Role of Gravity Waves in Determining Cirrus Cloud Properties

Cirrus clouds are important in the Earth's radiation budget. They typically exhibit variable physical properties within a given cloud system and from system to system. Ambient vertical motion is a key factor in determining the cloud properties in most cases. The obvious exception is convectively generated cirrus (anvils), but even in this case, the subsequent cloud evolution is strongly influenced by the ambient vertical motion field. It is well know that gravity waves are ubiquitous in the atmosphere and occur over a wide range of scales and amplitudes. Moreover, researchers have found that inclusion of statistical account of gravity wave effects can markedly improve the realism of simulations of persisting large-scale cirrus cloud features. Here, we use a 1 -dimensional (z) cirrus cloud model, to systematically examine the effects of gravity waves on cirrus cloud properties. The model includes a detailed representation of cloud microphysical processes (bin microphysics and aerosols) and is run at relatively fine vertical resolution so as to adequately resolve nucleation events, and over an extended time span so as to incorporate the passage of multiple gravity waves. The prescribed gravity waves "propagate" at 15 m s (sup -1), with wavelengths from 5 to 100 km, amplitudes range up to 1 m s (sup -1)'. Despite the fact that the net gravity wave vertical motion forcing is zero, it will be shown that the bulk cloud properties, e.g., vertically-integrated ice water path, can differ quite significantly from simulations without gravity waves and that the effects do depend on the wave characteristics. We conclude that account of gravity wave effects is important if large-scale models are to generate realistic cirrus cloud property climatology (statistics)

Two Layers of Melting Ice Particles Within a Single Radar Bright Band : Interpretation and Implications

Dual-frequency dual-polarization radar observations of the melting of two ice populations in a stratiform rainfall event are presented. The observed phenomenon occurs as a two-layer linear depolarization ratio (LDR) signature in a single radar bright band. Doppler spectra observations show that the upper LDR layer is caused by the melting of ice needles, potentially generated by the rime-splintering process, while the lower one is mainly due to the melting of background ice particles formed at the cloud top. The melting signal of small needles acts as a unique benchmark for detecting the onset of melting and is used to verify the current methods for the identification of melting layer boundaries. The radar-derived characteristics of the melting layer are found to be dependent on the radar variable and frequency used. The implications of the presented findings for radar-based studies of precipitation properties in and above the melting layer are also discussed.Peer reviewe

Retrievals Of The Deep Convective System Ice Cloud Microphysical Properties Using The Arm Radar And Aircraft In-Situ Measurements

This study presents an algorithm for retrieving the Deep Convective Systems (DCSs) ice cloud microphysical properties using the DOE Atmospheric Radiation Measurement (ARM) Ka-band Zenith Radar (KAZR) reflectivity during the Midlatitude Continental Convective Clouds Experiment (MC3E) at the ARM Southern Great Plain (SGP) site ( 36° 36\u27 18.0 N, 97° 29\u27 6.0 W) from April-June 2011. It is a challenge to retrieve DCS ice cloud microphysical properties due to the attenuation of cloud radar reflectivity, unknown particle size distributions (PSDs), and the bulk habit of the ice particles within the sample volume. To address the most pronounced of these radar limitations, the original KAZR reflectivity measurements have been adjusted using data collected with both a collocated unattenuated 915-MHz profiling radar system UHF ARM Zenith Radar (UAZR) and a Joss-Waldvogel impact disdrometer (JWD). Additionally, aircraft in-situ measurements provide PSDs and best-estimate ice water content (IWC) for validating radar retrievals. With the aid of the scattering database (SCATDB), the relationships between backscatter cross section (σ) and particle dimension (D) are parameterized for four ice crystal habits (bullet rosettes, snowflakes, columns and plates). The DCS ice cloud IWC and effective radius (re) on 20 May 2011 during the MC3E have been retrieved from adjusted KAZR reflectivity assuming a modified gamma distribution with size shape α and a bullet rosette σ-D relationship. The averaged IWC and re from radar retrievals over the stratiform rain (SR) region of the DCS are 0.34 g m-3 and 338 µm, in excellent agreement with aircraft in-situ measured IWC (0.34 g m-3) and re (337 µm). Over the anvil cloud (AC) region, the retrieved and measured IWCs are 0.18 g m-3 and 0.23 g m-3 and their respective re values are 250 µm and 305 µm. The radar retrieved re and IWC can increase to 283 µm and 0.23 g m-3 if a 2 dB uncertainty is added to the adjusted KAZR reflectivity over the AC region, following the sensitivities of 13%/2 dB in re and 26%/2 dB in IWC. These retrieval results are also compared with Geostationary Operational Environmental Satellite (GOES) retrieved cloud effective diameter (De) during MC3E. In addition to the spatially averaged GOES retrievals within a 1°Ã1° grid box centered over the ARM SGP site and the temporally averaged ARM retrievals within 1 hr (±0.5 hr GOES image), the ARM-retrieved De values were also averaged from cloud top down to where the reflectivity is around 0 dBZ to best match the GOES retrievals. During daytime, GOES retrieved De, on average, agrees with the ARM retrievals within ~25 µm despite the vastly different temporal and spatial resolutions of vertically pointing ground-based radar and cloud-top-viewing satellite instruments. GOES retrieved cloud top heights (CTHs) are also compared with ARM KAZR reflectivity profiles, having an excellent agreement with differences of ~0.2 km

Master of Science

thesisTo better understand the role of small particles in the microphysical processes and the radiative properties of cirrus, the reliability of the historical in situ measurement database must be understood. A means of establishing this validity is to assume that the in situ measurements are at least consistent, in a broad sense, with the remote sensing data, and vice versa. In this study, an algorithm using Doppler radar moments and Raman lidar extinction is developed to retrieve a bimodal particle size distribution and its uncertainty. Case studies and statistics compiled over an entire year show that the existence of high concentrations in excess of 1 cm-3 of small particles in cirrus is not consistent with any reasonable interpretation of remote sensing data and is therefore likely from an artifact of the in situ measurement process. This study shows that while the particle concentrations from the Two-Dimensional Cloud Probe generally agree well with the retrieval results, simultaneous concentrations from the Forward Scattering Spectrometer Probe are much higher than the concentrations of small particles implied by the remote sensing measurements. The one-year statistics of the cirrus microphysical properties, including the ice water content, the effective radius and the total particle concentration, show that the occurrence frequency of the concentrations larger than 1 cm-3 is below 1%, and, given the possibility of errors in retrieved concentration as large as 100%, this study concludes that the existence of particle concentrations in cirrus in excess of 1 cm-3 is extraordinarily rare instead of common as suggested by uncritical acceptance of in situ data

Characterization of snowfall using ground-based passive and active remote sensors.

Snowfall is a key quantity in the global hydrological cycle and has an impact on the global energy budget as well. In sub-polar and polar latitudes, snowfall is the predominant type of precipitation and rainfall is often initiated via the ice phase. Currently, the spatial distribution of snowfall is poorly captured by numerical weather prediction and climate models. In order to evaluate the models and to improve our understanding of snowfall microphysics, global observations of snowfall are needed. This can only be obtained by space-borne active and passive remote sensors. In order to be able to penetrate even thick snow clouds, sensors operating in the microwave frequency region are favoured. The challenge for snowfall retrieval development lies first in the complexity of snowfall microphysics and its interactions with liquid cloud water. Secondly, comprehensive knowledge is needed about the interaction of electromagnetic radiation with snowfall in order to finally relate the radiative signatures to physical quantities. A general advantage of ground-based observations is that simultaneous measurements of in-situ and remote sensing instruments can be obtained. Such a six-month dataset was collected within this thesis at an alpine site. The instrumentation included passive microwave radiometers that covered the frequency range from 22 up to \unit[150]{GHz} as well as two radar systems operating at 24.1 and 35.5 GHz. These data were complemented by optical disdrometer, ceilometer and various standard meteorological measurements. State-of-the-art single scattering databases for pristine ice crystals and complex snow aggregates were used within this thesis to investigate the sensitivity of ground--based passive and active remote sensors to various snowfall parameters such as vertical snow and liquid water distribution, snow particle habit, snow size distribution and ground surface properties. The comparison of simulations with measurements within a distinct case study revealed that snow particle scattering can be measured with ground--based passive microwave sensors at frequencies higher than 90 GHz. Sensitivity experiments further revealed that ground-based sensors have clear advantages over nadir measuring instruments due to a stronger snow scattering signal and lower sensitivity to variable ground surface emissivity. However, passive sensors were also found to be highly sensitive to liquid cloud water that was frequently observed during the entire campaign. The simulations indicate that the uncertainties of sizes distribution and snow particle habit are not distinguishable with a passive-only approach. In addition to passive microwave observations, data from a low-end radar system that is commonly used for rainfall were investigated for its capabilities to observe snowfall. For this, a snowfall specific data processing algorithm was developed and the re-processed data were compared to collocated measurements of a high-end cloud radar. If the focus can be narrowed down to medium and strong snowfall within the lowest 2-3 km height, the reflectivity and fall velocity measurements of the low-end system agree well with the cloud radar. The cloud radar dataset was used to estimate the uncertainty of retrieved snowfall rate and snow accumulation of the low-end system. Besides the intrinsic uncertainties of single-frequency radar retrievals the estimates of total snow accumulation by the low-end system lay within 7% compared to the cloud radar estimates. In a more general approach, the potential of multi-frequency radar systems for derivation of snow size distribution parameters and particle habit were investigated within a theoretical simulation study. Various single-scattering databases were combined to test the validity of dual-frequency approaches when applied to non-spheroid particle habits. It was found that the dual-frequency technique is dependent on particle habit. It could be shown that a rough distinction of snow particle habits can be achieved by a combination of three frequencies. The method was additionally tested with respect to signal attenuation and maximum particle size. The results obtained by observations and simulations within this thesis strongly suggest the further development of simultaneous ground-based in-situ and remote sensing observations of snowfall. Extending the sensitivity studies of this study will help to define the most suitable set of sensors for future studies. A combination of these measurements with a further development of single-scattering databases will potentially help to improve our understanding of snowfall microphysics