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NASA NTRS2001

Cloud Spatial Structure and 3D Radiative Transfer

Cloud radiative properties are sensitive to drop size and other parameters of cloud micro-structure, but also to cloud shape, spacing, and other parameters of cloud macro-structure, including internal fractal structure. New information on cloud structure is being derived from a variety of cloud radars and lidars. Ongoing field programs such as DoE/ARM (atmospheric radiation measurement) are improving the measurement and modelling of physical and radiative properties of clouds. A parallel effort is underway to improve cloud remote sensing, especially from the new suite of EOS (Earth observing system) instruments which are beginning to provide higher spectral, spatial resolution, and/or angular resolution. Key parameters for improving pixel-scale retrievals are cloud thickness and photon mean-free-path, which together determine the scale of 'radiative smoothing' of cloud fluxes and radiances. This scale has been observed as a change in the spatial spectrum of Landsat cloud radiances, and was also recently found with the Goddard micropulse lidar, by searching for returns from directions nonparallel to the incident beam. 'Offbeam' Lidar returns are now being used to estimate the cloud 'radiative Green's function' (G). G depends on cloud thickness and may be used to retrieve that important quantity. G is also being applied to improving simple estimates of cloud radiative properties that are based on the 'Independent Pixel Approximation' or IPA. This and other measurements of 3D (three dimensional) transfer in clouds, coupled with Monte Carlo and other 3D transfer methods, are beginning to provide a better understanding of the dependence of radiation on cloud inhomogeneity, and to suggest new retrieval and parameterization algorithms which take account of cloud inhomogeneity. An international 'Intercomparison of 3D Radiation Codes' (I3RC) program is underway to coordinate and evaluate the variety of 3D radiative transfer methods now available, and to make them more widely available. Information is on the Web at: http://climate.gsfc.nasa.gov/I3RC. Input consists of selected cloud fields derived from data sources such as radar, microwave and satellite, and from models involved in the GEWEX Cloud Systems Studies. Output is selected radiative quantities that characterize the large-scale properties of the fields of radiative fluxes and heating. Several example cloud fields will be used to illustrate the effects of cloud inhomogeneity and 3D radiation.

Cahalan, Robert F.↗
NASA NTRS2001

Cloud and Radiation Studies during SAFARI 2000

Though the emphasis of the Southern Africa Regional Science Initiative 2000 (SAFARI-2000) dry season campaign was largely on emission sources and transport, the assemblage of aircraft (including the high altitude NASA ER-2 remote sensing platform and the University of Washington CV-580, UK MRF C130, and South African Weather Bureau JRA in situ aircrafts) provided a unique opportunity for cloud studies. Therefore, as part of the SAFARI initiative, investigations were undertaken to assess regional aerosol-cloud interactions and cloud remote sensing algorithms. In particular, the latter part of the experiment concentrated on marine boundary layer stratocumulus clouds off the southwest coast of Africa. Associated with cold water upwelling along the Benguela current, the Namibian stratocumulus regime has received limited attention but appears to be unique for several reasons. During the dry season, outflow of continental fires and industrial pollution over this area can be extreme. From below, upwelling provides a rich nutrient source for phytoplankton (a source of atmospheric sulphur through DMS production as well as from decay processes). The impact of these natural and anthropogenic sources on the microphysical and optical properties of the stratocumulus is unknown. Continental and Indian Ocean cloud systems of opportunity were also studied during the campaign. Aircraft flights were coordinated with NASA Terra Satellite overpasses for synergy with the Moderate Resolution Imaging Spectroradiometer (MODIS) and other Terra instruments. An operational MODIS algorithm for the retrieval of cloud optical and physical properties (including optical thickness, effective particle radius, and water path) has been developed. Pixel-level MODIS retrievals (11 km spatial resolution at nadir) and gridded statistics of clouds in th SAFARI region will be presented. In addition, the MODIS Airborne Simulator flown on the ER-2 provided high spatial resolution retrievals (50 m at nadir). These retrievals will be discussed and compared with in situ observations.

Platnick, Steven↗
NASA NTRS2003

Derivation of Aerosol Columnar Mass from MODIS Optical Depth

In order to verify performance, aerosol transport models (ATM) compare aerosol columnar mass (ACM) with those derived from satellite measurements. The comparison is inherently indirect since satellites derive optical depths and they use a proportionality constant to derive the ACM. Analogously, ATMs output a four dimensional ACM distribution and the optical depth is linearly derived. In both cases, the proportionality constant requires a direct intervention of the user by prescribing the aerosol composition and size distribution. This study introduces a method that minimizes the direct user intervention by making use of the new aerosol products of MODIS. A parameterization is introduced for the derivation of columnar aerosol mass (AMC) and CCN concentration (CCNC) and comparisons between sunphotometer, MODIS Airborne Simulator (MAS) and in-measurements are shown. The method still relies on the scaling between AMC and optical depth but the proportionality constant is dependent on the MODIS derived r$_{eff}$,\eta (contribution of the accumulation mode radiance to the total radiance), ambient RH and an assumed constant aerosol composition. The CCNC is derived fkom a recent parameterization of CCNC as a function of the retrieved aerosol volume. By comparing with in-situ data (ACE-2 and TARFOX campaigns), it is shown that retrievals in dry ambient conditions (dust) are improved when using a proportionality constant dependent on r$ {eff}$ and \eta derived in the same pixel. In high humidity environments, the improvement inthe new method is inconclusive because of the difficulty in accounting for the uneven vertical distribution of relative humidity. Additionally, two detailed comparisons of AMC and CCNC retrieved by the MAS algorithm and the new method are shown. The new method and MAS retrievals of AMC are within the same order of magnitude with respect to the in-situ measurements of aerosol mass. However, the proposed method is closer to the in-situ measurements than the MODIS retrievals. The retrievals of CCNC are also within the same order of magnitude for both methods. The new method is applied to an actual MODIS retrieval and although no in-situ data is available to compare, it is shown that the proposed method yields more credible values than the MODIS retrievals. In addition, recent data available from the PRIDE (Puerto Rico Dust Experiment, July 2000) will be shown by comparing sunphotometer, MODIS and in-situ data.

Gasso, Santiago↗
NASA NTRS2007

Nonspherical and Spherical Characterization of Ice in Hurricane Erin for Wideband Passive Microwave Comparisons

In order to better understand the characteristics of frozen cloud particles in hurricane systems, computed brightness temperatures were compared with radiometric observations of Hurricane Erin (2001) from the NASA ER-2 aircraft. The focus was oil the frozen particle microphysics and the high frequencies (2 85 GHz) that are particularly sensitive to frozen particles. Frozen particles in hurricanes are an indicator of increasing hurricane intensity. In fact "hot towers" associated with increasing hurricane intensity are composed of frozen ice cloud particles. (They are called hot towers because their column of air is warmer than the surrounding air temperature, but above about 5-7 km to the tops of the towers at 15-19 km, the cloud particles are frozen.) This work showed that indeed, one can model information about cloud ice particle characteristics and indicated that nonspherical ice shapes, instead of spherical particles, provided the best match to the observations. Overall, this work shows that while non-spherical particles show promise, selecting and modeling a proper ice particle parameterization can be difficult and additional in situ measurements are needed to define and validate appropriate parameterizations. This work is important for developing Global Precipitation Measurement (GPM) mission satellite algorithms for the retrieval of ice characteristics both above the melting layer, as in Hurricane Erin, and for ice particles that reach the surface as falling snow.

Skofronick-Jackson, Gail↗
NASA NTRS2011

Satellite-Derived Distributions, Inventories and Fluxes of Dissolved and Particulate Organic Matter Along the Northeastern U.S. Continental Margin

Estuaries and the coastal ocean experience a high degree of variability in the composition and concentration of particulate and dissolved organic matter (DOM) as a consequence of riverine and estuarine fluxes of terrigenous DOM, sediments, detritus and nutrients into coastal waters and associated phytoplankton blooms. Our approach integrates biogeochemical measurements, optical properties and remote sensing to examine the distributions and inventories of organic carbon in the U.S. Middle Atlantic Bight and Gulf of Maine. Algorithms developed to retrieve colored DOM (CDOM), Dissolved (DOC) and Particulate Organic Carbon (POC) from NASA's MODIS-Aqua and SeaWiFS satellite sensors are applied to quantify the distributions and inventories of DOC and POC. Horizontal fluxes of DOC and POC from the continental margin to the open ocean are estimated from SeaWiFS and MODIS-Aqua distributions of DOC and POC and horizontal divergence fluxes obtained from the Northeastern North Atlantic ROMS model. SeaWiFS and MODIS imagery reveal the importance of estuarine outflow to the export of CDOM and DOC to the coastal ocean and a net community production of DOC on the shelf.

Mannino, A.↗
NASA NTRS2010

Dispersion and Lifetime of the SO2 Cloud from the August 2008 Kasatochi Eruption

Hemispherical dispersion of the SO2 cloud from the August 2008 Kasatochi eruption is analyzed using satellite data from the Ozone Monitoring Instrument (OMI) and the Goddard Trajectory Model (GTM). The operational OMI retrievals underestimate the total SO2 mass by 20-30% on 8-11 August, as compared with more accurate offline Extended Iterative Spectral Fit (EISF) retrievals, but the error decreases with time due to plume dispersion and a drop in peak SO2 column densities. The GTM runs were initialized with and compared to the operational OMI SO2 data during early plume dispersion to constrain SO2 plume heights and eruption times. The most probable SO2 heights during initial dispersion are estimated to be 10-12 km, in agreement with direct height retrievals using EISF algorithm and IR measurements. Using these height constraints a forward GTM run was initialized on 11 August to compare with the month-long Kasatochi SO2 cloud dispersion patterns. Predicted volcanic cloud locations generally agree with OMI observations, although some discrepancies were observed. Operational OMI SO2 burdens were refined using GTM-predicted mass-weighted probability density height distributions. The total refined SO2 mass was integrated over the Northern Hemisphere to place empirical constraints on the SO2 chemical decay rate. The resulting lower limit of the Kasatochi SO2 e-folding time is approx.8-9 days. Extrapolation of the exponential decay back in time yields an initial erupted SO2 mass of approx.2.2 Tg on 8 August, twice as much as the measured mass on that day.

Krotkov, N. A.↗
NASA NTRS2012

Spatial Distribution of Accuracy of Aerosol Retrievals from Multiple Satellite Sensors

Remote sensing of aerosols from space has been a subject of extensive research, with multiple sensors retrieving aerosol properties globally on a daily or weekly basis. The diverse algorithms used for these retrievals operate on different types of reflected signals based on different assumptions about the underlying physical phenomena. Depending on the actual retrieval conditions and especially on the geographical location of the sensed aerosol parcels, the combination of these factors might be advantageous for one or more of the sensors and unfavorable for others, resulting in disagreements between similar aerosol parameters retrieved from different sensors. In this presentation, we will demonstrate the use of the Multi-sensor Aerosol Products Sampling System (MAPSS) to analyze and intercompare aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Based on this intercomparison, we are determining geographical locations where these products provide the greatest accuracy of the retrievals and identifying the products that are the most suitable for retrieval at these locations. The analyses are performed by comparing quality-screened satellite aerosol products to available collocated ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations, during the period of 2006-2010 when all the satellite sensors were operating concurrently. Furthermore, we will discuss results of a statistical approach that is applied to the collocated data to detect and remove potential data outliers that can bias the results of the analysis.

Petrenko, Maksym↗
NASA NTRS2013

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

(GPCP) Global Precipitation Project analysis↗
NASA NTRS2016

Reducing Multisensor Satellite Monthly Mean Aerosol Optical Depth Uncertainty: 1. Objective Assessment of Current AERONET Locations

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

aerosol optical depth↗
NASA NTRS2018

Applicability of Neural Networks to Etalon Fringe Filtering in Laser Spectrometers

We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.

diode laser spectroscopy↗
NASA NTRS2021

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for sevenyears and continues to provide a vast quantity of global precipitation data at finer temporospatialresolutions with improved accuracy and coverage. GPM’s signature algorithm, the integratedmultisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expectedfor wide variety of research and operational applications. This study evaluates the latest version(V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatelliteprecipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensorsystem (MRMS) precipitation products over the conterminous United States (CONUS). The spatialdistributions of all products are analyzed. The error characteristics are further examined for 3B42 andIMERG in winter and summer by an error decomposition approach, which partitions total bias intohit bias, biases due to missed precipitation and false precipitation. The volumetric and categoricalstatistical metrics are used to quantitatively evaluate the performance of the two satellite-basedproducts. All products show a similar precipitation climatology with some regional differences.The two satellite-based products perform better in the eastern CONUS than in the mountainousWestern CONUS. The evaluation demonstrates the clear improvement in IMERG precipitationproduct in comparison with its predecessor 3B42, especially in reducing missed precipitation inwinter and summer, and hit bias in winter, resulting in better performance in capturing lighter andheavier precipitation.

Jianxin Wang↗
NASA NTRSDate not supplied

Integrated Multi-Satellite Retrievals for GPM (IMERG)

The Integrated Multi-satellitE Retrievals for GPM (IMERG) product from the U.S. Science Team of the NASA-JAXA Global Precipitation Measurement (GPM) mission provides global satellite precipitation estimates for a wide range of scientific research and societal applications. By adopting a constellation approach using low-Earth orbit passive microwave and geosynchronous-orbit infrared observations from domestic and international partners, IMERG is able to achieve a high resolution of 0.1° every half-hour globally, with three Runs to cater to applications with different latency requirements. With the completion of the reprocessing of the latest version, IMERG V07 now provides improved global satellite precipitation estimates for a period of more than 20 years. This presentation summarizes background on retrievals, the IMERG algorithm, and access to the data. As well, it showcases select results from the data, which might give participants ideas about how they can analyze the data themselves. The presentation ends with a prospectus for the timeline for the upcoming Version 08 and provides a list of references.

IMERG↗
NASA NTRS1991

Soil moisture measurements from airborne SAR

The preliminary results of algorithm development and testing for soil moisture retrieval at high incidence angles are reported. Based on first-order surface backscattering models, a physically based algorithm for retrieval of soil moisture was developed and evaluated using NASA/JPL aircraft synthetic aperture radar (SAR) data. It shows that the copolarization ratio is sensitive to soil moisture change but not to surface roughness at high incidence angles. This algorithm performed well at L-band and should be useful. This study suggests that incidence angles greater than 40 degrees are optimal for such monitoring.

Shi, Jiancheng↗
NASA NTRS1991

SAR-derived soil moisture measurements for bare fields

The results of algorithm development and testing for soil moisture retrieval at high incidence angles are reported. Based on first-order surface backscattering models, a physically based algorithm for retrieval of soil moisture has been developed and evaluated using NASA/JPL aircraft SAR (synthetic aperture radar) data. It shows that the co-polarization ratio is sensitive to soil moisture change but not to surface roughness at high incidence angles. This algorithm performed well at L-band and should be useful for repetitive, large-area soil moisture monitoring, without requiring surface roughness measurements. This study suggests that incidence angles greater than 40 deg are optimal for such monitoring.

Shi, Jiancheng↗
NASA NTRS2016

Considering Combined or Separated Roughness and Vegetation Effects in Soil Moisture Retrievals

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (tau(sub nad)) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB),soil roughness is modeled with a semi-empirical equation using four main parameters (Q(sub r), H(sub r), N(sub rp), with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N(sub rp) and H(sub r) on the SM and tau(sub nad) retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011-2015). In this study, Qr was set equal to zero and we assumed that N(sub rH)= N(sub rV). The retrievals were performed by varying N(sub rp) from −1 to 2 by steps of 1 and H(sub r) from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.

full-polarization↗
NASA NTRS2024

Mineral Dust Optical Properties for Remote Sensing and Global Modeling: A Review

Dust plays a key role in many Earth system processes and is ubiquitous in the Martian atmosphere. Various intensive field campaigns, laboratory analyses, space-based remote sensing missions, and global modeling efforts aim to characterize dust optical properties. This is a bountiful time for dust scientists, and yet the interpretation of retrievals and comparison to models remains complicated by various conflicting assumptions that are part of each algorithm. For example, the conversion of satellite radiance measurements into products like aerosol optical depth for model evaluation depends upon aerosol properties like particle size and shape that are often prescribed and not part of the retrieval. Conversely, the model calculation of aerosol optical depth often uses different assumptions. The goal of this review is to first document algorithmic assumptions by various satellite retrieval products and models, and identify where there is consistency and where there are differences. In general, the differences documented in this paper reflect uncertainties resulting from incomplete observational characterization of dust aerosols and limitations in our understanding. Second, we note what observations might reduce uncertainties in our knowledge and bring greater consistency to retrievals and models, allowing for a more rigorous and harmonious comparison. The lack of comprehensive and realistic shape models for dust is an outstanding issue, such that closure between forward modeling from particle refractive index, shape, and size and observed optical properties cannot be achieved. Limitations in the computational methods that must be applied to model scattering from complex shapes also makes accurate optical modeling for dust challenging. Field observations indicate the persistence of coarse and giant dust particles at higher altitudes and farther downwind from their source than previously expected. Remote sensing retrieval algorithms based on observations at visible wavelengths have limited sensitivity to these particles and generally do not consider them, although a recent product based on longwave radiances is encouraging. Current measurements of the refractive index of bulk dust and fundamental dust minerology components such as hematite vary widely, inhibiting attempts to represent the variability in dust optical properties and forcing, as expected from different major dust source regions on Earth that have varying mineralogical composition. Some remote sensing retrieval algorithms allow for limited refractive index variability in their inversion solutions through mixing with other fine mode aerosol models, or optimizing the single scattering albedo, but Earth system models surveyed for this paper assume a globally uniform, size-invariant refractive index. Although no Martian dust samples have yet been returned to Earth, remote sensing observations indicate that Martian dust is globally homogenous in composition, and a single spectral refractive index assumption has been widely adopted to represent Martian dust. The lack of comprehensive, statistically representative measurements of dust particle microphysical properties (size distribution, morphology, complex index of refraction spectra, internal structure heterogeneity), and the resulting optical properties, limits our ability to verify the fidelity of these assumptions. A chain of measurements is needed, ranging from characterizing individual dust mineralogy components (e.g., pure hematite and goethite) to in situ sampling of complex atmospheric aerosol mixtures. Such results could be applied to both remote sensing retrievals that characterize the optical properties of the total aerosol burden in the atmosphere from total radiance measurements, and to global models that represent the total aerosol burden in the atmosphere by building it up from the balance of individual aerosol sources and sinks.

Aeolian dust↗
NASA NTRS2008

Retrieval of Snow and Rain From Combined X- and W-B and Airborne Radar Measurements

Two independent airborne dual-wavelength techniques, based on nadir measurements of radar reflectivity factors and Doppler velocities, respectively, are investigated with respect to their capability of estimating microphysical properties of hydrometeors. The data used to investigate the methods are taken from the ER-2 Doppler radar (X-band) and Cloud Radar System (W-band) airborne Doppler radars during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment campaign in 2002. Validity is assessed by the degree to which the methods produce consistent retrievals of the microphysics. For deriving snow parameters, the reflectivity-based technique has a clear advantage over the Doppler-velocity-based approach because of the large dynamic range in the dual-frequency ratio (DFR) with respect to the median diameter Do and the fact that the difference in mean Doppler velocity at the two frequencies, i.e., the differential Doppler velocity (DDV), in snow is small relative to the measurement errors and is often not uniquely related to Do. The DFR and DDV can also be used to independently derive Do in rain. At W-band, the DFR-based algorithms are highly sensitive to attenuation from rain, cloud water, and water vapor. Thus, the retrieval algorithms depend on various assumptions regarding these components, whereas the DDV-based approach is unaffected by attenuation. In view of the difficulties and ambiguities associated with the attenuation correction at W-band, the DDV approach in rain is more straightforward and potentially more accurate than the DFR method.

Liao, Liang↗