scholarly journals Potential of Higher-Order Moments and Slopes of the Radar Doppler Spectrum for Retrieving Microphysical and Kinematic Properties of Arctic Ice Clouds

2017 ◽  
Vol 56 (2) ◽  
pp. 263-282 ◽  
Author(s):  
Maximilian Maahn ◽  
Ulrich Löhnert
Keyword(s):  
Higher Order ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
State Variables ◽  
Higher Order Moments ◽  
Radar Observations ◽  
Additional Information ◽  
Cloud Radar ◽  
Mass Size ◽  
Size Relation

AbstractRetrievals of ice-cloud properties from cloud-radar observations are challenging because the retrieval methods are typically underdetermined. Here, the authors investigate whether additional information can be obtained from higher-order moments and the slopes of the radar Doppler spectrum such as skewness and kurtosis as well as the slopes of the Doppler peak. To estimate quantitatively the additional information content, a generalized Bayesian retrieval framework that is based on optimal estimation is developed. Real and synthetic cloud-radar observations of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) dataset obtained around Barrow, Alaska, are used in this study. The state vector consists of the microphysical (particle-size distribution, mass–size relation, and cross section–area relation) and kinematic (vertical wind and turbulence) quantities required to forward model the moments and slopes of the radar Doppler spectrum. It is found that, for a single radar frequency, more information can be retrieved when including higher-order moments and slopes than when using only reflectivity and mean Doppler velocity but two radar frequencies. When using all moments and slopes with two or even three frequencies, the uncertainties of all state variables, including the mass–size relation, can be considerably reduced with respect to the prior knowledge.

scholarly journals Developing and Evaluating Ice Cloud Parameterizations for Forward Modeling of Radar Moments Using in situ Aircraft Observations

2015 ◽  
Vol 32 (5) ◽  
pp. 880-903 ◽  
Author(s):  
Maximilian Maahn ◽  
Ulrich Löhnert ◽  
Pavlos Kollias ◽  
Robert C. Jackson ◽  
Greg M. McFarquhar
Keyword(s):  
Size Distribution ◽  
Power Law ◽  
Particle Mass ◽  
Maximum Diameter ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Radar Observations ◽  
Size Relation ◽  
Particle Area

AbstractObserving ice clouds using zenith pointing millimeter cloud radars is challenging because the transfer functions relating the observables to meteorological quantities are not uniquely defined. Here, the authors use a spectral radar simulator to develop a consistent dataset containing particle mass, area, and size distribution as functions of size. This is an essential prerequisite for radar sensitivity studies and retrieval development. The data are obtained from aircraft in situ and ground-based radar observations during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) campaign in Alaska. The two main results of this study are as follows: 1) An improved method to estimate the particle mass–size relation as a function of temperature is developed and successfully evaluated by combining aircraft in situ and radar observations. The method relies on a functional relation between reflectivity and Doppler velocity. 2) The impact on the Doppler spectrum by replacing measurements of particle area and size distribution by recent analytical expressions is investigated. For this, higher-order moments such as skewness and kurtosis as well as the slopes of the Doppler spectrum are also used as a proxy for the Doppler spectrum. For the area–size relation, it is found that a power law is not sufficient to describe particle area and small deviations from a power law are essential for obtaining consistent higher moments. For particle size distributions, the normalization approach for the gamma distribution of Testud et al., adapted to maximum diameter as size descriptor, is preferred.

Advantages and limitations on combining radar dual - wavelength and polarimetric observations for ice microphysics retrievals

2021 ◽  
Author(s):  
Eleni Tetoni ◽  
Florian Ewald ◽  
Gregor Möller ◽  
Martin Hagen ◽  
Tobias Zinner ◽  
...  
Keyword(s):  
Dual Wavelength ◽  
Shape Estimation ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Aspect Ratios ◽  
Retrieval Scheme ◽  
Mass Size ◽  
Ice Microphysics ◽  
German Aerospace ◽  
Size Relation

<p>The challenge of the ice microphysical processes representation in numerical weather models is a well-known phenomenon as it can lead to high uncertainty due to the variety of ice microphysics. As ice microphysical properties can strongly affect the initiation of precipitation as well as the type and amount of it, we need to better understand the complexity of ice processes. To accomplish this, better microphysics information through ice retrievals from measurements is needed. The multi-wavelength radar method is nowadays becoming more and more popular in such microphysics retrievals. Taking advantage of different scattering regimes (Rayleigh or Mie), information about the size of atmospheric hydrometeors can be inferred using different radar bands. For this study, dual-wavelength reflectivity ratio measurements were combined with polarimetric measurements to estimate the size of ice hydrometeors. The measurements were obtained by using the synergy of the C-band POLDIRAD weather radar from the German Aerospace Center, located in Oberpfaffenhofen, and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilian University of Munich. Along with the dual-wavelength dataset, the Differential Reflectivity (Z<sub>DR</sub>) from POLDIRAD was used as a polarimetric contribution for the shape estimation of the detected ice particles. The radar observations were compared with T-matrix scattering simulations for the development of a retrieval scheme of ice microphysics. In the course of these studies, different assumptions were considered in the simulations. To capture the size variability, a Gamma particle size distribution (PSD) with different values of median volume diameter (MVD) was used. The soft spheroid approximation was used to approximate the ice particle shapes and to simplify the calculation and variation of their aspect ratios and effective densities. The selection of the most representative mass-size relation was the most crucial for the scattering simulations. In this study, we explored the modified Brown and Francis as well as the aggregates mass-size relation. After comparing the simulations to radar observations, we selected the better fitting one, i.e. aggregates, excluding the Brown and Francis as the simulated particles appeared to be too fluffy. Using the aggregates formulas, Look-Up tables (LUTs) for MVD, aspect ratio, and IWC were developed and used in the ice microphysics retrieval scheme. Here, we present preliminary microphysics retrievals of the median size, shape, and IWC of the detected hydrometeors combining the simulations in LUTs with the radar observations from different precipitation events over the Munich area.</p>

scholarly journals Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?

2019 ◽  
Vol 12 (6) ◽  
pp. 3151-3171 ◽  
Author(s):  
Maximilian Maahn ◽  
Fabian Hoffmann ◽  
Matthew D. Shupe ◽  
Gijs de Boer ◽  
Sergey Y. Matrosov ◽  
...  
Keyword(s):  
Radar Data ◽  
Reference Points ◽  
Radar Reflectivity ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Liquid Water Path ◽  
Cloud Radar ◽  
Microphysical Processes ◽  
Radar Calibration ◽  
Sudden Decrease

Abstract. Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum (γ), the radar mean Doppler velocity (W), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W, we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz Ka-band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1 dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

Association of Tropical Cirrus in the 10–15-km Layer with Deep Convective Sources: An Observational Study Combining Millimeter Radar Data and Satellite-Derived Trajectories

2006 ◽  
Vol 63 (2) ◽  
pp. 480-503 ◽  
Author(s):  
Gerald G. Mace ◽  
Min Deng ◽  
Brian Soden ◽  
Ed Zipser
Keyword(s):  
Deep Convection ◽  
Source Region ◽  
Winter Monsoon ◽  
Boreal Summer ◽  
Doppler Velocity ◽  
Boreal Winter ◽  
Radar Observations ◽  
Back Trajectories ◽  
Cloud Radar ◽  
Cloud Properties

Abstract In this paper, millimeter cloud radar (MMCR) and Geosynchronous Meteorological Satellite (GMS) data are combined to study the properties of tropical cirrus that are common in the 10–15-km layer of the tropical troposphere in the western Pacific. Millimeter cloud radar observations collected by the Atmospheric Radiation Measurement program on the islands of Manus and Nauru in the western and central equatorial Pacific during a 12-month period spanning 1999 and 2000 show differences in cirrus properties: over Manus, where clouds above 7 km are observed 48% of the time, the cirrus are thicker and warmer on average and the radar reflectivity and Doppler velocity are larger; over Nauru clouds above 7 km are observed 23% of time. To explain the differences in cloud properties, the relationship between tropical cirrus and deep convection is examined by combining the radar observations with GMS satellite-derived back trajectories. Using a data record of 1 yr, it is found that 47% of the cirrus observed over Manus can be traced to a deep convective source within the past 12 h while just 16% of the cirrus observed over Nauru appear to have a convective source within the previous 12 h. Of the cirrus that can be traced to deep convection, the evolution of the radar-observed cloud properties is examined as a function of apparent cloud age. The radar Doppler moments and ice water path of the observed cirrus at both sites generally decrease as the cirrus age increase. At Manus, it is found that cirrus during boreal winter typically advect over the site from the southeast from convection associated with the winter monsoon, while during boreal summer, the trajectories are mainly from the northeast. The properties of these two populations of cirrus are found to be different, with the winter cirrus having higher concentrations of smaller particles. Examining statistics of the regional convection using Tropical Rainfall Measuring Mission (TRMM), it is found that the properties of the winter monsoon convection in the cirrus source region are consistent with more intense convection compared to the convection in the summer source region.

scholarly journals Can liquid cloud microphysical processes be used for vertically-pointing cloud radar calibration?

2019 ◽  
Author(s):  
Maximilian Maahn ◽  
Fabian Hoffmann ◽  
Matthew D. Shupe ◽  
Gijs de Boer ◽  
Sergey Y. Matrosov ◽  
...  
Keyword(s):  
Radar Data ◽  
Reference Points ◽  
Radar Reflectivity ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Liquid Water Path ◽  
Cloud Radar ◽  
Microphysical Processes ◽  
Radar Calibration ◽  
Sudden Decrease

Abstract. Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single, robust method exists for assessing calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes of liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum (γ), the radar mean Doppler velocity (W), and the liquid water path (LWP). We identify reference points of these relationships and evaluate their potential for radar calibration. For γ and W, we use box model simulations to determine typical radar reflectivity values for these reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz Ka-band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that the methods are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that in 2016, the radar reflectivity at NSA was about 1 ± 1 dB too low, but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

scholarly journals Using artificial neural networks to predict riming from Doppler cloud radar observations

2021 ◽  
Author(s):  
Teresa Vogl ◽  
Maximilian Maahn ◽  
Stefan Kneifel ◽  
Willi Schimmel ◽  
Dmitri Moisseev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Doppler Velocity ◽  
Data Sets ◽  
Radar Observations ◽  
Cloud Radar ◽  
Target Values ◽  
In Situ Observations ◽  
Artificial Neural

Abstract. Riming, i.e. the accretion and freezing of SLW on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest, however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-band cloud radar and in-situ observations of snowfall. We focus on two configurations of input variables: ANN #1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width; SEW), and the skewness as input features. ANN #2 only uses Ze, SEW and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (riming index = 1) and yield low values (riming index ≤ 0.4) for unrimed snow. In general, the predictions of ANN #1 and ANN #2 are very similar, advocating the capability to predict riming without the use of MDV. It is demonstrated that both ANN setups are able to generalize to W-band radar data. The predictions of both ANNs for a wintertime convective cloud fit coinciding in-situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN #2 to an orographic case yields high riming index values coinciding with observations of solid graupel particles at the ground.

scholarly journals Long-Term Statistics of Riming in Nonconvective Clouds Derived from Ground-Based Doppler Cloud Radar Observations

2020 ◽  
Vol 77 (10) ◽  
pp. 3495-3508 ◽  
Author(s):  
Stefan Kneifel ◽  
Dmitri Moisseev
Keyword(s):  
Temperature Dependence ◽  
Supercooled Liquid ◽  
Terminal Velocity ◽  
Climatic Conditions ◽  
Doppler Velocity ◽  
Radar Observations ◽  
Cloud Radar ◽  
Similar Temperature Dependence ◽  
Different Seasons ◽  
Strong Relation

AbstractRiming is an efficient process of converting liquid cloud water into ice and plays an important role in the formation of precipitation in cold clouds. Due to the rapid increase in ice particle mass, riming enhances the particle’s terminal velocity, which can be detected by ground-based vertically pointing cloud radars if the effect of vertical air motions can be sufficiently mitigated. In our study, we first revisit a previously published approach to relate the radar mean Doppler velocity (MDV) to rime mass fraction (FR) using a large ground-based in situ dataset. This relation is then applied to multiyear datasets of cloud radar observations collected at four European sites covering polluted central European, clean maritime, and Arctic climatic conditions. We find that riming occurs in 1%–8% of the nonconvective ice containing clouds with median FR between 0.5 and 0.6. Both the frequency of riming and FR reveal relatively small variations for different seasons. In contrast to previous studies, which suggested enhanced riming for clean environments, our statistics indicate the opposite; however, the differences between the locations are overall small. We find a very strong relation between the frequency of riming and temperature. While riming is rare at temperatures lower than −12°C, it strongly increases toward 0°C. Previous studies found a very similar temperature dependence for the amount and droplet size of supercooled liquid water, which might be closely connected to the riming signature found in this study. In contrast to riming frequency, we find almost no temperature dependence for FR.

scholarly journals Proceed with Caution

2021 ◽  
pp. 1-20
Author(s):  
Annette Zimmermann ◽  
Chad Lee-Stronach
Keyword(s):  
Procedural Justice ◽  
Higher Order ◽  
Public Institutions ◽  
Decision Makers ◽  
Necessary Condition ◽  
Structural Injustice ◽  
Additional Information ◽  
Private And Public ◽  
Similarity Judgments

Abstract It is becoming more common that the decision-makers in private and public institutions are predictive algorithmic systems, not humans. This article argues that relying on algorithmic systems is procedurally unjust in contexts involving background conditions of structural injustice. Under such nonideal conditions, algorithmic systems, if left to their own devices, cannot meet a necessary condition of procedural justice, because they fail to provide a sufficiently nuanced model of which cases count as relevantly similar. Resolving this problem requires deliberative capacities uniquely available to human agents. After exploring the limitations of existing formal algorithmic fairness strategies, the article argues that procedural justice requires that human agents relying wholly or in part on algorithmic systems proceed with caution: by avoiding doxastic negligence about algorithmic outputs, by exercising deliberative capacities when making similarity judgments, and by suspending belief and gathering additional information in light of higher-order uncertainty.

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