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 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), adapted from Land Information System (LIS7). The dataset contains 28 parameters in a 0.10 degree spatial resolution and from January 2019 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Global Data Assimilation System (GDAS) data and Climate Hazards Group InfraRed Precipitation with Station Preliminary (CHIRPS-PRELIM) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit, restarted from CHIRPS-FINAL of the previous month. The simulation was initialized on January 1, 2019 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.

Contains data from Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) precipitation dataset. CHIRPS integrates 0.05° resolution satellite imagery with ground station data to generate gridded rainfall time series.

The datacude includes daily precipitation measurements from 01-Oct-2019 to 30-Sep-2021 for the Boeotikos Kifissos river basin.

• Dimentions:
  1. time: 1096
  2. latitude: 9
  3. longitude: 19
• Data variables: (time, latitude, longitude)
• Size: 759kB

Dimensions: (time: 1096, latitude: 9, longitude: 19)

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.

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.

This dataset consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM). The SPEI dataset covers the whole of the African continent for a 36-year-long period (1981–2016) at a horizontal resolution of 5 km (0.05 deg) and a monthly time resolution. The dataset is provided in NetCDF format with in a Geographic Lat/Lon projection. Due to the lower reliability of SPEI over areas with low hydro-climatic variability, the areas with barren or sparsely vegetated areas in Africa were masked out based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface type product (MCD12Q1).

This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.25 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is Southern Africa (34.75S, 5.75E, 6.75N, 51.25E). The files are in NetCDF format.

This simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS).

The simulation was initialized on 1 January 1982 using soil moisture and other state fields from a FLDAS VIC model climatology for that day of the year.

Modeling results with TETIS software, for Tugela River with a spatial resolution of 15 seconds, and temporal resolution daily. Experiment 21. Forcing Chirps

Includes results for the following variables:

• Total evapotranspiration flux in kg m-2 s-1
• Discharge flux in m-3 s-1
• Volumetric soil moisture content in percentage
• Total water storage content in kg m-2

The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with ‘Under-sampling with Repeated Advancing Phase’ (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.

This data set contains a series of land surface parameters simulated from the Noah 3.3 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 Western Africa (5.3N, 18.7W, 17.7N, 25.9E). The files are in NetCDF format.

This simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS).

The simulation was initialized on 1 January 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.

Chirp Ultrasound Dataset for StofNet

• TDK DK-CH101 Smartsonic Platform Sensor (see sensor_specs.yaml for details)
• 3 target classes: 1) wooden ruler, 2) metal stick, 3) paper chart
• 100 measurements per distance (20 test, 80 train) at 9 distances
• true distance by average of manufacturer estimates
• true measurements by average of multiple acquisitions
• averaging is accomplished from 200 separate samples (not part of test and train)

Author: Christopher Hahne
Year: 2023

CC-BY license
This work is licensed under a Creative Commons Attribution 4.0 International License.

Modeling results with TETIS software, for Tugela River with a spatial resolution of 0.0667 degrees, and temporal resolution daily. Experiment 20. Forcing Chirps

Includes results for the following variables:

• Discharge flux in m-3 s-1
• Volumetric soil moisture content in percentage
• Total water storage content in kg m-2
• Total evapotranspiration flux in kg m-2 s-1

[caption id="attachment_40950" align="alignright" width="400"] This data plot shows the time series of the radiance in window channels for Channel 1 (900 cm-1) and Channel 2 (2600 cm-1) in RS mode for Alaska S01 data.[/caption]

ARM is in the process of improving the temporal resolution of the atmospheric emitted radiance interferometer (AERI) to collect a sky spectrum every 15-30 seconds. The increased temporal resolution results in less averaging performed by the instrument; hence, the larger component of random noise in the sky spectra.

This VAP uses the high correlation in the observed radiance across the spectrum to reduce the uncorrelated random error in the data using principal component analysis (PCA). The VAP automatically determines the appropriate number of principal components to use in the reconstruction to eliminate as much random noise as possible.

A significant reduction in the uncorrelated random error in the data has been proven for both regular temporal data and rapid-sample data. For more details, see A Principal Component Analysis Noise Filter Value-Added Procedure to Remove Uncorrelated Noise from Atmospheric Emitted Radiance Interferometer (AERI) Observations.

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), adapted from Land Information System (LIS7). The dataset contains 28 parameters in a 0.10 degree spatial resolution and from January 2019 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Global Data Assimilation System (GDAS) data and Climate Hazards Group InfraRed Precipitation with Station Preliminary (CHIRPS-PRELIM) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit, restarted from CHIRPS-FINAL of the previous month. The simulation was initialized on January 1, 2019 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.