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)

BackgroundAccurate precipitation forecasting is crucial for various sectors, such as agriculture, hydrology, and disaster management. In recent years, machine learning (ML) techniques have proven invaluable in improving the accuracy of rainfall prediction and identifying the complex relationships between precipitation and other meteorological variables.MethodsThis paper presents acomprehensive analysis of the use of multivariable statistical and ML models to predict monthly rainfall at 13 locations in the eastern Amazon. Each model is trained separately for each month, allowing for a tailored representation of precipitation patterns and variations. Additionally, the performance of these models is evaluated via the time series cross-validation technique and an independent test.ResultsThe results indicate that for the points Serra Sul, Açailândia, and Ponta da Madeira, the multivariable models yielded the best monthly performance in 72.23% of the cases, mainly during the rainy season.DiscussionOur results highlighted several important aspects of precipitation prediction at different points across the selected study region, particularly concerning the influence of exogenous variables (mainly u10, t2m, TSA, and TNA) on precipitation in most months. Additionally, our findings indicate that the ARIMA, XGBoost, and CNN-1D models outperformed the other models in monthly rainfall forecasting for the Serra Sul, Açailândia, and Ponta da Madeira regions, respectively.

This dataset comprises of derived annual statistics for measures of rainfall, streamflow, temperature and stream acidity (pH) for a stream, draining a small, approximately 1.2 square kilometres, upland conifer catchment. The stream, Nant Trawsnant, drains into the Llyn Brianne reservoir, Powys, United Kingdom. The data are for a 31 year period covering 1st April 1982 to 1st April 2012. The streamflow and acidity data are derived from 15 minute resolution observations throughout the calendar year 2013 from associated stream gauging and water quality stations on the Nant Trawsnant. The monthly rainfall measures presented, were derived from local rain gauges. The monthly temperature measures presented were derived from observations at a weather station near Talgarth, Powys. Routines within the Lancaster University Computer-Aided Program for Time-series Analysis and Identification of Noisy Systems (CAPTAIN) Toolbox for Matlab were used to develop a dynamic model of these data. These models were then used to simulate the 31-year record for which monthly statistics were derived. The statistics were derived to develop greater understanding of the controls on the long-term dynamics of aquatic biodiversity observed by other researchers in this stream. The work was part of the Diversity in Upland River Ecosystem Service Sustainability (DURESS) project, NERC grant NE/J014826/1. Members of staff from the Lancaster Environment Centre, Lancaster University installed, maintained and downloaded the stream gauging and water quality stations and also carried out statistical analysis of the data.

BackgroundAccurate precipitation forecasting is crucial for various sectors, such as agriculture, hydrology, and disaster management. In recent years, machine learning (ML) techniques have proven invaluable in improving the accuracy of rainfall prediction and identifying the complex relationships between precipitation and other meteorological variables.MethodsThis paper presents acomprehensive analysis of the use of multivariable statistical and ML models to predict monthly rainfall at 13 locations in the eastern Amazon. Each model is trained separately for each month, allowing for a tailored representation of precipitation patterns and variations. Additionally, the performance of these models is evaluated via the time series cross-validation technique and an independent test.ResultsThe results indicate that for the points Serra Sul, Açailândia, and Ponta da Madeira, the multivariable models yielded the best monthly performance in 72.23% of the cases, mainly during the rainy season.DiscussionOur results highlighted several important aspects of precipitation prediction at different points across the selected study region, particularly concerning the influence of exogenous variables (mainly u10, t2m, TSA, and TNA) on precipitation in most months. Additionally, our findings indicate that the ARIMA, XGBoost, and CNN-1D models outperformed the other models in monthly rainfall forecasting for the Serra Sul, Açailândia, and Ponta da Madeira regions, respectively.

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) over 1984-2015. The validation was conducted between in situ rain gauge observation and satellite rainfall data and resulted in utilizing the MSWEP dataset in correlation with a bias correction grid. The created rainfall dataset was used to estimate stream flow in the region and determine suitable areas of aquifer recharge.

Accurate rainfall estimates are required to predict when and where rain-triggered landslides will occur. In regions with sparse region gauge networks, satellite rainfall products, owing to their easy availability, high temporal resolution, and improved spatial variability, could be used as an alternative. This study compares the utility of rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region: Idukki, along the Western Ghats, India. The GPM IMERG-L (Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM – Late) daily rainfall product was compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the rainfall. A conditional merging algorithm was applied to the GPM data to develop a product that combines rain gauge measures' accuracy and the satellite data's spatial variability. A comparison of the ability of the data products to capture the spatial spread of landslides was then carried out. The study area was divided into zones of influences corresponding to the rain gauge stations, and the landslides were classified according to their location within each zone. 5-day antecedent rainfall values were computed from both the rainfall products. Relying solely on the rain gauge derived values created many false positives and false negatives in landslide prediction. A total of 10.2% of the landslides fell in the true-positive category, while 51.3% was the overall false-negative rate. The study proposes using satellite products with improved spatial resolution and a denser rain gauge network to have reliable inputs for landslide prediction models.

CHIRPS is an abbreviation for Climate Hazards Group InfraRed Precipitation with Station Data (Version 2.0 final). The CHIRPS is a 30+ year quasi-global rainfall dataset and incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. The data of the CHIRPS pentad is derived from Google Earth Engine with earth engine snippet as https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2FUCSB-CHG_CHIRPS_PENTAD . With the dataset in a global format, it is clipped with the Cambodia boundary and generated the data visualized chart through the obtained data.

The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2023. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.

This version supersedes the previous version (202308) 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. These include the addition of data for calendar year 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 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. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection.

Analysis of ‘U.S. 15 Minute Precipitation Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a6756804-ab85-43c9-8728-1d54e5c3ba0a on 27 January 2022.

--- Dataset description provided by original source is as follows ---

U.S. 15 Minute Precipitation Data is digital data set DSI-3260, archived at the National Climatic Data Center (NCDC). This is precipitation data. The primary source of data for this file is approximately 2,000 mostly U.S. weather stations operated or managed by the U.S. National Weather Service. Stations are primary, secondary, or cooperative observer sites that have the capability to measure precipitation at 15 minute intervals. This dataset contains 15-minute precipitation data (reported 4 times per hour, if precip occurs) for U.S. stations along with selected non-U.S. stations in U.S. territories and associated nations. It includes major city locations and many small town locations. Daily total precipitation is also included as part of the data record. NCDC has in archive data from most states as far back as 1970 or 1971, and continuing to the present day. The major parameter is precipitation amounts at 15 minute intervals, when precipitation actually occurs.

--- Original source retains full ownership of the source dataset ---

This map is part of a series of global climate images produced by the Agrometeorology Group and based on data for mean monthly values of temperature, precipitation and cloudiness prepared in 1991 by R. Leemans and W. Cramer and published by the International Institute for Applied Systems Analysis (IIASA). For each of the weather stations used data have been assembled over a long time period - usually between 1961 and 1990 - and then averaged. Annual totals for rainfall were derived from the monthly values.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Rainfall in India (1901–2015)

Dataset Overview

This dataset provides comprehensive monthly, seasonal, and annual rainfall statistics across India's 36 meteorological subdivisions from 1901 to 2015. It offers a detailed view into India's historical rainfall patterns, making it highly valuable for climate research, time-series forecasting, and environmental studies.

Columns Description

Data Summary

Total Rows: 4,116 Total Columns: 19 Years Covered: 1901–2015 Regions: 36 subdivisions across India Data Types: Categorical: SUBDIVISION Numerical: Monthly and seasonal rainfall (in millimeters)

Missing Values: Some minor missing values across a few months and aggregated columns (mostly very few compared to the dataset size).

Sample Records

Potential Use Cases

📈 Trend Analysis: Study long-term changes in rainfall patterns due to climate change. 🌧 Monsoon Research: Analyze the strength and timing of monsoon seasons across different regions. 🌍 Environmental Studies: Explore the relationship between rainfall and agricultural or ecological changes. 🤖 Predictive Modeling: Build models to forecast future rainfall or detect extreme weather events. 🛰 Geospatial Analysis: Visualize and map rainfall trends across India's diverse subdivisions.

Acknowledgements

This dataset is a rich resource for researchers, meteorologists, data scientists, and students working in the areas of climatology, environment, and machine learning.

For both food security and biodiversity management in NEI, a biodiversity hot spot, is required dependable, consistent estimates of trend and modes of variability of rainfall, so that policy makers have an idea of the rainfall that can be expected during the coming decades. Hence, daily rainfall data over the North East India (NEI) for more than 100 years is required for any climate change impact assessment. However, the region is poorly sampled and none of the weather stations have operated continuously for such a period. This technical note describes combining conventional weather station records with rain-gauge records from a number of sources like privately owned tea estates to create a continuous daily rainfall record from 1 January 1920-31st December 2009 for the north-eastern region of India. We have been successful in creating a daily rainfall data set on a set of 24 well distributed fixed stations. The data extent back into the 1920 and stem from a variety of station observations. Remaining data gaps are less than 3% of the total data in each station. Every effort has been made to reconstruct the data gaps with the aim to improve assessments of the long-term changes in climate variability in NEI. The final reconstructed data set for NEI is well suited to estimate both long-term trend and multi-decadal variability of rainfall over the region.

Annual Monthly Data Summary

This dataset contains simulated monthly values for multiple years, along with annual totals. It is designed for time series analysis, forecasting, and trend evaluation.

Key Columns: - Year: Year identifier. - Jan - Dec: Monthly values for January through December. - Annual (ANN): Total annual value (sum of monthly values).

Highlights: - Time Series Data: Offers monthly and annual values for each year, supporting detailed trend analysis. - Modeling Ready: Ideal for forecasting, seasonal analysis, and historical trend evaluation. - Well-Structured: Clean data, suitable for machine learning, visualization, and statistical analysis. Applications: - Trend Analysis: Identify seasonal patterns and anomalies. - Forecasting: Predict future trends based on historical data. - Data Visualization: Create line charts, heatmaps, and more to visualize trends over time. Source: Simulated for research and educational purposes.

A statistical uncertainty analysis-based precipitation merging framework (SUPER) with the aim of enhancing precipitation merging accuracy over data-sparse regions.

SUPER v2: A 3-Hourly Global Precipitation Dataset Optimized for Sparse Data Challenges

The global rainfall and runoff software market is projected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX% during the forecast period. The growth of this market is primarily attributed to the increasing demand for accurate and reliable rainfall and runoff data for various applications, such as hydrologic modeling, flood routing, and weather prediction. Additionally, the growing adoption of cloud-based services and the advancement of artificial intelligence (AI) and machine learning (ML) technologies are expected to further drive market growth. The market is segmented based on application, type, and region. By application, the hydrologic modeling segment is expected to hold the largest market share during the forecast period. This growth is attributed to the increasing demand for accurate and reliable rainfall and runoff data for flood forecasting and water resource management. By type, the cloud segment is expected to grow at a higher CAGR during the forecast period. This growth is attributed to the increasing adoption of cloud-based services, which offer benefits such as flexibility, scalability, and cost-effectiveness. The Asia Pacific region is expected to be the fastest-growing region during the forecast period due to the increasing demand for rainfall and runoff data for infrastructure development and flood risk management in developing countries.

RIST (Rainfall Intensity Summarization Tool) is a Windows-based program designed to facilitate analysis of precipitation records. RIST has improved efficiency and output files suitable for input to runoff, erosion, and water quality models including RUSLE, WEPP, SWAT, and AnnAGNPS. RIST inputs text files in user-specified fixed-width or comma-delimited formats. Rainfall records may be time-and-date stamp, fixed interval, or variable interval (breakpoint) data. Standard outputs include: Standard RUSLE outputs include a storm-by-storm summary of total precipitation, duration, intensity, kinetic energy, and EI30; and bi-weekly and monthly rainfall summaries of rainfall, energy, EI30 and erosivity density. Optionally, storms with less than 0.5 in. (12.7mm) of precipitation may be excluded from the energy and intensity calculations. Standard WEPP outputs include daily rainfall, storm duration (reduced by excluding periods greater than 30 minutes without rain), ip, and tp. Output for SWAT and AnnAGNPS include daily precipitation and, optionally, sub-daily precipitation totals. RIST also includes the capability to generate (1) precipitation totals at any user-specified fixed time interval or (2) a storm-by-storm analysis including maximum intensities observed during 5, 10, 15, 20, 30, and 60 minute within-in storm periods. Resources in this dataset:Resource Title: RIST - Rainfall Intensity Summarization Tool. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=WPP-01&modecode=60-60-05-05 download page

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 2021.

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.

ensuring accurate representations in spatial and temporal data analyses.