Numerical Model Output Research Articles

Spiciness Anomalies of Subantarctic Mode Water in the South Indian Ocean

AbstractThis study investigates spreading and generation of spiciness anomalies of the Subantarctic Mode Water (SAMW) located on 26.6 to 26.8 σθ in the south Indian Ocean, using in situ hydrographic observations, satellite measurements, reanalysis datasets, and numerical model output. The amplitude of spiciness anomalies is about 0.03 psu or 0.13°C and tends to be large along the streamline of the subtropical gyre, whose upstream end is the outcrop region south of Australia. The speed of spreading is comparable to that of the mean current, and it takes about a decade for a spiciness anomaly in the outcrop region to spread into the interior up to Madagascar. In the outcrop region, interannual variability in mixed layer temperature and salinity tends to be density compensating, which indicates that Eulerian temperature or salinity changes account for the generation of isopycnal spiciness anomalies. It is known that wintertime temperature and salinity in the surface mixed layer determine the temperature and salinity relationship of a subducted water mass. Considering this, the mixed layer heat budget in the outcrop region is estimated based on the concept of effective mixed layer depth, the result of which shows the primary contribution from horizontal advection. The contributions from Ekman and geostrophic currents are comparable. Ekman flow advection is caused by zonal wind stress anomalies and the resulting meridional Ekman current anomalies, as is pointed out by a previous study. Geostrophic velocity is decomposed into large-scale and mesoscale variability, both of which significantly contribute to horizontal advection.

Kinetic energy of M2 tide in the Makassar Strait

The Makassar Strait (MS) is the main passage through the Pacific to Indonesian waters, which divides the islands of Kalimantan and Sulawesi. These waters are rich with natural resources. The research objective was to determine the energy produced by the M2 tides in the Makassar Strait. Ocean currents and energy are analyzed from the output of a two-dimensional numerical model. The results showed that the tidal flow velocity in the Makassar Strait was above 1 m/s, especially in Balikpapan (1.1975 m/s,) Mamuju (1.3379 m/s), and Pantoloan/Palu (1.2728 m/s). While the power or tidal current energy in Balikpapan is 3, 6238 × 104 kWatt, Mamuju is 2, 0215 × 105 kWatt, and Pantoloan/Palu is 7, 4006 × 105 kWatt. The tidal energy is intense in Pantoloan/Palu compared to other stations.

Scale‐Aware Space‐Time Stochastic Parameterization of Subgrid‐Scale Velocity Enhancement of Sea Surface Fluxes

AbstractStochastic representation of the influence of the subgrid‐scales on the resolved scales in weather and climate models has been shown to improve ensemble spread and resolved variability. We propose a statistical scale‐aware space‐time model to characterize the contribution of mesoscale wind variability to air‐sea exchanges. In an earlier study, we analyzed the difference between “true” fluxes computed from a high resolution simulation and “resolved” fluxes obtained by coarse graining. This discrepancy is modeled in space and time, conditioned on the coarse‐grained wind and precipitation fields, to parameterize the enhancement of fluxes by mesoscale velocity variations. Stochastic parameterization models have traditionally been developed for particular model resolutions without the explicit capability to adapt to model resolution. We present an approach to develop stochastic models that adapt to resolution in a scale‐aware fashion. The scale‐aware parameterization is developed from empirical results for systematically coarse‐grained high‐resolution numerical model output. The statistical model is fit from numerical model output at three different coarsening resolutions. From this scale‐aware parameterization, we derive a stochastic parameterization of flux enhancement by subgrid velocity variations for arbitrary resolutions and characterize the conditional distributions and space‐time structures of the flux enhancement across model resolutions.

Multi-task Learning Based Tropical Cyclone Intensity Monitoring and Forecasting through Fusion of Geostationary Satellite Data and Numerical Forecasting Model Output

Multi-task Learning Based Tropical Cyclone Intensity Monitoring and Forecasting through Fusion of Geostationary Satellite Data and Numerical Forecasting Model Output

Modeling of Indian monsoon extremes during 850-2000AD using the proxy-data from speleothems

This work highlights the potential significance of palaeoclimate model outputs in explaining the physical processes and mechanisms to better understand the speleothem-based proxy records of the precipitation and temperature signals during the Indian northeast and southwest monsoon from 850 to 2000AD. Thus, discussion is mainly focused on this conception by providing insights for aligning modeling preferences with the proxy records. δ18O speleothem values show that precipitation over northeast India (NEI) remained constantly higher than that over the summer core monsoon zone (CMZ) from 850-2000AD. Based on the dynamical numerical coupled model (CSIRO-Mk3L-1–2) outputs, the data are in alignment with the presence of four proxy-based major extreme climatic events: Dry Century (DC), Medieval Climatic Anomaly (MCA), Extreme Dry Century (EDC) and Little Ice Age (LIA) during the last millennia. In these extremes a significant trend in diurnal temperature range (DTR), in particular, is correspondingly observed. These extremes and associated mechanisms are examined using surface energetics and corresponding atmospheric-oceanic coupled processes. The analysis of downward radiative (shortwave and longwave), latent and sensible heat fluxes provided better rationale and understanding of associated physical processes and mechanisms. It is found that the latent and sensible heat fluxes were balanced by downward radiative fluxes and the remaining part of energy was anomalously trapped inside the surface, thus reflecting anomalous climatic behavior during these extremes.

Evaluating Wind Fields for Use in Basin‐Scale Distributed Snow Models

AbstractMountain winds are the driving force behind snow accumulation patterns in mountainous catchments, making accurate wind fields a prerequisite to accurate simulations of snow depth for ecological or water resource applications. In this study, we examine the effect that wind fields derived from different coarse data sets and downscaling schemes has on simulations of modeled snow depth at resolutions suitable for basin‐scale modeling (>50 m). Simulations are run over the Tuolumne River Basin, CA for the accumulation season of Water Year 2017 using the distributed snow model, SnowModel. We derived wind fields using observations from either sensor networks, the North American Land Data Assimilation System (12.5 km resolution), or High‐Resolution Rapid Refresh (HRRR, 3 km resolution) data, and downscaled using terrain‐based multipliers (MicroMet), a mass‐conserving flow model (WindNinja), or bilinear interpolation. Two wind fields derived from 3 km HRRR data and downscaled with respect to terrain produced snow depth maps that best matched observations of snow depth from airborne LiDAR. We find that modeling these wind fields at 100 and 50 m resolutions do not produce improvements in simulated snow depth when compared to wind fields modeled at a 150 m resolution due to their inability to represent wind dynamics at these scales. For input to distributed snow models at the basin‐scale, we recommend deriving wind fields from high resolution numerical weather prediction model output and downscaling with respect to terrain. Future studies should compare suspension schemes used in blowing snow models and investigate wind downscaling schemes of complexity between statistical and fluid dynamic models.

Reproducing pyroclastic density current deposits of the 79 CE eruption of the Somma–Vesuvius volcano using the box-model approach

Abstract. We use PyBox, a new numerical implementation of the box-model approach, to reproduce pyroclastic density current (PDC) deposits from the Somma–Vesuvius volcano (Italy). Our simplified model assumes inertial flow front dynamics and mass deposition equations and axisymmetric conditions inside circular sectors. Tephra volume and density and total grain size distribution of EU3pf and EU4b/c, two well-studied PDC units from different phases of the 79 CE Pompeii eruption, are used as input parameters. Such units correspond to the deposits from variably dilute, turbulent PDCs. We perform a quantitative comparison and uncertainty quantification of numerical model outputs with respect to the observed data of unit thickness, inundation areas and grain size distribution as a function of the radial distance to the source. The simulations consider (i) polydisperse conditions, given by the total grain size distribution of the deposit, or monodisperse conditions, given by the mean Sauter diameter of the deposit; (ii) axisymmetric collapses either covering the whole 360∘ (round angle) or divided into two circular sectors. We obtain a range of plausible initial volume concentrations of solid particles from 2.5 % to 6 %, depending on the unit and the circular sector. Optimal modelling results of flow extent and deposit thickness are reached on the EU4b/c unit in a polydisperse and sectorialized situation, indicating that using total grain size distribution and particle densities as close as possible to the real conditions significantly improves the performance of the PyBox code. The study findings suggest that the simplified box-model approach has promise for applications in constraining the plausible range of the input parameters of more computationally expensive models. This could be done due to the relatively fast computational time of the PyBox code, which allows the exploration of the physical space of the input parameters.

A Numerical Analysis of an Innovative Flow Ripple Reduction Method for External Gear Pumps

In this paper, an innovative solution to minimize noise emission, acting on the flow ripple, in a prototype External Gear Pump (EGP) is presented. Firstly, a new tool capable to completely simulate this pump’s typologies, called EgeMATor, is presented; the hydraulic model, adopted for the simulation, is based on a lumped parameter method using a control volume approach. Starting from the pump drawing, thanks to different subroutines developed in different environments interconnected, it is possible to analyze an EGP. Results have been compared with the outputs of a three-dimensional CFD numerical model built up using a commercial code, already used with success by the authors. In the second section, an innovative solution to reduce the flow ripple is implemented. This technology is called Alternative Capacitive Volumes (ACV) and works by controlling and uniformizing the reverse flow, performing a consistent reduction of flow non-uniformity amplitude. In particular, a high reduction of the flow non-uniformity is notable in the frequency domain on the second fundamental frequency. The technology is easy to accommodate in a pump housing, especially for high-pressure components, and it helps with reducing the fluid-borne noise.

A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables

Short-term (less than 1 h) forecast of the power generated by wind turbines in a wind farm is extremely challenging due to the lack of reliable data from meteorological towers and numerical weather model outputs at these timescales. A robust deep learning model is developed for short-term forecasts of wind turbine generated power in a wind farm using the state-of-the-art temporal convolutional networks (TCN) to simultaneously capture the temporal dynamics of the wind turbine power and relationship among the local meteorological variables. An orthogonal array tuning method based on the Taguchi design of experiments is utilized to optimize the hyperparameters of the proposed TCN model. The proposed TCN model is validated using twelve months of data from a 130 MW utility-scale wind farm with 86 wind turbines in comparison with some of the existing methods in the literature. The power curves obtained from the proposed TCN model show consistent improvements over existing methods at all wind speeds.

Incorporating the North Atlantic Oscillation into the post‐processing of MOGREPS‐G wind speed forecasts

AbstractChanges in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an incorrect representation of the polar jet stream and its associated physical processes, it is reasonable to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post‐processing methods through a regime‐dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS‐G. The mixture model offers substantial improvements upon conventional post‐processing methods when the local wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance otherwise. A measure of regime dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime‐dependent post‐processing. Implementing the regime‐dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.

Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity

The vision for sewage treatment plants is being revised and they are no longer considered as pollutant removing facilities but rather as water resources recovery facilities (WRRFs). However, the newly adopted bioprocesses in WRRFs are not fully understood from the microbiological and kinetic perspectives. Thus, large variations in the outputs of the kinetics-based numerical models are evident. In this research, data driven models (DDM) are proposed as a robust alternative towards modelling emerging bio- processes. Methanotrophs are multi-use bacterium that can play key role in revalorizing the biogas in WRRFs, and thus, a Multi-Layer Perceptron Artificial Neural Network (ANN) model was developed and optimized to simulate the cultivation of mixed methanotrophic culture considering multiple environmental conditions. The influence of the input variables on the outputs was assessed through de- veloping and analyzing several different ANN model configurations. The constructed ANN models demonstrate that the indirect and complex relationships between the inputs and outputs can be accurately considered prior to the full understanding of the physical or mathematical processes. Furthermore, it was found that ANN models can be used to better understand and rank the influence of dif- ferent input variables (i.e., the physical parameters that influence methanotrophs) on the microbial activity. Methanotrophic-based bio- processes are complex due to the interactions between the gaseous, liquid and solid phases. Yet, for the first time, this study success- fully utilized DDM to model methanotrophic-based bioprocesses. The findings of this research suggest that DDM are a powerful, al- ternative modeling tool that can be used to model emerging bioprocesses towards their implementation in WRRFs.

Orographic Turbulence Forecasting from Numerical Model Output Data

A method to forecast orographic turbulence based on the COSMO-Ru7 numerical model output data is presented. The calculation algorithm represents the critical amplitude approach for gravity waves and allows identifying zones of mountain wave breaking that generate turbulence. Its intensity is estimated through the wave drag value. The calculation was based on the model data for the territory of European Russia for April–September in 2019. Its results obtained from forecast data are in good agreement with those from initial model data for the forecast time. The turbulent zones are more intense at night than in the daytime and are situated over obstacles (for example, over the Crimean and Ural mountains) and on their lee slopes, in accordance with the physics of the phenomenon

Orographic Turbulence Forecasting from Numerical Model Output Data

A method to forecast orographic turbulence basing on the COSMO-Ru7 numerical model output data is presented. The calculation algorithm represents the critical amplitude approach for gravity waves and allows identifying zones of mountain wave breaking that generate turbulence. Its intensity is estimated through the wave drag value. The calculation is based on the model data for the territory of European Russia for April–September 2019. Its results obtained from forecast data are in a good agreement with those from initial model data for the forecast time. The turbulent zones are more intense at night than in the daytime and are situated over obstacles (for example, over the Crimean and Ural mountains) and on their lee slopes, in accordance with the physics of the phenomenon.

Режим ограниченной видимости на аэродромах европейской России

The characteristics are presented for the visibility regime at 42 aerodromes in European Russia calculated from the data of aerodrome observations reported in METAR telegrams with 30 minute (more rarely, 1 hour) time intervals. The occurrence frequency distributions of horizontal visibility ≤ 300 m and ≤ 800 m are calculated and analyzed over the period of 2001-2019. The tables are presented and discussed for the annual cycles of the occurrence frequency, as well as for its distributions under different weather phenomena. The occurrence frequency distributions depending on cloud base height, relative humidity, speed and direction of surface wind are presented. The results are also presented for the duration of low visibility episodes: it is demonstrated that such episodes are short, as a rule (for example, the visibility ≤ 300 m is continually observed for not more than 2 hours in 65-85 % of cases). The results of quantification of the correspondence between the occurrence or absence of low visibility and other weather characteristics observed at the same aerodromes demonstrate certain perspectives for developing (at least for several aerodromes) statistical methods to forecast this rather rare phenomenon basing on outputs of numerical atmosphere models. Keywords: visibility, aerodrome observations, annual cycle of low visibility, relative humidity, cloud base height, weather phenomena

Sensitivity Analysis and Effective Parametrization of PEM Fuel Cell Models

The cost of proton-exchange-membrane fuel cells (PEMFCs) remains a major hurdle in large-scale commercialization of this technology. To improve their performance and reduce cost, novel materials and electrode designs are continuously envisioned, e.g., non-PGM catalyst layers, ultra-thin Pt/Pt-Ni based catalyst layers, structured ionomer arrays or NSTF catalyst layers.1 Understanding the impact of these improvement strategies can be extremely time and cost intensive due to complex physical phenomena and large design space. We have previously developed a PEMFC modeling framework2 which has been a time and cost effective tool for understanding and optimizing the complex multi-physics phenomena within PEMFCs; however, several of the cell parameters used in the modeling have large spread in measured data.3 Furthermore, several transport parameters such as water adsorption kinetics have not been accurately measured and the approximations are spread over several orders of magnitude. These uncertainties cause problems in ascertaining accuracy of the modeling approach and they reduce the predictive power of the numerical models.The aim of this work is to identify the sensitivity of PEFC numerical model outputs to various input parameters. The previously in-house developed MEA modeling framework2 is used for PEMFC modeling. The sensitivity of the model outputs with respect to inputs parameters is obtained by analyzing the condition numbers for different output-input pairs at varying operating conditions. An example of the sensitivity analysis is shown in Figure 1. The condition numbers are obtained for the entire possible range of input parameters at varying operating conditions to identify the most crucial parameters of the PEMFC model. Based on our preliminary analysis, parameters related to kinetics (exchange current density and ECSA) and heat/water management in electrodes and ionomer (ionomer fraction, thermal conductivity) are most crucial.One of the major goals of this work is to identify the most crucial set of parameters towards which the model shows maximum sensitivity. This will guide future experimentalists to measure these properties with higher accuracy. Furthermore, the sensitivity analysis will also enable us to optimize the PEMFC performance by selectively targeting the most sensitive parameters and thereby making the largest impact. Acknowledgements The work is funded under the Fuel Cell Performance and Durability Consortium (FC-PAD), by the Fuel Cell Technologies Office (FCTO), Office of Energy Efficiency and Renewable Energy (EERE), of the U.S. Department of Energy under contract number DE-AC02-05CH11231. The authors would like to thank Nathan Craig at Robert Bosch LLC for his valuable input in designing the sensitivity analysis. The authors would also like to thank Giovanna Bucci and Matthias Hanauer at Robert Bosch for their valuable inputs and discussion. References P. K. Sinha, W. Gu, A. Kongkanand and E. Thompson, J. Electrochem. Soc., 158, B831 (2011).L. M. Pant, M. R. Gerhardt, N. Macauley, R. Mukundan, R. L. Borup and A. Z. Weber, Electrochim. Acta, 326, 134963 (2019).R. Vetter and J. O. Schumacher, ArXiv181110091 Phys. (2018). Figure 1

Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but they often suffer from inaccuracies, e.g., due to insufficient spatial resolution. sDoG (statistical Downscaling for Glacierized mountain environments) is a reanalysis data postprocessing tool designed to extend short-term weather station data from high mountain sites to the baseline climate. In this study, sDoG is applied to ERA-Interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. First, sDoG is trained and cross-validated using observations from 2002 to 2012 (cross-validation period). Then, the sDoG retrospective forecast and its cross-validation-based uncertainty estimates are evaluated for the period 1979–2001 (hereafter referred to as the true evaluation period). We demonstrate the ability of sDoG to model air temperature in the true evaluation period for different temporal scales: day-to-day variations, year-to-year and season-to-season variations, and the 23-year mean seasonal cycle. sDoG adds significant value over a selection of reference data sets available for the site at different spatial resolutions, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product. However, we identify limitations of sDoG in modeling summer air temperature variations particularly evident in the first part of the true evaluation period. This is most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. When comparing the performance of the considered reference data sets, we cannot demonstrate added value of the higher resolution data sets over the data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA-Interim (80 km resolution) both clearly outperform the higher resolution data sets ERA5-Land (9 km resolution), UERRA HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences among ERA5 and ERA-Interim, by contrast, are comparably small. Our study highlights the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas both for data users and model developers.

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices—Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)—were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05°).

Discussion on A high‐resolution bilevel skew‐tstochastic generator for assessing Saudi Arabia's wind energy resources

AbstractStatistical spatiotemporal environmental data analysis is rarely straightforward, with one having to face challenges relating to big data, non‐Gaussianity, nonstationarity, multiple scales of behavior, deterministic (numerical) model output, and more. One often has to rely heavily on good statistical parallel computing skills and sound knowledge of the application domain. The work of Tagle et al. (2020) overcomes all of these challenges, and is an excellent example of the tangible contributions spatiotemporal modeling and distribution theory can make to the environmental sciences at the policy level. In this discussion piece I focus on a few high‐level concepts in the paper of Tagle et al. (2020) that are relevant to related application domains. I also provide some technical suggestions that could be used to facilitate inference.

A synthetic case study of measuring the misfit between 4D seismic data and numerical reservoir simulation models through the Momenta Tree

Data assimilation is an important and time-consuming process in petroleum reservoir numerical simulation. It produces a set of calibrated models used to forecast and optimize oil and gas production. The process focuses on reducing uncertainties related to reservoir properties, yielding numerical reservoir models that plausibly reproduce measured data from the field, such as well rates and pressure.Besides the traditional well-production data, 4D seismic data are increasingly being used to reduce the uncertainty of numerical reservoir models, by providing dynamic spatial data to be matched. Although 4D seismic data reveal essential information about the dynamic behavior of the reservoir, its integration in data assimilation procedures is challenging, especially in a quantitative way, because of their noisy and uncertain nature and their larger resolution when compared to the resolution of simulated data from numerical reservoir models.The development of metrics able to efficiently estimate the discrepancies between 4D seismic data and numerical reservoir model outputs is a current research interest for data assimilation, given the challenges of integrating these different types of data.We introduce the Momenta Tree. It uses orthogonal moments supporting a multi-level data representation, where features are organized in nodes related to different levels of region detail. It supports the comparison of simulated data from numerical reservoir models and observed 4D images of seismic data, images, using different resolutions and considering various domains. The similarity between data is calculated with the extended Jaccard distance and is represented by a phylogenetic tree; the simulated models are represented as circles in branches, and their similarity is captured by connections. We apply the Momenta Tree to a controlled case, introduced in this paper, to validate and compare the new metric with traditional metrics, and a more complex representative case based on real oil industry data.Our results show that the Momenta Tree metric retains the same sequential similarity in environments affected by noise. The highest-ranked models using the Momenta Tree relate to forecast behavior closer to the reference data than the highest-ranked models obtained with traditional methods. An additional advantage of the Momenta Tree is its ability to enable data comparison in various domains (P-impedance and Water Saturation) at different resolutions of seismic and simulation data.

C-FOG: Life of Coastal Fog

AbstractC-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.