A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements
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...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2018/2095304 |
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| author | Augusto José Pereira Filho Felipe Vemado Guilherme Vemado Fábio Augusto Gomes Vieira Reis Lucilia do Carmo Giordano Rodrigo Irineu Cerri Cláudia Cristina dos Santos Eymar Silva Sampaio Lopes Marcelo Fischer Gramani Agostinho Tadashi Ogura José Eduardo Zaine Leandro Eugenio da Silva Cerri Oswaldo Augusto Filho Fernando Mazzo D’Affonseca Cláudio dos Santos Amaral |
| author_facet | Augusto José Pereira Filho Felipe Vemado Guilherme Vemado Fábio Augusto Gomes Vieira Reis Lucilia do Carmo Giordano Rodrigo Irineu Cerri Cláudia Cristina dos Santos Eymar Silva Sampaio Lopes Marcelo Fischer Gramani Agostinho Tadashi Ogura José Eduardo Zaine Leandro Eugenio da Silva Cerri Oswaldo Augusto Filho Fernando Mazzo D’Affonseca Cláudio dos Santos Amaral |
| author_sort | Augusto José Pereira Filho |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ea887945e85142398c1b572131a2a9f6 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-ea887945e85142398c1b572131a2a9f62025-02-03T01:24:00ZengWileyAdvances in Meteorology1687-93091687-93172018-01-01201810.1155/2018/20953042095304A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge MeasurementsAugusto José Pereira Filho0Felipe Vemado1Guilherme Vemado2Fábio Augusto Gomes Vieira Reis3Lucilia do Carmo Giordano4Rodrigo Irineu Cerri5Cláudia Cristina dos Santos6Eymar Silva Sampaio Lopes7Marcelo Fischer Gramani8Agostinho Tadashi Ogura9José Eduardo Zaine10Leandro Eugenio da Silva Cerri11Oswaldo Augusto Filho12Fernando Mazzo D’Affonseca13Cláudio dos Santos Amaral14Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, BrazilDepartamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, BrazilDepartamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, BrazilInstituto de Geociências e Ciências Exatas, Universidade Estadual Paulista, Rio Claro, São Paulo, BrazilInstituto de Geociências e Ciências Exatas, Universidade Estadual Paulista, Rio Claro, São Paulo, BrazilInstituto de Geociências e Ciências Exatas, Universidade Estadual Paulista, Rio Claro, São Paulo, BrazilInstituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, BrazilInstituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, BrazilInstituto de Pesquisas Tecnológicas, São Paulo, BrazilInstituto de Pesquisas Tecnológicas, São Paulo, BrazilInstituto de Geociências e Ciências Exatas, Universidade Estadual Paulista, Rio Claro, São Paulo, BrazilInstituto de Geociências e Ciências Exatas, Universidade Estadual Paulista, Rio Claro, São Paulo, BrazilEscola de Engenharia de São Carlos, Universidade de São Paulo, São Paulo, BrazilEberhard Karls Universität Tübingen, Tübingen, GermanyPetrobras Research and Development Center, Rio de Janeiro, BrazilAccurate 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.http://dx.doi.org/10.1155/2018/2095304 |
| spellingShingle | Augusto José Pereira Filho Felipe Vemado Guilherme Vemado Fábio Augusto Gomes Vieira Reis Lucilia do Carmo Giordano Rodrigo Irineu Cerri Cláudia Cristina dos Santos Eymar Silva Sampaio Lopes Marcelo Fischer Gramani Agostinho Tadashi Ogura José Eduardo Zaine Leandro Eugenio da Silva Cerri Oswaldo Augusto Filho Fernando Mazzo D’Affonseca Cláudio dos Santos Amaral A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements Advances in Meteorology |
| title | A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements |
| title_full | A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements |
| title_fullStr | A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements |
| title_full_unstemmed | A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements |
| title_short | A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements |
| title_sort | step towards integrating cmorph precipitation estimation with rain gauge measurements |
| url | http://dx.doi.org/10.1155/2018/2095304 |
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