What is he best known for?
The fields of study he is best known for:
- Meteorology
- Artificial intelligence
- Precipitation
Kuolin Hsu mainly investigates Precipitation, PERSIANN, Meteorology, Climatology and Quantitative precipitation estimation.
Kuolin Hsu interconnects Geosynchronous orbit and Diurnal cycle in the investigation of issues within Precipitation.
Kuolin Hsu has included themes like Artificial neural network, Snow, Component analysis, Range and Remote sensing in his PERSIANN study.
His research in the fields of Rain gauge overlaps with other disciplines such as Foyer.
His Climatology study integrates concerns from other disciplines, such as Tropical rainfall and Global precipitation.
His Quantitative precipitation estimation research is multidisciplinary, incorporating elements of Monsoon, Weather and climate, Weather forecasting and Scale.
His most cited work include:
- Artificial Neural Network Modeling of the Rainfall‐Runoff Process (1106 citations)
- Evaluation of PERSIANN system satellite-based estimates of tropical rainfall (851 citations)
- Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (537 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Precipitation, Meteorology, PERSIANN, Remote sensing and Climatology.
His studies in Precipitation integrate themes in fields like Artificial neural network and Streamflow.
He works mostly in the field of Artificial neural network, limiting it down to concerns involving Data mining and, occasionally, Artificial intelligence.
His biological study spans a wide range of topics, including Calibration, Flash flood, Flood forecasting and Hydrological modelling.
His PERSIANN study combines topics in areas such as Water resources and Scale.
Kuolin Hsu has researched Remote sensing in several fields, including Hydrometeorology and Geostationary orbit.
He most often published in these fields:
- Precipitation (50.96%)
- Meteorology (33.17%)
- PERSIANN (31.25%)
What were the highlights of his more recent work (between 2018-2021)?
- Precipitation (50.96%)
- Remote sensing (29.81%)
- PERSIANN (31.25%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Precipitation, Remote sensing, PERSIANN, Artificial neural network and Climatology.
To a larger extent, he studies Meteorology with the aim of understanding Precipitation.
Kuolin Hsu combines subjects such as Data processing, Hydrometeorology and Statistical power with his study of Remote sensing.
The study incorporates disciplines such as Multispectral image, Quantitative precipitation estimation and Scale in addition to PERSIANN.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Deep learning, Convolutional neural network and Multispectral satellite imagery.
His Climatology research includes elements of La Niña, Hydrology and Data set.
Between 2018 and 2021, his most popular works were:
- The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data. (51 citations)
- Improving Precipitation Estimation Using Convolutional Neural Network (35 citations)
- PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Meteorology
- Precipitation
His scientific interests lie mostly in Precipitation, PERSIANN, Artificial neural network, Climatology and Remote sensing.
Precipitation is a subfield of Meteorology that Kuolin Hsu studies.
His work on Global Precipitation Measurement, Monsoon and Quantitative precipitation forecast as part of his general Meteorology study is frequently connected to Constellation, thereby bridging the divide between different branches of science.
His Artificial neural network research incorporates elements of Deep learning and Convolutional neural network.
His work carried out in the field of Climatology brings together such families of science as Remote sensing, Hydrology, Natural disaster and Data set.
His work deals with themes such as Image resolution, Bootstrapping, Hydrometeorology and Moderate-resolution imaging spectroradiometer, which intersect with Remote sensing.
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