Snow or rain? hybrid AI deciphers surface precipitation phase from satellite observations

Domain: neuroscience
Published: 2026-02-16
Editorial outcome: major_revision

Abstract
Surface precipitation phase transition is conducive to devastating snowstorms and avalanches yet remains a global challenge due to the scarcity of surface observations. Here, we present the Real-time Precipitation Phase-Intensity Collaborative Retrieval Network (RePPIC-Net), a hybrid AI framework that quantifies surface precipitation phase from satellite observations. By integrating real-time 3D atmospheric physics fields from the AI-driven FuXi model with operational geostationary satellite observations through a hierarchical architecture, our system enables real-time monitoring of surface precipitation phase, as opposed to at least 4-hour latency of current operational systems. Validated against ground stations in China, RePPIC-Net achieves a Critical Success Index for Phase and Detection of 0.1574 (snowfall) and 0.3147 (rainfall) for 0.1-5 mm/h precipitation, outperforming 4-hour latency operational products’ respective scores of 0.1001 and 0.3064. The real-time precipitation phase discrimination capability of RePPIC-Net allows the development of a satellite-based surface precipitation phase nowcasting system, meeting the need for 1-3 hour global surface precipitation phase transition warnings. RePPIC-Net provides a replicable blueprint for AI-powered real-time weather monitoring, filling a gap in winter time weather disaster warnings.

Introduction
Accurate discrimination of precipitation phase is pivotal for mitigating cascading meteorological disasters, particularly in the context of accelerating climate change. Subtle phase shifts trigger divergent societal risks: transient snow-to-rain transitions destabilize snowpack structures, elevating avalanche hazards in alpine zones 1–4, while rain-to-snow conversions under cooling anomalies amplify blizzard risks, paralyzing critical infrastructure 5–8. The societal stakes are exemplified by China’s 2024 snow disasters, which affected 9.07 million people and claimed 24 lives through transport gridlock and supply chain failures 9. Climate warming exacerbates these threats by intensifying phase volatility across high-latitude and high-altitude regions—precisely where ground-based monitoring is most deficient due to wind-induced gauge undercatch and sparse station density 10. While satellite systems like the Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellite Retrievals (IMERG) product provide global coverage, their operational latency (>4 h) and coarse spatiotemporal resolution (>0.1°/30 min) 11,12 fail to resolve rapid phase transitions given the time-costing post-processing satellite observations and traditional numerical weather prediction (NWP) models 13. This system void undermines real-time hazard response, highlighting an urgent need for phase detection frameworks tailored to warming-sensitive terrains.

Artificial intelligence offers distinct methodological paradigms for precipitation monitoring and nowcasting 14–17, yet current technical frameworks face dual challenges of real-time multi-source data fusion and insufficient observational representativeness 18–20. Although deep learning models such as UNet can achieve precipitation retrieval at 5 km resolution by integrating multi-spectral satellite data 21, their purely data-driven approaches particularly result in intensity retrieval errors in complex terrain 10. While active radars and microwave sensors on polar-orbiting satellites can detect precipitation phase transitions by analyzing vertical hydrometeor profiles through reflectivity variations, current satellite-based phase retrieval still demonstrates significant bias compared to NWP-based phase results 22–26. Especially, geostationary satellites lack full-disk vertical detection capabilities and thus fail to produce spatial-continuous phase-resolved precipitation products 27,28. Meanwhile, meteorological forecast models (e.g., FuXi) generate physically consistent 3D atmospheric fields via Transformer architecture 29,30, outperforming traditional numerical models in synoptic-scale circulation forecasting 31–33. However, the absence of multi-source observations and inadequate parameterization of mesoscale dynamical processes hinder their ability to accurately resolve small-to-medium-scale precipitation events 34–36. This disconnect between vertical meteorological background fields with high-resolution real-time satellite observations leaves existing operational systems incapable of concurrently addressing critical requirements for enhanced spatiotemporal resolution, improved precipitation phase identification accuracy, and time-sensitive disaster alert capabilities.

To address this, we propose Real-time Precipitation Phase-Intensity Collaborative Retrieval Network (RePPIC-Net)—a hybrid framework integrating 3D meteorological physical fields (generated within seconds) with high-resolution satellite observations, establishing a real-time operational system enabling concurrent phase-resolved precipitation monitoring. Also, an operational nowcasting framework that seamlessly integrates satellite brightness temperature forecasts from a satellite nowcasting model to generate precipitation predictions with explicit rain-snow differentiation—a previously unmet need bridged by overcoming GPM IMERG’s reliance on lagged NWP assimilation for phase quantification. Validated by ground stations across China, RePPIC-Net demonstrates superior snow identification (Critical Success Index for Phase and Detection = 0.1574 vs. GPM IMERG’s 0.1001) at 0.1–5 mm/h precipitation sensitive range. This work not only provides a real-time monitoring tool for data-sparse regions but also establishes a technological paradigm to advance global early warnings.

Results
Evaluations of RePPIC-Net based on ground observation
As shown in Fig. 1a, a nationwide precipitation phase evaluation of RePPIC-Net against the GPM IMERG-Late product, utilizing over 2000 national Automatic Weather Stations (AWS) across China, revealed comparable rain-detection performance between the models with Critical Success Index (CSI) scores of 0.85 and 0.83, respectively. In addition, both models exhibited slight snowfall overestimation (Bias > 1.0) and rainfall underestimation (Bias < 1.0), with RePPIC-Net (snow: 1.02; rain: 0.99) closer to 1.0 than GPM IMERG (snow: 1.36; rain: 0.84). However, RePPIC-Net exhibited moderately reduced skill in snow identification (CSI: 0.69 vs. GPM IMERG’s 0.71), driven by elevated false-alarm rates at localized stations.

To further assess the monitoring capability for mixed-phase precipitation, a targeted analysis was performed for weak precipitation events within the 0.1–5 mm/h intensity range. The threshold is operationally significant, as snowfall in this category often corresponds to heavy or blizzard conditions, whereas rainfall represents light to moderate events. As illustrated in Fig. 1b, GPM IMERG outperformed RePPIC-Net in snowfall phase identification metrics, whereas RePPIC-Net excelled in snowfall detection accuracy (Critical Success Index for Detection, CSIn: 0.23 vs. 0.14). Meanwhile, for rainfall, RePPIC-Net demonstrated better phase discrimination capabilities, with both models performing similarly in precipitation detection. The consistently low uncertainties across all metrics indicate robust evaluation results, though detection CSI shows marginally higher uncertainty than phase classification. The superior comprehensive performance of RePPIC-Net, integrating spatiotemporal consistency and operational applicability, underscores its enhanced utility for monitoring small-scale snowfall systems. These findings suggest that while phase-specific algorithms in GPM IMERG provide robust discrimination, RePPIC-Net’s improved detection sensitivity aligns more closely with real-time disaster mitigation requirements, particularly in regions prone to rapid-onset winter storms.

The spatiotemporal error distributions
The national AWS observations allowed an examination of spatiotemporal error distribution characteristics of RePPIC-Net (Figs. 2 and 3). In general, both models demonstrated consistent spatial error distribution patterns, characterized by a relatively diminished capacity for rain discrimination over the Tibetan Plateau and a reduced ability to discriminate snow in southern China. Regarding rainfall, RePPIC-Net performed slightly better than GPM in the northwestern and northeastern regions with higher CSI (Fig. 2a, b) and POD (Fig. 2e, f). Nevertheless, this improvement was accompanied by a certain increase in False Alarm Ratio (FAR) (Fig. 2i, j). Regarding snowfall, both models were generally comparable; however, RePPIC-Net showed inferior performance in southern areas (Fig. 2c, d) due to lower POD (Fig. 2g, h), indicating that the model tends to miss snowfall in mountainous and similar terrains. These findings underscore the need for terrain-aware parameterizations and enhanced thermodynamic constraints in solid-precipitation retrievals.

Analysis of temporal variations further revealed the stability of model performance (Fig. 3). The study found that the discrimination results for snow phase exhibit significantly greater variation over time than those for rain, following certain patterns: rain shows lower CSI values in December and January, potentially related to the complex phase discrimination of winter rainfall in southern China; whereas snow demonstrates notably lower CSI in October and March, indicating that the model still faces challenges in accurately identifying phase during transition periods at the beginning and end of the snow season. In terms of monthly trends, RePPIC-Net and GPM IMERG show similar variation patterns, but the former consistently tends to classify more precipitation as snow across all months, correspondingly reducing rain classifications than the latter. Regarding overall precipitation detection capability, both models show relatively stable CSI performance for rain and snow across different months, with RePPIC-Net demonstrating superior snow detection ability throughout all months.

Case studies of mixed-phase and orographic snowfall
Under the influence of a strong cold air mass, a high-impact mixed precipitation event occurred across northeastern China from 20:00 UTC on 5 November 2023 to 20:00 UTC on 7 November 2023. The event featured heavy snowfall transitioning to rain-snow mixtures, with accumulations ranging from heavy snow (10–20 mm/day) to localized blizzard conditions (>30 mm/day) in central and western Inner Mongolia, northern and central Heilongjiang, Jilin, and Liaoning provinces. The China Meteorological Administration (CMA) issued a blue cold wave alert and an orange blizzard alert on 5 November. Joint consultations between the Ministry of Emergency Management and CMA emphasized preparedness for rapid temperature drops, ice accumulation, and complex precipitation phases driven by abundant moisture and dynamic cooling.

Quantitative assessments based on ground station data revealed significant discrepancies among satellite precipitation products (Fig. 4a). The official Fengyun-4B (FY-4B) precipitation product exhibited substantial overestimation with notable spatial distribution errors. The GPM IMERG product also overestimated total precipitation, while RePPIC-Net demonstrated superior accuracy, achieving the lowest magnitude error (RMSE: 1.65 mm/h). Critical to this event was the ability to distinguish rain from snow, particularly in regions where near-freezing temperatures caused rapid phase transitions. Both RePPIC-Net and GPM IMERG incorporate phase discrimination modules, enabling monitoring of snowfall. RePPIC-Net outperformed GPM IMERG in detecting blizzard conditions over northern China, attributed to its higher spatial resolution. For snowfall detection, RePPIC-Net achieved a Critical Success Index for Precipitation Phase and Detection (CSI-PD) of 0.64, surpassing GPM IMERG (CSI-PD: 0.39). For rainfall detection, RePPIC-Net also exhibited a slight advantage over GPM IMERG (CSI-PD: 0.49 vs. 0.43), suggesting its deep learning architecture effectively captures precipitation patterns despite GPM’s multi-sensor fusion approach.

Figure 5 illustrates a snowfall event over the Tibetan Plateau (19:00 UTC on 14 December 2023), emphasizing challenges in solid precipitation monitoring over complex high-altitude terrain. GPM IMERG exhibited limited capability in detecting snowfall. RePPIC-Net achieved more precise localization of snowfall with enhanced spatiotemporal resolution (0.05° spatial, 15 min temporal) by integrating profiles of meteorological variables, while providing certain insights for snowfall monitoring in data-sparse regions lacking ground stations. However, the precipitation detection capabilities of satellite-based precipitation observations for complex plateau precipitation remain generally limited (Supplementary Fig. 1) and require further research.

Performance of RePPIC-Net in nowcasting
Recent advances in geostationary satellite-based nowcasting have demonstrated the potential of data-driven models for cloud image forecasting. The DAYU model 37, leveraging Himawari-8 brightness temperature (BT) extrapolation, achieves correlation coefficients (CC) exceeding 0.9 within a 3-hour forecast window at 0.05° spatial resolution. Based on this framework, we adapted the architecture to process data from the FY-4B Advanced Geostationary Radiation Imager (AGRI) for brightness temperature (BT) forecasting. To evaluate the errors introduced by BT forecasts, we utilized 1, 2, and 3-hour lead time satellite BT data as respective inputs to the precipitation model. Quantitative verification of rainfall revealed decreased CSI-PD metrics with lead times increasing from 0.29 (1 h) to 0.28 (2 h) and 0.27 (3 h), while snowfall metrics remained relatively stable at 0.15 (1 h), 0.15 (2 h), 0.14 (3 h). This stability in snowfall detection may reflect beneficial smoothing effects from motion field extrapolation, which enhanced hit rates without significantly increasing false alarms.

Building on the previous analysis, we now present nowcasting results for the representative case study shown in Fig. 4. The refined DaYu-FY model exhibits progressive decay in forecast sharpness with increasing lead times, showing moderate blurring at 3 h while retaining discernible cloud structure and BT patterns (Fig. 6a). When applied to precipitation nowcasting via RePPIC-Net using these forecasting FY-4B BT inputs, the system maintains robust identification of snowfall systems across the 3-hour window, with a similar CSI-PD score (0.37). However, a slight performance decline emerges for rainfall prediction as the lead times increasing. To confirm this snowstorm case is representative, its heavy snowfall (>1 mm/h) CSI-PD scores (0.23 for both 1 h and 3 h forecasts) closely align with the 4-month systematic evaluation (more validation in Appendix), demonstrating consistent performance with the event given in Fig. 6. This divergence highlights RePPIC-Net’s inherent resilience to input degradation—despite increasing BT field uncertainty and spatial smoothing, the model preserves critical skill in precipitation localization. The results underscore the framework’s operational viability for short-term precipitation prediction while identifying key challenges for liquid-phase precipitation applications.

Relationship between meteorology and precipitation phase
To understand the physical mechanisms of the model and the influence of meteorological fields on precipitation phase, we identified the most influential meteorological variables for our precipitation model via a random forest feature importance analysis (Tables 1 and 2). Following this identification, we analyzed the systematic errors in the 12 h FuXi forecast fields relative to ERA5 reanalysis data over China (Supplementary Table 1). Subsequently, we investigated the impact of perturbing these top-ranked variables on precipitation.

The relative importance of contributing features in precipitation detection was systematically evaluated through quantitative analysis (Table 1). Satellite channel differences emerged as highly significant predictors, as they effectively capture the essential changes in cloud properties and atmospheric conditions, which are fundamental for accurate precipitation detection. Among meteorological parameters, total precipitation shows the greatest impact on precipitation detection due to its direct reflection of the intensity and distribution of rainfall events. In addition, the relative humidity at 700 hPa and 500 hPa ranked prominently, highlighting the critical role of mid-to upper-tropospheric water vapor advection in modulating precipitation initiation. The relative importance of contributing features in quantitative precipitation estimation was also systematically evaluated (Table 2). Notably, cloud cluster characteristics—specifically the distance to cloud boundary and the minimum 10.8 μm infrared brightness temperature within clusters—were found to exhibit high importance rankings, suggesting that incorporating cloud cluster information enhances quantitative precipitation estimate accuracy. In addition, lower-level divergence and upper-level humidity parameters occupied prominent positions in the ranking hierarchy, indicating their critical roles in modulating precipitation intensity. Conversely, single-channel infrared satellite data consistently demonstrated lower predictive importance, highlighting the limited utility of isolated infrared spectral information for quantitative precipitation retrieval.

Supplementary Table 1 presents the error analysis in the 12-hour FuXi forecasts and ERA5 reanalysis for meteorological variables used in precipitation retrieval models. FuXi meteorological fields demonstrate high overall accuracy, with temperature estimates exhibiting a higher correlation coefficient than relative humidity. With the national automatic weather station network in China, we evaluated the discrepancies of FuXi and ERA5 from October to March, 2023–2025. We found a systematic overestimation over temperature (except at 500 hPa) and underestimation of relative humidity in FuXi (across all pressure levels).

The comparable performance of FuXi and ERA5 results in similar precipitation phase and detection (Supplementary Table 2). However, a slight systematic bias observed by FuXi leads to simultaneously lower POD and FAR of precipitation detection for snow and rain. This pattern reveals that the precipitation model has an inherent tendency to predict more precipitation events under the specific thermodynamic profile of ERA5. This can be attributed to ERA5’s generally lower temperatures (except at 500 hPa) and higher humidity at all levels—conditions that favor precipitation generation, particularly given the high feature importance of relative humidity at 700 hPa (R700) in our model. Furthermore, the ERA5-driven model exhibits a greater propensity to predict snow. This is likely due to its lower near-surface temperatures, which yield lower wet-bulb temperatures. The FuXi’s slight warm bias appears to partially compensate for the inherent cool bias introduced by using pressure levels as a proxy for the actual surface conditions. Notably, inherent uncertainties in ERA5 within cloud systems may contribute to these differences 38.

Methods
Real-time Precipitation Phase-Intensity Collaborative Retrieval Network (RePPIC-Net)
Effective real-time monitoring of precipitation phase (rain/snow) and intensity requires synergistic integration of meteorological dynamics and satellite observations. We present RePPIC-Net, a hybrid framework that unifies phase-resolved retrieval and error-optimized intensity retrieval through hierarchical fusion of large-scale meteorological fields and fine-scale satellite features. The framework leverages the computational efficiency of the FuXi global weather prediction model, initialized with ERA5 reanalysis data, to generate 12 h forecasts of vertical atmospheric profiles (e.g., temperature, relative humidity). The atmospheric variables provide critical thermodynamic context, including wet-bulb temperature (Tw) derived from DEM-adjusted vertical interpolation, which replaces conventional air temperature for probabilistic liquid precipitation estimation.

Complementing this, a lightweight ResUNet architecture processes FY-4B AGRI Level-1 (L1) data to extract spatial features of precipitation clusters. Integrating near-instantaneous meteorological forecast outputs from the FuXi model (computationally generated within seconds) with multi-spectral satellite observations, RePPIC-Net achieves real-time full-disk quantitative precipitation estimates (QPE) at 0.05° spatial and 15 min temporal resolution (see Fig. 7). This real-time capability addresses the limitations of existing systems such as IMERG-Early, which rely on delayed JMA forecasts, and outperforms infrared-only methods for capturing heavy precipitation events.

Key innovations also include a probabilistic post processing layer that corrects systematic biases using joint occurrence-intensity distributions and a precipitation masking module that outputs precipitation occurrence probabilities alongside precipitation cluster intensity. By integrating probabilistic information, the framework achieves enhanced accuracy in intensity estimation.

Precipitation phase discrimination module
The precipitation phase classification algorithm in RePPIC-Net employs a probabilistic framework adapted from the GPM IMERG probability of liquid precipitation (PLP) methodology. Central to this module is the estimation of wet-bulb temperature (Tw), a thermodynamic variable recognized in previous studies as the most robust indicator for precipitation phase discrimination 39,40. Its established superiority over traditional approaches—including those based on dew point temperature, surface air temperature (Ta), and even certain machine learning techniques 41. Unlike IMERG-Final, which calculates PLP from ERA5 reanalysis data 11, our system integrates 12 h forecasts of temperature (T) and relative humidity (RH) from the FuXi model. Saturation vapor pressure is derived from FuXi’s T and RH using the Magnus-Tetens formula, with vertical interpolation optimized through DEM-adjusted elevation matching to select the nearest atmospheric layer to the surface. This ensures topographic consistency in thermodynamic calculations. The wet-bulb temperature is then iteratively solved from the psychrometric equation, which leverages the calculated saturation vapor pressure. PLP is calculated as follows:

PLP = 1 / (1 + exp(a * Tw + b)) (1)

where Tw is the wet-bulb temperature in °C, and a and b are parameters from the fitting performed separately over ocean and land, as referenced in Algorithm theoretical basis document for IMERG Version 07 11.

Quantitative precipitation estimation framework
The RePPIC-Net framework integrates three advancements to overcome limitations in geostationary satellite-based quantitative precipitation estimation (QPE). First, meteorological fields derived from the FuXi model replace satellite radar/microwave inputs, compensating for the constraints of infrared observations. Second, a ResUNet architecture incorporates six spatial features of precipitation clusters, including intra-cluster minimum brightness temperature (BT 10.8min), spectral differences (BT 12.3 - BT 10.8), distance to boundary, distance to center, BT slope, and precipitation cluster area. Third, a Bayesian refinement layer reduces systematic biases by leveraging phase likelihoods and joint intensity distributions.

Data preprocessing
FuXi forecast fields, FY-4B/AGRI L1 data, and geospatial ancillary datasets (e.g., DEM) are harmonized to a 0.05°/15 min resolution. Temperature and relative humidity profiles are vertically interpolated using DEM-adjusted elevations for Tw calculation, which serves as the driving variable for phase probability estimation. Precipitation labels are aligned with GPM DPR radar observations to ensure consistency during training and validation. The training dataset spans June 2022 to September 2023. To ensure data quality and model generalizability, rigorous preprocessing steps were implemented:
1. Invalid Data Filtering: Exclude samples with over 40% NaN values or precipitation slices with entirely zero values.
2. Zenith Angle Thresholding: Remove observations with zenith angles exceeding 65°, as larger angles degrade satellite measurement reliability due to increased atmospheric path length.
3. Label Truncation: Truncate precipitation intensity values at 100 mm/h to mitigate the influence of extreme outliers.
4. Normalization: Input features are normalized to zero mean and unit variance.
5. Sliding Window Sampling: Generate 32×32 pixels samples by sliding a window with a step size of 16, ensuring sufficient training while preserving spatial continuity. The 32×32 pixels input size aligns with typical convective-scale precipitation systems, capturing localized dynamics without excessive computational overhead.

Deep learning model for precipitation detection and retrieval
We employed the UNet and ResUNet architectures for precipitation detection and retrieval tasks, respectively, as illustrated in Fig. 8. Residual Blocks are designed to learn residual mappings, which stabilize training and enhance feature reuse.

The UNet framework implements an encoder-decoder structure and skip connections to preserve multi-scale spatial features. The 32×32 pixels inputs (27 channels) are processed through three hierarchical encoding stages. Each encoder stage employs dual 3×3 convolutions (ReLU-activated) followed by 2×2 max pooling, progressively increasing feature channels (64→128→256). The bottleneck layer expands to 512 channels via two 3×3 convolutions. The decoder is upsampled using the transpose convolution, connecting the skip connections of the encoder outputs and refining the merged features by dual 3×3 convolutions. The final 1×1 convolution layer with sigmoid activation generates 32×32 precipitation probability maps.

The ResUNet model integrates residual blocks within a UNet framework, enhancing feature propagation and mitigating gradient degradation. The encoder-decoder structure comprises four hierarchical levels, each containing two residual blocks with skip connections. Each residual block consists of sequential operations: a 3×3 convolutional layer, batch normalization, and ReLU activation. The encoder progressively downsamples spatial dimensions through max-pooling layers, while the decoder utilizes bilinear upsampling to restore resolution. The final layer applies a 1×1 convolution with sigmoid activation for precipitation probability estimation and linear activation for quantitative retrieval.

For model optimization, we employed the Adam optimizer with hyperparameters meticulously tuned as follows: the initial learning rate was set to α=0.001, balancing convergence stability and gradient descent efficiency; the batch size was configured at 16, a moderate choice that harmonizes computational tractability with gradient estimation reliability. For precipitation detection (a binary classification task with a 0.1 mm/h threshold), we utilized binary cross-entropy (BCE) loss to penalize probabilistic divergence between predicted class likelihoods and labels. For precipitation intensity retrieval, we employed a binary cross-entropy loss. This approach frames the retrieval of intensities exceeding a defined threshold as a probabilistic classification task, which demonstrated superior robustness to the inherent uncertainties in IR retrieval and the highly skewed distribution of precipitation values compared to regression losses (e.g., RMSE). The training epochs were task-specific: the detection model underwent 20 epochs with early stopping to prevent overfitting, while the retrieval model, requiring finer-grained parameter tuning for continuous target approximation, was trained for 150 epochs with periodic validation checks to monitor generalization performance. This dual-stage optimization framework ensures adaptive task-specific learning while maintaining algorithmic consistency, optimizing both discriminative accuracy for detection and parametric fidelity for retrieval. To align with the spatial resolution of GPM DPR precipitation data and minimize invalid values, the input and output dimensions were fixed to 32×32 pixels. This configuration balances computational efficiency and feature extraction capability for localized precipitation patterns. To validate the effectiveness of our approach, we conducted comprehensive comparisons against existing methods (random forests) 42,43 based on GPM DPR over the full-disk area. Our deep learning framework with meteorological field inputs achieved a CSI of 0.38 (at 0.1 mm/h threshold), significantly outperforming both point-to-point random forest approaches (0.31) and satellite-only deep learning configurations (0.33). Notably, even the random forest method augmented with meteorological fields reached only 0.36, underscoring the distinct advantage of our integrated architecture that combines area-based processing with atmospheric profile information.

Deep learning model for satellite nowcasting
We propose a spatiotemporal Vision Transformer-based ResUNet architecture named DaYu-FY for infrared brightness temperature prediction. This model integrates 3D patch embedding, hierarchical attention (via Pyramid Vision Transformer, PVT), and convolutional encoder-decoder blocks.

The DaYu-FY adopts a hybrid encoder-decoder architecture, combining convolutional ResUNet modules with a Transformer-based attention mechanism. The input—a multi-frame, multi-channel spatiotemporal tensor—is initially processed by a cube embedded module, which partitions it into patch-wise tokens. The encoder employs a series of ResNet convolutional layers to extract deep spatial features, while incorporating temporal embeddings. These encoded representations are further processed by a Pyramid Vision Transformer (PVT), which utilizes multi-stage hierarchical attention to capture long-range spatial and temporal dependencies. The decoder path consists of ResNet modules that progressively reconstruct the output, leveraging skip connections from corresponding encoder layers to preserve spatial detail. A final linear projection head followed by reshaping restores the high-resolution prediction, yielding outputs that are spatially aligned with the original input—i.e., infrared brightness temperature predictions.

For model optimization, we employed the AdamW optimizer with a carefully selected initial learning rate of 1×10⁻⁵, promoting stable and gradual convergence, particularly suited to transformer-based models. The loss function is Smooth L1 loss with a small β value of 0.0001, which balances robustness to outliers with sensitivity to fine-grained prediction errors—an effective choice for regression tasks such as brightness temperature forecasting. Training was conducted over 10 epochs, a compact yet adequate schedule for convergence under constrained batch dynamics. To accommodate memory limitations and support high-resolution input, the batch size was set to 1. A StepLR scheduler was utilized with a step size of 1 epoch and a decay factor (γ) of 0.3, allowing for rapid learning rate annealing and improved stability during the later stages of training.

Implement details
All model training and inference were conducted on the Computing for the Future at Fudan (CFFF) high-performance computing platform. Each computing node was equipped with two AMD EPYC 7H12 64-core processors and one NVIDIA RTX A6000 GPU (50 GB memory). The software environment consisted of Ubuntu 22.04 LTS, Python 3.10, PyTorch 2.0.0, and CUDA 11.8. The models were trained using AdamW optimizers, with learning rates of 1×10⁻⁴ for UNet/ResUNet and 1×10⁻⁵ for the DaYu-FY model. Batch sizes were set to 16 (for UNet/ResUNet) and 1 (for DaYu-FY). All experiments were performed under identical environments to ensure reproducibility. Supplementary Table 3 summarizes the computational complexity (FLOPs) and parameter counts (Parameters) of the major sub-models in the RePPIC-Net framework. The UNet and ResUNet modules are responsible for precipitation phase classification and quantitative precipitation retrieval, respectively. The FuXi short model generates real-time atmospheric physical fields, while the DaYu-FY model performs satellite BT extrapolation for precipitation nowcasting. Measured on an NVIDIA A6000 (50 GB) GPU setup, a single end-to-end full-disk (0.05°) inference combining precipitation phase classification and precipitation retrieval takes approximately 77.41 s; The subsequent PLP calculation for phase discrimination requires an additional 2 s. The runtime of the upstream forecasting models that supply inputs is: the FuXi short model takes about 5.1 s to produce a 6-hour forecast, and the DaYu-FY model takes about 2 s to produce a 30 min BT extrapolation. All timings are measured under the hardware/software environment stated in Methods and represent typical inference times for the reported resolution and product. Thus, a real-time forecast can be completed within 2 minutes.

Furthermore, we have provided a detailed explanation on the real-time operational capability of our product. The GPM IMERG-Early product, which requires the integration of multi-orbit and multi-sensor satellite observations and relies on meteorological fields from conventional numerical models, exhibits a latency of approximately 4 h. In contrast, although our model was trained and validated with ERA5 reanalysis data with a one-week delay, it can operationally utilize near-real-time ECMWF Operational Products (Ops), which have a latency of about 8 h. By incorporating the FuXi 12-hour forecast, meteorological fields covering approximately up to 4 h forecasts are obtained. This configuration enables our system to generate real-time precipitation monitoring and 1–3 h forecast products in approximately 2 min of computation.

Evaluation settings
We conducted a comprehensive evaluation of the Real-time Precipitation Phase-Intensity Collaborative Retrieval Network (RePPIC-Net), leveraging multi-source observational data from the GPM IMERG-Late product and China’s national ground stations (comprising over 2000 stations). The validation framework incorporated cross-comparisons with operational precipitation products, including full-disk precipitation intensity analysis against the FY-4B official precipitation products and systematic benchmarking using ground station observations against the GPM IMERG-Late dataset. Targeted assessments of precipitation intensity and phase classification accuracy focused on the low-intensity range (0.1–5 mm/h), as precise phase discrimination at these thresholds carries critical operational implications. This intensity band exhibits heightened sensitivity in meteorological impact differentiation–liquid precipitation within this range corresponds to light/moderate rainfall, while equivalent solid precipitation intensities may represent heavy snow or blizzard conditions.

Training and testing utilized FY-4B satellite observations from China’s latest geostationary meteorological satellite, operational since June 2022. The model was trained on data from June 2022 to September 2023, with independent validation in a subsampled 11-month period (one day every three days) covering two winter seasons (October-March 2023-2024 and 2024-2025), February 2024 was excluded due to satellite orbital adjustments. All reported metrics reflect performance on the held-out test set. Model capabilities were rigorously assessed through: (a) precipitation phase discrimination using CSI, FAR, Frequency Bias (FB), Success Ratio (SR) and Probability of Detection (POD), and (b) precipitation intensity estimation evaluated via Root Mean Square Error (RMSE) 23. Additionally, uncertainties were estimated following the method described in Jolliffe & Stephenson (2011) 44. To holistically assess snowfall and rainfall monitoring performance, we introduced a composite metric, Critical Success Index for Phase and Detection (CSI-PD), defined as:

CSI-PD = P(Phase ∩ Detection) = CSI_P * CSIn_D (2)

where: CSI_P represents phase classification accuracy (snow/rain discrimination), CSIn_D denotes neighborhood-based precipitation detection skill, computed using a 0.5° spatial tolerance to mitigate spatiotemporal localization uncertainties, and CSI-PD quantifies the capability to simultaneously detect precipitation occurrence and correctly classify its phase.

This probabilistic formulation explicitly disentangles phase identification fidelity (CSI_P) from precipitation occurrence detection reliability (CSIn_D). The joint optimization ensures balanced performance in both phase classification and detection tasks. The multiplicative combination enforces joint optimization, ensuring balanced performance across both tasks. The metric inherently penalizes scenarios where algorithms exhibit high precipitation detection rates but poor phase discrimination (or vice versa), precisely aligned with World Meteorological Organization (WMO) operational mandates for integrated precipitation assessment frameworks (WMO, 2023) 45.

Datasets and baselines
China ground station dataset. We utilized high-quality hourly precipitation data from over 2000 national standard meteorological stations in China. These datasets were employed to evaluate the precipitation phase products of RePPIC-Net and conduct comparative analyses with the GPM IMERG-Late products. All data are accessible through the National Meteorological Information Center (NMIC).

FY-4B dataset. We leveraged two datasets from the Fengyun-4B (FY-4B) satellite’s Advanced Geostationary Radiation Imager (AGRI): the full-disk L1 radiation observations and the Level-2 (L2) quantitative precipitation estimation product (FY-4B QPE-official) 46,47, both with a 4 km spatial resolution and a 15 min temporal resolution. We focused on the infrared spectral band from 6.25 to 13.3 μm to ensure continuous day-and-night precipitation monitoring. The FY-4B QPE-official product serves as the baseline for full-disk quantitative precipitation assessment. All FY-4B datasets, including observation data and related products, are available to the global community on the NSMC satellite data server website (http://satellite.nsmc.org.cn).

GPM dataset. The Global Precipitation Measurement (GPM) mission, a joint initiative of the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA), aims to accurately measure precipitation on a global scale.
Dual-frequency Precipitation Radar (DPR), aboard the GPM Core Observatory satellite, is an active radar designed for observing precipitation and its vertical structure. Surface precipitation rates from normal scans (NS) were selected as labels for training precipitation estimation models 11,48.

The Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM Late Run (IMERG-Late) combines various infrared (IR) and microwave (MW) data to provide precipitation estimates at relatively high spatial (0.1°) and temporal (30 min) resolution, with a latency of 14 h 11. IMERG-Late was selected as the reference dataset to evaluate the full-disk performance of the model product. These data are publicly available on the official website of the Goddard Earth Sciences Data and Information Services Center (GES DISC) (https://disc.gsfc.nasa.gov).

DEM (GEBCO_2023)
The GEBCO_2023 Grid is a globally unified elevation model (15 arc second resolution), integrating terrestrial elevation and oceanic bathymetry, jointly developed under the Nippon Foundation-GEBCO Seabed 2030 Project (GEBCO, 2023). This authoritative dataset is publicly available at https://www.gebco.net (accessed 25 November 2023).

Full-disk validation of different satellite precipitation products
To quantify full-disk precipitation retrieval accuracy, we benchmarked RePPIC-Net and FY-4B’s full-disk precipitation products against the multi-satellite GPM IMERG-Late dataset, a state-of-the-art reference for global precipitation estimation. Spatial error analysis (Supplementary Fig. 2) reveals that RePPIC-Net achieves comparable root-mean-square error (RMSE: 0.09) to FY-4B’s operational precipitation product but exhibits distinct regional biases: systematic underestimation near Antarctica and overestimation over oceanic regions. In contrast, FY-4B demonstrates pronounced precipitation overestimation at high southern latitudes and across the disk’s eastern quadrant—a pattern likely associated with increased atmospheric path length and signal attenuation at oblique viewing angles, which degrade radiometric fidelity. When evaluated against GPM IMERG’s surface precipitation rates and phase classifications (Supplementary Table 4), RePPIC-Net outperforms FY-4B in both rainfall and snowfall retrievals, particularly reducing the RMSE for snowfall by 58% (0.19 vs. 0.46). This performance gap persists despite excluding phase discrimination capabilities, underscoring RePPIC-Net’s superior capacity to resolve precipitation intensity across hydrometeor types. These results position RePPIC-Net as a robust framework for full-disk precipitation retrieval, especially for snowfall estimation.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of manuscript “Snow or Rain? Hybrid AI Deciphers Surface Precipitation from Satellite” submitted to Nature\nCommunications\nGeneral Comments\nThis manuscript deals with a new methodology to nowcast surface precipitation phase based on a hybrid AI framework.\nOverall the topic, development, and results are well suited for this journal. However, a number of clarifications and, mostly\nformal, corrections are needed to be considered acceptable for publication. I cannot recommend to publish it in its current\nform and I recommend a major review. Please see below a list of specific comments.\nSpecific Comments\n1. Page 1, line 1. Suggest: Precipitation -> Precipitation Phase\n2. Page 1, line 19. Please check English: avalanche -> avalanches\n3. Page 2, line 22 (and 25). Please check English: phases -> phase [typically singular is used in this type of sentences]\n4. Page 2, line 31. Please check English: ever-first -> first-ever\n5. Page 2, line 31-32. Please rephrase: aiding the need\n6. Page 2, line 38. Suggest rephrasing: transient snow-to-rain transitions\n7. Page 5, lines 98-99. Are temperature and relative humidity 12-hour forecast only at surface (or near surface) level? It is not\nclear why below ‘vertical atmospheric profiles’ are mentioned – these profiles could indeed include temperature and relative\nhumidity. Please rewrite to improve to clarify this sentence.\n8. Page 5, line 100. Authors identify here the term Twet with wet-bulb temperature, but later in the text Tw is used instead.\nPlease be consistent.\n9. Page 8. Is the CSI-PD formula equivalent to compute the CSI for phase type whenever precipitation is detected? Please\nclarify in text.\n10. Page 8, lines 151-152: “where: <cr> CSIp” -> “where CSIp” [remove colon and carriage return]\n11. Page 8, line 161. Could authors please add a reference regarding the WMO mandate mentioned?\n12. Page 9, first paragraph. Some verification score values are listed here with 3 decimal digits, unlike in other parts of the\ntext. I suggest always using the same number of decimal digits and also to include uncertainties for each score value –\ncomputation formulas for verification score uncertainties can be found, for example, in Jolliffe & Stephenson (2011). A\nsummary table showing both scores and uncertainties can be included in this section, or in an appendix. Complementary\nerror bars to some plots such as Fig. 2b.\n13. Page 10, Fig. 2b. Please be consistent with notation of variables: for example, CSI-P was introduced in line 150 with p\nas a subscript – similarly for others. Please check and correct.\n14. Page 11, lines 206-208. Please add the year of the dates mentioned.\n15. Page 12, Fig. 3 (and Fig. 7 too). Typo (twice in Fig. 3): IMERGE.\n16. Page 12, Fig. 3. Colour bar title: Precipitation [mm/h]; is it Precipitation rate or Rainfall rate? Similarly, Snowfall, shouldn’t\nbe Snowfall rate?\n17. Page 13, lines 239-240. Please check ‘integrating vertical meteorological fields’ do you mean ‘integrating profiles of\nmeteorological variables’?\n18. Page 14, Fig. 4 caption. Please add a full stop.\n19. Page 21. To better connect the current study with existing literature, either in this section or elsewhere, authors could\nmention other studies describing methodologies to classify surface precipitation phase – examples could be, for instance,\nCasellas et al. (2021), Gao et al. (2024) or Øydvin et al. (2025) – of course others are possible. Additionally, authors should\nalso comment, at least briefly, the recent article by Jennings et al. (2025) which is closely related with the manuscript\nsubmitted.\n20. Page 23, first paragraph. The validation period (Oct-2023 to Jan-2024) did present comparable characteristics to the\ntraining period (Jun-2022 to Sep-2023)? Please comment and if necessary add a brief justification in the text.\n21. Page 25, line 481. Please indicate here at which rainfall rate (or rates) was set the precipitation detection threshold.\n22. Page 30, line 598. Fig. 9 not found.\n23. Page 32, line 637. Is the ‘monitoring evaluation period’ the ‘validation’ period? Please use the same terms to avoid\npossible confusions.\n24. Page 32, line 650 (and 652, 654-655). Please check English: were downloaded and downloadable?\n25. Page 36, Fig. 6. Please add in the figure caption the IMERG version (Late?) as in Fig. 8.\n26. Page 37, Fig. 7 caption. The content of panels a) and b) should be indicated.", "reviewer_2": "The motivation of the work is highly significant operational meteorology, hydrology, and disaster management because\nReal-time, accurate precipitation phase distinction is a long-standing global challenge, especially in data-sparse\nmountainous and high-latitude regions where hazards like avalanches and blizzards are common.\nThe paper proposes an interesting combination of AI architectures, fusing physics-informed meteorological fields from a\nrapid AI weather model (FuXi) with high-resolution satellite observations and a nowcasting model.\nThe main results in the paper are that the authors want to develop a system for real-time operations that achieves\ncomparable rain-detection performance between their model and IMERG-Late with CSI scores of 0.95 and 0.96 and similar\nbias in snowfall overestimation and rainfall underestimation. In a given case study, the authors demonstrate the superior skill\nof the model in predicting a high impact snow blizzard, outperforming IMERG and FY-4B QPE product.\nMajor Critical Comments:\n(1) The proposed training dataset seems quite small, training over June 2022 to September 2023, which covers likely only\none snowfall season and one rainfall season, so it’s unclear if there was any overfitting of the model.\n(2) When looking at the evaluation time period of October 2023 to January 2024, that again seems very small to draw any\nconcrete conclusions from. It covers only the snowfall period and even then extending the evaluation period to include up\nuntil March 2024 would be better.\n(3) The evaluation should also cover the rainfall season so that we can understand any False alarm ratios better in cases\nwhen there is no rainfall as the model could be overfitting.\n(4) The paper claims to be highly performant for extreme precipitation events but only shows CSI for values up to 5mm/hr\nand only showcases one case study for a high impact event. From this case study it’s really hard to draw any conclusions\nthat the system is indeed highly performant over different scenarios.\n(5) Clarification of the \"Real-Time\" Capability: The central claim of the paper is its \"real-time\" operational capability, which is\nhighlighted as a key advantage over systems with a \"4-hour latency\" . However, the methods section states that the FuXi\nmodel is \"initialized with ERA5 data (about 8-hour latency)\". This creates a significant contradiction. While the FuXi model's\nforecast generation is computationally fast (\"within seconds\" ), its dependency on an 8-hour-old initial state seems to\nundermine the \"real-time monitoring\" claim. The authors must clarify this point. Is the system truly real-time, or is it more\naccurately described as a rapid-update system with an 8-hour data lag? A detailed discussion of how this latency in the\ninput data affects the final product's timeliness and accuracy is essential for the paper's credibility.\n(6) The paper claims accuracy at 0.05 degree spatial resolution but all results are against IMERG data which is at 0.01\ndegree resolution, so it’s unclear what the validation mechanism is for accuracy at 0.05 degrees.\n(7) The authors introduce a novel and useful composite metric, the Critical Success Index for Phase and Detection (CSI-PD),\ndefined as CSIP​⋅CSIDn​. While the rationale is explained as penalizing models that are good at one task but not the other ,\nthe specific choice of multiplication versus other methods of combination (e.g., a weighted average) is not justified. A brief\nsentence explaining why this multiplicative, probabilistic formulation was chosen would strengthen the methodological\ncontribution.", "reviewer_3": "Reviewer Report\nSummary:\nThe manuscript addresses the real-time monitoring of precipitation phase and intensity by\nintegrating AI-driven atmospheric forecasts (FuXi), geostationary satellite observations (FY-4B),\nand auxiliary geolocation information. The authors employ a U-Net architecture for phase\ndiscrimination and a ResU-Net for intensity estimation, trained using GPM DPR products as\nreference labels. The topic is timely and relevant for advancing real-time precipitation monitoring,\nespecially over data-sparse regions such as the Tibetan Plateau.\n\nHowever, the manuscript has major weaknesses in terms of novelty, methodological rigor, and\nthe positioning for a high-impact science journal of Nature Communications. Below are the\nspecific points of evaluation.\n\nStrengths:\n1. Timely Topic: Real-time phase-intensity discrimination is highly relevant for both weather\nand climate communities. Performance evaluation over challenging environments such as the\nTibetan Plateau adds practical value and scientific interest.\n2. Integration of Multiple Data Sources: Combining AI-driven atmospheric forecasts (FuXi),\ngeostationary observations (FY-4B), and probabilistic classification methodology shows an\nattempt toward an integrated solution.\n3. Operational Relevance: Emphasis on low-latency capability (leveraging FuXi forecasts and\nFY-4B observations), and short-term precipitation nowcasting (1–3 hours lead time) enhances\nthe practical utility of the system.\n\nMajor Concerns:\n1. Novelty and Conceptual Contribution\nThe core methodological contributions appear incremental rather than transformative. The use of\nUnet and ResUNet architectures for precipitation phase and intensity estimation is well-established\nin remote sensing and computer vision domains, and binary cross-entropy loss is a standard\napproach for classification problems. Furthermore, the phase classification algorithm relies heavily\non the existing GPM-IMERG PLP framework, raising concerns about whether the approach\nrepresents a novel methodology or a repurposing of an established algorithm. The study risks being\nperceived as an application note rather than a fundamental advance.\n\n2. Dependence on Latency and Real-Time Feasibility\nThe proposed system claims to enable real-time phase discrimination by combining FuXi forecasts\n(with ERA5 initialization) and geostationary satellite data. However, ERA5 initialization introduces\nan approximate 8-hour latency, which contradicts the claim of real-time operation. This undermines\nthe practicality and uniqueness of the proposed framework compared to operational systems.\nAdditionally, the reliance on FuXi’s 12-hour forecasts and the inherent uncertainties in short-term\natmospheric model outputs could introduce systematic biases, although there are optimization\ncorrections, it has not fully addressed in the manuscript. The authors should rigorously quantify how\nforecast errors propagate into phase discrimination and intensity estimation, particularly under\nextreme or rapidly evolving weather events.\n\n3. Validation Strategy and Robustness of Results\nThe study uses GPM DPR as the reference for both precipitation phase and intensity, but this raises\nseveral concerns. First, GPM DPR’s revisit time and sampling limitations make it an imperfect\nground truth for evaluating full-disk geostationary-based predictions. Second, the performance\nevaluation primarily focuses on statistical accuracy over large areas and special regions (e.g., the\nTibetan Plateau), but there is limited analysis of failure cases or uncertainty quantification in\nheterogeneous terrains and convective systems. Furthermore, the validation appears superficial and\nlacks comprehensive metrics, and temporal consistency checks. Without robust uncertainty analysis,\nthe reliability of the integrated system for operational purposes remains unclear.\n\n4. Positioning Against Existing Systems\nThe manuscript compares the proposed system to FY-4B and GPM IMERG-Late products, but these\ncomparisons are primarily descriptive and do not convincingly demonstrate a clear performance\nadvantage in terms of accuracy, latency, or operational applicability. The added value of integrating\nFuXi AI-driven forecasts with satellite observations needs to be explicitly quantified against state-\nof-the-art precipitation phase/intensity retrieval algorithms. In its current form, the manuscript does\nnot sufficiently justify why the approach is superior to existing multi-source fusion techniques,\nlimiting its perceived novelty and impact.\n\n5. Scientific Insight vs. Algorithmic Engineering\nWhile the technical implementation is sound, the study reads more like an engineering solution for\noperational nowcasting rather than a contribution that advances scientific understanding of\nprecipitation processes. The manuscript lacks physical interpretability and mechanistic insight.\nDoes FuXi better capture the 0°C isotherm or wet-bulb temperature structure? How does this\nimprovement manifest across different synoptic regimes, e.g., warm vs. cold precipitation systems,\nfreezing rain events, orographic snowfall? The authors could strengthen its scientific contribution\nby analyzing how integrated atmospheric forecasts and satellite signals improve phase\ndiscrimination under challenging physical conditions (e.g., mixed-phase precipitation, topographic\ninfluence on snow/rain transition), or by proposing a novel interpretable AI architecture informed\nby atmospheric physics. Currently, the discussion of underlying physical processes and their\ninteraction with the AI-driven framework is limited.\n\nMinor Comments:\n1. The overall organization of the manuscript requires further improvement. The manuscript’s\nstructure is somewhat disorganized, and the presentation lacks clarity. Some content is repeated\nacross sections, giving the text a redundant or “AI-generated” feel. In addition, some section of the\nmanuscript is described in a way that is not sufficiently detailed or precise, making it challenging\nfor readers to fully understand the methodology, assumptions, and experimental procedures.\n2. Using binary cross-entropy for precipitation intensity estimation is conceptually flawed, as\nprecipitation intensity is inherently a continuous variable with a strongly right-skewed and zero-\ninflated distribution. Converting it into discrete classes for classification and applying binary or\nmulti-class cross-entropy (BCE) results in a loss of important continuous information. This\ndiscretization introduces sensitivity to arbitrary thresholds and can lead to calibration biases,\nultimately limiting the model’s ability to accurately represent the true intensity distribution."}, "author_response": "Dear Reviewers,\nWe sincerely thank you for your insightful comments and valuable suggestions on\nour manuscript NCOMMS-25-50501A (originally titled “Snow or Rain? Hybrid AI\nDeciphers Surface Precipitation from Satellite”). In response to your feedback, we have\ncomprehensively revised and improved the manuscript, with the following key\nenhancements:\nFirstly, following your advice, we have refined the title to “Snow or Rain? Hybrid AI\nDeciphers Surface Precipitation Phase from Satellite” to better focus the scope of the\nstudy. Then, we have significantly extended the evaluation period (from October–\nMarch across 2023–2025) to ensure adequate representation of snowfall events,\nthereby enabling more robust assessment. We have also optimized the evaluation\nmetrics and strengthened the physical interpretation section to provide more\ncomprehensive analysis and insights. Moreover, we have clarified the real-time\napplicability of our model: ERA5 reanalysis data was used during model training and\nvalidation, near-real-time ECMWF operational analysis products with an 8-hour latency\ncan be employed as meteorological inputs in operational applications. Our results\ndemonstrate that the proposed RePPIC-Net model can generate the first phase-\ndiscriminable 1–3 hour precipitation forecast within 2 minutes, addressing a critical gap\nin operational forecasting.\nIn particular, to strengthen the rigor of our evaluation and enhance physical\ninterpretability, we have implemented the following three major improvements:\n1. Quantified uncertainties in precipitation phase identification and detection, and\nadded a new section (“The spatiotemporal error distributions”) to systematically\nanalyze the error distribution of the model;\n2. Included a new section (“Relationship between meteorology and precipitation\nphase”) to enhance the physical interpretability of the model and analyze the\nphysical mechanisms by which meteorological variables trigger precipitation;\n3. Added comparisons with state-of-the-art algorithms to highlight the novelty of\nour approach, and quantified the performance gains achieved by incorporating\nmeteorological inputs.\nWe believe that the revisions made in response to your expert suggestions have\nsignificantly enhanced the reliability and depth of the study’s conclusions, and we kindly\nrequest your re-evaluation of the revised manuscript. In this response letter, our\nresponse is presented in blue, and any direct quotations from the revised manuscript\nare underlined.\nThe authors are thankful to the reviewers for their valuable comments. Here is\nour detailed point-by-point responses…\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank authors for the corrected version and the detailed item-by-item response provided. I think the new corrected version fulfilled all items raised so it can now be accepted for publication.", "reviewer_2": "Thanks for providing detailed responses to the reviewer comments and updating the manuscript accordingly. Based on the updates, the paper seems to have been significantly stregnthened with robust results. The authors have effectively addressed the critical concerns regarding dataset size, operational latency, and physical interpretability.\nThe paper introduces RePPIC-Net, a hybrid AI framework that fuses 3D atmospheric physics from the FuXi model with geostationary satellite observations to quantify surface precipitation phase . This system represents a \"first-ever\" achievement in satellite-based nowcasting, offering a scalable template for global early warning systems.\nThe following key enhancements and justifications characterize this final version:\n1. The authors have convincingly resolved concerns regarding operational latency. By initializing the FuXi model with ECMWF Operational Products, the system bridges data lags to generate 0–3 hour nowcasts with a total computational time of approximately 2 minutes.\n2. The evaluation framework was significantly expanded to an 11-month period covering two complete winter seasons (2023–2025) . Accuracy at the 0.05° spatial resolution was independently validated against more than 2,000 national standard meteorological stations in China.\nI recommend an acceptance of this work based on it's impact during wintertime in data-sparse regions, and operational qualities."}, "author_response": "We sincerely thank you for your careful review and for confirming that our revised manuscript has adequately addressed all previous concerns. We are grateful for your time and insightful input throughout this process.\nWe thank you for your thoughtful assessment and for recognizing the improvements made to the manuscript. We are pleased that the clarifications regarding operational latency, evaluation framework, and physical interpretability have strengthened the paper.\nWe agree that providing clear and accessible code is important for reproducibility and community use. We have updated the README file in the repository to better describe the ML architecture, training workflow, and how users can adapt the framework for their own data or regions. Also, the code repository has also been archived on Zenodo to ensure long-term availability and citability. The DOI is: https://doi.org/10.5281/zenodo.18357366\nWe believe these steps enhance the utility and transparency of the code for researchers interested in applying or extending the RePPIC-Net framework.\nThank you once again for your supportive and encouraging recommendation."}]

Decision letter