Statistical-dynamical downscaling is one method to model urban climate using global and regional climate model (GCM and RCM) results. In this study, different strategies for statistical-dynamical downscaling are derived and evaluated. For the statistical part of downscaling, a Bivariate Skill Score is developed to quantify the overlap of the joint probability densities of two meteorological variables, which helps to quantify whether the statistically selected days are representative of the full climatology. Results show that for representing the winter climate of the selected urban area (Hamburg, Germany), more days need to be simulated than for summer climate (129 days vs. 40 days simulated). For the dynamical part of the downscaling (from similar to 20 km to 250 m), the mesoscale atmospheric model METRAS is used with two different grid structures: (a) downscaling with three one-way nested domains and (b) downscaling with one domain employing a non-uniform grid. The downscaling method with the non-uniform grid is less expensive in preparing and computing resources than the method with tree one-way nests. The evaluation shows that similar model results are achieved in both cases in the domain of interest. The evaluation of temperature, relative humidity, wind speed and wind direction with different metrics shows that METRAS performs well for summer and winter climate, and slightly better for summer. Furthermore, METRAS performs well with both downscaling methods and the evaluation measures are slightly better for the downscaling with the non-uniform grid. For further downscaling of GCM or RCM results to a very local scale, for example, with obstacle resolving models, using a non-uniform grid may be a good solution to reduce the number of necessary downscaling steps and the related work load for computer and humans.

Research and operational numerical weather prediction models rely on bulk-layer parameterization techniques - primarily, the Monin-Obukhov Similarity theory - to compute vertical turbulent fluxes of momentum within the atmospheric surface layer (ASL). In this way, the aerodynamic roughness length and consequently the turbulent drag over land is assumed to be an intrinsic property of the surface, ignoring characteristics of the overlying flow. Although recognized to be invalid near heterogeneous surfaces, to date, no suitable alternatives have been developed for ASL parameterization near coastal environments. In these regions, drastic spatial gradients in surface thermal and roughness properties drive cross-coastal flows, leading to phenomena that directly contradict bulk-flux assumptions. Here, we define a flow-dependent, local, inland coastal aerodynamic roughness length Z_0c for onshore flow conditions. Analysis of observations collected from a cross-shore array of inland flux towers anchored at the Monterey Bay, CA coastline from June to October 2021 during the Coastal Land-Air-Sea Interaction (CLASI) campaign reveals significant departures in Z_0c from the expected homogeneous values for increasing wind speeds and inland fetches within 8 kilometers of the coast. These findings inform development of a physical framework describing a non-dimensional Z_0c as a function of the residence time of the inland flow, a reference height, and a representative homogeneous roughness length. We explore these relationships using large-eddy simulations of a coastal onshore flow scenario to achieve general understanding of the spatial variability in Z_0c . Finally, we present a baseline empirical relationship for Z_0c based on the CLASI dataset under near-neutral, onshore flow conditions.

We present a recent study to implement and test schemes for diagnostic calculations of skin sea surface temperature in the Navy’s Coastal Ocean Model (NCOM). This includes three schemes for estimating the cool anomaly in the viscous sublayer (i.e., the ocean skin), and a fourth scheme that adds an estimate of a warm anomaly in the solar radiation-driven, thermally stratified diurnal layer at near-surface depths. Applications of these schemes are made, and their performances are evaluated against field measurements from the Coupled Air-Sea Processes and Electromagnetic Ducting Research East campaign (CASPER-East), showing overall good agreements. The statistics of the model-observation comparisons are similar and do not indicate any systematic bias towards any scheme, but differences in the model performances are noticeable and vary depending on the surface wind and solar conditions. To understand the discrepancies among the schemes, inter-model comparisons are analyzed based on the conditions of surface wind stress and solar radiation flux. The issues associated with making the warm-layer correction are discussed, in particular, including the sensitivity of the diagnostic warm-layer anomaly to the layer thickness specified a priori, and the risk of double-counting the effect of solar radiation penetration when using the high-resolution NCOM temperature fields.

Existing theories explain that fluid interactions with isolated island topography generate leeward turbulent wakes through internal processes, such as hydraulic dissipation or baroclinic tilting. Realistic island wake flows and resulting marine atmospheric boundary layer (MABL) structures subjected to diurnal changes in surface heat fluxes are not well understood, especially over islands with low topographic peaks (<1 km). Here, we elucidate diurnal characteristics of the MABL downstream of Barbados (maximum elevation = 340 m) during summertime conditions coinciding with the Moisture and Aerosol Gradients/Physics of Inversion Evolution (MAGPIE) campaign. We synthesize composited coupled ocean-atmosphere, mesoscale numerical weather prediction model solutions over 18 days in August with remote estimates of the 10-m winds obtained from synthetic aperture radar, revealing contrasting spatial and temporal characteristics of the downstream wake MABL between night and day. Overnight, windward surface drag and leeward boundary layer separation result in a laminar, shallow wake. Enhanced low-level stability reduces lateral mixing between the wake and environment, permitting retention of long, laminar wakes downstream. During the daytime, standing mountain waves weaken in favor of thermally induced leeward surface convergence, fueling a deep, buoyant MABL immediately offshore. Finally, we contrast the evolution of the simulated flow between 2 days which differ by the strength of the upstream wind forcing, thermal stratification, and surface heating over the island. We find that nuanced differences in these upstream parameters impact the downstream extent and diurnal transition of the wake, providing a link between the local MABL and regional environment.

For decades, carefully-selected and quality-controlled measurements of atmospheric turbulence and fluxes across a variety of environments – including open ocean, flat farmland, and complex mountainous terrain – has facilitated verification of classical theories of atmospheric turbulence upon which numerical weather model parameterizations, air quality studies and monitoring, and myriad other applications are based. However, measurements of coastal atmospheric boundary layer (ABL) turbulence are limited, in part due to the challenges in securing long-term measurements in addition to the inherent complexities in such environments. Specifically, coastal regions – spanning several tens of kilometers on either side of the coastline – are characterized by strong gradients in surface thermal and roughness properties and can feature complex topography, which modify the ABL beyond the canonical homogeneous theory. Consequently, the applicability of traditional theories and behavior of atmospheric turbulence within coastal environments remains an enigma.Here, we examine coastal turbulence data obtained at a novel coastal observing site, the Naval Research Laboratory Coastal Environmental Observation Station (NRL-CEOBS), located within the Monterey Bay region of Central California. We address challenges in quality controlling high-frequency turbulent data, and present preliminary eddy-covariance data collected across January to March 2024 collected from a flux tower outfitted with a sonic anemometer and gas, humidity, and temperature sensors located within 0.25 km of the coast. Computed velocity and temperature variance spectra and flux cospectra are compared with classical datasets (e.g., Kaimal et al 1972). Preliminary results reveal that the existence of a true inertial subrange hinges on the direction of the prevailing flow and therefore flux footprint. Additionally, turbulence isotropy is diagnosed using the Reynolds Stress tensor. The data reveal highly anisotropic turbulence is diagnosed more frequently during onshore-oriented flow compared to offshore flow. The proportion of isotropic data increases when decreasing the flux averaging time period, which removes large, energy-producing scales of motion from consideration. We also corroborate the flux tower measurements with upper boundary layer measurements obtained using profiling and scanning LiDAR modes, to elucidate environmental flow attributes which correlate with the near-surface turbulence behavior.

The fair‐weather (wind speeds 10 m/s), surface ( 4 m), nearshore wind field modification is examined with multiple cross‐shore arrays spanning from the coastline to 40 km offshore, deployed within four‐month‐long field experiments, measuring winds and air‐water temperatures. The over‐water to coastline winds ratio, , was previously explored for offshore winds with limited observations and minimally for onshore and alongshore winds. Array observations provide a complete picture of the nearshore wind field for all wind orientations. The over‐water wind and temperature are linearly related to coastline values near the coastline, with decreasing with distance from shore, , with a decorrelation scale of 10 km. Mean as a function of differs per wind orientation, consistent with prior work. An model is developed from exponential Gaussian Process Regression (GPR), which accurately predicts the wind field with 20% data set holdback to elucidate the cross‐shore patterns and variable co‐dependence. The modeled Partial Dependence Plots provide dependency as a function of on coastline winds, and temperature differences without preconceptions. A consistent nearshore wind slowing occurs that varies in amplitude and distance, and changes with variable co‐dependence for wind orientation. The onshore wind slowing is counterintuitive, though consistent with sophisticated numerical models. Wind gustiness exhibits dependence, with linear normalization akin to the open ocean but larger. The observations and GPR highlight nearshore winds' cross‐shore extent and complexity, which are important for atmospheric and oceanic studies.

Free space optical communication (FSO) is strongly affected by atmospheric effects such as scattering and optical turbulence. To predict availability and performance of terrestrial and ground to space systems, forecasts of these effects are needed. Unfortunately, global measurements of optical loss and scintillation are not available. So, it is not even possible to make predictions based on historical records. However, there have been many measurement campaigns that have produced data that connects optical turbulence parameters, such as C-n(2), to weather, as well as models that show reasonable agreement with experiment. Similarly, optical scattering loss can be related to rain rate and visibility. Thus, numerical weather prediction (NWP) systems may offer a way to predict FSO system performance globally. The Naval Research Laboratory's (NRL) Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) represents a state-of-the-art NWP, and includes both Nowcast capability and short-term (up to 72 hours) forecast tools applicable for any given region of the Earth in both the atmosphere and ocean. Recently COAMPS has added forecasts of C-n(2) to its capabilities. In this work we compare COAMPS predictions of optical turbulence, to measurements on NRL's Chesapeake Bay optical range. We measure scintillation on the range, and then use COAMPS C-n(2) calculations, together with a wave optic simulation code, to compare model and experiment.

Predicting evaporation duct in the stable boundary layer has been problematic as demonstrated by the discrepancies between the measured and calculated propagation loss. In particular, a limited number of modified refractive index (MRI) vertical profiles (dM/dz) observations exist when the lower atmosphere has stable thermal stratification. The evaporation duct heights calculated from the profiles in stable stratification deviate significantly when compared to those predicted by the Monin-Obukhov similarity theory (MOST). Preliminary analyses of the MRI profiles have also shown significantly different vertical structure for the thermally stable and unstable cases. These previous studies provide the motivation for focused efforts on examining the refractivity characteristics for when the surface layer is stable.

The NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) employed the NASA P-3, Stratton Park Engineering Company (SPEC) Learjet 35, and a host of satellites and surface sensors to characterize the coupling of aerosol processes, cloud physics, and atmospheric radiation within the Maritime Continent's complex southwest monsoonal environment. Conducted in the late summer of 2019 from Luzon, Philippines, in conjunction with the Office of Naval Research Propagation of Intraseasonal Tropical Oscillations (PISTON) experiment with its R/V Sally Ride stationed in the northwestern tropical Pacific, CAMP2Ex documented diverse biomass burning, industrial and natural aerosol populations, and their interactions with small to congestus convection. The 2019 season exhibited El Nino conditions and associated drought, high biomass burning emissions, and an early monsoon transition allowing for observation of pristine to massively polluted environments as they advected through intricate diurnal mesoscale and radiative environments into the monsoonal trough. CAMP2Ex's preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high-resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP2Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud, and radiation processes as well as a crucible for the design of future observing systems.

Traditional atmospheric surface layer theory assumes homogeneous surface conditions. Regardless, nearly all surface layer parameterization schemes employed within numerical weather prediction models utilize the same techni-ques within highly heterogeneous coastal regimes as for homogeneous environments. We compare predicted surface weather and fluxes of momentum, heat, and moisture -focusing mainly on momentum -from regional simulations using the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model to observations collected from offshore buoys, inland flux towers, and radiosonde profiles during the Coastal Land-Air-Sea Interaction (CLASI) project throughout the summer of 2021 around Monterey Bay, California. Results reveal that modeled cross-coastal sur-face flux gradients are spuriously discontinuous, leading to systematically overestimated fluxes and weak winds inland of the coastline during onshore flow periods. Additionally, contrary to observations, modeled surface exchange coefficients are insensitive to wind direction on both sides of the coast, which degrades predictive skill downstream from the coastline. Over the central bay, prediction degrades when near-surface wind directions deviate from the prevailing flow direction as the parameterized stress-wind relationship fails during these cases. Predictive skill over the bay is therefore linked to varia-tions in wind direction. Offshore of the geographically complex peninsula, systematic biases are less clear; however, bifur-cations in drag coefficients based on wind direction were measured here as well. Last, increasing the horizontal grid spacing from 333 m to 3 km does not significantly affect surface layer prediction. This work highlights the need to reevalu-ate surface layer parameterization methods for modeling within coastal regions.

The Coastal Land- Air-Sea Interaction (CLASI) project aims to develop new "coastaware" atmospheric boundary and surface layer parameterizations that represent the complex land-sea transition region through innovative observational and numerical modeling studies. The CLASI field effort involves an extensive array of more than 40 land- and ocean-based moorings and towers deployed within varying coastal domains, including sandy, rocky, urban, and mountainous shorelines. Eight Air-Sea Interaction Spar ( ASIS) buoys are positioned within the coastal and nearshore zone, the largest and most concentrated deployment of this unique, established measurement platform. Additionally, an array of novel nearshore buoys and a network of land-based surface flux towers are complemented by spatial sampling from aircraft, shore-based radars, drones, and satellites. CLASI also incorporates unique electromagnetic wave (EM) propagation measurements using a coherent array, drone receiver, and a marine radar to understand evaporation duct variability in the coastal zone. The goal of CLASI is to provide a rich dataset for validation of coupled, data assimilating large-eddy simulations (LES) and the Navy's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). CLASI observes four distinct coastal regimes within Monterey Bay, California (MB). By coordinating observations with COAMPS and LES simulations, the CLASI efforts will result in enhanced understanding of coastal physical processes and their representation in numerical weather prediction (NWP) models tailored to the coastal transition region. CLASI will also render a rich dataset for model evaluation and testing in support of future improvements to operational forecast models.

Our goal is to provide an overview of the microphysical measurements made during the C-FOG (Toward Improving Coastal Fog Prediction) field project. In addition, we evaluate microphysical parametrizations using the C-FOG dataset. The C-FOG project is designed to advance understanding of liquid fog formation, particularly its development and dissipation in coastal environments, so as to improve fog predictability and monitoring. The project took place along eastern Canada’s (Nova Scotia and Newfoundland) coastlines and open water environments from August−October 2018, where environmental conditions play an important role for late-season fog formation. Visibility, wind speed, and atmospheric turbulence along coastlines are the most critical weather-related factors affecting marine transportation and aviation. In the analysis, microphysical observations are summarized first and then, together with three-dimensional wind components, used for fog intensity (visibility) evaluation. Results suggest that detailed microphysical observations collected at the supersites and aboard the Research Vessel Hugh R. Sharp are useful for developing microphysical parametrizations. The fog life cycle and turbulence-kinetic-energy dissipation rate are strongly related to each other. The magnitudes of three-dimensional wind fluctuations are higher during the formation and dissipation stages. An array of cutting-edge instruments used for data collection provides new insight into the variability and intensity of fog (visibility) and microphysics. It is concluded that further modifications in microphysical observations and parametrizations are needed to improve fog predictability of numerical-weather-prediction models.

The deployment of small unmanned aerial systems (UASs) from marine vessels, integrated with meteorological sensors, has the potential to fill traditionally challenging observational gaps in the maritime environment. In this work we assessed observations from an iMET-XQ2 sensor used to characterize the marine atmospheric boundary layer (MABL) during a November 2019 field effort near San Clemente Island SCI). Complex atmospheric variability in the sampling region east of SCI was evident due primarily to westerly/northwesterly winds interacting with the changing elevations of SCI. The horizontal and vertical spatiotemporal variability made comparisons of UAS observations to the more traditional weather balloon radiosonde observations (RAOB) a challenge. However, approximately co-located (spatially and temporally) RAOB-UAS observations exhibit trends that are consistent with previous UAS studies that found iMET-XQ sensor aspiration was essential for proper performance. Specifically, at least 3 m/s of flow over the sensor is recommended, either in the form of UAS ascent speed, ambient wind speeds, or from propeller wash. The increased spatial and temporal resolution of MABL observations from UAS platforms will allow for meteorological verification of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®1) performance as well as verification of COAMPS driven electromagnetic (EM) propagation. These efforts are essential to the continued development and improvement of the Navy’s numerical weather prediction and EM radiofrequency (RF) propagation prediction capabilities.

C-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.

The objective of this work is to evaluate GOES-R (Geostationary Operational Environmental Satellites-R series) data-based fog conditions which occurred during the C-FOG (Toward Improving Coastal Fog Prediction) field campaign. The C-FOG campaign was designed to advance understanding of fog formation, development, and dissipation over coastal environments to improve predictability. The project took place along coastlines and open water environments of eastern Canada (Nova Scotia, and the Island of Newfoundland) during August−October of 2018 where environmental conditions play an important role for late season fog formation. During the C-FOG field campaign, coastal instruments were mainly located at the Ferryland supersite, Newfoundland, with two main sites, and five satellite sites, as well as on the Research Vessel Hugh R. Sharp. Key in-situ measurement instruments included microphysical, meteorological, radiation, and aerosol sensors. A fog spectral probe was used for measuring droplet spectra from 1–50 µm at the Ferryland supersite. A laser precipitation monitor with 100 µm to 10 mm size range and an optical particle counter with 0.3–17 µm at 16 spectral channels provided information for fog and drizzle discrimination. Remote sensing platforms, e.g. profiling microwave radiometer, ceilometer, microwave rain radar, lidar, meteorological towers, tethered balloons, and GOES-R products for fog coverage, and droplet size and liquid water path) were used to evaluate fog over horizontal and vertical dimensions. Results suggest that effective radius, phase, liquid water path, and liquid water content values obtained from GOES-R and the profiling microwave radiometer are comparable to ground-based in-situ observations. It is concluded that integration of observations and nowcasting products may help improve short-term local fog predictions.