Evaluation of model‐derived and remotely sensed precipitation products for continental South America

Abstract

[1] This paper investigates the reliability of some of the more important remotely sensed daily precipitation products available for South America as a precursor to the possible implementation of a South America Land Data Assimilation System. Precipitation data fields calculated as 6 hour predictions by the CPTEC Eta model and three different satellite-derived estimates of precipitation (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), National Environmental Satellite, Data and Information Service (NESDIS), and Tropical Rainfall Measuring Mission (TRMM)) are compared with the available observations of daily total rainfall across South America. To make this comparison, the threat score, fractionalcovered area, and relative volumetric bias of the model-calculated and remotely sensed estimates are computed for the year 2000. The results show that the Eta model-calculated data and the NESDIS product capture the area without precipitation within the domain reasonably well, while the TRMM and PERSIANN products tend to underestimate the area without precipitation and to heavily overestimate the area with a small amount of precipitation. In terms of precipitation amount the NESDIS product significantly overestimates and the TRMM product significantly underestimates precipitation, while the Eta model-calculated data and PERSIANN product broadly match the domain average observations. However, both tend to bias the zonal location of precipitation more heavily toward the equator than the observations. In general, the Eta model-calculated data outperform the several remotely sensed data products currently available and evaluated in the present study.

Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

“…Dinku et al [27] found that CMORPH and TRMM rainfall products performed relatively better over Ethiopia and Zimbabwe. In South America, de Goncalves et al [36] demonstrated that PERSIANN performed better than TRMM rainfall product. Gourley et al [30] analyzed unadjusted NEXRAD, NEXRAD Stage ІV, PERSIANN Cloud Classification System (CCS), rain gauge, and TRMM Multisatellite Precipitation Analysis (TMPA) products over the Ft. Cobb basin in Oklahoma, which has a high density rain gauge network.…”

Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South America

“…Remotely sensed estimates of precipitation inferred, for example, from infrared cloud-top temperatures may provide a means of filling gaps between surface observations in remote regions. However, the calibration and validation of such remotely sensed estimates must be carefully examined because ground-based observations are so sparse (de Goncalves et al 2006).…”

Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

“…Zhang et al [44] developed a power function model to estimate rainfall erosivity based on daily rainfall data that is widely used in China, including in the First National Water Conservancy Survey [8,12,45,46]. Zhang and Fu [47] estimated rainfall erosivity from daily, monthly, and annual rainfall and verified each model by EI 30 . Their results indicated that the performance of a daily-based model was obviously better with Remote Sens.…”

“…However, it is well recognized that rainfall estimates from TRMM satellites involve their own uncertainties. De Goncalves et al [30] used three satellite-derived products to estimate daily rainfall across South America and found that TRMM tends to underestimate areas without rainfall and overestimate areas with small amounts of rainfall. Tang et al [31] reported that the TRMM-based precipitation estimates show obvious overestimation over most inland water bodies, which is caused by the systematic anomalies of the TRMM product stemming from deficiencies in the TRMM's assumptions about water surface emissivity.…”

“…High resolution precipitation data are essential for the evaluation of rainfall erosivity. Traditionally, rainfall erosivity is determined by a function of a storm's kinetic energy, E, and its maximum 30 minute rainfall intensity, I 30 , known as the EI 30 [2], and is observed with conventional instruments such as rain gauges. High temporal resolution rainfall measurements, for example 15 min or 30 min, are important for the calculation of erosivity since heavy rainfall and extreme events are typically rare events of short duration [3][4][5][6].…”