Revisiting Liquid Water Content Retrievals in Warm Stratified Clouds: The Modified Frisch

Küchler, Nils; Kneifel, Stefan; Kollias, Pavlos; Löhnert, Ulrich

doi:10.1029/2018gl079845

Cited by 14 publications

(18 citation statements)

References 42 publications

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“…However, by illustrating how cloud droplet spectra measured by in situ measurements can be used as PAMTRA input for simulating radar Doppler spectra (Sect. 3.4 we could illustrate that higher moments of the spectra can be suitable as retrieval input as they show clear drizzle signatures (Acquistapace et al, 2019;Küchler et al, 2018). Along this line also the passive microwave signatures of drizzle can be simulated to support related retrieval development (Cadeddu et al, 2019).…”

Section: Summary and Future Perspectivesmentioning

confidence: 66%

“…The differences in the noise levels (especially Ka-band) are due to known saturation effects in the Ka-band receiver which enhances the spectral noise. The slight mismatch of the W-band noise level is due to height dependent chirp table configuration and associated variable sensitivity (Küchler et al, 2018). In this simulation, PAMTRA was configured to match the highest chirp sequence and hence the noise level at lower ranges is underestimated.…”

Section: Ground-based Perspectivementioning

confidence: 99%

“…Therefore higher moments of the Doppler spectra, e.g. skewness, kurtosis, contain information which can be used in retrieval algorithms to disentangle the cloud and the drizzle contribution to Z e (Küchler et al, 2018). Fingerprinting of characteristic hydrometeor signatures in the spectra becomes even more important 5 for frozen hydrometeors allowing detailed process studies (Kalesse et al, 2016).…”

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confidence: 99%

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PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere

Mech¹,

Maahn²,

Kneifel³

et al. 2020

Preprint

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Abstract. Forward models are a key tool to generate synthetic observations given the knowledge of the atmospheric state. In this way they are an integral part of inversion algorithms that aim to retrieve geophysical variables from observations or in data assimilation. Their application for the exploitation of the full information content of remote sensing observations becomes increasingly important when these are used to evaluate the performance of cloud resolving models (CRMs). Herein, CRMs profiles or fields provide the input to the forward model whose simulation results are subsequently compared to the observations. This paper introduces the freely available comprehensive microwave forward model PAMTRA (Passive and Active Microwave TRAnsfer), demonstrates its capabilities to simulate passive and active measurements across the microwave spectral region for up- and downward looking geometries, and illustrates how the forward simulations can be used to evaluate CRMs and to interpret measurements to improve our understanding of cloud processes. PAMTRA is unique as it treats passive and active radiative transfer (RT) in a consistent way with the passive forward model providing up- and down-welling polarized brightness temperatures and radiances for arbitrary observation angles. The active part is capable of simulating the full radar Doppler spectrum and its moments. PAMTRA is designed to be flexible with respect to instrument specifications, interfaces to many different formats of in- and output type, especially CRMs, spanning the range from bin-resolved microphysical output to one- and two-moment schemes, and to in situ measured hydrometeor properties. A specific highlight is the incorporation of the self-similar Rayleigh--Gans Approximation (SSRGA) both for active and passive applications which becomes especially important for the investigation of frozen hydrometeors.

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Section: Summary and Future Perspectivesmentioning

confidence: 66%

Section: Ground-based Perspectivementioning

confidence: 99%

mentioning

confidence: 99%

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PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere

Mech¹,

Maahn²,

Kneifel³

et al. 2020

Preprint

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show abstract

“…To reduce noise further, all DWRs within the group (or window) were smoothed by fitting to them. Motivated by previous studies which assume shallow clouds have linear LWC profiles (e.g., Küchler et al, 2018) fitting is performed using a second-order polynomial of the form…”

Section: Lwc Retrieval Configurationmentioning

confidence: 99%

The Vertical Structure of Liquid Water Content in Shallow Clouds as Retrieved From Dual‐Wavelength Radar Observations

et al. 2019

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Observations collected over 3 months by the beam‐matched second‐generation Ka/W band Scanning Cloud Radar located at the Atmospheric Radiation Measurement Program Eastern North Atlantic observatory are used to advance existing liquid water content (LWC) retrieval techniques, quantify retrieval uncertainty, and subsequently characterize the impact of cloud dynamics and rain rates on the vertical distribution of LWC in boundary layer clouds both precipitating and broken. A threefold technique is proposed that involves (1) temporally averaging measured radar reflectivities collected at two wavelengths to 30‐s resolution, (2) smoothing via fitting a second‐degree polynomial to their dual‐wavelength ratios within 187.5‐m vertical overlapping sliding windows, and (3) averaging the multiple LWC estimates produced at each height. It is estimated that this technique reduced LWC retrieval uncertainty to 0.10–0.65 g/m3, depending on cloud thickness. Although individual retrievals remained noisy, statistics on subgroups of height‐normalized LWC profiles show that, on average, the vertical distributions of LWC in most of the observed clouds followed a linear relationship with a degree of adiabaticity ranging from 0.6 to 0.2 for 200‐ to 600‐m thick clouds. However, nonlinear LWC profiles were present in subgroups of cloud segments presenting intense (0.1–0.5 mm/hr) drizzle rates where LWC was observed to pool near cloud base and in subgroups of cloud segments within strong (0.6 m/s) downdrafts near cloud top where LWC was coincidently reduced. This nonlinearity is inconsistent with the use of adiabatic cloud assumptions for process studies and supports further development of retrievals like the one proposed.

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“…The latter provides the vertically integrated LWP (Jacob et al, 2019b), which helps to approach the liquid water 60 content which is a key quantity to describe clouds in models like the ICON. The direct observation of the liquid water content profile is difficult (Crewell et al, 2009), but the LWP can be used to estimate the water content when combined with estimates of cloud vertical extend by lidar and radar either in a simple average approach or more sophisticated as a profile (Frisch et al, 1998;Küchler et al, 2018). In addition, dropsondes were released regularly during the flights to probe the temperature and humidity profile.…”

mentioning

confidence: 99%

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

Jacob

Kollias

Ament

et al. 2020

Preprint

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Airborne remote sensing observations over the tropical Atlantic Ocean upstream of Barbados are used to characterize trade wind shallow cumulus clouds and to benchmark two cloud-resolving ICON (ICOsahedral Nonhydrostatic) model simulations at kilo-and hectometer scales. The clouds were observed by an airborne nadir pointing backscatter lidar, a cloud radar, and a microwave radiometer in the tropical dry winter season during daytime. For the model benchmark, forward operators convert the model data into the observational space for considering instrument specific cloud detection thresholds. The forward 5 simulations reveal the different detection limits of the lidar and radar observations, i.e., most clouds with cloud liquid water content greater than 10 −7 kg kg −1 are detectable by the lidar, whereas the radar is primarily sensitive to the "rain"-category hydrometeors in the models and can detect even low amounts of rain.The observations reveal two prominent modes of cumulus cloud top heights separating the clouds into two layers. The lower mode relates to boundary layer convection with tops closely above the lifted condensation level, which is at about 700 m above 10 sea level. The upper mode is driven by shallow moist convection, also contains shallow outflow anvils, and is closely related to the trade inversion at about 2.3 km above sea level. The two cumulus modes are reflected differently by the lidar and the radar observations and under different liquid water path (LWP) conditions. The storm-resolving model (SRM) at kilometer scale reproduces the cloud modes barely and shows the most cloud tops slightly above the observed lower mode. The large-eddy model (LEM) at hectometer scale reproduces better the observed cloudiness distribution with a clear bimodal separation. We 15 hypothesize that slight differences in the autoconversion parametrizations could have caused the different cloud development in the models. Neither model seems to account for in-cloud drizzle particles that do not precipitate down to the surface but generate a stronger radar signal even in scenes with low LWP. Our findings suggest that even if the SRM is a step forward for better cloud representation in climate research, the LEM can better reproduce the observed shallow cumulus convection and should therefore in principle represent cloud radiative effects and water cycle better. The representation of low-level oceanic clouds contributes largely to differences between climate models in terms of equilibrium climate sensitivity (Schneider et al., 2017). Global atmospheric models with kilometer-scale resolution are considered as the way forward in forecasting future climate scenarios (Bony and Dufresne, 2005; Satoh et al., 2019). The increased model 25 resolution and better matching scales with measurements allow for a more direct observational assessment by comparing the present day representation in the models with atmospheric measurements and thus anchoring models to reality. Recently, Stevens et al. (2020) demonstrated the general advantage...

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Revisiting Liquid Water Content Retrievals in Warm Stratified Clouds: The Modified Frisch

Cited by 14 publications

References 42 publications

PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere

PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere

The Vertical Structure of Liquid Water Content in Shallow Clouds as Retrieved From Dual‐Wavelength Radar Observations

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

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