Snow Particle Size Distribution Research Articles

Snow microphysical retrieval from the NASA D3R radar during ICE-POP 2018

Abstract. A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single-scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r=0.95) and snow water equivalent rate (r=0.92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (−31 % and −8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r=0.74 and r=0.71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r=0.43 for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.

Laser attenuation in falling snow correlated with measurements of snow particle size distribution

We investigate transmission through falling snow using a 3D imaging lidar at 1.56-μm wavelength. The lidar is based on the time-correlated single-photon counting technique. Experimental transmission data are compared with Mie theory transmission calculations based on snow particle size distribution simultaneously measured with a laser disdrometer. The calculations were performed in two ways, using the Beer–Lambert approach where all radiation interacting with a hydrometeor is considered extinct and an approach that includes effects of the forward scattering. Comparison of these two methods shows that inclusion of the contribution from forward scattering gives better agreement between experiment and calculations than using extinction only. When comparing the results using scattering calculations with a curve fit approach based on precipitation rate, it is evident that both the Beer–Lambert approach and the forward scattering approach give a much better correlation between experiment and calculations than relying on precipitation rate, as measured with the disdrometer, only.

Evaluation of the AROME model's ability to represent ice crystal icing using in situ observations from the HAIC 2015 field campaign

AbstractSince pilots generally avoid intense convective areas, ice crystals icing (ICI) is an aeronautical weather incident that mainly occurs in the anvil of tropical deep convective clouds. Samples of favorable conditions for the occurrence of ICI and data from the High Altitude Ice Crystals (HAIC) 2015 field campaign in French Guiana are investigated and compared with simulations of the French operational mesoscale forecast system Application of Research to Operations at Mesoscales (AROME). To this end, a contextualization of convective systems into convective, stratiform, and cirriform regions is employed for both observations and AROME. General features of the microphysics of deep tropical convective systems are identified. The number concentration of crystals larger than 125 μm and total water content (TWC) are strongly correlated at each temperature level, and both decrease with increasing distance from convective cores. AROME can reproduce the general behavior of the observed microphysics, especially TWC, but seems unable to simulate extreme ICI events. Reasons are sought in the assumptions performed in the microphysical scheme ICE3, and guidelines are proposed to enhance its skills in the context of ICI. In particular, the representation of the snow particle size distribution is adjusted across observations using a generalized gamma shape. This shape is found to outperform the usual Marshall–Palmer and gamma shapes. Additionally, a temperature and snow content dependence of generalized gamma parameters is found. These changes are found to significantly improve the snow concentration diagnostic of ICE3, and these modifications open the way for improvements in the ICE3 scheme.

Polarimetric Radar Relations for Estimation of Visibility in Aggregated Snow

AbstractThe 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 KDP, 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.

Characteristics of Snow Particle Size Distribution in the PyeongChang Region of South Korea

Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel2 disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow (ρ), snowfall rate (S), and ice water content (IWC) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors (Dm, log10Nw, and μ) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The Dm decreases and log10Nw and μ increase as the density increases. The Dm and log10Nw increase and μ decreases with the increase of snowfall rate. The power-law relationship between ρ and Dm is obtained and the relationship between S and IWC is also derived.

Snow Particle Size Distribution From a 2-D Video Disdrometer and Radar Snowfall Estimation in East China

In this study, as part of an effort to study snowfall characteristics and quantify winter precipitation in East China, we investigated the microphysical properties of snowfall, including size, shape, density, and terminal velocity using a 2-D video disdrometer (2-DVD) and a weighing precipitation gauge in Nanjing (NJ), East China during the winters of 2015-2019. We obtained larger snow density and terminal velocity values than those reported in the literature for this region. Higher snow density could account for higher snowflake terminal velocity, after removing the effects of observation altitude and surface temperature. We then fit the snow particle size distributions (PSDs) to the gamma model and explored the interrelationships among the model parameters and snowfall rate (SR). The relationship between radar reflectivity factor (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ) and SR was derived based on snow PSD measurements and the snow density relation. Using this Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> -SR relationship, the estimated liquid-equivalent SRs are obtained from S-band NJ radar data collected during several snowfall events. Radar-inferred SRs showed reasonable agreement with those measured on the ground, with a mean absolute error of 16% for the collected snowfall events in NJ.

Characteristics Analysis of Snow Particle Size Distribution in Gangwon Region according to Topography

Characteristics Analysis of Snow Particle Size Distribution in Gangwon Region according to Topography

Polarimetric Radar Relations for Quantification of Snow Based on Disdrometer Data

AbstractAccurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in radar snow estimates that are based on the radar reflectivity factor Z is caused by the variability of snow particle size distributions and snow particle density as well as the large diversity among snow growth habits. In this study, a novel method for snow quantification that is based on the joint use of radar reflectivity Z and specific differential phase KDP is introduced. An extensive dataset of 2D-video-disdrometer measurements of snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice water content IWC in the forms of bivariate power-law relations S = and along with similar relations for the intercept N0s and slope Λs of the exponential snow size distribution. The physical basis of these relations is explained. Their multipliers are sensitive to variations in the width of the canting angle distribution and to a lesser extent the particles’ aspect ratios and densities, whereas the exponents are practically invariant. This novel approach is tested against the S(Z) relation using snow disdrometer measurements in three geographical regions (Oklahoma, Colorado, and Canada). Significant improvement in snow estimates relative to the traditional Z-based methods is demonstrated.

Estimating radar reflectivity - Snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations

Snowfall rate (SR) estimates over Antarctica are sparse and characterised by large uncertainties. Yet, observations by precipitation radar offer the potential to get better insight in Antarctic SR. Relations between radar reflectivity (Ze) and snowfall rate (Ze-SR relations) are however not available over Antarctica. Here, we analyse observations from the first Micro Rain Radar (MRR) in Antarctica together with an optical disdrometer (Precipitation Imaging Package; PIP), deployed at the Princess Elisabeth station. The relation Ze=A*SRB was derived using PIP observations and its uncertainty was quantified using a bootstrapping approach, randomly sampling within the range of uncertainty. This uncertainty was used to assess the uncertainty in snowfall rates derived by the MRR. We find a value of A = 18 [11–43] and B = 1.10 [0.97–1.17]. The uncertainty on snowfall rates of the MRR based on the Ze-SR relation are limited to 40%, due to the propagation of uncertainty in both Ze as well as SR, resulting in some compensation. The prefactor (A) of the Ze-SR relation is sensitive to the median diameter of the snow particles. Larger particles, typically found closer to the coast, lead to an increase of the value of the prefactor (A=44). Smaller particles, typical of more inland locations, obtain lower values for the prefactor (A=7). The exponent (B) of the Ze-SR relation is insensitive to the median diameter of the snow particles. In contrast with previous studies for various locations, shape uncertainty is not the main source of uncertainty of the Ze-SR relation. Parameter uncertainty is found to be the most dominant term, mainly driven by the uncertainty in mass-size relation of different snow particles. Uncertainties on the snow particle size distribution are negligible in this study as they are directly measured. Future research aiming at reducing the uncertainty of Ze-SR relations should therefore focus on obtaining reliable estimates of the mass-size relations of snow particles.

In situ observations of snow particle size distributions over a cold frontal rainband within an extratropical cyclone

Cloud microphysical properties of a mixed phase cloud generated by a typical extratropical cyclone in the Tongliao area, Inner Mongolia on 3 May 2014, are analyzed primarily using in situ flight observation data. This study is mainly focused on ice crystal concentration, supercooled cloud water content, and vertical distributions of fit parameters of snow particle size distributions (PSDs). The results showed several discrepancies of microphysical properties obtained during two penetrations. During penetration within precipitating cloud, the maximum ice particle concentration, liquid water content, and ice water content were increased by a factor of 2-3 compared with their counterpart obtained during penetration of a nonprecipitating cloud. The heavy rimed and irregular ice crystals obtained by 2D imagery probe as well as vertical distributions of fitting parameters within precipitating cloud show that the ice particles grow during falling via riming and aggregation process, whereas the lightly rimed and pristine ice particles as well as fitting parameters within non-precipitating cloud indicate the domination of sublimation process. During the two cloud penetrations, the PSDs were generally better represented by gamma distributions than the exponential form in terms of the determining coefficient (R2). The correlations between parameters of exponential /gamma form within two penetrations showed no obvious differences compared with previous studies.

Snow particle speeds in drifting snow

AbstractKnowledge of snow particle speeds is necessary for deepening our understanding of the internal structures of drifting snow. In this study, we utilized a snow particle counter (SPC) developed to observe snow particle size distributions and snow mass flux. Using high‐frequency signals from the SPC transducer, we obtained the sizes of individual particles and their durations in the sampling area. Measurements were first conducted in the field, with more precise measurements being obtained in a boundary layer established in a cold wind tunnel. The obtained results were compared with the results of a numerical analysis. Data on snow particle speeds, vertical velocity profiles, and their dependence on wind speed obtained in the field and in the wind tunnel experiments were in good agreement: both snow particle speed and wind speed increased with height, and the former was always 1 to 2 m s−1 less than the latter below a height of 1 m. Thus, we succeeded in obtaining snow particle speeds in drifting snow, as well as revealing the dependence of particle speed on both grain size and wind speed. The results were verified by similar trends observed using random flight simulations. However, the difference between the particle speed and the wind speed in the simulations was much greater than that observed under real conditions. Snow transport by wind is an aeolian process. Thus, the findings presented here should be also applicable to other geophysical processes relating to the aeolian transport of particles, such as blown sand and soil.

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Abstract. To simulate passive microwave radiances in all-sky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and, particularly in deep-convective areas, it produces too much scattering in mid-frequencies (e.g. 30–50 GHz) and too little scattering at high frequencies (e.g. 150–183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important, such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60%. However, this improvement is achieved with a simple "one-size-fits-all" shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

Snow particle characteristics in the saltation layer

AbstractShadowgraphy was employed to study snow saltation in boundary-layer wind tunnel experiments with fresh, naturally deposited snow. The shadowgraphy method allowed for a temporally and spatially high-resolution investigation of snow particle characteristics within a measurement area of up to 50 mm × 50 mm. Snow particle size and number characteristics, and their variation with height in the saltation layer, were analysed. The following observations and findings were made for the saltation layer: (1) the particle number decreases exponentially with height, (2) the mean particle diameter is fairly constant, with a very slight tendency to decrease with height, (3) the maximum particle diameter decreases linearly with height, and (4) the snow particle size distribution can be adequately described by gamma probability density functions. The shape and scale parameters of the gamma distribution were found to vary systematically, though only slightly, with height over ground and between experiments with different snowpack characteristics.

Review and uncertainty assessment of size-resolved scavenging coefficient formulations for below-cloud snow scavenging of atmospheric aerosols

Abstract. Theoretical parameterizations for the size-resolved scavenging coefficient for atmospheric aerosol particles scavenged by snow (Λsnow) need assumptions regarding (i) snow particle–aerosol particle collection efficiency E, (ii) snow-particle size distribution N(Dp), (iii) snow-particle terminal velocity VD, and (iv) snow-particle cross-sectional area A. Existing formulas for these parameters are reviewed in the present study, and uncertainties in Λsnow caused by various combinations of these parameters are assessed. Different formulations of E can cause uncertainties in Λsnow of more than one order of magnitude for all aerosol sizes for typical snowfall intensities. E is the largest source of uncertainty among all the input parameters, similar to rain scavenging of atmospheric aerosols (Λrain) as was found in a previous study by Wang et al. (2010). However, other parameters can also cause significant uncertainties in Λsnow, and the uncertainties from these parameters are much larger than for Λrain. Specifically, different N(Dp) formulations can cause one-order-of-magnitude uncertainties in Λsnow for all aerosol sizes, as is also the case for a combination of uncertainties from both VD and A. Assumptions about dominant snow-particle shape (and thus different VD and A) will cause an uncertainty of up to one order of magnitude in the calculated scavenging coefficient. In comparison, uncertainties in Λrain from N(Dp) are smaller than a factor of 5, and those from VD are smaller than a factor of 2. As expected, Λsnow estimated from empirical formulas generated from field measurements falls in the upper range of, or is higher than, the theoretically estimated values, which can be explained by additional processes/mechanisms that influence field-derived Λsnow but that are not considered in the theoretical Λsnow formulas. Predicted aerosol concentrations obtained by using upper range vs. lower range of Λsnow values (a difference of around two orders of magnitude in Λsnow) can differ by a factor of 2 for just a one-centimetre snowfall (liquid water equivalent of approximately 1 mm). Based on the median and upper range of theoretically generated Λsnow and Λsnow values, it is likely that, for typical rain and snow events, the removal of atmospheric aerosol particles by snow is more effective than removal by rain for equivalent precipitation amounts, although a firm conclusion requires much more evidence.

The Modified Gamma Size Distribution Applied to Inhomogeneous and Nonspherical Particles: Key Relationships and Conversions

Abstract The four-parameter modified gamma distribution (MGD) is the most general mathematically convenient model for size distributions of particle types ranging from aerosols and cloud droplets or ice particles to liquid and frozen precipitation. The common three-parameter gamma distribution, the exponential distribution (e.g., Marshall–Palmer), and power-law distribution (e.g., Junge) are all special cases. Depending on the context, the particle “size” used in a given formulation may be the actual geometric diameter, the volume- or area-equivalent spherical diameter, the actual or equivalent radius, the projected or surface area, or the mass. For microphysical and radiative transfer calculations, it is often necessary to convert from one size representation to another, especially when comparing or utilizing distribution parameters obtained from a variety of sources. Furthermore, when the mass scales with Db, with b &amp;lt; 3, as is typical for snow and ice and other particles having a quasi-fractal structure, an exponential or gamma distribution expressed in terms of one size parameter becomes an MGD when expressed in terms of another. The MGD model is therefore more fundamentally relevant to size distributions of nonspherical particles than is often appreciated. The central purpose of this paper is to serve as a concise single-source reference for the mathematical properties of, and conversions between, atmospheric particle size distributions that can expressed as MGDs, including exponential and gamma distributions as special cases. For illustrative purposes, snow particle size distributions published by Sekhon and Srivastava, Braham, and Field et al. are converted to a common representation and directly compared for identical snow water content, allowing large differences in their properties to be discerned and quantified in a way that is not as easily achieved without such conversion.

Snow microphysical observations in shallow mixed‐phase and deep frontal Arctic cloud systems

AbstractSnow particle size distributions (particle size &gt;400 µm) in the western Arctic measured with in situ aircraft instrumentation during the Surface Heat Budget of the Arctic/First ISCCP Regional Experiment ‐ Arctic Clouds Experiment and Mixed‐Phase Arctic Cloud Experiment are analysed. Three cases of shallow, precipitating mixed‐phase boundary‐layer clouds and two cases of deep, precipitating frontal clouds are examined. Overall, the shallow cases had much lower values of particle concentration and ice water content than the deep cases, indicating large differences in ice initiation and growth between these regimes. Within a given case for both the shallow and deep frontal systems, and for the dataset as a whole, crystal concentration had little correlation with temperature (height), despite an active aggregation process that was indicated by large aggregates (&gt;5 mm) observed in four out of the five cases. Exponential size distributions are fitted to the observations, allowing a direct comparison with the snow particle size distributions that are represented with exponential functions in many bulk microphysics schemes used in weather and climate models. Values of the fitted intercept parameter N0 are generally 2–10 times smaller for the shallow compared to the deep frontal cases as a result of differences in crystal concentration between these regimes. Values of N0 ∼ 107 m−4 specified for snow in many bulk microphysics schemes are broadly consistent with fitted N0 for the deep cases but larger than values for the shallow cases. The deep frontal cases also exhibit a relationship between N0 and temperature consistent with previous observations of midlatitude frontal systems. However, there are no consistent differences in N0 between the shallow and deep cases when partitioned by ice water content. Fitted values of slope parameter λ for the shallow and deep cases are generally consistent with previous studies of lower‐latitude cloud systems. Copyright © 2011 Royal Meteorological Society

Wind and drifting-snow gust factor in an Alpine context

AbstarctWind-transported snow is a common phenomenon in cold windy areas, creating snowdrifts and contributing significantly to the loading of avalanche release areas. It is therefore necessary to take into account snowdrift formation both in terms of predicting and controlling drift patterns. Particularly in an Alpine context, drifting snow is a nonstationary phenomenon, which has not been taken into account in physical modeling carried out in wind tunnels or in numerical simulations. Only a few studies have been conducted to address the relation between wind gusts and drifting-snow gusts. Consequently, the present study was conducted at the Lac Blanc pass (2700ma.s.l.) experimental site in the French Alps using a snow particle counter and a cup anemometer in order to investigate drifting-snow gusts. First, it was shown that the behavior of the wind gust factor was coherent with previous studies. Then the definition of wind gust factor was extended to a drifting-snow gust factor. Sporadic drifting-snow events were removed from the analysis to avoid artificially high drifting-snow gust factors. Two trends were identified: (1) A high 1 s peak and a mean 10 min drifting-snow gust factor, greater than expected, were observed for events that exhibited a gamma distribution on the particle width histogram. The values of drifting-snow gust factors decreased with increasing gust duration. (2) Small drifting-snow gusts (i.e. smaller than or of the same order of magnitude as wind gusts) were also observed. However, in this case, they were systematically characterized by a snow particle size distribution that differed from the two-parameter gamma probability density function.

Snow scattering signals in ground‐based passive microwave radiometer measurements

This paper investigates the influence of snow microphysical parameters on the enhancement of ground‐based passive microwave brightness temperature (TB) measurements. In addition to multispectral passive microwave observations between 20 and 150 GHz, a 35 GHz cloud radar and a 2‐D video disdrometer for in situ measurements of snowfall were deployed as part of the “towards an optimal estimation‐based snowfall characterization algorithm” campaign in the winter season of 2008–2009 at an Alpine environment located at 2650 m mean sea level. These observations are combined with nearby radiosonde ascents and surface standard meteorological measurements to reconstruct the atmospheric state (i.e., fields of temperature, humidity, snow, and liquid water contents) and are subsequently used as input for a microwave radiative transfer (RT) model. We investigate the sensitivity of the missing information about snow shape and snow particle size distribution (SSD) on the microwave TB measurements using the disdrometer data as a rough constraint. For an extended case study, we found that TBs at 90 and 150 GHz are significantly enhanced because of scattering of surface radiation at snow crystals and that this enhancement is clearly correlated with the radar derived snow water path (SWP &lt; 0.2 kg m−2). RT simulations highlight the strong influence of the vertical distribution of cloud liquid water (liquid water path LWP &lt; 0.1 kg m−2) on the TB, which in extreme cases, can fully obscure the snow scattering signal. TB variations of the same magnitude can also be caused by typical variations in SSD parameters and particle shape similar to results obtained by space‐borne studies. Ground‐based stations with their infrastructural capabilities in combining active and passive microwave observations have the potential to disentangle the influences of different snow shape, SSD, and SWP in snow retrievals, thus supporting current and future satellite missions.

A physical model to estimate snowfall over land using AMSU‐B observations

In this study, we present a physical model to retrieve snowfall rate over land using brightness temperature observations from NOAA's Advanced Microwave Sounder Unit‐B (AMSU‐B) at 89 GHz, 150 GHz, 183.3 ± 1 GHz, 183.3 ± 3 GHz, and 183.3 ± 7 GHz. The retrieval model is applied to the New England blizzard of 5 March 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the discrete‐dipole approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU‐B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori database in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU‐B observations to within about ±5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near‐concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Ze and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Ze‐R relationships fall in the range of previously established Ze‐R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU‐B measured brightness temperatures.

Snow Size Distribution Parameterization for Midlatitude and Tropical Ice Clouds

Abstract Many microphysical process rates involving snow are proportional to moments of the snow particle size distribution (PSD), and in this study a moment estimation parameterization applicable to both midlatitude and tropical ice clouds is proposed. To this end aircraft snow PSD data were analyzed from tropical anvils [Tropical Rainfall Measuring Mission/Kwajelein Experiment (TRMM/KWAJEX), Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE)] and midlatitude stratiform cloud [First International Satellite Cloud Climatology Project Research Experiment (FIRE), Atmospheric Radiation Measurement Program (ARM)]. For half of the dataset, moments of the PSDs are computed and a parameterization is generated for estimating other PSD moments when the second moment (proportional to the ice water content when particle mass is proportional to size squared) and temperature are known. Subsequently the parameterization was tested with the other half of the dataset to facilitate an independent comparison. The parameterization for estimating moments can be applied to midlatitude or tropical clouds without requiring prior knowledge of the regime of interest. Rescaling of the tropical and midlatitude size distributions is presented along with fits to allow the user to recreate realistic PSDs given estimates of ice water content and temperature. The effects of using different time averaging were investigated and were found not to be adverse. Finally, the merits of a single-moment snow microphysics versus multimoment representations are discussed, and speculation on the physical differences between the rescaled size distributions from the Tropics and midlatitudes is presented.