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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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NASA NTRS2000

Sensitivity of MODIS 2.1 micron Channel for Off-Nadir View Angles for Use in Remote Sensing of Aerosol

Remote sensing of aerosol over land, from MODIS will be based on dark targets using mid-IR channels 2.1 and 3.9 micron. This approach was developed by Kaufman et al (1997), who suggested that dark surface reflectance in the red (0.66 micron -- rho(sub 0.66)) channel is half of that at 2.2 micron (rho(sub 2.2)), and the reflectance in the blue (0.49 micron - rho(sub 0.49)) channel is a quarter of that at 2.2 micron. Using this relationship, the surface reflectance in the visible channels can be predicted within Delta.rho(sub 0.49) approximately Delat.rho(sub 0.66) approximately 0.006 from rho(sub 2.2) for rho(sub 2.2) <= 0.10. This was half the error obtained using the 3.75 micron and corresponds to an error in aerosol optical thickness of Delat.tau approximately 0.06. These results, though applicable to several biomes (e.g. forests, and brighter lower canopies), have only been tested at one view angle - the nadir (theta = 0 deg). Considering the importance of the results in remote sensing of aerosols over land surfaces from space, we are validating the relationships for off-nadir view angles using Cloud Absorption Radiometer (CAR) data. The CAR data are available for channels between 0.3 and 2.3 micron and for different surface types and conditions: forest, tundra, ocean, sea-ice, swamp, grassland and over areas covered with smoke. In this study we analyzed data collected during the Smoke, Clouds, and Radiation - Brazil (SCAR-B) experiment to validate Kaufman et al.'s (1997) results for non-nadir view angles. We will show the correlation between rho(sub 0.472), rho(sub 0.675), and rho(sub 2.2) for view angles between nadir (0 deg) and 55 deg off-nadir, and for different viewing directions in the backscatter and forward scatter directions.

Gatebe, C. K.↗
NASA NTRS1999

Observations of Smoke Aerosol from Biomass Burning in Mexico: Effect of Particle Aging on Radiative Forcing and Remote Sensing

We take advantage of the May 1998 biomass burning event in Southern Mexico to test the global applicability of a smoke aerosol size model developed from data observed in South America. The Mexican event is an unique opportunity to observe well-aged, residual smoke. Observations of smoke aerosol size distribution made from vertical profiles of airborne in situ measurements show an inverse relationship between concentration and particle size that suggests the aging process continues more than a week after the smoke is separated from its fire sources. The ground-based radiometer retrievals show that the column-averaged, aged, Mexican smoke particles are larger (diameter = 0.28 - 0.33 micrometers) than the mean smoke particles in South America (diameter = 0.22 - 0.30 micrometers). However, the difference (delta - 0.06 micrometer) translates into differences in backscattering coefficient of only 4-7% and an increase of direct radiative forcing of only 10%.

Remer, Lorraine A.↗
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↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

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) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the 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 pixels 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 multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 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 that 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 data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. 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. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
NASA NTRS1985

Solar-induced oscillations in the stratosphere - A myth or reality?

Chandra (1984) has provided an assessment of the solar cycle ozone relationship based on seven years of Nimbus 4 BUV (backscattered ultraviolet) data. It was found that the globally averaged ozone in the upper stratosphere, when corrected for the changes in instrument sensitivity, decreased from 1970 to 1976 by 3-4 percent. This decrease is in accordance with the current estimates of solar UV variability over a solar cycle. The present investigation has the objective to determine if measured changes in ozone and temperature in the upper stratosphere on a time scale of a solar rotation are of solar origin, i.e., directly induced by changes in solar irradiance. The conducted study is based on the first two years (1970-1972) of ozone and temperature data obtained from the Nimbus 4 BUV and the Selective Chopper Radiometer (SCR) experiments. Attention is given to the response of the stratosphere to changes in solar activity associated with the 27-day solar rotation.

Chandra, S.↗
NASA NTRS1993

Atlas 2 Animation

The Atmospheric Laboratory for Applications and Science-2 (ATLAS-2), was designed to collect data on the relationship between the sun's energy output and Earth's middle atmosphere and how these factors affect the ozone layer. The ATLAS-2 flew on the Space Shuttle Discovery's mission SST-56, launched on April 8, 1993. The videotape consists of an animated tour of the instruments that were included as part of the mission. The first half of the tape shows the various instruments, pointing to each in turn and identifying each by the associated initialisms. The instruments identified were: the Atmospheric Trace Molecule Spectroscopy (ATMOS), Millimeter Wave Atmospheric Sounder (MAS), Shuttle Solar Backscatter Ultraviolet/A (SSBUV/A) spectrometer, Solar Spectrum Measurement (SOLSPEC) instrument, Solar Ultraviolet Irradiance Monitor (SUSIM), Active Cavity Radiometer (ACR) and Solar Constant (SOLCON).) The second half of the animation shows the same tour without the pointing or the identification of the instruments.

Source record↗
NASA NTRS2001

Observation of Sea Ice Surface Thermal States Under Cloud Cover

Clouds interfere with the distribution of short-wave and long-wave radiations over sea ice, and thereby strongly affect the surface energy balance in polar regions. To evaluate the overall effects of clouds on climatic feedback processes in the atmosphere-ice-ocean system, the challenge is to observe sea ice surface thermal states under both clear sky and cloudy conditions. From laboratory experiments, we show that C-band radar (transparent to clouds) backscatter is very sensitive to the surface temperature of first-year sea ice. The effect of sea ice surface temperature on the magnitude of backscatter change depends on the thermal regimes of sea ice thermodynamic states. For the temperature range above the mirabilite (Na2SO4.10H20) crystallization point (-8.2 C), C-band data show sea ice backscatter changes by 8-10 dB for incident angles from 20 to 35 deg at both horizontal and vertical polarizations. For temperatures below the mirabilite point but above the crystallization point of MgCl2.8H2O (-18.0 C), relatively strong backwater changes between 4-6 dB are observed. These backscatter changes correspond to approximately 8 C change in temperature for both cases. The backscattering mechanism is related to the temperature which determines the thermodynamic distribution of brine volume in the sea ice surface layer. The backscatter is positively correlated to temperature and the process is reversible with thermodynamic variations such as diurnal insolation effects. From two different dates in May 1993 with clear and overcast conditions determined by the Advanced Very High Resolution Radiometer (AVHRR), concurrent Earth Resources Satellite 1 (ERS-1) C-band ice observed with increases in backscatter over first-year sea ice, and verified by increases in in-situ sea ice surface temperatures measured at the Collaborative-Interdisciplinary Cryosphere Experiment (C-ICE) site.

Nghiem, S. V.↗
NASA NTRS2006

The Aquarius Scatterometer: An Active System for Measuring Surface Roughness for Sea-Surface Brightness Temperature Correction

The Aquarius scatterometer is a total-power L-band radar system for estimating ocean surface roughness. Its measurements will enable the removal of wind effects from the Aquarius radiometer ocean-surface brightness temperature measurements being used to retrieve ocean salinity. The Aquarius scatterometer is a relatively simple, low-spatial resolution power-detecting radar, without ranging capability. But to meet its science requirement, it must be very stable, with repeatability on the order of 0.1 dB over several days, and calibrated accuracy to this level over several months. Data from this instrument over land as well as ocean areas will be available for a variety of geophysical applications.

scatterometers↗
NASA NTRS1985

Satellite observations of the nitric oxide dayglow Implications for the behavior of mesospheric and lower-thermospheric odd nitrogen

The solar backscattered ultraviolet spectral radiometer on the Nimbus 7 satellite routinely measures fluorescence emissions from the nitric oxide (1, 4) gamma band that are imposed on the large Rayleigh-scattered signal in the wavelength range 255-256 nm. The gamma band feature, when isolated from the background radiance, provide information on the seasonal and latitudinal variations in the nitric oxide column abundance over the altitude region from 40 to 45 km upward through the thermosphere. At latitudes from 30 deg to 45 deg in the Northern Hemisphere the measurements show an annual cycle with maximum nitric oxide abundance in summer. The Southern Hemisphere pattern is qualitatively similar to this, although the amplitude of the seasonal variation is substantially smaller. The most prominent feature of the data base is a large maximum in nitric oxide emission that develops poleward of 45 deg latitude in both Hemispheres during late autumn and early winter. These maxima dissipate rapidly as spring approaches and are no longer evident in the data for Northern Hemisphere March and Southern Hemisphere September.

Frederick, J. E.↗
NASA NTRS2007

RadSTAR L-Band Imaging Scatterometer: Performance Assessment

RadSTAR is an instrument development program aimed at combining a radiometer and a scatterometer system into a highly compact configuration that uses a single, electronically scanned antenna to provide co-located and simultaneous measurements of emission and backscatter for airborne and spaceborne applications [I]. The program was designed to map soil moisture and ocean salinity, both important components of the water cycle, and to map sea ice density and thickness, an important factor in ocean-atmosphere heat exchange in Polar Regions. The accuracy in estimation of these and a number of other Earth science parameters can be greatly enhanced by providing the co-aligned radar/radiometer microwave measurements. For instance, radiometer estimates of soil moisture from soil emission are affected by emission from vegetation, and from the roughness of the surface. Complementary measurements using the scatterometer can be used to evaluate the vegetation and surface roughness effects. Hence, the combined observations can provide an improved estimate. As with soil moisture, the ocean salinity is a function of the microwave emission from the sea surface temperature (SST) and sea roughness. There, the addition of radar backscatter measurements of sea roughness enables the correction of the emissivity and provide more accurate estimates of ocean salinity. Similar arguments can be made for other important Earth science parameters. This paper discusses the RadSTAR program, the radar system design, calibration, and digital beamforming techniques, and presents preliminary analysis of the data collected during the test flights. The data sets obtained during the flights and during the radar calibration in the anechoic chamber are also employed to asses the performance of the radar. The paper also discusses the Digital Beamforming Synthetic Aperture Radar (DBSAR) processor, a real-time processor recently developed for the LIS instrument which enables beam synthesis, fine resolutions, and large swaths.

Rincon, Rafael↗
DOE OSTI2022

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Total suspended solids↗
NASA NTRS1994

Performance evaluation of the solar backscatter ultraviolet radiometer, model 2 (SBUV/2) inflight calibration system

The Solar Backscatter Ultraviolet Radiometer, Model 2 (SBUV/2) instruments, as part of their regular operation, deploy ground aluminum reflective diffusers to deflect solar irradiance into the instrument's field-of-view. Previous SBUV instrument diffusers have shown a tendency to degrade in their reflective efficiencies. This degradation will add a trend to the ozone measurements if left uncorrected. An extensive in-flight calibration system was designed into the SBUV/2 instruments to effectively measure the degradation of the solar diffuser (Ball Aerospace Systems Division 1981). Soon after launch, the NOAA-9 SBUV/2 calibration system was unable to track the diffuser's reflectivity changes due, in part, to design flows (Frederick et al. 1986). Subsequently, the NOAA-11 SBUV/2 calibration system was redesigned and an analysis of the first 2 years of data (Weiss et al. 1991) indicated the NOAA-11 SBUV/2 onboard calibration system's performance to be exceeding preflight expectations. This paper will describe the analysis of the first three years NOAA-11 SBUV/2 calibration system data.

Weiss, H.↗
NASA NTRS2012

Characterizing the Vertical Profile of Aerosol Particle Extinction and Linear Depolarization over Southeast Asia and the Maritime Continent: The 2007-2009 View from CALIOP

Vertical profiles of 0.532 μm aerosol particle extinction coefficient and linear volume depolarization ratio are described for Southeast Asia and the Maritime Continent. Quality-screened and cloud-cleared Version 3.01 Level 2 NASA Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) 5-km Aerosol Profile datasets are analyzed from 2007 to 2009. Numerical simulations from the U.S. Naval Aerosol Analysis and Predictive System (NAAPS), featuring two-dimensional variational assimilation of NASA Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging Spectro- Radiometer quality-assured datasets, combined with regional ground-based lidar measurements, are considered for assessing CALIOP retrieval performance, identifying bias, and evaluating regional representativeness. CALIOP retrievals of aerosol particle extinction coefficient and aerosol optical depth (AOD) are high over land and low over open waters relative to NAAPS (0.412/0.312 over land for all data points inclusive, 0.310/0.235 when the per bin average is used and each is treated as single data points; 0.102/0.151 and 0.086/0.124, respectively, over ocean). Regional means, however, are very similar (0.180/0.193 for all data points and 0.155/0.159 when averaged per normalized bin), as the two factors offset one another. The land/ocean offset is investigated, and discrepancies attributed to interpretation of particle composition and a-priori assignment of the extinction-to-backscatter ratio ("lidar ratio") necessary for retrieving the extinction coefficient from CALIOP signals. Over land, NAAPS indicates more dust present than CALIOP algorithms are identifying, indicating a likely assignment of a higher lidar ratio representative of more absorptive particles. NAAPS resolvesmore smoke overwater than identified with CALIOP, indicating likely usage of a lidar ratio characteristic of less absorptive particles to be applied that biases low AOD there. Over open waters except within the Bay of Bengal, aerosol particle scattering is largely capped below 1.5 km MSL, though ground-based lidar measurements at Singapore differ slightly from this finding. Significant aerosol particle presence over land is similarly capped near 3.0 km MSL over most regions. Particle presence at low levels regionally, except over India, is dominated by relatively non-depolarizing particles. Industrial haze, sea salt droplets and fresh smoke are thus most likely present.

linear volume depolarization ratio↗
NASA NTRS2000

MM-Wave Radiometric Measurements of Low Amounts of Precipitable Water Vapor

An experiment was conducted during March, 1999 to study ways in which to improve techniques for measuring low amounts of total-column precipitable water vapor (PWV). The experiment was conducted at the DOE's ARM program's North Slope of Alaska/Adjacent Arctic Ocean Cloud and Radiation Testbed site (DoE ARM NSA/AAO CaRT) located just outside Barrow, Alaska. NASA and NOAA deployed a suite of radiometers covering 25 channels in the frequency range of 20 GHz up to 340 GHz including 8 channels around the 183 GHz water vapor absorption line. In addition to the usual CaRT site instrumentation the NOAA Depolarization and Backscatter Unattended Lidar (DABUL), the SUNY Rotating Shadowband Spectroradiometer (RSS) and other surface based meteorological instrumentation were deployed during the intensive observation period. Vaisala RS80 radiosondes were launched daily as well as nearby National Weather Service VIZ sondes. Atmospheric conditions ranged from clear calm skies to blowing snow and heavy multi-layer cloud coverage. Measurements made by the radiosondes indicate the PWV varied from approx. 1 to approx. 5 mm during the experiment. The near-surface temperature varied between about -40 C to - 15 C. In this presentation, an overview of the experiment with examples of data collected will be presented. Application of the data for assessing the potential and limitations of millimeter-wave radiometry for retrieving very low amounts of PWV will be discussed.

Racette, P.↗
NASA NTRS1992

ECLIPS at LMD: Preliminary results of phase 2

The LMD (Laboratoire de Meteorologie Dynamique) ground-based backscatter lidar station (48 degrees 4 N, 02 degrees 0 E) near Paris was operated daily during the phase 2 of the Experimental Closed Lidar Pilot Study (ECLIPS) from May 22 to June 19, 1991 simultaneously with a radiometric equipment recording the downward short wavelength (SW) and long wavelength (LW) fluxes. A day-by-day summary of the field measurement as a function of universal time (UT) is given. Considering a 5 days week, there are only two days of missing data (05/31 and 06/06) in our record. The nearby meteorological station (10 km away) provides rawindsounding at 00UT and 12UT. During two days, the French airborne lidar LEANDRE was flown over the ground-based station. The airborne equipment looked down while the ground based equipment looked up. The two lidars were operated at 532 nm, and the on-board radiometers were identical to the ones on the ground. The measurements were synchronized with Advanced Very High Resolution Radiometer (AVHIRR) overpasses as required. We also indicate the Meteosat data corresponding to the period of measurements. Two examples of simultaneous ground-based lidar, pygeometer and pyranometer data are displayed.

Valentin, R.↗
NASA NTRS2024

Opinion: Aerosol Remote Sensing Over the Next Twenty Years

More than two decades ago, aerosol remote sensing underwent a revolution with the launch of the Terra and Aqua satellites. Advancement continued via additional launches carrying new passive and active sensors. Capable of retrieving parameters characterizing aerosol loading, rudimentary particle properties and in some cases aerosol layer height, the satellite view of Earth’s aerosol system came into focus.The modeling communities have made similar advances. Now the efforts have continued long enough that we can see developing trends in both remote sensing and modeling communities, allowing us to speculate about the future and how the community will approach aerosol remote sensing twenty years from now. We anticipate technology that will replace today’s standard multi-wavelength radiometers with hyperspectral and/or polarimetry all viewing in multiple angles.These will be supported by advanced active sensors with the ability to measure profiles of aerosol extinction in addition to backscatter. The result will be greater insight into aerosol particle properties. Algorithms will move from being primarily physically-based to include an increasing degree of Machine Learning methods, but physically-based techniques will not go extinct. However, the practice of applying algorithms to a single sensor will be in decline. Retrieval algorithms will encompass multiple sensors and all available ground measurements into a unifying framework, and these inverted products will be ingested directly into assimilation systems, becoming “cyborgs”: half observations, half model. In twenty years we will see a true democratization in space with nations large and small, private organizations and commercial entities of all sizes launching space sensors. With this increasing amount of data and aerosol products available, there will be a lot of bad data. User communities will organize to set standards and the large national space agencies will lead the effort to maintain quality by deploying and maintaining validation ground networks and focused field experiments. Through it all, interest will remain high in the global aerosol system and how that system affects climate, clouds, precipitation and dynamics, air quality, the environment and public health, transport of pathogens and fertilization of ecosystems, and how these processes are adapting to a changing climate.

aerosol remote sensing↗
NASA NTRS2006

Long-term Evolution of Upper Stratospheric Ozone at Selected Stations of the Network for the Detection of Stratospheric Change (NDSC)

The long-term evolution of upper stratospheric ozone has been recorded by lidars and microwave radiometers within the ground-based Network for the Detection of Stratospheric Change (NDSC), and by the space-borne Solar Backscatter Ultra-Violet instruments (SBUV), Stratospheric Aerosol and Gas Experiment (SAGE), and Halogen Occultation Experiment (HALOE). Climatological mean differences between these instruments are typically smaller than 5% between 25 and 50 km. Ozone anomaly time series from all instruments, averaged from 35 to 45 km altitude, track each other very well and typically agree within 3 to 5%. SBUV seems to have a slight positive drift against the other instruments. The corresponding 1979 to 1999 period from a transient simulation by the fully coupled MAECHAM4-CHEM chemistry climate model reproduces many features of the observed anomalies. However, in the upper stratosphere the model shows too low ozone values and too negative ozone trends, probably due to an underestimation of methane and a consequent overestimation of ClO. The combination of all observational data sets provides a very consistent picture, with a long-term stability of 2% or better. Upper stratospheric ozone shows three main features: (1) a decline by 10 to 15% since 1980, due to chemical destruction by chlorine; (2) two to three year fluctuations by 5 to 10%, due to the Quasi-Biennial Oscillation (QBO); (3) an 11-year oscillation by about 5%, due to the 11-year solar cycle. The 1979 to 1997 ozone trends are larger at the southern mid-latitude station Lauder (45 S), reaching 8%/decade, compared to only about 6%/decade at Table Mountain (35 N), Haute Provence/Bordeaux (approximately equal to 45 N), and Hohenpeissenberg/Bern(approximately equal to 47 N). At Lauder, Hawaii (20 N), Table Mountain, and Haute Provence, ozone residuals after subtraction of QBO- and solar cycle effects have levelled off in recent years, or are even increasing. Assuming a turning point in January 1997, the change of trend is largest at southern mid-latitude Lauder, +11%/decade, compared to +7%/decade at northern mid-latitudes. This points to a beginning recovery of upper stratospheric ozone. However, chlorine levels are still very high and ozone will remain vulnerable. At this point the most northerly mid-latitude station, Hohenpeissenberg/Bern differs from the other stations, and shows much less clear evidence for a beginning recovery, with a change of trend in 1997 by only +3%/decade. In fact, record low upper stratospheric ozone values were observed at Hohenpeissenberg/Bern, and to a lesser degree at Table Mountain and Haute Provence, in the winters 2003/2004 and 2004/2005.

Steinbrecht, W.↗