Articles | Volume 19, issue 10
https://doi.org/10.5194/hess-19-4275-2015
https://doi.org/10.5194/hess-19-4275-2015
Research article
 | 
22 Oct 2015
Research article |  | 22 Oct 2015

Correction of real-time satellite precipitation with satellite soil moisture observations

W. Zhan, M. Pan, N. Wanders, and E. F. Wood

Related authors

Machine learning and Global Vegetation: Random Forests for Downscaling and Gapfilling
Barry van Jaarsveld, Sandra Hauswirth, and Niko Wanders
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-430,https://doi.org/10.5194/hess-2022-430, 2023
Preprint under review for HESS
Short summary
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023,https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
A data-driven model for Fennoscandian wildfire danger
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023,https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
DynQual v1.0: A high-resolution global surface water quality model
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-222,https://doi.org/10.5194/gmd-2022-222, 2022
Preprint under review for GMD
Short summary
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022,https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Stochastic approaches
Technical note: A stochastic framework for identification and evaluation of flash drought
Yuxin Li, Sisi Chen, Jun Yin, and Xing Yuan
Hydrol. Earth Syst. Sci., 27, 1077–1087, https://doi.org/10.5194/hess-27-1077-2023,https://doi.org/10.5194/hess-27-1077-2023, 2023
Short summary
Stochastic simulation of reference rainfall scenarios for hydrological applications using a universal multi-fractal approach
Arun Ramanathan, Pierre-Antoine Versini, Daniel Schertzer, Remi Perrin, Lionel Sindt, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 26, 6477–6491, https://doi.org/10.5194/hess-26-6477-2022,https://doi.org/10.5194/hess-26-6477-2022, 2022
Short summary
Atmospheric conditions favouring extreme precipitation and flash floods in temperate regions of Europe
Judith Meyer, Malte Neuper, Luca Mathias, Erwin Zehe, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 6163–6183, https://doi.org/10.5194/hess-26-6163-2022,https://doi.org/10.5194/hess-26-6163-2022, 2022
Short summary
A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance
Yuan Liu and Daniel B. Wright
Hydrol. Earth Syst. Sci., 26, 5241–5267, https://doi.org/10.5194/hess-26-5241-2022,https://doi.org/10.5194/hess-26-5241-2022, 2022
Short summary
Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective
Yuan Li, Zhiyong Wu, Hai He, and Hao Yin
Hydrol. Earth Syst. Sci., 26, 4975–4994, https://doi.org/10.5194/hess-26-4975-2022,https://doi.org/10.5194/hess-26-4975-2022, 2022
Short summary

Cited articles

Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Antecedent wetness conditions based on ERS scatterometer data, J. Hydrol., 364, 73–87, https://doi.org/10.1016/j.jhydrol.2008.10.007, 2009.
Brocca, L., Moramarco, T., Melone, F., and Wagner, W.: A new method for rainfall estimation through soil moisture observations, Geophys. Res. Lett., 40, 853–858, https://doi.org/10.1002/grl.50173, 2013.
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V.: Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data, J. Geophys. Res.-Atmos., 119, 5128–5141, https://doi.org/10.1002/2014JD021489, 2014.
Chopin, F., Berges, J., Desbois, M., Jobard, I., and Lebel, T.: Satellite Rainfall Probability and Estimation. Application to the West Africa During the 2004 Rainy Season, AGU Spring Meet. Abstr. A12, New Orleans, Louisiana, USA, 2005.
Ciabatta, L., Brocca, L., Massari, C., Moramarco, T., Puca, S., Rinollo, A., Gabellani, S., and Wagner, W.: Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory, J. Hydrometeorol., 16, 1341–1355, https://doi.org/10.1175/JHM-D-14-0108.1, 2015.