Luzon strait mesoscale eddy characteristics in HYCOM reanalysis, simulation, and forecasts

Trott, Corinne B.; Metzger, E. Joseph; Yu, Zhitao

doi:10.1007/s10872-023-00686-5

Cited by 2 publications

(1 citation statement)

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“…The HYCOM database is a collection of ocean data based on output from the HYCOM model, encompassing spatial distribution and time series information of multiple ocean variables, including temperature profiles [59,60]. The HYCOM system employs the Navy Coupled Ocean Data Assimilation (NCODA) system to assimilate observational data, ensuring the accuracy and reliability of the data [61,62].…”

Section: Data Description and Preprocessingmentioning

confidence: 99%

Machine Learning to Characterize Biogenic Isoprene Emissions and Atmospheric Formaldehyde with Their Environmental Drivers in the Marine Boundary Layer

Wang,

Xue

et al. 2024

Atmosphere

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Oceanic biogenic emissions exert a significant impact on the atmospheric environment within the marine boundary layer (MBL). This study employs the extreme gradient boosting (XGBoost) machine learning method and clustering method combined with satellite observations and model simulations to discuss the effects of marine biogenic emissions on MBL formaldehyde (HCHO). The study reveals that HCHO columnar concentrations peaked in summer with 8.25 × 1015 molec/cm2, but the sea–air exchange processes controlled under the wind and sea surface temperature (SST) made marine biogenic emissions represented by isoprene reach their highest levels in winter with 95.93 nmol/m2/day. Analysis was conducted separately for factors influencing marine biogenic emissions and affecting MBL HCHO. It was found that phytoplankton functional types (PFTs) and biological degradation had a significant impact on marine biogenic emissions, with ratio range of 0.07~15.87 and 1.02~5.42 respectively. Machine learning methods were employed to simulate the conversion process of marine biogenic emissions to HCHO in MBL. Based on the SHAP values of the learning model, the importance results indicate that the factors influencing MBL HCHO mainly included NO2, as well as temperature (T) and relative humidity (RH). Specifically, the influence of NO2 on atmospheric HCHO was 1.3 times that of T and 1.6 times that of RH. Wind speed affected HCHO by influencing both marine biogenic emission and the atmospheric physical conditions. Increased marine biogenic emissions in air masses heavily influenced by human activities can reduce HCHO levels to some extent. However, in areas less affected by human activities, marine biogenic emissions can lead to higher levels of HCHO pollution. This research explores the impact of marine biogenic emissions on the HCHO status of the MBL under different atmospheric chemical conditions, offering significant insights into understanding chemical processes in marine atmospheres.

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