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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2017

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of- atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked (“remembered”) over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance products. Furthermore, the geostationary results satisfactorily capture the movement of clouds and variations in atmospheric dust/aerosol concentrations, suggesting that high quality land surface and vegetation datasets from the advanced geostationary sensors can help complement and improve the corresponding EOS products.

geostationary satellite sensors↗
NASA NTRSDate not supplied

Fusing Surface BRDF from Geostationary and Polar-Orbiting Satellite Sensors

The bi-directional reflectance distribution function (BRDF) describes the fundamental optical property of a surface and therefore has been retrieved from both geostationary (GEO) and polar-orbiting (or Low-Earth Orbit, LEO) satellite observations. In theory, although GEO and LEO observations feature different illumination-view geometries, they reflect the same physical property and the retrieved BRDF should be mutually consistent. This fact also suggests that we may derive a better BRDF product by synergistically fusing the GEO and LEO datasets. Here we demonstrate the idea by fusing Terra/Aqua MODIS and GOES16/17 ABI surface BRDF with the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Our processing is implemented in two steps. First, we compile and project both MODIS and ABI data on the same GeoNEX common grid, and then use the MODIS MAIAC BRDF (MCD19A3) as the prior information to perform atmospheric correction of ABI data and retrieve surface BRDF with the ABI MAIAC code. In the second step, we run the MAIAC code in the forward mode to simulate MODIS and ABI TOA radiances from the jointly retrieved surface BRDF and the corresponding illumination-view geometries. By comparing the simulated TOA radiances with corresponding observations (e.g., MOD02 and ABI05), we can quantitatively evaluate whether the jointly retrieved ABI surface BRDF improves over the separately retrieved MODIS and ABI BRDF products. We expect that the jointly retrieved BRDF data are more robust than the standard products and can help us reduce uncertainties in higher-level earth observation satellite products.

Geostationary satellite↗
NASA NTRS1997

Correction of Rayleigh Scattering Effects in Cloud Optical Thickness Retrievals

We present results that demonstrate the effects of Rayleigh scattering on the 9 retrieval of cloud optical thickness at a visible wavelength (0.66 Am). The sensor-measured radiance at a visible wavelength (0.66 Am) is usually used to infer remotely the cloud optical thickness from aircraft or satellite instruments. For example, we find that without removing Rayleigh scattering effects, errors in the retrieved cloud optical thickness for a thin water cloud layer (T = 2.0) range from 15 to 60%, depending on solar zenith angle and viewing geometry. For an optically thick cloud (T = 10), on the other hand, errors can range from 10 to 60% for large solar zenith angles (0-60 deg) because of enhanced Rayleigh scattering. It is therefore particularly important to correct for Rayleigh scattering contributions to the reflected signal from a cloud layer both (1) for the case of thin clouds and (2) for large solar zenith angles and all clouds. On the basis of the single scattering approximation, we propose an iterative method for effectively removing Rayleigh scattering contributions from the measured radiance signal in cloud optical thickness retrievals. The proposed correction algorithm works very well and can easily be incorporated into any cloud retrieval algorithm. The Rayleigh correction method is applicable to cloud at any pressure, providing that the cloud top pressure is known to within +/- 100 bPa. With the Rayleigh correction the errors in retrieved cloud optical thickness are usually reduced to within 3%. In cases of both thin cloud layers and thick ,clouds with large solar zenith angles, the errors are usually reduced by a factor of about 2 to over 10. The Rayleigh correction algorithm has been tested with simulations for realistic cloud optical and microphysical properties with different solar and viewing geometries. We apply the Rayleigh correction algorithm to the cloud optical thickness retrievals from experimental data obtained during the Atlantic Stratocumulus Transition Experiment (ASTEX) conducted near the Azores in June 1992 and compare these results to corresponding retrievals obtained using 0.88 Am. These results provide an example of the Rayleigh scattering effects on thin clouds and further test the Rayleigh correction scheme. Using a nonabsorbing near-infrared wavelength lambda (0.88 Am) in retrieving cloud optical thickness is only applicable over oceans, however, since most land surfaces are highly reflective at 0.88 Am. Hence successful global retrievals of cloud optical thickness should remove Rayleigh scattering effects when using reflectance measurements at 0.66 Am.

Wang, Meng-Hua↗
NASA NTRS2024

Global Sensitivity Analysis of Simulated Remote Sensing Polarimetric Observations Over Snow

This study presents a detailed theoretical assessment of the information content of passive polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses the covariance of state parameters across their entire parameter space, hence favoring a more correct interpretation of inversion algorithms and the optimal design of their state vectors. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing particulates (LAPs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAPs in the VIS–NIR and to grain size at different depths in the snowpack in the NIR–SWIR. With adequate viewing geometries, mono-angle measurements of total reflectance in the VIS–SWIR (akin to those of the Moderate Resolution Imaging Spectroradiometer, MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS–NIR provides information on grain shape and microscale roughness. The simultaneous sensitivity in the VIS–NIR to both aerosols and snow-embedded impurities can be disentangled by extending the spectral range to the SWIR, which contains information on aerosol optical depth while remaining essentially unaffected when the same particulates are mixed with the snow. Multi-angle polarimetric observations can therefore (i) effectively partition LAPs between the atmosphere and the surface, which represents a notorious challenge for snow remote sensing based on measurements of total reflectance only and (ii) lead to better estimates of grain shape and roughness and, in turn, the asymmetry parameter, which is critical for the determination of albedo. The retrieval uncertainties are minimized when the degree of linear polarization is used in place of the polarized reflectance. The Sobol indices, which are the main metric for the GSA, were used to select the state parameters in retrievals performed on data simulated for multiple instrument configurations. Improvements in retrieval quality with the addition of measurements of polarization, multi-angle views, and different spectral channels reflect the information content, identified by the Sobol indices, relative to each configuration. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors. They can also aid flight planning activities, since the optimal exploitation of the information content of multi-angle measurements depends on the viewing geometry. The better characterization of surface and atmospheric parameters in snow-covered regions advances research opportunities for scientists of the cryosphere and ultimately benefits albedo estimates in climate models.

remote sensing↗
NASA NTRS2020

MODIS Reflective Solar Bands Lunar Calibration Update and Improvements

The MODerate resolution Imaging Spectroradiometer (MODIS) instruments on-board the Terra and Aqua spacecrafts were launched on December 18, 1999 and May 4, 2002, respectively. Each instrument has been scheduled to view the Moon through its Space View (SV) port approximately once per month in order to monitor the long-term radiometric stability of their reflective solar bands (RSB). The lunar calibration has played a key role in tracking the change in the response versus scan angle (RVS) of the instrument’s scan mirror, which is an essential part of the RSB calibration. The lunar irradiance observed by MODIS depends on the view geometry and it is crucial to remove the geometric effects in order to derive accurate RSB calibration coefficients. With a scheduled roll maneuver, the lunar phase angles are kept in a narrow range of 1° for each MODIS instrument such that the impact of these geometric effects is minimized. Nevertheless, it remains a challenge to completely remove the impact of geometric effects in the calibration coefficients derived from the lunar observations, considering the high expectations for the accuracy and quality of the results. In the current MODIS lunar calibration, the geometric effects on the lunar irradiance are corrected by the RObotic Lunar Observatory (ROLO) model. The overall relative uncertainty of the ROLO model for the MODIS calibration has been assessed to be about 1% in the selected lunar phase angle range of 1°. It could be as large as 4% beyond this small phase angle range, especially for the shortest wavelengths. This uncertainty induces noticeable oscillations in the calibration coefficients derived from the lunar observations. We developed a simple lunar model, which is based on MODIS lunar measurements, derived from scheduled lunar observations, as well as those obtained from intrusion of the Moon in the SV, referred to as unscheduled lunar observations, in the time period from 2005 to 2012. Here, the scheduled lunar observations for the entire mission are reprocessed with the new lunar model applied to correct the view geometry effects for each MODIS instrument. New calibration coefficients for the RSBs are calculated. We show that oscillations and noise in the derived lunar calibration coefficients are significantly reduced for both MODIS instruments.

Terra↗
NASA NTRS2012

Retrieval of Ocean Surface Windspeed and Rainrate from the Hurricane Imaging Radiometer (HIRAD) Brightness Temperature Observations

The Hurricane Imaging Radiometer (HIRAD) is a new airborne synthetic aperture passive microwave radiometer capable of wide swath imaging of the ocean surface wind speed under heavy precipitation e.g. in tropical cyclones. It uses interferometric signal processing to produce upwelling brightness temperature (Tb) images at its four operating frequencies 4, 5, 6 and 6.6 GHz [1,2]. HIRAD participated in NASA s Genesis and Rapid Intensification Processes (GRIP) mission during 2010 as its first science field campaign. It produced Tb images with ~70 km swath width and 3 km resolution from a ~ 20 km altitude. From this, ocean surface wind speed and column averaged atmospheric liquid water content can be retrieved across the swath. The column averaged liquid water then could be related to an average rain rate. The retrieval algorithm (and the HIRAD instrument itself) is a direct descendant of the nadir-only Stepped Frequency Microwave Radiometer that is used operationally by the NOAA Hurricane Research Division to monitor tropical cyclones [3,4]. However, due to HIRAD s slant viewing geometry (compared to nadir viewing SFMR) a major modification is required in the algorithm. Results based on the modified algorithm from the GRIP campaign will be presented in the paper.

Biswas, Sayak K.↗
NASA NTRS1981

Soybean canopy reflectance as a function of view and illumination geometry

Reflectances were calculated from measurements at four wavelength bands through eight view azimuth and seven view zenith directions, for various solar zenith and azimuth angles over portions of three days, in an experimental characterization of a soybean field by means of its reflectances and physical and agronomic attributes. Results indicate that the distribution of reflectance from a soybean field is a function of the solar illumination and viewing geometry, wavelength, and row direction, as well as the state of canopy development. Shadows between rows were found to affect visible wavelength band reflectance to a greater extent than near-IR reflectance. A model describing reflectance variation as a function of projected solar and viewing angles is proposed, which approximates the visible wavelength band reflectance variations of a canopy with a well-defined row structure.

Ranson, K. J.↗
NASA NTRS1997

Bidirectional Reflectance Functions for Application to Earth Radiation Budget Studies

Reflected solar radiative fluxes emerging for the Earth's top of the atmosphere are inferred from satellite broadband radiance measurements by applying bidirectional reflectance functions (BDRFs) to account for the anisotropy of the radiation field. BDRF's are dependent upon the viewing geometry (i.e. solar zenith angle, view zenith angle, and relative azimuth angle), the amount and type of cloud cover, the condition of the intervening atmosphere, and the reflectance characteristics of the underlying surface. A set of operational Earth Radiation Budget Experiment (ERBE) BDRFs is available which was developed from the Nimbus 7 ERB (Earth Radiation Budget) scanner data for a three-angle grid system, An improved set of bidirectional reflectance is required for mission planning and data analysis of future earth radiation budget instruments, such as the Clouds and Earth's Radiant Energy System (CERES), and for the enhancement of existing radiation budget data products. This study presents an analytic expression for BDRFs formulated by applying a fit to the ERBE operational model tabulations. A set of model coefficients applicable to any viewing condition is computed for an overcast and a clear sky scene over four geographical surface types: ocean, land, snow, and desert, and partly cloudy scenes over ocean and land. The models are smooth in terms of the directional angles and adhere to the principle of reciprocity, i.e., they are invariant with respect to the interchange of the incoming and outgoing directional angles. The analytic BDRFs and the radiance standard deviations are compared with the operational ERBE models and validated with ERBE data. The clear ocean model is validated with Dlhopolsky's clear ocean model. Dlhopolsky developed a BDRF of higher angular resolution for clear sky ocean from ERBE radiances. Additionally, the effectiveness of the models accounting for anisotropy for various viewing directions is tested with the ERBE along tract data. An area viewed from nadir and from the side give two different radiance measurements but should yield the same flux when converted by the BDRF. The analytic BDRFs are in very good qualitative agreement with the ERBE models. The overcast scenes exhibit constant retrieved albedo over viewing zenith angles for solar zenith angles less than 60 degrees. The clear ocean model does not produce constant retrieved albedo over viewing zenith angles but gives an improvement over the ERBE operational clear sky ocean BDRF.

Manalo-Smith, N.↗
NASA NTRS2004

Analysis Of AVIRIS Data From LEO-15 Using Tafkaa Atmospheric Correction

We previously developed an algorithm named Tafkaa for atmospheric correction of remote sensing ocean color data from aircraft and satellite platforms. The algorithm allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser s vector radiative transfer code. During the past few years we have extended the capabilities of the code. Current modifications include the ability to account for within scene variation in solar geometry (important for very long scenes) and view geometries (important for wide fields of view). Additionally, versions of Tafkaa have been made for a variety of multi-spectral sensors, including SeaWiFS and MODIS. In this proceeding we present some initial results of atmospheric correction of AVIRIS data from the 2001 July Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) at LEO-15.

Montes, Marcos J.↗
NASA NTRS1982

Soybean canopy reflectance as a function of view and illumination geometry

The results of an experiment designed to characterize a soybean field by its reflectance at various view and illumination angles and by its physical and agronomic attributes are presented. Reflectances were calculated from measurements at four wavelength bands through eight view azimuth and seven view zenith directions for various solar zenith and azimuth angles during portions of three days. An ancillary data set consisting of the agronomic and physical characteristics of the soybean field is described. The results indicate that the distribution of reflectance from a soybean field is a function of the solar illumination and viewing geometry, wavelength and row direction, as well as the state of development of the canopy. Shadows between rows greatly affected the reflectance in the visible wavelength bands and to a lesser extent in the near infrared wavelengths. A model is proposed that describes the reflectance variation as a function of projected solar and projected viewing angles. The model appears to approximate the reflectance variations in the visible wavelength bands from a canopy with well defined row structure.

Bauer, M. E.↗
NASA NTRS1984

Variation in spectral response of soybeans with respect to illumination, view, and canopy geometry

Comparisons of the spectral response for incomplete (well-defined row structure) and complete (overlapping row structure) canopies of soybeans indicated a greater dependence on Sun and view geometry for the incomplete canopies. Red and near-IR reflectance for the incomplete canopy decreased as solar zenith angle increased for a nadir view angle until the soil between the plant rows was completely shaded. Thereafter for increasing solar zenith angle, the red reflectance leveled off and the near-IR reflectance increased. A 'hot spot' effect was evident for the red and near-IR reflectance factors. The 'hot spot' effect was more pronounced for the red band based on relative reflectance value changes. The ratios of off-nadir to nadir acquired data reveal that off-nadir red band reflectance factors more closely approximated straightdown measurements for time periods away from solar noon. Normalized difference generally approximated straightdown measurements during the middle portion of the day.

Ranson, K. J.↗
NASA NTRS2013

Vertical Distribution of Dust and Water Ice Aerosols from CRISM Limb-geometry Observations

[1] Near-infrared spectra taken in a limb-viewing geometry by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on board the Mars Reconnaissance Orbiter provide a useful tool for probing atmospheric structure. Specifically, the observed radiance as a function of wavelength and height above the limb enables the vertical distribution of both dust and water ice aerosols to be retrieved. More than a dozen sets of CRISM limb observations have been taken so far providing pole-to-pole cross sections, spanning more than a full Martian year. Radiative transfer modeling is used to model the observations taking into account multiple scattering from aerosols and the spherical geometry of the limb observations. Both dust and water ice vertical profiles often show a significant vertical structure for nearly all seasons and latitudes that is not consistent with the well-mixed or Conrath-v assumptions that have often been used in the past for describing aerosol vertical profiles for retrieval and modeling purposes. Significant variations are seen in the retrieved vertical profiles of dust and water ice aerosol as a function of season. Dust typically extends to higher altitudes (approx. 40-50km) during the perihelion season than during the aphelion season (<20km), and the Hellas region consistently shows more dust mixed to higher altitudes than other locations. Detached water ice clouds are common, and water ice aerosols are observed to cap the dust layer in all seasons.

Atmosphere↗
NASA NTRS1986

Designing Delta-DOR acquisition strategies to determine highly elliptical earth orbits

Delta-DOR acquisition strategies are designed for use in determining highly elliptical earth orbits. The requirements for a possible flight demonstration are evaluated for the Charged Composition Explorer spacecraft of the Active Magnetospheric Particle Tracer Explorers. The best-performing strategy uses data spanning the view periods of two orthogonal baselines near the same orbit periapse. The rapidly changing viewing geometry yields both angular position and velocity information, but each observation may require a different reference quasar. The Delta-DOR data noise is highly dependent on acquisition geometry, varying several orders of magnitude across the baseline view periods. Strategies are selected to minimize the measurement noise predicted by a theoretical model. Although the CCE transponder is limited by S-band and a small bandwidth, the addition of Delta-DOR to coherent Doppler and range improves the one-sigma apogee position accuracy by more than an order of magnitude. Additional Delta-DOR accuracy improvements possible using dual-frequency (S/X) calibration, increased spanned bandwidth, and water-vapor radiometry are presented for comparison. With these benefits, the residual Delta-DOR data noise is primarily due to quasar position uncertainties.

Frauenholz, R. B.↗
NASA NTRS2020

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Jeff W. Atkins↗
NASA NTRS1992

Mars: Correcting surface albedo observations for effects of atmospheric dust loading

We have developed a radiative transfer model which allows the effects of atmospheric dust loading on surface albedo to be investigated. This model incorporates atmospheric dust opacity, the single scattering albedo and particle phase function of atmospheric dust, the bidirectional reflectance of the surface, and variable lighting and viewing geometry. The most recent dust particle properties are utilized. The spatial and temporal variability of atmospheric opacity (Tan) strongly influences the radiative transfer modelling results. We are currently using the approach described to determine Tan for IRTM mapping sequences of selected regions. This approach allows Tan to be determined at the highest spatial and temporal resolution supported by the IRTM data. Applying the radiative transfer modelling and determination of Tan described, IRTM visual brightness observations can be corrected for the effects of atmospheric dust loading a variety of locations and times. This approach allows maps of 'dust-corrected surface albedo' to be constructed for selected regions. Information on the variability of surface albedo and the amount of dust deposition/erosion related to such variability results. To date, this study indicates that atmospheric dust loading has a significant effect on observations of surface albedo, amounting to albedo corrections of as much as several tens of percent. This correction is not constant or linear, but depends upon surface albedo, viewing and lighting geometry, the dust and surface phase functions, and the atmospheric opacity. It is clear that the quantitative study of surface albedo, especially where small variations in observed albedo are important (such as photometric analyses), needs to account for the effects of the atmospheric dust loading. Maps of 'dust-corrected surface albedo' will be presented for a number of regions.

Lee, S. W.↗
NASA NTRS1986

ERBE bidirectional model consistency check

A short analysis is presented of Earth Radiation Budget Experiment (ERBE) errors inherent in the directional models used for data interpretation. The models were all developed on the basis of experience with the Nimbus-7 ERB experiment, which had a spatial resolution one-third that of ERBE instrumentation. A pseudo-directional model is defined to simulate the ERBE scanner data, using the assumptions that the average radiant exitance for any particular scene is independent of the viewing geometry, geographic location and time the data is collected. The directionality of the view angle and solar zenith angle is accounted for by a method of bins.

Baldwin, D. G.↗
NASA NTRS1996

SeaWiFS Calibration and Algorithm Validation

This is the fourth annual report on NASA grant NAGW 3543, titled 'Sea-viewing Wide Field of view Sensor (SeaWiFS) calibration and algorithm validation'. An extended field experiment was conducted at Lake Tahoe in late July 1996 that involved: experimental verification of radiative transfer computations for various viewing geometries; retrieval of an effective aerosol optical depth height; calibration of a large field of view (FOV) sensor; and an evaluation of spatial non-uniformities in surface reflectance. The investigation and development of novel methods for the calibration of field radiometers at a variety of signal levels has been continued during the past year.

Parada, Robert J., Jr.↗
NASA NTRS2011

Sensitivity of Spaceborne and Ground Radar Comparison Results to Data Analysis Methods and Constraints

With the availability of active weather radar observations from space from the Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TR.MM) satellite, numerous studies have been performed comparing PR reflectivity and derived rain rates to similar observations from ground-based weather radars (GR). These studies have used a variety of algorithms to compute matching PR and GR volumes for comparison. Most studies have used a fixed 3-dimensional Cartesian grid centered on the ground radar, onto which the PR and GR data are interpolated using a proprietary approach and/or commonly available GR analysis software (e.g., SPRINT, REORDER). Other studies have focused on the intersection of the PR and GR viewing geometries either explicitly or using a hybrid of the fixed grid and PR/GR common fields of view. For the Dual-Frequency Precipitation Radar (DPR) of the upcoming Global Precipitation Measurement (GPM) mission, a prototype DPR/GR comparison algorithm based on similar TRMM PR data has been developed that defines the common volumes in terms of the geometric intersection of PR and GR rays, where smoothing of the PR and GR data are minimized and no interpolation is performed. The PR and GR volume-averaged reflectivity values of each sample volume are accompanied by descriptive metadata, for attributes including the variability and maximum of the reflectivity within the sample volume, and the fraction of range gates in the sample average having reflectivity values above an adjustable detection threshold (typically taken to be 18 dBZ for the PR). Sample volumes are further characterized by rain type (Stratiform or Convective), proximity to the melting layer, underlying surface (land/water/mixed), and the time difference between the PR and GR observations. The mean reflectivity differences between the PR and GR can differ between data sets produced by the different analysis methods; and for the GPM prototype, by the type of constraints and categorization applied to the data. In this paper, we will show results comparing the 3-D gridded analysis "black box" approach to the GPM prototype volume-matching approach, using matching TRMM PR and WSR-88D ground radar data. The affects of applying data constraints and data categorizations on the volume-matched data to the results will be shown, and explanations of the differences in terms of data and analysis algorithm characteristics will be presented. Implications of the differences to the determination of PR/DPR calibration differences and use of ground radar data to evaluate the PR and DPR attenuation correction algorithms will be discussed.

Morris, Kenneth R.↗