Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields

Chensen Lin; Hao Li; Ruian Tie; Shihong Yi; Xiaohui Zhong

REVIEW 5 major objections 8 minor 20 references

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields

T0 review · 5 major / 8 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read FuXi-CFD claims that a neural network trained on CFD simulations can reconstruct 30-meter-resolution 3D wind fields, including vertical velocity and turbulence, from kilometer-scale 10 m wind inputs plus terrain maps.

desk verdict A solid CFD-emulation paper whose operational framing outruns the evidence: internal accuracy is fine, but the transfer from simulated to real winds is untested. read the letter →

arxiv 2505.12732 v1 pith:YRVO35X5 submitted 2025-05-19 physics.ao-ph

Chensen Lin , Ruian Tie , Shihong Yi , Xiaohui Zhong , Hao Li This is my paper · ORCID

classification physics.ao-ph

keywords windenergyterrain-awaredownscalingAIweatherforecastingcomputationalfluiddynamics3Dfieldreconstructionturbulentkineticcomplexterraindeeplearning

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 is trying to establish that coarse operational weather forecasts, which only provide horizontal wind at 10 meters over kilometer-scale grids, contain enough information to reconstruct the fine-scale, three-dimensional wind structure needed for wind energy siting and operations. It introduces FuXi-CFD, a deep learning model trained on more than 12,000 steady-state RANS simulations of real mountainous terrain, with inflow conditions deliberately withheld from the model. The model takes 9x9 kilometer, 1 kilometer-resolution 10 m u and v fields plus 30 m elevation and roughness as input, and outputs u, v, w, and turbulent kinetic energy at 30 m horizontal resolution across 27 vertical levels up to 300 m. The central claim is that this achieves CFD-comparable accuracy — relative errors below 7% for horizontal winds at 100 m and about 20% for the inferred vertical wind and turbulence — while reducing inference time from hours to under a second. If correct, it closes the resolution gap between AI weather models and wind-energy applications with a lightweight, terrain-aware post-processor.

What carries the argument

The central object is a shared Vision Transformer encoder with four task-specific decoder branches, which takes four 300 by 300 channels — upsample coarse 10 m u and v fields plus 30 m elevation and roughness — and maps them to a 300 by 300 by 27 by 4 volumetric output of u, v, w, and turbulent kinetic energy. The load-bearing design choice is the CFD dataset construction: over 12,000 steady-state RANS simulations of real mountainous terrain, with inflow speed and direction deliberately excluded from the training labels so the network must learn terrain-flow relationships from local context alone. A hybrid loss combining the Charbonnier spatial loss with a frequency-domain regularization term stabilizes training and improves reconstruction of the latent variables w and k that are absent from the input.

What would settle it

Take an actual mountainous wind site with mast or lidar measurements, feed FuXi-CFD with a real operational forecast's 10 m u/v fields plus the terrain maps, and compare the predicted 100 m u, v, w, and turbulent kinetic energy against the observations; if the horizontal wind relative errors exceed the claimed 7% or the inferred w and k show no skill, the central claim fails.

Watch

Extended reading notes

Core claim

The paper's central discovery is that an inverse-reconstruction formulation can replace forward CFD for microscale wind prediction: instead of specifying inflow boundary conditions, the model learns to infer the full local flow from terrain context and coarse near-surface wind. Evaluated against held-out CFD simulations, FuXi-CFD reproduces horizontal wind components with small scatter, captures the spatial patterns of vertical velocity with moderate accuracy, and recovers turbulent kinetic energy with the largest but still plausible scatter. Error profiles peak near 100 m above ground, the height most relevant to turbines, where terrain influence weakens while flow complexity remains high. The authors interpret this as evidence that kilometer-scale 10 m winds, together with globally available elevation and land-cover data, are sufficient inputs for reconstructing three-dimensional wind and turbulence fields in complex terrain.

Load-bearing premise

The entire accuracy claim is validated against the same family of steady, neutral CFD simulations used for training, so the load-bearing premise is that those simulations faithfully represent real wind over mountainous terrain and that their coarse 10 m inputs resemble the biased, error-laden outputs of real AI forecast models; the paper offers no observations or tests with real forecast outputs to support either half.

Editorial extensions

If this is right

If the central claim holds, a 1 km, 10 m horizontal wind forecast plus terrain maps is sufficient to generate 30 m resolution 3D wind and turbulence fields over a 9 by 9 km mountainous region in under a second.
Relative errors below 7% for horizontal winds and around 20% for vertical velocity and turbulent kinetic energy at 100 m would make micro-siting and turbine-level power forecasting feasible from operational AI weather model outputs.
Because vertical velocity and turbulence are inferred without direct input, FuXi-CFD could provide latent flow variables that coarse forecasts cannot deliver, enabling engineering assessments in data-sparse mountain regions.
The three-orders-of-magnitude speedup over CFD would allow high-resolution wind fields to be used in real-time operational workflows, not just offline planning studies.
The authors state that the model is currently limited to steady-state, neutral stability conditions, meaning its practical scope depends on extending the approach to stratified and unsteady atmospheric states.

Reading between the lines

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

The deliberate withholding of inflow conditions implies that terrain context carries much of the boundary-layer information; if so, the same inverse-reconstruction setup could be retrained to infer other unresolved fields, such as surface temperature or turbulence intensity, from suitable CFD or LES training data.
A natural stress test that the paper does not run is to feed FuXi-CFD with actual output from an operational AI weather model, whose 10 m winds contain real forecast biases, and compare against observations; this would directly test the simulation-to-reality transfer on which the application rests.
Because the training labels come from RANS simulations, the claimed accuracy inherits RANS turbulence-model biases; higher-fidelity large-eddy simulations or field observations could serve as replacement ground truth and likely reveal where the inferred w and k fields deviate most.
The error peak near 100 m, identified in the paper, suggests that terrain-aware downscaling is hardest exactly in the turbine layer; an editor-level expectation is that stability and gust effects, which are absent from the training set, would push those errors higher in real operational use.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper is a competent engineering effort: 12,000 steady-state RANS simulations over real mountainous terrain, a shared-encoder transformer with four decoder branches, and a clean demonstration that a neural net can reproduce 3D fields u, v, w, and k from coarse 10 m winds plus terrain. Internal evaluation on held-out cases shows horizontal wind relative errors below 7% at 100 m, w and k around 20%, and inference in seconds. That is a genuinely useful result for wind energy siting and a reasonable template for CFD-to-ML emulation.

The soft spots are where the paper reaches beyond its evidence. All evaluation uses the same RANS pipeline that generated the training data, so "CFD-comparable accuracy" is partly by construction. The operational claim—that real AI forecast outputs can be downscaled this way—rests on two unstated assumptions: RANS under neutral, steady conditions with a fixed 10 m/s inflow at 100 m (direction varies, speed and stability do not) faithfully represents real mountainous winds, and that the clean 9x9 km, 1 km samples from those CFD fields stand in for the biased, error-corrupted outputs of operational models. Neither assumption is tested: no observational data, no tall tower or lidar comparison, no test with actual AI forecast fields. The abstract also says predictions are based "solely on horizontal wind input at 10 m," while Section 2.2 requires elevation and roughness maps; that misstates the input set, though it is a presentation issue rather than a fatal one.

The circularity burden is moderate, not maximal: w and k are not in the input, so the model is doing a real mapping task, and the internal consistency of the results suggests the architecture works as an emulator. But because the training inflows collapse to a single speed and one stability class, the model has no way to learn to separate weak inflow from terrain sheltering in real forecasts. The conclusion's nod to stratification as future work is honest, but it conflicts with the abstract's general framing.

The citation pattern is fine, and the paper does not invent new physics or hide its limitations entirely. I would send this to peer review, but as a methods paper with major revision required: external validation, a learned baseline, and ideally code or data release. For my own work, I would not cite it yet without artifacts or real-world tests; it is a useful demonstration, not yet a proven tool.

Referee Report

5 major / 8 minor

Summary. The paper introduces FuXi-CFD, a transformer-based downscaling framework that maps coarse (1 km, 10 m AGL) horizontal wind components, together with 30 m terrain elevation and surface roughness, to full three-dimensional wind fields—u, v, w, and turbulent kinetic energy k—at 30 m horizontal resolution over 27 vertical levels up to about 300 m. The model is trained on a dataset of over 12,000 steady-state RANS simulations over real mountainous terrain, with inflow direction varied and inflow speed and stability fixed. The authors report parity plots, vertical profiles, and height-dependent error statistics, claiming relative errors below 7% for horizontal winds and about 20% for w and k at 100 m AGL, with inference time reduced from hours to seconds. The central claim is that this combination of coarse AI-forecast winds and terrain data is sufficient to reconstruct fine-scale wind fields with CFD-comparable accuracy, thereby enabling operational wind-energy applications.

Significance. If the accuracy claims survive independent validation, FuXi-CFD would be a practical and scalable tool for wind-resource assessment and power forecasting in complex terrain. The paper contributes a large, physically grounded training dataset, a clean encoder–decoder architecture with four output branches, and a genuinely nontrivial reconstruction task for latent variables w and k. The use of globally available inputs (SRTM elevation, land-cover roughness, and 10 m winds) makes the approach potentially deployable. However, the significance is conditional: the evaluation is entirely internal to the CFD pipeline that generated the training data, with no observational test and no test using real AI-forecast outputs, so the operational claim outruns the evidence presented.

major comments (5)

[§3.1, §2.1, Fig. 8] The accuracy claims in §3.1 and Figure 8 are based entirely on comparison with CFD results from the same steady-state RANS pipeline described in §2.1. Because the model is trained on outputs of that same pipeline, the reported <7% (u,v) and <20% (w,k) relative errors at 100 m are surrogate-emulation errors, not errors relative to real winds. These results do not by themselves establish the operational claim in the Abstract that the model provides site-specific wind fields for real applications. Please add validation against independent observations (e.g., tall-tower or lidar measurements over complex terrain), and if possible, test the model with actual AI-forecast output rather than CFD-derived 10 m winds.
[§2.1, Abstract] The dataset description states that every simulation uses a logarithmic atmospheric boundary layer profile with a 10 m/s reference wind at 100 m height, and that only the inflow direction is varied. The Abstract's and §2.1's description of "a wide range of ... inflow conditions" is therefore overstated. With a single wind speed and neutral stability, the model cannot learn to separate slow 10 m winds caused by weak synoptic inflow from slow winds caused by terrain sheltering, and it cannot represent stability-dependent profile shapes or TKE magnitudes. This limitation is acknowledged only in the conclusion as future work, but it directly affects the central claim of applicability to real kilometer-scale forecasts. Please retrain or fine-tune on simulations with variable inflow speed and stability, or at least provide a controlled sensitivity test showing how performance degrades when these parameters are outside the training distribution.
[Abstract, §2.2] The Abstract claims that FuXi-CFD predicts full 3D wind structures "based solely on horizontal wind input at 10 meters above ground," but §2.2 lists terrain elevation and surface roughness maps at 30 m resolution as additional model inputs, and Figure 4 confirms four input channels. This is a factual inconsistency in the paper's central statement. Please correct the Abstract to state that the model uses coarse 10 m winds together with terrain data, not winds alone.
[§3.1–§3.4, Figs. 6 and 8] No error bars, confidence intervals, or repeated-run statistics are reported for any quantitative result. The parity plots in Figure 6, the error curves in Figure 8, and the profile comparisons in Figure 9 appear to come from a single training run, so the reader cannot assess whether the <7% and <20% figures are stable across weight initializations, data splits, or hyperparameter choices. Please provide statistics over multiple seeds or at least bootstrap confidence intervals on the test set.
[§3.4, §3.3] The only baseline considered is bilinear interpolation followed by logarithmic extrapolation for u and v, and no baseline is given for w and k. This is insufficient to support the statement in §3.3 that the results represent "a meaningful advance over existing downscaling approaches." Please compare against at least one established data-driven downscaling method (e.g., a U-Net or CNN with the same inputs, or linear regression on terrain features) and, if feasible, a mesoscale model (e.g., WRF) at comparable resolution.

minor comments (8)

[Fig. 6] The caption says "Dashed lines represent perfect agreement," while the main text says the red diagonal line indicates perfect prediction; please reconcile the figure and text.
[Fig. 6 caption] The phrase "predictions fork" appears to be a typo; please correct it.
[§3.3] Please define the L2 relative error formula precisely (e.g., the ratio of the L2 norm of the prediction error to the L2 norm of the reference field, computed per vertical level or over the full volume), so the reported percentages are reproducible.
[§3.1, dataset] The data split is not described: please state how many of the 12,000 simulations are used for testing, how the "unseen terrain" regions are selected, and whether the test regions are spatially disjoint from the training regions.
[Introduction] In the paragraph listing CFD tools, the citations appear as "[1], [2], [3]" for Meteodyn WT, WindSim, and Envision Greenwich, which collide with the actual reference numbers for other works; please renumber these citations.
[§2.2] The model input is described as "10 m AGL wind components (u,v) sampled on a 9×9 grid at 1 km resolution, covering a 9×9 km2 area"; please clarify whether this consists of 81 point values or 9×9 grid cells, and how the 1 km sampling relates to the CFD domain padding described in §2.1.
[§2.2] The CFD simulations output 47 vertical levels up to 3 km, but the model outputs 27 levels up to approximately 300 m; please specify how the 27 levels are selected from the original grid.
[Fig. 5] Please state whether the validation metrics in Figure 5 are computed on a fixed held-out set and provide the units of the y-axis for each subplot.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the learned CFD-to-3D mapping is a genuine held-out emulation task, not a parameter fit disguised as a prediction.

full rationale

The paper's derivation chain is linear and self-contained: RANS CFD simulations over real terrain produce training targets; a transformer is trained to map coarse 10 m horizontal winds plus 30 m terrain fields to 3D u, v, w, and k; and evaluation is performed on unseen terrain against held-out CFD outputs. No equation or construction makes the output equal the input. The 10 m input winds are coarse samples of the same CFD solutions, but the targets w and k are not included in the input, and the model must generalize to terrain not seen in training, so the reported errors are a nontrivial measure of within-distribution generalization rather than a tautology. The phrase 'CFD-comparable accuracy' is explicitly benchmarked against CFD, which is exactly the data distribution used for training; this limits external validity to real atmospheres, but that is a domain-transfer and validation limitation, not circularity. The paper also openly states in the Conclusion that the model is trained on steady-state neutral simulations, acknowledging the gap to real stratified, unsteady conditions. The only self-citations (FuXi references [6,7]) are contextual mentions of existing AI weather models and carry no load-bearing argument. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. Therefore no circular step can be exhibited with the specificity required by the review rules, and the correct finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical entities are proposed. FuXi-CFD is a learned model architecture, not a new force, particle, dimension, or conserved quantity. The real cost of the paper is empirical: the accuracy claim depends on the fidelity of RANS simulations, on the dataset construction, and on a large number of fitted network weights that are not publicly available.

free parameters (2)

Neural network weights (encoder and four decoder branches) = Learned during training; not released
The central claim rests on an empirical fit to 12,000 CFD simulations. The network weights determine all reported errors and are not provided, so the result cannot be reproduced or audited independently.
Loss weighting between Charbonnier and frequency-domain terms = Not reported
The hybrid loss is described qualitatively in Section 2.2 and Figure 5. The relative weight of the frequency-domain regularization changes the optimized solution, but no value is given.

assumptions (4)

domain assumption RANS with standard turbulence models accurately represents real wind fields over complex mountainous terrain.
All training labels and evaluation ground truth come from these simulations (Section 2.1); no comparison to observational data is provided.
domain assumption Steady-state, neutral atmospheric boundary layer conditions are representative of wind-energy-relevant mountainous flows.
The dataset is generated under neutral stability and steady-state inflow; the conclusion notes stratification and unsteady flows are left for future work, so the current model is not valid under those conditions.
domain assumption The coarse 9 by 9 km, 1 km resolution 10 m wind inputs seen by the model are representative of AI weather model outputs.
The paper does not state how the inputs are produced from the CFD fields and never feeds actual AI forecast outputs through the model (Section 2.2).
domain assumption SRTM 30 m elevation and land-cover-derived roughness are sufficient inputs to define terrain-atmosphere interaction.
Elevation and roughness are the only terrain inputs (Section 2.1); any error or incompleteness in these maps propagates directly into predictions.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields." pith.science (2026). https://pith.science/paper/YRVO35X5

@misc{pith2026250512732,
  author       = {Pith},
  title        = {Pith review of: Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YRVO35X5}},
  note         = {Machine review of arXiv:2505.12732}
}

read the original abstract

High-resolution wind information is essential for wind energy planning and power forecasting, particularly in regions with complex terrain. However, most AI-based weather forecasting models operate at kilometer-scale resolution, constrained by the reanalysis datasets they are trained on. Here we introduce FuXi-CFD, an AI-based downscaling framework designed to generate detailed three-dimensional wind fields at 30-meter horizontal resolution, using only coarse-resolution atmospheric inputs. The model is trained on a large-scale dataset generated via computational fluid dynamics (CFD), encompassing a wide range of terrain types, surface roughness, and inflow conditions. Remarkably, FuXi-CFD predicts full 3D wind structures -- including vertical wind and turbulent kinetic energy -- based solely on horizontal wind input at 10 meters above ground, the typical output of AI-based forecast systems. It achieves CFD-comparable accuracy while reducing inference time from hours to seconds. By bridging the resolution gap between regional forecasts and site-specific wind dynamics, FuXi-CFD offers a scalable and operationally efficient solution to support the growing demands of renewable energy deployment.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

20 extracted references · 16 canonical work pages

[1]

IEEE Transactions on sustainable energy7(4), 1525–1537 (2016)

Wang, Q., Martinez-Anido, C.B., Wu, H., Florita, A.R., Hodge, B.-M.: Quantify- ing the economic and grid reliability impacts of improved wind power forecasting. IEEE Transactions on sustainable energy7(4), 1525–1537 (2016)

work page 2016
[2]

Wind Energy Science7(6), 2231–2254 (2022)

Clifton, A., Barber, S., St¨ okl, A., Frank, H., Karlsson, T.: Research challenges and needs for the deployment of wind energy in hilly and mountainous regions. Wind Energy Science7(6), 2231–2254 (2022)

work page 2022
[3]

Science382(6677), 1416–1421 (2023)

Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W.,et al.: Learning skillful medium-range global weather forecasting. Science382(6677), 1416–1421 (2023)

2023
[4]

arXiv preprint arXiv:2202.11214 (2022)

Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mar- dani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., et al.: Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214 (2022)

arXiv 2022
[5]

Nature619(7970), 533–538 (2023)

Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., Tian, Q.: Accurate medium-range 12 global weather forecasting with 3d neural networks. Nature619(7970), 533–538 (2023)

work page 2023
[6]

npj climate and atmospheric science6(1), 190 (2023)

Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., Li, H.: Fuxi: A cas- cade machine learning forecasting system for 15-day global weather forecast. npj climate and atmospheric science6(1), 190 (2023)

work page 2023
[7]

Nature Communications15(1), 6425 (2024)

Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C.,et al.: A machine learning model that outperforms conventional global subseasonal forecast models. Nature Communications15(1), 6425 (2024)

work page 2024
[8]

Nature communications13(1), 1–10 (2022)

Espeholt, L., Agrawal, S., Sønderby, C., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R., Andrychowicz, M., Hickey, J.,et al.: Deep learning for twelve hour precipitation forecasts. Nature communications13(1), 1–10 (2022)

work page 2022

Show all 20 references

[9]

arXiv preprint arXiv:2312.15796 (2023)

Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T.R., El-Kadi, A., Mas- ters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., et al.: Gencast: Diffusion-based ensemble forecasting for medium-range weather. arXiv preprint arXiv:2312.15796 (2023)

2023 arXiv
[10]

Quarterly journal of the royal meteorological society146(730), 1999–2049 (2020)

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor´ anyi, A., Mu˜ noz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,et al.: The era5 global reanal- ysis. Quarterly journal of the royal meteorological society146(730), 1999–2049 (2020)

2020
[11]

Renewable Energy 101, 1–9 (2017)

Dhunny, A., Lollchund, M., Rughooputh, S.: Wind energy evaluation for a highly complex terrain using computational fluid dynamics (cfd). Renewable Energy 101, 1–9 (2017)

2017
[12]

Geography Compass7(4), 249–265 (2013)

Schoof, J.T.: Statistical downscaling in climatology. Geography Compass7(4), 249–265 (2013)

2013
[13]

Journal of Marine Science and Engineering9(3), 318 (2021)

Martinez-Garc´ ıa, F.P., Contreras-de-Villar, A., Mu˜ noz-Perez, J.J.: Review of wind models at a local scale: advantages and disadvantages. Journal of Marine Science and Engineering9(3), 318 (2021)

2021
[14]

Climate dynamics39, 2497–2522 (2012)

Soares, P.M., Cardoso, R.M., Miranda, P.M., Medeiros, J., Belo-Pereira, M., Espirito-Santo, F.: Wrf high resolution dynamical downscaling of era-interim for portugal. Climate dynamics39, 2497–2522 (2012)

2012
[15]

Journal of Applied Meteorology and Climatology57(3), 733–753 (2018)

Fern´ andez-Gonz´ alez, S., Mart´ ın, M.L., Garc´ ıa-Ortega, E., Merino, A., Lorenzana, J., S´ anchez, J.L., Valero, F., Rodrigo, J.S.: Sensitivity analysis of the wrf model: Wind-resource assessment for complex terrain. Journal of Applied Meteorology and Climatology57(3), 733...

2018
[16]

In: 2016 International Conference on High Performance Computing & Simulation (HPCS), pp

Goger, B., Rotach, M.W., Gohm, A., Stiperski, I., Fuhrer, O.: Current challenges for numerical weather prediction in complex terrain: Topography representation 13 and parameterizations. In: 2016 International Conference on High Performance Computing & Simulation (HPCS), pp. 89...

2016
[17]

Boundary-layer meteorology174(1), 1–59 (2020)

Port´ e-Agel, F., Bastankhah, M., Shamsoddin, S.: Wind-turbine and wind-farm flows: a review. Boundary-layer meteorology174(1), 1–59 (2020)

2020
[18]

Geological Survey: Shuttle Radar Topography Mis- sion (SRTM) 1 Arc-Second Global

U.S. Geological Survey: Shuttle Radar Topography Mis- sion (SRTM) 1 Arc-Second Global. U.S. Geological Sur- vey (2014). https://www.usgs.gov/centers/eros/science/ usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1

2014
[19]

Zenodo (2021)

Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brock- mann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., Ramoino, F., Ari...

2021 doi
[20]

Boundary-Layer Meteorology109, 227–254 (2003) 14

Hasager, C.B., Nielsen, N.W., Jensen, N.O., Boegh, E., Christensen, J.H., Dellwik, E., Soegaard, H.: Effective roughness calculated from satellite-derived land cover maps and hedge-information used in a weather forecasting model. Boundary-Layer Meteorology109, 227–254 (2003) 14

2003

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

IEEE Transactions on sustainable energy7(4), 1525–1537 (2016)

Wang, Q., Martinez-Anido, C.B., Wu, H., Florita, A.R., Hodge, B.-M.: Quantify- ing the economic and grid reliability impacts of improved wind power forecasting. IEEE Transactions on sustainable energy7(4), 1525–1537 (2016)

work page 2016

[2] [2]

Wind Energy Science7(6), 2231–2254 (2022)

Clifton, A., Barber, S., St¨ okl, A., Frank, H., Karlsson, T.: Research challenges and needs for the deployment of wind energy in hilly and mountainous regions. Wind Energy Science7(6), 2231–2254 (2022)

work page 2022

[3] [3]

Science382(6677), 1416–1421 (2023)

Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W.,et al.: Learning skillful medium-range global weather forecasting. Science382(6677), 1416–1421 (2023)

2023

[4] [4]

arXiv preprint arXiv:2202.11214 (2022)

Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mar- dani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., et al.: Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214 (2022)

arXiv 2022

[5] [5]

Nature619(7970), 533–538 (2023)

Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., Tian, Q.: Accurate medium-range 12 global weather forecasting with 3d neural networks. Nature619(7970), 533–538 (2023)

work page 2023

[6] [6]

npj climate and atmospheric science6(1), 190 (2023)

Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., Li, H.: Fuxi: A cas- cade machine learning forecasting system for 15-day global weather forecast. npj climate and atmospheric science6(1), 190 (2023)

work page 2023

[7] [7]

Nature Communications15(1), 6425 (2024)

Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C.,et al.: A machine learning model that outperforms conventional global subseasonal forecast models. Nature Communications15(1), 6425 (2024)

work page 2024

[8] [8]

Nature communications13(1), 1–10 (2022)

Espeholt, L., Agrawal, S., Sønderby, C., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R., Andrychowicz, M., Hickey, J.,et al.: Deep learning for twelve hour precipitation forecasts. Nature communications13(1), 1–10 (2022)

work page 2022