Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

By Yang Hong, K. Hsu, Soroosh Sorooshian, Xiaogang Gao. 2004

782 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7568e91b9b2d79f11250504166a99b799e99721c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. A. Rowe
2. M. Martini
3. Simon P. Szoeke
4. Samson Hagos
5. Chaopeng Shen
6. Zhe Feng
7. Casey D. Burleyson

In Global and Planetary Change

1. A. Rowe
2. M. Martini
3. Simon P. Szoeke
4. Samson Hagos
5. Chaopeng Shen
6. Zhe Feng
7. Casey D. Burleyson
8. Jianwei Ma

Citations in the corpus

• Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign (2015, methodology): “366 using satellite infrared images [Hong et al., 2004] and to identify organized mesoscale cellular convective cells in stratocumulus clouds [Martini et al., 2014].”
• Utility of Quantitative Precipitation Estimates for high resolution hydrologic forecasts in mount (2012, methodology): “Neural Networks Adaptive Neural Network calibration of GOESS and TRMM using a cloud classification system Hong et al. (2004); Hong et al.”
• Performance evaluation of the self‐organizing map for feature extraction (2006, methodology): “A practical way to apply the SOM is proposed and demonstrated using several examples, including long time series of coastal ocean currents from the West Florida Shelf.”
• Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS (2018, neutral): “, 2004); (ii) infrared-based rainfall estimates derived using the PERSIANN-CCS algorithm (Hong et al., 2004); (iii) calibration of passive microwave-based P estimates to the Combined GMI-DPR P dataset (available up to almost 70 latitude) during the GPM era and the Combined TMI-PR P dataset (availabl”
• Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, methodology): “Several satellite-basedmainstream algorithms used for retrieving precipitation have been employed in recent decades (Ashouri et al., 2015; Hong et al., 2004; Hou et al., 2014; Huffman et al., 2007; Joyce et al., 2004).”
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “With the SDAE model, they directly provided the raw satellite image itself without much preprocessing, and the training target was the difference between Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) and the grou”