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LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration
Publisher: American Meteorological Society
Abstract: The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated ...
LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part II: Validation
Publisher: American Meteorological Society
Abstract: A new satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model ...
A Statistical Model for the Uncertainty Analysis of Satellite Precipitation Products
Publisher: American Meteorological Society
Abstract: arth-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 ...
An Artificial Neural Network Model to Reduce False Alarms in Satellite Precipitation Products Using MODIS and CloudSat Observations
Publisher: American Meteorological Society
Abstract: he Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical ...
Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System
Publisher: American Meteorological Society
Abstract: A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts ...
A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products
Publisher: American Meteorological Society
Abstract: espite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and ...
Using Densely Distributed Soil Moisture Observations for Calibration of a Hydrologic Model
Publisher: American Meteorological Society
Abstract: alibration is a crucial step in hydrologic modeling that is typically handled by tuning parameters to match an observed hydrograph. In this research, an alternative calibration scheme based on soil moisture was investigated ...
Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches
Publisher: American Meteorological Society
Abstract: n the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information ...
A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information
Publisher: American Meteorological Society
Abstract: AbstractCompared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based ...
Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin
Publisher: American Meteorological Society