A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model-observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of > 200 million realistic DSDs from one-dimensional bin microphysical rain shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the 6th and 9th) perform better than the lower-order moments (0th and 3rd) typically predicted by many bulk microphysics schemes

The development of rainfall retrievals from radar at Darwin

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.5194/amt-14-53-2021The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dualpolarization quantities was wavelength dependent for 1 10 mm h−1 . Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1 10 mm h−1 (deeper convective rain).Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract DE-AC02-06CH11357. This work has been supported by the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE) as part of the Climate Model Development and Validation activity. NOAA PSL contributes effort with funding from the Weather Program Office’s Precipitation Prediction Grand Challenge. The development of the Python ARM radar toolkit was funded by the ARM program part of the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE). The work from Monash University and the Bureau of Meteorology was partly supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0014063. BD contributions are supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0017977

Inertial and viscous flywheel sensing of nanoparticles

Rotational dynamics often challenge physical intuition while enabling unique realizations, from the rotor of a gyroscope that maintains its orientation regardless of the outer gimbals, to a tennis racket that rotates around its handle when tossed face-up in the air. In the context of inertial mass sensing, which can measure mass with atomic precision, rotational dynamics are normally considered a complication hindering measurement interpretation. Here, we exploit the rotational dynamics of a microfluidic device to develop a new modality in inertial resonant sensing. Combining theory with experiments, we show that this modality normally measures the volume of the particle while being insensitive to its density. Paradoxically, particle density only emerges when fluid viscosity becomes dominant over inertia. We explain this paradox via a viscosity-driven, hydrodynamic coupling between the fluid and the particle that activates the rotational inertia of the particle, converting it into a viscous flywheel. This modality now enables the simultaneous measurement of particle volume and mass in fluid, using a single, high-throughput measurement.Comment: 20 pages, 4 figures, 12 s. figures, 2 s. table

The Application of Modern Optimization Packages in Multisensor Data Assimilation

PyDDA is an expandable framework that integrates data from weather radars and forecasting models using SciPys optimization package to create meteorological fields

PyDDA: A New Pythonic Wind Retrieval Package

PyDDA (Pythonic Direct Data Assimilation) is a new community framework aimed at wind retrievals that depends only upon utilities in the SciPy ecosystem such as scipy, numpy, and dask. It can support retrievals of winds using information from weather radar networks constrained by high resolution forecast models over grids that cover thousands of kilometers at kilometer-scale resolution. Unlike past wind retrieval packages, this package can be installed using anaconda for easy installation and, with a focus on ease of use can retrieve winds from gridded radar and model data with just a few lines of code. The package is currently available for download at https://github.com/openradar/PyDDA

The measurement of Navier slip on individual nanoparticles in liquid

The Navier slip condition describes the motion of a liquid, relative to a neighboring solid surface, with its characteristic Navier slip length being a constitutive property of the solid-liquid interface. Measurement of this slip length is complicated by its small magnitude, expected in the nanometer range based on molecular simulations. Here, we report an experimental technique that interrogates the Navier slip length on individual nanoparticles immersed in liquid, with sub-nanometer precision. Proof-of-principle experiments on individual, citrate-stabilized, gold nanoparticles in water give a constant slip length of 2.7$\pm$0.6 nm (95% C.I.) - independent of particle size. Achieving this feature of size independence is central to any measurement of this constitutive property, which is facilitated through the use of individual particles of varying radii. This demonstration motivates studies that can now validate the wealth of existing molecular simulation data on slip.Comment: 12 pages, 4 figure

Analysis of a destructive wind storm on 16 November 2008 in Brisbane, Australia

During the late afternoon on 16 November 2008 the Brisbane (Queensland, Australia) suburb of “The Gap” experienced extensive wind damage caused by an intense local thunderstorm. The CP2 research radar nearby detected near-surface radial velocities exceeding 43 m s−1 above The Gap while hail size reports did not exceed golf ball size, and no tornadoes were reported. The storm environment was characterized by a layer of very moist near-surface air and strong storm-relative low-level flow, whereas the storm-relative winds aloft were weak. While the thermodynamic storm environment contained a range of downdraft-promoting ingredients such as a ~4-km-high melting level above a ~2-km-deep layer with nearly dry-adiabatic lapse rates mostly collocated with dry ambient air, a ~1-km-deep stable layer near the ground would generally lower expectations of destructive surface winds based on the downburst mechanism. Once observed reflectivities exceed 70 dBZ, downdraft cooling due to hail melting and downdraft acceleration based on hail loading are found to likely become nonnegligible forcing mechanisms. The event featured the close proximity of a hydrostatically and dynamically driven mesohigh at the base of the downdraft to a dynamically driven mesolow associated with a low-level circulation. This proximity was instrumental in the anisotropic horizontal acceleration of the near-ground outflow and the ultimate strength of the Gap storm surface winds. Weak storm-relative midlevel winds are speculated to have allowed the downdraft to descend close to the low-level circulation, which set up this strong horizontal perturbation pressure gradient

Statistical Evaluation of Three Distinct Automated Doppler Dealiasing Algorithms Using a Hand-Dealiased Shipborne Radar Dataset

No abstract availabl

Computer Science Research Institute 2005 annual report of activities.

This report summarizes the activities of the Computer Science Research Institute (CSRI) at Sandia National Laboratories during the period January 1, 2005 to December 31, 2005. During this period, the CSRI hosted 182 visitors representing 83 universities, companies and laboratories. Of these, 60 were summer students or faculty. The CSRI partially sponsored 2 workshops and also organized and was the primary host for 3 workshops. These 3 CSRI sponsored workshops had 105 participants, 78 from universities, companies and laboratories, and 27 from Sandia. Finally, the CSRI sponsored 12 long-term collaborative research projects and 3 Sabbaticals

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset