Coupled Ocean Atmosphere Mesoscale Prediction System Research Articles

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.

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.

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.

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.

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.

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.

The Effect of a Kona Low on the Eastern Pacific Valentine’s Day (2019) Atmospheric River

Abstract A high-impact atmospheric river (AR) event that made landfall on the U.S. West Coast on Valentine’s Day of 2019 and produced widespread flooding in California is examined. The U.S. Naval Research Laboratory cloud resolving and high-resolution Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) captures the main features impacting the life cycle and structure of the Valentine’s Day AR. Analysis of the model-simulated AR reveals the complex processes leading up to the initial northeastward surge of the water vapor and enhanced near-surface flow associated with this AR. These include the preexistence of a mesoscale cold-core kona low, a mesoscale anticyclone, and a strong low-level convergence in the corridor between the kona low and mesoscale anticyclone where the environment becomes supersaturated in a region of weak vertical wind shear. Model sensitivity experiments show that the eastward progression and magnitude of the AR water vapor surge are strongly sensitive to the magnitude of kona low circulation. Experiments with the kona low circulation amplitude reduced to less than 25% showed that the AR is not able to reach the U.S. West Coast. These results help to identify key new aspects of an important player—the kona low—and its significant contributions to the overall AR characteristics of this particular observed event.

Assimilating Near-Surface Wind Retrievals from High-Frequency Radars

Abstract High-frequency radars (HFR) are traditionally used in coastal environments to observe ocean current and wave characteristics. With an HFR forward model, HFR adjoint model, and the Simulating Waves Nearshore model, HFR Doppler spectra observations were used to estimate near-surface winds in the Southern California Bight in October 2017. The HFR 10-m wind retrievals were assimilated into the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) with the COAMPS four-dimensional variational (4DVar) assimilation system to integrate the HFR wind retrievals into the initial conditions. Impact of the HFR-derived winds on the forecast are evaluated in terms of adjoint-derived forecast sensitivity observation impact (FSOI), and by an observing system experiment that compared forecasts from simulations that assimilated the HFR wind retrievals to a control simulation that excluded HFR winds. The addition of the HFR-estimated wind observations reduced the error in the forecasted dry energy norm in the lowest model level and also contributed to small improvements in the 10-m wind field over a 25-day experiment. The potential benefit of this new method to estimate near-surface ocean winds near the coast for data assimilation and improved numerical weather prediction is an exciting advancement in remote sensing of coastal winds and expands the benefit of existing HFR networks beyond their intended use. More importantly, wind fields retrieved from HFR have the potential to fill an observation gap near the shoreline where ship and buoy observations are sparse and scatterometer observations are unavailable due to land contamination.

CLASI: Coordinating Innovative Observations and Modeling to Improve Coastal Environmental Prediction Systems

Abstract The Coastal Land–Air–Sea Interaction (CLASI) project aims to develop new “coast-aware” 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.

Forecast Skill for California Heavy Precipitation Periods from the High-Resolution Rapid Refresh Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System

Abstract Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

Track Dependence of Tropical Cyclone Intensity Forecast Errors in the COAMPS-TC Model

AbstractThis study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

The Circulation Response of a Two-Dimensional Frontogenetic Model to Optimized Moisture Perturbations

AbstractAn analysis of the influence and sensitivity of moisture in an idealized two-dimensional moist semigeostrophic frontogenesis model is presented. A comparison between a dry (relative humidity RH = 0%) version and a moist (RH = 80%) version of the model demonstrates that the impact of moisture is to increase frontogenesis, strengthen the transverse circulation (uag, w), generate a low-level potential-vorticity anomaly, and develop a low-level jet. The idealized model is compared with a real case simulated with the full-physics three-dimensional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model, establishing good agreement and thereby confirming that the idealized model retains the essential physical processes relevant for improving understanding of midlatitude frontogenesis. Optimal perturbations of mixing ratio are calculated to quantify the circulation response of the model through the computation of singular vectors, which determines the fastest-growing modes of a linearized version of the idealized model. The vertical velocity is found to respond strongly to initial-condition mixing-ratio perturbations such that small changes in moisture lead to large changes in the ascent. The progression of physical processes responsible for this nonlinear growth is (in order) jet/front transverse circulation → moisture convergence ahead of the front → latent heating at mid- to low elevations → reduction in static stability ahead of the front → strengthening of the transverse circulation, and the feedback cycle repeats. Together, these physical processes represent a pathway by which small perturbations of moisture can have a strong impact on a forecast involving midlatitude frontogenesis.

A Case Study of the Physical Processes Associated with the Atmospheric River Initial-Condition Sensitivity from an Adjoint Model

Abstract Analysis of a strong landfalling atmospheric river is presented that compares the evolution of a control simulation with that of an adjoint-derived perturbed simulation using the Coupled Ocean–Atmosphere Mesoscale Prediction System. The initial-condition sensitivities are optimized for all state variables to maximize the accumulated precipitation within the majority of California. The water vapor transport is found to be substantially enhanced at the California coast in the perturbed simulation during the time of peak precipitation, demonstrating a strengthened role of the orographic precipitation forcing. Similarly, moisture convergence and vertical velocities derived from the transverse circulation are found to be substantially enhanced during the time of peak precipitation, also demonstrating a strengthened role of the dynamic component of the precipitation. Importantly, both components of precipitation are associated with enhanced latent heating by which (i) a stronger diabatically driven low-level potential vorticity anomaly strengthens the low-level wind (and thereby the orographic precipitation forcing), and (ii) greater moist diabatic forcing enhances the Sawyer–Eliassen transverse circulation and thereby increases ascent and dynamic precipitation. A Lagrangian parcel trajectory analysis demonstrates that a positive moisture perturbation within the atmospheric river increases the moisture transport into the warm conveyor belt offshore, which enhances latent heating in the perturbed simulation. These results suggest that the precipitation forecast in this case is particularly sensitive to the initial moisture content within the atmospheric river due to its role in enhancing both the orographic precipitation forcing and the dynamic component of precipitation.

Adjoint Sensitivity of North Pacific Atmospheric River Forecasts

AbstractThe initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016–17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg−1 perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s−1 perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies.

Mean Offshore Refractive Conditions during the CASPER East Field Campaign

AbstractIn this study, we use observational and numerical model data from the Coupled Air Sea Processes and Electromagnetic Ducting Research (CASPER) field campaign to describe the mean refractive conditions offshore Duck, North Carolina. The U.S. Navy operational numerical weather prediction model known as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) performed well forecasting large-scale conditions during the experiment, with an observed warm bias in SST and cold and dry biases in temperature and humidity in the lowest 2000 m. In general, COAMPS underpredicted the number of ducts, and they were weaker and at lower height than those seen in observations. It was found that there is a noticeable diurnal evolution of the ducts, more over land than over the ocean. Ducts were found to be more frequent over land but overall were stronger and deeper over the ocean. Also, the evaporative duct height increases as one moves offshore. A case study was chosen to describe the electromagnetic properties under different synoptic conditions. In this case the continental atmospheric boundary layer dominates and interacts with the marine atmospheric boundary layer. As a result, the latter moves around 80 km offshore and then back inland after 2 h.

High-Resolution Seeded Simulations of Western North Pacific Ocean Tropical Cyclones in Two Future Extreme Climates

ABSTRACTA variant of downscaling is devised to explore the properties of tropical cyclones (TCs) that originate in the open ocean of the western North Pacific Ocean (WestPac) region under extreme climates. This variant applies a seeding strategy in large-scale environments simulated by phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate-model integrations together with embedded integrations of Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC), an operational, high-resolution, nonhydrostatic, convection-permitting numerical weather prediction (NWP) model. Test periods for the present day and late twenty-first century are sampled from two different integrations for the representative concentration pathway (RCP) 8.5 forcing scenario. Then seeded simulations for the present-day period are contrasted with similar seeded simulations for the future period. Reinforcing other downscaling studies, the seeding results suggest that the future environments are notably more conducive to high-intensity TC activity in the WestPac. Specifically, the future simulations yield considerably more TCs that exceed 96-kt (1 kt ≈ 0.5144 m s−1) intensity, and these TCs exhibit notably greater average life cycle maximum intensity and tend to spend more time above the 96-kt intensity threshold. Also, the future simulations yield more TCs that make landfall at >64-kt intensity, and the average landfall intensity of these storms is appreciably greater. These findings are supported by statistical bootstrap analysis as well as by a supplemental sensitivity analysis. Accounting for COAMPS-TC intensity forecast bias using a quantile-matching approach, the seeded simulations suggest that the potential maximum western North Pacific TC intensities in the future extreme climate may be approximately 190 kt.

Development and validation of a coastal ocean forecasting system for Puerto Rico and the U.S. virgin islands

An ocean forecasting system has been developed for the coastal area of Puerto Rico and the U.S. Virgin Islands. This paper presents the development and validation of the Puerto Rico Operational Regional Ocean Modeling System (PRO-ROMS) developed by the Caribbean Coastal Ocean Observing System (CARICOOS), which aims at improving currently available short-term forecasts of ocean currents for this region, and to resolve sub-mesoscale variability absent from the currently operational regional systems. The coastal ocean forecasting system is based on the Regional Ocean Modeling System (ROMS), and provides a 3-day forecast of ocean currents, water levels, temperature and salinity. Initial and lateral boundary conditions are derived from the U.S. Naval Oceanographic Office (NAVOCEANO) operational AmSeas forecasting system, a 3.6-km resolution application of the regional Navy Coastal Ocean Model (NCOM) that encompasses the Gulf of Mexico and Caribbean Sea. Meteorological conditions are interpolated from the Navy’s Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with the exception of surface stresses, which are computed from a 2-km application of the Weather Research and Forecasting (WRF) model used by National Centers for Environmental Protection’s (NCEP) National Digital Forecast Database (NDFD). Tidal variability is imposed using ROMS spectral forcing by specifying the harmonic phases and amplitudes from the TPXO global inverse tide solutions at the boundaries and sub-tidal conditions are imposed by filtering out high frequencies of barotropic variables from the lateral boundary conditions interpolated from AmSeas. Modeled results of sea surface height are validated with NOAA tide gauges, ocean currents are validated with Acoustic Doppler Current Profilers (ADCP) and High Frequency Radars (HFR). Computed statistics show improvement of the ocean current forecast in PRO-ROMS compared to AmSeas, especially at higher frequencies dominated by the tides and in small channels where the bathymetry is better represented by the higher resolution coastal ocean forecasting system.

On the Dynamics of Tropical Cyclone and Trough Interactions

Abstract The interaction between a tropical cyclone (TC) and an upper-level trough is simulated in an idealized framework using Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) for Tropical Cyclones (COAMPS-TC) on a β plane. We explore the effect of the trough on the environment, structure, and intensity of the TC. In a simulation that does not have a trough, environmental inertial stability is dominated by Coriolis, and outflow remains preferentially directed equatorward throughout the simulation. In the presence of a trough, negative storm-relative tangential wind in the base of the trough reduces the inertial stability such that the outflow shifts from equatorward to poleward. This interaction results in a ~24-h period of enhanced upper-level divergence coincident with intensification of the TC. Sensitivity tests reveal that if the TC is too far from the trough, favorable interaction does not occur. If the TC is too close to the trough, the storm weakens because of enhanced vertical wind shear. Only when the relative distance between the TC and the trough is 0.2–0.3 times the wavelength of the trough in x and 0.8–1.2 times the amplitude of the trough in y does favorable interaction and TC intensification occur. However, stochastic effects make it difficult to isolate the intensity change associated directly with the trough interaction. Outflow is found to be predominantly ageostrophic at small radii and deflects to the right (in the Northern Hemisphere) since it is unbalanced. The outflow becomes predominantly geostrophic at larger radii but not before a rightward deflection has already occurred. This finding sheds light on why the outflow accelerates toward but generally never reaches the region of lowest inertial stability.