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 accessAbstract
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.
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Adler, R. F., Huffman, G. J., Chang, A. T. C., Ferraro, R. R., Xie, P., Janowiak, J. E., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D. T., et al. (2003) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2003)004<1147:tvgpcp>2.0.co;2 · Cited by 5,425
Joyce, R. J., Janowiak, J. E., Arkin, P. A., & Xie, P. (2004) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2004)005<0487:camtpg>2.0.co;2 · Cited by 3,208
Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., & Susskind, J. (2001) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2001)002<0036:gpaodd>2.0.co;2 · Cited by 1,854
The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset
Huffman, G. J., Adler, R. F., Arkin, P., Chang, A., Ferraro, R., Gruber, A., Janowiak, J., McNab, A., Rudolf, B., & Schneider, U. (1997) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(1997)078<0005:tgpcpg>2.0.co;2 · Cited by 1,723
Ashouri, H., Hsu, K.-L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Nelson, B. R., & Prat, O. P. (2015) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-13-00068.1 · Cited by 1,384
Self-citationEvaluation of PERSIANN System Satellite–Based Estimates of Tropical Rainfall
Sorooshian, S., Hsu, K.-L., Gao, X., Gupta, H. V., Imam, B., & Braithwaite, D. (2000) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(2000)081<2035:eopsse>2.3.co;2 · Cited by 1,237
Self-citationPrecipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks
Hsu, K.-L., Gao, X., Sorooshian, S., & Gupta, H. V. (1997) · Journal of Applied Meteorology · doi:10.1175/1520-0450(1997)036<1176:pefrsi>2.0.co;2 · Cited by 1,096
Self-citationHong, Y., Hsu, K.-L., Sorooshian, S., & Gao, X. (2004) · Journal of Applied Meteorology · doi:10.1175/jam2173.1 · Cited by 851
Self-citationGlobally Gridded Satellite Observations for Climate Studies
Knapp, K. R., Ansari, S., Bain, C. L., Bourassa, M. A., Dickinson, M. J., Funk, C., Helms, C. N., Hennon, C. C., Holmes, C. D., Huffman, G. J., et al. (2011) · Bulletin of the American Meteorological Society · doi:10.1175/2011bams3039.1 · Cited by 376
Scofield, R. A., & Kuligowski, R. J. (2003) · Weather and Forecasting · doi:10.1175/1520-0434(2003)018<1037:saooos>2.0.co;2 · Cited by 300
Research works printed but not linked: 16
- 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).
- 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).
- 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).
- 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).
- Behrangi, A. et al. Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol. 397, 225–237 (2011). · DOI
- 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).
- 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
- 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
- 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
- Stokstad, E. Scarcity of rain, stream gages threatens forecasts. Science 285, 1199–1200 (1999). · doi:10.1126/science.285.5431.1199
- Nguyen, P. et al. Satellites track precipitation of super typhoon Haiyan. Eos 95, 133–135 (2014). · doi:10.1002/2014eo160002
- 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).
- 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
- 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
- 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
- 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
Tropical deforestation causes large reductions in observed precipitation
Smith, C., Baker, J. C. A., & Spracklen, D. V. (2023) · Nature · doi:10.1038/s41586-022-05690-1 · Cited by 270
Long-term spatiotemporal variability in high Andean wetlands in northern Chile
de la Fuente, A., Meruane, C., & Suárez, F. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.143830 · Cited by 52
Jiang, A.-L., Lee, M.-C., Zhou, G., Zhong, D., Hawaria, D., Kibret, S., Yewhalaw, D., Sanders, B. F., Yan, G., & Hsu, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-89576-8 · Cited by 14
Self-citationRegionalized Life-Cycle Water Impacts of Microalgal-Based Biofuels in the United States
Quiroz, D., Greene, J. M., & Quinn, J. C. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c05552 · Cited by 11
Jiang, A., Lee, M., Selvaraj, P., Degefa, T., Getachew, H., Merga, H., Yewhalaw, D., Yan, G., & Hsu, K. (2023) · GeoHealth · doi:10.1029/2023gh000868 · Cited by 3
Self-citation
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
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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