arXiv subjects

Heecheol Yang

Publications and source records attributed to Heecheol Yang.

8 recordsLinked to original sources

arXiv2026

Event-Aware Loss Design for Forecasting of Convective Precipitation and Lightning

Accurate forecasting of high-impact weather, specifically extreme precipitation and lightning, remains a significant challenge in numerical weather prediction (NWP) due to the complexity of atmospheric microphysics. While deep-learning models have shown promise in large-scale forecasting, they often suffer from systematic under-prediction of rare, high-intensity events and localized convective showers when optimized with conventional loss functions like Mean Squared Error (MSE). This study proposes an Event-Aware multi-task deep-learning post-processing framework designed to improve the representation of convective processes by leveraging lightning observations. The model jointly predicts precipitation amount, rainfall probability, and lightning occurrence using a shared-backbone Patch-cGAN (Conditional Generative Adversarial Network) architecture. To address the rare event problem, we introduce a lightning-informed loss-weighting strategy that element-wisely multiplies the MSE component by a spatial weight map derived from observed lightning strikes, forcing the model to prioritize accuracy in convective regions during training. Evaluations conducted over the Korean Peninsula during the 2025 Summer demonstrate that our framework outperforms standard AI benchmarks and conventional NWP models, particularly at intense rainfall thresholds (40 mm/6 h). Furthermore, the model exhibits superior skill in predicting lightning compared to conventional lightning parameterization and instability-index-based methods. These results indicate that integrating physical event indicators into the loss formulation effectively guides models to learn the meteorological signatures of deep convection, offering a pathway toward more reliable extreme weather forecasting.

physics.ao-ph↗

arXiv2025

Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams

Forecasting from atmospheric soundings is a fundamental task in operational meteorology, often requiring structured visual reasoning over Skew-T log-P diagrams by human forecasters. While recent advances in Vision-Language Models (VLMs) have shown promise in other scientific domains, their application to meteorological diagram interpretation remains largely unexplored. In this study, we present a lightweight AI assistant that interprets Skew-T diagrams using a small language model (LM) and a small VLM fine-tuned to emulate human forecasters. Using a curriculum learning framework, we first train the models to identify key atmospheric features from diagrams through visual question answering, followed by chain-of-thought reasoning tasks that estimate precipitation probability based on the derived visual groundings. Model inputs include either textual summaries or generated Skew-T diagrams derived from operational Numerical Weather Prediction (NWP) forecasts, paired with three-hour precipitation observations from South Korea's Auto Weather Stations network. Evaluation results demonstrate that the fine-tuned VLM achieves skill comparable to an operational NWP model, despite relying solely on static atmospheric profiles. Ablation studies reveal that visual grounding and reasoning supervision are critical for performance, while attention map analysis confirms that the model learns to focus on relevant meteorological features. These findings highlight the potential of compact, interpretable multimodal models to support weather forecasting tasks. The approach offers a computationally efficient alternative to large-scale systems, and future work could extend it to more complex applications.

physics.ao-ph↗

arXiv2025

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations

Accurate quantitative precipitation forecasting (QPF) remains one of the main challenges in numerical weather prediction (NWP), primarily due to the difficulty of representing the full complexity of atmospheric microphysics through parameterization schemes. This study introduces a deep learning-based post-processing model, DL-QPF, which diagnoses precipitation fields from meteorological forecasts by learning directly from high-resolution radar estimates precipitation. The DL-QPF model is constructed using a Patch-conditional Generative Adversarial Network (Patch-cGAN) architecture combined with a U-Net generator and a discriminator. The generator learns meteorological features relevant to precipitation, while the adversarial loss from the discriminator encourages the generation of realistic rainfall patterns and distributions. Training is performed on three years of warm-season data over the Korean Peninsula, with input variables derived from ECMWF's Integrated Forecasting System High-Resolution forecast (IFS-HRES). Model verification is conducted against multiple reference models, including global (IFS-HRES, KIM), regional (KIM-Regional, KIM-LENS), and AI-based (GraphCast) forecasts. Verification across multiple rainfall thresholds shows that DL-QPF achieves a frequency bias near one and superior success ratios. Particularly for heavy and intense rainfall events, DL-QPF outperforms both conventional NWP and an AI model, demonstrating improved skill in capturing high-intensity precipitation. This study highlights the potential of observational data-driven deep learning approaches in post-processing QPF. By directly learning from observations, DL-QPF reduces systematic biases and enhances the realism of forecasted rainfall distributions. These results demonstrate the model's potential to enhance QPF realism.

physics.ao-ph↗

arXiv2023

Towards 6G Hyper-Connectivity: Vision, Challenges, and Key Enabling Technologies

Technology forecasts anticipate a new era in which massive numbers of humans, machines, and things are connected to wireless networks to sense, process, act, and communicate with the surrounding environment in a real-time manner. To make the visions come true, the sixth generation (6G) wireless networks should be hyper-connected, implying that there are no constraints on the data rate, coverage, and computing. In this article, we first identify the main challenges for 6G hyper-connectivity, including terabits-per-second (Tbps) data rates for immersive user experiences, zero coverage-hole networks, and pervasive computing for connected intelligence. To overcome these challenges, we highlight key enabling technologies for 6G such as distributed and intelligence-aware cell-free massive multi-input multioutput (MIMO) networks, boundless and fully integrated terrestrial and non-terrestrial networks, and communication-aware distributed computing for computation-intensive applications. We further illustrate and discuss the hyper-connected 6G network architecture along with open issues and future research directions.

cs.IT↗

arXiv2018

Private Coded Computation for Machine Learning

In a distributed computing system for the master-worker framework, an erasure code can mitigate the effects of slow workers, also called stragglers. The distributed computing system combined with coding is referred to as coded computation. We introduce a variation of coded computation that protects the master's privacy from the workers, which is referred to as private coded computation. In private coded computation, the master needs to compute a function of its own dataset and one of the datasets in a library exclusively shared by the external workers. After the master recovers the result of the desired function through coded computation, the workers should not know which dataset in the library was desired by the master, which implies that the master's privacy is protected. We propose a private coded computation scheme for matrix multiplication, namely private polynomial codes, based on polynomial codes for conventional coded computation. As special cases of private polynomial codes, we propose private one-shot polynomial codes and private asynchronous polynomial codes. Whereas the private one-shot polynomial code achieves a lower communication load from the master to each worker, the private asynchronous polynomial code achieves faster computation than private one-shot polynomial codes. In terms of computation time and communication load, we compare private one-shot polynomial codes and private asynchronous polynomial codes with a conventional robust private information retrieval scheme which can be directly applied to coded computation.

cs.IT↗

arXiv2016

Topological Interference Management with Reconfigurable Antennas

We study the symmetric degrees-of-freedom (DoF) of partially connected interference networks under linear coding strategies at transmitters without channel state information beyond topology. We assume that the receivers are equipped with reconfigurable antennas that can switch among their preset modes. In such a network setting, we characterize the class of network topologies in which half linear symmetric DoF is achievable. Moreover, we derive a general upper bound on the linear symmetric DoF for arbitrary network topologies. We also show that this upper bound is tight if the transmitters have at most two co-interferers.

cs.IT↗

arXiv2016

Linear Degrees of Freedom for $K $-user MISO Interference Channels with Blind Interference Alignment

In this paper, we characterize the degrees of freedom (DoF) for $K $-user $M \times 1 $ multiple-input single-output interference channels with reconfigurable antennas which have multiple preset modes at the receivers, assuming linear coding strategies in the absence of channel state information at the transmitters, i.e., blind interference alignment. Our linear DoF converse builds on the lemma that if a set of transmit symbols is aligned at their common unintended receivers, those symbols must have independent signal subspace at their corresponding receivers. This lemma arises from the inherent feature that channel state's changing patterns of the links towards the same receiver are always identical, assuming that the coherence time of the channel is long enough. We derive an upper bound for the linear sum DoF, and propose an achievable scheme that exactly achieves the linear sum DoF upper-bound when both of the $\frac{n^{*}}{M}=R_{1} $ and $\frac{MK}{n^{*}}=R_{2} $ are integers. For the other cases, where either $R_1 $ or $R_2 $ is not an integer, we only give some guidelines how the interfering signals are aligned at the receivers to achieve the upper-bound. As an extension, we also show the linear sum DoF upper-bound for downlink/uplink cellular networks.

cs.IT↗

arXiv2015

Grouping Based Blind Interference Alignment for $K$-user MISO Interference Channels

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for $K$-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.

cs.IT↗