sepideh sarachi | University of California, Irvine (original) (raw)
Supervisors: Soroosh Sorooshian
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Papers by sepideh sarachi
Journal of Hydrometeorology, 2015
Earth-observing satellites provide a method to measure precipitation from space with good spatial... more Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5° × 5° over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used...
Journal of Hydrometeorology, 2015
A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological va... more A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.
Journal of Hydrometeorology, 2015
Earth-observing satellites provide a method to measure precipitation from space with good spatial... more Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5° × 5° over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used...
Journal of Hydrometeorology, 2015
A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological va... more A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.