Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow
Combines multi-temporal optical flow with a conditional GAN for radar rainfall nowcasting, improving accuracy over MAPLE for 0–3 h lead times.
Korea Meteorological Administration (KMA)
20 papers in the archive.
Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
Tests 24 schemes for deep-learning radar nowcasting in South Korea, finding U-Net beats ConvLSTM and balanced MSE loss helps heavy rain.
Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model
Tests a machine-learning radiation emulator in an NWP model across 0.25–100 km resolutions over Korea, finding larger heating-rate errors at fine scales.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning
SimVP-based radar nowcasting over South Korea improves 120-min CSI at 1 mm/h to 0.46 and handles typhoons and narrow convection better than prior models.
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network
WR-Net combines TV-L1 optical flow warping with a refinement network to interpolate and predict GK2A geostationary imagery from 4- to 2-minute intervals.
Evaluation of Deep-Learning-Based Very Short-Term Rainfall Forecasts in South Korea
Evaluates deep-learning very-short-term rainfall forecasts over South Korea; abstract not provided, so specific skill numbers are unknown.
Benefits of Stochastic Weight Averaging in Developing Neural Network Radiation Scheme for Numerical Weather Prediction
Stochastic weight averaging improves a neural radiation emulator in a Korean NWP model, giving a 60-fold radiation speedup and 12.6% week-long flux gains.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Short-Term Visibility Prediction Using Tree-Based Machine Learning Algorithms and Numerical Weather Prediction Data
Tree-based XGBoost trained on LDAPS NWP output beats raw LDAPS for Seoul visibility (MAE 2.04 km vs 4.66 km, R 0.88 vs 0.40).
Post-processing & calibrationKorea Meteorological Administration (KMA)
Effects of Cloud Microphysics on the Universal Performance of Neural Network Radiation Scheme
Tests a neural radiation emulator across 15 microphysics schemes; errors rise 8.6–41.3% in idealized cases, reducible by 26.5–50.4% with a compound parameterization.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Usefulness of Automatic Hyperparameter Optimization in Developing Radiation Emulator in a Numerical Weather Prediction Model
Automatic learning-rate tuning with Sherpa improves a neural radiation emulator in a Korean NWP model, reducing one-week flux and precipitation errors.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea
Random forests best downscale NWP wind speed to 100 m over South Korea for agriculture, with 48-hour RMSE near 0.8 m/s.
Downscaling & super-resolutionKorea Meteorological Administration (KMA)
Optimization of Neural Network Radiation Emulator using Sherpa
Source code and weights for a neural network radiation emulator in WRF, with hyperparameters tuned by Sherpa to speed up the radiation scheme.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning
Machine learning estimates 24-hour cloud cover from ground-based sky images, with support vector regression giving RMSE 1.45 tenths against human observers.
Observations & remote sensingKorea Meteorological Administration (KMA)
Compound Parameterization to Improve the Accuracy of Radiation Emulator in a Numerical Weather Prediction Model
Two compound parameterizations correct errors of a neural-network radiation emulator in a Korean NWP model, keeping its 60-fold speedup with lower error.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization
A neural-network emulator replaces KMA's radiation scheme in an operational model, giving a 60-fold speedup and 22–34% lower skin temperature and flux errors.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Neural Network Radiation Emulator (KMA/NIMS)
Releases code and training data for a neural network radiation emulator in an NWP model, tied to improved forecast accuracy.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Evaluation of Neural Network Emulations for Radiation Parameterization in Cloud Resolving Model
Neural network radiation emulators in a cloud-resolving model run 20–100 times faster and, run frequently, improve 6 h forecast accuracy over infrequent original radiation calls.
Hybrid physics-ML & parameterizationKorea Meteorological Administration (KMA)
Assessment of Cloud Retrieval for IASI 1D-Var Cloudy-Sky Assimilation and Improvement with an ANN Approach
An artificial neural network estimates IASI cloud-top pressure and fraction closer to optimal values, cutting retrieval time and improving 1D-Var cloudy-sky assimilation with near-neutral forecast impact.
Data assimilationMet OfficeKorea Meteorological Administration (KMA)
Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management
Machine learning corrects a coupled climate model's seasonal forecasts of daily air temperature for field-scale agricultural management.
Sub-seasonal to seasonalKorea Meteorological Administration (KMA)
Application of machine learning to an early warning system for very short-term heavy rainfall
Applies machine learning to an early warning system for very short-term heavy rainfall; abstract unavailable, classification based on title only.