Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Johanna Orellana"'
Publikováno v:
Meteorological Applications, Vol 31, Iss 5, Pp n/a-n/a (2024)
Abstract High spatio‐temporal variability is a characteristic of extreme rainfall. In mountainous regions like the Tropical Andes, where intricate orography and mesoscale atmospheric dynamics greatly impact rainfall systems, this particularly holds
Externí odkaz:
https://doaj.org/article/87b2150e5c934f0bbc214fa64d15ddde
Autor:
Samira Zander, Nazli Turini, Daniela Ballari, Steve Darwin Bayas López, Rolando Celleri, Byron Delgado Maldonado, Johanna Orellana-Alvear, Benjamin Schmidt, Dieter Scherer, Jörg Bendix
Publikováno v:
Atmosphere, Vol 14, Iss 8, p 1225 (2023)
Clouds play an important role in the climate system; nonetheless, the relationship between climate change in general and regional cloud occurrence is not yet well understood. This particularly holds for remote areas such as the iconic Galapagos archi
Externí odkaz:
https://doaj.org/article/9a29cea5cc5340acaa621882aa473e76
Autor:
Rütger Rollenbeck, Johanna Orellana-Alvear, Jörg Bendix, Rodolfo Rodriguez, Franz Pucha-Cofrep, Mario Guallpa, Andreas Fries, Rolando Célleri
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 824 (2022)
The coastal regions of South Ecuador and Peru belong to the areas experiencing the strongest impact of the El Niño Southern Oscillation phenomenon. However, the impact and dynamic development of weather patterns during those events are not well unde
Externí odkaz:
https://doaj.org/article/e5bb126966ca422e815534235e228ac7
Autor:
Nazli Turini, Boris Thies, Rütger Rollenbeck, Andreas Fries, Franz Pucha-Cofrep, Johanna Orellana-Alvear, Natalia Horna, Jörg Bendix
Publikováno v:
Atmosphere, Vol 12, Iss 12, p 1678 (2021)
Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Sat
Externí odkaz:
https://doaj.org/article/2b17452287a749bbab5e90bf1f531b0a
Publikováno v:
Hydrology, Vol 8, Iss 4, p 183 (2021)
Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWS
Externí odkaz:
https://doaj.org/article/93e554804bf04e3ea641fed975e1d294
Publikováno v:
Atmosphere, Vol 12, Iss 12, p 1561 (2021)
Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitivity and resolution, which makes them well suited for complex precipitation patterns. The first horizontal scanning weather radar in Peru was installed
Externí odkaz:
https://doaj.org/article/d415c59746e94a3bbe7690c38c8b9cf5
Publikováno v:
Remote Sensing, Vol 13, Iss 5, p 991 (2021)
Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defi
Externí odkaz:
https://doaj.org/article/9ed2fdbc8c6a4ec38174c077155dad4a
Publikováno v:
Atmosphere, Vol 12, Iss 2, p 238 (2021)
The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal d
Externí odkaz:
https://doaj.org/article/d5a27fa7569c4cc88e9dcf4ac3c7c642
Autor:
Zbyněk Sokol, Jan Szturc, Johanna Orellana-Alvear, Jana Popová, Anna Jurczyk, Rolando Célleri
Publikováno v:
Remote Sensing, Vol 13, Iss 3, p 351 (2021)
Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes
Externí odkaz:
https://doaj.org/article/c4be1ad7c1a240628be296fb4f68fb96
Autor:
Johanna Orellana-Alvear, Rolando Célleri, Rütger Rollenbeck, Paul Muñoz, Pablo Contreras, Jörg Bendix
Publikováno v:
Remote Sensing, Vol 12, Iss 12, p 1986 (2020)
Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchme
Externí odkaz:
https://doaj.org/article/28943bb0d8fe49428b968dc35b52dcbd