FlexCast: Adaptive Weather Forecasting from Arbitrary Field Sets

Yuang Zhang; Chen Hui; Weisi Lin; Haiqi Zhu; Xiulai Wang; Sun-Yuan Kung; Feng Jiang

cs.LGOct 4, 2026

FlexCast: Adaptive Weather Forecasting from Arbitrary Field Sets

Authors: Yuang Zhang, Chen Hui, Weisi Lin, Haiqi Zhu, Xiulai Wang, Sun-Yuan Kung, Feng Jiang

Organizations: Nanjing University of Information Science and Technology · Nanyang Technological University · Harbin Institute of Technology · Princeton University

Abstract

Most deep learning weather models assign a fixed set of variables and pressure levels to predefined channels, limiting transfer across atmospheric field configurations. This dependence on a fixed field set limits the transferability of trained models across atmospheric field configurations. We propose FlexCast, a field-adaptive weather forecasting model that uses a single set of parameters to produce identity-aligned forecasts for variable-cardinality subsets drawn from a 69-field ERA5 registry. Specifically, a metadata-conditioned adapter the first encodes variable identity, pressure level, and field type and combines them with spatial features. Then, shared rank-16 projec?tions are modulated by metadata-dependent gates to produce field?specific features, while masked set fusion aggregates the available fields into a fixed-width representation. Subsequently, a multiscale U-Transformer processes the fused atmospheric features, while an identity-aware query decoder produces forecasts for the requested fields. Finally, FlexCast learns a standardized six-hour increment and applies it recursively to generate forecasts at longer lead times. Experiments on the 2020 ERA5 test set demonstrate that FlexCast operates across varying field configurations. Compatible cross-field context is associated with lower forecast errors, whereas mismatched context increases them.

Figures & tables

Figure 1: Comparison of fixed-channel forecasting and FlexCast for arbitrary atmospheric field sets. (a) A fixed-channel model forecasts the complete 69-field state. (b) A changed input subset is incompatible with the fixed-channel model’s predefined layout. (c) FlexCast forecasts the supplied fields from valid subsets with a single model. Here, $\mathcal{G}$ denotes the 69-field registry, $X_{\mathcal{G}}$ is the complete input, $S_{t}\subseteq\mathcal{G}$ is the available subset, $N=|S_{t}|$ , and $Q_{t}=S_{t}$ are the requested forecast fields.

Figure 2: Illustration of the proposed FlexCast framework. (a) Overall forecasting pipeline. Each field in the available set $S_{t}$ is encoded using its spatial map and metadata. The adapted field features are fused and processed by a shared U-Transformer, while identity-aware queries produce forecasts for the requested set $Q_{t}=S_{t}$ , with $N=|S_{t}|$ . From left to right, the three addition nodes correspond to metadata conditioning, the gated low-rank feature update, and residual reconstruction using the de-standardized six-hour increment. (b) Metadata-conditioned modulation, where the shared projections $D$ and $U$ reduce and restore the feature width, and $\odot$ denotes element-wise multiplication by the metadata gate. (c) Masked set fusion, where learned slots attend to valid field tokens and are refined by slot self-attention before projection to the spatial representation.

Model	6 h	1 day	4 days	7 days	10 days
FengWu	0.1252	0.2028	0.4860	0.6887	0.8143
Pangu-Weather	0.1182	0.1859	0.4509	0.6960	0.7582
FuXi	0.1258	0.2050	0.4742	0.6484	0.7831
FlexCast(Full-69)	0.1421	0.2269	0.4246	0.5626	0.6341

Table 1: Multi-lead forecasting results on the 2020 ERA5 test set. Values are nRMSE (lower is better) under the same evaluation protocol. The best result in each column is shown in bold, and the second best is underlined.

Condition	Anchors	nRMSE	Change
Anchor only	13	0.1943	–
+Same-variable context	9	0.1695	$-10.2\%$
+Same-level context	9	0.1408	$-21.0\%$
+Broad matched context	13	0.1269	$-34.3\%$
+Broad mismatched context	13	0.2818	$+124.5\%$

Table 2: Cross-field context comparison at 6 h on the 2020 ERA5 test set. Values are mean anchor-field nRMSE (lower is better). Relative changes are computed for each anchor and then averaged: the same-variable, same-level, and broad-matched conditions are compared with the corresponding anchor-only condition, whereas the broad-mismatched condition is compared with the broad-matched condition.

$N$	6 h	12 h	18 h	24 h
8	0.1627	0.2232	0.2659	0.3007
16	0.1662	0.2186	0.2539	0.2822
32	0.1570	0.2032	0.2331	0.2563
48	0.1566	0.2003	0.2278	0.2489
69	0.1421	0.1997	0.2163	0.2269

Table 3: Forecasting results for different field-set cardinalities on the 2020 ERA5 test set.

N

denotes the number of available fields. Values are nRMSE under same-set forecasting (lower is better). The best result is shown in bold, and the second best is underlined.

Method	6 h	1 day	4 days	7 days	10 days
FlexCast (Full-69)	0.1421	0.2269	0.4246	0.5626	0.6341
w/o Metadata Addition	0.2420	0.4906	0.9178	1.1734	1.3965
w/o Modulation	0.1972	0.3844	0.6798	0.7641	0.8072

Table 4: Ablation study of the metadata addition and modulation in FlexCast. Mean nRMSE (lower is better) is reported across different lead times. The best result in each column is shown in bold, and the second best is underlined. “w/o” denotes “without”.

Explore similar work

VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting
Aug 10, 2026Zhisheng Chen, Jinhan Li, Yuxuan Li +6Weather Prediction Graph Representations
1Nanyang Technological University · 2Tsinghua University · 3Peking University +1
MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres
Aug 5, 2026M. L. Carroll, J. Li, S. D. Guzewich +3Climate Exoplanet Atmospheres
1) NASA Goddard Space Flight Center · 2) ADNet Systems, Inc.
StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting
Aug 20, 2026Xiao Wang, Changjian Chen, Rongwen Li +3Weather Prediction Spatio-Temporal Forecasting
Hunan University.

Aug 10, 2026, cs.LGJ/K move · Enter open · S save

VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting

Zhisheng Chen, Jinhan Li, Yuxuan Li, Yuan Gao+5

1Nanyang Technological University · 2Tsinghua University · 3Peking University+1

Weather Prediction Graph Representations

Open paper AskPDF arXiv

Global medium-range weather forecasting requires modeling structured yet state-dependent interactions among heterogeneous atmospheric fields. Existing data-driven models largely learn these interactions implicitly, whereas equation-level physical constraints may inherit approximation and model-form biases. We present VeinCast, a physics-guided dynamic field graph and graph-conditioned fusion framework that jointly forecasts 69 surface and upper-air fields. Within each local window, its Physics-Guided Dynamic Field Graph combines predefined atmospheric relations with state-dependent Top-K residual edges and adapts Earth-window attention using the resulting graph context. Graph-Conditioned Latent Fusion further employs graph context and source-node centrality to guide field-to-latent aggregation, while bounded feedback preserves field-specific information. On the

1.5^\circ

ERA5 benchmark, VeinCast demonstrates competitive forecasting performance across all 69 meteorological fields at lead times of up to 14 days, compared with representative global weather forecasting models including FuXi, Pangu-Weather, GraphCast, FengWu, and ARROW. Ablations confirm that the two modules provide complementary gains, demonstrating the effectiveness of relational-level physical guidance for data-driven weather forecasting.

Abstract

Explore similar work

VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting

MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres

StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting