Seguir
Mu-Chieh Ko
Mu-Chieh Ko
Sr. Research Associate, University of Miami/CIMAS; NOAA/AOML/Hurricane Research Division
Dirección de correo verificada de noaa.gov
Título
Citado por
Citado por
Año
Evaluation of hurricane Harvey (2017) rainfall in deterministic and probabilistic HWRF forecasts
MC Ko, FD Marks, GJ Alaka Jr, SG Gopalakrishnan
Atmosphere 11 (6), 666, 2020
162020
The Development of a consensus machine learning model for hurricane rapid intensification forecasts with hurricane weather research and forecasting (HWRF) data
MC Ko, X Chen, M Kubat, S Gopalakrishnan
Weather and Forecasting 38 (8), 1253-1270, 2023
42023
Development of a probabilistic tropical cyclone rainfall model: P-rain
FD Marks, BD McNoldy, MC Ko, AB Schumacher
100th American Meteorological Society Annual Meeting, 2020
42020
The development of a consensus machine learning model for hurricane rapid intensity (ri) forecasts with hurricane weather research and forecast (hwrf) data
MC Ko, M Kubat, S Gopalakrishnan, X Chen
AGU Fall Meeting Abstracts 2021, H35M-1182, 2021
22021
Precipitation evaluation of the real-time basin-scale HWRF in 2017
M Ko, F Marks, GJ Alaka, SG Gopalakrishnan
33rd Conference on Hurricanes and Tropical Meteorology. American …, 2017
22017
Effects of Feature-Space Reduction and Resampling on Machine Learning Performance for Hurricane Intensity Predictions
MC Ko, M Kubat, S Gopalakrishnan
101st American Meteorological Society Annual Meeting, 2021
12021
A review of support vector machine performance on tropical cyclone intensity prediction with imbalanced datasets
MC Ko, M Kubat, SG Gopalakrisnan, FD Marks
100th American Meteorological Society Annual Meeting, 2020
12020
Real-time Testing of Experimental Upgrades to the Hurricane Analysis and Forecast System, Version B (HAFSV1. 1B)
AT Hazelton, GJ Alaka, X Chen, MC Ko, W Ramstrom, Z Zhang, B Liu, ...
104th AMS Annual Meeting, 2024
2024
23AI An Updated Consensus Machine Learning Modeling Using the HAFS dataset for Hurricane Rapid Intensification (RI) Prediction
MC Ko, GJ Alaka
104th AMS Annual Meeting, 2024
2024
The Development of a Tropical Cyclogenesis Index with a Consensus Machine Learning Model using the HAFS Dataset
MC Ko, JP Dunion, GJ Alaka
103rd AMS Annual Meeting, 2023
2023
Developing a Multi-Storm Configuration of the Hurricane Analysis and Forecast System
GJ Alaka, MC Ko, JH Shin, A Hazelton, LJ Gramer, W Ramstrom, ...
103rd AMS Annual Meeting, 2023
2023
Real-Time and Retrospective Evaluation of the Hurricane Analysis and Forecast System (HAFS-S Version)
AT Hazelton, GJ Alaka, LJ Gramer, W Ramstrom, X Chen, MC Ko, ...
103rd AMS Annual Meeting, 2023
2023
Development of a Probabilistic Tropical Cyclone Rainfall Model
F Marks, B McNoldy, MC Ko, AB Schumacher
34th Conference on Hurricanes and Tropical Meteorology, 2021
2021
A Comparison of Utilizing Analysis Data and 6-h Forecast Data from the Hurricane Weather Research and Forecasting Model on Machine Learning Predictions for Hurricane Rapid …
MC Ko, M Kubat, S Gopalakrishnan
101st American Meteorological Society Annual Meeting, 2021
2021
A Rainfall Evaluation of Deterministic and Ensemble Basin-Scale HWRF
MC Ko, FD Marks, GJ Alaka, SG Gopalakrishnan
99th American Meteorological Society Annual Meeting, 2019
2019
The Basin-Scale HWRF: Developments and Evaluation of 2018 Real-Time Forecasts
GJ Alaka, J Poterjoy, MC Ko, X Zhang, S Gopalakrishnan, FD Marks, ...
99th American Meteorological Society Annual Meeting, 2019
2019
Withdrawn: The Hurricane Forecast Improvement Project: Basin-Scale HWRF Performance in 2018
GJ Alaka, S Gopalakrishnan, X Zhang, MC Ko, FD Marks
99th American Meteorological Society Annual Meeting, 2019
2019
Withdrawn: Evaluation of the Large-Scale Environment in NOAA Experimental Models
SW Diaz, SG Gopalakrishnan, SJ Lin, X Zhang, GJ Alaka, MJ Morin, ...
33rd Conference on Hurricanes and Tropical Meteorology, 2018
2018
An investigation of the cyclogenesis forecast ability of the Basin-Scale HWRF model by invoking the GFDL Vortex Tracker
MC Ko
2016
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–19