Engineering topics

Robert Meneghini

Publications and source records attributed to Robert Meneghini.

13 records

NASA NTRS2023

Retrieval of Normalized Gamma Size Distribution Parameters Using Precipitation Imaging Package (Pip) Snowfall Observations During Ice-Pop 2018

Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment - PyeongChang Olympics and Paralympic (ICE-POP 2018). Two of the gamma PSD parameters, the mass weighted particle diameter (Dmass) and the normalized intercept parameter NW, have median values of 1.15-1.31 mm and 2.84-3.04 log(mm-1 m-3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx-Deq, and the relationship between the Reynolds and Best numbers, Re-X. Normalization of snow water equivalent rate (SWER) and ice water content (W) by NW reduces the range in NW resulting in well fitted power law relationship, between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter (μ) ranging from -2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and μ = 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23-0.34 when μ = 2 and by 0.29-0.40 when μ = 3, while the MFB of SWER had a much narrower range (-0.03 to 0.04) for the same μ values.

Snowfall↗

NASA NTRS2022

A Generalized Dual-Frequency Ratio (DFR) Approach for Rain Retrievals

The dual-frequency ratio of radar reflectivity factors (DFR) has been shown to be a useful quantity as it is independent of the number concentration of the particle size distribution and primarily a function of the mass-weighted particle diameter, Dm. A drawback of DFR-related methods for rain estimation, however, is the non-unique relationship between Dm and DFR. At Ku- and Ka-band frequencies, two solutions for Dm exist when DFR is less than zero. This ambiguity generates multiple solutions for the range profiles of the particle size parameters. We investigate characteristics of these solutions for both the initial-value (forward) and final-value (backward) forms of the equations. To choose one of among many possible range profiles of Dm, number concentration and rain rate, R, independently measured path attenuations are used. For the backward approach the possibility exists of dispensing with externally measured path attenuations by achieving consistency between the input and output path attenuations. The methods are tested by means of a simulation based on disdrometer-measured raindrop size distributions and the results are compared with a simplified version of the operational R-Dm method.

Robert Meneghini↗

NASA NTRS2022

Correction of Path Integrated Attenuation Estimates Considering the Soil Moisture Effect for the Gpm Dual-Frequency Precipitation Radar

Spaceborne precipitation radars, including the Tropical Rainfall Measuring Mission’s Precipitation Radar (PR) and the Global Precipitation Measurement Mission’s Dual-Frequency Precipitation Radar (DPR), measure not only precipitation echoes but surface echoes as well, the latter of which are used to estimate the path-integrated attenuation (PIA) in the surface reference technique (SRT). In our previous study based on analyzing PR measurements, we found that attenuation-free surface backscattering cross sections (denoted by s0e) over land increased in the presence of precipitation. This behavior, called the soil moisture effect, causes an underestimate of the PIA by the SRT as the method does not explicitly consider this effect. In this study, measurements made by Ku-band Precipitation Radar (KuPR) and Ka-band Precipitation Radar (KaPR), which comprise the DPR, were analyzed to examine whether KuPR and KaPR exhibit similar dependencies on the soil moisture as does the PR. For both KuPR and KaPR, an increase in s0e was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld–Bordan (HB) method suggest that s0e increases with the surface precipitation rate for light precipitation events. Meanwhile, for heavy precipitation, owing to the degradation of the HB method, it is difficult to estimate s0e quantitatively. Thus, a correction method for PIA that considers the soil moisture effect was developed and implemented into the DPR standard algorithm. With this correction, the surface precipitation rate estimates increased by approximately 18%for KuPR and 15%for the normal scan of KaPR over land.

Shinta Seto↗

NASA NTRS2022

Stability of the Dual-Frequency Radar Equations and a New Method Applied to the GPM’s Dual-Frequency Precipitation Radar (DPR) Data

A new algorithm is proposed that estimates two parameters of the particle size distribution (PSD) at each range bin from the Global Precipitation Measurement’s (GPM’s) Dual-frequency Precipitation Radar data. The equation that expresses the relationship of the PSD parameters between adjacent range bins is derived. By including the attenuation effect within the bin in the discretized equation, the new algorithm alleviates the double-solution problem when attenuation within the bin is sufficiently large. The stability of the solutions to the equation depends on the value of the mean diameter D(sub m) and its gradient with respect to range in the case of liquid precipitation. If the critical diameter above which the dual-frequency ratio (DFR) of radar reflectivity factors becomes a monotonically increasing function of D(sub m), the backward processing of the equation provides a stable or moderately diverging solution, unlike the forward processing that often gives unstable solutions. To provide a set of initial conditions without using the surface reference technique (SRT) in the backward processing, an initialization method using the Hitschfeld-Bordan attenuation correction method is proposed and tested. The proposed algorithm may provide a tool for investigating the assumptions used in various algorithms.

GPM↗

NASA NTRS2022

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

The primary goal of the Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite is to infer precipitationrate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: 1)description of the DPR retrieval algorithm that uses an adjustable relationship between rainrate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dmsimultaneously; 2) evaluation of the DPR algorithm based on the physical simulationsthat employ measured DSD/PSD to understand the mechanism and errorcharacteristics of the retrieval method; 3) review of ground validation studies for theDPR product as well as analysis of the strengths and weaknesses of the ground radarand rain gauge/disdrometer validations. Overall, the DPR Version-6 algorithmprovides reasonably accurate estimates of R and Dm in rain. Non-uniformity in therain profile, however, tends to degrade the accuracy of the R and Dm estimates tosome extent as the range-independent assumption of the adjustable parameter () ofthe R-Dm relation is not able to fully account for natural variation of DSD in the verticalprofile. Underestimation of the DPR snow rate is found when compared with theindependent dual-frequency ratio (DFR) technique. This is possibly the result of theconstraint associated with the path integral attenuation (PIA)/differential PIA (dPIA)used in the DPR algorithm to find the best  and range-independent  assumption. Arange-variable  model, proposed in the DPR Version-7 algorithm, is expected toimprove rain and snow retrieval.

Liang Liao↗

NASA NTRS2020

The Precipitation Rate Retrieval Algorithms for the GPM Dual-frequency Precipitation Radar

In this paper, precipitation rate retrieval algorithms for the Global Precipitation Measurement mission's Dual-frequency Precipitation Radar (DPR) are developed. The DPR consists of a Ku-band radar (KuPR; 13.6 GHz) and a Ka-band radar (KaPR; 35.5 GHz). For the KuPR, an algorithm similar to the Tropical Rainfall Measuring Mission's Precipitation Radar algorithm is developed, with the relation between precipitation rate R and massweighted mean diameter D m (R−D m relation) replacing the relation between the specific attenuation k and effective radar reflectivity factor Z e . The R−D m relation can also be applied to the KaPR and dual-frequency algorithms. In both the single-frequency and dual-frequency algorithms, the forward retrieval method is applied with an assumed adjustment factor for the R−D m relation (ε) and the results are evaluated to select the best value of ε. The advantages of the dual-frequency algorithm are the availability of the dual-frequency surface reference technique and the ZfKa method, which is a method to use the attenuation-corrected radar reflectivity factor Z f of KaPR, to select ε as well as the possibility to selectively use measurements from KuPR or KaPR. This paper also describes the derivation of the scattering table and the R−D m relation as well as the procedure for non-uniform beam filling correction in detail. The outputs are then statistically analyzed to demonstrate algorithm performance.

precipitation radar↗

NASA NTRS2020

Path Attenuation Estimates for the GPM Dual-Frequency Precipitation Radar (DPR)

Estimation of path attenuation is a critical part of retrieving precipitation parameters using measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement Mission (GPM) satellite. In this paper, we describe the latest implementation of the Surface Reference Technique that uses surface scattering properties to infer path attenuation through the precipitation. Both single- and dual-frequency versions of this method are available and while the dual-frequency version appears to be more accurate at moderate rain rates, the single-frequency approach at Ku-band is needed when the Ka-band data are not available. Despite improvements afforded by the dual-frequency version of the method, other methods such as the Hitschfeld-Bordan and standard dual-frequency approaches offer advantages particularly at lighter rain rates and at near-nadir incidence angles over land. Weighted averages of the results from these methods appear to offer the best estimate of path attenuation presently available.

attenuation↗

NASA NTRS2020

Assessment of Ku- and Ka-band Dual-Frequency Radar for Snow Retrieval

Dual-frequency Ku/Ka-band radar retrievals of snow parameters such as liquid-equivalent snowfall rate (R) and mass-weighted diameter (Dm) have two principal errors, namely, the differences between the assumed particle size distribution (PSD) model from the actual PSD and inadequacies in characterizing the single-scattering properties of snowflakes. Regarding the first issue, this study, based on radar simulations from a large amount of observed PSD data, shows that there exist relatively high correlations between the estimated snow parameters and their true values derived directly from the measured PSD. For PSD data with R greater than 0.1 mm h−1, a gamma PSD model with a fixed shape factor (μ) equal to 0 (or exponential distribution) provides the best estimates of R and Dm. This is despite negative biases of up to −15 % in R and underestimates and overestimates in Dm for small and large Dm, respectively. The μ = 0 assumption, however, produces relatively poor estimates of normalized intercepts of a gamma PSD (Nw), whereas the best estimates are obtained when μ is considered either 3 or 6. However, the use of an inappropriate scattering table increases the errors in snow retrieval. Simple evaluations are made for cases where the scattering databases used for the algorithm input differ from that used for retrieval. The mismatched scattering databases alone could cause at least 30 %-50 % changes in the estimates of snow water content (SWC) and R and could affect the retrievals of Dm and Nw and their dependence on μ.

Liang Liao↗

NASA NTRS2020

Characteristics of DSD Bulk Parameters: Implication to Radar Rain Retrieval

With the use of 213,456 one-minute measured data of droplet-size distribution (DSD) of rain collected during several National Aeronautics and Space Administration (NASA)-sponsored field campaigns, the relationships between rainfall rate R, mass-weighted diameter D(m) and normalized intercept parameter N(w) of the gamma DSD are studied. It is found, based on the simulations of the gamma DSD model, that R, D(m) and N(w) are closely interrelated, and that the ratio of R to N(w) is solely a function of D(m), independent of the shape factor μ of the gamma distribution. Furthermore, the model-produced ratio agrees well with those from the DSD data. When a power-law equation is applied to fit the model data, we have: R=aN(w)D(sup b, sub m) , where a=1.588×10(exp −4) , b=4.706 . Analysis of two-parameter relationships such as R–D(m), N(w)–R and N(w)–D(m) reveals that R and D(m) are moderately correlated while N(w) and D(m) are negatively correlated. N(w) and R, however, are uncorrelated. The gamma DSD model also reveals that variation of R–D(m) relation is caused primarily by N(w). For the application of the Ku- and Ka-band dual-frequency radar for the retrieval of the DSD bulk parameters as well as the specific radar attenuations, the study is carried out to relate the dual-frequency radar reflectivity factors to the DSD and attenuation parameters.

Liang Liao↗

NASA NTRS2019

Assessment of Ku- and Ka-Band Dual-Frequency Radar for Retrieval of Snow Properties

Current scattering look-up tables for snow assume a constant mass density along with a Gamma particle size distribution (PSD). The first assumption is tested against scattering parameters from simulated particle models generated by Dr. Kwo-Sen Kuo at GSFC and Dr. Guosheng Liu at FSU. Good agreement of the scattering parameters is found with the FSU results if the mass is taken to be the same as the mass of the simulated particle and the mass density is taken to be 0.2 g/cm cu. For the GSFC data base, good agreement is found if the mass density is taken to be between 0.1 and 0.2 g/cm cu. The second assumption of a Gamma PSD is tested against measured PSD's along with a m-d (mass-dimension) relationship. The degree of agreement depends on the value of, the 'shape' parameter in the Gamma distribution but to a lesser degree on the m-d relationship (of the three that were examined). A shortcoming of the simulated snow particle data bases is the lack of large particles. As a consequence, larger values of DFR (dual-frequency ratio) that are commonly seen in airborne and spaceborne measurements cannot be reproduced from the tables. This situation is expected to improve as scattering parameters from larger particles are included in the databases.

Liang Liao↗

NASA NTRS2019

Physical Evaluation of GPM DPR Single- and Dual-Wavelength Algorithms

A physical evaluation of the rain profiling retrieval algorithms for the Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite is carried out by applying them to the hydrometeor profiles generated from measured raindrop size distributions (DSD). The DSD-simulated radar profiles are used as input to the algorithms, and their estimates of hydrometeors' parameters are compared with the same quantities derived directly from the DSD data (or truth). The retrieval accuracy is assessed by the degree to which the estimates agree with the truth. To check the validity and robustness of the retrievals, the profiles are constructed for cases ranging from fully correlated (or uniform) to totally uncorrelated DSDs along the columns. Investigation into the sensitivity of the retrieval results to the model assumptions is made to characterize retrieval uncertainties and identify error sources. Comparisons between the single- and dual-wavelength algorithm performance are carried out with either a single- or dual-wavelength constraint of the path integral or differential path integral attenuation. The results suggest that the DPR dual-wavelength algorithm generally provides accurate range-profiled estimates of rainfall rate and mass-weighted diameter with the dual-wavelength estimates superior in accuracy to those from the single-wavelength retrievals.

Liang Liao↗

NASA NTRS2017

The Global Precipitation Measurement (GPM) Mission for Science and Society

Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

Gail Skofronick-Jackson↗

NASA NTRS2003

Combined Radiometer-Radar Microphysical Profile Estimations with Emphasis on High Frequency Brightness Temperature Observations

Information about the vertical microphysical cloud structure is useful in many modeling and predictive practices. Radiometers and radars are used to observe hydrometeor properties. This paper describes an iterative retrieval algorithm that combines the use of airborne active and wideband (10 to 340 GHz) passive observations to estimate the vertical content and particle size distributions of liquid and frozen hydrometeors. The physically-based retrieval algorithm relies on the high frequencies (greater than 89 GHz) to provide details on the frozen hydrometeors. Neglecting the high frequencies yielded acceptable estimates of the liquid profiles, but the ice profiles were poorly retrieved. Airborne radar and radiometer observations from the third Convection and Moisture EXperiment (CAMEX-3) were used in the retrieval algorithm as constraints. Nadir profiles were estimated for a minute each of flight time (approximately 12.5 km along track) from an anvil, convection, and quasi- stratiform rain. The complex structure of the frozen hydrometeors required the most iterations for convergence for the anvil cloud type. The wideband observations were found to more than double the estimated frozen hydrometeor content as compared to retrievals using only 90-GHz and below. The convective and quasi-stratiform quickly reached convergence (minimized difference between observations and calculations using the estimated profiles). A qualitative validation using coincident in situ CAMEX-3 observations shows that the retrieved particle size distributions are well corroborated with independent measurements.

Gail Skofronick-Jackson↗