Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed K dp and Z DR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using Z DR and K dp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.
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Simple Analytical Expressions for Steady-State Vapor Growth and Collision–Coalescence Particle Size Distribution Parameter Profiles
This study derives simple analytical expressions for the theoretical height profiles of particle number concentrations (
While complex numerical models are often used to describe weather phenomenon, sometimes simple equations can instead provide equally good or comparable results. Thus, these simple equations can be used in place of more complicated models in certain situations and this replacement can allow for computationally efficient and elegant solutions. This study derives such simple equations in terms of exponential and power-law mathematical functions that describe how the average size and total number of snow or rain particles change at different atmospheric height levels due to growth from the vapor phase and aggregation (the sticking together) of these particles balanced with their fallout from clouds. We catalog these mathematical equations for different assumptions of particle characteristics and we then test these equations using spirally descending aircraft observations and ground-based measurements. Overall, we show that these mathematical equations, despite their simplicity, are capable of accurately describing the magnitude and shape of observed height and time series profiles of particle sizes and numbers. These equations can be used by researchers and forecasters along with radar measurements to improve the understanding of precipitation and the estimation of its properties.
- NSF-PAR ID:
- 10468788
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of the Atmospheric Sciences
- Volume:
- 80
- Issue:
- 10
- ISSN:
- 0022-4928
- Format(s):
- Medium: X Size: p. 2531-2544
- Size(s):
- ["p. 2531-2544"]
- Sponsoring Org:
- National Science Foundation
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Abstract The profiles of marine boundary layer (MBL) cloud and drizzle microphysical properties are important for studying the cloud‐to‐rain conversion and growth processes in MBL clouds. However, it is challenging to simultaneously retrieve both cloud and drizzle microphysical properties within an MBL cloud layer using ground‐based observations. In this study, methods were developed to first decompose drizzle and cloud reflectivity in MBL clouds from Atmospheric Radiation Measurement cloud radar reflectivity measurements and then simultaneously retrieve cloud and drizzle microphysical properties during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE‐ENA) campaign. These retrieved microphysical properties, such as cloud and drizzle particle size (
r candr m,d), their number concentration (N candN d) and liquid water content (LWC candLWC d), have been validated by aircraft in situ measurements during ACE‐ENA (~158 hr of aircraft data). The mean surface retrieved (in situ measured)r c,N c, andLWC care 10.9 μm (11.8 μm), 70 cm−3(60 cm−3), and 0.21 g m−3(0.22 g m−3), respectively. For drizzle microphysical properties, the retrieved (in situ measured)r d,N d, andLWC dare 44.9 μm (45.1 μm), 0.07 cm−3(0.08 cm−3), and 0.052 g m−3(0.066 g m−3), respectively. Treating the aircraft in situ measurements as truth, the estimated median retrieval errors are ~15% forr c, ~35% forN c, ~30% forLWC candr d, and ~50% forN dandLWC d. The findings from this study will provide insightful information for improving our understanding of warm rain processes, as well as for improving model simulations. More studies are required over other climatic regions. -
null (Ed.)Abstract A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning K a -band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from −17° to −15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between −10 and 10 dB Z . For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from −17° to −15°C toward the ground. This increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.more » « less
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Abstract The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase K DP , is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, . The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles’ densities, the width of the canting angle distribution, and particles’ aspect ratios, whereas the relation’s exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient.more » « less
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Hail and graupel are linked to lightning production and are important components of cloud evolution. Hail can also cause significant damage when it precipitates to the surface. The accurate prediction of the amount and location of hail and graupel and the effects on the other hydrometeor species depends upon the size distribution assumed. Here, we use ~310 km of in situ observations from flights of the South Dakota School of Mines and Technology T-28 storm-penetrating aircraft to constrain the representation of the particle size distribution (PSD) of hail. The maximum ~1-km hail water content encountered was 9 g m−3. Optical probe PSD measurements are normalized using two-moment normalization relations to obtain an underlying exponential shape. By linking the two normalizing moments through a power law, a parameterization of the hail PSD is provided based on the hail water content only. Preliminary numerical weather simulations indicate that the new parameterization produces increased radar reflectivity relative to commonly used PSD representations.
