Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 319

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data

Open access
Nguyen, Phu, Shearer, Eric J., Tran, Hoang, Ombadi, Mohammed, Hayatbini, Negin, Palacios, Thanh, Huynh, Phat, Braithwaite, Dan, Updegraff, Garr, Hsu, Kuolin, Kuligowski, Bob, Logan, Will S., Sorooshian, Soroosh·2019·Scientific Data

Abstract

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/sdata.2018.296

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 23 references

Research works printed but not linked: 16

  1. Hsu, K., Behrangi, A., Imam, B., Sorooshian, S. In Satellite Rainfall Applications for Surface Hydrology (eds. Gebremichael, M. & Hossain F. ) Ch. 4. (Springer Nature and Tsinghua Univ. Press, 2010).
  2. Lin, Y. & Mitchell, K. E. 1.2 the NCEP stage II/IV hourly precipitation analyses: Development and applications. In Proceedings of the 19th conference on Hydrology 9–13 (American Meteorological Society, San Diego, CA, 2005).
  3. Heidke, P. Berechnung des erfolges und der gute der windstarkevorhersagen im sturmwarnungsdienst (Measures of success and goodness of wind force forecasts by the gale-warning service). Geog. Ann 8, 301–349 (1926).
  4. Hanssen, A. W. & Kuipers, W. J. A. On the relationship between the frequency of rain and various meteorological parameters. Meded. Verh 81, 2–15 (1965).
  5. Behrangi, A. et al. Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol. 397, 225–237 (2011). · DOI
  6. Sorooshian, S. et al. In Extreme Natural Hazards, Disaster Risks and Societal Implications Ismail-Zadeh J., Urrutia F., Kijko K., Takeuchi K., Zaliapin I. eds. Ch. 8. (Cambridge Univ. Press, (2014).
  7. AghaKouchak, A., Habib, E. & Bárdossy, A. Modeling radar rainfall estimation uncertainties: random error model. J. of Hydrol. Eng 15, 265–274 (2010). · doi:10.1061/(asce)he.1943-5584.0000185
  8. McCollum, J. R., Frajewski, W. F., Ferraro, R. R. & Mamoudou, B. B. Evaluation of biases of satellite rainfall estimation algorithms over the continental United States. J. Appl. Meteor 41, 1065–1080 (2002). · doi:10.1175/1520-0450(2002)041
  9. Tian, Y., Peters-Lidard, C. D., Choudhury, B. J. & Garcia, M. Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. J. Hydrometeorol. 8, 1165–1183 (2007). · doi:10.1175/2007jhm859.1
  10. Stokstad, E. Scarcity of rain, stream gages threatens forecasts. Science 285, 1199–1200 (1999). · doi:10.1126/science.285.5431.1199
  11. Nguyen, P. et al. Satellites track precipitation of super typhoon Haiyan. Eos 95, 133–135 (2014). · doi:10.1002/2014eo160002
  12. Sorooshian, S. et al. In Extreme Natural Hazards, Disaster Risks and Societal Implications (eds. Ismail-Zadeh, A., Fucugauchi, J. U., Kijko, A., Takeuchi, K. & Zaliapin, I. ) Ch. 8. Cambridge Univ. Press, (2014).
  13. Zahraei, A. et al. Short-term quantitative precipitation forecasting using an object-based approach. J. Hydrol. 483, 1–15 (2013). · doi:10.1016/j.jhydrol.2012.09.052
  14. Juglea, S. et al. Soil moisture modeling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station. Hydrol. Earth Syst. Sci. 14, 1509–1525 (2010). · doi:10.5194/hess-14-1509-2010
  15. Gado, T. A., Hsu, K. & Sorooshian, S. Rainfall frequency analysis for ungauged sites using satellite precipitation products. J. Hydrol. 554, 646–655 (2017). · doi:10.1016/j.jhydrol.2017.09.043
  16. Nguyen, P. et al. Exploring trends through “RainSphere”: research data transformed into public knowledge. Bull. Amer. Meteor. Soc. 98, 653–658 (2017). · doi:10.1175/bams-d-16-0036.1

Cited by

Metadata

Authors
Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., Huynh, P., Braithwaite, D., Updegraff, G., Hsu, K., Kuligowski, B., Logan, W. S., & Sorooshian, S.
Venue
Scientific Data
Published
2019-01-08
Volume
6
Issue
1
Pages
180296-180296
ISSN
2052-4463 (ISSN-L)
OpenAlex
W2911001330
Cited by
319
References
23
Type
data paper
Language
en
Open access
gold
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.