Cooks working in fast food restaurants in the United States had a median hourly wage of ***** U.S. dollars as of May 2023. Meanwhile, ** percent of fast food cooks earned less than ***** U.S. dollars per hour.

Bartenders in the United States had a median hourly wage of ***** U.S. dollars as of May 2024. In comparison, ten percent of bartenders earned less than **** U.S. dollars per hour during the same period.

Short order cooks prepare and cook a variety of meals that require only a short time to prepare. In the United States, the median hourly wage of short order cooks was 16.41 U.S. dollars as of May 2023. Comparatively, ten percent of short order cooks earned less than 11.21 U.S. dollars per hour during that period.

Four-yearly Wage Structure Survey: Average earnings per hour by Autonomous Community, sex, type of working day and average and percentiles. Autonomous Community.

Food service managers are in charge of planning, directing, or coordinating activities of an organization or department that serves food and beverages. As of May 2023, ** percent of food service managers in the United States earned less than ***** U.S. dollars per hour. The median hourly wage of food service managers in that period was ***** U.S. dollars.

Rainfall gages throughout the County were selected based on spatial distribution and rainfall record length to determine the 85th and 95th percentile, 24-hour rainfall. The 85th and 95th percentile, 24-hour rainfall can be used to determine the stormwater quality design volume per Public Works Low Impact Design Manual.

You can also check out this data on an interactive map located here:

https://dpw.lacounty.gov/wrd/hydrologygis/

Click HERE to access the REST end point for 85th Percentile Rainfall data.

Click HERE to access the REST end point for 95th Percentile Rainfall data.

Waiters and waitresses in the United States had a median hourly wage of 15.36 U.S. dollars as of May 2023. In comparison, ten percent of waiters and waitresses earned less than 8.94 U.S. dollars per hour during this period.

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time, by region, and public and private sector, and non-profit bodies and mutual associations. Hourly and weekly estimates are provided for the pay period that included a specified date in April. They relate to employees on adult rates of pay, whose earnings for the survey pay period were not affected by absence. Estimates for 2020 and 2021 include employees who have been furloughed under the Coronavirus Job Retention Scheme (CJRS). Annual estimates are provided for the tax year that ended on 5th April in the reference year. They relate to employees on adult rates of pay who have been in the same job for more than a year.

BackgroundWater Sensitive Urban Design (WSUD) systems are frequently used as part of a stormwater harvesting treatment trains (e.g. biofilters (bio-retentions and rain-gardens) and wetlands). However, validation frameworks for such systems do not exist, limiting their adoption for end-uses such as drinking water. The first stage in the validation framework is pre-validation, which prepares information for further validation monitoring.ObjectivesA pre-validation roadmap, consisting of five steps, is suggested in this paper. Detailed methods for investigating target micropollutants in stormwater, and determining challenge conditions for biofilters and wetlands, are provided.MethodsA literature review was undertaken to identify and quantify micropollutants in stormwater. MUSIC V5.1 was utilized to simulate the behaviour of the systems based on 30-year rainfall data in three distinct climate zones; outputs were evaluated to identify the threshold of operational variables, including length of dry periods (LDPs) and volume of water treated per event.ResultsThe paper highlights that a number of micropollutants were found in stormwater at levels above various worldwide drinking water guidelines (eight pesticides, benzene, benzo(a)pyrene, pentachlorophenol, di-(2-ethylhexyl)-phthalate and a total of polychlorinated biphenyls). The 95th percentile LDPs was exponentially related to system design area while the 5th percentile length of dry periods remained within short durations (i.e. 2–8 hours). 95th percentile volume of water treated per event was exponentially related to system design area as a percentage of an impervious catchment area.ConclusionsThe out-comings of this study show that pre-validation could be completed through a roadmap consisting of a series of steps; this will help in the validation of stormwater treatment systems.

This processed data represents the estimated percentile level of percentile level of daily 8-hour annual average surface-level O3 concentrations. The data is from the NASA Socioeconomic Data and Applications Center, Requia, W. J., Di, Q., Silvern, R., Kelly, J. T., Koutrakis, P., Mickley, L. J., Sulprizio, M. P., Amini, H., Shi, L., & 2 Schwartz, J. (2020). An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Groundlevel Ozone in the Contiguous United States. Environ. Sci. Technol., 54(18):11037-11047. https://doi.org/10.1021/ acs.est.0c01791. . The census block data was converted into census tract data by the mean of the census blocks within a tract comprising the data associated with each tract. From there the percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale.For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

Hosts and hostesses in restaurants, lounges and coffee shops in the United States had a median hourly wage of 14.05 U.S. dollars as of May 2023. Meanwhile, ten percent of hosts and hostesses in restaurants, lounges and coffee shops earned less than 10.35 U.S. dollars per hour during this period.

Dishwashers in the United States had a median hourly wage of ** U.S. dollars as of May 2023. In comparison, ten percent of dishwashers earned less than ** U.S. dollars per hour during this period.

Data for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.41 is a summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The data of each panel is provided in a single file.

List of data provided

This datasets contains global and regional anomaly time-series for:

• near-surface air temperature (1850-2020)
• precipitation (1950-2014)
• sea ice extent (1979-2014)
• ocean heat content (1850-2014)

Data provided in relation to figure

near-surface air temperature (tas) -fig_3_41_tas_global.nc, fig_3_41_tas_land.nc, fig_3_41_tas_north_america.nc, fig_3_41_tas_central_south_america.nc, fig_3_41_tas_europe_north_africa.nc, fig_3_41_tas_africa.nc, fig_3_41_tas_asia.nc, fig_3_41_tas_australasia.nc, fig_3_41_tas_antarctic.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 4, stat = 0 (mean)

ocean heat content (ohc) -fig_3_41_ohc_global.nc: brown line: ncl5 = 0, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile) green line: ncl5 = 1, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile) black line: ncl5 = 2, ncl6 = 0 (mean)

precipitation (pr) -fig_3_41_pr_60N_90N.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 2, stat = 0 (mean)

sea ice extent (siconc) -fig_3_41_siconc_nh.nc, fig_3_41_siconc_sh.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 2, stat = 0 (mean)

The ensemble spread (shaded regions) of CMIP6 data shown in figure 3.41 are the mean, 5th and 95th percentiles.

The in-file metadata labels the same ensemble spread with mean, min and max.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

🇸🇪 스웨덴 English Annual traffic may include ÅMVD, ÅDT or similar averages for traffic volumes broken down into light and heavy vehicles. Furthermore, there are flows per hour, average speed, median speed, 85 percentile of speed, flow maximum hour in the morning and afternoon.

Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 (Muis et al. 2020). The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.

Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.

Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.

Data for the ACT is (C) Access canberra and licenced for reuse under the CC By 4.0 International, https://creativecommons.org/licenses/by/4.0/

Data for NSW was provided by the Office of Environment and Heritage, NSW Government.

Data for the Northern Territory was sourced from the Northern Territory Environment Protection Authority.

Data for Queensland was provided by the State of Queensland, Department of Science, Information Technology and Innovation.

Data for South Australia was created and supplied by the Environment Protection Authority, SA.

Data for Tasmania was provided by EPA Tasmania, DPIPWE.

Data for Victoria was provided by the Environment Protection Authority Victoria.

Data for Western Australia was provided by the Western Australian Department of Environment Regulation.

Data used to produce figure ATM36 of the Atmosphere theme of SoE2016 available at https://soe.environment.gov.au/theme/ambient-air-quality/topic/2016/ozone#ambient-air-quality-figure-ATM36

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.