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)

In 2024, the United States saw some 31.6 inches of precipitation. The main forms of precipitation include hail, drizzle, rain, sleet, and snow. Since the turn of the century, 2012 was the driest year on record with an annual precipitation of 27.5 inches. Regional disparities in rainfall Louisiana emerged as the wettest state in the U.S. in 2024, recording a staggering 71.25 inches (1.8 meters) of precipitation—nearly 14.4 inches (ca. 37 centimeters) above its historical average. In stark contrast, Nevada received only 9.53 inches (ca. 24 centimeters), underscoring the vast differences in rainfall across the nation. These extremes illustrate the uneven distribution of precipitation, with the southwestern states experiencing increasingly dry conditions that experts predict will worsen in the coming years. Drought concerns persist Drought remains a significant concern in many parts of the country. The Palmer Drought Severity Index (PDSI) for the contiguous United States stood at -3.39 in December 2024, indicating moderate to severe drought conditions. This reading follows three years of generally negative PDSI values, with the most extreme drought recorded in December 2023 at -3.93.

Typical annual rainfall data were summarized from monthly precipitation data and provided in millimeters (mm). The monthly climate data for global land areas were generated from a large network of weather stations by the WorldClim project. Precipitation and temperature data were collected from the weather stations and aggregated across a target temporal range of 1970-2000.

Weather station data (between 9,000 and 60,000 stations) were interpolated using thin-plate splines with covariates including elevation, distance to the coast, and MODIS-derived minimum and maximum land surface temperature. Spatial interpolation was first done in 23 regions of varying size depending on station density, instead of the common approach to use a single model for the entire world. The satellite imagery data were most useful in areas with low station density. The interpolation technique allowed WorldClim to produce high spatial resolution (approximately 1 km2) raster data sets.

The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR. This product consists of monthly statistics of the PR measurements at both a low (5 degrees x 5 degrees) and a high (0.5 degrees x 0.5 degrees) horizontal resolution. The low resolution grids are in the Planetary Grid 1 structure and include 1) mean and standard deviation of the rain rate, reflectivity, path-integrated attenuation (PIA), storm height, Xi, bright band height and the NUBF (Non-Uniform Beam Filling) correction; 2) rain fractions; 3) histograms of the storm height, bright-band height, snow-ice layer, reflectivity, rain rate, path-attenuation and NUBF correction; 4) correlation coefficients. The high resolution grids are in the Planetary Grid 2 structure and contain mean rain rate along with standard deviation and rain fractions.

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

In 2023, precipitation worldwide stood at 1.82 inches below the annual average recorded across the previous century (1901 to 2000). In the past half-century, 2023 was the driest year on record. In contrast, 2010 was the wettest of the indicated period, with almost 1.4 inches of rainfall above the annual average.

The datasets are in a gridded filed format at 0.25-degree spatial resolution where the location of the grid is in the filename itself. Three columns in a file are daily observed precipitation (mm), maximum temperature (degree C), and minimum temperature (degree C) datasets. IMD datasets are available from 1951-2021.

In 2024, Louisiana recorded 71.25 inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only 9.53 inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of 0.20 inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

Historical rainfall data from the Climate Change Knowledge Portal, World Bank Group (Website: http://sdwebx.worldbank.org/climateportal/index.cfm?page=downscaled_data_download&menu=historical)

Information on the spatio-temporal distribution of rainfall is very critical for addressing water related disasters, especially in the arid to semi-arid regions of the Middle East and North Africa region. However, availability of reliable rainfall datasets for the region is limited. In this study we combined observation from satellite-based rainfall data, in situ rain gauge observation and rainfall climatology to create a reliable regional rainfall dataset for Jordan, West Bank and Lebanon. First, we validated three satellite-based rainfall products using rain gauge observations obtained from Jordan (205 stations), Palestine (44 stations) and Lebanon (8 stations). We used the daily 25-km Tropical Rainfall Measuring Mission over 2000 – 2016; daily 10-km Rainfall Estimate for Africa (RFE) rainfall over 2001 – 2016; daily 5-km Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall over 1981-2015; daily 25-km Multi-Source Weighted-Ensemble Precipitation (MSWEP) ov ...

The rainfall data inter-comparison dataset is a collection of precipitation statistics calculated from the hourly nationwide German radar climatology (RADKLIM) and radar online adjustment (RADOLAN) composites provided by the German Weather Service (Deutscher Wetterdienst, DWD), which were combined with rainfall statistics derived from rain gauge data for inter-comparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset.

The dataset consists of two point shapefiles which are readable with all common GIS. It constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 - 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, this data collection has the potential to benefit all users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

• Spatial extent: Germany
• Spatial Resolution: 1 x 1 km
• Time period: 2006 - 2017
• Data Format: Two point shapefiles
• Compared precipitation datasets: RADKLIM, RADOLAN, rain gauge data
• Selection of calculated precipitation statistics:
  • Annual precipitation sum
  • Mean annual precipitation sum
  • Mean seasonal precipitation sums
  • Number of days exceeding a daily precipitation of 1 mm
  • Number of days exceeding a daily precipitation of 20 mm
  • Mean daily precipitation sum of all days exceeding a precipitation sum of 1 mm
  • Mean daily precipitation sum of all days exceeding a precipitation sum of 20 mm
  • Number of NoData entries

An Excel file with detailed information on all parameters and on original data sources is also included in the dataset.

Original data source URLs:

• RADKLIM (DWD): https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/reproc/2017_002/bin/
• RADOLAN (DWD): ftp://ftp-cdc.dwd.de/pub/CDC/grids_germany/hourly/radolan/historical/bin/
• Rain Gauge Data (DWD): https://opendata.dwd.de/climate_environment/CDC/observations_ germany/climate/1_minute/precipitation/
• Location of wind energy plants (UFZ): https://www.ufz.de/record/dmp/archive/5467/de/
• SRTM DEM (DLR): https://geoservice.dlr.de/egp/

Australian Bureau of Meteorology assembled this dataset of 191 Australian rainfall stations for the purpose of climate change monitoring and assessment. These stations were selected because they are believed to be the highest quality and most reliable long-term rainfall stations in Australia. The longest period of record is August 1840 to December 1990, but the actual periods vary by individual station. Each data record in the dataset contains at least a monthly precipitation total, and most records also have daily data as well.

The rainfall data are obtained from Vietnam's HydroMeteorological Data Center (http://www.hymetdata.gov.vn/), and cover daily observations from 172 weather stations. Most of them were actively operated through out the period 1975-2006. The list of weather stations with GIS coordinates is also provided.

The Boston Water and Sewer Commission (BWSC) maintains collection sites throughout the city. Those collection sites are equipped with solar powered rain gauges on top of public buildings which log measurements of precipitation and which report data every five minutes. Here you find the link to the Boston Water and Sewer Commission’s interface to the rainfall data, which is updated continually. You can search for rainfall data going as far back as 1999, depending on the year of installation for the various gauges.

The ground station's seasonal rainfall data have been updated for download from September 15th, 2023. Please switch to the new link before December 31st, 2023, as the old link will expire. For bulk data downloads, please apply for membership at the Meteorological Data Open Platform https://opendata.cwa.gov.tw/index

This shapefile contains rain statistics in towns of Ethiopia.

Using observation data from various agencies in Taiwan, including the Central Weather Bureau, Water Resources Agency, Irrigation Agency and Taiwan Power Company, supplementary, homogenization, and gridization operations were carried out to establish grid data with a resolution of 5 kilometers throughout Taiwan. This data was produced by the "Taiwan Climate Change Projection Information and Adaptation Knowledge Platform Project" of the National Science Council.

India weekly precipitation, 1979 through 1985, for 34 mainland divisions and an island, have been digitized at Florida State University, from India's "Weekly Weather Report". This is based on the stations that report operationally (probably 500-1000 stations). More stations are available in delayed time. The division precipitation for a week is the average of stations that did report during that week. The weeks are continuous. The division normals for the period 1901-1970 are included in the data.

The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.

The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2023.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset.

As drought is the major bottleneck for the rain fed tef (Eragrostis tef) production, developing workable strategy that can mitigate its impacts is mandatory. To draw this strategy knowledge on how the rainfall behaves in the past decades is important. The central theme for this paper is studying the rainfall behavior over the past six decades in relation to the major rainfall induced risks for the rain-fed “tef” production system using 59 years of rainfall data. Risk of dry spell during germination and flowering is computed whereas crop water requirement satisfaction index is generated using water balance approach. The study shows strong intra annual variation but no trend on the annual and monthly mean rainfall totals, and number of rain days. The existence of this intra annual variation has enabled a wide range of possible planting dates that runs from late June to late August and there was no indication of trend that the planting date has a tendency to be either later or earlier in recent years. The result also depicts once in five years early and once in nine years late onset of the rain. Existence of these wide range of possible planting dates, early and late onset of the rain, high intra year variability in rainfall amount and number of rain days and absence of any apparent trend on the rainfall amount and number of rain days may shed some light how farmers are now facing frequent extremes that may consequence frequent crop failures. This signifies the need for every year rainfall forecasts and their appropriate analysis to have successful planting as well to minimize related risks and consequently to have better and consistent production system.