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At least 55 records · Page 3

A Principal Component-Based Radiative Transfer Forward Model (PCRTM) for Vertically in Homogeneous Cloud

A principal-component based radiative transfer model (PCRTM) is developed for simulating the infrared spectral radiance at the top of the atmosphere (TOA). The PCRTM approach removes the redundancy in radiative transfer calculation in high resolution infrared spectra, and saves significant amount of computational time with great accuracy. In PCRTM, both ice and water clouds are treated as effective transmissivity and reflectivity stored in a pre-calculated lookup tables. These quantities are calculated using cloud single scattering properties such as cloud optical depth, cloud particle size, cloud phase, etc. The cloud can be inserted into any pressure layer in the PCRTM model (up to 100 layers). The effective temperature of each cloud layer is treated as a function of its optical depth. To test the accuracy of this approximation, the results are compared with the more rigorous DISORT model, which treats cloud as a plane parallel layer. The root-mean-square error of PCRTM, with respect to DISORT results, is generally less than 0.5 K in brightness temperature. However, the CPU time required by PCRTM was approximately two orders of magnitude less than that required by DISORT.

Li, Hui↗

Characteristics of Precipitation, Cloud, and Latent Heating Associated with the Madden-Julian Oscillation

This study investigates the evolution of cloud and rainfall structures associated with Madden Julian oscillation (MJO) using Tropical Rainfall Measuring Mission (TRMM) data. Two complementary indices are used to define MJO phases. Joint probability distribution functions (PDFs) of cloud-top temperature and radar echo-top height are constructed for each of the eight MJO phases. The genesis stage of MJO convection over the western Pacific (phases 1 and 2) features a bottom-heavy PDF, characterized by abundant warm rain, low clouds, suppressed deep convection, and higher sea surface temperature (SST). As MJO convection develops (phases 3 and 4), a transition from the bottom-heavy to top-heavy PDF occurs. The latter is associated with the development of mixed-phase rain and middle-to-high clouds, coupled with rapid SST cooling. At the MJO convection peak (phase 5), a top-heavy PDF contributed by deep convection with mixed-phase and ice-phase rain and high echo-top heights (greater than 5 km) dominates. The decaying stage (phases 6 and 7) is characterized by suppressed SST, reduced total rain, increased contribution from stratiform rain, and increased nonraining high clouds. Phase 7, in particular, signals the beginning of a return to higher SST and increased warm rain. Phase 8 completes the MJO cycle, returning to a bottom-heavy PDF and SST conditions similar to phase 1. The structural changes in rain and clouds at different phases of MJO are consistent with corresponding changes in derived latent heating profiles, suggesting the importance of a diverse mix of warm, mixed-phase, and ice-phase rain associated with low-level, congestus, and high clouds in constituting the life cycle and the time scales of MJO.

Lau, K-M.↗

Remote Sensing of Cloud Top Heights Using the Research Scanning Polarimeter

Clouds cover roughly two thirds of the globe and act as an important regulator of Earth's radiation budget. Of these, multilayered clouds occur about half of the time and are predominantly two-layered. Changes in cloud top height (CTH) have been predicted by models to have a globally averaged positive feedback, however observational changes in CTH have shown uncertain results. Additional CTH observations are necessary to better and quantify the effect. Improved CTH observations will also allow for improved sub-grid parameterizations in large-scale models and accurate CTH information is important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. RSP scans along the aircraft track and obtains measurements at 152 viewing angles at any aircraft location. The approach presented here aggregates measurements from multiple scans to a single location at cloud altitude using a correlation function designed to identify the location-distinct features in each scan. During NASAs SEAC4RS air campaign, the RSP was mounted on the ER-2 aircraft along with the Cloud Physics Lidar (CPL), which made simultaneous measurements of CTH. The RSPs unique method of determining CTH is presented. The capabilities of using single and combinations of channels within the approach are investigated. A detailed comparison of RSP retrieved CTHs with those of CPL reveal the accuracy of the approach. Results indicate a strong ability for the RSP to accurately identify cloud heights. Interestingly, the analysis reveals an ability for the approach to identify multiple cloud layers in a single scene and estimate the CTH of each layer. Capabilities and limitations of identifying single and multiple cloud layers heights are explored. Special focus is given to sources of error in the method including optically thin clouds, physically thick clouds, multi-layered clouds as well as cloud phase. When determining multi-layered CTHs, limits on the upper clouds opacity are assessed.

Cloud height indicators↗

Nearly a Decade of CALIPSO Observations of Asian and Saharan Dust Properties Near Source and Transport Regions

The lidar on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, makes robust measurements of dust and has generated a length of record that is significant both seasonally and inter-annually. We exploit this record to determine a multi-year climatology of the properties of Asian and Saharan dust, in particular seasonal optical depths, layer frequencies, and layer heights of dust gridded in accordance with the Level 3 data products protocol, between 2006-2015. The data are screened using standard CALIPSO quality assurance flags, cloud aerosol discrimination (CAD) scores, overlying features and layer properties. To evaluate the effects of transport on the morphology, vertical extent and size of the dust layers, we compare probability distribution functions of the layer integrated volume depolarization ratios, geometric depths and integrated attenuated color ratios near the source to the same distributions in the far field or transport region. CALIPSO is collaboration between NASA and Centre National D'études Spatiales (CNES), was launched in April 2006 to provide vertically resolved measurements of cloud and aerosol distributions. The primary instrument on the CALIPSO satellite is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a near-nadir viewing two-wavelength polarization-sensitive instrument. The unique nature of CALIOP measurements make it quite challenging to validate backscatter profiles, aerosol type, and cloud phase, all of which are used to retrieve extinction and optical depth. To evaluate the uncertainty in the lidar ratios, we compare the values computed from dust layers overlying opaque water clouds, considered nominal, with the constant lidar ratio value used in the CALIOP algorithms for dust. We also explore the effects of noise on the CALIOP retrievals at daytime by comparing the distributions of the properties at daytime to the nighttime distributions.

Omar, Ali H.↗

Dome Degradation Characterization of Wide-Field-of-View Nonscanner Aboard ERBE and Its Reprocessing

Earth Radiation Budget Experiment (ERBE) wide-field-of-view (WFOV) nonscanners aboard ERBS and NOAA- 9/NOAA-10 provided broadband shortwave and longwave irradiances from 1985 to 1999. The previous analysis showed dome degradation in the shortwave nonscanner instruments. The correction was performed with a constant spectral (gray assumption) degradation. We suspect that the gray assumption affected daytime longwave irradiance and led to a day-minus-night longwave flux differences (little change in night time longwave) increase over time. Based on knowledge from the CERES process, we will reprocess entire ERBE nonscanner radiation dataset by characterizing shortwave dome transmissivity with spectral dependent degradation using the solar data observed by these instruments. Once spectral dependent degradation is derived, imager derived cloud fraction and the cloud phase as well as surface type over the FOV of nonscanner instruments will be used to model unfiltering coefficients. This poster primarily explains the reprocessing techniques and includes initial comparison of several months of data processed with existing and our recent methods.

Shrestha, Alok K.↗

A New, Low Braking Index for the LMC Pulsar B0540-69

We report the results of a 16-month monitoring campaign using the Swift satellite of PSR 0540, a young pulsar in the Large Magellanic Cloud. Phase connection was maintained throughout the campaign so that a reliable ephemeris could be determined, and the length of the campaign is adequate to accurately determine the spin frequency and its first and second derivatives. The braking index n is 0.031 +/- 0.013 (90% confidence), a value much lower than previously reported for 0 540 and almost all other young pulsars. We use data from the extensive monitoring campaign with RXTE to showt hat timing noise is unlikely to significantly affect the measurement. This is the first measurement of the braking index in the pulsars recently discovered high spin-down state. We discuss possible mechanisms for producing the low braking index.

pulsars individual (PSR B0540−69)↗

Validation of Quasi-Invariant Ice Cloud Radiative Quantities with MODIS Satellite-Based Cloud Property Retrievals

Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If t(1v) and t(1vg) are conserved where t is optical thickness, v the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1wg)factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1w)(1(exp. 1/2)wg)]12, also tend to be similar.

Radiative transfer↗

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Khlopenkov, Konstantin↗

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, approximately 70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

MAIAC AO↗

Cloud Thermodynamic Phase Detection with Polarimetrically Sensitive Passive Sky Radiometers

The primary goal of this project has been to investigate if ground-based visible and near-infrared passive radiometers that have polarization sensitivity can determine the thermodynamic phase of overlying clouds, i.e. if they are comprised of liquid droplets or ice particles. While this knowledge is important by itself for our understanding of the global climate, it can also help improve cloud property retrieval algorithms that use total (unpolarized) radiance to determine Cloud Optical Depth (COD). This is a potentially unexploited capability of some instruments in the NASA Aerosol Robotic Network (AERONET), which, if practical, could expand the products of that global instrument network at minimal additional cost. We performed simulations that found, for zenith observations, cloud thermodynamic phase is often expressed in the sign of the Q component of the Stokes polarization vector. We chose our reference frame as the plane containing solar and observation vectors, so the sign of Q indicates the polarization direction, parallel (negative) or perpendicular (positive) to that plane. Since the quantity of polarization is inversely proportional to COD, optically thin clouds are most likely to create a signal greater than instrument noise. Besides COD and instrument accuracy, other important factors for the determination of cloud thermodynamic phase are the solar and observation geometry (scattering angles between 40 and 60 degrees are best), and the properties of ice particles (pristine particles may have halos or other features that make them difficult to distinguish from water droplets at specific scattering angles, while extreme ice crystal aspect ratios polarize more than compact particles). We tested the conclusions of our simulations using data from polarimetrically sensitive versions of the Cimel 318 sun photometerradiometer that comprise AERONET. Most algorithms that exploit Cimel polarized observations use the Degree of Linear Polarization (DoLP), not the individual Stokes vector elements (such as Q). For this reason, we had no information about the accuracy of Cimel observed Q and the potential for cloud phase determination. Indeed, comparisons to ceilometer observations with a single polarized spectral channel version of the Cimel at a site in the Netherlands showed little correlation. Comparisons to Lidar observations with a more recently developed, multi-wavelength polarized Cimel in Maryland, USA, show more promise. This divergence between simulations and observations has prompted us to begin the development of a small test instrument called the Sky Polarization Radiometric Instrument for Test and Evaluation (SPRITE). This instrument is specifically devoted to the accurate observation of Q, and the testing of calibration and uncertainty assessment techniques, with the ultimate goal of understanding the practical feasibility of these measurements.

Knobelspiesse, Kirk D.↗

Contributions of Mixed-Phase Clouds to Reduced Arctic Amplification

Earth’s Arctic is particularly sensitive to global warming. The climate record shows that Arctic changes in surface temperatures far exceed that of the global mean, a phenomenon referred to as Arctic amplification. Here, we show that warming of the Arctic atmosphere causes mixed-phase clouds in the region to contain less ice and more supercooled liquid, which in turn tends to increase their amount and thick- ness, thereby inducing a positive feedback mainly by increasing downward longwave (LW) radiation at the surface. The increased downward LW radiation decreases the positive lapse rate feedback in the Arctic, thus resulting in reduced Arctic amplification. The strength of this feedback depends on the initial mean-state supercooled liquid fraction (SLF) and the ice crystal effective radii. We also show that reduced precipitation rates can result from large mean-state ice effective radii being replaced by relatively more smaller liquid droplets in the cloud phase feedback, despite having high mean-state SLFs, demonstrating the importance of the representation of cloud microphysics in the Arctic.

Tan, Ivy↗

A Satellite-Based Estimate of Combustion Aerosol Cloud Microphysical Effects over the Arctic Ocean

Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol–cloud interactions. Here, we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify large scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (~10W/sq.m) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain the majority of this signal. For example, combustion aerosols explain at most 40% of the CF differences between the full dataset and the clean-condition subset, compared to between 57% and 91% of the differences that can be predicted by co-varying meteorology. After normalizing for meteorological regime, aerosol microphysical effects have small but significant impacts on CF, CP, and precipitation frequency on an Arctic-wide scale. These effects indicate that dominant aerosol–cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic.

Zamora, Lauren M.↗

Modified Geometric Truncation of the Scattering Phase Function

Phase function of light scattering on large atmospheric particles has very strong peak in forward direction constituting a challenge for accurate numerical calculations of radiance required in remote sensing problems. Scaling transformation replaces original phase function with a sum of the delta function and a new regular smooth phase function. Geometric truncation is one of the ways to construct such a smooth function. The replacement phase function coincides with the original one outside the forward cone and preserves the asymmetry parameter. It has discontinuity at the cone. Another simple functional form of the replacement phase function within the cone is suggested. It enables continuity and allows for a number of modifications. Three of them are considered in this study: preserving asymmetry parameter, providing continuity of the 1st derivative of the phase function, and preserving mean scattering angle. Yet another problem addressed in this study is objective selection of the width of the forward cone. That angle affects truncation fraction and values of the phase function within the cone. A heuristic approach providing unambiguous criterion of selection of the truncation angle is proposed. The approach has easy numerical implementation. Suggested modifications were tested on cloud phase function using discrete ordinates and Monte Carlo methods. It was shown that the modifications provide better accuracy of the radiance computation compare to the original geometric truncation with discrete ordinates while continuous derivative approach provides significant gain in computer time with Monte Carlo simulations.

Alexander Radkevich↗

The Impact of Neglecting Ice Phase on Cloud Optical Depth Retrievals from AERONET Cloud Mode Observations

Clouds present many challenges to climate modelling. To develop and verify the parameterisations needed to allow climate models to represent cloud structure and processes, there is a need for high-quality observations of cloud optical depth from locations around the world. Retrievals of cloud optical depth are obtainable from radiances measured by Aerosol Robotic Network (AERONET) radiometers in “cloud mode” using a two-wavelength retrieval method. However, the method is unable to detect cloud phase, and hence assumes that all of the cloud in a profile is liquid. This assumption has the potential to introduce errors into long term statistics of retrieved optical depth for clouds that also contain ice. Using a set of idealized cloud profiles we find that, for optical depths above 20, the fractional error in retrieved optical depth is a linear function of the fraction of the optical depth that is due to the presence of ice cloud (“ice fraction”). Clouds that are entirely ice have positive errors with magnitudes of the order of 55% to 70 %. We derive a simple linear equation that can be used as a correction at AERONET sites where ice fraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles from five sites of the Atmospheric Radiation Measurement programme. The dataset contains separate retrievals of ice and liquid retrievals; hence ice fraction can be estimated. The magnitude of the error at each location was related to the relative frequencies of occurrence in thick frontal cloud at the mid-latitude sites and of deep convection at the tropical sites – that is, of deep cloud containing both ice and liquid particles. The long-term mean optical depth error at the five locations spans the range 2– 4, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10% and surface flux to about 15 %.

Shonk, Jonathan K. P.↗

The Evolution of Lidar Networks: A US Perspective

Atmospheric aerosols and clouds play an important role in climate by directly scattering and absorbing sunlight. Aerosol-cloud interactions modify the particle properties causing indirect effects, alter aerosol deposition and rainfall, and contribute substantially to the uncertainties in predicting climate effects. Aerosols also affect air quality and both constituents modulate boundary layer dynamics to a certain extent, in turn impacting aerosol transport and aerosol-cloud interactions. The spatiotemporal distribution of aerosol and cloud layers is thus important, and lidar remains the primary instrument for determining the vertical distribution of aerosols and thin clouds. Lidar still provides important information for opaque cloud layers by determining base heights. Combined lidar and radar data provide a comprehensive coverage of aerosol and cloud vertical distributions, and thus locations where and when they interact. Lidar data provide a means of constraining column aerosol loading observations (e.g. AOD) to vertical extents. In addition, lidar has proven effective at determining a proxy for boundary layer height by examining aerosol gradients and/or cloud base heights in lidar profiles. Polarized lidars provide additional information on particle shape, allowing estimates of cloud phase and separation of dust or smoke from sulfate and sea salt aerosols. Given the importance of lidar observations and a critical maturation in technology and retrieval techniques, several organizations began to build operational lidar networks around 2000. The European Aerosol Research Lidar Network (EARLINET) was started as a research focused network of advanced lidars across Europe. The Asian Dust Lidar Network (ADNET) was also developed as a regional network providing lidar profiles of dust and pollution across Eastern Asia. In the US, the NASA Micro Pulse Lidar Network (MPLNET) was created to provide global lidar profiling at key sites in the NASA Aerosol Robotic Network (AERONET). The Network for the Detection of Atmospheric Composition Change (NDACC) pre-dates these networks and many sites have lidar, but it is not strictly a lidar network and at the time focused less on the lower troposphere. Finally, there were already existing ceilometer networks operated by various meteorological agencies, but in particular here in the US the profile data has not been available. The ceilometer networks were used to provide only clouds base heights and estimates of PBL height. Thus, the distinguishing feature between lidar and ceilometer networks was historically the ability to actually provide profile information (in addition to differences in wavelength, and advanced retrievals such as the raman technique). As time progressed, each lidar network matured and developed more operational capabilities and data sets, coupled with viable data centers providing DAAC services and access to near-real-time (NRT) data. In 2008 under WMO guidance, the Global Atmospheric Watch (GAW) Aerosol Lidar Observation Network (GALION) was formed as a global network made up of the existing lidar networks. The goal was to share information, best practices, and develop frameworks and techniques for quality data. GALION grew to include several other regional lidar networks and this has led to a vast increase in quality lidar sites worldwide.

Welton, Ellsworth J.↗

TPSAS-NF1676L-29931-DND

The ACE science working group satellite concept calls for a combination of high spectral resolution aerosol lidar and a polarimeter able to determine vertical profiles of aerosol extinction and clouds. Here we discuss why an HSRL lidar is required to achieve these requirements. The primary advantage of an HSRL over an elastic backscatter lidar is that the HSRL channel allows for the independent retrieval of vertically resolved aerosol extinction without constraint. Not requiring a constraint means greater availability of retrievals in polar regions, on the night side, and in broken cloud situations, plus greater accuracy particularly in the lowest part of the atmosphere and in scenes with aerosol heterogeneity. Advantages go beyond the extinction products. Unlike attenuated backscatter, which is an integrated quantity, the aerosol backscatter coefficient depends only on the aerosol at a specific level in the atmosphere, making it preferable for use in quantitative applications. The aerosol backscatter coefficient is more accurate from an HSRL system compared to the retrieval from a single-channel system. This is because errors in both extinction and backscatter from a single-channel system increase with decreasing altitude, due to the need to accurately characterize the overlying attenuation. This means that an HSRL will also have more valid data in and near the boundary layer where aerosol is of primary interest for air quality and weather applications and for many climate objectives including aerosol-cloud interaction studies. Even cloud properties that do not directly use the HSRL capability (cloud phase and thin cirrus retrievals) benefit from the more accurate calibration of an HSRL system. The improved accuracy of backscatter and extinction also lead to better capabilities for aerosol typing, improved retrievals of aerosol radiative effect, and increased information content for combined lidar + polarimeter retrievals of profiles of microphysical properties such as vertically resolved effective radius.

S P Burton↗

Changes in TOA SW Fluxes over Marine Clouds When Estimated via Semiphysical Angular Distribution Models

Top-of-atmosphere (TOA) shortwave (SW) angular distribution models (ADMs) approximate—per angular direction of an imagined upward hemisphere—the intensity of sunlight scattered back from a specific Earth–atmosphere scene. ADMs are, thus, critical when converting satellite-borne broadband radiometry into estimated radiative fluxes. This paper applies a set of newly developed ADMs with a more refined scene definition and demonstrates tenable changes in estimated fluxes compared to currently operational ADMs. Newly developed ADMs use a semiphysical framework to consider cloud-top effective radius (¯¯R_e) and above-cloud water vapor (ACWV), in addition to accounting for surface wind speed and clouds’ phase, fraction, and optical depth. In effect, instantaneous TOA SW fluxes for marine liquid-phase clouds had the largest flux differences (of up to 25 W m−2) for lower solar zenith angles and cloud optical depth greater than 10 due to extremes in ¯¯R_e or ACWV. In regions where clouds had persistently extreme levels of ¯¯R_e (here mostly for ¯¯R_e < 7μm and ¯¯R_e > 15μm) or ACWV, instantaneous fluxes estimated from Aqua, Terra, Meteosat-8, and Meteosat-9 satellites using the two ADMs differed systematically, resulting in significant deviations in daily mean fluxes (up to ±10 W m^(−2)) and monthly mean fluxes (up to ±5 W m^(−2)). Flux estimates using newly developed, semiphysical ADMs may contribute to a better understanding of solar fluxes over low-level clouds. It remains to be seen whether aerosol indirect effects are impacted by these updates.

angular distribution models (ADMs)↗

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗