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At least 1,279 records · Page 71

Retrieval of Dry Snow Parameters from Radiometric Data Using a Dense Medium Model and Genetic Algorithms

In this paper, GA-based techniques are used to invert the equations of an electromagnetic model based on Dense Medium Radiative Transfer Theory (DMRT) under the Quasi Crystalline Approximation with Coherent Potential to retrieve snow depth, mean grain size and fractional volume from microwave brightness temperatures. The technique is initially tested on both noisy and not-noisy simulated data. During this phase, different configurations of genetic algorithm parameters are considered to quantify how their change can affect the algorithm performance. A configuration of GA parameters is then selected and the algorithm is applied to experimental data acquired during the NASA Cold Land Process Experiment. Snow parameters retrieved with the GA-DMRT technique are then compared with snow parameters measured on field.

Tedesco, Marco↗

Using OMI Observations to Measure Aerosol Absorption of Biomass Burning Aerosols Above Clouds

The presence of absorbing aerosol layers above clouds is unambiguously detected by the TOMS/OMI UV Aerosol Index (AI) that uses satellite observations at two near-UV channels. A sensitivity study using radiative transfer calculations shows that the AI signal of resulting from the presence of aerosols above clouds is mainly driven by the aerosol absorption optical depth and the optical depth of the underlying cloud. Based on these results, an inversion algorithm has been developed to retrieve the aerosol optical depth (AOD) of aerosol layers above clouds. In this presentation we will discuss the sensitivity analysis, describe the retrieval approach, and present results of applications of the retrieval method to OMI observations over the South Atlantic Ocean. Preliminary error analyses, to be discussed, indicate that the AOD can be underestimated (up to -30%) or overestimated (up to 60%) depending on algorithmic assumptions.

Torres, Omar↗

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters: Equivalent Sensor Radiance Formulation - Part 1

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

Simulations↗

Status on Iterative Transform Phase Retrieval Applied to the GBT Data

This slide presentation reviews the use of iterative transform phase retrieval in the analysis of the Green Bank Radio Telescope (GBT) Data. It reviews the NASA projects that have used phase retrieval, and the testbed for the algorithm to be used for the James Webb Space Telescope. It shows the comparison of phase retrieval with an interferometer, and reviews the two approaches used for phase retrieval, iterative transform (ITA) or parametric (non-linear least squares model fitting). The concept of ITA Phase Retrieval is reviewed, and the application to Radio Antennas is reviewed. The presentation also examines the National Radio Astronomy Observatory (NRAO) data from the GBT, and the Fourier model that NRAO uses to analyze the data. The challenge for ITA phase retrieval is reviewed, and the coherent approximation for incoherent data is shown. The validity of the approximation is good for a large tilt. There is a review of the proof of concept of the Phase Review simulation using the input wavefront, and the initial sampling parameters estimate from the focused GBT data.

Dean, Bruce↗

MODIS 3km Aerosol Product: Algorithm and Global Perspective

After more than a decade of producing a nominal 10 km aerosol product based on the dark target method, the MODIS aerosol team will be releasing a nominal 3 km product as part of their Collection 6 release. The new product differs from the original 10 km product only in the manner in which reflectance pixels are ingested, organized and selected by the aerosol algorithm. Overall, the 3 km product closely mirrors the 10 km product. However, the finer resolution product is able to retrieve over ocean closer to islands and coastlines, and is better able to resolve fine aerosol features such as smoke plumes over both ocean and land. In some situations, it provides retrievals over entire regions that the 10 km product barely samples. In situations traditionally difficult for the dark target algorithm, such as over bright or urban surfaces the 3 km product introduces isolated spikes of artificially high aerosol optical depth (AOD) that the 10 km algorithm avoids. Over land, globally, the 3 km product appears to be 0.01 to 0.02 higher than the 10 km product, while over ocean, the 3 km algorithm is retrieving a proportionally greater number of very low aerosol loading situations. Based on collocations with ground-based observations for only six months, expected errors associated with the 3 km land product are determined to be greater than for the 10 km product: 0.05 0.25 AOD. Over ocean, the suggestion is for expected errors to be the same as the 10 km product: 0.03 0.05 AOD. The advantage of the product is on the local scale, which will require continued evaluation not addressed here. Nevertheless, the new 3 km product is expected to provide important information complementary to existing satellite-derived products and become an important tool for the aerosol community.

Remer, L. A.↗

Joint Retrieval of Surface BRDF from Geostationary and Polar-Orbiting Satellite Sensors

The latest geostationary sensors like GOES 16/17 ABI and Himawari 8/9 AHI provide high frequent observations of the Earth surface with continuously changing solar illumination geometries, which allow us to retrieve the surface Bidirectional Reflectance Distribution Function (BRDF) with dedicated atmospheric correction algorithms like MAIAC (Multi-Angle Implementation of Atmospheric Correction). However, because the viewing geometry of a specific location from the geostationary satellites are fixed, the angular sampling of surface BRDF by GEO (Geostationary Earth Orbit) sensors is far from comprehensive. This study tries to address this issue by exploring a GEO-LEO (Low-Earth-Orbit) synergy, in particular, jointly retrieving surface BRDF parameters with concurrent ABI/AHI and VIIRS top-of-atmosphere (TOA) reflectance for the near-infrared (NIR) band. The NIR band is chosen because the ABI, AHI, and VIIRS instruments have very similar spectral response functions in this band and therefore simplifies the requirements for cross-sensor radiometric calibration. We compile ABI/AHI and VIIRS TOA data with the best quality (cloud-free and low aerosol loading) at chosen AERONET sites. We then run the GeoNEX MAIAC algorithm to retrieve the Ross-Thick-Li-Sparse (RTLS) surface BRDF parameters with or without the AEORNET measured atmospheric aerosol optical depth (AOD) as inputs. The joint retrieval results are considered the best estimate of surface BRDF. We compare the joint BRDF retrievals with the corresponding MAIAC BRDF products, retrieved with ABI/AHI or VIIRS separately, to evaluate their differences. We expect that the jointly retrieved BRDF data are more robust than the standard products and may help us reduce uncertainties in higher-level earth observation satellite products.

Remote Sensing↗

A Semianalytical Ocean Color Inversion Algorithm with Explicit Water Column Depth and Substrate Reflectance Parameterization

A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002-2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, at(443), the particulate backscattering coefficient at 443 nm, bbp(443), and the diffuse attenuation coefficient at 488 nm, Kd(488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of at(443), bbp(443), and Kd(443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

Great Barrier Reef↗

Aerosol Correction for Improving OMPS/LP Ozone Retrieval

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on Oct. 28, 2011. Limb profilers measures the radiance scattered from the Earth's atmospheric in limb viewing mode from 290 to 1000 nm and infer ozone profiles from tropopause to 60 km. The recently released OMPS-LP Version 2 data product contains the first publicly released ozone profiles retrievals, and these are now available for the entire OMPS mission, which extends from April, 2012. The Version 2 data product retrievals incorporate several important improvements to the algorithm. One of the primary changes is to turn off the aerosol retrieval module. The aerosol profiles retrieved inside the ozone code was not helping the ozone retrieval and was adding noise and other artifacts. Aerosols including polar stratospheric cloud (PSC) and polar mesospheric clouds (PMC) have a detectable effect on OMPS-LP data. Our results show that ignoring the aerosol contribution would produce an ozone density bias of up to 10 percent in the region of maximum aerosol extinction. Therefore, aerosol correction is needed to improve the quality of the retrieved ozone concentration profile. We provide Aerosol Scattering Index (ASI) for detecting aerosols-PMC-PSC, defined as ln(Im-Ic) normalized at 45km, where Im is the measured radiance and Ic is the calculated radiance assuming no aerosols. Since ASI varies with wavelengths, latitude and altitude, we can start by assuming no aerosol profiles in calculating the ASIs and then use the aerosol profile to see if it significantly reduces the residuals. We also discuss the effect of aerosol size distribution on the ozone profile retrieval process. Finally, we present an aerosol-PMC-PSC correction scheme.

OMPS/LP↗

Retrieval of Forest Canopy Parameters for OTTER Using an Optimized Technique

An inversion algorithm based on a nonlinear optimization technique is used to retrieve a subset of forest canopy parameters that can be used in ecosystem modeling. The inversion algorithm uses a parametric model derived from a discrete component forest scattering model which includes all major scattering contributions to radar backscatter. The parametric model, however, is derived for cases where only one mechanism dominates the scattering process, in this case, branch layer volume scattering.The inversion algorithm is shown to be a useful tool in the quantative monitoring of canopy moisture status, as well as a building block of its future versions which will include a more comprehensive set of forest scattering parameters as unknowns.

forest↗

Combined Retrievals of Boreal Forest Fire Aerosol Properties with a Polarimeter and Lidar

Absorbing aerosols play an important, but uncertain, role in the global climate. Much of this uncertainty is due to a lack of adequate aerosol measurements. While great strides have been made in observational capability in the previous years and decades, it has become increasingly apparent that this development must continue. Scanning polarimeters have been designed to help resolve this issue by making accurate, multi-spectral, multi-angle polarized observations. This work involves the use of the Research Scanning Polarimeter (RSP). The RSP was designed as the airborne prototype for the Aerosol Polarimetery Sensor (APS), which was due to be launched as part of the (ultimately failed) NASA Glory mission. Field observations with the RSP, however, have established that simultaneous retrievals of aerosol absorption and vertical distribution over bright land surfaces are quite uncertain. We test a merger of RSP and High Spectral Resolution Lidar (HSRL) data with observations of boreal forest fire smoke, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS). During ARCTAS, the RSP and HSRL instruments were mounted on the same aircraft, and validation data were provided by instruments on an aircraft flying a coordinated flight pattern. We found that the lidar data did indeed improve aerosol retrievals using an optimal estimation method, although not primarily because of the constraints imposed on the aerosol vertical distribution. The more useful piece of information from the HSRL was the total column aerosol optical depth, which was used to select the initial value (optimization starting point) of the aerosol number concentration. When ground based sun photometer network climatologies of number concentration were used as an initial value, we found that roughly half of the retrievals had unrealistic sizes and imaginary indices, even though the retrieved spectral optical depths agreed within uncertainties to independent observations. The convergence to an unrealistic local minimum by the optimal estimator is related to the relatively low sensitivity to particles smaller than 0.1 ( m) at large optical thicknesses. Thus, optimization algorithms used for operational aerosol retrievals of the fine mode size distribution, when the total optical depth is large, will require initial values generated from table look-ups that exclude unrealistic size/complex index mixtures. External constraints from lidar on initial values used in the optimal estimation methods will also be valuable in reducing the likelihood of obtaining spurious retrievals.

Knobelspiesse, K.↗

Assessing CryoSat-2 Antarctic Snow Freeboard Retrievals Using Data From ICESat-2

NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) laser altimeter launched in Fall 2018, providing an invaluable addition to the polar altimetry record generated by ESA's CryoSat-2 radar altimeter. The simultaneous operation of these two satellite altimeters enables unique comparison studies of sea ice altimetry, utilizing the different frequencies and profiling strategies of the two instruments. Here, we use freeboard data from ICESat-2 to assess Antarctic snow freeboard retrievals from CryoSat-2. We first discuss updates made to a previously published CryoSat-2 retrieval process and show how this Version 2 algorithm improves upon the original method by comparing the new retrievals to ICESat-2 in specific along-track profiles as well as on the basin-scale. In two near-coincident along-track profiles, we find mean snow freeboard differences (standard deviations of differences) of 0.3 (9.3) and 7.6 cm (9.6 cm) with 25 km binned correlation coefficients of 0.77 and 0.89. Monthly mean freeboard differences range between −2.9 (10.8) and 6.6 cm (16.8 cm) basin wide, with the largest differences typically occurring in Austral fall months that is hypothesized to be related to new ice growth and the use of static snow backscatter coefficients in the retrieval. Monthly mean correlation coefficients range between 0.57 and 0.80. While coincident data show good agreement between the two sensors, they highlight issues related to geometric and frequency sampling differences that can impact the freeboard distributions.

S W Fons↗

Evaluation of Climate Fingerprinting Sounder Product (ClimFiSP) Air Temperature and H2O Trends

Climate fingerprinting Sounder Product (ClimFiSP) provides gridded (0.5x0.5) daily skin temperature, surface emissivity, air temperature, water vapor, trace gases, and cloud properties, which derived from IR hyper-spectral radiance measured by AIRS on Aqua and CrIS on SNPP and JPSS series. In this work, the ClimFiSP daily air temperature and H2O were retrieved on 98 pressure levels by applying the spectral fingerprint algorithm on 0.5x0.5 gridded daily mean radiances from Climate Hyperspectral Infrared Radiance Product (CHIRP). CHIRP is a stable climate-quality radiance time series spanning AIRS and CrIS. The air temperature and H2O monthly data and trends were calculated from the more than two decades long CHIRP AIRS data. Troposphere warming and stratosphere cooling can be seen in both ClimFiSP and ERA5 air temperature data. The ClimFiSP air temperature trends are also compared with the MW sounding air temperature trends to validate the ClimFiSP air temperature results. The H2O trends on upper troposphere are evaluated by comparing with the ERA5 H2O trends, and results show a reasonable agreement with two sets of results. The ClimFiSP algorithm provides a unique and fast way to retrieve the spatial-temporal change in climate variables from the corresponding change in radiances, gridded at the same spatial-temporal scale. This greatly facilitates the procession of long-term climate data records.

ClimFiSP↗

Interannual and Decadal Variability of Ocean Surface Latent Heat Flux as Seen from Passive Microwave Satellite Algorithms

Ocean surface turbulent fluxes are critical links in the climate system since they mediate energy exchange between the two fluid systems (ocean and atmosphere) whose combined heat transport determines the basic character of Earth's climate. Deriving physically-based latent and sensible heat fluxes from satellite is dependent on inferences of near surface moisture and temperature from coarser layer retrievals or satellite radiances. Uncertainties in these "retrievals" propagate through bulk aerodynamic algorithms, interacting as well with error properties of surface wind speed, also provided by satellite. By systematically evaluating an array of passive microwave satellite algorithms, the SEAFLUX project is providing improved understanding of these errors and finding pathways for reducing or eliminating them. In this study we focus on evaluating the interannual variability of several passive microwave-based estimates of latent heat flux starting from monthly mean gridded data. The algorithms considered range from those based essentially on SSM/I (e.g. HOAPS) to newer approaches that consider additional moisture information from SSM/T-2 or AMSU-B and lower tropospheric temperature data from AMSU-A. On interannual scales, variability arising from ENSO events and time-lagged responses of ocean turbulent and radiative fluxes in other ocean basins (as well as the extratropical Pacific) is widely recognized, but still not well quantified. Locally, these flux anomalies are of order 10-20 W/sq m and present a relevant "target" with which to verify algorithm performance in a climate context. On decadal time scales there is some evidence from reanalyses and remotely-sensed fluxes alike that tropical ocean-averaged latent heat fluxes have increased 5-10 W/sq m since the early 1990s. However, significant uncertainty surrounds this estimate. Our work addresses the origin of these uncertainties and provides statistics on time series of tropical ocean averages, regional space / time correlation analysis, and separation of contributions by variations in wind and near surface humidity deficit. Comparison to variations in reanalysis data sets is also provided for reference.

Robertson, Franklin R.↗

1D-VAR Retrieval Using Superchannels

Since modern ultra-spectral remote sensors have thousands of channels, it is difficult to include all of them in a 1D-var retrieval system. We will describe a physical inversion algorithm, which includes all available channels for the atmospheric temperature, moisture, cloud, and surface parameter retrievals. Both the forward model and the inversion algorithm compress the channel radiances into super channels. These super channels are obtained by projecting the radiance spectra onto a set of pre-calculated eigenvectors. The forward model provides both super channel properties and jacobian in EOF space directly. For ultra-spectral sensors such as Infrared Atmospheric Sounding Interferometer (IASI) and the NPOESS Airborne Sounder Testbed Interferometer (NAST), a compression ratio of more than 80 can be achieved, leading to a significant reduction in computations involved in an inversion process. Results will be shown applying the algorithm to real IASI and NAST data.

Liu, Xu↗

Comparing MODIS C6 'Deep Blue' and 'Dark Target' Aerosol Data

The MODIS Collection 6 Atmospheres product suite includes refined versions of both 'Deep Blue' (DB) and 'Dark Target' (DT) aerosol algorithms, with the DB dataset now expanded to include coverage over vegetated land surfaces. This means that, over much of the global land surface, users will have both DB and DT data to choose from. A 'merged' dataset is also provided, primarily for visualization purposes, which takes retrievals from either or both algorithms based on regional and seasonal climatologies of normalized difference vegetation index (NDVI). This poster present some comparisons of these two C6 aerosol algorithms, focusing on AOD at 550 nm derived from MODIS Aqua measurements, with each other and with Aerosol Robotic Network (AERONET) data, with the intent to facilitate user decisions about the suitability of the two datasets for their desired applications.

MODIS Collection 6↗

A Modified NASA Team Sea Ice Algorithm for the Antarctic

A recent comparative study of the NASA Team and Bootstrap passive microwave sea ice algorithms revealed significantly different sea ice concentration retrievals in some parts of the Antarctic. The study identified potential reasons for the discrepancies including the influence of sea ice temperature variability on the Bootstrap retrievals and the influence of ice surface reflectivity on the horizontally polarized emissivity in the NASA Team retrievals. In this study, we present a modified version of the NASA Team algorithm which reduces the error associated with the use of horizontally polarized radiance data, while retaining the relative insensitivity to ice temperature variations provided by radiance ratios. By retaining the 19 GHz polarization as an independent variable, we also maintain a relatively large dynamic range in sea ice concentration. The modified algorithm utilizes the 19 GHz polarization (PR19) and both gradient ratios, GRV and GRH defined by (37V-19V)/(37V+19V) and (37H-19H)/(37H+19H), respectively, rather than just GRV used in the current NASA Team algorithm. A plot of GRV versus GRH shows that the preponderance of points lie along a quadratic curve, whereas those points affected by surface reflectivity anomalies deviate from this curve. This serves as a method of identifying the problems points. The 19H brightness temperature of these problem points is increased so they too fall along quadratic curve. Sea ice concentrations derived from AVHRR imagery illustrate the extent to which this method reduces the error associated with surface layering.

Cavalieri, Donald J.↗

Volcanic Signatures in Estimates of Stratospheric Aerosol Size, Distribution Width, Surface Area, and Volume Deduced from Global Satellite-Based Observations

Volcanic signatures in the stratospheric aerosol layer are revealed by two independent techniques which retrieve aerosol information from global satellite-based observations of particulate extinction. Both techniques combine the 4-wavelength Stratospheric Aerosol and Gas Experiment (SAGE) II extinction measurements (0.385 <= lambda <= 1.02 microns) with the 7.96 micron and 12.82 micron extinction measurements from the Cryogenic Limb Array Etalon Spectrometer (CLAES) instrument. The algorithms use the SAGE II/CLAES composite extinction spectra in month-latitude-altitude bins to retrieve values and uncertainties of particle effective radius R(sub eff), surface area S, volume V and size distribution width sigma(sub R). The first technique is a multi-wavelength Look-Up-Table (LUT) algorithm which retrieves values and uncertainties of R(sub eff) by comparing ratios of extinctions from SAGE II and CLAES (e.g., E(sub lambda)/E(sub 1.02) to pre-computed extinction ratios which are based on a range of unimodal lognormal size distributions. The pre-computed ratios are presented as a function of R(sub eff) for a given sigma(sub g); thus the comparisons establish the range of R(sub eff) consistent with the measured spectra for that sigma(sub g). The fact that no solutions are found for certain sigma(sub g) values provides information on the acceptable range of sigma(sub g), which is found to evolve in response to volcanic injections and removal periods. Analogous comparisons using absolute extinction spectra and error bars establish the range of S and V. The second technique is a Parameter Search Technique (PST) which estimates R(sub eff) and sigma(sub g) within a month-latitude-altitude bin by minimizing the chi-squared values obtained by comparing the SAGE II/CLAES extinction spectra and error bars with spectra calculated by varying the lognormal fitting parameters: R(sub eff), sigma(sub g), and the total number of particles N(sub 0). For both techniques, possible biases in retrieved-parameters caused by assuming a unimodal functional form are removed using correction factors computed from representative in situ measurements of bimodal size distributions. Some interesting features revealed by the LUT and PST retrievals include: (1) Increases in S and V (but not R(sub eff)) after the Ruiz and Kelut injections, (2) Increases in S, V, R(sub eff) after Pinatubo, (3) Post-Pinatubo increases in S, V, and R(sub eff) that are more rapid in the tropics than elsewhere, (4) Mid-latitude post-Pinatubo increases in R(sub eff) that lag increases in S and V, (5) S and V returning to pre-Pinatubo values sooner than R(sub eff) does, (6) Sharp increases in sigma(sub g), after Pinatubo and slight increases in sigma(sub g) after Ruiz, Etna, Kelut, Spurr and Rabaul, and (7) Gradual declines in the heights at which R(sub eff), S and V peak after Pinatubo.

Bauman, J. J.↗

On the Retrieval of Ice Cloud Particle Shapes from POLDER Measurements

Shapes of ice crystals can significantly affect the radiative transfer in ice clouds. The angular distribution of the polarized reflectance over ice clouds strongly depends on ice crystal shapes. Although the angular-distribution features of the total or polarized reflectance over ice clouds implies a possibility of retrieving ice cloud particle shapes by use of remote sensing data, the accuracy of the retrieval must be evaluated. In this study, a technique, which applies single ice crystal habit and multidirectional polarized radiance to retrieve ice cloud particle shapes, is assessed. Our sensitivity studies show that the retrieved particle shapes &om this algorithm can be considered good approximations to those in actual clouds in calculation of the phase matrix elements. Although a fractal poly-crystal shape or an inhomogeneous hexagonal column may also produce this type of phase functions, more representative single-scattering properties from combinations of natural particle shapes and size distributions may still be necessary in accurate retrieval of other cloud properties such as optical thickness and particle size.

Sun, W.↗