billion in 1991). The magnitude of their efforts can be appreciated when one realizes that since their introduction as 'cracking catalysts' in the early 1960's, they have saved the equivalent of 60 percent of the total oil production from Alaska's North Slope. Thus the performance of zeolite catalysts can have a profound effect on the U.S. economy. It is estimated that a 1 percent increase in yield of the gasoline fraction per barrel of oil would represent a savings of 22 million barrels of crude oil per year, representing a reduction of $400 million in the United States' balance of payments. Thus any activity that results in improvement in zeolite catalyst performance is of significant scientific and industrial interest. In addition, due to their 'stability,' uniformity, and, within limits, their 'engineerable' structures, zeolites are being tested as potential adsorbents to purify gases and liquids at the parts-per-billion levels needed in today's electronic, biomedical, and biotechnology industries and for the environment. Other exotic applications, such as host materials for quantum-confined semiconductor atomic arrays, are also being investigated. Because of the importance of this class of material, extensive efforts have been made to characterize their structures and to understand their nucleation and growth mechanisms, so as to be able to custom-make zeolites for a desired application. To date, both the nucleation mechanics and chemistry (such as what are the 'key' nutrients) are, as yet, still unknown for many, if not all, systems. The problem is compounded because there is usually a 'gel' phase present that is assumed to control the degree of supersaturation, and this gel undergoes a continuous 'polymerization' type reaction during nucleation and growth. Generally, for structure characterization and diffusion studies, which are useful in evaluating zeolites for improving yield in petroleum refining as well as for many of the proposed new applications (e.g., catalytic membranes, molecular electronics, chemical sensors) large zeolites (greater than 100 to 1000 times normal size) with minimum lattice defects are desired. Presently, the lack of understanding of zeolite nucleation and growth precludes the custom design of zeolites for these or other uses. It was hypothesized that the microgravity levels achieved in an orbiting spacecraft could help to isolate the possible effects of natural convection (which affects defect formation) and minimize sedimentation, which occurs since zeolites are twice as dense as the solution from which they are formed. This was expected to promote larger crystals by allowing growing crystals a longer residence time in a high-concentration nutrient field. Thus it was hypothesized that the microgravity environment of Earth orbit would allow the growth of large, more defect-free zeolite crystals in high yield.

Sacco, Albert, Jr.↗
NASA NTRS1995

Data management and scientific integration within the Atmospheric Radiation Measurement Program

The Atmospheric Radiation Measurement (ARM) Program has been developed by the U.S. Department of Energy with the goal to improve the predictive capabilities of General Circulation Models (GCM's) in their treatment of clouds and radiative transfer effects. To achieve this goal, three experimental testbeds were designed for the deployment of instruments that will collect atmospheric data used to drive the GCM's. Each site, known as a Cloud and Radiation Testbed (CART), consists of a highly available, redundant data system for the collection of data from a variety of instrumentation. The first CART site was deployed in April 1992 in the Southern Great Plains (SGP), Lamont, Oklahoma, with the other two sites to follow in September 1995 in the Tropical Western Pacific and in 1997 on the North Slope of Alaska. Approximately 400 MB of data are transferred per day via the Internet from the SGP site to the ARM Experiment Center at Pacific Northwest Laboratory in Richland, Washington. The Experiment Center is central to the ARM data path and provides for the collection, processing, analysis, and delivery of ARM data. Data are received from the CART sites from a variety of instrumentation, observational systems, amd external data sources. The Experiment Center processes these data streams on a continuous basis to provide derived data products to the ARM Science Team in near real-time while providing a three-month running archive of data. A primary requirement of the ARM Program is to preserve and protect all data produced or acquired. This function is performed at Oak Ridge National Laboratory where leading edge technology is employed for the long-term storage of ARM data. The ARM Archive provides access to data for participation outside of the ARM Program. The ARM Program involves a collaborative effort by teams from various DOE National Laboratories, providing multi-disciplinary areas of expertise. This paper will discuss the collaborative methods in which the ARM teams translate the scientific goals of the Program into data products. By combining atmospheric scientists, systems engineers, and software engineers, the ARM Program has successfully designed and developed an environment where advances in understanding the parameterizations of GCM's can be made.

Gracio, Deborah K.↗
NASA NTRS1995

Ice Processes and Growth History on Arctic and Sub-Arctic Lakes Using ERS-1 SAR Data

A survey of ice growth and decay processes on a selection of shallow and deep sub-Arctic and Arctic lakes was conducted using radiometrically calibrated ERS-1 SAR images. Time series of radar backscatter data were compiled for selected sites on the lakes during the period ot ice cover (September to June) for the years 1991-1992 and 1992-1993. A variety of lake-ice processes could be observed, and significant changes in backscatter occurred from the time of initial ice formation in autumn until the onset of the spring thaw. Backscatter also varied according to the location and depth of the lakes. The spatial and temporal changes in backscatter were most constant and predictable at the shallow lakes on the North Slope of Alaska. As a consequence, they represent the most promising sites for long-term monitoring and the detection of changes related to global warming and its effects on the polar regions.

Morris, K.↗
NASA NTRS1998

Aerosol analysis techniques and results from micro pulse lidar

The effect of clouds and aerosol on the atmospheric energy balance is a key global change problem. Full knowledge of aerosol distributions is difficult to obtain by passive sensing alone. Aerosol and cloud retrievals in several important areas can be significantly improved with active remote sensing by lidar. Micro Pulse Lidar (MPL) is an aerosol and cloud profilometer that provides a detailed picture of the vertical structure of boundary layer and elevated dust or smoke plume aerosols. MPL is a compact, fully eyesafe, ground-based, zenith pointing instrument capable of full-time, long-term unattended operation at 523 nm. In October of 1993, MPL began taking full-time measurements for the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) site and has since expanded to ARM sites in the Tropical West Pacific (TWP) and the North Slope of Alaska (NSA). Other MPL's are moving out to some of the 60 world-wide Aerosol Robotic Network (AERONET) sites which are already equipped with automatic sun-sky scanning spectral radiometers providing total column optical depth measurements. Twelve additional MPL's have been purchased by NASA to add to the aerosol and cloud database of the EOS ground validation network. The original MPL vertical resolution was 300 meters but the newer versions have a vertical resolution of 30 meters. These expanding data sets offer a significant new resource for atmospheric radiation analysis. Under the direction of Jim Spinhirne, the MPL analysis team at NASA/GSFC has developed instrument correction and backscatter analysis techniques for ARM to detect cloud boundaries and analyze vertical aerosol structures. A summary of MPL applications is found in Hlavka (1997). With the aid of independent total column optical depth instruments such as the Multifilter Rotating Shadowband Radiometer (MFRSR) at the ARM sites or sun photometers at the AERONET sites, the MPL data can be calibrated, and time-resolved vertical profiles of aerosol optical depth as well as aerosol extinction can be calculated. The techniques used to calibrate the lidar, calculate the aerosol extinction-to-backscatter ratio, and produce profiles of aerosol extinction and aerosol optical depths, will be described. Results using these techniques will be presented for case studies at the ARM site in the Tropical West Pacific and later in the Southern Great Plains.

Hlavka, Dennis L.↗
NASA NTRS1998

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer

The interactions between sea ice, open ocean, atmospheric radiation, and clouds over the Arctic Ocean exert a strong influence on global climate. Uncertainties in the formulation of interactive air-sea-ice processes in global climate models (GCMs) result in large differences between the Arctic, and global, climates simulated by different models. Arctic stratus clouds are not well-simulated by GCMs, yet exert a strong influence on the surface energy budget of the Arctic. Leads (channels of open water in sea ice) have significant impacts on the large-scale budgets during the Arctic winter, when they contribute about 50 percent of the surface fluxes over the Arctic Ocean, but cover only 1 to 2 percent of its area. Convective plumes generated by wide leads may penetrate the surface inversion and produce condensate that spreads up to 250 km downwind of the lead, and may significantly affect the longwave radiative fluxes at the surface and thereby the sea ice thickness. The effects of leads and boundary layer clouds must be accurately represented in climate models to allow possible feedbacks between them and the sea ice thickness. The FIRE III Arctic boundary layer clouds field program, in conjunction with the SHEBA ice camp and the ARM North Slope of Alaska and Adjacent Arctic Ocean site, will offer an unprecedented opportunity to greatly improve our ability to parameterize the important effects of leads and boundary layer clouds in GCMs.

Krueger, Steven K.↗
NASA NTRS2001

Improvements in AVHRR Daytime Cloud Detection Over the ARM NSA Site

Clouds play an important role in the radiation budget over Arctic and Antarctic. Because of limited surface observing capabilities, it is necessary to detect clouds over large areas using satellite imagery. At low and mid-latitudes, satellite-observed visible (VIS; 0.65 micrometers) and infrared (IR; 11 micrometers) radiance data are used to derive cloud fraction, temperature, and optical depth. However, the extreme variability in the VIS surface albedo makes the detection of clouds from satellite a difficult process in polar regions. The IR data often show that the surface is nearly the same temperature or even colder than clouds, further complicating cloud detection. Also, the boundary layer can have large areas of haze, thin fog, or diamond dust that are not seen in standard satellite imagery. Other spectral radiances measured by satellite imagers provide additional information that can be used to more accurately discriminate clouds from snow and ice. Most techniques currently use a fixed reflectance or temperature threshold to decide between clouds and clear snow. Using a subjective approach, Minnis et al. (2001) found that the clear snow radiance signatures vary as a function of viewing and illumination conditions as well as snow condition. To routinely process satellite imagery over polar regions with an automated algorithm, it is necessary to account for this angular variability and the change in the background reflectance as snow melts, vegetation grows over land, and melt ponds form on pack ice. This paper documents the initial satellite-based cloud product over the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site at Barrow for use by the modeling community. Cloud amount and height are determined subjectively using an adaptation of the methodology of Minnis et al. (2001) and the radiation fields arc determined following the methods of Doelling et al. (2001) as applied to data taken during the Surface Heat and Energy Budget of the Arctic (SHEBA). The procedures and data produced in this empirically based analysis will also facilitate the development of the automated algorithm for future processing of satellite data over the ARM NSA domain. Results are presented for May, June, and July 1998. ARM surface data are use to partially validate the results taken directly over the ARM site.

Chakrapani, V.↗
NASA NTRS2001

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths with GOES Data Over ARM/SGP Site

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a one layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum et al used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. used a combination infrared (IR), visible (VIS), and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne proposed a 1.6 and 11 micron bispectral threshold method. While all of these methods have made progress in solving this stubborn problem none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the Atmospheric Radiation Measurement (ARM) domains and hence should be identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) used over the ARM Program's Southern Great Plains (SGP), and North Slope of Alaska (NSA) sites to study the capability of detecting overlapping clouds.

Kawamoto, K.↗
NASA NTRS2003

Remote Sensing of Liquid Water and Ice Cloud Optical Thickness and Effective Radius in the Arctic: Application of Airborne Multispectral MAS Data

A multispectral scanning spectrometer was used to obtain measurements of the reflection function and brightness temperature of clouds, sea ice, snow, and tundra surfaces at 50 discrete wavelengths between 0.47 and 14.0 microns. These observations were obtained from the NASA ER-2 aircraft as part of the FIRE Arctic Clouds Experiment, conducted over a 1600 x 500 km region of the north slope of Alaska and surrounding Beaufort and Chukchi Seas between 18 May and 6 June 1998. Multispectral images of the reflection function and brightness temperature in 11 distinct bands of the MODIS Airborne Simulator (MAS) were used to derive a confidence in clear sky (or alternatively the probability of cloud), shadow, and heavy aerosol over five different ecosystems. Based on the results of individual tests run as part of the cloud mask, an algorithm was developed to estimate the phase of the clouds (water, ice, or undetermined phase). Finally, the cloud optical thickness and effective radius were derived for both water and ice clouds that were detected during one flight line on 4 June. This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS data in Alaska, is quite capable of distinguishing clouds from bright sea ice surfaces during daytime conditions in the high Arctic. Results of individual tests, however, make it difficult to distinguish ice clouds over snow and sea ice surfaces, so additional tests were added to enhance the confidence in the thermodynamic phase of clouds over the Beaufort Sea. The cloud optical thickness and effective radius retrievals used 3 distinct bands of the MAS, with the newly developed 1.62 and 2.13 micron bands being used quite successfully over snow and sea ice surfaces. These results are contrasted with a MODIS-based algorithm that relies on spectral reflectance at 0.87 and 2.13 micron.

King, Michael D.↗
NASA NTRS2010

A Microwave Radiance Assimilation Study for a Tundra Snowpack

Recent studies have begun exploring the assimilation of microwave radiances for the modeling and retrieval of snow properties. At a point scale, and for short durations (i week), radiance assimilation (RA) results are encouraging. However, in order to determine how practical RA might be for snow retrievals when applied over longer durations, larger spatial scales, and/or different snow types, we must expand the scope of the tests. In this paper we use coincident microwave radiance measurements and station data from a tundra site on the North Slope of Alaska. The field data are from the 3rd Radio-brightness Energy Balance Experiment (REBEX-3) carried out in 1994-95 by the University of Michigan. This dataset will provide a test of RA over months instead of one week, and for a very different type of snow than previous snow RA studies. We will address the following questions: flow well can a snowpack physical model (SM), forced with local weather, match measured conditions for a tundra snowpack?; How well can a microwave emission model, driven by the snowpack model, match measured microwave brightnesses for a tundra snowpack?; How well does RA increase or decrease the fidelity of estimates of snow depth and temperatures for a tundra snowpack?

Kim, Edward↗
NASA NTRS2013

Positive-Buoyancy Rover for Under Ice Mobility

A buoyant rover has been developed to traverse the underside of ice-covered lakes and seas. The rover operates at the ice/water interface and permits direct observation and measurement of processes affecting freeze- over and thaw events in lake and marine environments. Operating along the 2- D ice-water interface simplifies many aspects of underwater exploration, especially when compared to submersibles, which have difficulty in station-keeping and precision mobility. The buoyant rover consists of an all aluminum body with two aluminum sawtooth wheels. The two independent body segments are sandwiched between four actuators that permit isolation of wheel movement from movement of the central tether spool. For normal operations, the wheels move while the tether spool feeds out line and the cameras on each segment maintain a user-controlled fixed position. Typically one camera targets the ice/water interface and one camera looks down to the lake floor to identify seep sources. Each wheel can be operated independently for precision turning and adjustments. The rover is controlled by a touch- tablet interface and wireless goggles enable real-time viewing of video streamed from the rover cameras. The buoyant rover was successfully deployed and tested during an October 2012 field campaign to investigate methane trapped in ice in lakes along the North Slope of Alaska.

Leichty, John M.↗
NASA NTRS2018

(GO)2-SIM: a GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase

General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar–radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.

Lamer, K.↗
NASA NTRS2013

TPSAS-NF1676L-16299-DND

As part of the American Recovery and Reinvestment Act (ARRA) of 2009, the DOE ARM program deployed a new ground-­‐based Arctic High Spectral Resolution Lidar (AHSRL) at the North Slope of Alaska (NSA) site. This lidar, which is based on the AHSRL that was part of the NOAA’s SEARCH (Study of Environmental Arctic Change) contribution to the Canadian Network for the Detection of Atmospheric Change (CANDAC) facility at Eureka, Nunavut Territory, is an autonomous lidar that provides calibrated profiles of particulate (i.e. aerosol and cloud) backscatter cross section, optical thickness and circular depolarization at 532 nm. The AHSRL provides measurements of both intensive and extensive particulate properties. The intensive parameters depend only on the nature of the particles (size, composition, shape), not on the quantity or concentration. Here we show examples of the AHSRL data during both clear and cloudy conditions to show how these measurements can be used to depict aerosols and clouds over the NSA.

Marian Clayton↗
NASA NTRS2020

Non-Turbulent Liquid-Bearing Polar Clouds: Observed Frequency of Occurrence and Simulated Sensitivity to Gravity Waves

A common feature of polar liquid-bearing clouds (LBCs) is radiatively-driven turbulence, which may variously alter cloud lifecycle via vertical mixing, droplet activation, and subsequent feedbacks. However, polar LBCs are commonly initiated under stable, non-turbulent conditions. Using long-term data from the North Slope of Alaska and McMurdo, Antarctica, we show that non-turbulent conditions prevail in ~25% of detected LBCs, surmised to be preferentially early in their lifecycle. We conclude that non-turbulent LBCs are likely common over the polar regions owing primarily to atmospheric temperature and stability. Such stable environments are known to support gravity wave activity. Using large-eddy simulations we find that short to intermediate period gravity waves may catalyze turbulence formation when aerosol particles available for activation are sufficiently small. We posit that the frequent occurrence of non-turbulent LBCs over the polar regions has implications for polar aerosol-cloud interactions and their parameterization in large-scale models.

Clouds↗
NASA NTRS2021

Evaluation of CloudSat Radiative Kernels Using ARM and CERES Observations and ERA5 Reanalysis

Despite the widespread use of the radiative kernel technique for studying radiative feedbacks and radiative forcings, there has not been any systematic, observation-based validation of the radiative kernel method. Here, we utilize observed and reanalyzed radiative fluxes and atmospheric profiles from the Atmospheric Radiation Measurement (ARM) program and ERA5 reanalysis to assess a set of observation-based radiative kernels from CloudSat for six ARM sites. The CloudSat radiative kernels, convoluted with the ERA5 state variables, can almost perfectly reconstruct the monthly anomalies of shortwave (SW) and longwave (LW) radiative fluxes in ERA5 at the surface (SFC) and top-of atmosphere (TOA) with correlations significantly being greater than 0.95. The biases of kernel-estimated flux anomalies calculated using the ARM-observed state variables can be more than twice as large when compared with the ARM-observed surface flux anomalies and Clouds and Earth’s Radiant Energy System (CERES) observed anomalies at the TOA. Generally, clouds contribute to most (>60%) of the variance of flux anomalies at Southern Great Plain (SGP), Tropical Western Pacific (TWP), and Eastern North Atlantic (ENA), and surface albedo dominates (>69%) the variance of SW flux anomalies at North Slope of Alaska (NSA). The radiative kernels exhibit the lowest correlation (r~[0.55,0.85]) when reconstructing SFC LW flux anomalies at SGP, TWP, and ENA, whose biases are related to the possibility that the kernels may not fully capture the characteristics 47 associated with MJO and ENSO at TWP and the presence of clouds at SGP and ENA.

Radiative kernels↗