Deriving Mixed-Phase Cloud Properties from Doppler Radar Spectra

In certain circumstances, millimeter-wavelength Doppler radar velocity spectra can be used to estimate the microphysical composition of both phases of mixed-phase clouds. This distinction is possible when the cloud properties are such that they produce a bimodal Doppler velocity spectrum. Under these conditions, the Doppler spectrum moments of the distinct liquid and ice spectral modes may be computed independently and used to quantitatively derive properties of the liquid droplet and ice particle size distributions. Additionally, the cloud liquid spectral mode, which is a tracer for clear-air motions, can be used to estimate the vertical air motion and to correct estimates of ice particle fall speeds. A mixed-phase cloud case study from the NASA Cirrus Regional Study of Tropical Anvils and Cloud Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) is used to illustrate this new retrieval approach. The case of interest occurred on 29 July 2002 when a supercooled liquid cloud layer based at 5 km AGL and precipitating ice crystals advected over a ground measurement site. Ground-based measurements from both 35- and 94-GHz radars revealed clear bimodal Doppler velocity spectra within this cloud layer. Profiles of radar reflectivity were computed independently from the liquid and ice spectral modes of the velocity spectra. Empirical reflectivity-based relationships were then used to derive profiles of both liquid and ice microphysical parameters, such a

Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics

Microphysical properties of the November 26 cirrus cloud retrieved by Doppler radar/IR radiometer technique

Gaining information about cirrus cloud microphysics requires development of remote sensing techniques. In an earlier paper. Matrosov et al. (1992) proposed a method to estimate ice water path (IWP) (i.e., vertically integrated ice mass content IMC) and characteristic particle size averaged through the cloud from combined groundbased measurements of radar reflectivities and IR brightness temperatures of the downwelling thermal radiation in the transparency region of 10-12 mu m. For some applications, the vertically averaged characteristic particle sizes and IWP could be the appropriate information to use. However, vertical profiles of cloud microphysical parameters can provide a better understanding of cloud structure and development. Here we describe a further development of the previous method by Matrosov et al. (1992) for retrieving vertical profiles of cirrus particle sizes and IMC rather than their vertically averaged values. In addition to measurements of radar reflectivities, the measurements of Doppler velocities are used in the new method. This provides us with two vertical profiles of measurements to infer two vertical profiles of unknowns, i.e., particle characteristic sizes and IMC. Simultaneous measurements of the IR brightness temperatures are still needed to resolve an ambiguity in particle size-fall velocity relationships

Remote sensing data from CLARET: A prototype CART data set

The data set containing radiation, meteorological , and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) Program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of the experiment, ARM scientists requested archival of subsets of the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory

An Intercomparison of Microphysical Retrieval Algorithms for Upper Tropospheric Ice Clouds

The large horizontal extent, location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the earth's radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud scale processes in large-scale models and for accurately predicting the earth's future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade, however, there is room for improvement. Members of the Atmospheric Radiation measurement program (ARM) Cloud properties Working Group are involved in an intercomparison of optical depth(tau), ice water path, and characteristic particle size in clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for improvement

Distinguishing between Warm and Stratiform Rain Using Polarimetric Radar Measurements

Modeled statistical differential reflectivity–reflectivity (i.e., ZDR–Ze) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, ZDR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A ZDR–Ze relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational ZDR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean ZDR–Ze relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations

Ice Hydrometeor Shape Estimations Using Polarimetric Operational and Research Radar Measurements

A polarimetric radar method to estimate mean shapes of ice hydrometeors was applied to several snowfall and ice cloud events observed by operational and research weather radars. The hydrometeor shape information is described in terms of their aspect ratios, r, which represent the ratio of particle minor and major dimensions. The method is based on the relations between depolarization ratio (DR) estimates and aspect ratios. DR values, which are a proxy for circular depolarization ratio, were reconstructed from radar variables of reflectivity factor, Ze, differential reflectivity, ZDR, and copolar correlation coefficient ρhv, which are available from radar systems operating in either simultaneous or alternate transmutation of horizontally and vertically polarized signals. DR-r relations were developed for retrieving aspect ratios and their sensitivity to different assumptions and model uncertainties were discussed. To account for changing particle bulk density, which is a major contributor to the retrieval uncertainty, an approach is suggested to tune the DR-r relations using reflectivity-based estimates of characteristic hydrometeor size. The analyzed events include moderate snowfall observed by an operational S-band weather radar and a precipitating ice cloud observed by a scanning Ka-band cloud radar at an Arctic location. Uncertainties of the retrievals are discussed

Influence of multiple scattering on CloudSat measurements in snow: A model

[1] The effects of multiple scattering on larger precipitating hydrometers have an influence on measurements of the spaceborne W-band (94 GHz) CloudSat radar. This study presents initial quantitative estimates of these effects in ''dry'' snow using radiative transfer calculations for appropriate snowfall models. It is shown that these effects become significant (i.e., greater than approximately 1 dB) when snowfall radar reflectivity factors are greater than about 10 -15 dBZ. Reflectivity enhancement due to multiple scattering can reach 4 -5 dB in heavier stratiform snowfalls. Multiple scattering effects counteract signal attenuation, so the observed CloudSat reflectivity factors in snowfall could be relatively close to the values that would be observed in the case of single scattering and the absence of attenuation. Citation: Matrosov, S. Y., and A. Battaglia (2009), Influence of multiple scattering on CloudSat measurements in snow: A model study, Geophys. Res. Lett., 36, L12806