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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

An Evaluation of MODIS-Retrieved Aerosol Optical Depth over a Mountainous AERONET Site in the Southeastern US

The literature shows that aerosol optical depth (AOD) derived from the MODIS Collection 5 (C5) dark target algorithm has been extensively validated by spatiotemporal collocation with AERONET sites on both global and regional scales.Although generally comparing well over the eastern US region, poor performance over mountains in other regions indicate the need to evaluate the MODIS product over a mountain site. This study compares MODIS C5 AOD at 550nm to AOD measured at the Appalachian State University AERONET site in Boone, NC over 30 months between August 2010 and September 2013. For the combined Aqua and Terra datasets, although more than 70% of the 500 MODIS AOD measurements agree with collocated AERONET AOD to within error envelope of +/- (0.05 + 15%), MODIS tends to have a low bias (0.02-0.03). The agreement between MODIS and AERONET AOD does not depend on MODIS quality assurance confidence (QAC) value. However, when stratified by satellite, MODIS-Terra data does not perform as well as Aqua, with especially poor correlation (r = 0.39) for low aerosol loading conditions (AERONET AOD less than 0.15).Linear regressions between Terra and AERONET possess statistically-different slopes for AOD < 0.15 and AOD > or = 0.15. AERONET AOD measured only during MODIS overpass hours is highly correlated with daily-averaged AERONET AOD. MODIS monthly-averaged AOD also tracks that of AERONET over the study period. These results indicate that MODIS is sensitive to the day-to-day variability, as well as the annual cycle of AOD over the Appalachian State AERONET site. The complex topography and high seasonality in AOD and vegetation indices allow us to specifically evaluate MODIS dark target algorithm surface albedo and aerosol model assumptions at a regionally-representative SE US mountain site.

air quality↗
NASA NTRS2017

An Investigation of Dust Storms Observed with the Mars Color Imager

Daily global imaging by the Mars Color Imager (MARCI) continues the record of the Mars Orbiter Camera (MOC) and has allowed creation of a long-duration record of Martian dust storms. We observe dust storms over the first two Mars years of the MARCI record, including tracking individual storms over multiple sols, as well as tracking the growth and recession of the seasonal polar caps. Using the combined 6 Mars year record of textured dust storms (storms with visible textures on the observed dust cloud tops), we study the relationship between textured dust storm activity and meteorology (as simulated by the MarsWRF general circulation model) and surface properties. We find that textured dust storms preferentially occur in places and seasons with above average surface wind stress. Textured dust storm occurrence also has a modest linear anti-correlation with surface albedo (0.43) and topography (0.40). Lastly, we perform an empirical orthogonal function (EOF) analysis on the distribution of occurrence of textured dust storms and find that over 50 of the variance in textured dust storm activity can be explained by two EOF modes. We associate the first EOF mode with cap-edge storms just before Ls = 180deg and the second EOF mode with flushing dust storms that occur from Ls = 180-210deg and again near Ls = 320deg.

Guzewich, Scott D.↗
NASA NTRS2017

Comparison of Passive Microwave-Derived Early Melt Onset Records on Arctic Sea Ice

Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice-ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.

sea ice↗
NASA NTRS2017

What is the Uncertainty in MODIS Aerosol Optical Depth in the Vicinity of Clouds?

MODIS dark-target (DT) algorithm retrieves aerosol optical depth (AOD) using a Look Up Table (LUT) approach. Global comparison of AOD (Collection 6 ) with ground-based sun photometer gives an Estimated Error (EE) of +/-(0.04 + 10%) over ocean. However, EE does not represent per-retrieval uncertainty. For retrievals that are biased high compared to AERONET, here we aim to closely examine the contribution of biases due to presence of clouds and per-pixel retrieval uncertainty. We have characterized AOD uncertainty at 550 nm, due to standard deviation of reflectance in 10 km retrieval region, uncertainty related to gas (H2O, O3) absorption, surface albedo, and aerosol models. The uncertainty in retrieved AOD seems to lie within the estimated over ocean error envelope of +/-(0.03+10%). Regions between broken clouds tend to have higher uncertainty. Compared to C6 AOD, a retrieval omitting observations in the vicinity of clouds (< or = 1 km) is biased by about +/- 0.05. For homogeneous aerosol distribution, clear sky retrievals show near zero bias. Close look at per-pixel reflectance histograms suggests retrieval possibility using median reflectance values.

Patadia, Falguni↗
NASA NTRS2019

A Terrestrial Validation of ICESat Elevation Measurements and Implications for Global Reanalyses

The primary goal of NASA's Ice, Cloud, and land Elevation Satellite (ICESat) mission was to detect centimeter-level changes in global ice sheet elevations at the spatial scale of individual ice streams. Confidence in detecting these small signals requires careful validation over time to characterize the uncertainty and stability of measured elevations. A common validation approach compares altimeter elevations to an independently characterized and stable reference surface. Using a digital elevation model (DEM) from geodetic surveys of one such surface, the salar de Uyuni in Bolivia, we show that ICESat elevations at this location have a 0.0-centimeter bias relative to the WGS84 ellipsoid, 4.0-centimeter (1-sigma) uncertainty overall, and 1.8-centimeter uncertainty under ideal conditions over short (50 kilometer) profiles. We observe no elevation bias between ascending and descending orbits, but we do find that elevations measured immediately after transitions from low to high surface albedo may be negatively biased. Previous studies have reported intercampaign biases (ICBs) between various ICESat observation campaigns, but we find no statistically significant ICBs or ICB trends in our data. We do find a previously unreported 3.1-centimeter bias between ICESat's Laser 2 and Laser 3, and we find even larger interlaser biases in reanalyzed data from other studies. For an altimeter with an exact repeat orbit like ICESat, we also demonstrate that validation results with respect to averaged elevation profiles along a single ground track are comparable to results obtained using reference elevations from an in situ survey.

Laser Ranging↗
NASA NTRS2019

Pixel-Based Model For High Latitude Dust Detection

Dust has implications on the energy budget, ocean biodiversity, and economy at regional and global scales. Dust detection relies on spectral sensitivity at visible (RGB) and infrared wavelengths. Radiative properties of high latitude dust and the background surface albedo in these regions (>40°N, >40°S) complicate current dust detection methods. Leveraging supervised machine learning (ML) methods, we propose a new method accounting for regional differences of dust occurrence.

High latitude dust↗
NASA NTRS2019

Satellite Mapping of PM2.5 Episodes in the Wintertime San Joaquin Valley: A "Static" Model Using Column Water Vapor

The use of satellite Aerosol Optical Thickness (AOT) from imaging spectrometers has been successful in quantifying and mapping high PM2.5 (particulate matter mass <2.5µm diameter) episodes for pollution abatement and health studies. However, some regions have high PM2.5 but poor estimation success. The challenges in using Aerosol Optical Thickness (AOT) from imaging spectrometers to characterize PM2.5 worldwide was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's attendant difficulties of high-albedo surfaces and very shallow, variable vertical mixing also occur in other significantly polluted regions around the world. We report on more accurate PM2.5 maps for the whole-winter period in the SJV, Nov 14, 2012?Dec 11, 2013. Intensive measurements by including NASA aircraft were made for several weeks in that winter, the DISCOVER-AQ California mission.We found success with a relatively simple method based on calibration and checking with surface monitors and a characterization of vertical mixing, and incorporating specific understandings of the region's climatology. We estimate PM2.5 to within ~7µgm?3 RMSE and with R values of ~0.9, based on remotely sensed MAIAC (Multi-Angle Implementation of Atmospheric Correction) observations, and that certain further work will improve that accuracy. Mapping is at 1km resolution. This allows a time sequence of mapped aerosols at 1km for cloud-free days. We describe our technique as a "static estimation". Estimation procedures like this one, not dependent on well-mapped source strengths or on transport error, should help full source-driven simulations by deconstructing processes. They also provide a rapid method to create a long-term climatology.Essential features of the technique are (a) daily calibration of the AOT to PM2.5 using available surface monitors, and (b) characterization of mixed-layer dilution using column water vapor (CWV, otherwise "precipitable water"). We noted that on multi-day timescales both water vapor and particles share near-surface sources and both fall to very low values with altitude; indeed, both are largely removed by precipitation. The existence of layers of H2O or aerosol not within the mixed layer adds complexity, but mixed-effects statistical regression captures essential proportionality of PM2.5 and the ratio variable (AOT/CWV). Accuracy is much higher than previous statistical models, and can be extended to the whole Aqua-satellite data record. The maps and time-series we show suggest a repeated pattern for large valleys like the SJV ? progressive stabilization of the mixing height after frontal passages: PM2.5 is somewhat more determined by day-by-day changes in mixing than it is by the progressive accumulation of pollutants (revealed as increasing AOT).

Chatfield, Robert B.↗
NASA NTRS2019

Decomposing Shortwave Top-of-Atmosphere and Surface Radiative Flux Variations in Terms of Surface and Atmospheric Contributions

Adiagnostic tool for determining surface and atmospheric contributions to interannual variations in top-of-atmosphere (TOA) reflected shortwave (SW) and net downward SW surface radiative fluxes is introduced. The method requires only upward and downward radiative fluxes at the TOA and surface as input and therefore can readily be applied to both satellite-derived and model-generated adiative fluxes. Observations from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.0 product show that 81% of the monthly variability in global mean reflected SW TOA flux anomalies is associated with atmospheric variations (mainly clouds),6%is from surface variations, and 13% is from atmosphere–surface covariability. Over the Arctic Ocean, most of the variability in both reflected SW TOA flux and net downward SW surface flux anomalies is explained by variations in sea ice and cloud fraction alone (r2 5 0.94). Compared to CERES, variability in two reanalyses—the ECMWF interim reanalysis (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)—show large differences in the regional distribution of variance for both the atmospheric and surface contributions to anomalies in net downward SW surface flux. For MERRA-2 the atmospheric contribution is 17% too large compared to CERES while ERA-Interim underestimates the variance by 15%. The difference is mainly due to how cloud variations are represented in the reanalyses. The overall surface contribution in both ERA-Interim and MERRA-2 is smaller than CERES EBAF by 15% for ERA-Interim and 58% for MERRA-2, highlighting limitations of the reanalyses in representing surface albedo variations and their influence on SW radiative fluxes.

Norman G. Loeb↗
NASA NTRSDate not supplied

Diagnosing Changes In Earth's Top-Of-Atmosphere Radiation Budget Since 2000

Earth’s radiation budget (ERB) is a key indicator of the climate system as it describes the delicate balance between how much of the sun’s energy reaching Earth is absorbed and how much thermal infrared radiation is emitted back to space. It is now well established that there is a positive net imbalance in ERB of 0.5-1 Wm-2. This imbalance, referred to as Earth’s Energy Imbalance (EEI), is shaped by natural and anthropogenic climate forcings and the climate system’s response to the forcing.When averaged over a long period, a positive EEI leads to an increase in surface temperature, sea levelrise, ocean heat content, melting of snow and sea-ice, and an acceleration of the hydrological cycle.EEI is also influenced by internal climate variability at interannual (e.g., El Niño/Southern Oscillation(ENSO)) and longer timescales (e.g., Pacific Decadal Oscillation (PDO)). Here we use CERES data toshow that since 2000, EEI has been increasing at a rate of 0.04+-0.03 Wm-2 per yr, corresponding to 0.8 Wm-1 over the 20-year CERES record. To understand the underlying causes for this increase, we analyze the CERES data in the context of internal climate variability indices and regional pattern changes. We also perform a partial radiative perturbation analysis that decomposes the TOA SW and LW radiation variations in terms of contributions by clouds, aerosols, surface albedo, temperature,water vapor and other gases (e.g., ozone, carbon dioxide, methane, etc.).

Norman G Loeb↗
NASA NTRS2021

Modeling Snow Ablation over the Western United States Mountains: Patterns and Controlling Factors

When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.

Snow↗
NASA NTRSDate not supplied

Habitability of Cloudy Worlds: Intersecting Constraints and Unknowns

"Follow the water" has long been a theme of astrobiology. Where there are terrestrial worlds with water, there are likely to be clouds. Earth's clouds carry active microbes, in addition to inactive life that comprises a significant minority of dry dust. Both Venus and Mars are believed to have had significant early water; on Venus, the limited water retained in the clouds has been suggested as a potential refuge for life, a parallel to Mars's subsurface water. Conversely, as microbes on Earth do not stay airborne for very long, a hypothetical exoplanet with more persistent cloud cover could be even more favorable to an airborne biosphere than Earth. Airborne microbiology is significant at a planetary scale for Earth. Effects include weather (microbes as condensation and ice nuclei), climate (alteration of cloud and surface albedo), and water and air chemistry (through metabolic processing). Some of these effects, if present on exoplanets, could be detectable through remote observation. Understanding the parameters that constrain the habitability of clouds is therefore necessary to help guide the search for life. Paper includes the extended abstract.

Habitability↗
NASA NTRS2020

Decay of the Snow Cover Over Arctic Sea Ice From ICESat‐2 Acquisitions During Summer Melt in 2019

From the onset of melt in early June, corresponding declines in Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) freeboard and surface albedo can be seen over the entire Arctic sea ice cover. In the 2019 summer, area‐averaged freeboard decreased from 34 cm prior to melt to a minimum of 12 cm in August while the area‐averaged albedo decreased from ~0.7 to 0.38 for the same period. Calculations using ICESat‐2 freeboards and modeled ice thickness from Pan‐Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) give area‐averaged snow depths ranging from 17 cm prior to melt to 3 cm in August over seasonal ice and from 34 to 4 cm over multiyear ice. Mean rates of snow ablation (including evaporation) in mid‐June were as high as 2 cm/day, comparable to field records from other years. Increases in freeboard after mid‐August in the high latitude (>80°N) multiyear ice cover, north of the Greenland coast, are likely due to earlier freeze‐up and snow accumulation in these regions with shorter melt seasons.

R Kwok↗
NASA NTRS2019

Explicit Aerosol Correction of OMI Formaldehyde Retrievals

Atmospheric aerosols are significant sources of uncertainty in air mass factor (AMF) calculations for trace gas retrievals using ultraviolet measurements from space. Current trace gas retrievals typically do not consider aerosols explicitly as cloud products partially account for aerosol effects. Here, we propose a new measurement-based approach to correct for aerosols explicitly in the AMF calculation, apply it to Ozone Monitoring Instrument (OMI) formaldehyde (HCHO) retrievals and quantify the aerosol-induced HCHO vertical column density difference for three aerosol types (smoke, dust, and sulfate) during 2006–2007. We use OMI aerosol retrievals for aerosol optical properties and vertical profiles to construct lookup tables of scattering weights as functions of geometry, surface pressure, surface albedo, and aerosol information. The average difference between the National Aeronautics and Space Administration operational OMI HCHO product (not considering aerosols) and the results obtained in this study on a global scale are 27%, 6%, and −0.3% for smoke, dust, and sulfate aerosols, respectively. The region with the largest aerosol effects is East China, where the explicit smoke aerosol correction enhances mean HCHO vertical column densities by 35%, with corrections to individual observations sometimes larger than 100%. The quantified aerosol effects are applicable under clear-sky conditions. This study highlights the need to implement aerosol corrections in the AMF calculation for HCHO retrievals. This is particularly relevant in regions with high levels of pollution where aerosols interfere the most with formaldehyde satellite observations.

Ozone Monitoring Instrument (OMI) formaldehyde (HC↗
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↗
NASA NTRSDate not supplied

Residence Time and Trajectory as Constraints on Cloud-Based Habitability

Rocky, geologically active worlds with water, including Venus, are often considered potential origins for Earthlike biochemistries. They are also some of the likeliest worlds to have clouds.Yet the requirements for cloud habitability – the potential for aerobiospheres – is rarely studied. Earth's clouds carry significant numbers of active microbes (Amato et al., 2017), which affect planetary weather(as condensation and ice nuclei), climate (alteration of cloud and surface albedo), and water and air chemistry (through metabolic processing) (Delort et al., 2010).However, we have not yet observed airborne microbial reproduction in the field, and one likely reason is that microbes suspended in clouds on Earth have residence times at best equivalent to typical generation times – hours to days.

Residence↗
NASA NTRSDate not supplied

Arctic Amplification: Process Drivers and Sources of Uncertainty

Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This presentation synthesizes current knowledge of AA and describes a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA.

Patrick C Taylor↗
NASA NTRS2023

A Surface Radiation Balance Data Set from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

A field campaign at Siple Dome in West Antarctica during the austral summer 2019-2020 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-poweredequipment, includes surface energy balance measurements, meteorology and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model's snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-micron atmospheric window reveal cloud phase transitions associated with cloud lifecycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

cloud remote sensing↗
NASA NTRS2023

The CHROMA Cloud-Top Pressure Retrieval Algorithm for the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Satellite Mission

This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.

PACE↗