This dataset has 1-day (daily) averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

This dataset has monthly averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

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.

Scientists at Famine and Early Warning System (FEWS NET) who are members of the SERVIR Applied Sciences Team used 30+ years' (1981-present) worth of multiple satellite data sources and ground observations to produce an unprecedented, global, spatially and temporally consistent and continuous 30-year record of satellite-derived rainfall data. Spanning 50°S-50°N (and all longitudes), 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. This CHIRPS global dataset makes it possible to accurately assess and monitor large-scale rainfall patterns and analyze how they may be affected by climate change.

Two CHIRPS products, both reported in millimeters (mm), are produced operationally: a rapid preliminary version, and a later final version. The preliminary CHIRPS product is available, for the entire domain, two days after the end of a pentad (2nd, 7th, 12th, 17th, 22nd and 27th). The preliminary CHIRPS uses two station sources, the World Meteorological Organization's (WMO) Global Telecommunication System (GTS) and Mexico. The final CHIRPS product takes advantage of several other stations sources and is complete sometime in the third week of the following month.

The data are available in various formats for download via the Climate Hazard Center FTP site. (see below)

Through ClimateSERV (https://climateserv.servirglobal.net), the SERVIR Program provides the ability to extract zonal statistics (average, min, max) over a user-specified area of interest (AOI) for a specific time period. Data are downloadable as charts and underlying tabular data (in comma separated values - .csv files). Subsets of the data in raster format (.tif files) for an AOI can also be extracted. ClimateSERV also exposes an API to allow data retrieval requests into third party applications. ClimateSERV combines CHIRPS data with the most recently available CHIRP (no stations) data, which is overwitten as new CHIRPS data become available.

Please see Online Resources further below for links.

For more information on FEWS NET, visit https://fews.net For more information on SERVIR, visit https://servirglobal.net

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.

This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly datasets will no longer be available and have been superseded by the global monthly dataset. The simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit.The simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.In November 2020, all FLDAS data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.

Chirp

## Overview

Chirp is a dataset for object detection tasks - it contains Chirp Signals annotations for 1,099 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Chirp PA Birds

## Overview

Chirp PA Birds is a dataset for instance segmentation tasks - it contains Birds annotations for 703 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

Śledzenie historii cen Chirp umożliwia traderom kryptowalut łatwe monitorowanie wyników ich inwestycji. Możesz na bieżąco wygodnie śledzić nie tylko wartość otwarcia, maksimum i zamknięcie dla Chirp, ale także wolumen transakcji. Ponadto możesz natychmiast wyświetlić dzienną zmianę jako wartość procentową, co ułatwia identyfikację dni ze znacznymi wahaniami. Zgodnie z naszymi danymi dotyczącymi historii cen Chirp, wartość wzrosła do rekordowego szczytu w 2025-01-20 roku, przekraczając $0.2987 USD. Z drugiej strony, najniższy punkt w trajektorii cenowej Chirp, powszechnie określany jako „Chirp all-time low”, wystąpił w dniu 2025-04-07. Gdyby ktoś kupił Chirp w tym czasie, obecnie cieszyłby się znaczącym zyskiem w wysokości 93%. Z założenia zostanie utworzone 79,781,464 Chirp. Obecnie podaż w obiegu Chirp wynosi około 69,678,830. Wszystkie ceny podane na tej stronie pochodzą z wiarygodnego źródła Bitget. Ważne jest, aby polegać na jednym źródle w celu sprawdzenia inwestycji, ponieważ wartości mogą się różnić w zależności od sprzedawcy. Nasz historyczny zbiór danych cenowych Chirp obejmuje zakres w odstępach 1 minuty, 1 dnia, 1 tygodnia i 1 miesiąca (otwarcia/maksimum/minimum/zamknięcia/wolumenu). Te zbiory danych zostały poddane rygorystycznym testom w celu zapewnienia spójności, kompletności i dokładności. Są one specjalnie zaprojektowane w celach symulacji handlu i testów historycznych, łatwo dostępne do bezpłatnego pobrania i aktualizowane w czasie rzeczywistym.

This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.

The archive contains :

• AgERA5 (doi:10.24381/cds.6c68c9bb) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022
• CHIRPS v2.0 (doi:10.1038/sdata.2015.66) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022

This data has been extracted from their original sources using the SARRA-data-downloader tool.

The applicable licences are the licences of the respective datasets.

RAIN4PE is a novel daily gridded precipitation dataset obtained by merging multi-source precipitation data (satellite-based Climate Hazards Group InfraRed Precipitation, CHIRP (Funk et al. 2015), reanalysis ERA5 (Hersbach et al. 2020), and ground-based precipitation) with terrain elevation using the random forest regression method. Furthermore, RAIN4PE is hydrologically corrected using streamflow data in catchments with precipitation underestimation through reverse hydrology. Hence, RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in-situ observations, multiple precipitation sources, elevation data, and is supplemented by streamflow data to correct the precipitation underestimation over páramos and montane catchments. The RAIN4PE data are available for the terrestrial land surface between 19°S-2°N and 82-67°W, at 0.1° spatial and daily temporal resolution from 1981 to 2015. The precipitation dataset is provided in netCDF format. For a detailed description of the RAIN4PE development and evaluation of RAIN4PE applicability for hydrological modeling of Peruvian and Ecuadorian watersheds, readers are advised to read Fernandez-Palomino et al. (2021).

The monthly climatology dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of 12 monthly files, each representing the monthly data averaged over 35 years from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data. The data are in 0.10 degree resolution and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly climatology datasets will no longer be available and have been superseded by the global monthly climatology dataset.

More information about the monthly FLDAS can be found from the dataset landing page for FLDAS_NOAH01_C_GL_M_001 and the FLDAS README document.

In November 2020, all FLDAS data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.

This deposit contains the code and data used in "Evaluating Discharge Estimates from Passive Microwave Radiometry vs. Stage-Based Rating for the Bucao River (Zambales)," which has been submitted for peer review.

• 250509_Bco_Q.R is the R script used for formal analysis. It also contains the code to reproduce the figures in the manuscript. Comments are provided to guide the user. Please change the directory path names as needed. Please also download and install the required libraries.
• Bco_CSVs is a directory containing the files used as inputs to the above R code.
  • Bco_DPWH_in is a subdirectory containing Bucao_dpwh.csv. This file is the benchmark discharge data for the Bucao River from the Department of Public Works and Highways (DPWH); you may also access the same statio data (code R03.005) from the DPWH Streamflow Management System: https://apps.dpwh.gov.ph/streams_public/station_public.aspx. The CSV file has the following attributes: "YEAR", "DAY", and months ("JAN" through "DEC"). Discharge units are in lps.
  • RiverWatch_PH - DFO-RW_v4.5.csv contains the discharge estimates for different Philippine satellite gauging reaches (SGRs) from the Dartmouth Flood Observatory's River Watch (DFO-RW) v4.5. It has the following attributes: "SGR"; "Date"; "Q_cms" is discharge in m³/s; "m" is the slope of the rating curve equation used; "b" is the intercept of the rating curve equation; and "HR" is the remote sensing signal (referred to in the manuscript as "M/C ratio"; it is dimensionless).
  • Bco_chirps_points_loc.csv contains the locations of the equidistant grid points used to sample and derive (by inverse distance weighting) the catchment-averaged rainfall estimates from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data for the 2003–2006 and 2007–2010 subperiods (data fetched from the ClimateSERV server). It has the following attributes: "fid" is the index; "HubDist" is the distant of the point to the Bucao Watershed's centroid; "Bco_chirps_points_xcoord" is the longitude; and "Bco_chirps_points_ycoord" is the latitude.
  • S2216_dforw_Q.csv contains the discharge estimates from DFO-RW v1.0 for the Bucao River (ID: 2216 for "Pinatubo River North" in the DFO-RW v1.0 SGR list). It has the following attributes: "Date" and "Q_cms" (discharge in m³/s).

Please don't hesitate to reach out to RCC at rccornelio@up.edu.ph for any clarifications. Depending on the peer review, this deposit may be updated.

Two separate NETCDF containing time series of the Seasonal Location Index (SLI) and Rainfall Cluster index (RCI). These indices are the basis for the results presented in Seregina et al. (2020, DOI:). Both indices were calculated according the method of Riddle and Wilks (2013, DOI: 10.1002/joc.3429) from the Climate Hazards InfraRed Precipitation with Stations (CHIRPS, DOI: 10.1038/sdata.2015.66) for the time period 1981–2018 over the Greater Horn of Africa.