This dataset contains information about daily weather statistic

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Present summary statistics for biometeorological variables for NEON weather stations at core TIS sites. Statistics will include means, standard deviations, maxima, and minima for periods of days, months, and years. Engineering-grade product only.

This dataset contains information about Weather statistics – Current

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in 1895. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Historical Weather Data (2010 -2023)

Dataset Overview

This dataset contains historical weather data from the year 2010 to 2023, collected from the website wunderground.com. It includes a comprehensive set of meteorological variables for each day of every year, providing valuable insights into weather patterns and conditions.

Columns

1. Date: The date of the recorded weather data.
2. Year: The year of the recorded data .
3. Month: The month of the recorded data.
4. Max Temperature: The maximum temperature recorded for the day in degrees Celsius.
5. Avg Temperature: The average temperature recorded for the day in degrees Celsius.
6. Min Temperature: The minimum temperature recorded for the day in degrees Celsius.
7. Max Dew Point: The maximum dew point recorded for the day in degrees Celsius.
8. Avg Dew Point: The average dew point recorded for the day in degrees Celsius.
9. Min Dew Point: The minimum dew point recorded for the day in degrees Celsius.
10. Max Humidity: The maximum relative humidity recorded for the day as a percentage.
11. Avg Humidity: The average relative humidity recorded for the day as a percentage.
12. Min Humidity: The minimum relative humidity recorded for the day as a percentage.
13. Max Wind Speed: The maximum wind speed recorded for the day in kilometers per hour (km/h).
14. Avg Wind Speed: The average wind speed recorded for the day in kilometers per hour (km/h).
15. Min Wind Speed: The minimum wind speed recorded for the day in kilometers per hour (km/h).
16. Max Pressure: The maximum atmospheric pressure recorded for the day in hectopascals (hPa).
17. Avg Pressure: The average atmospheric pressure recorded for the day in hectopascals (hPa).
18. Min Pressure: The minimum atmospheric pressure recorded for the day in hectopascals (hPa).
19. Total Precipitation: The total precipitation (rainfall) recorded for the day in millimeters (mm).

Data Source

This dataset was obtained by scraping weather data from the wunderground.com website. Please note that the accuracy and reliability of the data are dependent on the source website, and any discrepancies or anomalies should be considered in the analysis.

Data Usage

Researchers, meteorologists, and data enthusiasts can utilize this dataset for various purposes, including climate studies, weather forecasting model training, trend analysis, and more. It provides a comprehensive historical record of weather conditions throughout the year 2010 to 2023.

Important Notes

1. Be aware that while this dataset provides valuable historical weather data, it should not be used as the sole source for critical decision-making or safety-related applications. It is always advisable to cross-reference weather information with official sources for such purposes.
2. The data is from the year 2010 to 2023 and may not represent current climate patterns or conditions. It is essential to consider this when conducting any analysis or research.
3. Respect the terms of use and policies of wunderground.com when using this dataset, and ensure compliance with ethical data usage practices.
4. Data preprocessing and quality checks may be necessary before using this dataset for specific analyses or applications.

Acknowledgments

We acknowledge wunderground.com for providing access to historical weather data, which has been made available in this dataset for research and educational purposes.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios. Temperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.

Descriptive statistics of weather conditions used for the analysis.

This dataset contain information about Weather statistics in 15-minutes

This dataset contains information about hourly weather statistic

According to a survey carried out in 2024, 43 percent of the respondents worldwide expressed that they experienced worse-than-usual extreme weather events in their communities. Around the same share of people responded that they experienced about the same as usual in their communities.

Weather catastrophes caused economic losses of 329 billion U.S. dollars worldwide in 2021. Sudden cataclysmic disasters cause devastation on impact. Some weather and climate-related extreme events are storms, floods, heat waves, cold waves, droughts, and forest fires. Climate-related hazards pose risks to human health and can lead to substantial economic losses.

Global natural disaster economic loss The economic damage caused by disasters varies based on geography and affects natural resources. Capital assets and infrastructure, along with the loss of life, disrupt the economic structure. In 2021, the economic loss due to natural disasters globally was about 343 billion U.S. dollars, and flooding generated the highest loss that year.

Billion-dollar natural disaster events in the United States

The United States experienced nearly two dozen billion-dollar disasters in 2021. At an economic loss of around 75 billion U.S. dollars, Hurricane Ida, a Category 4 storm that landed on the Louisiana coast in August, was the costliest.

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.

According to a survey carried out in 2024, half of the respondents in the United Kingdom expressed that they experienced worse-than-usual extreme weather events in their communities. On the other hand, another 44 percent of the respondents responded that they experienced the same things as usual in their communities.

Rainfall: Quy Nhon data was reported at 2,518.300 mm in 2016. This records an increase from the previous number of 1,351.400 mm for 2015. Rainfall: Quy Nhon data is updated yearly, averaging 1,904.900 mm from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 2,684.900 mm in 2010 and a record low of 1,291.000 mm in 2006. Rainfall: Quy Nhon data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.RI015: Weather Statistics: Sunshine, Rainfall, Mean Humidity, Mean Air Temperature.

Average Rainfall (mm) and average Temperature (centigrade) for the North East England and East England Met Office Climate district, which includes Lincolnshire.

This dataset shows the average Rainfall in millimetres and average Temperature in centigrade, by month, meteorological season, and annual calendar year.

The data is sourced from the UK Met Office website. See the Source link for more information about the data and the area it covers.