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This dataset contains radar moments corrected for clutter, noise, aliasing, and attenuation from the X-band ARM Scanning Precipitation Radar 2 (XSAPR-2).
Various corrections are needed in order to extract the best value of the measurements from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility’s scanning precipitation radars. The Corrected Moments in Antenna Coordinates (CMAC) Value-Added Product provides an application chain for providing radar moments that are corrected for artifacts such as aliasing, attenuation, multi-trip echoes, and beam blockage. This technical report provides details on the entire process of correcting the ARM scanning precipitation radar data using CMAC. CMAC has been used at multiple ARM sites and ARM Mobile Facility deployments including the Tracking Aerosol Convection Interactions Experiment (TRACER), Cloud, Aerosol, and Complex Terrain Interactions (CACTI), and the Surface Atmosphere Integrated Laboratory (SAIL).
For the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, Corrected Moments in Antenna Coordinates (CMAC) is a set of algorithms and code that does corrections to CSU X-Precipitation Radar PPI data. Additionally, CMAC adds calculations of derived snowfall rates to the original data in order to provide precipitation estimates for the campaign.
C Scanning ARM Precipitation Radar 2nd generation (CSAPR2), Corrected Moments in Antenna Coordinates (CMAC)
This is the X-SAPR RHI scan data processed using Corrected Moments in Antenna Coordinates 2.0.
In 2010 the Atmospheric Radiation Measurement user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. In order to maximize the scientific impact, the program supported the development of an application chain to correct for various phenomena in order to retrieve the “point” values of moments of the radar spectrum and polarimetric measurements. This report details the motivation, science, and progress to date as well as charting a path forward.
In 2010 the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. To maximize the scientific impact, ARM supported the development of an application chain to correct for various phenomena in order to retrieve the “point” values of moments of the radar spectrum and polarimetric measurements. We have now used the lessons learned from the processing of the 3- and 5-cm wavelength radars obtained by ARM to help process X-band radar data from the Surface Atmospheric Integrated Field Laboratory (SAIL) field campaign. This report details the motivation, science, and progress to date as well as charting a path forward.
Correction for ‘Tuning the melting point of selected ionic liquids through adjustment of the cation’s dipole moment’ by Brooks D. Rabideau et al. , Phys. Chem. Chem. Phys. , 2020, 22 , 12301–12311, https://doi.org/10.1039/D0CP01214A.
Raw data from ARM precipitation radars must be corrected for atmospheric phenomena and instrument characteristics (e.g., attenuation, clutter) to retrieve precipitation properties. The Corrected Moments in Antenna Coordinates Version 2 (CMAC2) value-added product (VAP) is a set of algorithms and code that does such corrections, and it also retrieves precipitation quantities from the radar measurements. Similar to the X-SAPR radars at the ARM main site, C-SAPR2 radar data also needs corrections and improvements. CMAC 2.0 has been updated to work with the ARM C-SAPR 2 radar. Data and fields that have been processed to: correct for velocity aliasing, unfold and generate a cross-polarimetric phase difference that is monotonically increasing, removing impulses caused by non-uniform beam filling and phase shift on backscatter, recalculate specific differential phase using a 20-point Sobel filter on the aforementioned phase, correct for liquid path attenuation using the polarimetric signals, estimate rainfall rates at the gate using the specific attenuation. In addition, CMAC writes the data out into a community-standard format netCDF File using the CF/Radial conventions. The data are therefore compatible with new and existing National Center for Atmospheric Research (NCAR) tools such as RadXConvert for converting to a variety of popular file formats.
Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we benchmark a quantum-classical hybrid solver tailored for particle-hole symmetric AIMs, using the variational quantum eigensolver to prepare the ground state of the model with shallow quantum circuits. The solver uses shallow quantum ansätze and one set of variational parameters to prepare the ground state and its particle and hole excitations, enabling the construction of the impurity Green’s function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment correction to the variational energies, and benchmark the approach by comparing the density of states computed from the impurity Green’s function against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green’s function construction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.
The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign generated an unprecedented, multisensory record of cold-season precipitation in the Upper Colorado River Basin (UCRB). To complement the Surface Quantitative Precipitation Estimation (SQUIRE) snow water equivalent (SWE) product derived from the Colorado State University (CSU) X-band radar, a hydrometeor phase classification data set was developed to systematically identify phase of the near-surface precipitation. The algorithm uses Corrected Moments in Antenna Coordinates (CMAC) fields with semi-supervised classification (SSC) and fuzzy-logic hydrometeor classification (FHC) methods and maps hydrometeor classes to a compact set of phase classes suitable for Quantitative Precipitation Estimation (QPE) and hydrologic applications. We used SQUIRE grid projections to mitigate beam-blockage issues by selecting the near-surface gates for each beam as well as to make it directly usable for the QPE products.
NSA X-SAPR radar data processed with Corrected Moments in Antenna Coordinates (CMAC 2.0)
Corrected Moments in Antenna Coordinates (CMAC) calculates quantitative precipitation estimates (QPE) from empirical relationships based on equivalent radar reflectivity factor, specific differ- ential phase, and specific attenuation. To evaluate these empirical relationships, the Extracted Radar Columns and In-Situ Sensors (RADclss) product was developed. Utilizing Py-ART, RAD- clss extracts CMAC radar columns above various ARM and partner locations. These columns are then spatiotemporally synced with in-situ observations at the surface utilizing the Atmospheric data Community Toolkit (ACT; Theisen et al. 2025), allowing direct comparison of radar parameters with rain gauges and laser disdrometers for further investigation.
In this note we discuss subtleties associated with the efficiency corrections for measurements of off-diagonal cumulants and factorial moments for a situation when one deals with overlapping sets of particles, such as correlations between numbers of protons and positively charged particles. In particular, we discuss the situation commonly encountered in heavy-ion experiments, where first all charges are reconstructed and then protons are selected from these charges by an additional particle identification procedure. We present the efficiency correction formulas for the case when the detection efficiencies follow a binomial distribution.
The first magnetic 2D material discovered, monolayer (ML) CrI3, is particularly fascinating due to its ground state ferromagnetism. However, because ML materials are difficult to probe experimentally, much remains unresolved about ML CrI3’s structural, electronic, and magnetic properties. Here, we leverage Density Functional Theory (DFT) and high-accuracy Diffusion Monte Carlo (DMC) simulations to predict lattice parameters, magnetic moments, and spin–phonon and spin–lattice coupling of ML CrI3. We exploit a recently developed surrogate Hessian DMC line search technique to determine CrI3’s ML geometry with DMC accuracy, yielding lattice parameters in good agreement with recently published STM measurements—an accomplishment given the ~10% variability in previous DFT-derived estimates depending upon the functional. Strikingly, we find that previous DFT predictions of ML CrI3’s magnetic spin moments are correct on average across a unit cell but miss critical local spatial fluctuations in the spin density revealed by more accurate DMC. DMC predicts that magnetic moments in ML CrI3 are 3.62 μB per chromium and -0.145 μB per iodine, both larger than previous DFT predictions. The large disparate moments together with the large spin–orbit coupling of CrI3’s I-p orbital suggest a ligand superexchange-dominated magnetic anisotropy in ML CrI3, corroborating recent observations of magnons in its 2D limit. We also find that ML CrI3 exhibits a substantial spin–phonon coupling of ~3.32 cm-1. Our work, thus, establishes many of ML CrI3’s key properties, while also continuing to demonstrate the pivotal role that DMC can assume in the study of magnetic and other 2D materials.
Kinetic simulations of relativistic gases and plasmas are critical for understanding diverse astrophysical and terrestrial systems, but the accurate construction of the relativistic Maxwellian, the Maxwell–Jüttner distribution, on a discrete simulation grid is challenging. Difficulties arise from the finite velocity bounds of the domain, which may not capture the entire distribution function, as well as errors introduced by projecting the function onto a discrete grid. Here, we present a novel scheme for iteratively correcting the moments of the projected distribution applicable to all grid-based discretizations of the relativistic kinetic equation. In addition, we describe how to compute the needed nonlinear quantities, such as Lorentz boost factors, in a discontinuous Galerkin scheme through a combination of numerical quadrature and weak operations. The resulting method accurately captures the distribution function and ensures that the moments match the desired values to machine precision.
The KAZR-ARSCL VAP provides cloud boundaries and best-estimate time-height fields of radar moments. The VAP merges corrected, but uncalibrated, KAZR moments from all active radar modes with cloud base and cloud mask observations from the micropulse lidar (MPL), cloud base from the ceilometer, as well information from soundings, rain gauge, and microwave radiometer instruments to produce two data streams, one with best-estimate cloud base and cloud layer boundaries, and another which also includes best-estimate time-height fields of radar moments. This DOI is for the data stream that contain cloud layer boundaries only. Please note that the reflectivity used in this level c0 product is uncalibrated.
The KAZRCFRCOR Value Added Product (VAP) performs several corrections to the ingested KAZR data and also creates a significant detection mask for each radar mode. The VAP computes gaseous attenuation as a function of time and radial distance from the radar antenna, based on ambient relative humidity, and corrects observed reflectivities for that effect. KAZR reflectivities are also corrected in the antenna ‘nearfield’ range to compensate for the near-field antenna pattern and gain. KAZRCFRCOR also dealiases mean Doppler velocities, to correct velocities whose magnitudes exceed the radar’s Nyquist velocity. This dataset contains the 'kazrpr' (precipitation) mode output from this VAP.