Results for “Microwave Backscattering Models” — Page 6

NASA NTRS1981

Microwave scatterometer measurements of oceanic wind vector

Results from Seasat-A scatterometer (SASS) remote sensing of oceanic wind vectors are reviewed, together with the theory of radar backscattering. The SASS was designed to measure the surface wind stress and neutral stability wind vector at 19.5 m altitude. Requirements included windspeeds from 4-24 m/sec to within 2 m/sec or 10%, a 1000 km swath, directions from 0-360 deg to within 20 deg, a 50 km resolution cell, and cross-track and along-track spacing between resolution cells of 100 km. The method was based on Bragg scattering of microwaves from cm-length capillary ocean waves. The strength of the backscatter is proportional to the capillary wave amplitude, which is in equilibrium with the ocean surface wind speed. The measurement grouping, least-squares estimator, and dB deviation model function table for the SASS geophysical algorithm are discussed. Comparisons of SASS, GOASEX, and ground truth wind data are made, and a 0.92 correlation between SASS and ground truth data is determined from regression analysis.

Jones, W. L.↗

NASA NTRS2005

Assimilation of MLS and OMI Ozone Data

Ozone data from Aura Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) were assimilated into the ozone model at NASA's Global Modeling and Assimilation Office (GMAO). This assimilation produces ozone fields that are superior to those from the operational GMAO assimilation of Solar Backscatter Ultraviolet (SBUV/2) instrument data. Assimilation of Aura data improves the representation of the "ozone hole" and the agreement with independent Stratospheric Aerosol and Gas Experiment (SAGE) III and ozone sonde data. Ozone in the lower stratosphere is captured better: mean state, vertical gradients, spatial and temporal variability are all improved. Inclusion of OMI and MLS data together, or separately, in the assimilation system provides a way of checking how consistent OMI and MLS data are with each other, and with the ozone model. We found that differences between OMI total ozone column data and model forecasts decrease after MLS data are assimilated. This indicates that MLS stratospheric ozone profiles are consistent with OMI total ozone columns. The evaluation of error characteristics of OMI and MLS ozone will continue as data from newer versions of retrievals becomes available. We report on the initial step in obtaining global assimilated ozone fields that combine measurements from different Aura instruments, the ozone model at the GMAO, and their respective error characteristics. We plan to use assimilated ozone fields in estimation of tropospheric ozone. We also plan to investigate impacts of assimilated ozone fields on numerical weather prediction through their use in radiative models and in the assimilation of infrared nadir radiance data from NASA's Advanced Infrared Sounder (AIRS).

Stajner, I.↗

NASA NTRS2000

A Melting Layer Model for Passive/Active Microwave Remote Sensing Applications: Model Formulation and Comparison with Observations - Part 1

In this study, a 1-D steady-state microphysical model which describes the vertical distribution of melting precipitation particles is developed. The model is driven by the ice-phase precipitation distributions just above the freezing level at applicable gridpoints of "parent" 3-D cloud-resolving model (CRM) simulations. It extends these simulations by providing the number density and meltwater fraction of each particle in finely separated size categories through the melting layer. The depth of the modeled melting layer is primarily determined by the initial material density of the ice-phase precipitation. The radiative properties of melting precipitation at microwave frequencies are calculated based upon different methods for describing the dielectric properties of mixed phase particles. Particle absorption and scattering efficiencies at the Tropical Rainfall Measuring Mission Microwave Imager frequencies (10.65 to 85.5 GHz) are enhanced greatly for relatively small (approx. 0.1) meltwater fractions. The relatively large number of partially-melted particles just below the freezing level in stratiform regions leads to significant microwave absorption, well-exceeding the absorption by rain at the base of the melting layer. Calculated precipitation backscatter efficiencies at the Precipitation Radar frequency (13.8 GHz) increase in proportion to the particle meltwater fraction, leading to a "bright-band" of enhanced radar reflectivities in agreement with previous studies. The radiative properties of the melting layer are determined by the choice of dielectric models and the initial water contents and material densities of the "seeding" ice-phase precipitation particles. Simulated melting layer profiles based upon snow described by the Fabry-Szyrmer core-shell dielectric model and graupel described by the Maxwell-Garnett water matrix dielectric model lead to reasonable agreement with radar-derived melting layer optical depth distributions. Moreover, control profiles that do not contain mixed-phase precipitation particles yield optical depths that are systematically lower than those observed. Therefore, the use of the melting layer model to extend 3-D CRM simulations appears justified, at least until more realistic spectral methods for describing melting precipitation in high-resolution, 3-D CRM's are implemented.

Olson, William S.↗

NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗

NASA NTRS2017

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Active radar backscatter (s◦) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s◦ and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s◦ or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s◦ and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

GLEAM↗

NASA NTRS1985

Active microwave remote sensing of an anisotropic random medium layer

A two-layer anisotropic random medium model has been developed to study the active remote sensing of the earth. The dyadic Green's function for a two-layer anisotropic medium is developed and used in conjunction with the first-order Born approximation to calculate the backscattering coefficients. It is shown that strong cross-polarization occurs in the single scattering process and is indispensable in the interpretation of radar measurements of sea ice at different frequencies, polarizations, and viewing angles. The effects of anisotropy on the angular responses of backscattering coefficients are also illustrated.

Lee, J. K.↗

NASA NTRS2017

Evaluation of the Ozone Fields in NASA's MERRA-2 Reanalysis

The assimilated ozone product from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), produced at NASAs Global Modeling and Assimilation Office (GMAO) is summarized. The reanalysis begins in 1980 with the use of retrieved partial-column ozone concentrations from a series of Solar Backscatter Ultraviolet Radiometer (SBUV) instruments on NASA and NOAA spacecraft. Beginning in October 2004, retrieved ozone profiles from the Microwave Limb Sounder (MLS) and total column ozone from the Ozone Monitoring Instrument (OMI) on NASAs EOS Aura satellite are assimilated. While this change in data streams does lead to a discontinuity in the assimilated ozone fields in MERRA-2, making it not useful for studies in decadal (secular) trends in ozone, this choice was made to prioritize demonstrating the value NASAs high-quality research data in the reanalysis context. The MERRA-2 ozone is compared with independent satellite and ozonesonde data, focusing on the representation of the spatial and temporal variability of stratospheric and upper-tropospheric ozone. The comparisons show agreement within 10 (standard deviation of the difference) between MERRA-2 profiles and independent satellite data in most of the stratosphere. The agreement improves after 2004, when EOS Aura data are assimilated. The standard deviation of the differences between the lower-stratospheric and upper-tropospheric MERRA-2 ozone and ozonesondes is 11.2 and 24.5, respectively, with correlations of 0.8 and above. This is indicative of a realistic representation of the UTLS ozone variability in MERRA-2. After 2004, the upper tropospheric ozone in MERRA-2 shows a low bias compared to the sondes, but the covariance with independent observations is improved compared to earlier years. Case studies demonstrate the integrity of MERRA-2 analyses in representing important features such as tropopause folds.

Ozone↗

NASA NTRS1995

Microwave Backscatter and Attenuation Dependence of Leaf Area Index for Flooded Rice Fields

Wetlands are important for their role in global climate as a source of methane and other reduced trace gases. As part of an effort to determine whether radar is suitable for wetland vegetation monitoring, we have studied the dependence of microwave backscatter and attenuation on leaf area index (LAI) for flooded rice fields. We find that the radar return from a flooded rice field does show dependence on LAI. In particular, the C-band VV cross section per unit area decreases with increasing LAI. A simple model for scattering from rice fields is derived and fit to the observed HH and VV data. The model fit provides insight into the relation of backscatter to LAI and is also used to calculate the canopy path attenuation as a function of LAI.

Durden, Stephen L.↗

NASA NTRS1988

Important changes in microwave scattering properties of young snow-covered sea ice as indicated from dielectric modelling

Winter measurements of the properties of young snow-covered fast ice are input into a mixture model to calculate the complex dielectric constant. Large changes in the electromagnetic properties of the medium occur over the 46 day period of measurements. This early evolution of the surface is shown to have an important impact upon predicted radar backscattering properties at GHz frequencies. This has consequences for the unambiguous identification of young ice forms from synthetic aperture or side-looking airborne radar images.

Drinkwater, M. R.↗

NASA NTRS1980

Ocean wave-radar modulation transfer functions from the West Coast experiment

Short gravity-capillary waves, the equilibrium, or the steady state excitations of the ocean surface are modulated by longer ocean waves. These short waves are the predominant microwave scatterers on the ocean surface under many viewing conditions so that the modulation is readily measured with CW Doppler radar used as a two-scale wave probe. Modulation transfer functions (the ratio of the cross spectrum of the line-of-sight orbital speed and backscattered microwave power to the autospectrum of the line-of-sight orbital speed) were measured at 9.375 and 1.5 GHz (Bragg wavelengths of 2.3 and 13 cm) for winds up to 10 m/s and ocean wave periods from 2-18 s. The measurements were compared with the relaxation-time model; the principal result is that a source of modulation other than straining by the horizontal component of orbital speed, possibly the wave-induced airflow, is responsible for most of the modulation by waves of typical ocean wave period (10 s). The modulations are large; for unit coherence, spectra of radar images of deep-water waves should be proportional to the quotient of the slope spectra of the ocean waves by the ocean wave frequency.

Wright, J. W.↗

NASA NTRSDate not supplied

Recent Developments in the Assimilation of Microwave and Radar Observations Into NWP Models

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗

NASA NTRSDate not supplied

Enhancing CRTM All-Sky Simulations and Implementation of A New Active Sensor Module

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗

NASA NTRSDate not supplied

CRTM Microwave Cloud Scattering Lookup Tables and Radar Simulator

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗

NASA NTRS1989

Stereological characterization of dry alpine snow for microwave remote sensing

A persistent problem in investigations of electromagnetic properties of snow, from reflectance at visible wavelengths to emission and backscattering in the microwave, has been the proper characterization of the snow's physical properties. It is suggested that the granular and laminar structure of snow can be measured in its aggregated state by stereology performed on sections prepared from snow specimens, and that these kinds of measurements can be incorporated into models of the electromagnetic properties. With careful sampling, anisotropy in the snow microstructure at various scales can be quantified. It is shown how stereological parameters can be averaged over orientation and optical depth for radiative transfer modeling.

Davis, Robert E.↗

NASA NTRS1977

Active microwave measurement from space of sea-surface winds

Radar backscatter measurements from the ocean were made at 13.9 GHz from Skylab. The radar signal increased rapidly with wind speed over the entire range of winds encountered, and for angles of incidence of 30 deg larger. Signals observed were normalized to a nominal incidence angle and to a nominal upwind observation direction, using a theoretical model that has been verified as approximately true with aircraft experiments. Observations during the summer and winter Skylab missions were treated separately because of possible differences caused by an accident to the antenna between the two sets of observations. The results are in general agreement with the theory in all cases. The objective analysis method used for determining surface-truth winds in the Skylab experiment was tested by comparing results obtained at weather ships with the observations made by the weather ships themselves. In most cases, the variance about the regression line between objective analysis and weather-ship data actually exceeded that about the regression line between objective analysis and backscatter data

Young, J. D.↗

NASA NTRS2014

Diurnal Variations of Stratospheric Ozone Measured by Ground-Based Microwave Remote Sensing at the Mauna Loa NDACC Site: Measurement Validation and GEOSCCM Model Comparison

There is presently renewed interest in diurnal variations of stratospheric and mesospheric ozone for the purpose of supporting homogenization of records of various ozone measurements that are limited by the technique employed to being made at certain times of day. We have made such measurements for 19 years using a passive microwave remote sensing technique at the Mauna Loa Observatory (MLO) in Hawaii, which is a primary station in the Network for Detection of Atmospheric Composition Change (NDACC). We have recently reprocessed these data with hourly time resolution to study diurnal variations. We inspected differences between pairs of the ozone spectra (e.g., day and night) from which the ozone profiles are derived to determine the extent to which they may be contaminated by diurnally varying systematic instrumental or measurement effects. These are small, and we have reduced them further by selecting data that meet certain criteria that we established. We have calculated differences between profiles measured at different times: morning-night, afternoon-night, and morning-afternoon and have intercompared these with like profiles derived from the Aura Microwave Limb Sounder (Aura-MLS), the Upper Atmosphere Research Satellite Microwave Limb Sounder (UARS-MLS),t he Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES), and Solar Backscatter Ultraviolet version 2 (SBUV2) measurements. Differences between averages of coincident profiles are typically less than 1.5 percent of typical nighttime values over most of the covered altitude range with some exceptions. We calculated averages of ozone values for each hour from the Mauna Loa microwave data, and normalized these to the average for the first hour after midnight for comparison with corresponding values calculated with the Goddard Earth Observing System Chemistry Climate Model (GEOSCCM). We found that the measurements and model output mostly agree to better than 1.5 percent of the midnight value, with one noteworthy exception: The measured morning-night values are significantly (2-3 percent) higher than the modeled ones from 3.2 to 1.8 hPa (approximately 39-43 km), and there is evidence that the measured values are increasing compared to the modeled values before sunrise in this region.

GEOSCCM model comparison↗

DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Survey of in-situ and remote sensing methods for soil moisture determination

General methods for determining the moisture content in the surface layers of the soil based on in situ or point measurements, soil water models and remote sensing observations are surveyed. In situ methods described include gravimetric techniques, nuclear techniques based on neutron scattering or gamma-ray attenuation, electromagnetic techniques, tensiometric techniques and hygrometric techniques. Soil water models based on column mass balance treat soil moisture contents as a result of meteorological inputs (precipitation, runoff, subsurface flow) and demands (evaporation, transpiration, percolation). The remote sensing approaches are based on measurements of the diurnal range of surface temperature and the crop canopy temperature in the thermal infrared, measurements of the radar backscattering coefficient in the microwave region, and measurements of microwave emission or brightness temperature. Advantages and disadvantages of the various methods are pointed out, and it is concluded that a successful monitoring system must incorporate all of the approaches considered.

Schmugge, T. J.↗