Improving satellite precipitation products

An accurate characterization of the global hydrologic cycle is essential not only to study and forecast climate variations, but also for extreme events mitigation (floods and droughts), for agricultural planning and water resources management. Because precipitation is the major driving force of the hydrological cycle, the implementation of current and future satellite precipitation missions is critical to determining global precipitation estimates. Satellite-based precipitation data are critical for a wide range of applications, such as hazard mitigation, terrestrial hydrology, climate change studies, as well as agriculture and irrigation practices.

Error estimates associated with satellite precipitation retrievals are of crucial importance in hydrological applications and climate studies. Specifically, they allow inferences about the reliability of satellite products and their operational applications. The team has been involved in several research activities to develop frameworks to estimate and validate uncertainty estimates associated with fine resolution satellite precipitation products. The estimation of these errors is challenging because of the complex interactions of the satellite retrieval uncertainty (satellite measurement and sampling errors) with the natural space-time variability of precipitation, especially at fine temporal and spatial resolution. Addressing this issue has important practical implications concerning the use of future (GPM era) satellite data in hydrology, climate and weather studies.

Related published work:

  • Falck, A., V. Maggioni, J. Tomasella, F. Diniz, Y. Mei, C. Beneti, D. Herdies, D. R. Neundorf, R. Caram, and D. Rodriguez, 2018: Improving the use of ground-based radar rainfall data for monitoring and predicting floods in the Iguaçu river basin, Journal of Hydrology, in print.
  • Khan S., V. Maggioni, P.E. Kirstetter, 2018: Investigating the potential of using satellite-based precipitation radars as reference for evaluating multi-satellite merged products, Journal of Geophysical Research – Atmospheres,
  • Oliveira R., V. Maggioni, D. Vila, L. Porcacchia, 2018: Using satellite error modeling to improve GPM-Level 3 rainfall, Remote SensingSpecial Issue onRemote Sensing Precipitation Measurement, Validation, and Applications”, 10(2), p.336, doi: 10.3390/rs10020336.
  • Porcacchia L., P.E. Kirstetter, J.J. Gourley, V. Maggioni, B.L. Cheong, M.N. Anagnostou, 2018: Toward a polarimetric radar classification scheme for coalescence dominant precipitation: Application to complex terrain, Journal of Hydrometeorology, 18(12), pp. 3199-3215.
  • Maggioni, V. and C. Massari, 2018: On the performance of satellite precipitation products in riverine flood modeling: A review, Journal of Hydrology, 558 214–224.
  • Nikolopoulos, N.I., E. Destro, V. Maggioni, F. Marra, and M. Borga, 2017: Satellite-rainfall estimates for debris flow prediction: An evaluation based on rainfall depth-duration thresholds, Journal of Hydrometeorology, vol. 18, no. 8,
  • Maggioni V., E.I. Nikolopoulos, E.N. Anagnostou, M. Borga, 2017: Modeling satellite precipitation errors over mountainous terrain: The influence of gauge density, seasonality, and temporal resolution, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 4130-4140, doi: 10.1109/TGRS.2017.2688998.
  • Oliveira R., V. Maggioni, D. Vila, C. Morales, 2016: Characteristics and diurnal cycle of GPM rainfall estimates over the Central Amazon Region, Remote SensingSpecial Issue onUncertainties in Remote Sensing”, 8(7), 544; doi: 10.3390/rs8070544.
  • Maggioni V., P.C. Meyers, M.D. Robinson, 2016: A review of merged high resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) – Era, Journal of Hydrometeorology – International Precipitation Working Group 7 Special Collection, vol. 17, no. 4, 1101-1117,
  • Maggioni V., M.R.P. Sapiano, R.F. Adler, 2016: Estimating uncertainties in high-resolution satellite precipitation products: systematic or random error?, Journal of Hydrometeorology, vol. 17, no. 4, 1119-1129,
  • Falck A., V. Maggioni, J. Tomasella, D.A. Vila, F.L.R. Diniz, 2015: Propagation of satellite precipitation uncertainties through a distributed hydrologic model: a case study in the Tocantins-Araguaia basin in Brazil, Journal of Hydrology, 527, 943–957, http://doi:10.1016/j.jhydrol.2015.05.042.
  • Maggioni V., M.R.P. Sapiano, R.F. Adler, Y. Tian, G.J. Huffman, 2014: An error model for uncertainty quantification in high-time resolution precipitation products, Journal of Hydrometeorology, vol. 15, no. 3, 1274–1292.