A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements

Nenhuma Miniatura disponível




Pereira Filho, Augusto Jose
Vemado, Felipe
Vemado, Guilherme
Gomes Vieira Reis, Fabio Augusto [UNESP]
Giordano, Lucilia do Carmo [UNESP]
Cerri, Rodrigo Irineu [UNESP]
Santos, Claudia Cristina dos
Sampaio Lopes, Eymar Silva
Gramani, Marcelo Fischer
Ogura, Agostinho Tadashi

Título da Revista

ISSN da Revista

Título de Volume


Hindawi Ltd


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 (3mmh(-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 100mm. 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.



Como citar

Advances In Meteorology. London: Hindawi Ltd, 24 p., 2019.