Pith. sign in

REVIEW 4 major objections 6 minor 37 references

Data-driven Precipitation Nowcasting Using Satellite Imagery

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Rain up to six hours ahead can be predicted at 2 km resolution from geostationary satellite imagery alone, without radar or numerical weather models.

desk verdict Useful dataset and a credible two-stage satellite-to-radar nowcaster for Korea, but the radar-free generalization claim outruns the evidence. read the letter →

arxiv 2412.11480 v1 pith:36ZQMHX6 submitted 2024-12-16 cs.CV eess.IV

Young-Jae Park , Doyi Kim , Minseok Seo , Hae-Gon Jeon , Yeji Choi This is my paper · ORCID
classification cs.CVeess.IV
keywords precipitationnowcastinggeostationarysatelliteimageryinfraredchannelswatervaporvideopredictionimage-to-imagetranslationcriticalsuccessindexradar-freeforecasting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that short-range precipitation nowcasting can be done from geostationary satellite imagery alone, at $2~\mathrm{km}$ resolution with hourly updates out to six hours, without ground radar or numerical weather model inputs. The authors build the Neural Precipitation Model (NPM), a two-stage model that first predicts how precipitating clouds will move and grow in infrared and water-vapor channels, then translates the predicted cloud fields into radar-style rain-rate maps with a generative adversarial network. On their Sat2Rdr benchmark over the Korean Peninsula, NPM reports higher Critical Success Index scores than six video-prediction baselines across light, moderate, and heavy rain thresholds, and a zero-shot flood case in a radar-free region shows the predicted rain pattern matching independent estimates. If those results transfer to other regions, the approach would give countries without radar networks a real-time flood-warning tool at a fraction of the cost of ground-based infrastructure.

What carries the argument

The machinery is an end-to-end two-stage generative pipeline. The first stage is a satellite video prediction model: an encoder-translator-decoder network whose translator is built from spatio-temporal large-kernel attention blocks, taking six historical satellite frames and forecasting the next six, with a temporal-consistency constraint that penalizes physical discontinuities between consecutive predicted frames. The second stage is a satellite-to-radar image translation model: a conditional GAN whose generator is trained with a combination of MSE and adversarial loss to turn each predicted satellite frame into a radar precipitation map. Two conditioning devices carry the temporal information the satellite channels lack: sinusoidal day-of-year and hour-of-day positional embeddings, and a season-aware sampling strategy that draws equal numbers of training samples from each calendar month. The paper's ablation shows the day embedding is the single largest performance contributor.

What would settle it

Take the released NPM checkpoint and evaluate it zero-shot on a radar-equipped region outside East Asia, such as a European winter or a South Asian monsoon, computing the Critical Success Index at $1~\mathrm{mm/h}$ and $8~\mathrm{mm/h}$ thresholds for six-hour lead times; if the scores fall below a persistence or climatology baseline, the transfer assumption that the radar-free claim rests on is refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that precipitation nowcasting can be re-framed as a satellite-only, radar-free task: first forecast the future cloud field, then decode rainfall from it. Using three geostationary channels—infrared at $10.5~\mu\mathrm{m}$, upper water vapor at $6.3~\mu\mathrm{m}$, and lower water vapor at $7.3~\mu\mathrm{m}$—plus a digital elevation model, NPM predicts rain-rate maps at $2~\mathrm{km}$ grid resolution for lead times of one to six hours. On the Sat2Rdr test set, NPM beats six video-prediction baselines on the Critical Success Index at $1~\mathrm{mm/h}$, $4~\mathrm{mm/h}$, and $8~\mathrm{mm/h}$ thresholds, and it is the only method to keep a nonzero score for heavy rain at six hours. In a zero-shot North Korea flood event with no local radar, the model placed heavy rain over the affected river basin, predicting $27.45~\mathrm{mm}$ of six-hour accumulation against $46.49~\mathrm{mm}$ from an independent satellite product and roughly $60~\mathrm{mm}$ observed, showing systematic underprediction but correct location and timing.

Load-bearing premise

The paper acknowledges it still needs satellite data; the deeper load-bearing premise is that the cloud-to-rain mapping learned from Korean radar labels transfers to other climates and regions without retraining.

Editorial extensions

If this is right

  • A region with geostationary satellite coverage but no radar could generate its own $2~\mathrm{km}$, hourly-updated rainfall forecasts from public satellite data and elevation, with no supercomputing infrastructure.
  • The two-stage design predicts cloud motion before decoding rain, so precipitation forming from clouds with no current radar echo can still be anticipated, something radar-only nowcasting cannot do.
  • Underprediction of event totals in the zero-shot flood case means the outputs are more suitable for triggering early warnings than for quantifying flood volumes.
  • Seasonal conditioning is the strongest model component: adding day embeddings produces the largest jump in Critical Success Index in the ablation, so future satellite-nowcasting models should encode time-of-year explicitly.
  • Heavy rain at $8~\mathrm{mm/h}$ remains a hard target for every baseline at long lead times, and NPM's nonzero scores there, though small, suggest the satellite predictors plus seasonal conditioning add genuine information rather than noise.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the Korean-trained cloud-to-rain mapping transfers, the same checkpoint could serve as a free nowcasting layer for flood-prone regions in the developing world, with local gauge data used only for calibration; this extends the paper's motivation rather than a claim it tests.
  • Because changing the day embedding alone changes the predicted cloud field, the model could be probed counterfactually to ask how the same synoptic situation might rain in different seasons, a climate-sensitivity experiment the paper does not run.
  • The intermediate cloud-prediction representation could be reused for other radar-blind products, such as snowfall detection or hail proxies, since the first stage is modality-agnostic and only the decoder is rain-specific.
  • A direct extension would be to train the second stage separately on regions with sparse radar while keeping the first stage shared, yielding a semi-supervised recipe for expanding coverage without any new radar installations; the paper does not explore this split.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes the Neural Precipitation Model (NPM), a two-stage deep-learning system for precipitation nowcasting from geostationary satellite imagery. Stage 1 is a video prediction model that forecasts future satellite frames (IR 10.5 μm, WV 6.3 μm, WV 7.3 μm, plus DEM) using a spatio-temporal large-kernel attention architecture with day/hour positional embeddings and a temporal consistency loss. Stage 2 translates the predicted satellite frames to radar precipitation maps using StegoGAN. The authors introduce the Sat2Rdr dataset, built from GK2A satellite data and 10 Korean ground radars from 2019 to 2024, and evaluate CSI at 1, 4, and 8 mm/h thresholds for lead times up to 6 hours. They report consistent improvements over six OpenSTL video prediction baselines, provide ablations, and present a North Korea flood case study as evidence of zero-shot operation in regions without radar. Code and dataset are promised publicly.

Significance. If the reported results are robust, the paper makes a useful contribution to satellite-only precipitation nowcasting, an important direction for regions lacking ground radar infrastructure. The main strengths are: (1) a new publicly intended dataset pairing geostationary satellite channels with radar ground truth over Korea; (2) a clean two-stage framework that decouples cloud-motion prediction from satellite-to-radar mapping; (3) an ablation showing that day/hour embeddings substantially improve skill, which is a practically relevant finding; and (4) reproducible baselines in the OpenSTL framework. The key risk is that the paper's central practical claim—radar-free nowcasting for developing countries—is only tested in one zero-shot case using a satellite-based reference, not independent surface observations. The evidence on the Korean test domain is credible, but the geographic generalization claim needs more support or a more cautious framing.

major comments (4)
  1. [Flood Case Study in North Korea] The only evidence for the zero-shot generalization claim is the July 2024 North Korea flood case. The quantitative comparison there is against NASA GPM IMERG-late, which is itself a satellite-based precipitation retrieval that relies in part on infrared brightness-temperature relationships, and the paper explicitly states that 'due to observation limitations, it is not possible to confirm the actual ground precipitation amount in the masked area.' Thus the case study does not validate surface precipitation in a radar-free region. Because the abstract and conclusions claim that NPM enables precipitation forecasting in regions without radar, this is load-bearing evidence, and the current support is insufficient. The authors should either validate on independent observations (e.g., rain gauges, or another country's radar network after retraining or as a transfer test) or explicitly reframe the North Korea result as an illustrative qualitative example rather than a demonstration of generalization.
  2. [Temporal Consistency Constraint, Eq. (8)] The symbol sigma in Eq. (8) is never defined. The equation writes a KL divergence between sigma(Delta Y_hat_i) and sigma(Delta Y_i), but it is unclear whether sigma is a softmax over space, a fixed-variance Gaussian likelihood, or some other normalization. Without a definition, the loss function is not reproducible and the claim that this term penalizes discontinuity between frames is not fully specified. Please define sigma and state exactly how the KL divergence is computed over the spatial dimensions.
  3. [Qualitative Results, Figure 4] The paper states 'the 6-hour average CSI of our approach is 0.29, demonstrating its ability to outperform PreDiff' after noting that PreDiff reports 0.14 for 1 mm precipitation over 6 hours. This comparison is not a controlled experiment: PreDiff is a radar-to-radar model evaluated on a different task and dataset (SEVIR-style radar input), not adapted and evaluated on Sat2Rdr. The numbers are therefore not directly comparable. Either adapt PreDiff (or another radar-to-radar model) to the satellite-input setting and include it in Table 1, or clearly label the 0.14 as an external reference not comparable to the Sat2Rdr results.
  4. [Main Results, Table 1] The central quantitative claim—that NPM outperforms six baselines—is based on a single train/test split with no confidence intervals, bootstrap resampling, or significance tests. This matters particularly for CSI 8 mm, where many scores are 0.00 or near zero, and for the differences between NPM and the best baseline (often 0.01–0.03). Without uncertainty estimates, it is difficult to determine whether the reported gaps are statistically meaningful. Please provide error bars over multiple evaluation runs or resamples, or a small number of temporal test folds.
minor comments (6)
  1. [Conclusions] There is a typo in 'anyy radar coverage' in the final paragraph; also, the dataset is called 'Sat2Radar' here but 'Sat2Rdr' elsewhere—please standardize the name.
  2. [Qualitative Results] The first sentence of the Qualitative Results section says 'Figure 4 shows a quantitative comparison,' but the figure presents qualitative visualization; please change 'quantitative' to 'qualitative'.
  3. [Satellite Prediction Model, Eq. (5)] In the positional encoding formula, the dimension variable d is not explicitly defined; clarify whether d is the embedding dimension of the concatenated day/hour vector or the dimension of each individual scalar embedding.
  4. [Sat2Rdr Dataset] The dataset description says the data spans September 2019 to June 2024, while the text later says training is from September 2019 to June 2023 and the test set is July 2023 to June 2024; please make the temporal split explicit and consistent.
  5. [Season-aware Sampling Strategy] Equation (3) states that each month is sampled with probability 1/12, but the following sentence mentions oversampling for underperforming months; the oversampling procedure is not formalized or evaluated, so please clarify whether it is used in the reported experiments or only suggested as future work.
  6. [Limitation] The Limitation section acknowledges the reliance on satellite imagery but does not mention the more important limitation that the satellite-to-radar mapping is trained only on Korean radar labels; an explicit statement about the geographic transferability assumption would improve transparency.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: NPM's satellite-to-radar predictions are evaluated against held-out radar ground truth, and no fitted parameter is renamed as a prediction.

full rationale

The paper's derivation chain is self-contained and does not reduce to its inputs. Stage one trains a video prediction model F_theta on satellite frames with an MSE plus temporal-consistency loss (Eq. 9), and stage two trains a satellite-to-radar generator G_Phi against radar ground truth with MSE plus adversarial loss (Eq. 2); neither loss defines the target metric or the held-out test labels. The CSI evaluation (Eq. 10) is computed against the reserved July 2023-June 2024 radar test period, so the headline Table 1 result is not forced by construction. The ablation study in Table 4 adds components and measures their marginal effect, which is a genuine empirical contribution rather than a circular restatement. The self-citations to LT3P (Park et al. 2024) and DGDM (Yoon et al. 2023) appear only in the related-work survey and are not load-bearing: the method's architecture relies on external works such as SimVP, large-kernel attention, StegoGAN, and SEVIR. The North Korea flood case study is explicitly qualified by the authors, who write that 'due to observation limitations, it is not possible to confirm the actual ground precipitation amount in the masked area'; comparing against IMERG, itself a satellite-based retrieval, weakens the external-validation claim but does not make the derivation circular. The central scientific question of whether the Korean-trained IR/WV-to-rainfall mapping transfers to radar-free regions is an external-validity risk, not a circularity in the paper's derivation. No equation reduces to a fitted value, and no prediction is equivalent to its training target by definition.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on two fitted loss weights and a set of domain assumptions about satellite-to-radar correspondence. No new physical entities are introduced; the only invented artifact is the Sat2Rdr dataset, which is a data contribution rather than an entity.

free parameters (2)
  • lambda (adversarial loss weight) = not reported
    Balances MSE and GAN loss in Eq. 2; chosen by hand, value not given.
  • alpha (temporal consistency weight) = 0.1
    Weight on the KL regularization in Eq. 9; reported in Implementation Details.
assumptions (5)
  • domain assumption Satellite IR 10.5 um and WV 6.3/7.3 um brightness temperatures are informative proxies for cloud properties and surface precipitation.
    Invoked in the Introduction and Method; based on classical satellite rainfall estimation literature such as Arkin 1979 and PERSIANN.
  • domain assumption The merged radar product from 10 Korean stations is a reliable ground truth for training and CSI evaluation.
    Used throughout experiments; radar QPE errors are not quantified in the paper.
  • domain assumption GK2A satellite and KMA radar frames are correctly registered and matched in time.
    Dataset construction states 'merged, registered, and matched' without validation details.
  • ad hoc to paper Day and hour positional embeddings let the model infer seasonal and diurnal precipitation patterns.
    Introduced as a solution to missing NWP inputs; no proof or analysis that embeddings capture these patterns beyond the ablation.
  • ad hoc to paper The sigma mapping in Eq. 8 converts frame differences into comparable distributions for a KL divergence.
    Sigma is not defined; the KL interpretation is asserted without specifying the probability distributions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Data-driven Precipitation Nowcasting Using Satellite Imagery." pith.science (2026). https://pith.science/paper/36ZQMHX6

@misc{pith2026241211480,
  author       = {Pith},
  title        = {Pith review of: Data-driven Precipitation Nowcasting Using Satellite Imagery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/36ZQMHX6}},
  note         = {Machine review of arXiv:2412.11480}
}
abstract

Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are space-constrained and have high maintenance costs. Consequently, most developing countries depend on a global numerical model with low resolution, instead of operating their own radar systems. To mitigate this gap, we propose the Neural Precipitation Model (NPM), which uses global-scale geostationary satellite imagery. NPM predicts precipitation for up to six hours, with an update every hour. We take three key channels to discriminate rain clouds as input: infrared radiation (at a wavelength of 10.5 $\mu m$), upper- (6.3 $\mu m$), and lower- (7.3 $\mu m$) level water vapor channels. Additionally, NPM introduces positional encoders to capture seasonal and temporal patterns, accounting for variations in precipitation. Our experimental results demonstrate that NPM can predict rainfall in real-time with a resolution of 2 km. The code and dataset are available at https://github.com/seominseok0429/Data-driven-Precipitation-Nowcasting-Using-Satellite-Imagery.

Figures

Figures reproduced from arXiv: 2412.11480 by the authors.

Figure 1
Figure 1. (a) Our +6 hour forecasting results without radar. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of NPM Architecture. First, the Satellite Prediction Model takes season-aware sampled satellite sequences [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Performance comparison of CSI 1 mm, CSI 4 mm, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comparison between the Radar2Radar state-of-the-art method, PreDiff, and our Sat2Rdr approach. (a) shows ours, [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Precipitation forecasting results of 2024-07-26 heavy rainfall case in North Korea. (a) is the prediction result of NPM, [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Comparison of Day embedding. (a) shows the re [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 27 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Andrychowicz, M.; Espeholt, L.; Li, D.; Merchant, S.; Merose, A.; Zyda, F.; Agrawal, S.; and Kalchbrenner, N. 2023. Deep learning for day forecasts from sparse observations. arXiv preprint arXiv:2306.06079

    arXiv 2023

  4. [4]

    Arkin, P. A. 1979. The relationship between fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Monthly weather review, 107(10): 1382--1387

    work page 1979

  5. [5]

    Bi, K.; Xie, L.; Zhang, H.; Chen, X.; Gu, X.; and Tian, Q. 2023. Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619(7970): 533--538

    2023

  6. [6]

    Chen, L.; Cao, Y.; Ma, L.; and Zhang, J. 2020. A deep learning-based methodology for precipitation nowcasting with radar. Earth and Space Science, 7(2): e2019EA000812

    work page 2020

  7. [7]

    C.; Alexander, C

    Dowell, D. C.; Alexander, C. R.; James, E. P.; Weygandt, S. S.; Benjamin, S. G.; Manikin, G. S.; Blake, B. T.; Brown, J. M.; Olson, J. B.; Hu, M.; et al. 2022. The High-Resolution Rapid Refresh (HRRR): An hourly updating convection-allowing forecast model. Part I: Motivation and system description. Weather and Forecasting, 37(8): 1371--1395

    work page 2022

  8. [8]

    Gao, Z.; Shi, X.; Han, B.; Wang, H.; Jin, X.; Maddix, D.; Zhu, Y.; Li, M.; and Wang, Y. B. 2024. Prediff: Precipitation nowcasting with latent diffusion models. Proceedings of the Neural Information Processing Systems (NeurIPS)

    work page 2024

Show all 37 references
  1. [9]

    Gao, Z.; Tan, C.; Wu, L.; and Li, S. Z. 2022. Simvp: Simpler yet better video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2022

  2. [10]

    Gruca, A.; Serva, F.; Lliso, L.; R \' podas, P.; Calbet, X.; Herruzo, P.; Pihrt, J.; Raevskyi, R.; S im \'a nek, P.; Choma, M.; et al. 2023. Weather4cast at neurips 2022: Super-resolution rain movie prediction under spatio-temporal shifts. In NeurIPS 2022 Competition Track. PMLR

    2023

  3. [11]

    L.; and Thome, N

    Guen, V. L.; and Thome, N. 2020. Disentangling physical dynamics from unknown factors for unsupervised video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2020

  4. [12]

    Guo, M.-H.; Lu, C.-Z.; Liu, Z.-N.; Cheng, M.-M.; and Hu, S.-M. 2023. Visual attention network. Computational Visual Media, 9(4): 733--752

    2023

  5. [13]

    Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Hor \'a nyi, A.; Mu \ n oz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999--2049

    2020

  6. [14]

    J.; Adler, R

    Huffman, G. J.; Adler, R. F.; Bolvin, D. T.; and Nelkin, E. J. 2010. The TRMM multi-satellite precipitation analysis (TMPA). Satellite rainfall applications for surface hydrology, 3--22

    2010

  7. [15]

    Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2017

  8. [16]

    Lam, R.; Sanchez-Gonzalez, A.; Willson, M.; Wirnsberger, P.; Fortunato, M.; Alet, F.; Ravuri, S.; Ewalds, T.; Eaton-Rosen, Z.; Hu, W.; et al. 2023. Learning skillful medium-range global weather forecasting. Science, 382(6677): 1416--1421

    2023

  9. [17]

    Larvor, G.; Berthomier, L.; Chabot, V.; Le Pape, B.; Pradel, B.; and Perez, L. 2020. Meteonet, an open reference weather dataset. https://meteonet.umr-cnrm.fr/

    2020

  10. [18]

    A.; Simmons, A.; Vamborg, F.; and Rodwell, M

    Lavers, D. A.; Simmons, A.; Vamborg, F.; and Rodwell, M. J. 2022. An evaluation of ERA5 precipitation for climate monitoring. Quarterly Journal of the Royal Meteorological Society, 148(748): 3152--3165

    2022

  11. [19]

    Li, B.; Xue, K.; Liu, B.; and Lai, Y.-K. 2023. Bbdm: Image-to-image translation with brownian bridge diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2023

  12. [20]

    D.; Rhoades, A

    Ombadi, M.; Risser, M. D.; Rhoades, A. M.; and Varadharajan, C. 2023. A warming-induced reduction in snow fraction amplifies rainfall extremes. Nature, 619(7969): 305--310

    2023

  13. [21]

    Park, Y.-J.; Seo, M.; Kim, D.; Kim, H.; Choi, S.; Choi, B.; Ryu, J.; Son, S.; Jeon, H.-G.; and Choi, Y. 2024. Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data. In Proceedings of the International Conference on Learning Representat...

    2024

  14. [22]

    Ravuri, S.; Lenc, K.; Willson, M.; Kangin, D.; Lam, R.; Mirowski, P.; Fitzsimons, M.; Athanassiadou, M.; Kashem, S.; Madge, S.; et al. 2021. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878): 672--677

    2021

  15. [23]

    K.; Espeholt, L.; Heek, J.; Dehghani, M.; Oliver, A.; Salimans, T.; Agrawal, S.; Hickey, J.; and Kalchbrenner, N

    S nderby, C. K.; Espeholt, L.; Heek, J.; Dehghani, M.; Oliver, A.; Salimans, T.; Agrawal, S.; Hickey, J.; and Kalchbrenner, N. 2020. Metnet: A neural weather model for precipitation forecasting. arXiv preprint arXiv:2003.12140

    2020 arXiv

  16. [24]

    Song, H.-J. 2023. Long-term variations of cloud top patterns associated with heavy rainfall over the Korean peninsula. Journal of Hydrology: Regional Studies, 46: 101337

    2023

  17. [25]

    V.; Imam, B.; and Braithwaite, D

    Sorooshian, S.; Hsu, K.-L.; Gao, X.; Gupta, H. V.; Imam, B.; and Braithwaite, D. 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of the American Meteorological Society, 81(9): 2035--2046

    2000

  18. [26]

    Tan, C.; Gao, Z.; Li, S.; and Li, S. Z. 2022. SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning. arXiv preprint arXiv:2211.12509

    2022 arXiv

  19. [27]

    Tan, C.; Gao, Z.; Wu, L.; Xu, Y.; Xia, J.; Li, S.; and Li, S. Z. 2023 a . Temporal attention unit: Towards efficient spatiotemporal predictive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2023

  20. [28]

    Tan, C.; Li, S.; Gao, Z.; Guan, W.; Wang, Z.; Liu, Z.; Wu, L.; and Li, S. Z. 2023 b . Openstl: A comprehensive benchmark of spatio-temporal predictive learning. Proceedings of the Neural Information Processing Systems (NeurIPS)

    2023

  21. [29]

    Tang, S.; Li, C.; Zhang, P.; and Tang, R. 2023. Swinlstm: Improving spatiotemporal prediction accuracy using swin transformer and lstm. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

    2023

  22. [30]

    J.; and Jezek, K

    Van Der Veen, C. J.; and Jezek, K. C. 1993. Seasonal variations in brightness temperature for central Antarctica. Annals of Glaciology, 17: 300--306

    1993

  23. [31]

    Veillette, M.; Samsi, S.; and Mattioli, C. 2020. Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology. Proceedings of the Neural Information Processing Systems (NeurIPS)

    2020

  24. [32]

    T.-J.; and Carbone, R

    Wang, C.-C.; Chen, G. T.-J.; and Carbone, R. E. 2004. A climatology of warm-season cloud patterns over East Asia based on GMS infrared brightness temperature observations. Monthly Weather Review, 132(7): 1606--1629

    2004

  25. [33]

    Y.; and Long, M

    Wang, Y.; Wu, H.; Zhang, J.; Gao, Z.; Wang, J.; Philip, S. Y.; and Long, M. 2022. Predrnn: A recurrent neural network for spatiotemporal predictive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(2): 2208--2225

    2022

  26. [34]

    Wedi, N.; Bauer, P.; Denoninck, W.; Diamantakis, M.; Hamrud, M.; Kuhnlein, C.; Malardel, S.; Mogensen, K.; Mozdzynski, G.; and Smolarkiewicz, P. 2015. The modelling infrastructure of the Integrated Forecasting System: Recent advances and future challenges. European Centre for ...

    2015

  27. [35]

    Wu, S.; Chen, Y.; Mermet, S.; Hurni, L.; Schindler, K.; Gonthier, N.; and Landrieu, L. 2024. StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2024

  28. [36]

    Yoon, D.; Seo, M.; Kim, D.; Choi, Y.; and Cho, D. 2023. Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting. arXiv preprint arXiv:2312.02819

    2023 arXiv

  29. [37]

    Yu, D.; Li, X.; Ye, Y.; Zhang, B.; Luo, C.; Dai, K.; Wang, R.; and Chen, X. 2024. Diffcast: A unified framework via residual diffusion for precipitation nowcasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.