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The Korean Health Eating Index (KHEI) of young adults was the lowest compared to other age groups. Food Literacy (FL) is the ability to keep a healthy eating habits for an individual and can be an important tool for preventing chronic diseases among young adults. The objectives of this study were to classify the types of dietary behaviors among young adults based on their food intake behaviors and to explore the level of FL according to those types. We conducted an online cross-sectional survey of representative sample of 1,059 young adults (19 to 39 years) in South Korea in 2021. FL questionnaire was comprised of 33 questions with 5-point scale. The total FL scores were calculated by adding all the responses and multiplied by 0.61 to make the highest score to be 100. Principal component analysis (PCA) was used to classify the key characteristics of dietary behaviors, using 19 behavior questions such as frequencies of eating three meals and night snacking, frequency of using take-outs, cooking at home, and emotional eating. Statistical analysis was conducted using STATA/SE 17.0. Four types of dietary behaviors were extracted using PCA: healthy, trendy, convenient and emotional eating. Each participant had tendency scores for four eating types: the higher score meaning higher tendency of having that specific eating type. The FL scores increased from Q1(55.4) to Q5(67.8) according to the quintiles of their healthy diet tendency scores after adjusting for sex and age. The FL scores by the tendency of trendy diet, which is characterized by trying trendy diet such as intermittent fasting, increased from Q1(59.1) to Q5(66.9). In contrast, the higher tendency for emotional diet was associated with lower FL scores(Q1 = 62.5, Q5 = 59.8). Korean young adult population had different levels of FL according to their eating characteristics. It is important to develop targeted nutrition education programs for improving FL among young adults with unhealthy eating practices such as higher tendency of emotional eating. Ministry of Food and Drug Safety; Government of Seoul; National Research Foundation of Korea (NRF) funded by the Ministry of Education. (2021R1I1A306024811, 2021R1A6A1A03044501) [ABSTRACT FROM AUTHOR]

Food literacy(FL) is originally derived from health literacy and includes personal skills and knowledge in various aspects of food consumption. Recently, the authors have proposed a broader definition of FL, including the ability to select food by considering community, society, and the environment as well as individual functional competency. It consists of nutrition and safety (NS), cultural and relational (CS), and socio-ecological (SE) domains. To identify the level of FL among Seoul citizens and the association with food intake and happiness in life Cross-sectional study using a representative sample of 4,039 citizens FL questionnaire was comprised of 33 questions. Happiness in life, satisfaction on food, and importance of food on pursuing happiness were measured with a scale of 0 (most negative) to 10 (most positive). Descriptive statistics and regression analysis were performed to analyze the distribution of FL scores according to the sociodemographic factors. The analysis was conducted using Stata/SE 17.0. Men, students, people who are 18-29 years or 70 years and older had the lowest scores for all domains of FL. The highest quartile group of the NS scores had higher probabilities of consuming adequate servings of vegetables(Q4 vs. Q1, OR = 1.69) and fruits(Q4 vs. Q1, OR = 1.83), which both showed significant linear trends (P <0.001). Also, as the overall FL score increased, the happiness in life increased by 0.92(Q4 vs. Q1) points, the satisfaction on food increased by 1.13(Q4 vs. Q1) points, and the importance of food on pursuing happiness increased by 0.87(Q4 vs. Q1) points. All of three domains of FL showed a linear increase with happiness, satisfaction, and importance of food after controlling for various sociodemographic factors (P < 0.001). This study identified the vulnerable groups of FL and found that FL is closely related to happiness in life and food-related life satisfaction after controlling for other covariates. It is necessary to develop a program to improve FL especially for vulnerable groups for happier and more satisfactory life. Government of Seoul; National Research Foundation of Korea (NRF) funded by the Ministry of Education(2021R1I1A306024811, 2021R1A6A1A03044501) [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the quality of clouds simulated by the National Centers for Environmental Prediction global forecast system (GFS) model and to examine the causes for some systematic errors seen in the simulations through use of satellite and ground-based measurements. In general, clouds simulated by the GFS model had similar spatial patterns and seasonal trends as those retrieved from passive and active satellite sensors, but large systematic biases exist for certain cloud regimes especially underestimation of low-level marine stratocumulus clouds in the eastern Pacific and Atlantic oceans. This led to the overestimation (underestimation) of outgoing longwave (shortwave) fluxes at the top-of-atmosphere. While temperature profiles from the GFS model were comparable to those obtained from different observational sources, the GFS model overestimated the relative humidity field in the upper and lower troposphere. The cloud condensed water mixing ratio, which is a key input variable in the current GFS cloud scheme, was largely underestimated due presumably to excessive removal of cloud condensate water through strong turbulent diffusion and/or an improper boundary layer scheme. To circumvent the problem associated with modeled cloud mixing ratios, we tested an alternative cloud parameterization scheme that requires inputs of atmospheric dynamic and thermodynamic variables. Much closer agreements were reached in cloud amounts, especially for marine stratocumulus clouds. We also evaluate the impact of cloud overlap on cloud fraction by applying a linear combination of maximum and random overlap assumptions with a de-correlation length determined from satellite products. Significantly better improvements were found for high-level clouds than for low-level clouds, due to differences in the dominant cloud geometry between these two distinct cloud types. [ABSTRACT FROM AUTHOR]

Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth's radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds. [ABSTRACT FROM AUTHOR]

Purpose: To assess the levels of stress and the prevalence of cardiovascular disease (CVD) risk factors in female law enforcement officers (LEOs). Methods: Self-reported data including job-related stress and CVD risk factors were obtained from 65 female LEOs. Stress scores were compared with 429 males LEOs and CVD risk factors were compared with 1,213 demographically similar female respondents to the CDC BRFSS survey. All subjects resided in Iowa. Results: Female LEOs had more stress (perceived stress, p < 0.01), more job-related stress (job strain, vital exhaustion and effort-reward imbalance, p < 0.01 for all), but similar social support (social provision scale, p = 0.412) than male LEOs. Female LEOs had a significantly higher prevalence of hypercholesterolemia than the general Iowa female population (46.2 vs. 29.3%, p < 0.01). There was a trend towards a higher prevalence of diabetes in female LEOs (9.2 vs. 4.5%, p = 0.084). The most commonly cited contributor to their perceived CVD risk was stress (77%). Female LEOs who felt that being either a LEO (67.7%) or a female LEO (41.5%) contributed to their risk for chronic diseases had more stress and a higher prevalence of overweight and obesity than female LEOs who felt differently. Conclusions: Female LEOs have higher stress than male LEOs. The prevalences of hypercholesterolemia and diabetes are higher in female LEOs than that of the general female population. Thus, female LEOs may be at a greater risk for CVD than their male counterparts. [ABSTRACT FROM AUTHOR]

Climatologies, which depict mean fields of oceanographic variables on a regular geographic grid, and atlases, which provide graphical depictions of specific areas, play pivotal roles in comprehending the societal vulnerabilities linked to ocean acidification (OA). This significance is particularly pronounced in coastal regions where most economic activities, such as commercial and recreational fisheries and aquaculture industries, occur. In this paper, we unveil a comprehensive data product featuring coastal ocean acidification climatologies and atlases, encompassing the fugacity of carbon dioxide, pH on the total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle factor, total dissolved inorganic carbon content, and total alkalinity content. These variables are provided on 1° × 1° spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 m, along the North American ocean margins, defined as the region between the coastline and a distance of 200 nautical miles (∼370 km) offshore. The climatologies and atlases were developed using the World Ocean Atlas (WOA) gridding methods of the NOAA National Centers for Environmental Information (NCEI) based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA), along with the 2021 update to the Global Ocean Data Analysis Project version 2 (GLODAPv2.2021) data product. The relevant variables were adjusted to the index year of 2010. The data product is available in NetCDF (https://doi.org/10.25921/g8pb-zy76 , Jiang et al., 2022b) on the NOAA Ocean Carbon and Acidification Data System: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0270962.html (last access: 15 July 2024). It is recommended to use the objectively analyzed mean fields (with "_an" suffix) for each variable. The atlases can be accessed at https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/nacoastal.html (last access: 15 July 2024). [ABSTRACT FROM AUTHOR]

Climatologies, which depict mean fields of oceanographic variables on a regular geographic grid, and atlases, which provide graphical depictions of specific areas, play pivotal roles in comprehending the societal vulnerabilities linked to ocean acidification (OA). This significance is particularly pronounced in coastal regions where most economic activities related to commercial and recreational fisheries as well as aquaculture industries occur. In this paper, we unveil a comprehensive data product featuring coastal climatologies and atlases for ten OA indicators, including fugacity of carbon dioxide, pH on the total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. These indicators are provided on 1°×1° degree spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 meters, along the North American ocean margins – defined as the region between the coastline and a distance of 200 nautical miles (∼370 km) offshore. The climatologies and atlases were developed using the World Ocean Atlas (WOA) gridding methods of the NOAA National Centers for Environmental Information (NCEI), based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA), along with the 2021 update to the Global Ocean Data Analysis Project version 2 (GLODAPv2.2021) data product. The relevant variables were adjusted to the index year of 2010. The data product is available in NetCDF (DOI: 10.25921/g8pb-zy76) at the NOAA Ocean Carbon and Acidification Data System: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0270962.html. It is recommended to use the objectively analyzed mean fields (with '_an' suffix) for each variable. The atlases can be accessed at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/nacoastal.html. [ABSTRACT FROM AUTHOR]

Knowledge of cloud vertical structure is important for meteorological and climate studies due to the impact of clouds on both the Earth's radiation budget and atmospheric adiabatic heating. Yet it is among the most difficult quantities to observe. In this study, we develop a long-term (10 years) radiosonde-based cloud profile product over the Southern Great Plains and along with ground-based and space-borne remote sensing products, use it to evaluate cloud layer distributions simulated by the National Centers for Environmental Prediction global forecast system (GFS) model. The primary objective of this study is to identify advantages and limitations associated with different cloud layer detection methods and model simulations. Cloud occurrence frequencies are evaluated on monthly, annual, and seasonal scales. Cloud vertical distributions from all datasets are bimodal with a lower peak located in the boundary layer and an upper peak located in the high troposphere. In general, radiosonde low-level cloud retrievals bear close resemblance to the ground-based remote sensing product in terms of their variability and gross spatial patterns. The ground-based remote sensing approach tends to underestimate high clouds relative to the radiosonde-based estimation and satellite products which tend to underestimate low clouds. As such, caution must be exercised to use any single product. Overall, the GFS model simulates less low-level and more high-level clouds than observations. In terms of total cloud cover, GFS model simulations agree fairly well with the ground-based remote sensing product. A large wet bias is revealed in GFS-simulated relative humidity fields at high levels in the atmosphere. [ABSTRACT FROM AUTHOR]