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At least 37 records · Page 2

Estimation of raindrop size distribution parameters from a dual-parameter spaceborne radar measurement

A method to estimate raindrop size distribution (DSD) parameters from a combined Zm profile and path-integrated attenuation is shown, and a test result of the method using the data from an aircraft experiment is presented. The 'semi' dual-parameter (SDP) measurement is employed to estimate DSD parameters using the data obtained from an aircraft experiment conducted by Communications Research Laboratory, Tokyo, in conjunction with NASA. The validity of estimated DSD parameters is examined using measured Ka-band radar reflectivities. The estimated path-averaged N(0) is consistent with the Ka/X Ze ratio, and the use of estimated DSD shows excellent agreement between the rain rates estimated from the X-band and K-band Zes. The feasibility of estimating DSD parameters from space is confirmed.

Kozu, Toshiaki

Stochastic Filtering of Rain Profiles Using Radar, Surface-Referenced Radar, or Combined Radar-Radiometer Measurements

This paper describes a computationally efficient nearly optimal Bayesian algorithm to estimate rain (and drop size distribution) profiles, given a radar reflectivity profile at a single attenuating wavelength. In addition to estimating the averages of all the mutually ambiguous combinations of rain parameters that can produce the data observed, the approach also calculates the n-ns uncertainty in its estimates (this uncertainty thus quantifies "the amount of ambiguity" in the "solution"). The paper also describes a more general approach that can make estimates based on a radar reflectivity profile together with an approximate measurement of the path-integrated attenuation, or a radar reflectivity profile and a set of passive microwave brightness temperatures. This more general "combined" algorithm is currently being adapted for the Tropical Rainfall Measuring Mission.

Haddad, Ziad S.

An Initial Assessment of the Surface Reference Technique Applied to Data from the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite

It has long been recognized that path-integrated attenuation (PIA) can be used to improve precipitation estimates from high-frequency weather radar data. One approach that provides an estimate of this quantity from airborne or spaceborne radar data is the surface reference technique (SRT), which uses measurements of the surface cross section in the presence and absence of precipitation. Measurements from the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) satellite afford the first opportunity to test the method for spaceborne radar data at Ka band as well as for the Ku-band-Ka-band combination. The study begins by reviewing the basis of the single- and dual-frequency SRT. As the performance of the method is closely tied to the behavior of the normalized radar cross section (NRCS or sigma(0)) of the surface, the statistics of sigma(0) derived from DPR measurements are given as a function of incidence angle and frequency for ocean and land backgrounds over a 1-month period. Several independent estimates of the PIA, formed by means of different surface reference datasets, can be used to test the consistency of the method since, in the absence of error, the estimates should be identical. Along with theoretical considerations, the comparisons provide an initial assessment of the performance of the single- and dual-frequency SRT for the DPR. The study finds that the dual-frequency SRT can provide improvement in the accuracy of path attenuation estimates relative to the single-frequency method, particularly at Ku band.

space-borne weather radars

Dual-parameter radar rainfall measurement from space - A test result from an aircraft experiment

An aircraft experiment has been conducted with a dual-frequency (X/Ka-bands) radar to test various rainfall retrieval methods from space. The authors test a method to derive raindrop size distribution (DSD) parameters from the combination of a radar reflectivity profile and a path-integrated attenuation derived from surface return, which may be available from most spaceborne radars. The estimated DSD parameters are reasonable in that the values generally fall within the range of commonly measured ones and that shifts in DSD parameters appear to be correlated with changes in storm type. The validity of the estimation result is also demonstrated by a consistency check using the Ka-band reflectivity profile which is independent of the DSD estimation process. Although errors may occur in the cases of nonuniform beam filling, these test results indicate the feasibility of the dual-parameter radar measurement from space in achieving a better accuracy in quantitative rainfall remote measurements.

Kozu, Toshiaki

Correction of Sampling Errors in Ocean Surface Cross-Sectional Estimates from Nadir-Looking Weather Radar

The return from the ocean surface has a number of uses for airborne meteorological radar. The normalized surface cross section has been used for radar system calibration, estimation of surface winds, and in algorithms for estimating the path-integrated attenuation in rain. However, meteorological radars are normally optimized for observation of distributed targets that fill the resolution volume, and so a point target such as the surface can be poorly sampled, particularly at near-nadir look angles. Sampling the nadir surface return at an insufficient rate results in a negative bias of the estimated cross section. This error is found to be as large as 4 dB using observations from a high-altitude airborne radar. An algorithm for mitigating the error is developed that is based upon the shape of the surface echo and uses the returned signal at the three range gates nearest the peak surface echo.

Caylor, I. Jeff

Rainfall Measurement with a Ground Based Dual Frequency Radar

Dual frequency methods are one of the most useful ways to estimate precise rainfall rates. However, there are some difficulties in applying this method to ground based radars because of the existence of a blind zone and possible error in the radar calibration. Because of these problems, supplemental observations such as rain gauges or satellite link estimates of path integrated attenuation (PIA) are needed. This study shows how to estimate rainfall rate with a ground based dual frequency radar with rain gauge and satellite link data. Applications of this method to stratiform rainfall is also shown. This method is compared with single wavelength method. Data were obtained from a dual frequency (10 GHz and 35 GHz) multiparameter radar radiometer built by the Communications Research Laboratory (CRL), Japan, and located at NASA/GSFC during the spring of 1997. Optical rain gauge (ORG) data and broadcasting satellite signal data near the radar t location were also utilized for the calculation.

Takahashi, Nobuhiro

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

Tests of Radar Rainfall Retrieval Algorithms

The NASA/JPL Airborne Rain Mapping Radar (ARMAR) operates at 14 GHz. ARMAR flew on the NASA DC-8 aircraft during Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean Atmosphere Response Experiment (COARE), collecting data in oceanic mesoscale convective systems, similar to those now being observed by the Tropical Rainfall Measuring Mission (TRMM) satellite, which includes a 14-GHz precipitation radar. Several algorithms for retrieving rain rate from downward looking radars are in existence. These can be categorized as deterministic and stochastic. Deterministic algorithms use the path integrated attenuation (PIA), measured by the surface reference technique, as a constraint. One deterministic algorithm corrects the attenuation-rainfall (k-R) relation, while another corrects the reflectivity rainfall (ZR) relation. Stochastic algorithms apply an Extended Kalman Filter to the reflectivity profile. One employs radar reflectivity only; the other additionally uses the PIA. We find that the stochastic algorithm with PIA is the most robust algorithm with regard to incorrect assumptions about the drop-size distribution (DSD). The deterministic algorithm that uses the PIA to adjust the Z-R relation is also fairly robust and produces rain rates similar to the stochastic algorithm that uses the PIA. The deterministic algorithm that adjusts only the k-R relation and the stochastic radar-only algorithm are more sensitive to assumptions about the DSD. It is likely that they underestimate convective rainfall, especially if the DSD is erroneously assumed to be appropriate for stratiform rain conditions. The underestimation is illustrated in the diagram. The algorithm labeled D IS initially assumes a DSD that is appropriate for stratiform. rain, while the rain is most likely convective. The PIA constraint causes the k-R relation to be adjusted, resulting in a much lower rain rate than the other algorithms. Additional information is contained in the original.

Durden, Stephen L.

Comparison of Radar Rainfall Retrieval Algorithms in Convective Rain During TOGA COARE

The authors compare deterministic and stochastic rain-rate retrieval algorithms by applying them to 14-GHz nadir-looking airborne radar reflectivity profiles acquired in tropical convective rain during the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment, The deterministic algorithms both use the path-integrated attenuation (PIA), measured by the surface reference technique, as a constraint. One deterministic algorithm corrects the k-R relation, while the second corrects the Z-R relation. The stochastic algorithms are based on applying an extended Kalman filter to the reflectivity profile. One employs radar reflectivity only; the other additionally uses the PIA. The authors find that the stochastic algorithm, which uses the PIA, is the most robust algorithm with regard to incorrect assumptions about the drop size distribution (DSD). The deterministic algorithm that uses the PIA to adjust the Z-R relation is also fairly robust and produces rain rates similar to the stochastic algorithm that uses the PIA, The deterministic algorithm that adjusts only the k-R relation and the stochastic radar-only algorithm are more sensitive to assumptions about the DSD. It is likely that they underestimate convective rainfall, especially if the DSD is erroneously assumed to be appropriate for stratiform rain conditions.

Durden, Stephen L.

Interannual Variability of the Tropical Water Cycle: Capabilities in the TRMM Era and Challenges for GPM

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30" NE) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Although it is well documented that El Nino-Southern Oscillation (ENSO) events with marked SST changes over the tropical oceans, produce significant regional changes in precipitation, water vapor, and radiative fluxes in the tropics, we still cannot yet adequately quantify the associated net integrated changes to water and heat balance over the entire tropical oceanic or land sectors. Robertson et al., [2001 GRL] for example, showed that substantial disagreement exists among contemporary satellite estimates of interannual variations in tropical rainfall that are associated with SST changes. Berg et al., [2002 J. Climate] have documented the distinct differences between precipitation structure over the eastern and western Pacific ITCZ and noted how various satellite precipitation algorithms may respond quite differently to ENSO modulations of these precipitation regimes. Resolving this uncertainty is important since precipitation and latent heat release variations over land and ocean sectors are key components of the tropical heat balance in its most aggregated form. Rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) averaged over the tropical oceans have not solved this issue and, in fact, show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. In this paper we will focus on findings that uncertainties in microphysical assumptions necessitated by the single-frequency PR measurement pose difficulties for detecting climate-related precipitation signals. Recent work has shown that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series, yet the PR rainfall interannual variability (and attenuation derived predominantly from reflectivity) differs even in sign. We will explore these apparent inconsistencies and detail their impact on estimates of how ENSO events perturb the tropical rainfall. We will place these results in perspective by considering requirements for precipitation accuracy for global climate variability and change studies involving ENSO, monsoon dynamics and variations, and climate model improvement and validation. The discussion will conclude with an assessment of the implications of these findings for Global Precipitation Mission (GPM) requirements.

Robertson, Franklin R.

Interannual Variability of Tropical Rainfall as Seen From TRMM

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30deg N/S) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Although it is well documented that El Nino-Southern Oscillation (ENSO) events with marked SST changes over the tropical oceans produce significant regional changes in precipitation, water vapor, and radiative fluxes in the tropics, we still cannot yet adequately quantify the associated net integrated changes to water and heat balance over the entire tropical oceanic or land sectors. Resolving this uncertainty is important since precipitation and latent heat release variations over land and ocean sectors are key components of the tropical heat balance in its most aggregated form. Rainfall estimates from the Version 5 Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) averaged over the tropical oceans have not solved this issue and, in fact, show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. In this paper we will focus on findings that uncertainties in microphysical assumptions necessitated by the single-frequency PR measurement pose difficulties for detecting climate-related precipitation signals. Recent work has shown that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series, yet the PR rainfall interannual variability (and attenuation derived predominantly from reflectivity) differs even in sign. We will explore these apparent inconsistencies and examine changes in new TRMM Version 6 retrievals. To place these results in a tropical water balance perspective we also examine interannual variations in evaporation over the tropical oceans made from TRMM and SSM/I (Special Sensor Microwave Imager) measurements of surface winds and humidity. Evaporation estimates from reanalysis and several global model experiments will also be compared to the TRMM findings and evaluated for consistency. The ability to detect regional shifts in freshwater flux over the oceans (equivalently, integrated moisture convergence) and moisture transport will be discussed.

Robertson, Franklin R.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

Masunaga, Hirohiko

Standard Deviation of Spatially-Averaged Surface Cross Section Data from the TRMM Precipitation Radar

We investigate the spatial variability of the normalized radar cross section of the surface (NRCS or Sigma(sup 0)) derived from measurements of the TRMM Precipitation Radar (PR) for the period from 1998 to 2009. The purpose of the study is to understand the way in which the sample standard deviation of the Sigma(sup 0) data changes as a function of spatial resolution, incidence angle, and surface type (land/ocean). The results have implications regarding the accuracy by which the path integrated attenuation from precipitation can be inferred by the use of surface scattering properties.

Meneghini, Robert

Near-Nadiral Normalized Radar Cross Section of the SEA Surface at Ku, Ka, and W-Bands: Comparison of Measurements and Models

Understanding the relationship between wind speed and direction and the near-nadiral normalized radar cross section (NRCS) of the sea surface is important in many oceanographic and atmospheric remote sensing applications: (1) wind speed retrievals in traditional altimeter systems (2) assistance in calibration and path integrated attenuation processing for atmospheric profiling radars The desired wind speed (and direction in some cases) retrieval requires a clear understanding of the relationship between the relevant geophysical quantities and the observed NRCS Such understanding is available from existing electromagnetic models, but the presence of many such models, as well as implicit descriptions of the sea surface, motivates continued evaluation of model performance.

CloudSat

Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions

A simplied framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameter of a gamma size distribution andthe melted-equivalent mass median diameter D0, the particle density, dielectric mixing formula, and the choice of complex index of refraction for ice. We examine these properties at selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz; and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles arefound to be extremely sensitive to the melted-equivalent mass median diameter D0 ofthe size distribution. Similar large sensitivities are found for variations in the ice vol-ume fraction whenever the geometric mass median diameter exceeds approximately 1/8th of the wavelength. At 94 GHz the two-way path integrated attenuation is potentially large for dense compact particles. The distribution parameter mu has a relatively weak effect on any observable: less than 1-2 K in brightness temperature and up to 2.7 dB difference in the effective radar reflectivity. Reversal of the roles of ice and air in the MaxwellGarnett dielectric mixing formula leads to a signicant change in both microwave brightness temperature (10 K) and radar reflectivity (2 dB). The choice of Warren (1984) or Warren and Brandt (2008) for the complex index of refraction of ice can produce a 3%-4% change in the brightness temperature depression.

Microwave properties

Evaluation of Cloud Microphysics in JMA-NHM Simulations Using Bin or Bulk Microphysical Schemes through Comparison with Cloud Radar Observations

Numerical weather prediction (NWP) simulations using the Japan Meteorological Agency NonhydrostaticModel (JMA-NHM) are conducted for three precipitation events observed by shipborne or spaceborneW-band cloud radars. Spectral bin and single-moment bulk cloud microphysics schemes are employed separatelyfor an intercomparative study. A radar product simulator that is compatible with both microphysicsschemes is developed to enable a direct comparison between simulation and observation with respect to theequivalent radar reflectivity factor Ze, Doppler velocity (DV), and path-integrated attenuation (PIA). Ingeneral, the bin model simulation shows better agreement with the observed data than the bulk modelsimulation. The correction of the terminal fall velocities of snowflakes using those of hail further improves theresult of the bin model simulation. The results indicate that there are substantial uncertainties in the masssizeand sizeterminal fall velocity relations of snowflakes or in the calculation of terminal fall velocity of snowaloft. For the bulk microphysics, the overestimation of Ze is observed as a result of a significant predominanceof snow over cloud ice due to substantial deposition growth directly to snow. The DV comparison shows thata correction for the fall velocity of hydrometeors considering a change of particle size should be introducedeven in single-moment bulk cloud microphysics.

cloud radar

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

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