Pith. sign in

REVIEW 5 major objections 6 minor 25 references

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A deep learning model converts atmospheric circulation into 6-hour precipitation maps whose heavy rainfall is more consistent with observations than other AI weather models and competitive with global numerical weather prediction models.

desk verdict A useful precipitation diagnostic architecture undermined by a target-definition choice that invalidates the headline comparisons. read the letter →

arxiv 2411.12640 v1 pith:LKXDYZ5R submitted 2024-11-19 physics.ao-ph cs.LG

Weiwen Ji , Jin Feng , Yueqi Liu , Yulu Qiu , Hua Gao This is my paper · ORCID
classification physics.ao-phcs.LG
keywords heavyprecipitationinformationbalanceschemedeeplearningdiagnosticmodelCMORPHlong-taildistributionthreatscorefractionsskill
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

The paper aims to close a known weak spot in deep learning weather forecasting: precipitation, especially heavy rain, which predictive models handle poorly because heavy events are rare and the data distribution is long-tailed. Leadsee-Precip is a global diagnostic model that takes a single time step of atmospheric circulation fields (five upper-air variables on 13 pressure levels plus four surface variables from the ERA5 reanalysis) and outputs a 6-hour accumulated precipitation field. Two design choices carry the argument: an information balance loss that weights each sample's error by the negative log frequency of its precipitation bin, so rare heavy rain errors count more, and training against the satellite-radar CMORPH product instead of ERA5 precipitation. On the global test set, the model reaches a Threat Score of 0.185 and a Fractions Skill Score of 0.570 at the 25 mm/6h threshold, and its heavy-rain fields line up better with observations than the native precipitation outputs of other AI weather models while staying competitive with global numerical weather prediction models.

What carries the argument

The central object is the information balance (IB) scheme, a loss-weighting rule that multiplies each sample's squared error by its normalized negative log frequency, $W_i = [-\log P(y_i)]^\tau / \sum_i [-\log P(y_i)]^\tau$, where $P(y_i)$ is the empirical frequency of the precipitation bin containing the target value and $\tau = 2$. The scheme converts the long-tail distribution of precipitation into a per-sample importance weight, making the optimizer more sensitive to rare heavy-rain errors than to the frequent zero and light-precipitation samples that dominate ordinary MSE training. The second load-bearing ingredient is the training target, the satellite-radar CMORPH precipitation aggregated to 6 hours and max-pooled to 0.25 degrees, which the paper uses in place of the ERA5 precipitation field because that reanalysis product is known to be biased.

What would settle it

Verify Leadsee-Precip's 6-hour, >25 mm precipitation against an independent gauge-based network (e.g., the roughly 10,470 Chinese stations the paper uses) on the same events, computing Threat Scores for Leadsee-Precip and the paper's global numerical baseline side by side. If the numerical model's Threat Score is higher, the central claim of competitive heavy-precipitation skill is falsified.

Watch

Extended reading notes

Core claim

Leadsee-Precip is presented as a solution to the long-tail precipitation problem in deep learning weather models. The model uses an encoder-decoder architecture with separate 3D and 2D convolutions for upper-air and surface variables, a MogaNet (multi-order gated aggregation) bottleneck, and a shortcut connection to reconstruct 0.25-degree global precipitation. The information balance scheme computes a per-sample weight $W_i = [-\log P(y_i)]^\tau / \sum_i [-\log P(y_i)]^\tau$ over 92 magnitude bins, multiplying the MSE loss so that rare, high-magnitude precipitation errors dominate training. With $\tau = 2$ and CMORPH-derived 6-hour targets resampled by max-pooling, the model achieves TS 0.185 and FSS 0.570 for 6-hour precipitation exceeding 25 mm on a global test set from April to September 2022, and station-based evaluation over China shows TS 0.11 at the 25 mm/6h threshold. A LoRA (low-rank adaptation) fine-tuned version at 5 km resolution over China, trained on the CROA observational analysis, improves these station-based scores (TS 0.14 at 25 mm/6h) and better captures the location of heavy rainbands. The paper also shows the model can be driven by the circulation forecasts of an AI weather model (FuXi); the resulting precipitation fields are weaker than with reanalysis input but still capture heavy events in North China better than FuXi's native precipitation.

Load-bearing premise

The CMORPH-derived 6-hour accumulated precipitation product is treated as accurate ground truth, including for heavy rain, after a max-pooling interpolation to 0.25 degrees; if CMORPH is biased for extreme rain, especially over land, the model learns the retrieval's biases and the reported TS and FSS do not measure skill for actual precipitation.

Editorial extensions

If this is right

  • When driven by the ERA5 circulation fields, Leadsee-Precip acts as a global diagnostic tool that reconstructs 6-hour precipitation at 0.25 degrees from a single atmospheric state.
  • Because it accepts circulation fields as input, the model can be attached to any global circulation model to issue precipitation forecasts, at the cost of weaker intensity when the input is a model's predicted rather than reanalyzed circulation.
  • Fine-tuning the upsampling branch on a 5 km regional observational analysis over China raises Threat Scores and lowers bias, and the same procedure can in principle be applied to other regions with high-quality analyses.
  • The reported skill at the 25 mm/6h threshold (TS 0.185, FSS 0.570) gives other AI weather models a concrete precipitation benchmark to beat, in addition to the usual circulation-variable metrics.
  • Replacing an AI weather model's native precipitation output with a separate diagnostic model trained on satellite-radar targets can improve heavy-rain realism without changing the circulation forecast core.

Reading between the lines

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

  • The information balance weighting is a generic recipe for any regression task with a long-tailed target distribution, such as extreme winds, air quality, or streamflow; the paper only applies it to precipitation.
  • Because the training targets are made by max-pooling high-resolution CMORPH rain rates into 0.25-degree cells, the heavy-rain labels are likely inflated relative to grid-cell averages, which may explain why the station-based Threat Score (0.11) is lower than the CMORPH-based one (0.185) at 25 mm/6h.
  • Coupling a diagnostic precipitation model to an autoregressive circulation forecast makes the precipitation skill inherit the circulation model's error growth; a stronger test would be to fine-tune on the circulation model's own forecast fields and verify over longer lead times, as the paper suggests but does not carry out.
  • The fine-tuning result, which raises TS from 0.11 to 0.14 at 25 mm/6h despite a 5 km output grid, suggests that resolution alone is not the bottleneck for heavy-rain skill; the training target's fidelity and the loss weighting are more likely levers.
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

5 major / 6 minor

Summary. The paper proposes Leadsee-Precip, a global deep learning diagnostic model that maps ERA5 circulation fields to 6-hour accumulated precipitation at 0.25° resolution, trained against CMORPH precipitation with a new information balance (IB) loss intended to emphasize rare heavy precipitation. A LoRA fine-tuned version at 5 km resolution is presented for China using the CROA dataset. The authors report TS and FSS scores on a global test set, station-based verification over China, and a single case study comparing against FuXi and ECMWF HRES. The central claim is that heavy precipitation from Leadsee-Precip is more consistent with observations than AI global weather models and competitive with global numerical weather prediction models.

Significance. If the headline claim were fully supported, Leadsee-Precip would be a practically useful diagnostic tool, particularly for post-processing circulation forecasts from AI weather models. The IB weighting scheme is a reasonable and clearly motivated approach to the long-tail precipitation regression problem, and the use of independent station data for evaluation is a genuine strength. The paper also acknowledges limitations candidly (e.g., no significant heavy-precipitation improvement from fine-tuning in Fig. 6). However, the manuscript currently lacks the systematic baseline comparisons needed to support its central comparative claim, and the max-pooled CMORPH target raises a representativeness issue that affects the interpretation of all global skill scores. The results may still be valid, but the evidence as presented is insufficient to establish the abstract's claim.

major comments (5)
  1. [Section 3.5.2 and Table 1] The max-pooling interpolation used to convert CMORPH from its native 8 km resolution to the 0.25° target grid produces a value equal to the largest 8-km precipitation within each target cell, not the cell-area mean. Because NWP and AI weather models predict grid-cell mean precipitation, the TS and FSS scores in Table 1 do not measure skill for the same quantity that these baseline models are designed to predict. Please either justify the choice of predicting sub-grid maxima (with physical or application-specific reasons) or repeat the training and evaluation using averaging or conservative remapping, and report the sensitivity of the headline scores to this choice.
  2. [Abstract and Section 4] The abstract's claim that heavy precipitation from Leadsee-Precip is more consistent with observations than AI global weather models, and competitive with global NWP models, is not supported by any systematic evaluation in the manuscript. The only direct side-by-side comparison is the single North China case in Fig. 5. Please add a table reporting the same TS and FSS thresholds for baseline models (e.g., FuXi, GraphCast, ECMWF HRES) computed on the same test period and target grid, ideally with the same station-based and grid-based protocols, together with uncertainty estimates.
  3. [Section 3.2] The information balance weights in Eq. (1) depend on the 92-bin partition used to estimate P(y_i) and on the temperature coefficient τ. The bin edges are not specified anywhere in the paper, and τ=2 is reported only as the result of ablation experiments that are not shown. Since these choices directly determine the loss function and therefore the trained model, please provide the bin boundaries and the frequency curve used to define P(y_i), and report the ablation over τ either in the main text or in a supplement.
  4. [Tables 1–3] No uncertainty estimates are provided for the TS, FSS, or bias values. This is particularly important for the high-threshold rows with very few positive samples (e.g., TS=0.003 at 100 mm/6h in Table 1). Confidence intervals, bootstrap estimates, or at least the number of event days and grid points used would be needed to judge whether differences between models, or between Tables 2 and 3, are meaningful.
  5. [Section 4.3 and Fig. 6] The text in Section 4.3 states that the fine-tuned model shows no significant improvement in heavy precipitation compared with the original model (Fig. 6), yet Table 3 and the conclusion highlight higher TS scores. Please report the per-threshold improvements explicitly and temper the conclusion accordingly; if the improvement is concentrated at lower thresholds or in overall pattern, this should be stated clearly so that readers do not over-read the headline result.
minor comments (6)
  1. [Section 3.4, Eq. (3)] In the FSS formula, P and T are not explicitly defined as neighborhood-averaged fractions; please add that these are mean values over a 7×7 pixel window and clarify that the same window is used for all thresholds.
  2. [Section 4.1] The text lists the thresholds as 0.1, 1, 5, 10, 25, and 50 mm/6h, but Table 1 also includes 100 mm/6h; the list should be corrected to include all seven thresholds.
  3. [Fig. 5 caption] There is a typo: 'Panle' should be 'Panel'. Additionally, the caption refers to 24-hour accumulated precipitation while the text discusses 25 mm/6h; please clarify which accumulation interval the thresholds refer to in the figure.
  4. [Section 3.5.1] Please clarify how the hourly ERA5 data from 2013–2022 are aggregated to match the 6-hourly data from 1998–2012, and whether the 6-hourly data are instantaneous or accumulated values. The input consistency across the mixed-resolution training set affects the reproducibility of the model.
  5. [Section 3.2, Eq. (1)] Please specify whether the normalization in Eq. (1) is performed over the full training set or per batch. If the latter, the loss magnitude depends on the batch composition, which should be stated for reproducibility.
  6. [References] Reference [25] appears unrelated to station precipitation data; please verify that this citation is appropriate and consider citing the original station dataset source instead.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the model is trained on external precipitation targets and evaluated against held-out CMORPH and independent station observations; no target result is assumed in the derivation.

full rationale

Leadsee-Precip is a supervised diagnostic model: the predictor (69 ERA5 circulation channels) and the target (NOAA CMORPH 6-hour accumulations, Sect. 3.5.2) are independent data sources, and the claimed scores are computed on a temporal holdout (April to September 2022) as well as on third-party station observations that were not used in training (Sect. 3.5.4). The information-balance weights in Eq. (1) depend only on the marginal frequency of precipitation bins and on the fitted temperature parameter tau set to 2 after ablation; these are standard loss-reweighting and hyperparameter choices, not quantities that encode the evaluation metric or the test outcome. The qualitative comparison against FuXi and ECMWF HRES in Fig. 5 uses station observations as ground truth, so the central comparison is externally anchored. The only apparent self-citations ([10] and [25]) are related-work context and a data-source citation for the station dataset; they do not carry the derivation, and the station data is externally falsifiable. The max-pooling interpolation of CMORPH (Sect. 3.5.2) is a legitimate target-construction concern that affects interpretation of the global TS/FSS against grid-box-mean model output, but it is not circular: the model is trained and evaluated on exactly the quantity the authors define, and that definition does not presuppose the model's outputs. No self-definitional reduction, fitted-input-renamed-as-prediction, or imported uniqueness theorem is present.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper rests on several data-quality and modeling assumptions: reanalysis circulation as input, CMORPH and CROA observations as targets, frequency-bin probabilities as weights, and the sufficiency of instantaneous circulation for 6-hour precipitation. The only tuned scalar in the loss is tau, set by an unshown ablation, and the bin partition is not reported.

free parameters (3)
  • temperature coefficient tau = 2
    Controls the information-balance weighting in Eq. 1; set after ablation experiments in Sect. 3.2 but no ablation results or sensitivity are shown.
  • precipitation bin partition (92 bins) = not reported
    The bins define P(yi) and therefore all loss weights; Fig. 2 shows the first bin is 0 to 0.1 and the maximum is 400 mm per 6 hours, but exact boundaries are not given.
  • LoRA fine-tuning hyperparameters = not reported
    Rank and training settings for the 5 km China model are not specified, so the Table 3 improvement cannot be reproduced or assessed.
assumptions (4)
  • domain assumption ERA5 circulation fields are the correct atmospheric state for diagnosis.
    Sect. 3.5.1 uses ERA5 as input without quantifying analysis error or the effect of temporal-resolution mixing.
  • domain assumption NOAA CMORPH precipitation retrievals are accurate enough to serve as training targets, especially for heavy precipitation.
    Sect. 3.5.2 replaces ERA5 precipitation with CMORPH based on the claim it is more accurate; no independent validation of CMORPH for the evaluated thresholds is provided.
  • domain assumption A single time step of circulation fields determines the 6-hour accumulated precipitation.
    Fig. 4 and Sect. 4.1 describe diagnosis from 69 variables at a single time step, but the model is trained on 6-hour accumulated CMORPH targets.
  • ad hoc to paper The information-balance weighting with tau equals 2 is a valid loss for heavy-precipitation skill.
    Sect. 3.2 introduces the weighting and chooses tau by ablation, but no ablation evidence is included.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation." pith.science (2026). https://pith.science/paper/LKXDYZ5R

@misc{pith2026241112640,
  author       = {Pith},
  title        = {Pith review of: Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LKXDYZ5R}},
  note         = {Machine review of arXiv:2411.12640}
}
read the original abstract

Recently, deep-learning weather forecasting models have surpassed traditional numerical models in terms of the accuracy of meteorological variables. However, there is considerable potential for improvements in precipitation forecasts, especially for heavy precipitation events. To address this deficiency, we propose Leadsee-Precip, a global deep learning model to generate precipitation from meteorological circulation fields. The model utilizes an information balance scheme to tackle the challenges of predicting heavy precipitation caused by the long-tail distribution of precipitation data. Additionally, more accurate satellite and radar-based precipitation retrievals are used as training targets. Compared to artificial intelligence global weather models, the heavy precipitation from Leadsee-Precip is more consistent with observations and shows competitive performance against global numerical weather prediction models. Leadsee-Precip can be integrated with any global circulation model to generate precipitation forecasts. But the deviations between the predicted and the ground-truth circulation fields may lead to a weakened precipitation forecast, which could potentially be mitigated by further fine-tuning based on the predicted circulation fields.

Figures

Figures reproduced from arXiv: 2411.12640 by the authors.

Figure 1
Figure 1. Structure of Leadsee-Precip. The model consists of feature extraction, hidden translator, and precipitation [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Distribution of precipitation. The precipitation data is categorized into 92 bins, with the precipitation intensity [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. LoRA fine-tuning 5-km model structure. The feature extraction part is frozen, the MogaNet hidden part uses [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: An illustrative example of a global 6-hour accumulated precipitation prediction generated by Leadsee-Precip [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: 24-hour accumulated precipitation at weather station locations: observed and different model results. Panel [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Comparison of 6-hour precipitation results between fine-tuning and initial Leadsee-Precip model. Panel [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 17 canonical work pages

  1. [1]

    The era5 global reanalysis

    Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al. The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020

    work page 1999

  2. [2]

    Fourcastnet: A global data-driven high- resolution weather model using adaptive fourier neural operators

    Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al. Fourcastnet: A global data-driven high- resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214, 2022

    arXiv 2022

  3. [3]

    Evaluation of ECMWF forecasts

    Thomas Haiden, Martin Janousek, Frederic Vitart, Maliko Tanguy, Fernando Prates, and Matthieu Chevallier. Evaluation of ECMWF forecasts. European Centre for Medium Range Weather Forecasts Reading, UK, 2024

    work page 2024

  4. [4]

    Swinrdm: integrate swinrnn with diffusion model towards high-resolution and high-quality weather forecasting

    Lei Chen, Fei Du, Yuan Hu, Zhibin Wang, and Fan Wang. Swinrdm: integrate swinrnn with diffusion model towards high-resolution and high-quality weather forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 322–330, 2023

    work page 2023

  5. [5]

    Accurate medium-range global weather forecasting with 3d neural networks

    Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. Accurate medium-range global weather forecasting with 3d neural networks. Nature, 619(7970):533–538, 2023

    2023

  6. [6]

    Graphcast: Learning skillful medium-range global weather forecasting

    Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, et al. Graphcast: Learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794, 2022

    arXiv 2022

  7. [7]

    Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast

    Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Yuan Qi, and Hao Li. Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast. npj Climate and Atmospheric Science, 6(1):190, 2023

    work page 2023

  8. [8]

    Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead

    Kang Chen, Tao Han, Junchao Gong, Lei Bai, Fenghua Ling, Jing-Jia Luo, Xi Chen, Leiming Ma, Tianning Zhang, Rui Su, et al. Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead. arXiv preprint arXiv:2304.02948, 2023

    arXiv 2023

Show all 25 references
  1. [9]

    Aifs-ecmwf’s data-driven forecasting system

    Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian Pinault, Baudouin Raoult, Mariana CA Clare, Christian Lessig, Michael Maier-Gerber, Linus Magnusson, et al. Aifs-ecmwf’s data-driven forecasting system. arXiv preprint arXiv:2406.01465, 2024

    2024 arXiv

  2. [10]

    W-mae: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

    Xin Man, Chenghong Zhang, Jin Feng, Changyu Li, and Jie Shao. W-mae: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting. arXiv preprint arXiv:2304.08754, 2023. 9 Leadsee-Precip: A Deep Learning Precipitation Diagnostic Model

    2023 arXiv

  3. [11]

    An evaluation of era5 precipitation for climate monitoring

    David A Lavers, Adrian Simmons, Freja Vamborg, and Mark J Rodwell. An evaluation of era5 precipitation for climate monitoring. Quarterly Journal of the Royal Meteorological Society, 148(748):3152–3165, 2022

    2022

  4. [12]

    A systematic study of the class imbalance problem in convolutional neural networks

    Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski. A systematic study of the class imbalance problem in convolutional neural networks. Neural networks, 106:249–259, 2018

    2018

  5. [13]

    Large-scale long-tailed recognition in an open world

    Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu. Large-scale long-tailed recognition in an open world. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2537–2546, 2019

    2019

  6. [14]

    Long-tailed classification by keeping the good and removing the bad momentum causal effect

    Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. Long-tailed classification by keeping the good and removing the bad momentum causal effect. Advances in neural information processing systems, 33:1513–1524, 2020

    2020

  7. [15]

    Distribution alignment: A unified framework for long-tail visual recognition

    Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, and Jian Sun. Distribution alignment: A unified framework for long-tail visual recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2361–2370, 2021

    2021

  8. [16]

    Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition

    Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. Advances in Neural Information Processing Systems , 35:34077–34090, 2022

    2022

  9. [17]

    Customized deep learning for precipitation bias correction and downscaling

    Fang Wang, Di Tian, and Mark Carroll. Customized deep learning for precipitation bias correction and downscaling. Geoscientific Model Development, 16(2):535–556, 2023

    2023

  10. [18]

    Smaat-unet: Precipitation nowcasting using a small attention-unet architecture, 2021

    Kevin Trebing, Tomasz Stanczyk, and Siamak Mehrkanoon. Smaat-unet: Precipitation nowcasting using a small attention-unet architecture, 2021

    2021

  11. [19]

    Segnet: A deep convolutional encoder-decoder architecture for image segmentation.IEEE transactions on pattern analysis and machine intelligence, 39(12):2481– 2495, 2017

    Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation.IEEE transactions on pattern analysis and machine intelligence, 39(12):2481– 2495, 2017

    2017

  12. [20]

    Moganet: Multi-order gated aggregation network

    Siyuan Li, Zedong Wang, Zicheng Liu, Cheng Tan, Haitao Lin, Di Wu, Zhiyuan Chen, Jiangbin Zheng, and Stan Z Li. Moganet: Multi-order gated aggregation network. In The Twelfth International Conference on Learning Representations, 2023

    2023

  13. [21]

    Deep learning for day forecasts from sparse observations.arXiv preprint arXiv:2306.06079, 2023

    Marcin Andrychowicz, Lasse Espeholt, Di Li, Samier Merchant, Alexander Merose, Fred Zyda, Shreya Agrawal, and Nal Kalchbrenner. Deep learning for day forecasts from sparse observations.arXiv preprint arXiv:2306.06079, 2023

    2023 arXiv

  14. [22]

    Long-tail learning via logit adjustment

    Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit adjustment. arXiv preprint arXiv:2007.07314, 2020

    2007 arXiv

  15. [23]

    A long-term assessment of precipitation forecast skill using the fractions skill score

    Marion Mittermaier, Nigel Roberts, and Simon A Thompson. A long-term assessment of precipitation forecast skill using the fractions skill score. Meteorological Applications, 20(2):176–186, 2013

    2013

  16. [24]

    Cmorph: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution

    Robert J Joyce, John E Janowiak, Phillip A Arkin, and Pingping Xie. Cmorph: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of hydrometeorology, 5(3):487–503, 2004

    2004

  17. [25]

    Enhancing surface wind speed and temperature prediction using surface-layer emulator and transfer learning

    Jin Feng and Yanjie Li. Enhancing surface wind speed and temperature prediction using surface-layer emulator and transfer learning. Monthly Weather Review, 152(10):2361–2377, 2024. 10

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.