scholarly journals Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations

2021 ◽  
Vol 21 (22) ◽  
pp. 17133-17166
Author(s):  
Markus Karrer ◽  
Axel Seifert ◽  
Davide Ori ◽  
Stefan Kneifel
Keyword(s):  
Size Distribution ◽  
3D Model ◽  
Doppler Radar ◽  
Terminal Velocity ◽  
Critical Parameters ◽  
Distribution Width ◽  
Radar Observations ◽  
Functional Relationships ◽  
Definition Of ◽  
The Impact

Abstract. Aggregation is a key microphysical process for the formation of precipitable ice particles. Its theoretical description involves many parameters and dependencies among different variables that are either insufficiently understood or difficult to accurately represent in bulk microphysics schemes. Previous studies have demonstrated the valuable information content of multi-frequency Doppler radar observations to characterize aggregation with respect to environmental parameters such as temperature. Comparisons with model simulations can reveal discrepancies, but the main challenge is to identify the most critical parameters in the aggregation parameterization, which can then be improved by using the observations as constraints. In this study, we systematically investigate the sensitivity of physical variables, such as number and mass density, as well as the forward-simulated multi-frequency and Doppler radar observables, to different parameters in a two-moment microphysics scheme. Our approach includes modifying key aggregation parameters such as the sticking efficiency or the shape of the size distribution. We also revise and test the impact of changing functional relationships (e.g., the terminal velocity–size relation) and underlying assumptions (e.g., the definition of the aggregation kernel). We test the sensitivity of the various components first in a single-column “snowshaft” model, which allows fast and efficient identification of the parameter combination optimally matching the observations. We find that particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while the sticking efficiency and the cloud ice habit have less influence. The setting which optimally matches the observations is then implemented in a 3D model using the identical scheme setup. Rerunning the 3D model with the new scheme setup for a multi-week period revealed that the large overestimation of aggregate size and terminal velocity in the model could be substantially reduced. The method presented is expected to be applicable to constrain other ice microphysical processes or to evaluate and improve other schemes.

scholarly journals Improving the Representation of Aggregation in a Two-moment Microphysical Scheme with Statistics of Multi-frequency Doppler Radar Observations

2021 ◽  
Author(s):  
Markus Karrer ◽  
Axel Seifert ◽  
Davide Ori ◽  
Stefan Kneifel
Keyword(s):  
Size Distribution ◽  
Doppler Radar ◽  
Critical Parameters ◽  
Microphysics Scheme ◽  
Distribution Width ◽  
Radar Observations ◽  
Particle Properties ◽  
Differential Sedimentation ◽  
Definition Of ◽  
Cloud Ice

Abstract. The simulation of aggregation of ice particles is critical for precipitation prediction, but still a major challenge. Its simulation requires assumptions about numerous parameters, many of which are either not well known or difficult to represent accurately in bulk microphysics schemes. However, knowing the sensitivity of aggregation to various simplified assumptions can help to identify critical parameters. By comparison with suitable observations, these critical parameters can even be constrained. We investigate the sensitivity of the model variables, and the modeled multi-frequency and Doppler radar observables to different parameters in a two-moment microphysics scheme. Therefore, we revise hydrometeor parameters by using a recently published dataset of particle properties, modify the formulations of the aggregation process (which allows using an area-based differential sedimentation kernel) and update other ice microphysical parameters in the scheme such as the sticking efficiency Estick and the shape of the size distribution. Overall, particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while Estick and the cloud ice habit have less influence. Finally, we run multi-week simulations with the most promising parameter combinations. The statistical comparison between real and synthetic observables shows a reduction in the velocity and snow particle size. With this study, we show a possible way to revise processes in microphysical schemes by using statistics of detailed cloud radar observations.

scholarly journals Validation of technological process of preparation hymoding cream with hyaluronic acid

2021 ◽  
pp. 27-35
Author(s):  
Т. Г. Ярних ◽  
Г. М. Мельник ◽  
О. А. Рухмакова
Keyword(s):  
Risk Assessment ◽  
Hyaluronic Acid ◽  
Technological Process ◽  
Critical Parameters ◽  
Documentary Evidence ◽  
Technological Parameters ◽  
Eligibility Criteria ◽  
Critical Control Points ◽  
Definition Of ◽  
The Impact

To date, in the mass production of medicines both in pharmaceutical companies and in pharmacies, more and more attention is paid to the practice of process validation. Validation is a key condition for the implementation of Good Manufacturing (GMP) and Pharmacy (GPP) practices, the standards of which are mandatory for medicines worldwide and in Ukraine. The aim of the work – validation of the technological process of preparation extemporaneous hymoding cream with hyaluronic acid in order to obtain documentary evidence of effective reproduction of the preparation of this medicine. The study design is based on research on the development of dosage form technology, analysis of the impact of critical manufacturing points and evaluation of their impact on the final quality of the cream, taking into account the requirements of GPP. The object of validation is the technological process of preparation hymoding cream with hyaluronic acid. The validation procedure was performed on 3 experimental batches of the medicine. In order to check and optimize the selected technological process of preparation hymoding cream in pharmacies, its validation was carried out. According to the calculated quantities of the components of the studied cream, as well as the identified critical parameters, three batches of the medicine of 100.00 g were developed. On the series of cream, the optimization of technological parameters, testing of critical parameters for all planned stages of production with the definition of eligibility criteria and validation scheme was carried out. Acceptance criteria for all critical parameters were determined during the optimization of technology on batches of the studied medicine. The general risk assessment was carried out at the stage of pharmaceutical development according to the procedure «identification – analysis – risk assessment» to determine the scheme of validation work on batches of the medicine. During the technological process, critical indicators were checked and appropriate forms were filled out. A study on the validation of the technological process of preparation hymoding cream with hyaluronic acid in pharmacies was performed. Documentary evidence of effective reproduction of the manufacture of this medicine has been obtained. A comprehensive analysis of the developed manufacturing process makes it possible to identify critical control points that minimize the occurrence of possible risks in the manufacture of the investigated medicine. According to the results of the research, it can be concluded that the developed cream with hyaluronic acid meets the requirements of State Pharmacopoeia of Ukraine in all quality indicators, which allows to recommend it for use in dermatology.

Clarifying remotely-retrieved precipitation of shallow marine clouds from the NSF/NCAR Gulfstream V

2021 ◽  
Author(s):  
Mampi Sarkar ◽  
Paquita Zuidema ◽  
Virendra Ghate
Keyword(s):  
Doppler Radar ◽  
Rain Attenuation ◽  
Doppler Velocity ◽  
Distribution Width ◽  
Raindrop Size Distribution ◽  
Remote Measurements ◽  
Raindrop Size ◽  
Rain Rates ◽  
The Impact

AbstractPrecipitation is a key process within the shallow cloud lifecycle. The Cloud System Evolution in the Trades (CSET) campaign included the first deployment of a 94 GHz Doppler radar and 532 nm lidar. Despite a larger sampling volume, initial mean radar/lidar retrieved rain rates (Schwartz et al. 2019) based on the upward-pointing remote sensor datasets are systematically less than those measured by in-situ precipitation probes in the cumulus regime. Subsequent retrieval improvements produce rainrates that compare better to in-situ values, but still underestimate. Retrieved shallow cumulus drop sizes can remain too small and too few, with an overestimated shape parameter narrowing the raindrop size distribution too much. Three potential causes for the discrepancy are explored: the gamma functional fit to the dropsize distribution, attenuation by rain and cloud water, and an underaccounting of Mie dampening of the reflectivity. A truncated exponential fit may represent the dropsizes below a showering cumulus cloud more realistically, although further work would be needed to fully evaluate the impact of a different dropsize representation upon the retrieval. The rain attenuation is within the measurement uncertainty of the radar. Mie dampening of the reflectivity is shown to be significant, in contrast to previous stratocumulus campaigns with lighter rain rates, and may be difficult to constrain well with the remote measurements. An alternative approach combines an a priori determination of the dropsize distribution width based on the in-situ data with the mean radar Doppler velocity and reflectivity. This can produce realistic retrievals, although a more comprehensive assessment is needed to better characterize the retrieval errors.

scholarly journals Assimilation of Synthetic GOES-R ABI Infrared Brightness Temperatures and WSR-88D Radar Observations in a High-Resolution OSSE

2016 ◽  
Vol 144 (9) ◽  
pp. 3159-3180 ◽  
Author(s):  
Rebecca M. Cintineo ◽  
Jason A. Otkin ◽  
Thomas A. Jones ◽  
Steven Koch ◽  
David J. Stensrud
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Forecast Accuracy ◽  
Central Plains ◽  
Radar Observations ◽  
Brightness Temperatures ◽  
Observing System Simulation Experiment ◽  
The Impact

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95-μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4–5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated.

scholarly journals An algorithm for topology optimization of gear reducer housing elements

2019 ◽  
Vol 287 ◽  
pp. 01020
Author(s):  
Stoyan D. Slavov ◽  
Mariya Iv. Konsulova-Bakalova
Keyword(s):  
Topology Optimization ◽  
3D Model ◽  
Optimization Methods ◽  
Design Stage ◽  
Control Parameters ◽  
Optimization Software ◽  
Optimization Control ◽  
Different Types ◽  
Definition Of ◽  
The Impact

In recent years, topology optimization methods are becoming more widely used in many engineering fields, and they are already being successfully integrated at the design stage of the different types of products. An active field of research in this area is the definition of appropriate constraints in topology optimization models in order to facilitate the production of the optimized objects. An algorithm for topology optimization of housing elements from gear reducers by using the capabilities of CAD-CAE topology optimization software is presented in the current work. The purposed algorithm is taking into account the resulting loads during operation of the reducer, the geometrical and manufacturing constraints of the production process of these housing elements. Obtained results from conducted Taguchi experimental study to investigate the impact of some topology optimization control parameters over optimized 3D-model also are shown and discussed. Conclusions on the applicability of the algorithm have been made.

Impacts of 4DVAR Assimilation of Airborne Doppler Radar Observations on Numerical Simulations of the Genesis of Typhoon Nuri (2008)

2014 ◽  
Vol 53 (10) ◽  
pp. 2325-2343 ◽  
Author(s):  
Zhan Li ◽  
Zhaoxia Pu ◽  
Juanzhen Sun ◽  
Wen-Chau Lee
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Numerical Simulations ◽  
Doppler Radar ◽  
Initial Conditions ◽  
Radar Data ◽  
Radar Observations ◽  
Wind Analysis ◽  
The Impact ◽  
Airborne Doppler Radar

AbstractThe Weather Research and Forecasting Model and its four-dimensional variational data assimilation (4DVAR) system are employed to examine the impact of airborne Doppler radar observations on predicting the genesis of Typhoon Nuri (2008). Electra Doppler Radar (ELDORA) airborne radar data, collected during the Office of Naval Research–sponsored Tropical Cyclone Structure 2008 field experiment, are used for data assimilation experiments. Two assimilation methods are evaluated and compared, namely, the direct assimilation of radar-measured radial velocity and the assimilation of three-dimensional wind analysis derived from the radar radial velocity. Results show that direct assimilation of radar radial velocity leads to better intensity forecasts, as this process enhances the development of convective systems and improves the inner-core structure of Nuri, whereas assimilation of the radar-retrieved wind analysis is more beneficial for tracking forecasts, as it results in improved environmental flows. The assimilation of both the radar-retrieved wind and the radial velocity can lead to better forecasts in both intensity and tracking, if the radial velocity observations are assimilated first and the retrieved winds are then assimilated in the same data assimilation window. In addition, experiments with and without radar data assimilation led to developing and nondeveloping disturbances in numerical simulations of Nuri’s genesis. The improved initial conditions and forecasts from the data assimilation imply that the enhanced midlevel vortex and moisture conditions are favorable for the development of deep convection in the center of the pouch and eventually contribute to Nuri’s genesis. The improved simulations of the convection and associated environmental conditions produce enhanced upper-level warming in the core region and lead to the drop in sea level pressure.

scholarly journals Assimilation of Satellite Infrared Radiances and Doppler Radar Observations during a Cool Season Observing System Simulation Experiment

2013 ◽  
Vol 141 (10) ◽  
pp. 3273-3299 ◽  
Author(s):  
Thomas A. Jones ◽  
Jason A. Otkin ◽  
David J. Stensrud ◽  
Kent Knopfmeier
Keyword(s):  
Radial Velocity ◽  
Simulation Experiment ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
System Simulation ◽  
Radar Data ◽  
Cool Season ◽  
Radar Observations ◽  
Observing System Simulation Experiment ◽  
The Impact

Abstract An observing system simulation experiment is used to examine the impact of assimilating water vapor–sensitive satellite infrared brightness temperatures and Doppler radar reflectivity and radial velocity observations on the analysis accuracy of a cool season extratropical cyclone. Assimilation experiments are performed for four different combinations of satellite, radar, and conventional observations using an ensemble Kalman filter assimilation system. Comparison with the high-resolution “truth” simulation indicates that the joint assimilation of satellite and radar observations reduces errors in cloud properties compared to the case in which only conventional observations are assimilated. The satellite observations provide the most impact in the mid- to upper troposphere, whereas the radar data also improve the cloud analysis near the surface and aloft as a result of their greater vertical resolution and larger overall sample size. Errors in the wind field are also significantly reduced when radar radial velocity observations were assimilated. Overall, assimilating both satellite and radar data creates the most accurate model analysis, which indicates that both observation types provide independent and complimentary information and illustrates the potential for these datasets for improving mesoscale model analyses and ensuing forecasts.

scholarly journals Towards the connection between snow microphysics and melting layer: Insights from multi-frequency and dual-polarization radar observations during BAECC

2020 ◽  
Author(s):  
Haoran Li ◽  
Jussi Tiira ◽  
Annakaisa von Lerber ◽  
Dmitri Moisseev
Keyword(s):  
Doppler Radar ◽  
Strong Dependence ◽  
Precipitation Intensity ◽  
Dual Polarization ◽  
Radar Observations ◽  
Melting Layer ◽  
Microphysical Properties ◽  
Microphysical Processes ◽  
Biogenic Aerosols ◽  
The Impact

Abstract. In stratiform rainfall, the melting layer is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the melting layer and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured melting layer properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide an additional constraint on the retrieval of ice cloud microphysical properties, we have investigated this link. This study is divided into two parts. Firstly, surface-based snowfall measurements are used to devise a method for classifying rimed and unrimed snow from X- and Ka-band Doppler radar observations. In the second part, this classification is used in combination with multi-frequency and dual-polarization radar observations to investigate the impact of precipitation intensity, aggregation, riming, and dendritic growth on melting layer properties. The radar-observed melting layer characteristics show strong dependence on precipitation intensity as well as detectable differences between unrimed and rimed snow. This study is based on the data collected during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) experiment, that took place in 2014 in Hyytiala, Finland.

scholarly journals Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo (1997) Using the Ensemble Kalman Filter

2012 ◽  
Vol 69 (11) ◽  
pp. 3147-3171 ◽  
Author(s):  
Humberto C. Godinez ◽  
Jon M. Reisner ◽  
Alexandre O. Fierro ◽  
Stephen R. Guimond ◽  
Jim Kao
Keyword(s):  
Kalman Filter ◽  
Latent Heat ◽  
Ensemble Kalman Filter ◽  
Wind Shear ◽  
Doppler Radar ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Radar Observations ◽  
The Impact ◽  
Dual Doppler Radar

Abstract In this work the authors determine key model parameters for rapidly intensifying Hurricane Guillermo (1997) using the ensemble Kalman filter (EnKF). The approach is to utilize the EnKF as a tool only to estimate the parameter values of the model for a particular dataset. The assimilation is performed using dual-Doppler radar observations obtained during the period of rapid intensification of Hurricane Guillermo. A unique aspect of Guillermo was that during the period of radar observations strong convective bursts, attributable to wind shear, formed primarily within the eastern semicircle of the eyewall. To reproduce this observed structure within a hurricane model, background wind shear of some magnitude must be specified and turbulence and surface parameters appropriately specified so that the impact of the shear on the simulated hurricane vortex can be realized. To identify the complex nonlinear interactions induced by changes in these parameters, an ensemble of model simulations have been conducted in which individual members were formulated by sampling the parameters within a certain range via a Latin hypercube approach. The ensemble and the data, derived latent heat and horizontal winds from the dual-Doppler radar observations, are utilized in the EnKF to obtain varying estimates of the model parameters. The parameters are estimated at each time instance, and a final parameter value is obtained by computing the average over time. Individual simulations were conducted using the estimates, with the simulation using latent heat parameter estimates producing the lowest overall model forecast error.

scholarly journals Towards the connection between snow microphysics and melting layer: insights from multifrequency and dual-polarization radar observations during BAECC

2020 ◽  
Vol 20 (15) ◽  
pp. 9547-9562 ◽  
Author(s):  
Haoran Li ◽  
Jussi Tiira ◽  
Annakaisa von Lerber ◽  
Dmitri Moisseev
Keyword(s):  
Doppler Radar ◽  
Precipitation Intensity ◽  
Dual Polarization ◽  
Radar Observations ◽  
Melting Layer ◽  
Bright Band ◽  
Radar Signatures ◽  
Microphysical Processes ◽  
The Impact ◽  
Precipitation Formation

Abstract. In stratiform rainfall, the melting layer (ML) is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the ML and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured ML properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide new insights into radar signatures of snow growth processes, we have investigated this link. This study is divided into two parts. Firstly, surface-based snowfall measurements are used to develop a new method for identifying rimed and unrimed snow from X- and Ka-band Doppler radar observations. Secondly, this classification is used in combination with multifrequency and dual-polarization radar observations collected during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) experiment in 2014 to investigate the impact of precipitation intensity, aggregation, riming and dendritic growth on the ML properties. The results show that the radar-observed ML properties are highly related to the precipitation intensity. The previously reported bright band “sagging” is mainly connected to the increase in precipitation intensity. Ice particle riming plays a secondary role. In moderate to heavy rainfall, riming may cause additional bright band sagging, while in light precipitation the sagging is associated with unrimed snow. The correlation between ML properties and dual-polarization radar signatures in the snow region above appears to be arising through the connection of the radar signatures and ML properties to the precipitation intensity. In addition to advancing our knowledge of the link between ML properties and snow processes, the presented analysis demonstrates how multifrequency Doppler radar observations can be used to get a more detailed view of cloud processes and establish a link to precipitation formation.

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