CHIRPS

2000-2018年全球0.05°降水产品逐日数据集

内容包含全球陆表尺度降水估算值，基于CHIRPS（Climate Hazards Group Infrared Precipitation with Station Data）数据整合而来，以毫米（mm）为单位，空间范围50°N-50°S，空间分辨率0.05°（约5 km），以栅格（.tif）形式存储和数据组织文件。数据集存储格式为.tif， 由6,940个文件组成，数据量为372 GB。

GCIP/ESOP-97 Surface: Daily Precipitation Composite. Version 1.0

The Daily Precipitation Composite was formed from several data sources (i.e., National Centers for Environmental Prediction daily precipitation, National Weather Service Cooperative Observers daily precipitation data, and the daily precipitation data extracted from the ESOP-97 Hourly Precipitation Composite). Data from these sources were quality controlled and merged to form this precipitation composite.

Russian river precipitation data

Data to generate precipitation values in manuscript

Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0-14 day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer timescales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden-Julian Oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, re-evaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Reconstructed Precipitation and Temperature CFSv2 Forecasts for 2011-2018

Temperature and precipitation forecasts from operational CFSv2 (Climate Forecast System), for biweekly dates in 2011-2018, with a prediction window of 3-6 weeks and interpolated to a 1x1 grid containing the western contiguous United States.

Earth Analytics Bootcamp | Final Project Dataset | Precipitation Data for Boulder, CO and San Francisco, CA for 1948 to 2018

This dataset contains: 1. boulder-monthly-precip-mm-1948-2018.csv: total monthly precipitation (millimeters) for each month and year for Boulder, CO between 1948 and 2018. The data are organized with a row for each for each year (in order from 1948 to 2018) and one column for month (in order from January to December). This file does not contain headers. Original data from U.S. National Oceanic and Atmospheric Administration (NOAA): https://www.esrl.noaa.gov/psd/boulder/Boulder.mm.precip.html 2. san-franscisco-monthly-precip-mm-1948-2018.csv: total monthly precipitation (millimeters) for each month and year for San Francisco, CA between 1948 and 2018. The data are organized with a row for each for each year (in order from 1948 to 2018) and one column for month (in order from January to December). This file does not contain headers. Original data from the National Weather Service: https://w2.weather.gov/climate/xmacis.php?wfo=mtr

Consecutive Dry Days for Far Future (2080 - 2100)

Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data. [1] http://www.csag.uct.ac.za/

Climate projections of chill hours and implications for olive cultivation in Minas Gerais, Brazil

Abstract: The objective of this work was to determine the accumulation of chill hours and to define the areas with aptitude for olive (Olea europaea) cultivation in the state of Minas Gerais, Brazil, as well as to analyze the impacts of climate change projections on chilling-hour requirements and climatic zoning, in two radiative forcing scenarios. The trigonometric method was used to quantify the number of chill hours, considering base temperatures (Tb) of 7.0, 9.5, and 13°C (high, medium, and low chill, respectively), and was applied to present climate (1983-2012) and to two future climate (2041-2070 and 2071-2100) conditions. The present climate data were obtained from 47 conventional weather stations, and the future climate data were obtained from three Earth system models (IPSL-CM5A-LR, MRI-CGCM3, and MIROC5). Future projections point to a decrease in the suitable areas for olive crop cultivation, particularly under representative concentration pathway (RCP) 8.5 and for olive cultivars with a high-chilling requirement (Tb=7.0ºC). Of the olive cultivars requiring medium chill (Tb=9.5ºC), only 2.6% (RCP 4.5) and 1.6% (RCP 8.5) will be suitable in the extreme south and in higher altitude areas of Minas Gerais, while, of those requiring low chill (Tb=13ºC), 11.8% (RCP 4.5) and 6.7% (RCP 8.5) will be suitable. If the climate projections become true, the cultivation of olive crops will be viable in the southern region and in higher altitude areas of the state of Minas Gerais.

Anticyclonic Rossby wave breaking (AWB)-associated vertical wind shear anomalies, 1979-2019

This repository comprises data generated for the study "Winter Rossby Wave Breaking Persistence in Extended-range Seasonal Forecasts of Atlantic Tropical Cyclone Activity" (Jones et al. 2021). The repository includes: i) The first four leading modes of tropical Atlantic vertical wind shear (seasonal); ii) Six-hourly (0Z, 6Z, 12Z, 18Z), monthly and seasonal indices of vertical wind shear anomalies associated with the downstream edge of anticyclonic Rossby wave breaking (AWB) detected over the North Atlantic region (20-40N, 100-5W); iii) An index of subtropical zonal wind anomalies projected against the correlation pattern between the second leading mode of July-September tropical Atlantic vertical wind shear and the January-March 200 hPa zonal wind field. The reanalysis dataset used is the ECMWF ERA-5 Reanalysis dataset. The wave breaking detection algorithm used to generate this index is outlined in Papin et al. (2020) and calculation of the AWB-associated shear is outlined in Jones et al. (2020). The data captures the main drivers (both tropical and subtropical) of summer tropical Atlantic vertical wind shear and is used in our study to quantify the added skill in extended-range statistical forecasts of tropical cyclone activity. For the original scripts that produced the archived indices, please contact the author at jhordanne.jones@colostate.edu or jhrdnnjones3@gmail.com.

Extreme precipitation records in Antarctica [Dataset]

This is the dataset associated to the research 'Extreme precipitation records in Antarctica' published in International Journal of Climatology. This repository contains: Precipitation extremes for each model at every grid point for a duration of xxx days. Files named: [model]_PCP_max_[xxx]d.csv Dimensions for ERA5: [lons, lats] Dimensions for RACMO2: [grid_x, grid_y] Units: mm Dimensions to plot the precipitation extremes: lons (longitudes), lats (latitudes) and duration. Files named: [model]_PCP_max_lats.csv Dimensions for ERA5: [lats] Dimensions for RACMO2: [grid_x, grid_y] Units: degrees [model]_PCP_max_lons.csv Dimensions for ERA5: [lons] Dimensions for RACMO2: [grid_x, grid_y] Units: degrees [model]_PCP_max_duration.csv Dimensions: [time] Units: days World Precipitation Records from 1 day. File named: Max_WR_from1day.csv (first row duration [days]; second row precipitation [mm]) How to cite If you use this dataset, please cite the accompanying paper as: González-Herrero, S.,Vasallo, F., Bech, J., Gorodetskaya, I., Elvira, B., & Justel, A. (2023). Extreme precipitation records in Antarctica.International Journal of Climatology, 43(7), 3125–3138. https://doi.org/10.1002/joc.8020 Complementary code You can find the jupyter notebooks to complement the research in: https://doi.org/10.1002/joc.8020 Contact If you have any question, please contact with Sergi at sergi.gonzalez@slf.ch

COHMEX: PAM-II Data. Version 1.0

This dataset is a single tar file of data from 50 PAM-II stations as part of the combined Microburst and Severe Thunderstorm (MIST) and Satellite Precipitation and Cloud Experiment (SPACE) projects.

Appendix A. Comparison of climatic predictors among 18 study sites and the 69 sites historically occupied by pika from which they were selected via random sampling stratified by latitude, longitude, and elevation.

Comparison of climatic predictors among 18 study sites and the 69 sites historically occupied by pika from which they were selected via random sampling stratified by latitude, longitude, and elevation.

NAME: Precipitation Hourly Multi-Network Composite. Version 1.0

This Hourly Precipitation Composite is one of several precipitation datasets provided for the North American Monsoon Experiment (NAME) 2004. This precipitation composite was formed from several data sources (i.e. Army Range Dugway Proving Grounds (DPG) Precipitation Data, Army Range Ft. Huachuca Proving Grounds (EPG) Precipitation Data, Army Range White Sands Missile Range (WSMR) Precipitation Data, Army Range Yuma Proving Ground (YPG) Precipitation Data, Mesonet Arizona Maricopa County ALERT Precipitation Network, Arizona Meteorological Network (AZMET) Precipitation Data, Mesonet Arizona Mohave County ALERT Precipitation Data, Mesonet Arizona Pima County ALERT Precipitation Data, Mesonet Arizona Yavapai County ALERT Precipitation Data, NOAA/NWS ALERT Network Precipitation Data [Jamison], Mesonet FSL MADIS Precipitation Data [NCAR/EOL], Mesonet LDM Surface METAR Precipitation Data, Mesonet New Mexico State University Precipitation Data, Mexican Navy SEMAR R/V Altair Meteorological and Navigational Parameters Precipitation Data, Mexico Agriculture Automated Weather Station Precipitation Data (Sonora), Mexico Navy SEMAR Automated Weather Station Precipitation Data, Mexico SMN-CNA Automated Weather Station Precipitation Data, NCAR Integrated Sounding System (ISS) Precipitation Data [NCAR/EOL], NCDC RecRainga Hourly Precipitation, NCEP/EMC U.S. Gage-only Hourly Precipitation Data [NCAR/EOL], NOAA/AL NAME Supersite (Obispo) Meteorological Precipitation Data, El Puma Mexico Navy Research Vessel (R/V), and the Northwest Mexico NAME Event Raingage Network (NERN) Hourly Data). Data from these sources were gross limit checked and merged to form this precipitation composite. This composite contains data for the NAME 2004 Tier 3 domain.

GCIP/ESOP-97 Surface: Hourly Precipitation Composite. Version 1.0

The Hourly Precipitation Composite was formed from several data sources (i.e., National Climatic Data Center hourly precipitation data, National Centers for Environmental Prediction hourly precipitation, National Soil Tilth Laboratory hourly precipitation, and the precipitation data extracted from the ESOP-97 Hourly Surface Composite). Data from these sources were quality controlled and merged to form this precipitation composite.

SGP99: ABRFC WSR-88D Stage III Daily Precipitation. Version 1.0

This dataset contains data from the NOAA Arkansas-Red Basin River Forecast Center. The ABRFC routinely ingests WSR-88D precipitation derived products (Level III) from each of the radar sites with coverage in the Arkansas-Red River Basin (15 radars). In addition the ABRFC ingests real-time precipitation data from a total of approximately 500 gages. The ABRFC produces a number of derived or "Stage" products using the radar and precipitation gage data. A Stage II product is produced by merging radar precipitation estimates (Stage I) with ground truth data provided by the gages. The Stage II estimates are then composited into a Stage III precipitation 4 x 4 km resolution area averaged mosaic for the Arkansas-Red River Basin.

WRF simulation data

We investigated the effect of a (non-)scale-aware convective parameterization scheme on simulations of heavy rainfall events in Korea. This file contains WRF simulation data of 48-hour accumlated precipitation used in this paper. The spatial domain is 4x4 km (D03) and the format is NetCDF.

North American watershed precipitation from WRF

Precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model. Each file contains the following variables for the stated watershed: lat: latitude of each grid point, in degrees north lon: longitude of each grid point, in degrees north time: day of simulation RAINC: accumulated total cumulus precipitation, in mm RAINNC: accumulated total grid scale precipitation, in mm SNOWNC: accumulated total grid scale snow and ice, in mm watershed mask: matrix where 1 indicates the grid point is inside the watershed, 0 is outside watershed mountain mask: matrix where 1 indicates the grid point is within the mountainous portion of the watershed, 0 is outside

Linear trend in annual total precipitation from very high daily rainfall (1921-2015) defined as when daily rainfall (RR) > 99th percentile of the baseline average

An updated analysis of trends in South African rainfall is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). A total of 60 weather stations were used for the rainfall trend analysis spanning the period 1921-2015. Apart from the trends in the annual rainfall totals, long-term changes in rainfall can manifest in changes in rainfall extremes. An extreme rainfall trend analysis is also performed, based on rainfall extreme indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013). The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level. Based on the data, there is strong evidence of statistically significant increases in rainfall occurring over the southern interior regions, extending from the western interior of the Eastern Cape and eastern interior of the Western Cape northwards into the central interior region of the Northern Cape, over the period 1921-2015. Extreme daily rainfall events have increased over these same areas, with these increases also being statistically significant and extending northwards into North West, the Free State and Gauteng. Over Limpopo there is strong evidence of statistically significant decreases in annual rainfall totals.

SMEX02: ABRFC Stage III Gridded WSR-88D Daily Precipitation Data. Version 1.0

This dataset contains daily resolution 4 km by 4 km gridded precipitation fields produced by the National Oceanic and Atmospheric Administration (NOAA) Arkansas-Red Basin River Forecast Center (ABRFC). Data covers the period from June 1 to July 31, 2002. The Data are in netCDF format.

Multi-Model Ensemble Climate Projections for Ontario, Canada

Multi-model ensembles for climate modeling, created by combining seven Global Climate Models (GCM) and Regional Climate Models (RCM), are assessed at twelve stations, as well as for the entire domain of Ontario, Canada. Two multi-model ensembles were produced, one using the mean of the seven GCM and RCM combinations and the other using the median of the same seven GCM and RCM combinations. Three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature) were used to evaluate the performance of the models. Data obtained from the North American Coordinated Regional Downscaling Experiment were compared with gridded data based on observations from the Climactic Research Unit’s TS v4.00 dataset, as well as observed station data from the Digital Archive of Canadian Climatological Data provided by Environment and Climate Change Canada.

内容包含全球陆表尺度降水估算值，基于CHIRPS（Climate Hazards Group Infrared Precipitation with Station Data）数据整合而来，以毫米（mm）为单位，空间范围50°N-50°S，空间分辨率0.05°（约5 km），以栅格（.tif）形式存储和数据组织文件。数据集存储格式为.tif， 由6,940个文件组成，数据量为372 GB。

The Daily Precipitation Composite was formed from several data sources (i.e., National Centers for Environmental Prediction daily precipitation, National Weather Service Cooperative Observers daily precipitation data, and the daily precipitation data extracted from the ESOP-97 Hourly Precipitation Composite). Data from these sources were quality controlled and merged to form this precipitation composite.

Data to generate precipitation values in manuscript

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0-14 day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer timescales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden-Julian Oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, re-evaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Temperature and precipitation forecasts from operational CFSv2 (Climate Forecast System), for biweekly dates in 2011-2018, with a prediction window of 3-6 weeks and interpolated to a 1x1 grid containing the western contiguous United States.

This dataset contains: 1. boulder-monthly-precip-mm-1948-2018.csv: total monthly precipitation (millimeters) for each month and year for Boulder, CO between 1948 and 2018. The data are organized with a row for each for each year (in order from 1948 to 2018) and one column for month (in order from January to December). This file does not contain headers. Original data from U.S. National Oceanic and Atmospheric Administration (NOAA): https://www.esrl.noaa.gov/psd/boulder/Boulder.mm.precip.html 2. san-franscisco-monthly-precip-mm-1948-2018.csv: total monthly precipitation (millimeters) for each month and year for San Francisco, CA between 1948 and 2018. The data are organized with a row for each for each year (in order from 1948 to 2018) and one column for month (in order from January to December). This file does not contain headers. Original data from the National Weather Service: https://w2.weather.gov/climate/xmacis.php?wfo=mtr

Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data. [1] http://www.csag.uct.ac.za/

Abstract: The objective of this work was to determine the accumulation of chill hours and to define the areas with aptitude for olive (Olea europaea) cultivation in the state of Minas Gerais, Brazil, as well as to analyze the impacts of climate change projections on chilling-hour requirements and climatic zoning, in two radiative forcing scenarios. The trigonometric method was used to quantify the number of chill hours, considering base temperatures (Tb) of 7.0, 9.5, and 13°C (high, medium, and low chill, respectively), and was applied to present climate (1983-2012) and to two future climate (2041-2070 and 2071-2100) conditions. The present climate data were obtained from 47 conventional weather stations, and the future climate data were obtained from three Earth system models (IPSL-CM5A-LR, MRI-CGCM3, and MIROC5). Future projections point to a decrease in the suitable areas for olive crop cultivation, particularly under representative concentration pathway (RCP) 8.5 and for olive cultivars with a high-chilling requirement (Tb=7.0ºC). Of the olive cultivars requiring medium chill (Tb=9.5ºC), only 2.6% (RCP 4.5) and 1.6% (RCP 8.5) will be suitable in the extreme south and in higher altitude areas of Minas Gerais, while, of those requiring low chill (Tb=13ºC), 11.8% (RCP 4.5) and 6.7% (RCP 8.5) will be suitable. If the climate projections become true, the cultivation of olive crops will be viable in the southern region and in higher altitude areas of the state of Minas Gerais.

This repository comprises data generated for the study "Winter Rossby Wave Breaking Persistence in Extended-range Seasonal Forecasts of Atlantic Tropical Cyclone Activity" (Jones et al. 2021). The repository includes: i) The first four leading modes of tropical Atlantic vertical wind shear (seasonal); ii) Six-hourly (0Z, 6Z, 12Z, 18Z), monthly and seasonal indices of vertical wind shear anomalies associated with the downstream edge of anticyclonic Rossby wave breaking (AWB) detected over the North Atlantic region (20-40N, 100-5W); iii) An index of subtropical zonal wind anomalies projected against the correlation pattern between the second leading mode of July-September tropical Atlantic vertical wind shear and the January-March 200 hPa zonal wind field. The reanalysis dataset used is the ECMWF ERA-5 Reanalysis dataset. The wave breaking detection algorithm used to generate this index is outlined in Papin et al. (2020) and calculation of the AWB-associated shear is outlined in Jones et al. (2020). The data captures the main drivers (both tropical and subtropical) of summer tropical Atlantic vertical wind shear and is used in our study to quantify the added skill in extended-range statistical forecasts of tropical cyclone activity. For the original scripts that produced the archived indices, please contact the author at jhordanne.jones@colostate.edu or jhrdnnjones3@gmail.com.

This is the dataset associated to the research 'Extreme precipitation records in Antarctica' published in International Journal of Climatology. This repository contains: Precipitation extremes for each model at every grid point for a duration of xxx days. Files named: [model]_PCP_max_[xxx]d.csv Dimensions for ERA5: [lons, lats] Dimensions for RACMO2: [grid_x, grid_y] Units: mm Dimensions to plot the precipitation extremes: lons (longitudes), lats (latitudes) and duration. Files named: [model]_PCP_max_lats.csv Dimensions for ERA5: [lats] Dimensions for RACMO2: [grid_x, grid_y] Units: degrees [model]_PCP_max_lons.csv Dimensions for ERA5: [lons] Dimensions for RACMO2: [grid_x, grid_y] Units: degrees [model]_PCP_max_duration.csv Dimensions: [time] Units: days World Precipitation Records from 1 day. File named: Max_WR_from1day.csv (first row duration [days]; second row precipitation [mm]) How to cite If you use this dataset, please cite the accompanying paper as: González-Herrero, S.,Vasallo, F., Bech, J., Gorodetskaya, I., Elvira, B., & Justel, A. (2023). Extreme precipitation records in Antarctica.International Journal of Climatology, 43(7), 3125–3138. https://doi.org/10.1002/joc.8020 Complementary code You can find the jupyter notebooks to complement the research in: https://doi.org/10.1002/joc.8020 Contact If you have any question, please contact with Sergi at sergi.gonzalez@slf.ch

This dataset is a single tar file of data from 50 PAM-II stations as part of the combined Microburst and Severe Thunderstorm (MIST) and Satellite Precipitation and Cloud Experiment (SPACE) projects.

Comparison of climatic predictors among 18 study sites and the 69 sites historically occupied by pika from which they were selected via random sampling stratified by latitude, longitude, and elevation.

This Hourly Precipitation Composite is one of several precipitation datasets provided for the North American Monsoon Experiment (NAME) 2004. This precipitation composite was formed from several data sources (i.e. Army Range Dugway Proving Grounds (DPG) Precipitation Data, Army Range Ft. Huachuca Proving Grounds (EPG) Precipitation Data, Army Range White Sands Missile Range (WSMR) Precipitation Data, Army Range Yuma Proving Ground (YPG) Precipitation Data, Mesonet Arizona Maricopa County ALERT Precipitation Network, Arizona Meteorological Network (AZMET) Precipitation Data, Mesonet Arizona Mohave County ALERT Precipitation Data, Mesonet Arizona Pima County ALERT Precipitation Data, Mesonet Arizona Yavapai County ALERT Precipitation Data, NOAA/NWS ALERT Network Precipitation Data [Jamison], Mesonet FSL MADIS Precipitation Data [NCAR/EOL], Mesonet LDM Surface METAR Precipitation Data, Mesonet New Mexico State University Precipitation Data, Mexican Navy SEMAR R/V Altair Meteorological and Navigational Parameters Precipitation Data, Mexico Agriculture Automated Weather Station Precipitation Data (Sonora), Mexico Navy SEMAR Automated Weather Station Precipitation Data, Mexico SMN-CNA Automated Weather Station Precipitation Data, NCAR Integrated Sounding System (ISS) Precipitation Data [NCAR/EOL], NCDC RecRainga Hourly Precipitation, NCEP/EMC U.S. Gage-only Hourly Precipitation Data [NCAR/EOL], NOAA/AL NAME Supersite (Obispo) Meteorological Precipitation Data, El Puma Mexico Navy Research Vessel (R/V), and the Northwest Mexico NAME Event Raingage Network (NERN) Hourly Data). Data from these sources were gross limit checked and merged to form this precipitation composite. This composite contains data for the NAME 2004 Tier 3 domain.

The Hourly Precipitation Composite was formed from several data sources (i.e., National Climatic Data Center hourly precipitation data, National Centers for Environmental Prediction hourly precipitation, National Soil Tilth Laboratory hourly precipitation, and the precipitation data extracted from the ESOP-97 Hourly Surface Composite). Data from these sources were quality controlled and merged to form this precipitation composite.

This dataset contains data from the NOAA Arkansas-Red Basin River Forecast Center. The ABRFC routinely ingests WSR-88D precipitation derived products (Level III) from each of the radar sites with coverage in the Arkansas-Red River Basin (15 radars). In addition the ABRFC ingests real-time precipitation data from a total of approximately 500 gages. The ABRFC produces a number of derived or "Stage" products using the radar and precipitation gage data. A Stage II product is produced by merging radar precipitation estimates (Stage I) with ground truth data provided by the gages. The Stage II estimates are then composited into a Stage III precipitation 4 x 4 km resolution area averaged mosaic for the Arkansas-Red River Basin.

We investigated the effect of a (non-)scale-aware convective parameterization scheme on simulations of heavy rainfall events in Korea. This file contains WRF simulation data of 48-hour accumlated precipitation used in this paper. The spatial domain is 4x4 km (D03) and the format is NetCDF.

Precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model. Each file contains the following variables for the stated watershed: lat: latitude of each grid point, in degrees north lon: longitude of each grid point, in degrees north time: day of simulation RAINC: accumulated total cumulus precipitation, in mm RAINNC: accumulated total grid scale precipitation, in mm SNOWNC: accumulated total grid scale snow and ice, in mm watershed mask: matrix where 1 indicates the grid point is inside the watershed, 0 is outside watershed mountain mask: matrix where 1 indicates the grid point is within the mountainous portion of the watershed, 0 is outside

An updated analysis of trends in South African rainfall is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). A total of 60 weather stations were used for the rainfall trend analysis spanning the period 1921-2015. Apart from the trends in the annual rainfall totals, long-term changes in rainfall can manifest in changes in rainfall extremes. An extreme rainfall trend analysis is also performed, based on rainfall extreme indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013). The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level. Based on the data, there is strong evidence of statistically significant increases in rainfall occurring over the southern interior regions, extending from the western interior of the Eastern Cape and eastern interior of the Western Cape northwards into the central interior region of the Northern Cape, over the period 1921-2015. Extreme daily rainfall events have increased over these same areas, with these increases also being statistically significant and extending northwards into North West, the Free State and Gauteng. Over Limpopo there is strong evidence of statistically significant decreases in annual rainfall totals.

This dataset contains daily resolution 4 km by 4 km gridded precipitation fields produced by the National Oceanic and Atmospheric Administration (NOAA) Arkansas-Red Basin River Forecast Center (ABRFC). Data covers the period from June 1 to July 31, 2002. The Data are in netCDF format.

Multi-model ensembles for climate modeling, created by combining seven Global Climate Models (GCM) and Regional Climate Models (RCM), are assessed at twelve stations, as well as for the entire domain of Ontario, Canada. Two multi-model ensembles were produced, one using the mean of the seven GCM and RCM combinations and the other using the median of the same seven GCM and RCM combinations. Three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature) were used to evaluate the performance of the models. Data obtained from the North American Coordinated Regional Downscaling Experiment were compared with gridded data based on observations from the Climactic Research Unit’s TS v4.00 dataset, as well as observed station data from the Digital Archive of Canadian Climatological Data provided by Environment and Climate Change Canada.

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