This data set contains seasonal number for fecal coliform, E. coli, and enterococci. This dataset is associated with the following publication: Selvakumar, A., and T. Oconnor. Seasonal Variation in Indicator Organisms Infiltrating from Permeable Pavement Parking Lots at the Edison Environmental Center, New Jersey. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 94(9): e10791, (2022).

This entry covers single-level data aggregated on a monthly time resolution. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

The Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020, is a multi-sensor, multi-decadal, data set of global microwave backscatter, for 1993 to 2020. It assembles data from C-band sensors onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) covering 1993-2000, Advanced Scatterometer (ASCAT) onboard EUMETSAT satellites for 2007-2020, and the Ku-band sensor onboard the QuikSCAT satellite for 1999-2009, onto a common spatial grid (0.05 degree latitude /longitude resolution) and time step (both monthly and seasonal). Data are provided for all land (except high latitudes and islands), and for urban grid cells, based on a specific masking that removes grid cells with > 50% open water or < 20% built land. The all-land data allows users to choose and evaluate other urban masks. There is an offset between C-band and Ku-band backscatter from both vegetated and urban surfaces that is not spatially constant. There is a strong linear correlation (overall R-squared value = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8,450 urban grid cells (0.05 degree resolution) from large cities in Europe, China, and the United States.

This entry covers pressure-level data aggregated on a monthly time resolution. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

About Transportation Services Index

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.

For charts and discussion on the relationship of the TSI to the economy, see our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

Explore the statistics for Holidays & Seasonal eCommerce in 2025, including store count by region and platform, estimated sales amount by platform and region, products sold by platform and region, and total app spend by platform and region. Gain insights into regional preferences, market penetration, consumer trends, and technological investments within the Holidays & Seasonal sector. Discover the leading regions and platforms, as well as the dynamics of sales and product volumes. Stay informed about the evolving landscape of Holidays & Seasonal online stores for a comprehensive understanding of the market.

Temperature at 30 sites around the country from at least 1972 to 2022. We report annual and seasonal trends for the period 1972 to 2022 as well as rate of temperature change per decade. We provide data on average, minimum, and maximum for daily, annual, and seasonal temperatures. Trends are reported for annual and seasonal statistics. Temperature change can have a significant effect on agriculture, energy demand, ecosystems, and recreation.Climate change projections for New Zealand suggest the greatest warming will be in summer/autumn and the least in winter and spring (MfE, 2018). Variables: site: NIWA monitoring site statistic: Statistic: (mean of Average, Minimum or Maximum daily temperature) season: Spring, Summer, Autumn, Winter, or Annual p_value: P value slope, conf_low, conf_high: Rate of change per year and their lower and upper confidence intervals conf_level: confidence level (66% or 90% to match IPCC likelihood levels) intercept, r_squared, sigma: Linear model statistics trend_method: Whether the information in this row correspond to the Linear model slope or the Mann-Kendall test n: number of observations used to calculate the trend note: analysis note s, var_s, tau: Mann-Kendall trend statistics z: Z score alternative: the alternative hypothesis used for the Mann-Kendall test trend_likelihood: Likelihood categories adapted from IPCC. Indicates the likelihood that a trend is increasing, decreasing, or indeterminate period_start: Start of the period for which the trend was assessed period_end: End of the period for which the trend was assessed lat :Latitude lon: Longitude Ministry for the Environment. (2018). Climate Change Projections for New Zealand: Atmosphere Projections Based on Simulations from the IPCC Fifth Assessment, 2nd Edition (Publication No. ME 1385). https://www.mfe.govt.nz/publications/climate-change/climate-change-projections-new-zealand

Trend Detection and Forecasting

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. Time series analysis is a powerful technique that can be used to understand the various temporal patterns in our data by decomposing data into different cyclic trends. Time series analysis can also be used to predict how levels of a variable will change in the future, taking into account what has happened in the past.

Learning Objectives

1. Choose appropriate time series analyses for trend detection and forecasting
2. Discuss the influence of seasonality on time series analysis
3. Interpret and communicate results of time series analyses

This statistic depicts the expected annual consumer spending in the United States in 2017, by seasonal event. That year, the Easter season was expected to generate about **** billion U.S. dollars in spending.

Consumer holiday spending

If you look on consumer spending throughout the year, it is easy to identify several peaks around seasonal events and holidays. According to the National Retail Federation (NRF), the spring season contains Valentine's Day, Presidents Day, Easter, Mother's Day, Memorial Day, Father's Day, and Independence Day. In comparison, the fall season is home to Labor Day, Rosh Hashanah, Yom Kippur, Columbus Day, Halloween, Election Day, Veterans Day, Thanksgiving, Christmas, New Year's Day, and Martin Luther King Day.

The highest in-store traffic and consumer spending usually occurs during the holiday season starting ************ until *************, especially on the 4-day Thanksgiving weekend plus Cyber Monday and the remaining Saturdays before Christmas. The NRF indicated that about *** fifth of the annual retail sales stem from the entire holiday season occurring in November and December. In 2014, the seasonal traffic in brick-and-mortar stores peaked not on Black Friday, as most people would assume, but on the last Saturday before Christmas. Industry experts explained the fact that Black Friday didn’t continue to perform as the busiest shopping day with retailers’ stretching their deals and promotions across November and December. The shift in traffic has even gone so far that people started calling Thanksgiving Day ‘Gray Thursday’.

The chart provides an insightful analysis of the estimated sales amounts for Holidays & Seasonal stores across various platforms. Custom Cart stands out, generating a significant portion of sales with an estimated amount of $2.36B, which is 40.84% of the total sales in this category. Following closely, Magento accounts for $1.97B in sales, making up 34.10% of the total. WooCommerce also shows notable performance, contributing $632.57M to the total sales, representing 10.97%. This data highlights the sales dynamics and the varying impact of each platform on the Holidays & Seasonal market.

SEAS comprises ensembles of individual forecasts coupled to an ocean model and post-processed products of average conditions (e.g. monthly averages) with the associated uncertainty. Products are available up to 7 months ahead.

The following sub-sets are available:

V-i: Monthly means of ensemble means

Field computed from data of the daily individual forecast runs (section V-v) and averaged over all ensemble members. The fields are provided in GRIB code.

The data in this dataset were generated from the GISTEMP dataset found at NOAA Physical Sciences Laboratory, using this script. These are the 2-meter air temperature data gridded onto a 2° x 2° longitude-latitude grid, with 1200-km smoothing applied to help spread out more isolated points, and to make the maps of data less blocky in earlier years. The original data, stored in NetCDF format, were on the following 3-d grid:

• longitude: -180° to 180°
• latitude: -90° to 90°
• time: Jan, 1880 to Jan. 2022, monthly anomalies (differences from a long-term monthly average)

These data were then averaged into different datasets: annual, seasonal, winter and summer anomaly, and stored in new NetCDF files. Finally, trend (actually temperature change) datasets were calculated and saved, storing annual, winter and summer trends. These trends covered 51 air temperature trends, from 1900-2020 to 1950-2020, for all spatial gridboxes. Thus, maps of these trends can be drawn using, for instance, Cartopy. Here is a list of the files and their contents:

• seasonally-1.nc: Seasonal average anomalies for the 'atmospheric seasons' December-January-February (DJF), MAM, JJA, SON;
• summer-1.nc: 'Atmospheric summer' average anomalies for June-July-August (JJA) in the Northern Hemisphere, and DJF in the Southern Hemisphere;
• summtrnd-2020-1.nc: Air temperature trends based on summer mean anomalies, from 1900-2020 to 1950-2020;
• trnd-2020-1.nc: Air temperature trends based on annual mean anomalies, same temporal coverage as 2020;
• winter-1.nc: 'Atmospheric winter' average anomalies for DJF in the Northern Hemisphere, and JJA in the Southern Hemisphere;
• winttrnd-2020-1.nc: Air temperature trends based on winter mean anomalies, from 1900-2020 to 1950-2020;
• ne_110m_coastline.shp: A coastline shapefile extracted from the Cartopy python module.

An example of how to plot a single map of winter trends (1930 to 2020 trends) is available in the Winter trend graphing script.

It is noteworthy that there will be a noticeable discontinuity across the Equator specifically for the winter and summer trend datasets, since those seasons are defined for different months in the Northern and Southern Hemispheres, all the way to the Equator. This was done simply for continuity in the maps. Seasonal effects are generally smaller near the Equator, but trends can be calculated for any 3-month period from the 'seasonally-1a.nc' file, and annual trends are already available for plotting in the 'trnd-2020-1.nc' file.

This entry covers single-level data and soil-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable). Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

Our data sheds light on the distribution of Holidays & Seasonal stores across different online platforms. Shopify leads with a substantial number of stores, holding 6.93K stores, which accounts for 40.31% of the total in this category. WooCommerce follows with 4.71K stores, making up 27.39% of the Holidays & Seasonal market. Meanwhile, Custom Cart offers a significant presence as well, with 1.36K stores, or 7.90% of the total. This chart gives a clear picture of how stores within the Holidays & Seasonal sector are spread across these key platforms.

Feature Articles on Finance - Seasonal Adjustment of Current Account Statistics in Hong Kong's Balance of Payments

This dataset contains seasonal statistics by five-degree squares of temperature, salinity, oxygen, oxygen saturation, potential density, and specific volume. The number of observations, mean, and standard deviation of each parameter for each season are given at each standard level from the sea surface through a depth of 4000m.

This USGS data release represents the input data used to identify trends in New Jersey streams, water years 1971-2011 and the results of Weighted Regression on Time, Discharge, and Season (WRTDS) models and seasonal rank-sum tests. The data set consists of CSV tables and Excel workbooks of: • trends_InputData_NJ_1971_2011: Reviewed water-quality values and qualifiers at selected stream stations in New Jersey over water years 1971-2011 • trends_WRTDS_AnnualValues_NJ_1971_2011: Annual concentrations and fluxes for each water-quality characteristic at each station from WRTDS models • trends_WRTDS_Changes_NJ_1971_2011: Changes and trends in flow-normalized concentrations and fluxes determined from WRTDS models • trends_SeasonalRankSum_results_NJ_1971_2011: Results of seasonal rank-sum tests to identify step trends between concentrations in the 1970s, 1980s, 1990s, and 2000s at selected stations on streams in New Jersey. These data support the following publication: Hickman, R.E., and Hirsch, R.M., 2017, Trends in the quality of water in New Jersey streams, water years 1971-2011: U.S. Geological Survey Scientific Investigations Report 2016-5176, 58 p., https://doi.org/10.3133/sir20165176.

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. The frequency of measurement and the timespan of the dataset can vary widely. At its most simple, a time series model includes an explanatory time component and a response variable. Mixed models can include additional explanatory variables (check out the nlme and lme4 R packages). We will be covering a few simple applications of time series analysis in these lessons.

Opportunities

Analysis of time series presents several opportunities. In aquatic sciences, some of the most common questions we can answer with time series modeling are:

• Has there been an increasing or decreasing trend in the response variable over time?

• Can we forecast conditions in the future?

  Challenges

Time series datasets come with several caveats, which need to be addressed in order to effectively model the system. A few common challenges that arise (and can occur together within a single dataset) are:

• Autocorrelation: Data points are not independent from one another (i.e., the measurement at a given time point is dependent on previous time point(s)).

• Data gaps: Data are not collected at regular intervals, necessitating interpolation between measurements. There are often gaps between monitoring periods. For many time series analyses, we need equally spaced points.

• Seasonality: Cyclic patterns in variables occur at regular intervals, impeding clear interpretation of a monotonic (unidirectional) trend. Ex. We can assume that summer temperatures are higher.

• Heteroscedasticity: The variance of the time series is not constant over time.

• Covariance: the covariance of the time series is not constant over time. Many of these models assume that the variance and covariance are similar over the time-->heteroschedasticity.

  Learning Objectives

After successfully completing this notebook, you will be able to:

1. Choose appropriate time series analyses for trend detection and forecasting

2. Discuss the influence of seasonality on time series analysis

3. Interpret and communicate results of time series analyses

Delving into the Holidays & Seasonal sector, our data presents a revealing look at store distribution by region, highlighting regional preferences and market penetration in this niche. United States leads with 6.75K stores, which is 50.54% of the total. United Kingdom follows, contributing 1.92K stores, which is 14.40% of the total. Australia comes third, with 719 stores, making up 5.38% of the total.

Provide the statistical table of the number of audit cases and audit results for each agency and each quarter of the year.