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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Evaluation of Cloud Microphysics Simulated using a Meso-Scale Model Coupled with a Spectral Bin Microphysical Scheme through Comparison with Observation Data by Ship-Borne Doppler and Space-Borne W-Band Radars

Equivalent radar reflectivity factors (Ze) measured by W-band radars are directly compared with the corresponding values calculated from a three-dimensional non-hydrostatic meso-scale model coupled with a spectral-bin-microphysical (SBM) scheme for cloud. Three case studies are the objects of this research: one targets a part of ship-borne observation using 95 GHz Doppler radar over the Pacific Ocean near Japan in May 2001; other two are aimed at two short segments of space-borne observation by the cloud profiling radar on CloudSat in November 2006. The numerical weather prediction (NWP) simulations reproduce general features of vertical structures of Ze and Doppler velocity. A main problem in the reproducibility is an overestimation of Ze in ice cloud layers. A frequency analysis shows a strong correlation between ice water contents (IWC) and Ze in the simulation; this characteristic is similar to those shown in prior on-site studies. From comparing with the empirical correlations by the prior studies, the simulated Ze is overestimated than the corresponding values in the studies at the same IWC. Whereas the comparison of Doppler velocities suggests that large-size snowflakes are necessary for producing large velocities under the freezing level and hence rules out the possibility that an overestimation of snow size causes the overestimation of Ze. Based on the results of several sensitivity tests, we conclude that the source of the overestimation is a bias in the microphysical calculation of Ze or an overestimation of IWC. To identify the source of the problems needs further validation research with other follow-up observations.

Iguchi, T.↗
DOE OSTI2023

Leipzig University W-Band Cloud Radar, Gothic (Colorado), SAIL Campaign Second Winter (15.11.2022 - 05.06.2023)

The instrument is a polarimetric W-Band simultaneous transmission simultaneous reception (STSR) Doppler Cloud Radar manufactured by Radiometer Physics GmbH (RPG, instrument type RPG-FMCW-94-DP). It was deployed about 500m away from the ARM M1 facility in Gothic (CO) near the Ore House between Nov 15, 2022 and Jun 5, 2023 at an altitude of 2913 m. The Doppler cloud radar was mounted on a prototype of a cold temperature scanner manufactured by RPG. The dataset contains common radar variables like equivalent radar reflectivity factor, mean Doppler velocity, Doppler spectrum width as well as common polarimetric radar variables like differential phase shift (PhiDP), specific differential phase shift (KDP), correlation coefficient (RHV) and differential reflectivity (ZDR). Between Nov 15, 2022, and Feb 7, 2023 the radar was operated at 40° constant elevation and 151° azimuth (towards the KAZR at the AMF1). Between Feb 7 and Mar 15, 2023 the following scan cycle was repeated in 15min intervals: zenith pointing measurements, RHI scans from 0° to 90° and 90° to 0° elevation at 151° azimuth, constant elevation measurements at 40° elevation and 151° azimuth and PPI scans at 85° elevation for wind retrievals (one PPI per hour). Between Mar 15 18:18 UTC and Apr 8, 2023 11:04 UTC, the scanner was stuck at 72° elevation, which resulted in corrupted files for this period. For that period, the files containing data are uploaded but should be treated with care. Between Apr 8 and Apr 14, 2023 the scanning pattern described above was performed again. Afterwards, except for May 17 and May 18, 2023 where scanning patterns as described above were performed, measurements continued at 40° elevation and 151° azimuth until May 22, 2023 09:00 UTC. The scanning pattern was then started again, but without hourly PPI measurements until May 31, 2023. Between May 31 and Jun 5, 2023 solely zenith-pointing observations were performed. The data provided here are the RPG Level 1 (L1) files, Doppler spectra Level 0 (L0) files are available upon request.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Exploring Environmental and Aerosol Impacts on Maritime Tropical Convection using Airborne Radiometer, Radar, Lidar, and Dropsondes

The field deployment phase of NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) took place around the Philippines during August–October 2019, with the primary goal of jointly investigating aerosols and tropical meteorology at the micro-β to meso-β scales. A suite of instruments was deployed on NASA’s P-3B Orion aircraft to accomplish this mission, including: the Advanced Microwave Precipitation Radiometer (AMPR), Airborne Precipitation and cloud Radar 3rd Generation (APR-3), High Spectral Resolution Lidar 2 (HSRL2), and Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes. To examine potential aerosol influences on maritime tropical convection, P-3 flight segments throughout CAMP2Ex were binned into similar environmental groups using “low,” “medium,” and “high” values of nine AVAPS-derived parameters with known physical connections to convective frequency and/or intensity. Aerosol concentrations in each flight segment were evaluated using three HSRL2 variables at 355 and 532 nm: aerosol backscatter, aerosol extinction, and aerosol optical thickness. A set of radiometer- and radar-derived variables directly related to convective frequency and/or intensity was used to characterize convection, which included: AMPR-derived integrated cloud liquid water; polarization-corrected temperatures at 10.7, 19.35, 37.1, and 85.5 GHz; peak equivalent radar reflectivity factor (ZH); peak height of 30-dBZ ZH; and the number of radar data columns with composite ZH > 30 dBZ. The ZH analyses were performed using both Ku- and Ka-band APR-3 data. For each flight segment, correlation coefficients were calculated between each convective parameter and aerosol concentrations within the “low,” “medium,” and “high” groups for each environmental variable. The environmental stratification thresholds were then varied in a series of sensitivity tests. Several noteworthy correlations were observed between the convective and aerosol parameters within the environmental groups, especially when stratifying the environments based on their 850–500-hPa temperature lapse rate, 700–500-hPa temperature lapse rate, and K-Index. The convective parameters were often correlated most strongly with 355-nm extinction, 532-nm extinction, and 532-nm backscatter, while the presence of precipitation-sized liquid and ice hydrometeors contributed to some unexpected negative correlations. In general, as environmental conditions became more favorable for convection, a stronger correlation was observed between the convective parameters and aerosol concentrations. However, a “Goldilocks” zone of medium aerosol concentration was correlated most strongly with the convective parameters in many cases. These results stress the importance of considering environmental and aerosol conditions together when evaluating their impacts on convection. This presentation will provide a detailed discussion of these results and their implications, a summary of the limitations associated with such an analysis, and suggestions for future work.

Corey G. Amiot↗
NASA NTRS2007

Methods of Attenuation Correction for Dual-Wavelength and Dual-Polarization Weather Radar Data

In writing the integral equations for the median mass diameter and number concentration, or comparable parameters of the raindrop size distribution, it is apparent that the forms of the equations for dual-polarization and dual-wavelength radar data are identical when attenuation effects are included. The differential backscattering and extinction coefficients appear in both sets of equations: for the dual-polarization equations, the differences are taken with respect to polarization at a fixed frequency while for the dual-wavelength equations, the differences are taken with respect to frequency at a fixed polarization. An alternative to the integral equation formulation is that based on the k-Z (attenuation coefficient-radar reflectivity factor) parameterization. This-technique was originally developed for attenuating single-wavelength radars, a variation of which has been applied to the TRMM Precipitation Radar data (PR). Extensions of this method have also been applied to dual-polarization data. In fact, it is not difficult to show that nearly identical equations are applicable as well to dualwavelength radar data. In this case, the equations for median mass diameter and number concentration take the form of coupled, but non-integral equations. Differences between this and the integral equation formulation are a consequence of the different ways in which attenuation correction is performed under the two formulations. For both techniques, the equations can be solved either forward from the radar outward or backward from the final range gate toward the radar. Although the forward-going solutions tend to be unstable as the attenuation out to the range of interest becomes large in some sense, an independent estimate of path attenuation is not required. This is analogous to the case of an attenuating single-wavelength radar where the forward solution to the Hitschfeld-Bordan equation becomes unstable as the attenuation increases. To circumvent this problem, the equations can be expressed in the form of a final-value problem so that the recursion begins at the far range gate and proceeds inward towards the radar. Solving the problem in this way traditionally requires estimates of path attenuation to the final gate: in the case of orthogonal linear polarizations, the attenuations at horizontal and vertical polarizations (same frequency) are required while in the dual-wavelength case, attenuations at the two frequencies (same polarization) are required.

Meneghini, R.↗
DOE OSTI2026

BNF C-band Scanning ARM Precipitation Radar 2nd Generation (CSAPR-2) Extracted Radar Columns and In-Situ Sensors (RadCLss)

Corrected Moments in Antenna Coordinates (CMAC) calculates quantitative precipitation estimates (QPE) from empirical relationships based on equivalent radar reflectivity factor, specific differ- ential phase, and specific attenuation. To evaluate these empirical relationships, the Extracted Radar Columns and In-Situ Sensors (RADclss) product was developed. Utilizing Py-ART, RAD- clss extracts CMAC radar columns above various ARM and partner locations. These columns are then spatiotemporally synced with in-situ observations at the surface utilizing the Atmospheric data Community Toolkit (ACT; Theisen et al. 2025), allowing direct comparison of radar parameters with rain gauges and laser disdrometers for further investigation.

bankhead↗
NASA NTRS1991

Nested rain cell contour statistics derived from radar measurements in the mid-Atlantic coast of the United States

During a period spanning more than 5 years, a series of low elevation rain radar measurements encompassing 17 rain days were systematically executed in the mid-Atlantic coast of the United States. Drop size distribution measurements with a nearby disdrometer were also acquired during the same rain days. The drop size data were utilized to convert the radar reflectivity factors to estimated rain rates for the respective rain days of operation. Applying developed algorithms to the radar and disdrometer data, 'core' values of rain intensities and nested families of rain rate isopleths enveloping them were identified and their equi-circle diameters were statistically analyzed.

Goldhirsh, Julius↗
NASA NTRS1992

Nimbus-7 SMMR precipitation observations calibrated against surface radar during TAMEX

This paper represents a continuation of work begun by Petty and Katsaros (1990) on refining an attenuation-based technique for estimating rainfall parameters from polarized 37-GHz brightness temperatures. In the present work, Nimbus-7 SMMR normalized 37-GHz polarization differences P are compared with surface digital radar observations of oceanic precipitation, made during the Taiwan Area Mesoscale Experiment (TAMEX) for cases when the SMMR and the radar coverages of significant precipitation features were nearly simultaneous. After the radar data were corrected for range-dependent errors, relationships were determined between the 37-GHz P and the radar reflectivity factor Z. The relationship was used to generate a large set of simulated SMMR observations from all available TAMEX radar scans, to produce histograms and mean values of pixel-averaged rain rate as a function of P.

Petty, Grant W.↗
NASA NTRS2012

3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer

Determining the Z-R relationship (where Z is the radar reflectivity factor and R is rainfall rate) from disdrometer data has been and is a common goal of cloud physicists and radar meteorology researchers. The usefulness of this quantity has traditionally been limited since radar represents a volume measurement, while a disdrometer corresponds to a point measurement. To solve that problem, a 3D-DSD (drop-size distribution) method of determining an equivalent 3D Z-R was developed at the University of Central Florida and tested at the Kennedy Space Center, FL. Unfortunately, that method required a minimum of three disdrometers clustered together within a microscale network (.1-km separation). Since most commercial disdrometers used by the radar meteorology/cloud physics community are high-cost instruments, three disdrometers located within a microscale area is generally not a practical strategy due to the limitations of these kinds of research budgets. A relatively simple modification to the 3D-DSD algorithm provides an estimate of the 3D-DSD and therefore, a 3D Z-R measurement using a single disdrometer. The basis of the horizontal extrapolation is mass conservation of a drop size increment, employing the mass conservation equation. For vertical extrapolation, convolution of a drop size increment using raindrop terminal velocity is used. Together, these two independent extrapolation techniques provide a complete 3DDSD estimate in a volume around and above a single disdrometer. The estimation error is lowest along a vertical plane intersecting the disdrometer position in the direction of wind advection. This work demonstrates that multiple sensors are not required for successful implementation of the 3D interpolation/extrapolation algorithm. This is a great benefit since it is seldom that multiple sensors in the required spatial arrangement are available for this type of analysis. The original software (developed at the University of Central Florida, 1998.- 2000) has also been modified to read standardized disdrometer data format (Joss-Waldvogel format). Other modifications to the software involve accounting for vertical ambient wind motion, as well as evaporation of the raindrop during its flight time.

Lane, John↗
NASA NTRS2007

Managing Uncertainty Due to a Fundamental Error Source Arising from Scatterer Distribution Complexity in Radar Remote Sensing of Precipitation

The assumption that cloud and rain drops are spatially distributed according to a Poisson distribution within a scattering volume probed by a radar being used to estimate precipitation has represented bedrock theory in establishing 'rules of the game' for pulse averaging--the process needed to beat down noise to an acceptable level in the measurement of radar reflectivity factor. Based on relatively recent observations of 'realistic' spatial distributions of hydrometeor scatterers in a cloudy atmosphere motivates a renewed examination of the consequences of using a too simplified assumption underlying volume scattering--particularly in regards to the standard pulse averaging rule. Our investigation addresses two extremes, simple to complex, insofar as allowed for complexities in an underlying scatterer distribution. It is demonstrated that as the spatial distribution ranges from Poisson (a narrow distribution) to multi-fractal (much broader distribution), uncertainty in a measurement increases if the rule for pulse averaging goes unchanged from its Poisson distribution reference county. [A bounded cascade is used for the multi-fractal distribution, a regularly observed distribution vis-a-vis cloud liquid water content.] The resultant measurement uncertainty leads to a fundamental source of error in the estimation of rain rate from radar measurements, one that has been disregarded since the early 1950s when radar sets first began to be used for rainfall measuring. It is shown how this source of error can be 'managed'--under the assumption that number of data analysis experiments would be carried out, experiments involving pulse-by-pulse measurements obtained from a radar set modified to output individual pulses of reflectivity factor. For practical applications, a new parameter called normalized k-sample intensity invariance is developed to enable defining the required pulse average count according to a preferred degree of uncertainty.

Smith, Eric A.↗
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↗
NASA NTRS1993

Preliminary results from multiparameter airborne rain radar measurement in the western Pacific

Preliminary results are presented from multiparameter airborne radar measurements of tropical storms. The experiment was conducted in the western Pacific in September 1990 with the NASA DC-8 aircraft that was equipped with a dual-wavelength radar at X and Ka bands and several microwave radiometers. The modification to dual-polarization at X-band radar enabled measurements of the linear depolarization ratio (LDR). Vertical profiles of dual-polarization and dual-frequency observables for an example of stratiform rain and three examples of convective rain cells are examined. It is shown that at nadir incidence the LDR measurement often can be used to distinguish the phase states of the hydrometeors and to identify the melting layer. In addition to the information concerning particle shape and orientation from LDR, the ratio of the radar reflectivity factors in two frequency bands (X and Ka bands) provides insight into particle size. The capabilities of dual-wavelength and dual-polarization radar in the identification of particle size and phase will be important considerations in the design of future spaceborne weather radars.

Kumagai, Hiroshi↗
NASA NTRS2006

The Potential of Water Vapor & Precipitation Estimation with a Differential-frequency Radar

In the presence of rain, the radar return powers from a three-frequency radar, with center frequency at 22.235 GHz and upper and lower frequencies chosen with equal water vapor absorption coefficients, can be used to estimate water vapor density and parameters of the precipitation. A linear combination of differential measurements between the center and lower frequencies on one hand and the upper and lower frequencies on the other provide an estimate of differential water vapor absorption. Conversely, the difference in radar reflectivity factors (in dB) between the upper and lower frequencies is independent of water vapor absorption and can be used to estimate the median mass diameter of the hydrometeors. For a down-looking radar, path-integrated estimates of water vapor absorption may be possible under rain-free as well as raining conditions by using the surface returns at the three frequencies. Cross-talk or interference between the precipitation and water vapor estimates depends on the frequency separation of the channels as well as on the phase state and the median mass diameter of the hydrometeors. Simulations of the retrieval of water vapor absorption show that the largest source of variability arises from the variance in the measured radar return powers while the largest biases occur in the mixed-phase region. Use of high pulse repetition frequencies and signal whitening methods may be needed to obtain the large number of independent samples required. Measurements over a fractional bandwidth, defined as the ratio of the difference between the upper and lower frequencies to the center frequency, up to about 0.2 should be passible in a differential frequency mode, where a single transceiver and antenna are used. Difficulties in frequency allocation may require alternative choices of frequency where the water vapor absorptions at the low and high frequencies are unequal. We consider the degradation in the retrieval accuracy when the frequencies are not optimum.

Meneghini, Robert↗
NASA NTRS1992

Three-dimensional numerical simulation of the 20 June 1991, Orlando microburst

On 20 June 1991, NASA's Boeing 737, equipped with in-situ and look-ahead wind-shear detection systems, made direct low-level penetrations (300-350 m AGL) through a microburst during several stages of its evolution. This microburst was located roughly 20 km northeast of Orlando International Airport and was monitored by a Terminal Doppler Weather Radar (TDWR) located about 10 km south of the airport. The first NASA encounter with this microburst (Event 142), at approximately 2041 UTC, was during its intensification phase. At flight level, in-situ measurements indicated a peak 1-km (averaged) F-factor of approximately 0.1. The second NASA encounter (Event 143) occurred at approximately 2046 UTC, about the time of microburst peak intensity. It was during this penetration that a peak 1-km F-factor of approximately 17 was encountered, which was the largest in-situ measurement of the 1991 summer deployment. By the third encounter (Event 144), at approximately 2051 UTC, the microburst had expanded into a macroburst. During this phase of evolution, an in-situ 1-km F-factor of 0.08 was measured. The focus of this paper is to examine this microburst via numerical simulation from an unsteady, three-dimensional meteorological cloud model. The simulated high-resolution data fields of wind, temperature, radar reflectivity factor, and precipitation are closely examined so as to derive information not readily available from 'observations' and to enhance our understanding of the actual event. Characteristics of the simulated microburst evolution are compared with TDWR and in-situ measurements.

Proctor, Fred H.↗
NASA NTRS1994

Case study of a low-reflectivity pulsating microburst: Numerical simulation of the Denver, 8 July 1989, storm

On 8 July 1989, a very strong microburst was detected by the Low-Level Windshear Alert system (LLWAS), within the approach corridor just north of Denver Stapleton Airport. The microburst was encountered by a Boeing 737-200 in a 'go-around' configuration which was reported to have lost considerable air speed and altitude during penetration. Data from LLWAS revealed a pulsating microburst with an estimated peak velocity change of 48 m/s. Wilson et al. reported that the microburst was accompanied by no apparent visible clues such as rain or virga, although blowing dust was present. Weather service hourly reports indicated virga in all quadrants near the time of the event. A National Center for Atmospheric Research (NCAR) research Doppler radar was operating; but according to Wilson et al., meaningful velocity could not be measured within the microburst due to low radar-reflectivity factor and poor siting for windshear detection at Stapleton. This paper presents results from the three-dimensional numerical simulation of this event, using the Terminal Area Simulation System (TASS) model. The TASS model is a three-dimensional nonhydrostatic cloud model that includes parameterizations for both liquid and ice phase microphysics, and has been used in investigations of both wet and dry microburst case studies. The focus of this paper is the pulsating characteristic and the very-low radar reflectivity of this event. Most of the surface outflow contained no precipitation. Such an event may be difficult to detect by radar.

Proctor, Fred H.↗
NASA NTRS2011

NEXRAD-In-Space: A Geostationary Orbiting Doppler Radar for Hurricane Monitoring and Studies

Under NASA's Earth Science Technology Program, a novel mission concept has been developed for detailed monitoring of hurricanes, cyclones, and severe storms from a geostationary orbit: "NEXRAD in Space" (NIS). By operating in the Geostationary Earth Orbit (GEO), NIS would enable rapid-update sampling (less than or equal to 1 hour cadence) of three dimenional fields of 35 GHz (Ka-band) radar reflectivity factor (Z) and line-of-sight Doppler velocity (VD) profiles, at mesoscale horizontal resolutions (approx. 10 km) over a circular Earth region of approximately 5300 km in diameter (equivalent to much of an oceanic basin, such as the Atlantic). NIS GEO-radar concept was chosen as one of only four potential post-2020 missions for the Weather Focus area in the 2007-2016 NASA Science Mission Directorate (SMD) Science Plan. The results of the first project aiming at developing the NIS concept highlighted the enormous potential of such mission, and the technological challenges presented by it. In essence, it is because of its rapid-cadence capability that NIS science planning is focusing on hurricane monitoring and prediction. Hurricanes, or generically tropical cyclones (TCs), have always been among the most devastating natural phenomena. This has been painfully reiterated in recent years with a number of powerful TCs landfalling in North America and elsewhere. In April 2007, the first NIS Science Workshop was convened at the University of Miami to galvanize the scientific community's interest in NIS's measurement capabilities for improved TC monitoring and prediction. The general consensus of the workshop was that a GEO Doppler radar would provide a major breakthrough in regards to the observation of TCs, and, when combined with cloud-resolving numerical weather prediction (NWP) models. This paper presents brief summaries of the instrument concept, the current technology status, the anticipated impacts on hurricane monitoring and model prediction, and the future science and technology roadmap.

precipitation↗
NASA NTRS1994

Measuring rainwater content by radar using propagation differential phase shift

While radars measure several quantities closely coupled to the rainfall rate, for frequencies less than 15 GHz, estimates of the rainwater content W are traditionally computed from the radar reflectivity factor Z or the rate of attenuation A--quantities only weakly related to W. Consequently, instantaneous point estimates of W using Z and A are often erroneous. A more natural, alternative parameter for estimating W at these frequencies is the specific polarization propagation differential phase shift phi(sub DP), which is a measure of the change in the difference between phases of vertically (V) and horizontally (H) polarized waves with increasing distance from a radar. It is now well known that W is nearly linearly related to phi(sub DP) divided by (1 - reversed R), where reversed R is the mass-weighted mean axis ratio of the raindrops. Unfortunately, such relations are not widely used in part because measurements of phi(sub DP) are scarce but also because one must determine reversed R. In this work it is shown that this parameter can be estimated using the differential reflectivity (Z(sub H)/Z(sub V) at 3 GHz. An alternative technique is suggested for higher frequencies when the differential reflectivity becomes degraded by attenuation. While theory indicates that it should be possible using phi(sub DP) to estimate W quite accurately, measurement errors increase the uncertainty to +/- 18%-35% depending on reversed R. While far from ideal, it appears that these estimates are likely to be considerably more accurate than those deduced using currently available methods.

Jameson, A. R.↗
NASA NTRS1997

Microphysical Retrievals Over Stratiform Rain Using Measurements from an Airborne Dual-Wavelength Radar-Radiometer

The need to understand the complementarity of the radar and radiometer is important not only to the Tropical Rain Measuring Mission (TRMM) program but to a growing number of multi-instrumented airborne experiment that combine single or dual-frequency radars with multichannel radiometers. The method of analysis used in this study begins with the derivation of dual-wavelength radar equations for the estimation of a two-parameter drop size distribution (DSD). Defining a "storm model" as the set of parameters that characterize snow density, cloud water, water vapor, and features of the melting layer, then to each storm model there will usually correspond a set of range-profiled drop size distributions that are approximate solutions of the radar equations. To test these solutions, a radiative transfer model is used to compute the brightness temperatures for the radiometric frequencies of interest. A storm model or class of storm models is considered optimum if it provides the best reproduction of the radar and radiometer measurements. Tests of the method are made for stratiform rain using simulated storm models as well as measured airborne data. Preliminary results show that the best correspondence between the measured and estimated radar profiles usually can be obtained by using a moderate snow density (0.1-0.2 g/cu cm), the Maxwell-Garnett mixing formula for partially melted hydrometeors (water matrix with snow inclusions), and low to moderate values of the integrated cloud liquid water (less than 1 kg/sq m). The storm-model parameters that yield the best reproductions of the measured radar reflectivity factors also provide brightness temperatures at 10 GHz that agree well with the measurements. On the other hand, the correspondence between the measured and modeled values usually worsens in going to the higher frequency channels at 19 and 34 GHz. In searching for possible reasons for the discrepancies, It is found that changes in the DSD parameter Mu, the radar constants, or the path-integrated attenuation can affect the high frequency channels significantly. In particular, parameters that cause only modest increases in the median mass diameter of the snow, and which have a minor effect on the radar returns or the low frequency brightness temperature, can produce a strong cooling of the 34 GHz brightness temperature.

Meneghini, Robert↗
NASA NTRS1992

A dual-frequency microwave technique for measuring rainwater content

This study shows that the dual-frequency (38, 25 GHz) differential attenuation (A(38-25)) coefficient can provide accurate estimates of rainwater content W potentially over a wide range of rainwater contents. While measurements along a microwave link are fairly easily implemented, radar estimates of A(38-25) can become clouded by differences between the radar reflectivity factors at the two frequencies (Z38, Z25). Root-mean-square deviations (epsilon) of the estimated W from the actual W are calculated for a wide variety of drop-size distributions and rainwater contents. The computed epsilon include the effects of standard measurement errors and differences between Z38 and Z25. Accurate estimates appear possible using a 38-25-GHz radar when W is not less than 1.5-2 g/cu m, depending upon the desired spatial resolution, and along a microwave link when W is not less than 0.5 g/cu m.

Jameson, A. R.↗