Abstract
Accurate daily rainfall estimation is required in several applications such as in hydrology, hydrometeorology, water resources management, geomorphology, civil protection, and agriculture, among others. CMORPH daily rainfall estimations were integrated with rain gauge measurements in Brazil between 2000 and 2015, in order to reduce daily rainfall estimation errors by means of the statistical objective analysis scheme (SOAS). Early comparisons indicated high discrepancies between daily rain gauge rainfall measurements and respective CMORPH areal rainfall accumulation estimates that tended to be reduced with accumulation time span (e.g., yearly accumulation). Current results show CMORPH systematically underestimates daily rainfall accumulation along the coastal areas. The normalized error variance (NEXERVA) is higher in sparsely gauged areas at Brazilian North and Central-West regions. Monthly areal rainfall averages and standard deviation were obtained for eleven Brazilian watersheds. While an overall negative tendency (3 mm·h−1) was estimated, the Amazon watershed presented a long-term positive tendency. Monthly areal mean precipitation and respective spatial standard deviation closely follow a power-law relationship for data-rich watersheds, i.e., with denser rain gauge networks. Daily SOAS rainfall accumulation was also used to calculate the spatial distribution of frequencies of 3-day rainfall episodes greater than 100 mm. Frequencies greater than 3% were identified downwind of the Peruvian Andes, the Bolivian Amazon Basin, and the La Plata Basin, as well as along the Brazilian coast, where landslides are recurrently triggered by precipitation.
Highlights
In recent years, many new environmental satellites have been deployed to monitor Earth from space [1] at an increasing spatial-temporal resolution
Sohn et al [14] compared the performance by diurnal cycle and season of Tropical Rainfall Measuring Mission (TRMM), TMPA, Center morphing technique (CMORPH), PERSIANN, and Naval Research Laboratory (NRL) with rain gauges in Korea and observed that TMPA provided the best estimate in real time
It is most active at the north most portion in September and at south in April, with precipitation in excess of 400 mm along the shores of Para State. e South Atlantic convergence zone (SACZ) precipitation footprint is apparent between November and March and most active over the Southeast region in January, with a maximum greater than 250 mm
Summary
Many new environmental satellites have been deployed to monitor Earth from space [1] at an increasing spatial-temporal resolution. Shanhu et al [3] compared TMPA and CMORPH precipitation estimates with rain gauge measurements over watersheds. Zhang et al [8] compared CMORPH areal precipitation estimates with respective areal precipitation measurements with weather radars and rain gauges during winter and summer. Zubieta et al [9] used TMPA, CMORPH, and PERSIANN precipitation estimates for rain-runoff modeling in Amazon Basin, while Buarque et al [10] compared rain gauge measurements with TRMM and CMORPH in Amazon. Filho et al [20] compared daily, monthly, seasonal, and annual CMORPH precipitation estimates with respective rain gauge measurements in South America and showed that the CMORPH estimates are better correlated with rain gauges with increasing accumulation.
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