Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

Abstract

Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of three QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks -Cloud Classification System) , a satellite-based QPE. The second and the third are QPEs from a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that all the QPEs were well below the rain gauge values in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. The main conclusion, assuming the information from the rain gauges as ground truth, is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.

Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

“…One of the causes could be the difference in the spatial sampling of the rain gauge and the SBP product since the first provides point measurements and the last delivers spatial averages over the area of a grid cell. The rain gauge may not be detecting convective precipitation events located over the area of the grid cell of the SBP product [16].…”

Bathymetry Time Series Using High Spatial Resolution Satellite Images

“…Mar Menor is a coastal lagoon with a high degree of hydrodynamic confinement and, as such, is the final recipient of all the transport processes that take place in the large (1350 km 2 ) watershed of Campo de Cartagena. The soil uses in the coastal area has undergone significant transformations in the last decade, which have contributed to the acceleration of these transport processes and, consequently, the bottom of the lagoon have experienced an increasing accumulation of sediments (Figure 15 Mar Menor underwent a generalized silting up (an average of +18 cm) between 2008 and 2016, mainly the result of sediment contributions due to the very frequent extreme precipitation events [47]. Very intense rainfall events with a high return period occurred in the Campo of Cartagena in the last decade.…”

“…Very intense rainfall events with a high return period occurred in the Campo of Cartagena in the last decade. As examples, the event of 27-28 September 2009 corresponded to a 200-year return period, where the CA12 rain gauge (located in La Palma) recorded 268 mm in 30 h. Then, during the event registered between 17 and 19 December 2016 in Campo of Cartagena (with a 500-year return period), the rainfall exceeded 50 mm in one hour in the Torre Pacheco (TP42) and San Javier (TP22) rain gauges [47]. Finally, the CoLs event named dana of 12-15 September 2019 generated maximum hourly rainfall intensities of 70.4 mm and 60.6 mm at the Torre Pacheco and Pozo Estrecho rain gauges, respectively.…”

“…The energy in the water reaches the optical sensor placed above the water. The relationship between the water-leaving reflectance just above the surface and the water-leaving reflectance just below the surface is constant [46][47][48]. If the water is clear, the energy arrives from the bottom through the water column [49], in this case it is possible to determinate the water depth.…”

Editorial for Special Issue “Remote Sensing of Precipitation”

“…In their paper, Cánovas-García et al [4] study the accuracy of three Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars and the extent to which they could complement or even be an alternative to rain gauge readings in the Iberian Peninsula. The first QPE is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System, a satellite-based QPE.…”