Forecasting the Skies: A New Approach to Predicting Rainfall

Author: Denis Avetisyan

Researchers have developed a novel deep learning model that significantly improves the accuracy and realism of short-term precipitation forecasts.

Spatial and spatio-temporal enhancements within the STLDM model demonstrate differing levels of predictive detail on the HKO-7 test set, with focused examination of the initial five frames revealing nuanced distinctions in performance.

This paper introduces STLDM, a Spatio-Temporal Latent Diffusion Model that combines forecasting and enhancement stages for state-of-the-art precipitation nowcasting.

Accurate and reliable precipitation nowcasting remains a challenge due to the inherent stochasticity and complexity of weather systems. This paper introduces STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting, a novel approach that decomposes the task into deterministic forecasting and subsequent enhancement via a latent diffusion process. Experimental results demonstrate that STLDM surpasses state-of-the-art methods in both predictive accuracy and visual fidelity, while also improving inference efficiency. Could this hybrid architecture represent a new paradigm for spatio-temporal prediction tasks beyond weather forecasting?

The Challenge of Accurate Precipitation Prediction

The ability to accurately predict precipitation in the very near future – a process known as nowcasting – is critically important for a wide range of societal needs. Beyond simply knowing if an afternoon picnic will be rained out, precise short-term forecasts directly impact public safety through timely severe weather alerts, enabling effective disaster preparedness and minimizing potential harm. Furthermore, efficient resource management hinges on anticipating rainfall; agriculture, urban drainage systems, and even energy grids all benefit from accurate predictions that allow for proactive adjustments and optimized operations. Despite advancements in meteorological science, however, nowcasting remains a substantial challenge due to the chaotic and rapidly evolving nature of atmospheric systems, particularly convective storms which can develop and dissipate with surprising speed and localized intensity.

Conventional precipitation nowcasting techniques frequently rely on extrapolation – essentially, assuming a storm’s current movement will continue unchanged into the future. However, atmospheric processes are inherently non-linear; even slight variations in temperature, humidity, or wind can dramatically alter a storm’s path and intensity. This complexity renders simple extrapolation unreliable, particularly for rapidly developing convective storms. Because these methods struggle to account for the cascading effects of small changes, predictions often diverge quickly from reality, resulting in inaccurate forecasts regarding storm location, rainfall rates, and the potential for severe weather events. Consequently, improvements in nowcasting necessitate models capable of capturing these intricate, non-linear dynamics.

Initial forays into deep learning for precipitation nowcasting, exemplified by models like PredRNN and ConvLSTM, demonstrated a promising ability to capture spatiotemporal dependencies in radar data, surpassing traditional extrapolation techniques. However, these early architectures encountered significant limitations. The recurrent nature of these models, while enabling the processing of sequential data, demanded substantial computational resources, hindering real-time forecasting capabilities. Furthermore, their representational capacity – the ability to effectively encode complex storm dynamics – proved insufficient to accurately predict rapidly evolving, non-linear phenomena. The inherent difficulty in modeling chaotic systems, coupled with the models’ architectural constraints, often resulted in forecasts that degraded rapidly with increasing lead time, necessitating the development of more efficient and powerful approaches to accurately anticipate localized precipitation events.

Unlike deterministic models which produce blurry forecasts and generative models prone to inaccuracies, our STLDM achieves both accurate predictions and visually appealing results.

Generative Models: A New Path to Accurate Forecasts

Diffusion Models (DMs) represent a class of generative models that have recently achieved state-of-the-art results in various data modalities, notably image generation. Unlike Generative Adversarial Networks (GANs), which rely on adversarial training and can suffer from instability, and Variational Autoencoders (VAEs), which often produce blurry samples, DMs excel in generating high-fidelity outputs. This is achieved through a probabilistic process of progressively adding Gaussian noise to data until it becomes pure noise, then learning to reverse this process to generate new samples. Critically, DMs are trained with a likelihood-based objective – specifically, maximizing the data likelihood – which provides a more stable and theoretically sound training process compared to the adversarial or reconstruction losses used in GANs and VAEs. This focus on likelihood maximization contributes to their superior performance in terms of both sample quality – assessed via metrics like Fréchet Inception Distance (FID) – and probability estimation.

Direct application of Diffusion Models (DMs) to high-resolution radar data presents significant computational challenges due to the large dimensionality of the input data and the iterative nature of the diffusion process. Each diffusion step requires substantial memory and processing power, scaling unfavorably with image resolution. To mitigate these costs, Latent Diffusion Models (LDMs) operate by first encoding the high-dimensional radar data into a lower-dimensional latent space using an autoencoder. The diffusion and denoising processes are then performed within this latent space, drastically reducing computational requirements without significant loss of information. This approach allows for efficient generation and nowcasting of high-resolution radar imagery, making it feasible to apply DMs to practical weather forecasting scenarios.

Latent Diffusion Models (LDMs) address the computational limitations of applying diffusion models directly to high-dimensional data, such as high-resolution radar imagery, by performing the diffusion and denoising processes within a lower-dimensional latent space. This is achieved through the use of a learned autoencoder; an encoder compresses the original data into a latent representation, and a decoder reconstructs the data from this representation. By conducting the diffusion process on the lower-dimensional latent variables, the computational cost is significantly reduced, enabling efficient generation and, specifically, nowcasting – the prediction of future radar reflectivity based on current and past observations – without substantial loss of fidelity. The autoencoder is trained to minimize reconstruction error, ensuring that the generated samples in the latent space accurately reflect the original data distribution when decoded.

The proposed STLDM model utilizes a Variational Autoencoder, a Conditioning Network, and a Spatio-Temporal Latent Denoising Network <span class="katex-eq" data-katex-display="false">D_{\theta}</span>-detailed with Linearized Spatial Attention in the yellow and green boxes-to process input radar frames <span class="katex-eq" data-katex-display="false">X_{1:M}</span> and generate denoised predictions <span class="katex-eq" data-katex-display="false">Y^\_{1:N}</span> and initial estimations <span class="katex-eq" data-katex-display="false">\overline{Y}_{1:N}</span> from Gaussian noise <span class="katex-eq" data-katex-display="false">z^T_{1:N}</span>. — The proposed STLDM model utilizes a Variational Autoencoder, a Conditioning Network, and a Spatio-Temporal Latent Denoising Network $D_{\theta}$ -detailed with Linearized Spatial Attention in the yellow and green boxes-to process input radar frames $X_{1:M}$ and generate denoised predictions $Y^\_{1:N}$ and initial estimations $\overline{Y}_{1:N}$ from Gaussian noise $z^T_{1:N}$ .

Introducing STLDM: A Spatio-Temporal Architecture

STLDM utilizes Latent Diffusion Models (LDMs) to forecast precipitation by representing the spatiotemporal evolution of weather patterns within a lower-dimensional latent space. This approach significantly reduces computational demands compared to directly modeling precipitation in pixel space; LDMs learn a compressed representation of the data, allowing the model to operate on this latent representation instead of the full-resolution radar data. By performing diffusion and denoising processes within the latent space, STLDM maintains forecast accuracy while requiring substantially less memory and processing power, enabling more efficient and scalable precipitation nowcasting.

The Translator network functions as the initial encoding stage within STLDM, converting raw radar reflectivity data into a lower-dimensional latent representation. This encoding process incorporates Gated Spatio-Temporal Attention (gSTA), a mechanism designed to selectively focus on relevant spatial and temporal features within the radar sequences. Specifically, gSTA utilizes learned gate weights to modulate the influence of neighboring radar echoes across both space and time, enabling the network to prioritize crucial precipitation patterns and filter out noise. This latent representation, capturing the essential characteristics of the precipitation field, then serves as input to the subsequent Latent Denoising Network for forecast refinement.

The Latent Denoising Network (LDN) functions as a refinement stage following the initial forecast generated by the Translator network. This network is constructed using a series of ResBlocks, which facilitate the propagation of information and allow for deep network architectures without the vanishing gradient problem. The LDN operates through a diffusion process, iteratively adding and then removing noise to the latent representation. This process encourages the model to learn the underlying data distribution and generate accurate precipitation forecasts by effectively “denoising” the initial prediction. The iterative nature of the diffusion process allows for a progressive refinement of the forecast, ultimately leading to improved predictive performance.

Both DiffCast and STLDM struggled to predict precipitation events originating from the left side of the HKO-7 test set due to a restricted observational field of view.

Empirical Validation and Performance Analysis

To rigorously assess the capabilities of STLDM, a comprehensive evaluation was conducted utilizing three distinct datasets – SEVIR, HKO-7, and MeteoNet – each presenting unique challenges in precipitation forecasting. The model’s performance wasn’t judged on a single measure, but through a suite of established metrics designed to capture both the accuracy and perceptual quality of its forecasts. Critical Success Index (CSI) and Heidke Skill Score (HSS) quantified the model’s ability to correctly identify precipitation events, while Structural Similarity Index Measure (SSIM) gauged the visual fidelity of the generated forecasts compared to ground truth observations. Further refining the evaluation, the Learned Perceptual Image Patch Similarity (LPIPS) metric measured the perceptual differences between generated and actual precipitation patterns, offering insights into how realistically the model captures the nuances of weather phenomena. This multi-faceted approach ensured a thorough and nuanced understanding of STLDM’s strengths and limitations.

Rigorous evaluation confirms that STLDM consistently delivers superior performance when contrasted with existing precipitation forecasting models. Across the SEVIR, HKO-7, and MeteoNet datasets, the model achieves notable gains in key quantitative metrics, most prominently exceeding baseline Critical Success Index (CSI) values on each dataset tested. This improvement isn’t limited to numerical accuracy; qualitative assessments also demonstrate a marked enhancement in forecast quality. The consistent outperformance suggests that STLDM effectively captures complex precipitation patterns, leading to more reliable and accurate predictions compared to current state-of-the-art methodologies.

Classifier-Free Guidance (CFG) proves instrumental in refining the precipitation forecasts generated by STLDM, allowing the model to produce outputs that are both more accurate and visually realistic. Evaluations on the HKO-7 dataset reveal that STLDM attains the lowest Learned Perceptual Image Patch Similarity (LPIPS) scores – a key indicator of perceptual quality – surpassing the performance of competing models in generating images that closely resemble ground truth. Importantly, this enhanced performance is achieved with reduced training demands; STLDM converges more efficiently than comparable architectures, demonstrating a significant advantage in computational cost and development time. This combination of improved accuracy, perceptual fidelity, and training efficiency positions STLDM as a promising advancement in precipitation nowcasting.

The study reveals that STLDM significantly accelerates precipitation forecasting through remarkably fast sampling speeds. Compared to existing diffusion-based models, STLDM consistently achieves the shortest inference times across all tested spatial resolutions – 128, 256, and 512. This efficiency stems from architectural optimizations within the model, allowing for quicker generation of high-resolution precipitation forecasts without sacrificing accuracy. The reduced computational demands not only facilitate real-time applications but also broaden the accessibility of advanced precipitation modeling, offering a practical solution for time-sensitive weather prediction and analysis.

STLDM, trained with varying strategies detailed in Section 4.3.2, demonstrates prediction capabilities on the SEVIR test set.

The presented work, STLDM, embodies a pursuit of essentiality in predictive modeling. It distills complex atmospheric dynamics into a latent space, prioritizing information density over superfluous detail. This mirrors a core tenet of efficient design – minimizing cognitive load to maximize understanding. As Robert Tarjan aptly stated, “Sometimes the hardest part of a problem isn’t solving it, but recognizing what doesn’t need to be solved.” STLDM’s two-stage approach – forecasting followed by enhancement – reflects this principle. By initially focusing on core prediction and subsequently refining visual fidelity, the model avoids unnecessary computational expense while maintaining high accuracy, demonstrating that true innovation often lies in strategic subtraction.

What Remains?

The pursuit of increasingly detailed predictive models often obscures a fundamental truth: a perfect forecast is not merely difficult, but conceptually flawed. This work, while demonstrating notable advances in precipitation nowcasting, implicitly acknowledges this by requiring a ‘forecasting’ and an ‘enhancement’ stage. A system that needs correction has, at its core, already failed to fully grasp the underlying principles. The refinement process suggests the initial prediction is, by necessity, incomplete – a sketch, not a finished portrait.

Future iterations should, therefore, prioritize not simply more data or complex architectures, but a rigorous distillation of existing information. The challenge lies in identifying the irreducible elements – the minimal set of observations sufficient to describe the system’s evolution. The focus should shift from generating plausible continuations to discerning the inherent constraints that govern atmospheric behavior.

Ultimately, the true measure of success will not be an incremental improvement in numerical accuracy, but a fundamental simplification of the problem. A model that anticipates, rather than reacts, requires less instruction. Clarity, after all, is courtesy – both to the researcher and to the system itself.

Original article: https://arxiv.org/pdf/2512.21118.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Challenge of Accurate Precipitation Prediction

Generative Models: A New Path to Accurate Forecasts

Introducing STLDM: A Spatio-Temporal Architecture

Empirical Validation and Performance Analysis

What Remains?

See also: