Uncertainties in Microwave Properties of Frozen Precipitation: Implications for Remote Sensing and Data Assimilation

Abstract

A combined active/passive modeling system that converts CloudSat observations to simulated microwave brightness temperatures (T B ) is used to assess different ice particle models under precipitating conditions. Simulation results indicate that certain ice models (e.g., low-density spheres) produce excessive scattering and implausibly low simulated T B s for stratiform precipitation events owing to excessive derived ice water paths (IWPs), while other ice models produce unphysical T B depressions due to the combined effects of elevated derived IWP and excessive particle size distribution-averaged extinction. An ensemble of nonspherical ice particle models, however, consistently produces realistic results under most circumstances and adequately captures the radiative properties of frozen hydrometeors associated with precipitation-with the possible exception of very high IWP events. Large derived IWP uncertainties exceeding 60% are also noted and may indicate IWP retrieval accuracy deficiencies using high-frequency passive microwave observations. Simulated T B uncertainties due to the ice particle model ensemble members approach 9 (5) K at 89 (157) GHz for high ice water path conditions associated with snowfall and ;2-3 (;1-2) K under typical stratiform rain conditions. These uncertainties, however, display considerable variability owing to ice water path, precipitation type, satellite zenith angle, and frequency. Comparisons between 157-GHz simulations and observations under precipitating conditions produce low biases (,1.5 K) and high correlations, but lower-frequency channels display consistent negative biases of 3-4 K in precipitating regions. Sample error correlations and covariance matrices for select microwave frequencies also show strong functional relationships with ice water path and variability depending on precipitation type.

CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

“…Some research RTMs that have more sophisticated radiative transfer treatment produced comparable results with observations (e.g., Davis et al, 2007;Kulie et al, 2010). However, some of the major operational RTMs still have large biases in highfrequency microwave channels, which result in poor usage of cloudy/precipitating scenes observed by instruments such as MHS and SSMI.…”

“…These measurements provide useful pairs for instrument calibration (e.g., John et al, 2012), cross-validation of a particular variable (e.g., Wang et al, 2010), or development of new retrieval methods (e.g., Lamquin et al, 2008). In this paper, we will be focusing on the last application.…”

G band atmospheric radars: new frontiers in cloud physics

“…The advantage of CloudSat's cloud profiling radar is that one can derive information on the vertical distribution of snow as well as small cloud ice particles and thus estimate the surface snowfall rate even during relatively light precipitation cases (Liu, 2008b;Matrosov et al, 2008;Kulie and Bennartz, 2009). However, radar-based algorithms rely on statistical relations between the equivalent radar reflectivity factor Z e and snowfall rate S, which are in turn a function of particle fall velocity, particle habit (Petty and Huang, 2010;Kulie et al, 2010) and particle size distribution (PSD). The large natural variability of such properties can lead to uncertainties greater than 100 % in snowfall estimates (Hiley et al, 2011).…”

“…In this context lidar and radar can provide useful complementary and synergetic information (Battaglia and Delanöe, 2013, and references therein). By combining multi-frequency measurements from active and passive microwave remote sensing instruments, essential assumptions on particle type and size distribution have been evaluated through consistency checks with radiative transfer modelling in snow clouds (Löhnert et al, 2011;Kneifel et al, 2010;Kulie et al, 2010). These assumptions can be constrained further by in situ measurements and continuous temperature and humidity profile information.…”

Interpreting an evaluation of the ECMWF global model with CloudSat observations: ambiguities due to radar reflectivity forward operator uncertainties

“…The IFS model does not provide explicit parameters for the radar reflectivity forward operator as there are currently a number of different assumptions used for different microphysical processes and, as for many models, a lack of consistency between the cloud, precipitation, convection and radiation parametrizations. Recently, Kulie et al (2010) and Hiley et al (2011) proposed a methodology to bound the uncertainties in the forward modelling of microwave brightness temperatures and in the snowfall retrieval from radar observations. Following a similar approach, the microphysical assumptions made in ZmVar are replaced with possible alternatives in order to evaluate the impact on the synthetic reflectivities.…”