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.

This map displays the Quantitative Precipitation Forecast (QPF) for the next 72 hours across the contiguous United States. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.The dataset includes incremental and cumulative precipitation data in 6-hour intervals. In the ArcGIS Online map viewer you can enable the time animation feature and select either the "Amount by Time" (incremental) layer or the "Accumulation by Time" (cumulative) layer to view a 72-hour animation of forecast precipitation. All times are reported according to your local time zone.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces forecast data of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.qpf.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

AWIS Weather Services has delivered weather data from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

Our Extensive Historical Weather Database is full of 100% quality checked weather data from over 30,000 observation sites worldwide. The data is REAL WEATHER OBSERVATIONS and visually checked by humans each day.

This service is your access to that database as it gets updated.

You choose the variables you need. You choose the cities you need covered. We'll handle the data pulling, updating, and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for Live Weather Data Feed available for most locations are Max Temperature Min Temperature Total Precipitation Average Wind Speed Average Cloud Cover Average Temperature Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Total Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Average Soil Temperature Snow Fall Snow Depth

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our historical databases.

PRICING ESTIMATES: (The number of variables requested could change the price slightly) $1.50 per site, per month if you need less than 1000 sites. $1.25 per site, per month if you need 1001-5000 sites. $0.75 per site, per month if you need 5001-10000 sites. $0.25 per site, per month if you need over 10k sites.

Discounts available for long term deals. HISTORICAL DATA available upon request at a reduced rate. Reach out to us for more details and we can provide a targeted proposal within hours.

This record is for Approval for Access product AfA501 for approximately 1000 automatic rainfall data from the Environment Agency rainfall API.

The data is available on an update cycle which varies across the country, typically updated daily but updated faster is rainfall is detected. This is update frequency is usually increased during times of flooding, etc.

Readings are transferred via telemetry to internal and external systems in or close to real-time.

Measurement of the rainfall is taken in millimetres (mm) accumulated over 15 minutes. Note that rainfall data is recorded in GMT, so during British Summer Time (BST) data may appear to be an hour old. Data comes from a network of over 1000 gauges across England. Data shown is raw data collected from the gauges and is subject to quality control procedures. As a result, values may change after publication on this website.

Continuous rainfall information is also stored on our hydrometric archive, Wiski, and can be provided in non real-time on request through our customer contact centre. This raw rainfall data is provided to the Met Office for quality control along with all the data from our registered daily storage gauges (c.1400). The quality controlled dataset is covered in AfA148 Quality Controlled Daily and Monthly Raingauge Data from Environment Agency Gauges.

Data from a small selection of Met Office raingauges are included in our open data feed. This data is also available from the Met Office as open data.

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.

The metadata record summarizes information for live weather measurements of the latest hour from the 41 meteo stations of ASTA. The hourly data is shown in local wintertime (GMT+1) and is each hour updated. The kind of measurements differ from station to station. This dataset is available via WMS (https://wms.inspire.geoportail.lu/geoserver/mf/wms?service=WMS&version=1.3.0&request=GetCapabilities) and WFS (https://wms.inspire.geoportail.lu/geoserver/mf/wfs?service=WFS&version=2.0.0&request=GetCapabilities) API protocols. Data is transformed into INSPIRE data model. Description copied from catalog.inspire.geoportail.lu.

Gridded files of radar-derived 5 minute precipitation accumulations, corrected by rain gauge data. Radar data over the Netherlands and surrounding area measured by Dutch, Belgian, and German radars are corrected by available data from automatic rain gauges. Time interval is 5 minutes. See data set nl_rdr_data_rtcor_5m_tar/1.0 for an archive that goes back to 2018. Starting with data from 31 January 2023 - 10.45 UTC onwards, this dataset is created using improved algorithms. This includes correction for signal attenuation, correction for vertical variation of precipitation, correction for fast-moving showers and use of uncertainty information in merging data from multiple radars.

This map displays projected visible surface smoke across the contiguous United States for the next 48 hours in 1 hour increments. It is updated every 24 hours by NWS. Concentrations are reported in micrograms per cubic meter.Where is the data coming from?The National Digital Guidance Database (NDGD) is a sister to the National Digital Forecast Database (NDFD). Information in NDGD may be used by NWS forecasters as guidance in preparing official NWS forecasts in NDFD. The experimental/guidance NDGD data is not an official NWS forecast product.Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndgd/GT.aq/AR.conus/ds.smokes01.binSource data archive can be found here: https://www.ncei.noaa.gov/products/weather-climate-models/national-digital-guidance-database look for 'LXQ...' files by date. These are the Binary GRIB2 files that can be decoded via DeGRIB tool.Where can I find other NDGD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.RevisionsJuly 11, 2022: Feed updated to leverage forecast model change by NOAA, whereby the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) forecast model system was replaced with the Rapid Refresh (RAP) forecast model system. Key differences: higher accuracy with RAP now concentrated at 0-8 meter detail vs HYSPLIT at 0-100 meter; earlier data delivery by 6 hrs; forecast output extended to 51 hrs.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The dataset contains the blended (gauge and satellite estimates) monthly mean precipitation rates (unit: mm/day) for Canada for the period from January 1979 to December 2007, at a half degree spatial resolution. Please refer to the paper below for the details of the blending algorithm and input gauge and satellite data. Reference: Lin, A. and X. L. Wang, 2011: An algorithm for Blending Multiple Satellite Precipitation Estimates with in-situ Precipitation Measurements in Canada. JGR-Atmospheres, 116, D21111, doi:10.1029/2011JD016359.

-PROVISONALLY UNVALIDATED DATA- Gridded files of daily unvalidated precipitation sum in the Netherlands measured on 100-300 locations of the voluntary network from 08:00-08:00 UT. Grids are calculated based on unvalidated data (limited number of stations) as soon as the data is available, typically around 14:00. The data has only been automatically pre-validated. The complete validated dataset is Rd1. Version 2 is based on RobuKIS source data.

This map displays the expected total accumulation of new snow over the next 72 hours across the contiguous United States. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.The dataset includes incremental and cumulative snowfall data in 6-hour intervals. In the ArcGIS Online map viewer you can enable the time animation feature and select either the amount by time (incremental) or accumulation by time (cumulative) layers to view a 72-hour animation of forecast precipitation. All times are reported according to your local time zone.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.snow.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Abstract: Rain depth is collected using model 6011-A tipping bucket rain gauges manufactured by All Weather Inc. (formerly Qualimetrics). Two raingauges (RG1 and RG2) are installed at each of eight stations. Each rain gauge tip represents a depth of 0.01 inches of rainfall. Data are recorded by a data logger at the station and telemetered hourly to UMBC, where the data are stored in a data base. The rain gauges are not heated and therefore snow and ice storms are removed from the published record. The QA/QC procedure applied to the raw data includes removal of false tips and snow/ice events, accumulating tip data to a time series in inches/min, applying a laboratory-based calibration curve to the data, and converting corrected data to a one-minute time series in units of mm/min for publication. Type of instrument: Tipping bucket rain gauge Manufacturer: All Weather Inc. (formerly Qualimetrics Inc.) http://www.allweatherinc.com/ Model number: 6011-A Orifice opening: 8 in diameter (20 cm) Sensitivity: 0.01 in (0.25 mm) Manufacturer's specified calibrated accuracy: +/-0.5% at 0.5 in/hr. Manufacturer's specified repeatability: +/-3% Station information Each of eight stations consists of two tipping bucket rain gauges, a data logger (Campbell Scientific CR10X), a power source (10 W solar panel, solar controller, 12V 42 amp-hr battery), and a device for data transmission (Sierra Wireless AirLink Raven RV50). A contract is held with AT+T for data transmission. Raw data (time stamps of 0.01 inch tips) are recorded to the data logger and transmitted hourly via the Raven to University of Maryland, Baltimore County (UMBC) and stored in a data base at UMBC. Data streaming into UMBC are checked after every storm. Raw data can be viewed online at http://his10.umbc.edu/Precip/. If a station fails to transmit data, the station is visited after a storm for troubleshooting. Otherwise, stations are visited every 60 days to remove debris and trim weeds, check wiring and moving parts, clean solar panels, and remove any spider webs and insect nests. The rain gauges are not heated and therefore do not accurately record precipitation during snow and ice events. The rain gauges are deployed at locations listed in Table 1. The following QA/QC procedure is applied to the raw data to prepare for publication. (1) False tips are removed from the records; (2) snow and ice events are removed from the records; (3) a script is applied to the raw data to (a) accumulate the data to one-minute increments to derive a rain-rate time series; (b) apply a laboratory-derived calibration curve to the rain-rate time series, where a calibration is unique to a rain gauge; and � convert the data to desired units for publication (e.g., mm/min). Station name Station ID serial number* RG2 serial number* Carrie Murray Nature Center, WXCMNC, 2821, 2238 Carroll Park Golf Course, WXCPGC, 2244, 2494 Dead Run near Catonsville, WXDRNC, 2473 , 2486 Glyndon Elementary School, WXGFGL, 2126, 2255 Gwynns Falls Near Delight , WXGFND , 2157, 2168 McDonogh School , WXMCDO, 2231 , 2248 Oregon Ridge Park, WXORDG, ,2250 , 2124 UMBC Campus , WXUMBC, 2252 , 2873 * Deployment locations as of 12/31/2017 For further information contact: Claire Welty, UMBC, weltyc@umbc.edu Locations Carrie Murray Nature Center, 39d18m26.09sN, 76d41m42.26sW Carroll Park Golf Course , 39d16m24.93sN, 76d38m54.91sW Dead Run Near Catonsville (DR5), 39d17m45.19sN, 76d44m38.50sW Glyndon Elementary School, 39d28m05.60sN, 76d48m37.80sW Gywnns Falls Near Delight , 39d26m38.01sN, 76d46m57.61sW McDonogh School, 39d23m46.81sN, 76d46m17.07sW Oregon Ridge Park , 39d29m47.65sN, 76d41m20.42sW UMBC, 39d15m15.48sN, 76d42m08.42sW

This dataset contains Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Quasi-global satellite and observation based precipitation estimates over land from 1981 to near-real time. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.

A NEW GLOBAL SCALE RAINFALL PRODUCT obtained from satellite soil moisture data through the SM2RAIN algorithm (Brocca et al., 2014), at 0.25 degree/daily spatial-temporal resolution, has been delivered (Ciabatta et al., 2018). The SM2RAIN method was applied to the ESA CCI soil moisture Active and Passive products (Liu et al., 2011, 2012; Wagner et al., 2012) for the period from January 1998 to December 2015 (18 years).

The CCI-derived rainfall datasets (in mm/day) is gridded over a 0.25-degree grid on a global scale. The number of dates is 6574 (1998/01/01 – 2015/12/31). The product represents the cumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. A climatological correction has been applied to the data at monthly scale.

The rainfall dataset is provided in netCDF format. A total of 18 netCDF files, one per year, are provided.

The rainfall dataset is obtained by applying the SM2RAIN algorithm to the ESA CCI soil moisture Active and Passive products at version 03.1 separately. Then, an integration procedure based on a weighted average is applied in order to obtain the rainfall estimate. The algorithm has been calibrated during three different periods (1998-2001, 2002-2006 and 2007-2013) against the Global Precipitation Climatology Centre Full-Data daily dataset (GPCC-FDD, Schamm et al., 2015). The quality flag provided within the raw soil moisture observations has been used to mask out low quality data, as well as the areas characterized by high topographic complexity, high frozen soil and snow probability and presence of tropical forests.

References

Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489.

Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267-280, https://doi.org/10.5194/essd-10-267-2018, 2018.

Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436, doi:10.5194/hess-15-425-2011.

Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014.

Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U. (2015). GPCC Full Data Daily Version 1.0 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. DOI: 10.5676/DWD_GPCC/FD_D_V1_100.

Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., Ertl, M. (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321.

This map displays projected visible surface smoke across the contiguous United States for the next 48 hours in 1 hour increments. It is updated every 24 hours by NWS. Concentrations are reported in micrograms per cubic meter.Where is the data coming from?The National Digital Guidance Database (NDGD) is a sister to the National Digital Forecast Database (NDFD). Information in NDGD may be used by NWS forecasters as guidance in preparing official NWS forecasts in NDFD. The experimental/guidance NDGD data is not an official NWS forecast product.Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndgd/GT.aq/AR.conus/ds.smokes01.binSource data archive can be found here: https://www.ncei.noaa.gov/products/weather-climate-models/national-digital-guidance-database look for 'LXQ...' files by date. These are the Binary GRIB2 files that can be decoded via DeGRIB tool.Where can I find other NDGD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.RevisionsJuly 11, 2022: Feed updated to leverage forecast model change by NOAA, whereby the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) forecast model system was replaced with the Rapid Refresh (RAP) forecast model system. Key differences: higher accuracy with RAP now concentrated at 0-8 meter detail vs HYSPLIT at 0-100 meter; earlier data delivery by 6 hrs; forecast output extended to 51 hrs.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This dataset contains dekadal rainfall indicators computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2, aggregated by subnational administrative units.

Included indicators are (for each dekad):

• 10 day rainfall mm
• rainfall 1-month rolling aggregation mm
• rainfall 3-month rolling aggregation mm
• rainfall long term average mm
• rainfall 1-month rolling aggregation long term average mm
• rainfall 3-month rolling aggregation long term average mm
• rainfall anomaly %
• rainfall 1-month anomaly %
• rainfall 3-month anomaly %

The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the n_pixelscolumn.

The GPM Ground Validation Naval Research Laboratory (NRL) Near-Real Time Rain Rates IFloodS data product was created for the GPM Iowa Flood Studies (IFloodS) field campaign from April 23, 2013 through June 30, 2013. The IFloodS field campaign was a ground measurement campaign that took place in eastern Iowa. The goals of the campaign were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This NRL real time rain rates data product was produced using the Probability Matching Method with rain gauge, Defense Meteorological Satellite Program (DMSP) F15 Special Sensor Microwave - Imager (SSM/I), and DMSP F16 Special Sensor Microwave - Imager/Sounder (SSMIS) data. This data product includes rain rate estimates and files are available in netCDF-4 and binary formats, as well as corresponding browse imagery in JPG format.

The global satellite-based precipitation radar market is projected to reach USD XXX million by 2033, growing at a CAGR of XX% during the forecast period (2023-2033). This growth can be attributed to the increasing demand for accurate and timely precipitation data for various applications, such as weather forecasting, climate monitoring, and hydrological modeling. The market is also being driven by the growing adoption of satellite-based precipitation radar systems by governments and research institutions worldwide. The market is segmented into application and type. By application, the market is segmented into TRMM Satellite and GPM Satellite. By type, the market is segmented into Single Frequency Radar and Dual Frequency Radar. North America is expected to hold the largest share of the market during the forecast period, followed by Europe and Asia Pacific. The growing adoption of satellite-based precipitation radar systems by governments and research institutions in these regions is driving the market growth. Asia Pacific is also expected to witness significant growth due to the increasing investment in infrastructure development and the rising demand for accurate precipitation data for agricultural and water management purposes.

This file contains the Supplement (both raw observed precipitation data and figures obtained as output of the analysis) accompanying the manuscript 'hess-2016-453' submitted to the HESS journal (http://www.hydrology-and-earth-system-sciences.net/).

Title: "Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile"

Abstract

Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SRE) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.

This study attempts to exhaustively evaluate -for the first time- the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-adj, MSWEPv1.1 and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25° grid if necessary). The modified Kling-Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.

Results revealed that most SRE products performed better for the humid South (36.4-43.7°S) and Central Chile (32.18-36.4°S), in particular at low- and mid-elevation zones (0-1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons autumn and winter (MAM-JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all time scales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1,while CMORPH, PERSIANN-CDR and the real-time PERSIANN-CCS-adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resources assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.

Rainfall videos and the rainfall data measured by a rain gauge.