scholarly journals Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model

2020 ◽  
Author(s):  
Robert S. Schrom ◽  
Marcus van Lier-Walqui ◽  
Matthew R. Kumjian ◽  
Jerry Y. Harrington ◽  
Anders A. Jensen ◽  
...  
Keyword(s):  
Vapor Deposition ◽  
Growth Parameters ◽  
Particle Density ◽  
Optimal Parameter ◽  
Number Concentration ◽  
Department Of Energy ◽  
The Arctic ◽  
Model Parameters ◽  
Growth Ratio ◽  
Radar Measurements

AbstractThe potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model-observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

scholarly journals Atmospheric Ice Particle Shape Estimates from Polarimetric Radar Measurements and In Situ Observations

2017 ◽  
Vol 34 (12) ◽  
pp. 2569-2587 ◽  
Author(s):  
Sergey Y. Matrosov ◽  
Carl G. Schmitt ◽  
Maximilian Maahn ◽  
Gijs de Boer
Keyword(s):  
Aspect Ratio ◽  
Particle Shape ◽  
Characteristic Size ◽  
In Situ Measurements ◽  
Department Of Energy ◽  
Depolarization Ratio ◽  
Suggested Approach ◽  
Aspect Ratios ◽  
Radar Measurements

AbstractA remote sensing approach to retrieve the degree of nonsphericity of ice hydrometeors using scanning polarimetric Ka-band radar measurements from a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program cloud radar operated in an alternate transmission–simultaneous reception mode is introduced. Nonsphericity is characterized by aspect ratios representing the ratios of particle minor-to-major dimensions. The approach is based on the use of a circular depolarization ratio (CDR) proxy reconstructed from differential reflectivity ZDR and copolar correlation coefficient ρhυ linear polarization measurements. Essentially combining information contained in ZDR and ρhυ, CDR-based retrievals of aspect ratios are fairly insensitive to hydrometeor orientation if measurements are performed at elevation angles of around 40°–50°. The suggested approach is applied to data collected using the third ARM Mobile Facility (AMF3), deployed to Oliktok Point, Alaska. Aspect ratio retrievals were also performed using ZDR measurements that are more strongly (compared to CDR) influenced by hydrometeor orientation. The results of radar-based retrievals are compared with in situ measurements from the tethered balloon system (TBS)-based video ice particle sampler and the ground-based multiangle snowflake camera. The observed ice hydrometeors were predominantly irregular-shaped ice crystals and aggregates, with aspect ratios varying between approximately 0.3 and 0.8. The retrievals assume that particle bulk density influencing (besides the particle shape) observed polarimetric variables can be deduced from the estimates of particle characteristic size. Uncertainties of CDR-based aspect ratio retrievals are estimated at about 0.1–0.15. Given these uncertainties, radar-based retrievals generally agreed with in situ measurements. The advantages of using the CDR proxy compared to the linear depolarization ratio are discussed.

scholarly journals Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations

2012 ◽  
Vol 12 (4) ◽  
pp. 1785-1810 ◽  
Author(s):  
Y. Qian ◽  
C. N. Long ◽  
H. Wang ◽  
J. M. Comstock ◽  
S. A. McFarlane ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Probability Distribution ◽  
Vertical Structure ◽  
Cloud Amount ◽  
Cloud Fraction ◽  
Department Of Energy ◽  
The Arctic ◽  
Radiative Fluxes ◽  
The Tropics ◽  
Ipcc Ar4

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM long-term ground-based measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Comparisons are performed for three climate regimes as represented by the Department of Energy Atmospheric Radiation Measurement (ARM) sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). Our intercomparisons of three independent measurements of CF or sky-cover reveal that the relative differences are usually less than 10% (5%) for multi-year monthly (annual) mean values, while daily differences are quite significant. The total sky imager (TSI) produces smaller total cloud fraction (TCF) compared to a radar/lidar dataset for highly cloudy days (CF > 0.8), but produces a larger TCF value than the radar/lidar for less cloudy conditions (CF < 0.3). The compensating errors in lower and higher CF days result in small biases of TCF between the vertically pointing radar/lidar dataset and the hemispheric TSI measurements as multi-year data is averaged. The unique radar/lidar CF measurements enable us to evaluate seasonal variation of cloud vertical structures in the GCMs. Both inter-model deviation and model bias against observation are investigated in this study. Another unique aspect of this study is that we use simultaneous measurements of CF and surface radiative fluxes to diagnose potential discrepancies among the GCMs in representing other cloud optical properties than TCF. The results show that the model-observation and inter-model deviations have similar magnitudes for the TCF and the normalized cloud effect, and these deviations are larger than those in surface downward solar radiation and cloud transmissivity. This implies that other dimensions of cloud in addition to cloud amount, such as cloud optical thickness and/or cloud height, have a similar magnitude of disparity as TCF within the GCMs, and suggests that the better agreement among GCMs in solar radiative fluxes could be a result of compensating effects from errors in cloud vertical structure, overlap assumption, cloud optical depth and/or cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation. The GCM performance in simulating the probability distribution, transmissivity and vertical profiles of cloud are comprehensively evaluated over the three ARM sites. The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF at SGP and cloud transmissivity is less susceptible to the change of TCF than observed. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high-level CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating discrepancies in the representation of ice cloud associated with convection in the models. While the GCMs generally capture the maximum CF in the boundary layer and vertical variability, the inter-model deviation is largest near the surface over the Arctic.

scholarly journals A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part II: Single-Column Modeling of Arctic Clouds

2005 ◽  
Vol 62 (6) ◽  
pp. 1678-1693 ◽  
Author(s):  
H. Morrison ◽  
J. A. Curry ◽  
M. D. Shupe ◽  
P. Zuidema
Keyword(s):  
Climate Models ◽  
Heat Budget ◽  
Number Concentration ◽  
The Arctic ◽  
Liquid Water Path ◽  
Microphysics Scheme ◽  
Radiative Fluxes ◽  
Arctic Clouds ◽  
Single Column ◽  
Double Moment

Abstract The new double-moment microphysics scheme described in Part I of this paper is implemented into a single-column model to simulate clouds and radiation observed during the period 1 April–15 May 1998 of the Surface Heat Budget of the Arctic (SHEBA) and First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) field projects. Mean predicted cloud boundaries and total cloud fraction compare reasonably well with observations. Cloud phase partitioning, which is crucial in determining the surface radiative fluxes, is fairly similar to ground-based retrievals. However, the fraction of time that liquid is present in the column is somewhat underpredicted, leading to small biases in the downwelling shortwave and longwave radiative fluxes at the surface. Results using the new scheme are compared to parallel simulations using other microphysics parameterizations of varying complexity. The predicted liquid water path and cloud phase is significantly improved using the new scheme relative to a single-moment parameterization predicting only the mixing ratio of the water species. Results indicate that a realistic treatment of cloud ice number concentration (prognosing rather than diagnosing) is needed to simulate arctic clouds. Sensitivity tests are also performed by varying the aerosol size, solubility, and number concentration to explore potential cloud–aerosol–radiation interactions in arctic stratus.

scholarly journals A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 126 ◽  
Author(s):  
Lina Aboulmouna ◽  
Shakti Gupta ◽  
Mano Maurya ◽  
Frank DeVilbiss ◽  
Shankar Subramaniam ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Metabolic Regulation ◽  
Metabolic Flux ◽  
Optimal Parameter ◽  
Gene Knockout ◽  
Model Parameters ◽  
Mouse Bone Marrow ◽  
Optimization Approach ◽  
Substrate Uptake ◽  
Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

scholarly journals Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

2017 ◽  
Vol 14 (12) ◽  
pp. 3129-3155 ◽  
Author(s):  
Hakase Hayashida ◽  
Nadja Steiner ◽  
Adam Monahan ◽  
Virginie Galindo ◽  
Martine Lizotte ◽  
...  
Keyword(s):  
Sea Ice ◽  
Water Column ◽  
Dimethyl Sulfide ◽  
Phytoplankton Bloom ◽  
Model Simulation ◽  
Field Measurements ◽  
Sulfur Cycle ◽  
The Arctic ◽  
Model Parameters ◽  
Ocean Ecosystem

Abstract. Sea ice represents an additional oceanic source of the climatically active gas dimethyl sulfide (DMS) for the Arctic atmosphere. To what extent this source contributes to the dynamics of summertime Arctic clouds is, however, not known due to scarcity of field measurements. In this study, we developed a coupled sea ice–ocean ecosystem–sulfur cycle model to investigate the potential impact of bottom-ice DMS and its precursor dimethylsulfoniopropionate (DMSP) on the oceanic production and emissions of DMS in the Arctic. The results of the 1-D model simulation were compared with field data collected during May and June of 2010 in Resolute Passage. Our results reproduced the accumulation of DMS and DMSP in the bottom ice during the development of an ice algal bloom. The release of these sulfur species took place predominantly during the earlier phase of the melt period, resulting in an increase of DMS and DMSP in the underlying water column prior to the onset of an under-ice phytoplankton bloom. Production and removal rates of processes considered in the model are analyzed to identify the processes dominating the budgets of DMS and DMSP both in the bottom ice and the underlying water column. When openings in the ice were taken into account, the simulated sea–air DMS flux during the melt period was dominated by episodic spikes of up to 8.1 µmol m−2 d−1. Further model simulations were conducted to assess the effects of the incorporation of sea-ice biogeochemistry on DMS production and emissions, as well as the sensitivity of our results to changes of uncertain model parameters of the sea-ice sulfur cycle. The results highlight the importance of taking into account both the sea-ice sulfur cycle and ecosystem in the flux estimates of oceanic DMS near the ice margins and identify key uncertainties in processes and rates that should be better constrained by new observations.

scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 5 (3) ◽  
pp. 1641-1675 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on the half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

Understanding the role of different geographical features in the hydrological response of humid mountainous areas through a stepwise clustering scheme

2021 ◽  
Author(s):  
Yaqian Yang ◽  
Jintao Liu
Keyword(s):  
Bulk Density ◽  
Optimal Parameter ◽  
Soil Bulk Density ◽  
Model Parameters ◽  
Hydrological Response ◽  
Runoff Generation ◽  
Explanatory Variables ◽  
Hydrological Responses ◽  
Rainfall Patterns

&lt;p&gt;In the mountainous basins with less anthropogenic influence, the hydrological function is mainly affected by climate and landscape, which makes it possible to measure hydrological similarity indirectly by geographical features. Due to the mechanisms of runoff generation can vary geographically, in this study, a simple stepwise clustering scheme was proposed to explore the role of geographical features at different spatial hierarchy in indicating hydrological response. Research methods mainly include (1) Stepwise regression was used to quantitatively show the correlation between 35 geographical features and 35 flow features and identify the important explanatory variables for hydrological response; (2) 64 basins were divided by stepwise clustering scheme, and the overall ability of the scheme to capture hydrological similarity was tested by comparing the optimal parameters; (3) The hydrological similarity of basin groups was measured by the leave-one cross validation of hydrological model parameters. The results showed that: (1) Rainfall features, elevation, slope and soil bulk density are the main explanatory variables. (2) The NSE of basin groups based on stepwise clustering is 0.64, reaches 80% of the optimal parameter sets (NSE=0.80). The NSE of 90% basins is greater than 0.5, 80% is greater than 0.6, and 49% is greater than 0.7. (3) In humid areas, the hydrological responses of the basins with more uniform monthly rainfall and more abundant summer rainfall are more similar, e.g., the NSE of Class 4 is 0.77. Under similar rainfall patterns, the hydrological responses of the basins with higher average altitude, greater slope, more convergent of shape and richer vegetation are more similar, e.g., the NSE of Class 3-2 is 0.72 and that of Class 1-2 is 0.70. In the case of similar rainfall patterns and landforms, the hydrological responses of the basins with smaller soil bulk density are more similar, e.g., the NSE of Class 3-2-2 is 0.80. In conclusion, the stepwise clustering enhances the interpretability of basin classification, and the effect of different geographical features on hydrological response can show the applicability of hydrological simulation in ungauged basins.&lt;/p&gt;

scholarly journals Growth of U-Shaped Graphene Domains on Copper Foil by Chemical Vapor Deposition

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1887
Author(s):  
Ming Pan ◽  
Chen Wang ◽  
Hua-Fei Li ◽  
Ning Xie ◽  
Ping Wu ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Chemical Vapor Deposition ◽  
Vapor Deposition ◽  
Growth Parameters ◽  
Copper Substrate ◽  
Chemical Vapor ◽  
Morphology Evolution ◽  
Force Microscopy ◽  
Smooth Edge ◽  
Transmission Electron

U-shaped graphene domains have been prepared on a copper substrate by chemical vapor deposition (CVD), which can be precisely tuned for the shape of graphene domains by optimizing the growth parameters. The U-shaped graphene is characterized by using scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), and Raman. These show that the U-shaped graphene has a smooth edge, which is beneficial to the seamless stitching of adjacent graphene domains. We also studied the morphology evolution of graphene by varying the flow rate of hydrogen. These findings are more conducive to the study of morphology evolution, nucleation, and growth of graphene domains on the copper substrate.

Estimation of growth parameters based on length-stratified age samples

2020 ◽  
Vol 77 (3) ◽  
pp. 439-450 ◽  
Author(s):  
Andrea M.J. Perreault ◽  
Nan Zheng ◽  
Noel G. Cadigan
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Growth Parameters ◽  
Age Distribution ◽  
Growth Parameter ◽  
Model Parameters ◽  
Second Phase ◽  
Science Literature ◽  
Fisheries Science ◽  
Optimal Approach

Response-selective stratified sampling (RSSS) has been well studied in the statistical literature; however, the application of the resulting statistical theories and methods to a specific case of RSSS in fisheries studies, namely length-stratified age sampling (LSAS), is inadequate. We review nine estimation approaches for RSSS found in the statistical and fisheries science literature in terms of three sampling components: the first phase length composition sample, the second phase age composition sample, and the sampling scheme. We compare the performance in terms of RRMSE (relative root mean squared error) for von Bertalanffy (vonB) growth model parameter estimation using an extensive simulation study. We further demonstrate methods by applying the two best-performing and the most popular methods to estimate the vonB model parameters for American plaice (Hippoglossoides platessoides) in NAFO Divisions 3LNO. Our simulations demonstrated that mis-specifying one or more of the three sampling components increases the RRMSEs, and this effect is magnified when the age distribution is incorrectly specified. The optimal approach for data based on LSAS is the empirical proportion approach, and we recommend this method for growth parameter estimation based on LSAS data.

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