Coastal-Marine Automated Network Research Articles

A statistical analysis method for significant wave height and spectral peak frequency considering the random and time-varying effects based on copula function and Bayesian inference

Random and time-varying effects are important factors for statistical analysis of wave characteristic variables, including the significant wave height and spectral peak frequency. This paper proposes a statistical analysis method for the accurate statistical analysis of the state of the ocean. Several common distributions are applied as candidates for describing a specific variable, denoted as the marginal distribution. The joint distribution for the wave characteristic variables is constructed using copula functions based on the marginal distributions. The probability and unknown parameters of the marginal distributions are then determined by fully Bayesian inference. The best-fitting marginal distribution is selected based on the posterior probabilities of the candidates. Then, unknown parameters of the candidate copula functions are estimated by maximum likelihood estimation. The best-fitting copula function is selected based on Akaike information criterion, root mean squared error and Nash Sutcliffe efficiency. The proposed method is verified using the National Data Buoy Center dataset for 2019. However, this dataset, collected from a network of almost 100 moored buoys and Coastal-Marine Automated Network (CMAN) stations, contains incomplete data. The results reveal that the best-fitting marginal distribution and copula function may vary with the month. The average and maximum values of the improved RMSE using the proposed method are only 0.0064 and 0.0187, respectively. This indicates the high accuracy of the proposed method for the statistical analysis of wave states even though missing some data.

Signatures of Oceanic Wind Events in Convection-Resolving WRF Model Simulations

Abstract Analyses of cloud-top temperature and lightning characteristics of 48 Weather Research and Forecasting (WRF) Model–simulated ocean-based wind events, with 1-min temporal and 0.5-km horizontal resolution, revealed signatures similar to the corresponding 13 observed events detected by buoys and Coastal-Marine Automated Network (C-MAN) stations as shown in prior research on ocean-based wind events by the first author. These events occurred in the eastern Gulf of Mexico and in the Atlantic Ocean from Florida northward through South Carolina. The coldest WRF cloud-top temperature (WCTT) and peak WRF-estimated lightning flash rate values of the model-simulated events, where each event was required to have a negative vertical velocity of at least 10 m s−1 in the lowest 2 km associated with a convective storm, occurred at an average of 4.2 and 1.1 min prior to the events, respectively. With 36 of the events, the peak estimated flash rate occurred within 5 min of the coldest WCTT. Cloud depth typically increased as the WCTT decreased, and the maximum depth occurred at an average of 2.9 min prior to the events. Thermal cooling and precipitation loading provided negative buoyancy needed to help drive the wind events. Environmental characteristics of the model-simulated ocean-based wind events also resembled those associated with land-based wet downbursts, including moist air near the surface, lapse rates near moist adiabatic, and low cloud bases.

Signatures of Oceanic Wind Events in Geostationary Cloud Top Temperature and Lightning Data

AbstractA total of 13 ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1-min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1-min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning and did not exceed 11 flashes per minute for 9 of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

Long-Wave Response of the West Florida Shelf to the Landfall of Hurricane Wilma, October 2005

Direct observations of the storm surge and the subsequent long-wave response induced by Hurricane Wilma's landfall on the West Coast of Florida on 24 October 2005 are presented. The data set consists of weeklong time series of storm surge and barometric pressure measured by the U.S. Geological Survey (USGS) Florida Integrated Science Center. The survey area spanned more than 100 km alongshore from the landfall site northward. The USGS data were augmented with measurements at the National Oceanic and Atmospheric Administration tide gauge and Coastal Marine Automated Network (CMAN) stations. At the time of Wilma's arrival, the storm surge was minimal to the north of landfall site but was significant in the southern sector. Subsequently, it evolved into an edge wave pulse propagating northward as Wilma moved inland and the surge was no longer sustained by the wind forcing. The height of the wave pulse exceeded 1.5 m in detided sea level data. However, its magnitude was somewhat obscured in direct surge measurements because the wave pulse propagated during the low tide. The duration of this wave pulse was approximately 6 hours. The propagation speed of the wave front was ∼25 m s−1, while the pulse crest traveled at a lower speed of ∼10 m s−1, which indicates the dispersion effects. A relatively low phase speed suggests that the wave energy was trapped nearshore, in the water depth of 10–20 m. The wave pulse was followed by a train of weaker wavelike undulations, also propagating northward. The edge wave pulse seemed to be attenuated by a complex topography in the vicinity of Sanibel Island–Pine Island.

Determination of the Influence of Wind on the Keweenaw Current in the Lake Superior Basin as Identified by Advanced Very High Resolution Radiometer (AVHRR) Imagery

The Keweenaw Current is a warm coastal current in Lake Superior that flows northeastward along the northern shore of Michigan's Keweenaw Peninsula. This study focuses on the fate of the current at the tip of the Keweenaw Peninsula. Results of this study suggest that the path of the current beyond the peninsula is primarily controlled by wind. In this study, eleven surface temperature maps derived from Advanced Very High Resolution Radiometric (AVHRR) data using computer assisted image processing techniques were used to identify the Keweenaw Current. Wind data from two moored data buoys, a Coastal Marine Automated Network (CMAN) fixed buoy, and three airport weather stations, collected on the same day as each of the images and for the two days preceding the image date, are used to determine whether wind direction and speed influence the path of the Keweenaw Current past the tip of the peninsula. In nine images the current's path is similar to the surface Ekman transport direction predicted from wind data. All eleven images show a similarity between the current's actual path and a path calculated when net Ekman transport is assumed. Results of this study also show that there may be a possible lag time of one day between a change in wind direction and the current's adjustment to that change.