Search Results (28)

The eddy dissipation rate (EDR, or turbulence dissipation rate) is a crucial parameter in the study of the atmospheric boundary layer (ABL). However, the existing Doppler lidar-based estimates of EDR seldom offer long-term comparisons that span the entire ABL. Building upon prior research utilizing Doppler lidar wind-field data, we optimized the EDR retrieval algorithm using a genetic adaptive approach. The newly developed algorithm demonstrates enhanced accuracy in EDR estimation. The daily evolution of EDR reveals a distinct diurnal pattern in its variation. A detailed four consecutive days study of turbulence generated via low-level jets (LLJs) indicated that EDR driven by heat flux (~10⁻² m²/s³) is significantly stronger than that produced through wind shear (~10⁻³ m²/s³). Subsequently, we examined seasonal variations in EDR at different mixing layer heights (MLH, Zi): elevated EDR values in summer (~7 × 10⁻³ m²/s³ at 0.1Zi) contrasted with reduced levels in winter (~6 × 10⁻⁴ m²/s³ at 0.1Zi). In the early morning, EDR decreases with height for 1 magnitude, while in later stages, it remains relatively stable within 0.1 order of magnitude across 0.1Zi to 0.9Zi. Notably, the EDR during DJF exceeds that of MAM and SON in the afternoon. This suggests that ML turbulence is not solely dependent on surface fluxes (SHF + LHF) but may also be influenced by MLH. A lower MLH (smaller volume), even with reduced surface fluxes, could potentially result in a stronger EDR. Finally, we compared the evolution of the EDR and MLH in the boundary layer using Doppler lidar data from ARM sites and the PBL (Planetary Boundary Layer) Moving Active Profiling System (PBLMAPS) Airborne Doppler Lidar (ADL). The results show that the vertical wind data exhibit strong consistency (R = 0.96) when the ADL is positioned near ARM Southern Great Plains (SGP) sites C1 or E37. The ADL’s mobility and flexibility provide significant advantages for future field experiments, particularly in challenging environments such as mountainous or complex terrains. This study not only highlights the potential of utilizing Doppler lidar alone for EDR calculations but also extensively explores the development patterns of EDR within the ABL. Full article

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization. Full article

The uncertainty in estimating the wave spectrum from the records of wave elevation by heave–pitch–roll buoys is studied, considering the effects of the estimation method and the spectral resolution adopted in the process. This investigation utilizes measurements from a wave buoy moored in deep water in the South Atlantic Ocean. First, the spectra are computed using the autocorrelation function and the direct Fourier method. Second, the spectral resolution is tested in terms of degrees of freedom. The degrees of freedom are varied, and the resulting spectra and integrated parameters are computed, showing significant variability. A simple and robust methodology for determining the wave spectrum is suggested, which involves calculating the average energy density in each frequency band. The results of this methodology reduce the variability of the estimated parameters, improving overall accuracy while preserving frequency resolution, which is crucial in complex sea states. Additionally, to demonstrate the feasibility of the implemented approach, the final spectrum is fitted using an empirical model ideal for that type of spectrum. Finally, the performance and the goodness of the fit process for the final averaged curve are checked by widely used statistical metrics, such as R² = 0.97 and root mean square error = 0.49. Full article

A storm tracking and nowcasting model was developed for the contiguous US (CONUS) by combining observations from the advanced baseline imager (ABI) and numerical weather prediction (NWP) short-range forecast data, along with the precipitation rate from CMORPH (the Climate Prediction Center morphing technique). A random forest based model was adopted by using the maximum precipitation rate as the benchmark for convection intensity, with the location and time of storms optimized by using optical flow (OF) and continuous tracking. Comparative evaluations showed that the optimized models had higher accuracy for severe storms with areas equal to or larger than 5000 km² over smaller samples, and loweraccuracy for cases smaller than 1000 km², while models with sample-balancing applied showed higher possibilities of detection (PODs). A typical convective event from August 2019 was presented to illustrate the application of the nowcasting model on local severe storm (LSS) identification and warnings in the pre-convection stage; the model successfully provided warnings with a lead time of 1–2 h before heavy rainfall. Importance score analysis showed that the overall impact from ABI observations was much higher than that from NWP, with the brightness temperature difference between 6.2 and 10.3 microns ranking at the top in terms of feature importance. Full article

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset. Full article

In this paper, we analyze the surface winds of ECMWF ERA5 reanalysis in the Atlantic Ocean. The first part addresses a reanalysis validation, studying the spatial distribution of the errors and the performance as a function of the percentiles, with a further investigation under cyclonic conditions. The second part proposes and compares two calibration models, a simple least-squares linear regression (LR) and the quantile mapping method (QM). Our results indicate that ERA5 provides high-quality winds for non-extreme conditions, especially at the eastern boundaries, with bias between −0.5 and 0.3 m/s and RMSE below 1.5 m/s. The reanalysis errors are site-dependent, where large RMSE and severe underestimation are found in tropical latitudes and locations following the warm currents. The most extreme winds in tropical cyclones show the worst results, with RMSE above 5 m/s. Apart from these areas, the strong winds at extratropical locations are well represented. The bias-correction models have proven to be very efficient in removing systematic bias. The LR works well for low-to-mild wind intensities while the QM is better for the upper percentiles and winds above 15 m/s—an improvement of 10% in RMSE and 50% for the bias compared to the original reanalysis is reported. Full article

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology. Full article

This paper investigated the development of a hybrid model for wind speed forecast, ranging from 1 to 46 days, in the northeast of Brazil. The prediction system was linked to the widely used numerical weather prediction from the ECMWF global ensemble forecast, with neural networks (NNs) trained using local measurements. The focus of this study was on the post-processing of NNs, in terms of data structure, dimensionality, architecture, training strategy, and validation. Multilayer perceptron NNs were constructed using the following inputs: wind components, temperature, humidity, and atmospheric pressure information from ECMWF, as well as latitude, longitude, sin/cos of time, and forecast lead time. The main NN output consisted of the residue of wind speed, i.e., the difference between the arithmetic ensemble mean, derived from ECMWF, and the observations. By preserving the simplicity and small dimension of the NN model, it was possible to build an ensemble of NNs (20 members) that significantly improved the forecasts. The original ECMWF bias of −0.3 to −1.4 m/s has been corrected to values between −0.1 and 0.1 m/s, while also reducing the RMSE in 10 to 30%. The operational implementation is discussed, and a detailed evaluation shows the considerable generalization capability and robustness of the forecast system, with low computational cost. Full article

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs. Full article

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic operational intensity models used by the National Hurricane Center (NHC) are briefly reviewed. Results show that none of the deterministic models had RI utility from 1991 to about 2015 due to very low probability of detection, very high false alarm ratio, or both. Some ability to forecast RI has emerged since 2015, with dynamical models being the best guidance for the Atlantic and statistical models the best RI guidance for the eastern North Pacific. The first probabilistic RI guidance became available in 2001, with several upgrades since then leading to modest skill in recent years. A tool introduced in 2018 (DTOPS) is currently the most skillful among NHC’s probabilistic RI guidance. To measure programmatic progress in forecasting RI, the Hurricane Forecast Improvement Program has introduced a new RI metric that uses the traditional mean absolute error but restricts the sample to only those cases where RI occurred in the verifying best track or was forecast. By this metric, RI forecasts have improved by ~20–25% since the 2015–2017 baseline period. Full article

The prediction of rapid intensification (RI) in tropical cyclones (TCs) is a challenging problem. In this study, the RI process and factors contributing to it are compared for two TCs: an axis-symmetric case (Hurricane Irma, 2017) and an asymmetric case (Hurricane Michael, 2018). Both Irma and Michael became major hurricanes that made significant impacts in the United States. The Hurricane Weather Research and Forecasting (HWRF) Model was used to examine the connection between RI with forcing from the large-scale environment and the subsequent evolution of TC structure and convection. The observed large-scale environment was reasonably reproduced by HWRF forecasts. Hurricane Irma rapidly intensified in an environment with weak-to-moderate vertical wind shear (VWS), typically favorable for RI, leading to the symmetric development of vortical convective clouds in the cyclonic, vorticity-rich environment. Conversely, Hurricane Michael rapidly intensified in an environment of strong VWS, typically unfavorable for RI, leading to major asymmetries in the development of vortical convective clouds. The tangential wind momentum budget was analyzed for these two hurricanes to identify similarities and differences in the pathways to RI. Results suggest that eddy transport terms associated with convective processes positively contributed to vortex spin up in the early stages of RI and inhibited spin up in the later stages of RI in both TCs. In the early stages of RI, the mean transport terms exhibited notable differences in these TCs; they dominated the spin-up process in Irma and were of secondary importance to the spin-up process in Michael. Favorable aspects of the environment surrounding Michael appeared to aid in the RI process despite hostile VWS. Full article

The intensity and structure of simulated tropical cyclones (TCs) are known to be sensitive to the planetary boundary layer (PBL) parameterization in numerical weather prediction models. In this paper, we use an idealized version of the Hurricane Weather Research and Forecast system (HWRF) with constant sea-surface temperature (SST) to examine how the configuration of the PBL scheme used in the operational HWRF affects TC intensity change (including rapid intensification) and structure. The configuration changes explored in this study include disabling non-local vertical mixing, changing the coefficients in the stability functions for momentum and heat, and directly modifying the Prandtl number (Pr), which controls the ratio of momentum to heat and moisture exchange in the PBL. Relative to the control simulation, disabling non-local mixing produced a ~15% larger storm that intensified more gradually, while changing the coefficient values used in the stability functions had little effect. Varying Pr within the PBL had the greatest impact, with the largest Pr (~1.6 versus ~0.8) associated with more rapid intensification (~38 versus 29 m s⁻¹ per day) but a 5–10 m s⁻¹ weaker intensity after the initial period of strengthening. This seemingly paradoxical result is likely due to a decrease in the radius of maximum wind (~15 versus 20 km), but smaller enthalpy fluxes, in simulated storms with larger Pr. These results underscore the importance of measuring the vertical eddy diffusivities of momentum, heat, and moisture under high-wind, open-ocean conditions to reduce uncertainty in Pr in the TC PBL. Full article

The Loop Current (LC) system controls the connectivity between the northern Gulf of Mexico (GoM) region and the Straits of Florida. The evolution of the LC and the shedding sequence of the LC anticyclonic ring (Eddy Franklin) were crucial for the fate of the hydrocarbons released during the Deepwater Horizon (DwH) oil spill in 2010. In a previous study, we identified LC-related anticyclonic eddies in the southern GoM, named “Cuba anticyclones” (“CubANs”). Here, we investigate the relation between these eddies and LC evolution in 2010, focusing on the DwH period. We use high-resolution model results in tandem with observational data to describe the connection between the LC system evolution within the GoM (LC extensions, Eddy Franklin and LC Frontal Eddies—LCFEs) and the mesoscale dynamics within the Straits of Florida where CubANs propagate. Five periods of CubAN eddy activity were identified during the oil spill period, featuring different formation processes under a combination of local and regional conditions. Most of these cases are related to the retracted LC phases, when the major LC anticyclone (Eddy Franklin in 2010) is detached from the main body and CubAN eddy activity is most likely. However, two cases of CubAN eddy presence during elongated LC were detected, which led to the attenuation of the eastward flows of warm waters through the Straits (Florida Current; outflow), allowing the stronger supply of Caribbean waters through the Yucatan Channel into the Gulf (inflow), which contributed to short-term LC northward extensions. Oceanographic (LCFEs) and meteorological (wind-induced upwelling) conditions contributed to the release of CubANs from the main LC body, which, in tandem with other processes, contributed to the LC evolution during the DwH oil spill incident. Full article

This paper reviews the evolution of planetary boundary layer (PBL) parameterization schemes that have been used in the operational version of the Hurricane Weather Research and Forecasting (HWRF) model since 2011. Idealized simulations are then used to evaluate the effects of different PBL schemes on hurricane structure and intensity. The original Global Forecast System (GFS) PBL scheme in the 2011 version of HWRF produces the weakest storm, while a modified GFS scheme using a wind-speed dependent parameterization of vertical eddy diffusivity (K_m) produces the strongest storm. The subsequent version of the hybrid eddy diffusivity and mass flux scheme (EDMF) used in HWRF also produces a strong storm, similar to the version using the wind-speed dependent K_m. Both the intensity change rate and maximum intensity of the simulated storms vary with different PBL schemes, mainly due to differences in the parameterization of K_m. The smaller the K_m in the PBL scheme, the faster a storm tends to intensify. Differences in hurricane PBL height, convergence, inflow angle, warm-core structure, distribution of deep convection, and agradient force in these simulations are also examined. Compared to dropsonde and Doppler radar composites, improvements in the kinematic structure are found in simulations using the wind-speed dependent K_m and modified EDMF schemes relative to those with earlier versions of the PBL schemes in HWRF. However, the upper boundary layer in all simulations is much cooler and drier than that in dropsonde observations. This model deficiency needs to be considered and corrected in future model physics upgrades. Full article

Much of the vertical transport near the surface of the ocean, which plays a critical role in the transport of dissolved nutrients and gases, is thought to be associated with ageostrophic submesoscale phenomena. Vertical velocities are challenging not only to model accurately, but also to measure because of how difficult they are to locate in the surface waters of the ocean. Using unique massive drifter releases during the Lagrangian Submesoscale Experiment (LASER) campaign in the Gulf of Mexico and the Coherent Lagrangian Pathways from the Surface Ocean to the Interior (CALYPSO) experiment in the Mediterranean Sea, we investigate the generation of submesoscale structures along two different mesoscale fronts. We use a novel method to project Lagrangian trajectories to Eulerian velocity fields, in order to calculate horizontal velocity gradients at the surface, which are used as a proxy for vertical transport. The velocity reconstruction uses a squared-exponential covariance function, which characterizes velocity correlations in horizontal space and time, and determines the scales of variation using the data itself. SST and towed CTD measurements support the findings revealed by the drifter data. Due to the production of a submesoscale instability eddy in the Gulf of Mexico, convergence magnitudes of up to ∼20 times the planetary vorticity, f, are observed, the value of which is almost 3 times larger than that found in the mesoscale dominated Western Mediterranean Sea. Full article