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To cover its large dynamic range, radar reflectivity factors have historically been displayed and used on a logarithmic scale, that is, decibels of reflectivity (dBZ). Logarithmic reflectivity has also been used for data assimilation without being questioned or well validated. However, fundamental limitations exist with directly assimilating logarithmic reflectivity, such as strong nonlinearity of the observation forward operator and the fact that the impacts of small reflectivity values are amplified, leading to exaggerated increments when mapped back into physical space. In this study, we power‐transform both reflectivity and hydrometeor mixing ratios to alleviate the aforementioned issues with using conventional logarithmic reflectivity. Forecast evaluation across eight severe convection events demonstrates that applying the Box‐Cox power transformations to both reflectivity and hydrometeor mixing ratios effectively reduces the nonlinearity between the observations and control variables. This approach significantly improves analyses of model hydrometeor variables and forecasts of composite reflectivity and hourly precipitation.

Producing metallic hydrogen has been a great challenge in condensed matter physics. Metallic hydrogen may be a room-temperature superconductor and metastable when the pressure is released and could have an important impact on energy and rocketry. We have studied solid molecular hydrogen under pressure at low temperatures. At a pressure of 495 gigapascals, hydrogen becomes metallic, with reflectivity as high as 0.91. We fit the reflectance using a Drude free-electron model to determine the plasma frequency of 32.5 ± 2.1 electron volts at a temperature of 5.5 kelvin, with a corresponding electron carrier density of 7.7 ± 1.1 × 1023 particles per cubic centimeter, which is consistent with theoretical estimates of the atomic density. The properties are those of an atomic metal. We have produced the Wigner-Huntington dissociative transition to atomic metallic hydrogen in the laboratory.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

Neutron and x-ray reflectometry (NR and XRR) are powerful techniques to investigate the structural, morphological and even magnetic properties of solid and liquid thin films. While neutrons and x-rays behave similarly in many ways and can be described by the same general theory, they fundamentally differ in certain specific aspects. These aspects can be exploited to investigate different properties of a system, depending on which particular questions need to be answered. Having demonstrated the general applicability of neural networks to analyze XRR and NR data before (Greco et al 2019 J. Appl. Cryst. 52 1342), this study discusses challenges arising from certain pathological cases as well as performance issues and perspectives. These cases include a low signal-to-noise ratio, a high background signal (e.g. from incoherent scattering), as well as a potential lack of a total reflection edge (TRE). By dynamically modifying the training data after every mini batch, a fully-connected neural network was trained to determine thin film parameters from reflectivity curves. We show that noise and background intensity pose no significant problem as long as they do not affect the TRE. However, for curves without strong features the prediction accuracy is diminished. Furthermore, we compare the prediction accuracy for different scattering length density combinations. The results are demonstrated using simulated data of a single-layer system while also discussing challenges for multi-component systems.

The impact of assimilating radar reflectivity and radial velocity data with an intermittent, cycled three-dimensional variational assimilation (3DVAR) system is explored using an idealized thunderstorm case and a real data case on 8 May 2003. A new forward operator for radar reflectivity is developed that uses a background temperature field provided by a numerical weather prediction model for automatic hydrometeor classification. Three types of experiments are performed on both the idealized and real data cases. The first experiment uses radial velocity data only, the second experiment uses both radial velocity and reflectivity data without hydrometeor classification, and the final experiment uses both radial velocity and reflectivity data with hydrometeor classification. All experiments advance the analysis state to the next observation time using a numerical model prediction, which is then used as the background for the next analysis. Results from both the idealized and real data cases show that, assimilating only radial velocity data, the model can reconstruct the supercell thunderstorm after several cycles, but the development of precipitation is delayed because of the well-known spinup problem. The spinup problem is reduced dramatically when assimilating reflectivity without hydrometeor classification. The analyses are further improved using the new reflectivity formulation with hydrometeor classification. This study represents a successful first effort in variational convective-scale data assimilation to partition hydrometeors using a background temperature field from a numerical weather prediction model.

The physics of periodic systems are of fundamental importance and result in various phenomena that govern wave transport and interference. However, deviations from periodicity may result in higher complexity and give rise to a number of surprising effects. One such deviation can be found in the field of optics in the realization of photonic quasicrystals, a class of structures made from building blocks that are arranged using well-designed patterns but lack translational symmetry. Nevertheless, these structures, which lie between periodic and disordered structures, still show sharp diffraction patterns that confirm the existence of wave interference resulting from their long-range order. In this Review, we discuss the beautiful physics unravelled in photonic quasicrystals of one, two and three dimensions, and describe how they can influence optical transmission and reflectivity, photoluminescence, light transport, plasmonics and laser action. The optical characteristics and applications of the quasicrystal, a special form of aperiodic engineered structure, are explored in this Review article.

Geofluid discrimination plays an important role in the fields of hydrogeology, geothermics, and exploration geophysics. A geofluid discrimination approach incorporating linearized poroelasticity theory and pre-stack seismic reflection inversion with Bayesian inference is proposed in this study to identify the types of geofluid underground. Upon the review of the development of different geofluid indicators, the fluid modulus is defined as the geofluid indicator mainly affected by the fluid contained in reservoirs. A novel linearized P-wave reflectivity equation coupling the fluid modulus is derived to avoid the complicated nonlinear relationship between the fluid modulus and seismic data. Model examples illustrate the accuracy of the proposed linearized P-wave reflectivity equation comparing to the exact P-wave reflectivity equation even at moderate incident angle, which satisfies the requirements of the parameter estimations with P-wave pre-stack seismic data. Convoluting this linearized P-wave reflectivity equation with seismic wavelets as the forward solver, a pragmatic pre-stack Bayesian seismic inversion method is presented to estimate the fluid modulus directly. Cauchy and Gaussian probability distributions are utilized for prior information of the model parameters and the likelihood function, respectively, to enhance the inversion resolution. The preconditioned conjugate gradient method is coupled in the optimization of the objective function to weaken the strong degree of correlation among the four model parameters and enhance the stability of those parameter estimations simultaneously. The synthetic examples demonstrate the feasibility and stability of the proposed novel seismic coefficient equation and inversion approach. The real data set illustrates the efficiency and success of the proposed approach in differentiating the geofluid filled reservoirs.

The X‐ray reflectivity technique can provide out‐of‐plane electron‐density profiles of surfaces, interfaces, and thin films, with atomic resolution accuracy. While current methodologies require high surface flatness, this becomes challenging for naturally curved surfaces, particularly for liquid metals, due to the very high surface tension. Here, the development of X‐ray reflectivity measurements with beam sizes of a few tens of micrometres on highly curved liquid surfaces using a synchrotron diffractometer equipped with a double crystal beam deflector is presented. The proposed and developed method, which uses a standard reflectivity gθ–2gθ scan, is successfully applied to study in situ the bare surface of molten copper and molten copper covered by a graphene layer grown in situ by chemical vapor deposition. It was found that the roughness of the bare liquid surface of copper at 1400 K is 1.25 ± 0.10 Å, while the graphene layer is separated from the liquid surface by a distance of 1.55 ± 0.08 Å and has a roughness of 1.26 ± 0.09 Å. A method of X‐ray reflectivity data reconstruction from scattering measurements on spherically curved surfaces is proposed and tested on solid and liquid surfaces.

Infrastructure-based heat reduction strategies can help cities adapt to high temperatures, but simulations of their cooling potential yield widely varying predictions. We systematically review 146 studies from 1987 to 2017 that conduct physically based numerical modelling of urban air temperature reduction resulting from green-blue infrastructure and reflective materials. Studies are grouped into two modelling scales: neighbourhood scale, building-resolving (i.e. microscale); and city scale, neighbourhood-resolving (i.e. mesoscale). Street tree cooling has primarily been assessed at the microscale, whereas mesoscale modelling has favoured reflective roof treatments, which are attributed to model physics limitations at each scale. We develop 25 criteria to assess contextualization and reliability of each study based on metadata reporting and methodological quality, respectively. Studies have shortcomings with respect to neighbourhood characterization, reporting areal coverages of heat mitigation implementations, evaluation of base case simulations, and evaluation of modelled physical processes relevant to heat reduction. To aid comparison among studies, we introduce two metrics: the albedo cooling effectiveness (ACE), and the vegetation cooling effectiveness (VCE). A sub-sample of 47 higher quality studies suggests that high reflectivity coatings or materials offer ≈0.2 °C–0.6 °C cooling per 0.10 neighbourhood albedo increase, and that trees yield ≈0.3 °C cooling per 0.10 canopy cover increase, for afternoon clear-sky summer conditions. VCE of low vegetation and green roofs varies more strongly between studies. Both ACE and VCE exhibit a striking dependence on model choice and model scale, particularly for albedo and roof-level implementations, suggesting that much of the variation of cooling magnitudes between studies may be attributed to model physics representation. We conclude that evaluation of the base case simulation is not a sufficient prerequisite for accurate simulation of heat mitigation strategy cooling. We identify a three-phase framework for assessment of the suitability of a numerical model for a heat mitigation experiment, which emphasizes assessment of urban canopy layer mixing and of the physical processes associated with the heat reduction implementation. Based on our findings, we include recommendations for optimal design and communication of urban heat mitigation simulation studies.