High-resolution Weather Models Research Articles

Using sub-limb observations to measure gravity waves excited by convection

Convective gravity waves are a major driver of atmospheric circulation, including the stratospheric and mesospheric quasi-biennial oscillation (QBO) and the Brewer–Dobson circulation. Previous work shows clear evidence that these waves can be excited by both single convective cells and by mesoscale convective complexes acting as a single unit. However, the partitioning of the generated waves and, crucially for atmospheric model development, the flux of momentum they transport between these two types of excitation process remains highly uncertain due to a fundamental lack of suitable observations at the global scale. Here, we use both theoretical calculations and sampled output from a high-resolution weather model to demonstrate that a satellite instrument using a sub-limb geometry would be well suited to characterising the short-vertical short-horizontal gravity waves these systems produce, and hence to provide the scientific knowledge needed to identify the relative wave-driving contribution of these two types of convective wave excitation.

The Territorial Debris Flow Early Warning System of Piemonte (North-western Italy)

Debris flows are one of the most frequent and dangerous phenomena affecting the Alpine environment; they are responsible of 36% of casualties due to gravitational phenomena in the Italian Alps during the last century. In the Western Italian Alps (Piemonte, Italy) a Territorial Debris Flow Early Warning System (Te-DFEWS) aimed to forecast and predict the occurrence of sediment mass-transport has been developed, based on the characterization of small Alpine catchments (< 50 km2) and the processes that take place in these. The Te-DFWES is based on the identification of predisposing and triggering factors that determinate debris flow occurrence. The Te-DFEWS operates from 2010 in present-time and nowcasting using weather radar observations as input data. Recently (2021), the Te-DFEWS has been improved extending the forecasting window (up to 48h from simulation) by the introduction of Quantitative Precipitation Estimate/Forecast (QPE/QPF) input from COSMO-2I and ICON-IT, local high-resolution weather models. The Te-DFEWS, named DEFENSE (DEbris Flows triggEred by storms - Nowcasting SystEm) and the related warning procedures are presented as an operational tool integrated in the Regional Warning System for Geo-hydrological and Hydraulic Risk at the Functional Centre of Piemonte, managed by the Regional Agency for Environmental Protection of Piemonte (Arpa Piemonte).

Climate change is increasing the risk of a California megaflood

Despite the recent prevalence of severe drought, California faces a broadly underappreciated risk of severe floods. Here, we investigate the physical characteristics of “plausible worst case scenario” extreme storm sequences capable of giving rise to “megaflood” conditions using a combination of climate model data and high-resolution weather modeling. Using the data from the Community Earth System Model Large Ensemble, we find that climate change has already doubled the likelihood of an event capable of producing catastrophic flooding, but larger future increases are likely due to continued warming. We further find that runoff in the future extreme storm scenario is 200 to 400% greater than historical values in the Sierra Nevada because of increased precipitation rates and decreased snow fraction. These findings have direct implications for flood and emergency management, as well as broader implications for hazard mitigation and climate adaptation activities.

Flash flood warnings in context: combining local knowledge and large-scale hydro-meteorological patterns

Abstract. The small spatial and temporal scales at which flash floods occur make predicting events challenging, particularly in data-poor environments where high-resolution weather models may not be available. Additionally, the uptake of warnings may be hampered by difficulties in translating the scientific information to the local context and experiences. Here we use social science methods to characterise local knowledge of flash flooding among vulnerable communities along the flat Lake Malawi shoreline in the district of Karonga, northern Malawi. This is then used to guide a scientific analysis of the factors that contribute to flash floods in the area using contemporary global datasets, including geomorphology, soil and land-use characteristics, and hydro-meteorological conditions. Our results show that communities interviewed have detailed knowledge of the impacts and drivers of flash floods (deforestation and sedimentation), early warning signs (changes in clouds, wind direction, and rainfall patterns), and distinct hydro-meteorological processes that lead to flash flood events at the beginning and end of the wet season. Our analysis shows that the scientific data corroborate this knowledge and that combining local and scientific knowledge provides improved understanding of flash flood processes within the local context. We highlight the potential of linking large-scale global datasets with local knowledge to improve the usability of flash flood warnings.

Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.

Image Collection Simulation Using High-Resolution Atmospheric Modeling

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

Reproducibility of Surface Wind and Tracer Transport Simulations over Complex Terrain Using 5-, 3-, and 1-km-Grid Models

AbstractThe reproducibility of surface wind and tracer transport simulations from high-resolution weather and transport models was studied over complex terrain in wintertime in Japan. The horizontal grid spacing was varied (5-, 3-, and 1-km grids), and radioactive cesium (Cs-137) from the Fukushima nuclear power plant was used as a tracer. Fukushima has complex terrain, such as mountains and valleys. The model results were validated by observations collected from the national networks of the automated meteorological data acquisition system and the hourly air pollution sampling system. The reproducibility depended on the model resolution, topographic complexity, and synoptic weather conditions. Higher model resolution led to higher reproducibility of surface winds, especially in mountainous areas when the Siberian winter monsoon was disturbed. In contrast, the model improvement was negligible or nonexistent over plain/coastal areas when the synoptic field was steady. The statistical scores of the tracer transport simulations often deteriorated as a result of small errors in the plume locations. However, the higher-resolution models advantageously performed better transport simulations in the mountainous areas because of the lower numerical diffusion and higher reproducibility of the mass flux. The reproducibility of the tracer distribution in the valley of the Fukushima mountainous region was dramatically improved with increasing model resolution. In the range of mesoscale model resolutions (commonly 1–10 km), it was concluded that a higher-resolution model is definitely recommended for tracer transport simulations over mountainous terrain.

Using a Balloon-Launched Unmanned Glider to Validate Real-Time WRF Modeling.

The use of small unmanned aerial systems (sUAS) for meteorological measurements has expanded significantly in recent years. SUAS are efficient platforms for collecting data with high resolution in both space and time, providing opportunities for enhanced atmospheric sampling. Furthermore, advances in mesoscale weather research and forecasting (WRF) modeling and graphical processing unit (GPU) computing have enabled high resolution weather modeling. In this manuscript, a balloon-launched unmanned glider, complete with a suite of sensors to measure atmospheric temperature, pressure, and relative humidity, is deployed for validation of real-time weather models. This work demonstrates the usefulness of sUAS for validating and improving mesoscale, real-time weather models for advancements toward reliable weather forecasts to enable safe and predictable sUAS missions beyond visual line of sight (BVLOS).

Snowstorms on Mars

The Martian atmosphere has water-ice clouds, and water ice has been detected on the surface. It has been thought that snow on Mars would fall by slow gentle sedimentation of individual ice particles. Instead, Spiga and colleagues use high-resolution weather models to show that convective plumes can form in and below clouds during the cold Martian nights, producing powerful snowstorms.

A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain

Abstract Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

Giant and Ultragiant Aerosol Particle Variability over the Eastern Great Lakes Region

Abstract Numerous studies have indicated the potential for giant and ultragiant aerosol particles to expedite the warm-rain process as a result of their extreme sizes. The central question regarding their importance is, Are they present in large enough numbers to influence the microphysics of the clouds significantly? Thus, quantification of these particles and their variability is paramount. New observations collected during the second Alliance Icing Research Study (AIRS II) are presented as evidence of the presence and variability of giant and ultragiant aerosol particles over a continental region—in this case, within the eastern Great Lakes region and parts of the midwestern United States and Canada during one month in winter 2003. Sources and factors contributing to the amount of these particles observed in the lower atmosphere were difficult to identify separately; future studies incorporating high-resolution weather modeling are likely needed.

Mitigating atmospheric noise for InSAR using a high resolution weather model

A high resolution weather model is used to predict atmospheric delays for the acquisition times of synthetic aperture radar images over Hawaii. Refraction of the radar by water vapor in the atmosphere in Hawaii leads to apparent ground‐motions with wavelengths and magnitudes similar to the actual ground motions generated by tectonic and volcanic processes. We examine the potential for a weather model to help characterize the atmospheric component in InSAR scenes and find that in the best cases it models the observed delays well, reducing the variance at wavelengths of 30 km and greater by ∼60%, while even in the worst cases it provides an independent means of quantifying the expected variance in the image due to the atmosphere.

Evaluation of a Linear Phase Observation Operator with CHAMP Radio Occultation Data and High-Resolution Regional Analysis

Abstract In this study a nonlocal, linear observation operator for assimilating radio occultation data is evaluated. The operator consists of modeling the excess phase, that is, integrating the refractivity along straight lines tangent to rays, below a certain height. The corresponding observable is the excess phase integrated through the Abel-retrieved refractivity, along the same lines, below the same height. The operator allows very simple implementation (computationally efficient) while accurately accounting for the horizontal refractivity gradients. This is due to significant cancellation of the linearization and discretization errors when modeling the observable. Evaluation of the operator with Challenging Minisatellite Payload (CHAMP) radio occultation data and grid refractivity fields from high-resolution regional analysis over the continental United States showed reduction of the observation error in the troposphere (below 7 km) 1.5–2 times, compared to the error of local refractivity. The operator is useful for the assimilation of radio occultation data by high-resolution weather models in the troposphere.

Assessing the Accuracy of a Linearized Observation Operator for Assimilation of Radio Occultation Data: Case Simulations with a High-Resolution Weather Model

Abstract Assimilation into numerical weather models of the refractivity, Abel-retrieved from radio occultations, as the local refractivity at ray tangent point may result in large errors in the presence of strong horizontal gradients (atmospheric fronts, strong convection). To reduce these errors, other authors suggested modeling the Abel-retrieved refractivity as a nonlocal linear function of the 3D refractivity, which can be used as a linear observation operator for assimiliation. The authors of this study introduce their approach for the nonlocal linear observation operator, which consists of modeling the excess phase path, calculated along certain trajectories below the top of an atmospheric model. In this study (not aimed at development of an observation operator for any specific atmospheric model), both approaches are validated by assessing the accuracy of both linearized observation operators by numerical simulations with the high-resolution Weather Research and Forecasting (WRF) model and comparing them to the accuracy of interpretation of the Abel-retrieved refractivity as local. Improvement of the accuracy of about an order of magnitude is found with the nonlocal refractivity and further improvement is found with the excess phase path. The effect of horizontal resolution of an atmospheric model on the accuracy of modeling local and nonlocal linear observables is also investigated, and it is demonstrated that the nonlocal linear modeling of radio occultation observables is especially important for weather prediction models with sufficiently high horizontal resolution, grid size <100 km (mesoscale models).