Mesoscale Prediction Research Articles

Distinct Element macro-crack networks for expedited discontinuum seismic analysis of large-scale URM structures

Discontinuum analysis is a powerful tool for the detailed seismic assessment of unreinforced masonry (URM) structures, whose widespread use is however still mostly limited to small-scale assemblies or isolated components. Despite their unique capabilities, including the explicit simulation of separation phenomena, collapses and out-of-plane (OOP) failures that are hardly replicable using traditional continuum solutions, the high computational cost entailed by micro-to-meso-scale discontinuum modelling strategies, which are the standard approaches in applied research, presently prevent their employment for building-scale problems. The few available macro-scale discontinuum models specifically conceived for URM, on the other hand, rely on complex geometrical discretization processes that require costly manual work, as well as on less efficient deformable block formulations. In this paper, a new simplified discontinuum macro-model is presented and validated against full-scale laboratory test outcomes on walls, pier-spandrel systems and building specimens, in addition to various meso-scale numerical results, under either in-plane (IP) or OOP actions, and considering quasi-static (monotonic, cyclic) or dynamic loadings. The proposed simulation technique, implemented in a robust Distinct Element Method (DEM) framework, leverages a semi-automated Equivalent Frame discretization algorithm that idealizes masonry piers, spandrels and nodes as an assembly of interlocked rigid macro-blocks connected by a network of zero-thickness interface springs. The latter are herein demonstrated to effectively replicate URM damage at the component-level through fracture energy contact laws, providing macro-scale yet representative failure patterns, as well as adequate predictions of overall strength and deformation capacities. Results obtained show a good agreement between macro- and more sophisticated meso-scale predictions, as well as with experimental outcomes, albeit dissimilarities among measured and computed force-displacement responses – similarly to other simplified strategies – were detected as vertical overburden increases. Notably, for analogous levels of accuracy, the analysis time required by our new macro-models is up to 150 times lower than its meso-counterparts. The use of the proposed simplified strategy also enabled the satisfactory simulation of the quasi-static cyclic and seismic responses of full-scale building-scale specimens, prohibitive tasks using traditional detailed DEM models, while also being up to 10 times faster than previous deformable macro-models.

Tropical cyclone wave data assimilation impact on air-ocean-wave coupled Hurricane Harvey (2017) forecast

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) air-ocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey’s 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series

Predictive modeling of 3D textile composites using realistic micromechanical representations

An innovative and efficient progressive damage model, informed by coupon tests, and using minimal, readily available material parameters, is proposed in this paper to comprehensively predict the tensile, compressive and shear stress–strain response and strengths of a 3D woven angle interlock composite within 5% of test data. Realistic, high-fidelity microgeometries, with process-induced defects, and devoid of geometric assumptions, are constructed using the digital element approach. Excellent correlations with test micrographs are observed for yarn cross-section areas, aspect ratios and crimp ratios, that are on average within 5.5%, 1.9% and 1% of the micrograph measurements respectively. The mesoscale predictions capture the detailed failure mechanisms under each loading condition. Analytical stiffness expressions are derived in terms of the constituent material elastic properties and architecture geometry, which also predict the stiffnesses within 10% error margin. A robust workflow is established that combines the high-fidelity microgeometry generation, automated conformal finite element model creation and comprehensive property prediction capabilities to provide a holistic solution.

Impact of Land Surface Snow Processes on the Arctic Stable Boundary Layer

Abstract The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods

Abstract This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts. Significance Statement As the general Navy meteorological and oceanographic (METOC) community engages in collaboration with the broader scientific community, our goal is to harness community tools like MET for the systematic evaluation of weather forecasts, with a specific focus on variables crucial to the Navy. Clouds, given their significant impact on visibility, hold particular importance in our investigations. Cloud forecasts pose unique challenges, primarily attributable to the intricate physics governing cloud development and the complexity of representing these processes within numerical models. Cloud observations are also constrained by limitations, arising from both top-down satellite measurements and bottom-up ground-based measurements. This study illustrates that, with a comprehensive understanding of community tools, cloud forecasts can be consistently verified. This verification encompasses traditional evaluation methods, measuring general qualities such as bias and root-mean-squared error, as well as newer techniques like spatial and object-based methods designed to account for displacement errors.

Economical microscale predictions of wind over complex terrain from mesoscale simulations using machine learning

The ability to assess detailed wind patterns in real time is increasingly important for a variety of applications, including wind energy generation, urban comfort and environmental health, and drone maneuvering in complex environments. Machine Learning techniques are helping to develop accurate and reliable models for predicting local wind patterns. In this paper, we present a method for obtaining wind predictions with a higher resolution, similar to those from computational fluid dynamics (CFD), from coarser, and therefore less expensive, mesoscale predictions of wind in real weather conditions. This is achieved using supervised learning techniques. Four supervised learning approaches are tested: linear regression (SGD), support vector machine (SVM), k-nearest neighbors (KNn) and random forest (RFR). Among the four tested approaches, SVM slightly outperforms the others, with a mean absolute error of 1.81 m/s for wind speed and 40.6∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} for wind direction. KNn however achieves the best results in predicting wind direction. Speedup factors of about 290 are achieved by the model with respect to using CFD.

Preliminary Use of Convection-allowing Models in Fire Weather

Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.

Soil Moisture Influences on Summer Arctic Cyclones and Their Associated Poleward Moisture Transport

Abstract Moisture transport into the Arctic is an important modulator for clouds, radiative forcing, and sea ice change. Transport events—namely, moist-air intrusions—are often associated with Arctic cyclones, and during the summer season we find that the high-latitude land surface is a significant moisture source for intrusions. Summer Arctic cyclones typically originate from the surrounding continental interior and shorelines where during the early stages of intensification the warm sector experiences strong latent heat fluxes from the land surface. In this study, we use multiyear reanalysis data and back-trajectory calculations to quantify the linkages between key continental moisture source regions and water vapor within cyclone-induced intrusions. We also conduct regional soil moisture sensitivity experiments using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to diagnose the land surface moisture contribution for an August 2016 Arctic cyclone case. Results from reanalysis show that land regions on average account for more than 30% of the total moist-air intrusion flux at 70°N during summer. COAMPS case-study experiments reaffirm this result, showing that land surface moisture flux on average accounts for 30% of the intrusion water vapor content. COAMPS experiments further reveal that land surface moisture impacts cyclone intensification and moist-air intrusion cloud water vapor. When the regional soil moisture is reduced, intrusion cloud cover is also reduced, resulting in an increase in the surface solar radiation > 90 W m−2. These results demonstrate that the high-latitude land surface plays an important role in the Arctic summer hydrological cycle and may be increasingly impactful as traditionally cold or frozen soils warm. Significance Statement The purpose of this study is to better understand to what extent high-latitude land surface moisture flux is entrained into summer Arctic cyclones and their poleward moisture transport. Moisture transport into the Arctic is important for regional clouds, radiative forcing, and sea ice change. Our results show that land surface evaporation augments cyclone-induced moisture transport into the Arctic on average by 30%. Results are important because traditionally cold or partially frozen high-latitude soils continue to warm and exhibit more positive evaporative trends, including areas undergoing permafrost thaw.

Machine Learning–Based Cloud Forecast Corrections for Fusions of Numerical Weather Prediction Model and Satellite Data

Abstract Given the diversity of cloud-forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning–based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numerical weather prediction model error trends as well as improving the accuracy and sensitivity of the forecasts. The framework implements a UNet convolutional neural network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite-16 (GOES-16) as well as clouds predicted by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper-tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3–12 h). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application and a comparative assessment of results for upper-tropospheric clouds. Significance Statement Clouds are difficult to forecast because they require, in addition to spatial location, accurate height, depth, and cloud type. Satellite imagery is useful for verifying geographical location but is limited by 2D technology. Multiple cloud types can coexist at various heights within the same pixel. In this situation, cloud/no cloud verification does not convey much information about why the forecast went wrong. Sorting clouds by physical attributes such as cloud-top height, atmospheric stability, and cloud thickness contributes to a better understanding since very different physical mechanisms produce various types of clouds. Using a fusion of numerical model outputs and GOES-16 observations, we derive variables related to atmospheric conditions that form and move the clouds for our machine learning–based cloud forecast. The resulting verification over the U.S. mid-Atlantic region revealed our machine learning–based cloud forecast corrects systematic errors associated with high atmospheric clouds and provides accurate and consistent cloud forecasts from 3 to 12 h lead times.

Antarctic atmospheric Richardson number from radiosonde measurements and AMPS

Abstract. Monitoring a wide range of atmospheric turbulence over the Antarctic continent is still tricky, while the atmospheric Richardson number (Ri; a valuable parameter which determines the possibility that turbulence could be triggered) is easier to obtain. The Antarctic atmospheric Ri, calculated from the potential temperature and wind speed, was investigated using the daily results from the radiosoundings and forecasts of the Antarctic Mesoscale Prediction System (AMPS). Radiosoundings for a year at three sites (McMurdo – MM, South Pole – SP, and Dome C – DC) were used to quantify the reliability of the AMPS forecasts. The AMPS-forecasted Ri can identify the main spatiotemporal characteristics of atmospheric turbulence over the Antarctic region. The correlation coefficients (Rxy) of log 10(Ri) at McMurdo, the South Pole, and Dome C are 0.71, 0.59, and 0.53, respectively. The Ri was generally underestimated by the AMPS and the AMPS could better capture the trend of log 10(Ri) at relatively unstable atmospheric conditions. The seasonal median of log 10(Ri) along two vertical cross-sections of the AMPS forecasts are presented, and it shows some zones where atmospheric turbulence can be highly triggered in Antarctica. The Ri distributions appear to be reasonably correlated to some large-scale phenomena or local-scale dynamics (katabatic winds, polar vortices, convection, gravity wave, etc.) over the Antarctic plateau and surrounding ocean. Finally, the log 10(Ri) at the planetary boundary layer height (PBLH) were calculated and their median value is 0.316. This median value, in turn, was used to estimate the PBLH and agrees well with the AMPS-forecasted PBLH (Rxy>0.69). Overall, our results suggest that the Ri estimated by AMPS are reasonable and the turbulence conditions in Antarctica are well revealed.

Preconditioning and Intensification of Upstream Extratropical Cyclones through Surface Fluxes

Abstract The influence of the surface latent and surface sensible heat flux on the development and interaction of an idealized extratropical cyclone (termed “primary”) with an upstream cyclone (termed “upstream”) using the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) is analyzed. The primary cyclone develops from an initial perturbation to a baroclinically unstable jet stream, while the upstream cyclone results from Rossby wave dispersion at the surface where a bottom-up style development occurs. The intensity of the upstream cyclone is strongly enhanced by surface latent heat fluxes and, to a lesser degree, by surface sensible heat fluxes. Forward trajectories initiated from the postfrontal sector of the primary cyclone travel south of the upstream anticyclone and feed into the atmospheric river and warm conveyor belt region of the upstream cyclone. Substantial moistening of this airstream is a result of upward surface latent heat flux present in both the primary cyclone’s postfrontal sector and along the southern flank of the anticyclone. Backward trajectories initiated from the same region show that these air parcels originate from a broad area north of both the anticyclone and the primary cyclone in the lower troposphere. The airstream identified represents a new pathway through which dry, descending air that is preconditioned through surface moistening enhances the development of an upstream cyclone through diabatically generated potential vorticity.

Impact of assimilating wind retrievals from high‐frequency radar on COAMPS forecasts in the Chesapeake Bay

AbstractWind retrievals from high‐frequency radars (HFRs) provide high‐density, hourly wind estimates over the ocean near the coast. These wind retrievals are a promising new source of near‐surface wind estimates in coastal regions where it is difficult to deploy large networks of buoys and where scatterometers cannot observe because of land contamination. In addition to improving ocean monitoring, these wind retrievals are also useful in numerical weather prediction. The wind estimates are retrieved from the HFR Doppler spectra in conjunction with the Simulating WAves Nearshore (SWAN) model, HFR forward model, and HFR adjoint model. In this work, wind retrievals were generated from three Coastal Ocean Dynamics Applications Radar (CODAR) SeaSonde HFR sites located near the mouth of the Chesapeake Bay in August 2017 and assimilated in the Coupled Ocean/Atmosphere Mesoscale Prediction System® (COAMPS) model. The impact of the assimilated HFR wind retrievals on near‐surface weather forecasts is measured with adjoint‐derived forecast sensitivity observation impact (FSOI) and a comparison to forecasts from a data‐denial observing system experiment (OSE). The FSOI measurement indicates the HFR winds had neutral impact on the 12‐h forecast while the OSE comparison suggests a small improvement in the 10‐m u and v winds at lead times up to 36 h. Compared to a similar study in the Southern California Bight, the small impact on the forecast seen in this Chesapeake Bay study could be related to there being a smaller number of wind retrievals in a smaller region during a period where the HFR wind retrievals were already similar to the background wind field. We suggest that HFR wind retrievals used for numerical weather prediction may be more beneficial when generated for long swaths of coastlines rather than small, isolated areas.

Evaluating Atmospheric Surface Layer Flux Parameterization within the Coastal Regime

Abstract 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 techniques 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 surface 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 variations in wind direction. Offshore of the geographically complex peninsula, systematic biases are less clear; however, bifurcations 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 reevaluate surface layer parameterization methods for modeling within coastal regions. Significance Statement Understanding surface layer weather is critical for many purposes, such as infrastructure design and weather forecasting. Within the context of numerical modeling and weather prediction, skillful forecasts of surface winds and temperature rely on accurate portrayal of the surface layer. By comparing observations collected during the Coastal Land-Air-Sea Interaction field program to numerical model solutions, we show that prediction of the surface layer fluxes of momentum, heat, and moisture break down near the coastline, which leads to biases in the predicted surface layer weather both inland and over the water. As surface layer parameterization methods across nearly all numerical models are rooted in the same practices, our results call into question the use of traditional methods near the coastline.

Discovery of Calm Astronomical Sites Over the Antarctic Continent

A calm astronomical site means a site where astronomical observation would be less likely to be interfered with by optical turbulence. Previous turbulence measurements at a few sites in Antarctica have demonstrated very calm atmospheric conditions here. So far, to realize a wide range of measurements of the turbulence conditions above the Antarctic plateau will be a great hardship. Thus, in this study, the numerical weather model outputs provided by the Antarctic Mesoscale Prediction System (AMPS) have been used. Based on the AMPS outputs, the boundary layer height and the atmospheric Richardson number were obtained, from which the turbulence conditions above the Antarctic plateau have been evaluated. Finally, a statistical conclusion evaluating the total atmospheric turbulence above the whole Antarctic continent for an entire year is first reported. We find some sites (or regions) have a calmer atmosphere than Dome A; this is of great instructional significance for planning the next generation of ground-based optical astronomical telescopes.

Examining the sensitivity of ocean response to oceanic grid resolution in COAMPS-TC during Hurricane Irma (2017)

Using the Navy's Coupled Ocean-Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC), we investigate the sensitivity of upper ocean momentum processes to varying ocean horizontal and vertical resolutions during Hurricane Irma (2017). This study aims to examine the impact of ocean resolution sensitivity on the representation of the ocean mixed layer (OML) and thermocline heat and salt budget when the tropical cyclone (TC) is at maximum intensity east of the Lesser Antilles. Results indicate that changes in ocean resolution over the forecast period do not greatly affect TC track but have variable influence on TC intensity. OML budget analyses reveal that at coarser horizontal resolutions, the overall OML response is sensitive to vertical ocean resolution. Additionally, the dominant OML processes change as horizontal resolution increases with the dependence shifting from horizontal advection to vertical mixing. Examining budget tendencies with respect to TC quadrant reveals that the right-rear quadrant ocean response is least sensitive to grid spacing. However, the OML response within the front quadrants is most sensitive to resolution changes. Moreover, thermocline downwelling is apparent in both front quadrants and the left-rear quadrant. This downwelling response is caused by a mass convergence within the OML, pumping surface water into the ocean interior. Ocean grid resolution sensitivities in coupled TC forecast models are vital to understand since OML response is indicative of air-sea response and, thus, may contribute to forecasted TC intensity. • Modeled TC intensity varies up to 12 kt at 24 h by only changing ocean grid resolution. • Dominant oceanic processes are sensitive to ocean grid resolution and change with respect to TC quadrant. • Consider increasing upper ocean vertical resolution for horizontal resolutions ≥ 10 km.

The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica

Abstract. The McMurdo Dry Valleys (MDV) are a polar desert, where glacial melt is the main source of water to streams and the ecosystem. Summer air temperatures are typically close to zero, and therefore foehn events can have a large impact on the meltwater production. A 14-month record of automatic weather station (AWS) data on Joyce Glacier is used to force a 1D surface energy balance model to study the impact of foehn events on the energy balance. AWS data and output of the Antarctic Mesoscale Prediction System (AMPS) on a 1.7 km grid are used to detect foehn events at the AWS site. Foehn events at Joyce Glacier occur under the presence of cyclones over the Ross Sea. The location of Joyce Glacier on the leeward side of the Royal Society Range during these synoptic events causes foehn warming through isentropic drawdown. This mechanism differs from the foehn warming through gap flow that was earlier found for other regions in the MDV and highlights the complex interaction of synoptic flow with local topography of the MDV. Shortwave radiation is the primary control on melt at Joyce Glacier, and melt often occurs with subzero air temperatures. During foehn events, melt rates are enhanced, contributing to 23 % of the total annual melt. Foehn winds cause a switch from a diurnal stability regime in the atmospheric surface layer to a continuous energy input from sensible heat flux throughout the day. The sensible heating during foehn, through an increase in turbulent mixing resulting from gustier and warmer wind conditions, is largely compensated for by extra heat losses through sublimation. Melt rates are enhanced through an additional energy surplus from a reduced albedo during foehn.

On the dynamics of the eradication of a warm core mesoscale eddy after the passage of Hurricane Irma (2017)

Mesoscale oceanic eddies are known to influence air-sea interactions during tropical cyclone (TC) passage by either hindering or sustaining air-sea fluxes into the TC core. However, little is known as to how TCs alter the dynamical structure of such eddies during and after TC passage. Through simulations of Hurricane Irma (2017) using the Navy’s Coupled Ocean Atmospheric Mesoscale Prediction Scheme for Tropical Cyclones, it is found that a simulated mesoscale warm core eddy (WCE) located to the right of the model track disappeared shortly after the TC had passed over it. To further explore this phenomena, time series of ocean kinetic energy, available potential energy (APE), a vorticity budget, and a potential vorticity (PV) budget are examined to understand how TC-induced upper ocean currents influenced the eddy disappearance. Time series of APE within the model-simulated eddy show APE reaching a minimum as the circulation of the eddy disappeared in the model field and positive vorticity, dominated by vertical vorticity advection, became a maximum approximately 6 h after TC passage. These results were confirmed by the PV budget. The succession of subsurface vorticity changes suggest that the WCE was eradicated by a near-inertial wave wake that occurred earlier than expected. As found in a prior study, the early-onset of a near-inertial wake occurs when the model simulated TC with a small radius of maximum winds interacts with the subsurface ocean current field. The findings are important with regards to TC forecasting because the accurate representation and placement of eddies in the model field is necessary to correctly predict the coupled air-sea response during TC passage and for subsequent TCs with similar tracks. Such feedbacks from TCs can also have near-term and long-term influence on the prediction of air-sea interaction in the local and regional marine environment.

On the Influence of Surface Latent Heat Fluxes on Idealized Extratropical Cyclones

Abstract An analysis of the surface latent heat flux (SLHF) influence on the accumulated precipitation associated with an idealized extratropical cyclone using the Coupled Ocean–Atmosphere Mesoscale Prediction System is presented. There are two distinct precipitation regions found to be strongly influenced by the SLHF, referred to as the primary maximum and the cold-frontal precipitation. A substantial reduction by approximately 30 mm (35%) and 15 mm (75%) is observed in the accumulated precipitation of the two regions, respectively, when the SLHF is eliminated domain wide at 96 h—the starting time of the most rapid cyclone deepening. The source of this reduction is investigated by systematically controlling the SLHF in three cyclone sectors, which are the low-latitude, baroclinic, and high-latitude sectors. The precipitation in the primary maximum is most strongly controlled by the baroclinic sector, which experiences strong upward SLHF due to its dry environmental air. In contrast, the precipitation in the cold-frontal zone is most strongly controlled by the low-latitude sector, which experiences only a moderate amount of upward SLHF into the already warm and moist boundary layer air. The results underscore the crucial role of SLHF and boundary layer processes in precipitation predictions and demonstrate the need for accurate forecasts of air–sea temperature contrast, surface level winds, and moisture to properly simulate air–sea interactions.

Mesoscale evaluation of AMPS using AWARE radar observations of a wind and precipitation event over the Ross Island region of Antarctica

AbstractSurface, upper‐air, and radar observations are used to assess the performance of the Antarctic Mesoscale Prediction System (AMPS) in simulating the mesoscale aspects of a wind and precipitation event over the Ross Island region of Antarctica that spanned January 16–20, 2016. The observations, collected during the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE), provide a unique dataset for evaluating AMPS, especially the radar observations that facilitate a three‐dimensional depiction of winds and precipitation. Comparisons of AMPS forecast data with surface meteorology, balloon‐sounding, and profiling radar observations at and above sites near McMurdo Station reveal a mixture of similarities and differences. A generally southerly flow is evident at low levels in both the AMPS simulations and observed Doppler radial velocities. AMPS winds are comparable to those observed at the surface and aloft in terms of magnitude, direction, and timing but the strongest simulated southerly flow is displaced eastward relative to the observations. AMPS‐simulated reflectivity over the broader Ross Island region is more limited in areal extent and smaller in magnitude than observed by a scanning Doppler radar. Three episodes of surface precipitation are observed near McMurdo Station over the five‐day event with peak rates of ∼3 mm h−1 and a total accumulation of ∼22 mm. However, AMPS produces no surface precipitation at that location over the five‐day event due to a low‐level dry bias in the forecasts. The results show the first observationally based three‐dimensional understanding of meteorology in the Ross Island region.

Antarctic data impact experiments with Polar WRF during the YOPP‐SH summer special observing period

AbstractData impact experiments are conducted employing the Polar Weather Research and Forecasting (WRF) model during the YOPP‐SH summer special observing period (SOP) using the Antarctic Mesoscale Prediction System (AMPS) framework to determine the forecast impact of numerous additional radiosondes collected during the SOP. Hybrid variational‐ensemble three‐dimensional data assimilation is performed on model forecast domains over Antarctica and the Southern Ocean using all regular observations normally available (Experiment “NoSOP”) and using the same set plus the extra soundings launched for the SOP (Experiment “SOP”). The SOP results show better near‐surface temperature and wind‐speed forecasts than the NoSOP results, primarily over West Antarctica. Radiosonde profiles confirm that temperature and wind‐speed forecasts are improved throughout the troposphere with the addition of the SOP radiosonde data, but the results for relative humidity are variable. Temperatures are improved at lower levels early in the forecasts, whereas wind speeds are better at higher levels later in the forecasts. An evaluation against the ERA5 global reanalysis that provides a much broader perspective reveals that the improved forecast skill for the SOP experiment persists up to 72 hours for temperature, wind speed, and relative humidity. The gains, however, are primarily confined to the Antarctic continent, consistent with the additional radiosonde spatial coverage being mainly poleward of 60°S. With extra radiosondes concentrated over the Antarctic Peninsula, SOP forecasts of the region downstream of the Peninsula were significantly improved compared to NoSOP forecasts. In addition, it is found that the assimilation of the additional radiosonde data can have a greater impact on the forecasts of strong cyclones, as shown for a major coastal cyclone affecting West Antarctica, with improvements in its magnitude and track. The results also suggest that increasing radiosonde launches at lower southern latitudes would improve forecasts over the Southern Ocean, especially during austral winter.