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Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

Air quality forecasts produced by the National Air Quality Forecasting Capability (NAQFC) help air quality forecasters across the United States in making informed decisions to protect public health from acute air pollution episodes. However, errors in air quality forecasts limit their value in the decision-making process. This study aims to enhance the accuracy of NAQFC air quality forecasts and reliably quantify their uncertainties using a statistical–dynamical method called the analog ensemble (AnEn), which has previously been found to efficiently generate probabilistic forecasts for other applications. AnEn estimates of the probability of the true state of a predictand are based on a current deterministic numerical prediction and an archive of prior analogous predictions paired with prior observations. The method avoids the complexity and real-time computational expense of model-based ensembles and is proposed here for the first time for air quality forecasting. AnEn is applied with forecasts from the Community Multiscale Air Quality (CMAQ) model. Relative to CMAQ raw forecasts, deterministic forecasts of surface ozone (O3) and particulate matter of aerodynamic diameter smaller than 2.5 μm (PM2.5) based on AnEn’s mean have lower systemic and random errors and are overall better correlated with observations; for example, when computed across all sites and lead times, AnEn’s root-mean-square error is lower than CMAQ’s by roughly 35% and 30% for O3 and PM2.5, respectively, and AnEn improves the correlation by 50% for O3 and PM2.5. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

The multilayer urban canopy models (UCMs) building effect parameterization (BEP) and BEP + building energy model (BEM; a building energy model integrated in BEP) are added to the Yonsei University (YSU) planetary boundary layer (PBL) parameterization in the Weather Research and Forecasting (WRF) Model. The additions allow for the first analysis of the detailed effects of buildings on the urban boundary layer in a nonlocal closure scheme. The modified YSU PBL parameterization is compared with the other 1.5-order local PBL parameterizations that predict turbulent kinetic energy (TKE), Mellor–Yamada–Janjić and Bougeault–Lacarerre, using both ideal and real cases. The ideal-case evaluation confirms that BEP and BEP+BEM produce the expected results in the YSU PBL parameterization because the simulations are qualitatively similar to the TKE-based PBL parameterizations in which the multilayer UCMs have long existed. The modified YSU PBL parameterization is further evaluated for a real case. Similar to the ideal case, there are larger differences among the different UCMs (simple bulk scheme, BEP, and BEP+BEM) than across the PBL parameterizations when the UCM is held fixed. Based on evaluation against urban near-surface wind and temperature observations for this case, the BEP and BEP+BEM simulations are superior to the simple bulk scheme for each PBL parameterization.

Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events. The skill of the multi‐model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0–5 day model accumulation than for the first 24 h, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias correction. Compared to individual models, the multi‐model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 h lead times.

The simulated winds within the urban canopy of landfalling tropical cyclones are sensitive to the representation of the planetary-boundary and urban-canopy layers in numerical weather prediction models. In order to assess the sub-grid-scale parameterizations of these layers, mesoscale model simulations were executed and evaluated against near-surface observations as the outer wind field of Hurricane Irma (2017) interacted with the built-up region from downtown Miami northward to West Palm Beach. Four model simulations were examined, comprised of two different planetary boundary layer (PBL) parameterizations (a local closure scheme with turbulent kinetic energy prediction and a nonlocal closure scheme) and two different urban canopy models (UCMs) [a zeroth order bulk scheme and a multilayer Building Effect Parameterization (BEP) that mimics the three-dimensionality of buildings]. Overall, the simulated urban canopy winds were weakly sensitive to the PBL scheme and strongly sensitive to the UCM. The bulk simulations compared most favorably to an analyzed wind swath in the urban environment, while the BEP simulations had larger negative biases in the same region. There is uncertainty in magnitude of the urban environment biases due to the lack of many urban sheltered measurements in the wind swath analysis. Biases in the rural environment were similar among the bulk and BEP simulations. An improved comparison with the analyzed wind swath in the urban region was obtained by reducing the drag coefficient in BEP in one of the PBL schemes. The usefulness of BEP was demonstrated in its ability to predict realistic heterogeneous near-surface velocity patterns in urban regions.

A fire-spotting parameterization was developed for the WRF-Fire component of the WRF model version 4.0.1. The parameterization uses a Lagrangian particle transport framework and is coupled to the f...

As a first step toward achieving full physics urban weather simulation capabilities within the resident-GPU large-eddy simulation (LES) FastEddy® model, we have implemented and verified/validated a method for explicit representation of building effects. Herein, we extend the immersed body force method (IBFM) from Chan and Leach (2007, https://doi.org/10.1175/2006JAMC1321.1) to (i) be scale independent and (ii) control building surface temperatures. Through a specific drag-like term in the momentum equations, the IBFM is able to enforce essentially zero velocities within the buildings, in turn resulting in a no-slip boundary condition at the building walls. In addition, we propose similar forcing terms in the energy and mass conservation equations that allow an accurate prescription of the building temperature. The extended IBFM is computationally efficient and has the potential to be coupled to building energy models. The IBFM exhibits excellent agreement with laboratory experiments of an array of staggered cubes at a grid spacing of (Formula presented.) mm, demonstrating the applicability of the method beyond the atmospheric scale. In addition, the IBFM is validated at atmospheric scale through simulations of downtown Oklahoma City ((Formula presented.) m) using data collected during the Joint Urban 2003 (JU03) field campaign. Our LES IBFM results for mean wind speed, turbulence kinetic energy, and SF6 transport and dispersion compare well to observations and produce turbulence spectra that are in good agreement with sonic anemometer data. Statistical performance metrics for the JU03 simulations are within the range of other LES models in the literature.

Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPG use it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble’s members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere’s future state at DPG’s observing sites. Army meteorologists view E-4DWX’s output via customized figures posted to a restricted website. Some of these figures summarize collective results—for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped—for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

Operational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in situ soil moisture observations. Bias correction of these soil moisture analyses using data from 42 North American Soil Moisture Database stations throughout the Intermountain West reduces both the afternoon and early morning bias errors and improves forecasts of upper-level temperature and stability. These results illustrate that the assimilation of in situ and remotely sensed soil moisture observations, including those from the recently launched NASA Soil Moisture Active Passive (SMAP) mission, have the potential to greatly improve land surface analyses and near-surface temperature forecasts over arid regions.

Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series–based methods—a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead time, location, error magnitude, and day-to-day error variability.Results show that the analog methods are generally more effective than time series–based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors. When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series–based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude. The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times.

This paper reviews the benefits of a better understanding of atmospheric exchange processes over mountains. These processes affect weather and climate variables that are important in meteorological applications related to many scientific disciplines and sectors of the economy. We focus this review on examples of meteorological applications in hydrology, ecology, agriculture, urban planning, wind energy, transportation, air pollution, and climate change. These examples demonstrate the benefits of a more accurate knowledge of atmospheric exchange processes over mountains, including a better understanding of snow redistribution, microclimate, land-cover change, frost hazards, urban ventilation, wind gusts, road temperatures, air pollution, and the impacts of climate change. The examples show that continued research on atmospheric exchange processes over mountains is warranted, and that a recognition of the potential benefits can inspire new research directions. An awareness of the links between basic research topics and applications is important to the success and impact of new efforts that aim at better understanding atmospheric exchange processes over mountains. To maximize the benefits of future research for meteorological applications, coordinated international efforts involving scientists studying atmospheric exchange processes, as well as scientists and stakeholders representing many other scientific disciplines and economic sectors are required.

The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. AnEn estimates the probability of future observations of a predictand based on a current deterministic numerical weather prediction and an archive of prior analog predictions paired with prior observations. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. The authors apply AnEn to observations from the Environmental Protection Agency's (EPA's) AIRNow network and to forecasts from the Community Multiscale Air Quality (CMAQ). Compared to raw forecasts from CMAQ, deterministic forecasts of O3 and PM2.5 based on AnEn's mean have lower errors, both systemic and random, and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5–0.8 K; the 2-m specific humidity has biases from −0.5–−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2–1.18 m/s and −0.5–4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.

Weather Research and Forecasting Model forecasts over the Great Salt Lake Desert erroneously underpredict nocturnal cooling over the sparsely vegetated silt loam soil area of Dugway Proving Ground in northern Utah, with a mean positive bias error in temperature at 2 m AGL of 3.4°C in the early morning [1200 UTC (0500 LST)]. Positive early-morning bias errors also exist in nearby sandy loam soil areas. These biases are related to the improper initialization of soil moisture and parameterization of soil thermal conductivity in silt loam and sandy loam soils. Forecasts of 2-m temperature can be improved by initializing with observed soil moisture and by replacing Johansen's 1975 parameterization of soil thermal conductivity in the Noah land surface model with that proposed by McCumber and Pielke in 1981 for silt loam and sandy loam soils. Case studies illustrate that this change can dramatically reduce nighttime warm biases in 2-m temperature over silt loam and sandy loam soils, with the greatest improvement during periods of low soil moisture. Predicted ground heat flux, soil thermal conductivity, near-surface radiative fluxes, and low-level thermal profiles also more closely match observations. Similar results are anticipated in other dryland regions with analogous soil types, sparse vegetation, and low soil moisture.

This study describes a stepwise methodology used to provide daily high-spatial-resolution water surface temperatures from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data for use nearly in real time for the Great Salt Lake (GSL). Land surface temperature (LST) is obtained each day and then goes through the following series of steps: land masking, quality control based on other concurrent datasets, bias correction, quality control based on LSTs from recent overpasses, temporal compositing, spatial hole filling, and spatial smoothing. Although the techniques described herein were calibrated for use on the GSL, they can also be applied to any other inland body of water large enough to be resolved by MODIS, as long as several months of in situ water temperature observations are available for calibration. For each of the buoy verification datasets, these techniques resulted in mean absolute errors for the final MODIS product that were at least 62% more accurate than those from the operational Real-Time Global analysis. The MODIS product provides realistic cross-lake temperature gradients that are representative of those directly observed from individual MODIS overpasses and is also able to replicate the temporal oscillations seen in the buoy datasets over periods of a few days or more. The increased accuracy, representative temperature gradients, and ability to resolve temperature changes over periods down to a few days can be vital for providing proper surface boundary conditions for input into numerical weather models.

Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn2 are compared with nighttime profiles of Cn2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.

The field of atmospheric science has been enhanced by its long-standing collaboration with entities with specific needs. This chapter and the two subsequent ones describe how applications have worked to advance the science at the same time that the science has served the needs of society. This chapter briefly reviews the synergy between the applications and advancing the science. It specifically describes progress in weather modification, aviation weather, and applications for security. Each of these applications has resulted in enhanced understanding of the physics and dynamics of the atmosphere, new and improved observing equipment, better models, and a push for greater computing power.

The study describes a method of evaluating numerical weather prediction models by comparing the characteristics of temporal changes in simulated and observed 10-m (AGL) winds. The method is demonstrated on a 1-yr collection of 1-day simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) over southern New Mexico. Temporal objects, or wind events, are defined at the observation locations and at each grid point in the model domain as vector wind changes over 2 h. Changes above the uppermost quartile of the distributions in the observations and simulations are empirically classified as significant; their attributes are analyzed and interpreted.It is demonstrated that the model can discriminate between large and modest wind changes on a pointwise basis, suggesting that many forecast events have an observational counterpart. Spatial clusters of significant wind events are highly continuous in space and time. Such continuity suggests that displaying maps of surface wind changes with high temporal resolution can alert forecasters to the occurrence of important phenomena. Documented systematic errors in the amplitude, direction, and timing of wind events will allow forecasters to mentally adjust for biases in features forecast by the model.

Diffusion that is implicit in the odd-ordered advection schemes in early versions of the Advanced Research core of the Weather Research and Forecasting (WRF) model is sometimes insufficient to remove noise from kinematical fields. The problem is worst when grid-relative wind speeds are low and when stratification is nearly neutral or unstable, such as in weakly forced daytime boundary layers, where noise can grow until it competes with the physical phenomena being simulated. One solution to this problem is an explicit, sixth-order numerical diffusion scheme that preserves the WRF model’s high effective resolution and uses a flux limiter to ensure monotonicity. The scheme, and how it was added to the WRF model, are explained. The scheme is then demonstrated in an idealized framework and in simulations of salt breezes and lake breezes in northwestern Utah.

The Pentagon, and its 25,000+ occupants, represents a likely target for a future terrorist attack using chemical, biological, or radiological material released into the atmosphere. Motivated by this, a building-protection system, called Pentagon Shield, is being developed and deployed by a number of government, academic, and private organizations. The system consists of a variety of data-assimilation and forecast models that resolve processes from the mesoscale to the city scale to the building scale, and assimilate meteorological and contaminant data that are measured by remote and in situ sensors. This paper reports on a field program that took place in 2004 in the area of the Pentagon, where the aim was to provide meteorological data and concentration data from tracer releases, and to support the development and evaluation of the system. In particular, the results of the field program are being used to improve our understanding of urban meteorological processes, verify the overall effectiveness of the ...

Emerging application areas such as air pollution in megacities, wind energy, urban security, and operation of unmanned aerial vehicles have intensified scientific and societal interest in mountain meteorology. To address scientific needs and help improve the prediction of mountain weather, the U.S. Department of Defense has funded a research effort—the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program—that draws the expertise of a multidisciplinary, multi-institutional, and multinational group of researchers. The program has four principal thrusts, encompassing modeling, experimental, technology, and parameterization components, directed at diagnosing model deficiencies and critical knowledge gaps, conducting experimental studies, and developing tools for model improvements. The access to the Granite Mountain Atmospheric Sciences Testbed of the U.S. Army Dugway Proving Ground, as well as to a suite of conventional and novel high-end airborne and surface measurement platforms, has provided an unprecedented opportunity to investigate phenomena of time scales from a few seconds to a few days, covering spatial extents of tens of kilometers down to millimeters. This article provides an overview of the MATERHORN and a glimpse at its initial findings. Orographic forcing creates a multitude of time-dependent submesoscale phenomena that contribute to the variability of mountain weather at mesoscale. The nexus of predictions by mesoscale model ensembles and observations are described, identifying opportunities for further improvements in mountain weather forecasting.

The authors evaluate whether the structure and intensity of simulated squall lines can be explained by “RKW theory,” which most specifically addresses how density currents evolve in sheared environments. In contrast to earlier studies, this study compares output from four numerical models, rather than from just one. All of the authors’ simulations support the qualitative application of RKW theory, whereby squall-line structure is primarily governed by two effects: the intensity of the squall line’s surface-based cold pool, and the low- to midlevel environmental vertical wind shear. The simulations using newly developed models generally support the theory’s quantitative application, whereby an optimal state for system structure also optimizes system intensity. However, there are significant systematic differences between the newer numerical models and the older model that was originally used to develop RKW theory. Two systematic differences are analyzed in detail, and causes for these differences are proposed.

Meteorologists and other weather enthusiasts sometimes lament that they live in weather holes—places that receive less exciting weather than do their surroundings . This belief seems to stem from countless hours spent gazing at thunderstorms on displays of radar reflectivity. To test objectively whether radar observations truly bear out this belief, the authors analyzed 6 yr of composite reflectivity from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Statistics for 28 target cities, selected for their prominent meteorological communities, are compared with statistics for random points in the conterminous United States to see whether any of the targets is truly a weather hole or, perhaps, a hot spot—the counterpart to a hole. Holes and hot spots are defined by the frequency of convective echoes at a target relative to echoes in the surrounding region, and by the probability that convective echoes near a target were followed shortly by a convective echo at that target. The data do, indeed, ...

A series of numerical experiments were conducted to investigate the sensitivity of the WRF Model to the advection scheme. The differences in the simulated results between third- and fifth-order schemes are not large, however the vertical velocity field of the former is somewhat smoother. On the other hand, fourth- and sixth-order schemes easily produce grid-scale computational noise, although the time step is set to be smaller than the recommended one. Artificial second-order diffusion damps the noise, however it negates the advantage of the high-order advection scheme. Explicit fourth-order diffusion damps the noise, however it smoothes the field more than the inherent, implicit diffusion in the third-order scheme. Contrary to this, explicit sixth-order diffusion damps noise, but maintains detailed structure and high energy spectral density of the vertical velocity. We recommend the fifth-order upwind scheme for use in the WRF Model.

The authors demonstrate that much can be learned about the performance of a numerical weather prediction (NWP) model by examining the temporal modes of its simulated rainfall. Observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network are used to evaluate the rainfall frequency, and its diurnal and semidiurnal modes, in simulations made by a preliminary version of the Weather Research and Forecasting (WRF) model for the conterminous United States during the summer of 2003. Simulations and observations were broadly similar in the normalized amplitudes of their diurnal and semidiurnal modes, but not in the modes' phases, and not in overall frequency of rain. Simulated rain fell too early, and light rain was too frequent. The model also did not produce the distinct, nocturnal maximum in rainfall frequency that is integral to the hydrologic cycle of the Great Plains. The authors provide evidence that there were regional and phenomenological dependencies to the WRF model's perform...

The Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) is a research investigation using highly mobile platforms to examine the life cycles of mesoscale convective systems. It represents a combination of two related investigations to study (a) bow echoes, principally those that produce damaging surface winds and last at least 4 h, and (b) larger convective systems that produce long-lived mesoscale convective vortices (MCVs). The field phase of BAMEX utilized three instrumented research aircraft and an array of mobile ground-based instruments. Two long-range turboprop aircraft were equipped with pseudo-dual-Doppler radar capability, the third aircraft was a jet equipped with dropsondes. The aircraft documented the environmental structure of mesoscale convective systems (MCSs), observed the kinematic and thermodynamic structure of the convective line and stratiform regions (where rear-inflow jets and MCVs reside), and captured the structure of mature MCVs. The ground-based instruments augmented sou...

The authors examine the degree of hydrostatic and gradient balances in a mesoscale convective vortex (MCV) in the stratiform region of a mesoscale convective system (MCS) that crossed Oklahoma on 1 August 1996. Results indicate that the MCV was partially unbalanced because the cool layer at the base of its core was too cool and too shallow to balance the tangential winds about the MCV's axis. The apparent imbalance may have been due to strong, unsteady forcing on the vortex; insufficient or unrepresentative data; approximations used in the analysis; or reasons that are unknown.

The authors employ data from the NOAA Wind Profiler Network to present a scale-discriminating vorticity budget for a mesoscale convective vortex (MCV) that was generated by a mesoscale convective system (MCS) in Oklahoma and Kansas on 1 August 1996. A spatial bandpass filter was used to divide observed wind into mesoscale and synoptic components. Then the authors sought sources and sinks of vorticity in those two components over 9 h of the MCV's lifetime. The vorticity budget reveals that both the mesoscale and synoptic winds supplied significant vorticity to the MCV. The vortex's origin could not be proved, but data weakly suggest that tilting may have been mostly responsible. Convergence of absolute vorticity by the mesoscale wind was the reason the MCV grew deeper and stronger as the MCS weakened. Finally, tilting of synoptic and mesoscale vorticity by gradients in mesoscale vertical motion was responsible for a secondary deepening of the MCV as the MCS dissipated. The budget suggests that, if...

The authors present a unique, scale-discriminating study of the environment-relative circulations within a mesoscale convective system (MCS) and mesoscale convective vortex (MCV). The MCS, a leading convective line and trailing stratiform region that became asymmetric, passed through the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) in Kansas and Oklahoma on 1 August 1996. The MCV appeared in the MCS’s stratiform region just prior to the system’s mature stage and grew to a depth of over 12 km as the MCS dissipated. The MCV did not apparently survive to the next day. A spatial bandpass filter was used to divide observed wind into a component that was predominantly synoptic background wind and a component that was predominantly a mesoscale perturbation on that background wind. A mesoscale updraft, mesoscale downdraft, and divergent outflows in the lower and upper troposphere were evident after the synoptic background wind was removed, so these four circulations were internal and fundamental to the MCS. The mesoscale perturbation in wind in the middle troposphere extended farther behind the MCS than ahead of it, consistent with analytic studies and numerical simulations of gravity waves generated by heat sources characteristic of MCSs with leading convective lines and trailing stratiform regions. Deepening of the MCV appeared to be reflected in the vertical wind shear at the vortex’s center: as the MCV strengthened, the mesoscale shear through its lower part decreased, perhaps as wind became more vortical at increasing altitudes. Mesoscale and synoptic vertical shears were of similar magnitude, so an average of environmental soundings outside an MCS probably does not accurately represent the shear that affects an MCV. This suggests the need to reevaluate how the kinematical settings of MCVs are diagnosed.

Nocturnal boundary-layer phenomena in regions of complex topography are extremely diverse and respond to a multiplicity of forcing factors, acting primarily at the mesoscale and microscale. The interaction between different physical processes, e.g., drainage promoted by near-surface cooling and ambient flow over topography in a statically stable environment, may give rise to special flow patterns, uncommon over flat terrain. Here we present a climatography of boundary-layer flows, based on a 2-year archive of simulations from a high-resolution operational mesoscale weather modelling system, 4DWX. The geographical context is Dugway Proving Ground, in north-western Utah, USA, target area of the field campaigns of the MATERHORN (Mountain Terrain Atmospheric Modeling and Observations Program) project. The comparison between model fields and available observations in 2012–2014 shows that the 4DWX model system provides a realistic representation of wind speed and direction in the area, at least in an average sense. Regions displaying strong spatial gradients in the field variables, thought to be responsible for enhanced nocturnal mixing, are typically located in transition areas from mountain sidewalls to adjacent plains. A key dynamical process in this respect is the separation of dynamically accelerated downslope flows from the surface.

On the evening of 28 July 1997 the city of Fort Collins, Colorado, experienced a devastating flash flood that caused five fatalities and over 200 million dollars in damage. Maximum accumulations of rainfall in the western part of the city exceeded 10 in. in a 6-h period. This study presents a multiscale meteorological overview of the event utilizing a wide variety of instrument platforms and data including rain gauge, CSU—CHILL multiparameter radar, Next Generation Radar, National Lightning Detection Network, surface and Aircraft Communication Addressing and Reporting System observations, satellite observations, and synoptic analyses. Many of the meteorological features associated with the Fort Collins flash flood typify those of similar events in the western United States. Prominent features in the Fort Collins case included the presence of a 500-hPa ridge axis over northeastern Colorado; a weak shortwave trough on the western side of the ridge; postfrontal easterly upslope flow at low levels; w...

By animating enhanced coarse surface pressure observations of 12 1985 Preliminary Regional Experiment for Storm-Scale Operational Research Meteorology (PRE-STORM) mesoscale convective systems (MCSs) the authors exposed 92 transitory highs and lows living within virtually all of the systems’ mesohighs and wake lows. A quasi-Lagrangian (feature following, not material following) analysis of the pressure fields produced five primary results. First, these transients, with magnitudes of a few millibars, horizontal dimensions of order 100 km, and average lifetimes of about 2 h, collectively composed spatial and temporal envelopes that contributed at least part of the total pressure field within mesohighs and wake lows. Transients did not apparently favor formation or dissipation in any location of the envelopes. Second, as the MCSs matured, the difference between each complex’s transitory highs’ mean pressure and transitory lows’ mean pressure increased in 78% of the conclusive cases. Apparently, one frequent role of MCSs is locally to magnify storm-scale pressure gradients. Third, transient paths reflect the frequent symmetric-to-asymmetric metamorphoses of the MCSs. Fourth, the temporal fluctuations of the numbers and apparent sizes of transients within a composite MCS partially support theories of MCS upscale evolution. Finally, the composite’s transient numbers and apparent sizes varied almost identically with time in a pattern that closely resembles the fluctuation of stratiform and convective volumetric rain rates of MCSs.

A variety of high-resolution atmospheric-model applications are running on the high performance computer cluster at Dugway Proving Ground (DPG), with the objective of providing accurate weather information to forecasters, test managers and planners, and decision-support staff at ATEC test ranges. The foundation of these applications is the 4D Weather (4DWX) forecasting system, which is built upon the Real-Time Four Dimensional Data Assimilation (RTFDDA) process and the Weather Research and Forecasting (WRF) and Penn State University/NCAR Mesoscale Model version 5 (MM5) forecasting models. The RTFDDA has been expanded for generating ensemble analyses and probabilistic forecasts (the E-RTFDDA system), and a 30-member E-RTFDDA system has been implemented and operated on the HPC cluster at DPG since August 2007. The system contains three nested domains with a fine mesh having a 3.3-km grid increment. The system generates 48-h forecasts every 6 hours. E-RTFDDA design features, capabilities, and real-time operation at DPG are introduced. This application, crucially depending on high performance computerpsilas computing power, is unique advanced technology that can serve as a national asset to support homeland security and homeland defense.

During the first ascent of the Matterhorn, a remarkable optical effect comprising three crosses surrounded by a great arch was observed by Edward Whymper, the British mountaineer. The authors review previous published explanations of the apparition. There are no photographs, only a woodcut and sketch, so the size of the apparition is not known, and it is not possible to make a definitive conclusion about what caused it. A fogbow and ice crystal arcs could have produced a circle and crosses in a direction consistent with the apparition. Some simulations are presented; one has a form approximating Whymper’s sketch. However, while this simulation used a crystal type that can occur, it required an unusual alignment that would be very rare.