Polarimetric Observations and Simulations of Sublimating Snow: Implications for Nowcasting

2021 ◽  
Author(s):  
Jacob T. Carlin ◽  
Heather D. Reeves ◽  
Alexander V. Ryzhkov
Keyword(s):  
Radar Data ◽  
Lead Times ◽  
Start Time ◽  
Good Correspondence ◽  
Wide Range ◽  
Potential Benefits ◽  
Microphysical Processes ◽  
Ice Production ◽  
Differential Reflectivity

AbstractSnow sublimating in dry air is a forecasting challenge and can delay the onset of surface snowfall and affect storm-total accumulations. Despite this, it remains comparatively less studied than other microphysical processes. Herein, the characteristics of sublimating snow and the potential for nowcasting snowfall reaching the surface are explored through the use of dual-polarization radar. Twelve cases featuring prolific sublimation were analyzed using range-defined quasi-vertical profiles (RDQVPs) and compared with environmental model analyses. Overall, reflectivity Z significantly decreases, differential reflectivity ZDR slightly decreases, and copolar-correlation coefficient ρhv remains nearly constant through the sublimation layer. Regions of enhanced specific differential phase Kdp were frequently observed in the sublimation layer and are believed to be polarimetric evidence of secondary ice production via sublimation. A 1D bin model was initialized using particle size distributions retrieved from the RDQVPs using numerous novel polarimetric snowretrieval relations for a wide range of forecast lead times, with the model environment evolving in response to sublimation. It was found that the model was largely able to predict the snowfall start time up to six hours in advance, with a 6-h median bias of just -18.5 minutes. A more detailed case study of the 08 December 2013 snowstorm in the Philadelphia region was also performed, demonstrating good correspondence with observations and examples of model fields (e.g., cooling rate) hypothetically available from such a tool. The proof-of-concept results herein demonstrate the potential benefits of incorporating spatially averaged radar data in conjunction with simple 1D models into the nowcasting process.

scholarly journals Quasi-Vertical Profiles—A New Way to Look at Polarimetric Radar Data

2016 ◽  
Vol 33 (3) ◽  
pp. 551-562 ◽  
Author(s):  
Alexander Ryzhkov ◽  
Pengfei Zhang ◽  
Heather Reeves ◽  
Matthew Kumjian ◽  
Timo Tschallener ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Radar Data ◽  
Vertical Profiles ◽  
Remote Sensors ◽  
Weather Radars ◽  
X Band ◽  
Potential Benefits ◽  
High Vertical Resolution ◽  
Microphysical Processes ◽  
Differential Reflectivity

AbstractA novel methodology is introduced for processing and presenting polarimetric data collected by weather surveillance radars. It involves azimuthal averaging of radar reflectivity Z, differential reflectivity ZDR, cross-correlation coefficient ρhv, and differential phase ΦDP at high antenna elevation, and presenting resulting quasi-vertical profiles (QVPs) in a height-versus-time format. Multiple examples of QVPs retrieved from the data collected by S-, C-, and X-band dual-polarization radars at elevations ranging from 6.4° to 28° illustrate advantages of the QVP technique. The benefits include an ability to examine the temporal evolution of microphysical processes governing precipitation production and to compare polarimetric data obtained from the scanning surveillance weather radars with observations made by vertically looking remote sensors, such as wind profilers, lidars, radiometers, cloud radars, and radars operating on spaceborne and airborne platforms. Continuous monitoring of the melting layer and the layer of dendritic growth with high vertical resolution, and the possible opportunity to discriminate between the processes of snow aggregation and riming, constitute other potential benefits of the suggested methodology.

Insights into the Evolving Microphysical and Kinematic Structure of Northeastern U.S. Winter Storms from Dual-Polarization Doppler Radar

2017 ◽  
Vol 145 (3) ◽  
pp. 1033-1061 ◽  
Author(s):  
Matthew R. Kumjian ◽  
Kelly A. Lombardo
Keyword(s):  
Doppler Radar ◽  
Elevation Angle ◽  
Model Performance ◽  
Radar Data ◽  
Added Value ◽  
Dual Polarization ◽  
Differential Phase Shift ◽  
Winter Storms ◽  
Microphysical Processes ◽  
Ice Production

The recent Weather Surveillance Radar-1988 Doppler (WSR-88D) network upgrade to dual-polarization capabilities allows for bulk characterization of microphysical processes in northeastern U.S. winter storms for the first time. In this study, the quasi-vertical profile (QVP) technique (wherein data from a given elevation angle scan are azimuthally averaged and the range coordinate is converted to height) is extended and applied to polarimetric WSR-88D observations of six Northeast winter storms to survey their evolving, bulk vertical microphysical and kinematic structures. These analyses are supplemented using hourly analyses from the Rapid Refresh (RAP) model. Regions of ascent inferred from QVPs were consistently associated with notable polarimetric signatures, implying planar crystal growth when near −15°C, and riming and secondary ice production at higher temperatures. The heaviest snowfall occurred most often when ascent and enhanced propagation differential phase shift ([Formula: see text]) occurred near −15°C. When available, limited surface observations confirmed heavy snowfall rates and revealed large snow-to-liquid ratios at these times. Other cases revealed sudden, large melting-layer excursions associated with precipitation-type transitions near the surface. RAP analyses failed to capture such complex evolution, demonstrating the added value of dual-polarization radar observations in these scenarios and the potential use of radar data for assessing model performance in real time. These insights are a preliminary step toward better understanding the complex processes in northeastern U.S. winter storms.

scholarly journals A method to employ the spatial organization of catchments into semi-distributed rainfall–runoff models

2017 ◽  
Vol 21 (8) ◽  
pp. 4259-4282
Author(s):  
Henning Oppel ◽  
Andreas Schumann
Keyword(s):  
Spatial Organization ◽  
Hydrological Model ◽  
Rainfall Runoff ◽  
Catchment Characteristics ◽  
Natural Rainfall ◽  
Wide Range ◽  
Potential Benefits ◽  
Definition Of ◽  
Two Parameters

Abstract. A distributed or semi-distributed deterministic hydrological model should consider the hydrologically most relevant catchment characteristics. These are heterogeneously distributed within a watershed but often interrelated and subject to a certain spatial organization which results in archetypes of combined characteristics. In order to reproduce the natural rainfall–runoff response the reduction of variance of catchment properties as well as the incorporation of the spatial organization of the catchment are desirable. In this study the width-function approach is utilized as a basic characteristic to analyse the succession of catchment characteristics. By applying this technique we were able to assess the context of catchment properties like soil or topology along the streamflow length and the network geomorphology, giving indications of the spatial organization of a catchment. Moreover, this information and this technique have been implemented in an algorithm for automated sub-basin ascertainment, which included the definition of zones within the newly defined sub-basins. The objective was to provide sub-basins that were less heterogeneous than common separation schemes. The algorithm was applied to two parameters characterizing the topology and soil of four mid-European watersheds. Resulting partitions indicated a wide range of applicability for the method and the algorithm. Additionally, the intersection of derived zones for different catchment characteristics could give insights into sub-basin similarities. Finally, a HBV96 case study demonstrated the potential benefits of modelling with the new subdivision technique.

scholarly journals Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study

2020 ◽  
Vol 13 (9) ◽  
pp. 4727-4750
Author(s):  
Viswanathan Bringi ◽  
Kumar Vijay Mishra ◽  
Merhala Thurai ◽  
Patrick C. Kennedy ◽  
Timothy H. Raupach
Keyword(s):  
Size Distribution ◽  
Lower Order ◽  
Drop Size ◽  
Radar Data ◽  
Drop Size Distribution ◽  
Polarimetric Radar ◽  
X Band ◽  
Double Moment ◽  
Differential Reflectivity

Abstract. The lower-order moments of the drop size distribution (DSD) have generally been considered difficult to retrieve accurately from polarimetric radar data because these data are related to higher-order moments. For example, the 4.6th moment is associated with a specific differential phase and the 6th moment with reflectivity and ratio of high-order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution for the DSD. Many double-moment “bulk” microphysical schemes predict the total number concentration (the 0th moment of the DSD, or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted by the generalized gamma (G-G) distribution. The two reference moments are M3 and M6, which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity, and specific attenuation (from the iterative correction of measured reflectivity Zh using the total Φdp constraint, i.e., the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower-order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with about 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower-order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved from radar and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was <15 % in magnitude, with Pearson’s correlation coefficient >0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved mass-weighted mean diameter with M0 resulted in coherent “time tracks” that can potentially lead to studies of precipitation evolution that have not been possible so far.

scholarly journals Discrimination of Biological Scatterers in Polarimetric Weather Radar Data: Opportunities and Challenges

2020 ◽  
Vol 12 (3) ◽  
pp. 545 ◽  
Author(s):  
Sidney Gauthreaux ◽  
Robert Diehl
Keyword(s):  
Model Development ◽  
Model Performance ◽  
Radar Data ◽  
Classification Performance ◽  
Velocity Spectrum ◽  
Precise Knowledge ◽  
Machine Learning Model ◽  
Polarimetric Weather Radar ◽  
Differential Reflectivity

For radar aeroecology studies, the identification of the type of scatterer is critically important. Here, we used a random forest (RF) algorithm to develop a variety of scatterer classification models based on the backscatter values in radar resolution volumes of six radar variables (reflectivity, radial velocity, spectrum width, differential reflectivity, correlation coefficient, and differential phase) from seven types of biological scatterers and one type of meteorological scatterer (rain). Models that discriminated among fewer classes and/or aggregated similar types into more inclusive classes classified with greater accuracy and higher probability. Bioscatterers that shared similarities in phenotype tended to misclassify against one another more frequently than against more dissimilar types, with the greatest degree of misclassification occurring among vertebrates. Polarimetric variables proved critical to classification performance and individual polarimetric variables played central roles in the discrimination of specific scatterers. Not surprisingly, purposely overfit RF models (in one case study) were our highest performing. Such models have a role to play in situations where the inclusion of natural history can play an outsized role in model performance. In the future, bioscatter classification will become more nuanced, pushing machine-learning model development to increasingly rely on independent validation of scatterer types and more precise knowledge of the physical and behavioral properties of the scatterer.

scholarly journals Retrieval of Lower-Order Moments of the Drop Size Distribution using CSU-CHILL X-band Polarimetric Radar: A Case Study

2020 ◽  
Author(s):  
Viswanathan Bringi ◽  
Kumar Vijay Mishra ◽  
Merhala Thurai ◽  
Patrick C. Kennedy ◽  
Timothy H. Raupach
Keyword(s):  
Size Distribution ◽  
Lower Order ◽  
Drop Size ◽  
Radar Data ◽  
Drop Size Distribution ◽  
Polarimetric Radar ◽  
X Band ◽  
Double Moment ◽  
Differential Reflectivity

Abstract. The lower order moments of the drop size distribution (DSD) have generally been considered as difficult to retrieve accurately from polarimetric radar data because these are related to higher order moments. For example, the 4.5th moment is associated with specific differential phase, 6th moment with reflectivity and ratio of high order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution. Many double-moment bulk microphysical schemes predict the total number concentration (the 0th moment or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, to forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted to the generalized gamma (G-G) distribution. The two reference moments are M3 and M6 which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity and specific attenuation (from the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was  0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved characteristic diameter with M0 resulted in coherent time tracks that can potentially lead to studies of precipitation evolution that have not been possible so far.

Site specific agricultural soil management with the use of new technologies

2013 ◽  
Vol 16 (1) ◽  
pp. 59-67
Keyword(s):  
New Technologies ◽  
Digital Interface ◽  
Appropriate Treatment ◽  
Wide Range ◽  
Soil Variation ◽  
Soil Information ◽  
Remote Sensing Techniques ◽  
Agricultural Areas

<p>The Soil Science Institute of Thessaloniki produces new digitized Soil Maps that provide a useful electronic database for the spatial representation of the soil variation within a region, based on in situ soil sampling, laboratory analyses, GIS techniques and plant nutrition mathematical models, coupled with the local land cadastre. The novelty of these studies is that local agronomists have immediate access to a wide range of soil information by clicking on a field parcel shown in this digital interface and, therefore, can suggest an appropriate treatment (e.g. liming, manure incorporation, desalination, application of proper type and quantity of fertilizer) depending on the field conditions and cultivated crops. A specific case study is presented in the current work with regards to the construction of the digitized Soil Map of the regional unit of Kastoria. The potential of this map can easily be realized by the fact that the mapping of the physicochemical properties of the soils in this region provided delineation zones for differential fertilization management. An experiment was also conducted using remote sensing techniques for the enhancement of the fertilization advisory software database, which is a component of the digitized map, and the optimization of nitrogen management in agricultural areas.</p>

Oxford Studies in Ancient Philosophy, Volume 55

2018 ◽  
Keyword(s):  
Middle Ages ◽  
Ancient Philosophy ◽  
Recent Book ◽  
Loss Of Control ◽  
Ancient Greek ◽  
Original Essays ◽  
Constant Change ◽  
Wide Range ◽  
Metaphysical Principle

Oxford Studies in Ancient Philosophy provides, twice each year, a collection of the best current work in the field of ancient philosophy. Each volume features original essays that contribute to an understanding of a wide range of themes and problems in all periods of ancient Greek and Roman philosophy, from the beginnings to the threshold of the Middle Ages. From its first volume in 1983, OSAP has been a highly influential venue for work in the field, and has often featured essays of substantial length as well as critical essays on books of distinctive importance. Volume LV contains: a methodological examination on how the evidence for Presocratic thought is shaped through its reception by later thinkers, using discussions of a world soul as a case study; an article on Plato’s conception of flux and the way in which sensible particulars maintain a kind of continuity while undergoing constant change; a discussion of J. L. Austin’s unpublished lecture notes on Aristotle’s Nicomachean Ethics and his treatment of loss of control (akrasia); an article on the Stoics’ theory of time and in particular Chrysippus’ conception of the present and of events; and two articles on Plotinus, one that identifies a distinct argument to show that there is a single, ultimate metaphysical principle; and a review essay discussing E. K. Emilsson’s recent book, Plotinus.

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

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