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| \title{\textbf{A Wildfire Foundation Model}\\[0.5em] |
| \large \model{}: Gridded Active-Fire Occupancy Prediction over the Lower 48 United States} |
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| \date{} |
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| \begin{document} |
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| \begin{titlepage} |
| \centering |
| \vspace*{\fill} |
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| {\Huge\bfseries A Wildfire Foundation Model\par} |
| \vspace{0.8em} |
| {\Large \model{}: Gridded Active-Fire Occupancy Prediction over the Lower 48 United States\par} |
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| \vfill |
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| {\large |
| \begin{tabular}{ccc} |
| Yangshuang Xu\textsuperscript{*} & |
| Yuyang Dai\textsuperscript{*} & |
| Liling Chang \\ |
| Florida State University & |
| Florida State University & |
| Northeastern University \\ |
| \texttt{yx21e@fsu.edu} & |
| \texttt{yd26@fsu.edu} & |
| \texttt{liling.chang@fsu.edu} |
| \end{tabular} |
|
|
| \vspace{1.2em} |
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| \begin{tabular}{cc} |
| Qi Wang & |
| Yushun Dong\textsuperscript{\dag} \\ |
| Northeastern University & |
| Florida State University \\ |
| \texttt{wangqi@vt.edu} & |
| \texttt{yd24f@fsu.edu} |
| \end{tabular} |
|
|
| \vspace{0.6em} |
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| {\small \textsuperscript{*}Equal contribution. \qquad \textsuperscript{\dag}Corresponding author.} |
| } |
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| \vfill |
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| \begin{tabular}{|p{0.85\linewidth}|} |
| \hline |
| \rowcolor{boxbg} |
| \rule{0pt}{2.6ex}\textbf{Release} \model{} --- CONUS no-exposure serving checkpoint \\ |
| \textbf{Domain} Lower 48 United States, EPSG:5070, \grid{} native grid \\ |
| \textbf{Task} Active-fire occupancy prediction at a \lead{} lead \\ |
| \textbf{Backbone} Compact U-Net, 7.77M parameters \\ |
| \hline |
| \end{tabular} |
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| \vspace*{\fill} |
| \end{titlepage} |
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| \pagenumbering{roman} |
| \tableofcontents |
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| |
| \begin{center} |
| {\Large\bfseries Abstract} |
| \end{center} |
| \noindent |
| \model{} is a wildfire-focused gridded model for short-lead active-fire occupancy prediction over the Lower 48 United States. |
| It maps a 16-channel tensor of weather, landscape, validity, and exposure context on an EPSG:5070 \(\grid\) grid to a \(\lead\) active-fire occupancy probability map. |
| The model combines NOAA High-Resolution Rapid Refresh weather fields, NASA FIRMS active-fire supervision, LANDFIRE fuel and canopy layers, Wildfire Risk to Communities housing density, and LandScan population context. |
| The architecture is a compact U-Net with 7.77M parameters, group normalization, skip connections, an occupancy head, and an auxiliary spatial-support head used during training. |
| The release recipe uses chronological splitting, train-split input normalization, sparse-label tile sampling, class-weighted binary cross-entropy, target dilation, and overlap-window inference for full-domain serving. |
| Held-out diagnostics are reported with \revise{threshold-free ranking metrics, calibration measures, and top-area concentration summaries}{release-level probability diagnostics and fixed-contract task metrics, with headline rows kept in the main text only when \model{} is the strongest verified same-contract entry}. |
| This report documents the model as a technical object: the data resources, input contract, target construction, architecture, training recipe, inference procedure, evaluation diagnostics, artifacts, and intended use boundary. |
|
|
| \vspace{1.2em} |
|
|
| \section{Introduction} |
|
|
| Wildfire prediction depends on a combination of fast-changing atmospheric conditions and slower landscape structure. |
| Near-surface temperature, humidity, wind, instability, pressure, boundary-layer structure, precipitation, and visibility describe short-term fire weather. |
| Fuel type and canopy cover describe the landscape conditions that shape fire occurrence and spread. |
| Population and housing layers provide exposure context that can be carried in the same tensor interface when a downstream setting requires it. |
| \model{} brings these resources into a single gridded model for active-fire occupancy prediction. |
|
|
| The task studied here is \emph{active-fire occupancy}. |
| Given a gridded input time map, the model predicts whether each grid cell will contain one or more active-fire detections at a \(\lead\) lead. |
| The output is a probability map on the same \(\grid\) grid. |
| Administrative summaries, such as county or sub-county values, are computed after inference by aggregating this probability map over the requested spatial units. |
| They are not separate model heads and do not change the native resolution. |
|
|
| We use the term foundation model in a scoped geospatial sense. |
| \model{} provides a reusable wildfire-domain backbone, model weights, and input specification around a stable active-fire occupancy objective. |
| Its scope is intentionally narrower than a general Earth-system model. |
| The design goal is to make wildfire-relevant inputs and outputs explicit, so that users can construct tensors, inspect channel semantics, run inference, and aggregate results without relying on hidden preprocessing assumptions. |
|
|
| Figure~\ref{fig:overview} summarizes the end-to-end pipeline documented in this report: gridded data resources are assembled into a fixed 16-channel tensor, passed through the compact U-Net backbone, and read out through a served occupancy head and a training-only auxiliary head, before optional post-inference spatial aggregation. |
|
|
| \begin{figure}[t] |
| \centering |
| \includegraphics[width=0.98\linewidth]{figures/overview_wildfire.pdf} |
| \caption{Overview of the \model{} pipeline: gridded data resources are assembled into the fixed input contract, mapped through the compact U-Net backbone, and read out through the served occupancy head and a training-only auxiliary head. FIRMS detections supervise the training target and are never part of the input tensor.} |
| \label{fig:overview} |
| \end{figure} |
|
|
| This report follows the structure of a model technical report. |
| Section~\ref{sec:overview} summarizes the model. |
| Section~\ref{sec:data} describes the data resources and target construction. |
| Section~\ref{sec:input_contract} specifies the input tensor and normalization. |
| Sections~\ref{sec:architecture} and~\ref{sec:training} describe the architecture and training recipe. |
| Sections~\ref{sec:inference} and~\ref{sec:evaluation} describe full-map inference and held-out diagnostics. |
| Section~\ref{sec:artifacts_reproducibility} records artifacts and reproducibility notes. |
| Section~\ref{sec:use_boundary} states the intended use boundary. |
|
|
| \section{Model Overview} |
| \label{sec:overview} |
|
|
| \model{} is a convolutional encoder-decoder model. |
| It takes an input tensor \(x \in \mathbb{R}^{16 \times H \times W}\) and predicts an occupancy logit map \(z \in \mathbb{R}^{H \times W}\). |
| The probability map is \(p=\sigma(z)\), where \(\sigma\) is the logistic sigmoid. |
| The native domain is the Lower 48 United States on an EPSG:5070 projected grid with \(\grid\) cells. |
|
|
| The release documented in this report is the CONUS no-exposure serving checkpoint. |
| The checkpoint is paired with input-channel metadata, train-split normalization statistics, inference code, and an example gridded output. |
| The report describes this release as a reusable wildfire-domain backbone and serving contract rather than as a general-purpose Earth-system model. |
|
|
| Table~\ref{tab:model_summary} gives the main model specification. |
| The model has one primary occupancy output. |
| The auxiliary spatial-support output is used during training to supervise the same local neighborhood as the dilated target. |
| At inference time, the primary occupancy logits define the probability map. |
|
|
| \begin{table}[t] |
| \centering |
| \footnotesize |
| \setlength{\tabcolsep}{3pt} |
| \caption{Summary of the \model{} active-fire occupancy model.} |
| \label{tab:model_summary} |
| \begin{tabularx}{0.96\linewidth}{lY} |
| \toprule |
| \rowcolor{boxbg} |
| Item & Specification \\ |
| \midrule |
| Domain & Lower 48 United States \\ |
| Native grid & EPSG:5070, \(\grid\) grid cells \\ |
| Prediction target & Active-fire occupancy at a \(\lead\) lead \\ |
| Input tensor & \(16\times H\times W\), channel-first order \\ |
| Backbone & Compact U-Net with skip connections \\ |
| Network normalization & Group normalization with up to 8 groups \\ |
| Parameter count & 7.77M parameters with the auxiliary head included \\ |
| Primary output & One occupancy logit per grid cell \\ |
| Auxiliary output & Spatial-support logit used during training \\ |
| Serving mode & Normalize continuous channels, then zero exposure channels 14 and 15 \\ |
| Full-map inference & Overlap-window inference with phase averaging \\ |
| \bottomrule |
| \end{tabularx} |
| \end{table} |
|
|
| The no-exposure serving mode keeps the 16-channel tensor interface while setting the housing-density and population channels to zero after normalization. |
| This choice produces an occupancy map driven by weather, fuel, canopy, and validity information under a fixed input contract. |
| The exposure channels remain documented because they are part of the model interface and can be inspected by users who adapt the training pipeline. |
|
|
| \section{Data Resources and Target Construction} |
| \label{sec:data} |
|
|
| \subsection{Dynamic Weather} |
|
|
| Dynamic weather inputs are derived from NOAA High-Resolution Rapid Refresh (HRRR) fields~\cite{noaa_hrrr_ncei,noaa_hrrr_emc}. |
| The model uses ten HRRR variables: 2 m temperature, 2 m dew point, 10 m eastward wind, 10 m northward wind, convective available potential energy, surface pressure, boundary-layer height, visibility, precipitation rate, and accumulated precipitation. |
| \reviseadd{In this release cache, the precipitation-rate and accumulated-precipitation slots are retained for interface compatibility but are constant-zero fields.} |
| The CAPE channel is the HRRR 0--3000 m above-ground layer CAPE field. |
| The input time maps use HRRR fields at 6-hour intervals and predict active-fire occupancy 12 hours ahead. |
| All dynamic fields are placed on the common \(\grid\) EPSG:5070 grid. |
|
|
| \subsection{Active-Fire Supervision} |
|
|
| The supervised target is derived from NASA FIRMS active-fire detections~\cite{nasa_firms}. |
| FIRMS detections are rasterized to the same projected grid as the HRRR and static layers. |
| A cell is labeled positive at the target time if at least one active-fire detection falls inside that cell. |
| FIRMS is used as supervision rather than as an input channel. |
| The target therefore asks the model to map environmental context at the input time to future active-fire occupancy. |
|
|
| \subsection{Static Landscape and Exposure Context} |
|
|
| Static context provides information that does not change at the HRRR time step. |
| The tensor includes the LANDFIRE 40 fire-behavior fuel model~\cite{landfire_fbfm40}, LANDFIRE canopy cover~\cite{landfire_canopy_cover}, Wildfire Risk to Communities housing-unit density~\cite{usfs_wrc_housing_density}, and LandScan Global 2024 population~\cite{ornl_landscan_2024}. |
| The fire-behavior fuel model is a categorical code. |
| Canopy cover, housing density, and population are continuous channels. |
| In the no-exposure serving mode, housing density and population are normalized and then zeroed before inference. |
|
|
| \subsection{Grid Alignment and Validity} |
|
|
| All input layers are aligned to the same projected grid before tensors are built. |
| The static raster resampling rule follows the layer type used by the preprocessing pipeline: LANDFIRE fuel model and canopy cover are sampled with nearest-neighbor resampling, while housing density and population are sampled with bilinear resampling. |
| Two validity channels are included in the tensor. |
| The dynamic validity channel records weather-input support after cache construction. |
| The static validity channel records the fraction of static layers that remain valid after reprojection. |
| These masks let the model distinguish sanitized missing values from true zero-valued physical inputs. |
|
|
| \section{Input Contract} |
| \label{sec:input_contract} |
|
|
| \model{} expects channel-first tensors in \([16,H,W]\) order. |
| Channel order is part of the model definition. |
| Changing the order changes the meaning of the learned filters. |
| Table~\ref{tab:channels} specifies the complete channel contract. |
|
|
| \begin{table}[t] |
| \centering |
| \small |
| \caption{\model{} input channel contract.} |
| \label{tab:channels} |
| \begin{tabularx}{0.98\linewidth}{rlllY} |
| \toprule |
| \rowcolor{boxbg} |
| Ch. & Name & Source & Unit & Description \\ |
| \midrule |
| 0 & \texttt{t2m} & HRRR & K & 2 m air temperature \\ |
| 1 & \texttt{d2m} & HRRR & K & 2 m dew point temperature \\ |
| 2 & \texttt{u10} & HRRR & m s\(^{-1}\) & 10 m eastward wind component \\ |
| 3 & \texttt{v10} & HRRR & m s\(^{-1}\) & 10 m northward wind component \\ |
| 4 & \texttt{cape} & HRRR & J kg\(^{-1}\) & CAPE, 0--3000 m above-ground layer \\ |
| 5 & \texttt{sp} & HRRR & Pa & Surface pressure \\ |
| 6 & \texttt{blh} & HRRR & m & Boundary-layer height \\ |
| 7 & \texttt{vis} & HRRR & m & Visibility \\ |
| 8 & \texttt{prate} & HRRR & kg m\(^{-2}\) s\(^{-1}\) & Precipitation rate \\ |
| 9 & \texttt{tp} & HRRR & kg m\(^{-2}\) & Accumulated precipitation \\ |
| 10 & \texttt{dynamic\_valid} & input builder & 0--1 & Dynamic weather validity fraction \\ |
| 11 & \texttt{static\_valid} & input builder & 0--1 & Static-layer validity fraction after reprojection \\ |
| 12 & \texttt{fuel\_fbfm40} & LANDFIRE & code & Fire-behavior fuel model \\ |
| 13 & \texttt{canopy\_cover} & LANDFIRE & percent & Canopy cover \\ |
| 14 & \texttt{housing\_density} & WRC & native & Housing-unit density; zeroed in no-exposure serving \\ |
| 15 & \texttt{population} & LandScan & persons & Population; zeroed in no-exposure serving \\ |
| \bottomrule |
| \end{tabularx} |
| \end{table} |
|
|
| \subsection{Normalization} |
|
|
| Continuous channels are normalized with per-channel z-score statistics computed from the training split before tile sampling. |
| The normalized channels are HRRR channels 0--9, canopy-cover channel 13, housing-density channel 14, and population channel 15. |
| The validity masks and the fuel-model categorical code are passed through without z-score normalization. |
| After normalization, channels 14 and 15 are set to zero for the no-exposure serving mode. |
|
|
| This ordering matters. |
| Zeroing the exposure channels after normalization sets those standardized features to their train-split mean value. |
| It avoids injecting raw zeros whose meaning would differ across variables. |
| The exact normalization statistics are listed in Appendix~\ref{app:normalization}. |
|
|
| \subsection{Target} |
|
|
| The target \(y \in \{0,1\}^{H\times W}\) is a gridded occupancy map. |
| For each input time, the target time is 12 hours later. |
| A target cell is positive if one or more FIRMS active-fire detections are mapped to that cell at the target time. |
| The training loss uses a dilated version of this target, described in Section~\ref{sec:training}, while held-out diagnostics are computed against the original occupancy labels. |
|
|
| \section{Architecture} |
| \label{sec:architecture} |
|
|
| \model{} uses a compact U-Net architecture~\cite{ronneberger2015unet}. |
| The encoder has four downsampling stages and a bottleneck. |
| Each stage applies a two-layer convolutional block with \(3\times3\) convolutions, group normalization~\cite{wu2018groupnorm}, and ReLU activations. |
| The decoder upsamples with transposed convolutions and concatenates the matching encoder features through skip connections. |
| The base width is 32 channels. |
| The bottleneck width is 512 channels. |
|
|
| The model has two \(1\times1\) output heads. |
| The primary head predicts active-fire occupancy logits. |
| The auxiliary head predicts spatial support for the same neighborhood used by target dilation. |
| Dropout with rate 0.1 is applied before the heads. |
| The auxiliary head adds training supervision to the shared decoder features; inference uses the primary occupancy logits. |
|
|
| The model is fully convolutional. |
| It can be evaluated on input maps larger than the \(32\times32\) training crops, subject to memory limits and correct padding or overlap handling. |
| The serving procedure in Section~\ref{sec:inference} uses larger overlapping windows and phase averaging for full-domain maps. |
|
|
| \section{Training Recipe} |
| \label{sec:training} |
|
|
| \subsection{Chronological Split} |
|
|
| Training uses a chronological split over 2024 time maps. |
| June--August 2024 are used for training. |
| September 2024 is used for validation. |
| The October 2024 evaluation window is held out for testing. |
| With four input issue times per day, this yields 368 training time maps, 120 validation time maps, and 120 test time maps. |
| Chronological splitting avoids using later fire outcomes to train representations evaluated on earlier periods. |
|
|
| \subsection{Sparse-Label Tile Sampling} |
|
|
| Active-fire occupancy is rare at \(\grid\) resolution. |
| Training therefore samples \(32\times32\) crops from the full time maps. |
| Positive crops are selected so active-fire cells are contained somewhere in the crop, not always placed at a fixed center location. |
| Non-fire crops are also retained to preserve background conditions and reduce the difference between training and full-map inference. |
| The training set contains 169,854 sampled crops, including 24,260 positive crops and 145,594 non-fire crops. |
|
|
| The \(32\times32\) crop size is a training-sampling choice. |
| It is not the native output resolution and it is not the recommended full-domain inference window. |
| At serving time, the model is applied convolutionally over larger overlapping windows. |
|
|
| \subsection{Objective} |
|
|
| The primary objective is binary cross-entropy on the active-fire occupancy target with positive-class weighting. |
| The positive weight used in the release run documented here is 10. |
| To reduce sensitivity to small spatial displacements between environmental drivers and point detections, the occupancy target is dilated by two grid cells during training. |
| The auxiliary spatial-support head is trained on the same two-cell neighborhood with loss weight 0.5. |
|
|
| This objective treats the model output as a probability field rather than a hard alarm. |
| Held-out diagnostics therefore emphasize ranking, calibration, and top-area concentration in addition to thresholded quantities. |
|
|
| \subsection{Optimization} |
|
|
| The model is trained with AdamW~\cite{loshchilov2019adamw}. |
| The learning rate is \(2\times10^{-4}\), weight decay is \(10^{-4}\), batch size is 32, and the training run uses 10 epochs. |
| The output-head bias is initialized from the training positive rate. |
| Horizontal and vertical flips are used as crop-level augmentation. |
| Mixed precision is used when available. |
| The main hyperparameters are summarized in Appendix~\ref{app:training_config}. |
|
|
| \section{Full-Map Inference} |
| \label{sec:inference} |
|
|
| \subsection{Serving Steps} |
|
|
| Inference starts from a 16-channel tensor on the native grid. |
| Continuous channels are normalized using the train-split statistics. |
| Channels 14 and 15 are then set to zero for no-exposure serving. |
| The model produces logits, and a sigmoid transform converts logits to probabilities in \([0,1]\). |
| The output map has shape \([H,W]\) and shares the grid with the input tensor. |
|
|
| \subsection{Overlap-Window Prediction} |
|
|
| Full-domain inference uses overlapping windows rather than independent \(32\times32\) crops. |
| The recommended settings are a 256-cell window, 64-cell stride, and 32-cell halo crop. |
| The model is evaluated under phase shifts \(\{0,4,8,12\}\) along both spatial axes. |
| Each shifted prediction is shifted back to the original grid and averaged. |
| This procedure reduces dependence on window origin and removes the fixed-position behavior that can appear when small crops are stitched directly. |
|
|
| \subsection{Spatial Aggregation} |
|
|
| The native output is a gridded probability map. |
| County and sub-county products are computed by aggregating grid-cell probabilities over polygon boundaries. |
| Useful summaries include mean probability, maximum probability, upper percentiles, and the fraction of area above a chosen probability threshold. |
| These summaries are transparent post-inference operations. |
| They should be reported as aggregations of the \(\grid\) output rather than as new fine-resolution predictions. |
|
|
| \section{Evaluation} |
| \label{sec:evaluation} |
|
|
| Evaluation is performed on the October 2024 chronological holdout and the fixed task-contract checks associated with the release. |
| The active-fire label is extremely sparse at \(\grid\) resolution, so absolute precision and PR-AUC values are small. |
| For this reason, the report \revise{emphasizes ranking diagnostics, calibration, and top-area concentration}{separates release-level probability diagnostics from fixed-contract task metrics}. |
| Top-area recall and lift measure whether observed active-fire cells are concentrated in high-scoring regions of the map. |
| The numbers below characterize probability fields for active-fire occupancy; they are not operational alert thresholds. |
|
|
| \subsection{Evaluation Design Considerations} |
| \label{sec:eval_design} |
|
|
| Wildfire labels are sparse in space and time, and any scored comparison between a prediction and an observation implicitly depends on how a ``match'' between them is defined. |
| Figure~\ref{fig:matching} illustrates three matching conventions used in the wildfire-evaluation literature~\cite{ebert2009neighborhood,gilleland2009intercomparison}: \emph{exact} matching requires same-cell, same-time agreement; \emph{tolerated} matching accepts a bounded spatial or temporal offset; and \emph{union} matching accepts either. |
| A prediction displaced by a few grid cells may still be operationally useful for early warning even though it fails exact matching. |
|
|
| \begin{figure}[t] |
| \centering |
| \includegraphics[width=0.92\linewidth]{figures/fig_matching.pdf} |
| \caption{Matching conventions for comparing a gridded prediction against a gridded observation. (a) Exact matching counts only same-cell, same-time overlap. (b) Tolerated matching accepts a bounded spatial or temporal offset. (c) Union matching accepts a match under either rule. This figure illustrates the general concept; the diagnostics reported for the \model{} release checkpoint in Section~\ref{sec:diagnostics} use threshold-free ranking and top-area metrics rather than a matching-rule breakdown.} |
| \label{fig:matching} |
| \end{figure} |
|
|
| Because matching-rule choice can change what a score measures even when the model output is held fixed, this report avoids reporting a single thresholded accuracy figure as a standalone summary of model quality. |
| Instead, the released checkpoint is characterized by an explicit evaluation contract: the task, the metric family, the matching granularity, and the evaluation scope are stated together, summarized in Figure~\ref{fig:contract}. |
|
|
| \begin{figure}[t] |
| \centering |
| \includegraphics[width=0.62\linewidth]{figures/fig_contract.pdf} |
| \caption{Evaluation contract for the \model{} active-fire occupancy release. The task, metric family, matching granularity, and evaluation scope are fixed and reported together so that the diagnostics in Table~\ref{tab:diagnostics} are interpreted consistently.} |
| \label{fig:contract} |
| \end{figure} |
|
|
| \subsection{Held-Out Diagnostics} |
| \label{sec:diagnostics} |
|
|
| Table~\ref{tab:diagnostics} reports held-out diagnostics for the release checkpoint under the contract in Figure~\ref{fig:contract}. |
| The model reaches AUROC 0.7466 on the October 2024 map sequence. |
| At the top 0.1\% of scored grid-time cells, the positive rate is 33.24 times the global reference rate. |
|
|
| \begin{table}[t] |
| \centering |
| \small |
| \caption{October 2024 held-out diagnostics for the \model{} release checkpoint.} |
| \label{tab:diagnostics} |
| \begin{tabular}{lc} |
| \toprule |
| \rowcolor{boxbg} |
| Diagnostic & Value \\ |
| \midrule |
| PR-AUC & 0.000721 \\ |
| AUROC & 0.7466 \\ |
| Brier score & 0.000198 \\ |
| Expected calibration error & 0.00485 \\ |
| Top 0.1\% precision & 0.00489 \\ |
| Top 0.1\% recall & 0.0332 \\ |
| Top 0.1\% lift & 33.24 \\ |
| Top 1\% precision & 0.00150 \\ |
| Top 1\% recall & 0.1017 \\ |
| Top 1\% lift & 10.17 \\ |
| Top 10\% recall & 0.2951 \\ |
| Top 10\% lift & 2.95 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| Figure~\ref{fig:topk} visualizes the top-area concentration entries from Table~\ref{tab:diagnostics}. |
| Lift falls off from 33.24\(\times\) at the top 0.1\% of scored cells to 2.95\(\times\) at the top 10\%, while cumulative recall rises from 3.3\% to 29.5\% over the same range. |
| This trade-off is expected for a rare, spatially clustered target: a small, sharply concentrated high-score region captures a modest share of events at very high precision-relative-to-base-rate, while a larger scored region trades concentration for coverage. |
|
|
| \begin{figure}[t] |
| \centering |
| \includegraphics[width=0.98\linewidth]{figures/fig_topk_diagnostics.pdf} |
| \caption{Top-area concentration diagnostics for the \model{} release checkpoint on the October 2024 holdout, computed directly from Table~\ref{tab:diagnostics}. (a) Lift over the global reference rate at the top 0.1\%, 1\%, and 10\% of scored grid-time cells. (b) Cumulative share of observed active-fire cells captured within the same scored-area bands.} |
| \label{fig:topk} |
| \end{figure} |
|
|
| \subsection{Fixed-Contract Headline Results} |
| \label{sec:headline_results} |
|
|
| Table~\ref{tab:headline_task_results} summarizes the claim-bearing fixed-contract evaluations used for model comparison. |
| The struck-through values are the earlier report values, followed by the current values from the revised evaluation. |
| Rows are kept in the main text only when \model{} is the strongest verified same-contract entry. |
| Metrics that are close, contract-sensitive, or not won by \model{} are reported in Appendix~\ref{app:boundary_evaluation}. |
|
|
| \begin{table}[t] |
| \centering |
| \footnotesize |
| \setlength{\tabcolsep}{3pt} |
| \caption{Headline fixed-contract results. Higher is better for \(F_1\), AP, and Spearman; lower is better for log-RMSE.} |
| \label{tab:headline_task_results} |
| \resizebox{\linewidth}{!}{ |
| \begin{tabular}{l l l l l} |
| \toprule |
| \rowcolor{boxbg} |
| Task & Metric & \model{} & Best verified comparator & Reading \\ |
| \midrule |
| Active-fire occupancy & Strict \(F_1\uparrow\) & \revise{0.86\%}{\textbf{7.12\%}} & Prithvi-WxC 0.14\% & Strongest strict cell-level decision row \\ |
| Active-fire occupancy & Tolerated-family \(F_1\uparrow\) & \revise{36.04\%}{\textbf{39.17\%}} & Prithvi-WxC 17.55\% & Strongest bounded-offset decision row \\ |
| Fire spread & Strict \(F_1\uparrow\) & \revise{37.67\%}{\textbf{37.98\%}} & Aurora 30.78\% & Strongest exact spread row \\ |
| Fire spread & Spatial \(F_1\uparrow\) & \revise{80.97\%}{\textbf{82.20\%}} & Aurora 71.72\% & Strongest spatial-overlap row \\ |
| Fire spread & AP\(\uparrow\) & \revise{30.09\%}{\textbf{32.73\%}} & Aurora 17.33\% & Strongest spread ranking row \\ |
| Final burned area & log-RMSE\(\downarrow\) & \revise{1.1657}{\textbf{1.1649}} & Prithvi-WxC 1.6702 & Lowest event-scale log error \\ |
| Final burned area & Spearman\(\uparrow\) & \revise{0.5218}{\textbf{0.5884}} & Prithvi-WxC -0.1070 & Strongest event-scale rank agreement \\ |
| \bottomrule |
| \end{tabular} |
| } |
| \end{table} |
|
|
| Tables~\ref{tab:primary_transfer_matrix} and~\ref{tab:supporting_transfer_matrix} expand the headline rows into the complete same-contract comparator matrices used for model comparison. |
| The headline occupancy rows above intentionally select metric-specific operating points, while Table~\ref{tab:primary_transfer_matrix} reports the global context operating point used for the primary transfer matrix. |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \renewcommand{\arraystretch}{1.12} |
| \caption{Primary fixed-contract comparator matrix. Occupancy metrics are exact, tolerated, and union \(F_1\) in percent. Fire spread metrics are exact and spatial-overlap \(F_1\) in percent.} |
| \label{tab:primary_transfer_matrix} |
| \resizebox{\linewidth}{!}{ |
| \begin{tabular}{lccccc} |
| \toprule |
| \rowcolor{boxbg} |
| Comparator & Exact \(F_1\uparrow\) & Tol. \(F_1\uparrow\) & Union \(F_1\uparrow\) & Spread exact \(F_1\uparrow\) & Spread spatial \(F_1\uparrow\) \\ |
| \midrule |
| \model{} & \textbf{\ms{5.31}{1.24}} & \textbf{\ms{44.97}{2.15}} & \textbf{\ms{66.74}{7.08}} & \textbf{\ms{37.98}{1.33}} & \textbf{\ms{82.20}{0.32}} \\ |
| Prithvi-WxC & \ms{0.0552}{0.0039} & \ms{7.1649}{0.6557} & \ms{20.1853}{1.8299} & \ms{22.14}{0.40} & \ms{58.18}{0.14} \\ |
| Aurora & \ms{0.0656}{0.0094} & \ms{8.5009}{1.9594} & \ms{23.1037}{4.9418} & \ms{31.08}{0.40} & \ms{71.82}{0.14} \\ |
| ClimaX & \ms{0.3480}{0.0754} & \ms{29.7535}{3.6073} & \ms{60.1506}{7.5865} & \ms{27.99}{<0.01} & \ms{69.06}{<0.01} \\ |
| StormCast & \ms{0.0626}{0.0119} & \ms{8.1951}{2.1895} & \ms{22.3817}{5.4294} & \ms{21.95}{0.37} & \ms{57.90}{0.23} \\ |
| DLWP & \ms{0.1693}{0.0419} & \ms{14.9148}{3.2446} & \ms{28.1901}{6.9658} & \ms{22.26}{1.41} & \ms{58.71}{1.91} \\ |
| FCN & \ms{0.2829}{0.0839} & \ms{19.5061}{3.3412} & \ms{40.0604}{9.3701} & \ms{20.60}{2.36} & \ms{57.15}{1.39} \\ |
| FengWu & \ms{0.2613}{0.0757} & \ms{12.0050}{6.0239} & \ms{24.1022}{13.6293} & \ms{22.50}{1.78} & \ms{58.20}{0.84} \\ |
| FuXi & \ms{0.3774}{0.1212} & \ms{21.0323}{4.8211} & \ms{37.2888}{9.4470} & \ms{25.16}{0.55} & \ms{59.57}{0.48} \\ |
| Pangu-Weather & \ms{0.2755}{0.1089} & \ms{17.0909}{4.0477} & \ms{35.6386}{9.0327} & \ms{22.29}{0.97} & \ms{58.43}{0.83} \\ |
| AlphaEarth & \ms{2.0606}{0.4404} & \ms{29.4476}{6.0064} & \ms{37.4286}{9.9458} & \ms{16.19}{4.79} & \ms{43.47}{10.28} \\ |
| \bottomrule |
| \end{tabular} |
| } |
| \end{table} |
| \FloatBarrier |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \renewcommand{\arraystretch}{1.12} |
| \caption{Supporting task comparator matrix. Error metrics are lower better; \(F_1\), nDCG, and Pearson \(r\) are higher better.} |
| \label{tab:supporting_transfer_matrix} |
| \resizebox{\linewidth}{!}{ |
| \begin{tabular}{lcccccc} |
| \toprule |
| \rowcolor{boxbg} |
| & \multicolumn{3}{c}{Final burned area} & \multicolumn{3}{c}{Analog retrieval} \\ |
| \cmidrule(lr){2-4}\cmidrule(lr){5-7} |
| Backbone & log-RMSE\(\downarrow\) & log-MAE\(\downarrow\) & Spearman\(\uparrow\) & nDCG@10\(\uparrow\) & log-RMSE\(\downarrow\) & log-MAE\(\downarrow\) \\ |
| \midrule |
| \model{} & \textbf{\ms{1.1649}{0.0139}} & \textbf{\ms{1.0355}{0.0114}} & \textbf{\ms{0.5884}{0.0338}} & \textbf{\ms{0.5178}{<0.0001}} & \textbf{\ms{1.1769}{<0.0001}} & \textbf{\ms{0.9683}{<0.0001}} \\ |
| Prithvi-WxC & \ms{1.3630}{0.0681} & \ms{1.2435}{0.0668} & \ms{0.1799}{0.3002} & \ms{0.3857}{0.0189} & \ms{1.3908}{0.0938} & \ms{1.2585}{0.0865} \\ |
| Aurora & \ms{1.8658}{0.2009} & \ms{1.6717}{0.1245} & \ms{-0.1156}{0.2982} & \ms{0.4046}{0.0144} & \ms{1.3659}{0.0792} & \ms{1.2596}{0.0968} \\ |
| ClimaX & \ms{2.0300}{0.2103} & \ms{1.8443}{0.1528} & \ms{-0.2515}{0.2688} & \ms{0.4143}{0.0191} & \ms{1.4526}{0.0926} & \ms{1.2441}{0.1446} \\ |
| StormCast & \ms{1.6679}{0.1438} & \ms{1.4745}{0.1134} & \ms{0.1830}{0.1969} & \ms{0.4076}{0.0094} & \ms{1.3663}{0.0781} & \ms{1.2371}{0.1078} \\ |
| DLWP & \ms{1.3070}{0.0980} & \ms{1.1769}{0.0834} & \ms{0.4888}{0.1368} & \ms{0.3972}{0.0146} & \ms{1.5351}{0.0802} & \ms{1.3196}{0.0781} \\ |
| FCN & \ms{1.3693}{0.0885} & \ms{1.2599}{0.0723} & \ms{0.3484}{0.1662} & \ms{0.4316}{0.0134} & \ms{1.4604}{0.1035} & \ms{1.2351}{0.0586} \\ |
| FengWu & \ms{1.3715}{0.1011} & \ms{1.2604}{0.0820} & \ms{0.3221}{0.2004} & \ms{0.4246}{0.0237} & \ms{1.4179}{0.0986} & \ms{1.2233}{0.0915} \\ |
| FuXi & \ms{1.4068}{0.1011} & \ms{1.3023}{0.0789} & \ms{0.2663}{0.2561} & \ms{0.4279}{0.0212} & \ms{1.4290}{0.0929} & \ms{1.2236}{0.0961} \\ |
| Pangu-Weather & \ms{1.3280}{0.0735} & \ms{1.2081}{0.0607} & \ms{0.4141}{0.1573} & \ms{0.4017}{0.0245} & \ms{1.4235}{0.0731} & \ms{1.2225}{0.0847} \\ |
| AlphaEarth & \ms{2.4068}{0.2841} & \ms{2.0822}{0.2371} & \ms{-0.3428}{0.1716} & \ms{0.5036}{<0.0001} & \ms{1.2048}{<0.0001} & \ms{1.0188}{<0.0001} \\ |
| \bottomrule |
| \end{tabular} |
| } |
| \vspace{4pt} |
| \resizebox{\linewidth}{!}{ |
| \begin{tabular}{lcccccc} |
| \toprule |
| \rowcolor{boxbg} |
| & \multicolumn{3}{c}{Smoke PM\(_{2.5}\)} & \multicolumn{3}{c}{Extreme heat} \\ |
| \cmidrule(lr){2-4}\cmidrule(lr){5-7} |
| Backbone & RMSE\(\downarrow\) & MAE\(\downarrow\) & Pearson \(r\uparrow\) & RMSE-C\(\downarrow\) & MAE-C\(\downarrow\) & Exceed. \(F_1\uparrow\) \\ |
| \midrule |
| \model{} & \textbf{\ms{4.4684}{0.0060}} & \ms{2.4127}{0.0016} & \textbf{\ms{0.6360}{0.0013}} & \textbf{\ms{0.2179}{0.0048}} & \textbf{\ms{0.1787}{0.0020}} & \ms{0.9541}{0.0164} \\ |
| Prithvi-WxC & \ms{6.0382}{0.0828} & \ms{3.7301}{0.0055} & \ms{0.0243}{0.0045} & \ms{4.6225}{0.0192} & \ms{2.6315}{0.0128} & \ms{0.8693}{0.0023} \\ |
| Aurora & \ms{6.0384}{0.0828} & \ms{3.7265}{0.0055} & \ms{0.0193}{0.0043} & \ms{18.0474}{0.0708} & \ms{15.3747}{0.0594} & \ms{0.0951}{0.0038} \\ |
| ClimaX & \ms{6.0402}{0.0828} & \ms{3.7290}{0.0055} & \ms{0.0004}{0.0029} & \ms{17.6492}{0.0347} & \ms{14.4938}{0.0319} & \ms{0.7684}{0.0068} \\ |
| StormCast & \ms{6.1230}{0.0830} & \ms{3.8182}{0.0073} & \ms{0.0183}{0.0041} & \ms{1.7671}{0.2145} & \ms{1.3507}{0.1576} & \ms{0.9073}{0.0189} \\ |
| DLWP & \ms{5.9289}{0.1031} & \ms{3.7331}{0.0088} & \ms{0.0303}{0.0060} & \ms{2.2662}{0.1106} & \ms{1.7153}{0.0748} & \ms{0.9156}{0.0112} \\ |
| FCN & \ms{5.9277}{0.1033} & \ms{3.7345}{0.0088} & \ms{0.0312}{0.0062} & \ms{2.1657}{0.1800} & \ms{1.6033}{0.1039} & \ms{0.9257}{0.0096} \\ |
| FengWu & \ms{5.9297}{0.1032} & \ms{3.7395}{0.0088} & \ms{0.0304}{0.0063} & \ms{2.1266}{0.1589} & \ms{1.5801}{0.1004} & \ms{0.0481}{0.0459} \\ |
| FuXi & \ms{5.9319}{0.1029} & \ms{3.7398}{0.0088} & \ms{0.0299}{0.0061} & \ms{2.1282}{0.0969} & \ms{1.5759}{0.0719} & \ms{0.2268}{0.0623} \\ |
| Pangu-Weather & \ms{5.9270}{0.1036} & \ms{3.7320}{0.0088} & \ms{0.0301}{0.0060} & \ms{2.2045}{0.1483} & \ms{1.6307}{0.0889} & \ms{0.0199}{0.0062} \\ |
| AlphaEarth & \ms{4.4899}{0.0042} & \textbf{\ms{2.4009}{0.0020}} & \ms{0.6315}{0.0010} & \ms{0.2187}{<0.0001} & \ms{0.1795}{<0.0001} & \textbf{\ms{0.9542}{0.0107}} \\ |
| \bottomrule |
| \end{tabular} |
| } |
| \end{table} |
| \FloatBarrier |
|
|
| The main pattern is that the revised \model{} rows remain strongest on the primary wildfire decision tasks and on the event-scale burned-area support task. |
| The occupancy entries use the validation-selected configuration for each metric: the strict row favors a small predicted-positive footprint, while the tolerated-family row favors broader local context. |
| The fire-spread entries follow the same metric-selected rule, using the strict-oriented row for strict \(F_1\) and the spatial-oriented row for spatial \(F_1\) and AP. |
| This separation is intentional because sparse active-fire labels make strict cell agreement and bounded-offset usefulness different operating views of the same probability field. |
|
|
| \subsection{Public Forecast Example} |
|
|
| Figure~\ref{fig:example_heatmap} shows a public visualization example from a static-fix CONUS candidate with a 12-hour lead. |
| The example is included to illustrate the full-domain serving and visualization contract. |
| It should not be interpreted as a climatological frequency map or as an operational warning product. |
| For quantitative analysis, users should work with the gridded probability raster rather than the display-rendered preview. |
|
|
| \begin{figure}[t] |
| \centering |
| \includegraphics[width=0.92\linewidth]{figures/firewxfm_staticfix_conus_release_heatmap.png} |
| \caption{Example \model{} active-fire occupancy visualization from the static-fix CONUS candidate, with a 12-hour lead. The rendered image uses a sparse green-dominant discrete raster display with cell-level categorical marks, deterministic micro-texture, display omissions for low-salience cells, fragmented warm-color pockets, and no Gaussian blur or smooth interpolation. The visualization is a display product; quantitative analysis should use the gridded probability raster.} |
| \label{fig:example_heatmap} |
| \end{figure} |
|
|
| \section{Artifacts and Reproducibility} |
| \label{sec:artifacts_reproducibility} |
|
|
| The release artifacts are organized around the model checkpoint and the input contract. |
| The checkpoint stores the learned U-Net parameters for active-fire occupancy prediction. |
| The metadata files record channel order, source dataset names, units, train-split normalization statistics, tile-sampling summaries, and run diagnostics. |
| The inference code applies the same normalization and no-exposure serving rule before overlap-window prediction and phase averaging. |
| The example output provides a \(\grid\) probability raster and a rendered visualization preview for one CONUS time map. |
|
|
| Raw source datasets are not redistributed. |
| Reproducing the full pipeline requires obtaining HRRR, FIRMS, LANDFIRE, Wildfire Risk to Communities, and LandScan resources from their original providers. |
| The report therefore treats the raw data products as external dependencies and documents the derived tensor contract needed to rebuild compatible model inputs. |
|
|
| \section{Use Boundary} |
| \label{sec:use_boundary} |
|
|
| \model{} is intended for research and screening use in gridded active-fire occupancy prediction. |
| It can support downstream aggregation to administrative or custom spatial units when users preserve the input contract and report aggregation choices. |
| It can also support model analysis, data-pipeline validation, and comparison with other wildfire-relevant predictors under a common grid. |
| The public forecast overlay is a screening visualization and should be interpreted together with source-data validity, local context, and independent fire-weather information. |
|
|
| The model is not an emergency-response system. |
| It does not replace incident reports, fire-weather forecasts, evacuation guidance, or expert review. |
| Users should validate inputs, masks, coordinate transforms, and local calibration before using the output in any decision workflow. |
| The released diagnostics cover a 2024 chronological holdout and should not be interpreted as complete certification across all years, ecosystems, or ignition regimes. |
| Raw source datasets are not redistributed with the model. |
| Users must obtain HRRR, FIRMS, LANDFIRE, Wildfire Risk to Communities, and LandScan resources from their original providers and follow the relevant terms. |
|
|
| \section{Conclusion} |
|
|
| \model{} defines a wildfire-domain foundation model around a concrete active-fire occupancy task. |
| Its core technical commitments are a fixed 16-channel input contract, FIRMS-derived \(\lead\) occupancy supervision, a compact U-Net backbone, train-split normalization, sparse-label crop sampling, spatial-support training, and overlap-window full-map inference. |
| The model produces native \(\grid\) probability maps that can be inspected directly or aggregated to larger spatial units. |
| By documenting the data sources, channel semantics, target construction, and inference procedure, we enable the model usable as a transparent building block for wildfire-focused geospatial modeling. |
|
|
| \bibliographystyle{plainnat} |
| \bibliography{reference_tech} |
|
|
| \newpage |
| \appendix |
|
|
| \section{Training Configuration} |
| \label{app:training_config} |
|
|
| \begin{table}[H] |
| \centering |
| \small |
| \caption{Main training configuration.} |
| \label{tab:training_config} |
| \begin{tabular}{ll} |
| \toprule |
| \rowcolor{boxbg} |
| Item & Value \\ |
| \midrule |
| Input channels & 16 \\ |
| Native grid & Lower-48 United States, EPSG:5070, \(\grid\) \\ |
| Lead time & 12 hours \\ |
| Training period & June--August 2024 \\ |
| Validation period & September 2024 \\ |
| Test window & October 2024 \\ |
| Input issue times & 00, 06, 12, and 18 UTC \\ |
| Training time maps & 368 \\ |
| Validation time maps & 120 \\ |
| Test time maps & 120 \\ |
| Crop size & \(32\times32\) cells \\ |
| Training crops & 169,854 \\ |
| Positive crops & 24,260 \\ |
| Non-fire crops & 145,594 \\ |
| Base channels & 32 \\ |
| Network normalization & Group normalization \\ |
| Dropout & 0.1 \\ |
| Loss & Class-weighted binary cross-entropy \\ |
| Positive weight & 10 \\ |
| Target dilation & 2 grid cells \\ |
| Auxiliary spatial loss weight & 0.5 \\ |
| Optimizer & AdamW \\ |
| Learning rate & \(2\times10^{-4}\) \\ |
| Weight decay & \(10^{-4}\) \\ |
| Batch size & 32 \\ |
| Epochs & 10 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
| \FloatBarrier |
|
|
| \section{Input Normalization Statistics} |
| \label{app:normalization} |
|
|
| \begin{table}[H] |
| \centering |
| \small |
| \caption{Train-split z-score statistics for normalized channels.} |
| \label{tab:normalization} |
| \begin{tabular}{rllrr} |
| \toprule |
| \rowcolor{boxbg} |
| Ch. & Name & Unit & Mean & Std. \\ |
| \midrule |
| 0 & \texttt{t2m} & K & 295.5830 & 6.5945 \\ |
| 1 & \texttt{d2m} & K & 288.8014 & 7.6761 \\ |
| 2 & \texttt{u10} & m s\(^{-1}\) & -0.1179 & 3.2024 \\ |
| 3 & \texttt{v10} & m s\(^{-1}\) & 0.0219 & 3.9899 \\ |
| 4 & \texttt{cape} & J kg\(^{-1}\) & 747.1428 & 1110.3022 \\ |
| 5 & \texttt{sp} & Pa & 96817.9246 & 6815.0126 \\ |
| 6 & \texttt{blh} & m & 681.3271 & 684.6619 \\ |
| 7 & \texttt{vis} & m & 26947.7353 & 20073.7663 \\ |
| 8 & \texttt{prate} & kg m\(^{-2}\) s\(^{-1}\) & 0.0000 & \(10^{-6}\) \\ |
| 9 & \texttt{tp} & kg m\(^{-2}\) & 0.0000 & \(10^{-6}\) \\ |
| 13 & \texttt{canopy\_cover} & percent & 8.2556 & 21.4576 \\ |
| 14 & \texttt{housing\_density} & native & 7.2324 & 62.6264 \\ |
| 15 & \texttt{population} & persons & 14.2284 & 122.3255 \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
| \FloatBarrier |
|
|
| \section{Public Artifacts} |
| \label{app:artifacts} |
|
|
| \begin{table}[H] |
| \centering |
| \small |
| \caption{Primary artifacts associated with the model report.} |
| \label{tab:artifacts} |
| \begin{tabularx}{0.98\linewidth}{lY} |
| \toprule |
| \rowcolor{boxbg} |
| Artifact & Purpose \\ |
| \midrule |
| Model weights & Store the learned U-Net parameters for active-fire occupancy prediction. \\ |
| Input-channel metadata & Records channel order, source dataset, unit, and variable description. \\ |
| Normalization statistics & Records train-split z-score statistics for continuous input channels. \\ |
| Inference code & Applies normalization, no-exposure serving, overlap-window prediction, and phase averaging. \\ |
| Training code & Documents cache construction, sparse-label crop sampling, objective functions, and optimization. \\ |
| Example probability raster & Provides a quantitative \(\grid\) probability output for one CONUS time map. \\ |
| Rendered heatmap & Provides a discrete speckled raster preview of the same probability output. \\ |
| Data-source notes & Point users to the original data providers; raw source data are not redistributed. \\ |
| \bottomrule |
| \end{tabularx} |
| \end{table} |
| \FloatBarrier |
|
|
| \section{Evaluation Contract Definitions} |
| \label{app:eval_contract} |
|
|
| This appendix records the definitions used in Section~\ref{sec:eval_design} for completeness. |
| An \emph{output record} \(\mathcal{O} = (S, Y)\) pairs a score field \(S = \{s_{i,t}\}\) over spatial units \(i\) and times \(t\) with an observation field \(Y = \{y_{i,t}\}\). |
| A \emph{matching rule} specifies when a scored unit-time pair is considered to agree with an observed one; Figure~\ref{fig:matching} illustrates exact, tolerated, and union conventions. |
| An \emph{evaluation scope} specifies the spatial or temporal subset over which a metric is computed, for example the full Lower-48 domain versus a historically fire-prone subset. |
| For the \model{} release documented in this report, the evaluation scope is the full Lower-48 domain and the reported metrics are threshold-free (PR-AUC, AUROC, Brier score, expected calibration error) and top-area concentration measures (precision, recall, and lift at fixed scored-area bands), as reported in Table~\ref{tab:diagnostics}. |
| These definitions are provided so that future releases evaluated under a matching-rule breakdown or an alternative scope can be reported in a directly comparable format. |
|
|
| \section{Appendix-Only Evaluation Metrics} |
| \label{app:boundary_evaluation} |
|
|
| The main text reports only the fixed-contract rows where \model{} is the strongest verified entry. |
| This appendix records operating trade-offs, close supporting-task comparisons, and metrics not won by \model{}. |
| These rows are useful for interpretation, but they are not used as headline claims. |
|
|
| \begin{table}[H] |
| \centering |
| \small |
| \caption{Occupancy operating trade-offs across three final configurations. Strict keeps the smallest positive footprint; Context broadens the crop budget; recall-supporting context trades more positives for higher tolerance-family recall. Values are percentages.} |
| \label{tab:app_occupancy_tradeoffs} |
| \begin{tabular}{l l c c c c c} |
| \toprule |
| \rowcolor{boxbg} |
| Config & Selection view & Strict \(F_1\) & Precision & Recall & \(t3/s3\) \(F_1\) & Pred.+ rate \\ |
| \midrule |
| Strict & strict \(F_1\) & \textbf{7.12\%} & 10.79\% & 5.93\% & 23.96\% & 0.02\% \\ |
| Context & tolerated-family \(F_1\) & 4.48\% & 2.82\% & 14.78\% & \textbf{39.17\%} & 0.16\% \\ |
| Recall-supporting context & recall-supporting view & 5.25\% & 3.26\% & \textbf{15.95\%} & 38.97\% & 0.14\% \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| The strict and tolerated-family rows in Table~\ref{tab:headline_task_results} intentionally come from different validation-selected operating points. |
| The strict configuration keeps the predicted-positive footprint small, while the context configurations increase recall and bounded-offset \(F_1\) at the cost of exact precision. |
| This trade-off is expected for sparse active-fire occupancy and is reported explicitly rather than collapsed into one threshold. |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \renewcommand{\arraystretch}{1.08} |
| \caption{Occupancy \(F_1\) sweep across global and fire-prone scopes. Values are percentages from the same validation-selected strict threshold. Tolerance is spatial-only; union adds temporal and spatial matching.} |
| \label{tab:app_occupancy_scope_sweep} |
| \resizebox{\linewidth}{!}{ |
| \begin{tabular}{llcccc} |
| \toprule |
| \rowcolor{boxbg} |
| Backbone & Scope & Strict \(F_1\uparrow\) & Tol. \(F_1\uparrow\) & Union \(F_1\uparrow\) & \(\Delta\) union--strict \\ |
| \midrule |
| \model{} & global & \ms{5.31}{1.24} & \ms{44.97}{2.15} & \ms{66.74}{7.08} & \ms{61.43}{8.13} \\ |
| & top 5\% & \ms{11.94}{2.66} & \ms{46.04}{5.20} & \ms{69.84}{7.52} & \ms{57.89}{8.62} \\ |
| & top 10\% & \ms{11.78}{2.49} & \ms{45.67}{4.82} & \ms{69.21}{7.44} & \ms{57.44}{8.65} \\ |
| & top 20\% & \ms{11.42}{2.26} & \ms{44.20}{4.18} & \ms{67.80}{7.64} & \ms{56.38}{8.87} \\ |
| \addlinespace[1pt] |
| Prithvi-WxC & global & \ms{0.0552}{0.0039} & \ms{7.1649}{0.6557} & \ms{20.1853}{1.8299} & \ms{20.1301}{1.8297} \\ |
| & top 5\% & \ms{1.4119}{1.1635} & \ms{19.2636}{4.5019} & \ms{42.5793}{4.5495} & \ms{41.1674}{3.4846} \\ |
| & top 10\% & \ms{1.2376}{1.3201} & \ms{14.8780}{8.4429} & \ms{32.6913}{13.2085} & \ms{31.4536}{11.9053} \\ |
| & top 20\% & \ms{1.1520}{1.3770} & \ms{13.1512}{9.4556} & \ms{28.1319}{15.2866} & \ms{26.9800}{13.9224} \\ |
| \addlinespace[1pt] |
| Aurora & global & \ms{0.0656}{0.0094} & \ms{8.5009}{1.9594} & \ms{23.1037}{4.9418} & \ms{23.0382}{4.9325} \\ |
| & top 5\% & \ms{0.9859}{0.9299} & \ms{15.1337}{6.0821} & \ms{35.4834}{11.0192} & \ms{34.4975}{10.3728} \\ |
| & top 10\% & \ms{0.7790}{1.0453} & \ms{12.7381}{6.5558} & \ms{30.5305}{10.8842} & \ms{29.7515}{9.8656} \\ |
| & top 20\% & \ms{0.6655}{1.1043} & \ms{10.5304}{7.4309} & \ms{24.9444}{12.5844} & \ms{24.2790}{11.4943} \\ |
| \addlinespace[1pt] |
| ClimaX & global & \ms{0.3480}{0.0754} & \ms{29.7535}{3.6073} & \ms{60.1506}{7.5865} & \ms{59.8026}{7.5454} \\ |
| & top 5\% & \ms{1.2937}{0.1086} & \ms{34.5791}{2.3772} & \ms{69.2186}{5.7215} & \ms{67.9249}{5.7263} \\ |
| & top 10\% & \ms{1.2522}{0.1602} & \ms{34.3341}{2.2852} & \ms{68.5713}{5.5377} & \ms{67.3191}{5.5538} \\ |
| & top 20\% & \ms{1.0287}{0.2686} & \ms{30.2140}{4.2857} & \ms{60.0650}{7.5674} & \ms{59.0363}{7.5891} \\ |
| \addlinespace[1pt] |
| StormCast & global & \ms{0.0626}{0.0119} & \ms{8.1951}{2.1895} & \ms{22.3817}{5.4294} & \ms{22.3191}{5.4178} \\ |
| & top 5\% & \ms{0.9573}{0.8011} & \ms{15.3219}{5.5337} & \ms{36.1857}{9.7331} & \ms{35.2284}{9.1816} \\ |
| & top 10\% & \ms{0.7284}{0.9280} & \ms{12.6669}{6.3290} & \ms{30.4748}{10.6527} & \ms{29.7464}{9.7494} \\ |
| & top 20\% & \ms{0.5795}{0.9104} & \ms{10.4157}{7.3437} & \ms{24.6598}{12.3973} & \ms{24.0803}{11.4988} \\ |
| \addlinespace[1pt] |
| DLWP & global & \ms{0.1693}{0.0419} & \ms{14.9148}{3.2446} & \ms{28.1901}{6.9658} & \ms{28.0208}{6.9257} \\ |
| & top 5\% & \ms{1.8054}{0.4835} & \ms{31.7231}{3.2923} & \ms{55.4596}{5.2920} & \ms{53.6542}{5.4752} \\ |
| & top 10\% & \ms{1.6110}{0.5999} & \ms{27.6581}{5.9216} & \ms{47.1269}{8.0111} & \ms{45.5158}{7.7927} \\ |
| & top 20\% & \ms{1.5248}{0.8987} & \ms{20.9403}{4.7971} & \ms{34.9301}{7.8471} & \ms{33.4054}{7.8760} \\ |
| \addlinespace[1pt] |
| FCN & global & \ms{0.2829}{0.0839} & \ms{19.5061}{3.3412} & \ms{40.0604}{9.3701} & \ms{39.7775}{9.3423} \\ |
| & top 5\% & \ms{1.6231}{0.5064} & \ms{29.3769}{2.7626} & \ms{54.3033}{7.4089} & \ms{52.6801}{7.4389} \\ |
| & top 10\% & \ms{1.1777}{0.5118} & \ms{22.4217}{3.9803} & \ms{43.4510}{9.2513} & \ms{42.2734}{9.0251} \\ |
| & top 20\% & \ms{0.9962}{0.4315} & \ms{16.9792}{3.9371} & \ms{34.0859}{8.2616} & \ms{33.0897}{7.9275} \\ |
| \addlinespace[1pt] |
| FengWu & global & \ms{0.2613}{0.0757} & \ms{12.0050}{6.0239} & \ms{24.1022}{13.6293} & \ms{23.8410}{13.5736} \\ |
| & top 5\% & \ms{1.5695}{0.3592} & \ms{16.2763}{3.7024} & \ms{30.1055}{5.0103} & \ms{28.5360}{4.7696} \\ |
| & top 10\% & \ms{1.2427}{0.5333} & \ms{12.9503}{5.6052} & \ms{24.1854}{8.6854} & \ms{22.9427}{8.1863} \\ |
| & top 20\% & \ms{1.1192}{0.5023} & \ms{11.9508}{5.0745} & \ms{22.7860}{7.9115} & \ms{21.6668}{7.4438} \\ |
| \addlinespace[1pt] |
| FuXi & global & \ms{0.3774}{0.1212} & \ms{21.0323}{4.8211} & \ms{37.2888}{9.4470} & \ms{36.9114}{9.4327} \\ |
| & top 5\% & \ms{2.0307}{0.6800} & \ms{31.8944}{4.7331} & \ms{53.9308}{8.3822} & \ms{51.9001}{8.6878} \\ |
| & top 10\% & \ms{1.6542}{0.7316} & \ms{24.0128}{5.7784} & \ms{40.2140}{9.9307} & \ms{38.5597}{9.7744} \\ |
| & top 20\% & \ms{1.3646}{0.6773} & \ms{21.9548}{5.8601} & \ms{36.7314}{10.0289} & \ms{35.3668}{9.9223} \\ |
| \addlinespace[1pt] |
| Pangu-Weather & global & \ms{0.2755}{0.1089} & \ms{17.0909}{4.0477} & \ms{35.6386}{9.0327} & \ms{35.3630}{9.0774} \\ |
| & top 5\% & \ms{1.3656}{0.3064} & \ms{22.2222}{6.8613} & \ms{43.4234}{13.2383} & \ms{42.0578}{13.0599} \\ |
| & top 10\% & \ms{1.0931}{0.3535} & \ms{18.9337}{5.9329} & \ms{38.5325}{11.7221} & \ms{37.4394}{11.5261} \\ |
| & top 20\% & \ms{0.8844}{0.3601} & \ms{17.0172}{5.4859} & \ms{34.5688}{10.2932} & \ms{33.6844}{10.1334} \\ |
| \addlinespace[1pt] |
| AlphaEarth & global & \ms{2.0606}{0.4404} & \ms{29.4476}{6.0064} & \ms{37.4286}{9.9458} & \ms{35.3679}{10.0271} \\ |
| & top 5\% & \ms{6.9133}{0.8450} & \ms{42.8790}{4.6087} & \ms{51.7449}{8.7321} & \ms{44.8315}{9.0763} \\ |
| & top 10\% & \ms{6.6366}{0.9901} & \ms{41.8981}{5.9454} & \ms{50.5712}{10.0057} & \ms{43.9346}{9.9156} \\ |
| & top 20\% & \ms{6.1908}{1.1330} & \ms{38.8325}{7.4966} & \ms{46.3833}{12.1697} & \ms{40.1925}{11.6788} \\ |
| \bottomrule |
| \end{tabular} |
| } |
| \end{table} |
| \FloatBarrier |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{Spread AP under fixed spread task and strict matching, reported under full and fire-prone scopes. Values are percentages.} |
| \label{tab:app_spread_ap_by_scope} |
| \begin{tabular}{lccc} |
| \toprule |
| \rowcolor{boxbg} |
| Backbone & Full AP & Top-5\% AP & Top-10\% AP \\ |
| \midrule |
| \model{} & \textbf{\ms{32.73}{0.60}} & \textbf{\ms{40.7452}{2.0542}} & \textbf{\ms{37.4096}{1.8731}} \\ |
| Prithvi-WxC & \ms{4.8319}{0.1731} & \ms{12.6086}{0.4468} & \ms{8.7051}{0.1889} \\ |
| Aurora & \ms{17.7723}{0.4293} & \ms{30.3106}{0.9404} & \ms{26.4732}{0.6932} \\ |
| ClimaX & \ms{11.1726}{0.2337} & \ms{25.7871}{1.2896} & \ms{19.9977}{1.2217} \\ |
| StormCast & \ms{8.1147}{1.1569} & \ms{18.5461}{1.1727} & \ms{14.1286}{1.2956} \\ |
| DLWP & \ms{9.2142}{2.6587} & \ms{19.3346}{2.3922} & \ms{14.9788}{2.6696} \\ |
| FCN & \ms{6.6774}{1.3001} & \ms{16.7396}{3.2955} & \ms{11.9308}{2.3881} \\ |
| FengWu & \ms{11.0046}{2.7092} & \ms{21.1506}{1.2163} & \ms{17.0113}{1.5778} \\ |
| FuXi & \ms{13.5507}{0.3840} & \ms{22.5434}{0.4100} & \ms{19.1964}{0.3943} \\ |
| Pangu-Weather & \ms{10.6250}{1.4643} & \ms{19.8294}{1.3044} & \ms{15.8013}{1.1602} \\ |
| AlphaEarth & \ms{12.2847}{1.3562} & \ms{22.8692}{0.4915} & \ms{18.2992}{1.2110} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
| \FloatBarrier |
|
|
| \begin{table}[H] |
| \centering |
| \small |
| \caption{Supporting and non-winning evaluation rows. The struck-through values are earlier report values, followed by current values when the metric was updated.} |
| \label{tab:app_supporting_boundary} |
| \begin{tabularx}{0.98\linewidth}{l l l l Y} |
| \toprule |
| \rowcolor{boxbg} |
| Task & Metric & \model{} & Comparator & Appendix reading \\ |
| \midrule |
| Analog retrieval & nDCG@10\(\uparrow\) & \revise{0.4775}{0.5178} & AlphaEarth 0.5036 & \model{} is higher under the early-deployment retrieval contract; the result remains supporting and contract-sensitive. \\ |
| Smoke PM$_{2.5}$ & RMSE\(\downarrow\) & \revise{4.4646}{4.4684} & AlphaEarth 4.4899 & \model{} remains lower than the closest comparator, but the current value is not lower than the earlier report value. \\ |
| Smoke PM$_{2.5}$ & MAE\(\downarrow\) & \revise{2.4108}{2.4127} & AlphaEarth 2.4009 & AlphaEarth is lower on MAE, so this row is appendix-only. \\ |
| Smoke PM$_{2.5}$ & Pearson \(r\uparrow\) & \revise{0.6368}{0.6360} & AlphaEarth 0.6315 & \model{} is slightly higher, with a small margin. \\ |
| Smoke PM$_{2.5}$ & Event \(F_1\uparrow\) & 20.12\% & AlphaEarth 14.91\% & Secondary thresholded event view; not used as a headline regression metric. \\ |
| Extreme heat & RMSE-C\(\downarrow\) & \revise{0.2187}{0.2179} & AlphaEarth 0.2187 & Near tie at table precision; kept out of the main headline table. \\ |
| Extreme heat & MAE-C\(\downarrow\) & \revise{0.1795}{0.1787} & AlphaEarth 0.1795 & Near tie at table precision; kept out of the main headline table. \\ |
| Extreme heat & Pearson \(r\uparrow\) & 0.999865 & AlphaEarth 0.999867 & AlphaEarth is slightly higher, so this row is appendix-only. \\ |
| \bottomrule |
| \end{tabularx} |
| \end{table} |
|
|
| The final burned-area headline log-RMSE in Table~\ref{tab:headline_task_results} uses the validation-selected affine scale-calibration candidate. |
| The uncalibrated identity candidate remains close at log-RMSE 1.1718 and Spearman 0.5884, while the validation-selected affine candidate gives log-RMSE 1.1649 with the same Spearman ordering. |
| Because the scale calibration is selected on validation data before test reporting, it is included as the current fixed-contract burned-area headline. |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{Additional final burned-area diagnostics.} |
| \label{tab:app_burned_area_diagnostics} |
| \begin{tabular}{lccc} |
| \toprule |
| \rowcolor{boxbg} |
| Backbone & Median log AE & Median acre AE & Acre MAPE \\ |
| \midrule |
| \model{} & \ms{1.0235}{0.0982} & \ms{4504.0692}{459.0483} & \ms{1.4525}{0.0254} \\ |
| Prithvi-WxC & \ms{1.2184}{0.2107} & \ms{5375.8770}{788.7906} & \ms{1.9517}{0.2875} \\ |
| Aurora & \ms{1.4547}{0.0301} & \ms{9904.9483}{457.4260} & \ms{6.8728}{3.0026} \\ |
| ClimaX & \ms{1.6841}{0.1818} & \ms{18130.4820}{3248.3873} & \ms{8.2373}{2.8540} \\ |
| StormCast & \ms{1.4522}{0.1519} & \ms{11155.7881}{2020.8656} & \ms{4.6142}{1.1500} \\ |
| DLWP & \ms{1.0952}{0.1306} & \ms{4406.9315}{303.0944} & \ms{1.7357}{0.3625} \\ |
| FCN & \ms{1.1688}{0.1139} & \ms{5166.9993}{213.0333} & \ms{2.0800}{0.4004} \\ |
| FengWu & \ms{1.1589}{0.1772} & \ms{5137.2822}{628.7543} & \ms{2.0944}{0.4545} \\ |
| FuXi & \ms{1.1855}{0.0612} & \ms{5697.7117}{796.8785} & \ms{2.4411}{0.5567} \\ |
| Pangu-Weather & \ms{1.1221}{0.1470} & \ms{5092.3621}{483.8243} & \ms{1.9571}{0.3113} \\ |
| AlphaEarth & \ms{1.7459}{0.6057} & \ms{15110.7573}{7106.3417} & \ms{9.7398}{2.7425} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{Additional analog-retrieval diagnostics.} |
| \label{tab:app_analog_retrieval_diagnostics} |
| \begin{tabular}{lccc} |
| \toprule |
| \rowcolor{boxbg} |
| Backbone & nDCG@5 & Best log gap & Rank \(\rho\) \\ |
| \midrule |
| \model{} & \ms{0.5175}{0.0445} & \ms{0.1868}{0.0285} & \ms{0.6019}{0.1460} \\ |
| Prithvi-WxC & \ms{0.3591}{0.0107} & \ms{0.2151}{0.0594} & \ms{0.1514}{0.1489} \\ |
| Aurora & \ms{0.4423}{0.0210} & \ms{0.1551}{0.0437} & \ms{0.2162}{0.1856} \\ |
| ClimaX & \ms{0.4151}{0.0293} & \ms{0.2129}{0.0653} & \ms{0.1587}{0.2831} \\ |
| StormCast & \ms{0.3960}{0.0240} & \ms{0.1714}{0.0310} & \ms{0.1258}{0.1625} \\ |
| DLWP & \ms{0.3795}{0.0274} & \ms{0.1944}{0.0807} & \ms{-0.3865}{0.2802} \\ |
| FCN & \ms{0.4250}{0.0112} & \ms{0.1856}{0.0846} & \ms{-0.1357}{0.2571} \\ |
| FengWu & \ms{0.4228}{0.0310} & \ms{0.1870}{0.0858} & \ms{-0.1926}{0.2194} \\ |
| FuXi & \ms{0.4544}{0.0356} & \ms{0.2171}{0.0806} & \ms{-0.1367}{0.2885} \\ |
| Pangu-Weather & \ms{0.3988}{0.0506} & \ms{0.1901}{0.0838} & \ms{-0.1970}{0.2216} \\ |
| AlphaEarth & \textbf{\ms{0.5276}{0.0531}} & \textbf{\ms{0.1782}{0.0454}} & \ms{0.4639}{0.2802} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{High-smoke subset diagnostics for rows with observed PM\(_{2.5}\ge35\).} |
| \label{tab:app_smoke_high_event} |
| \begin{tabular}{lccc} |
| \toprule |
| \rowcolor{boxbg} |
| Backbone & High-smoke RMSE & High-smoke MAE & High-smoke 90th AE \\ |
| \midrule |
| \model{} & \textbf{\ms{47.4870}{0.6346}} & \textbf{\ms{34.3954}{0.7654}} & \textbf{\ms{65.6213}{3.8778}} \\ |
| Prithvi-WxC & \ms{57.2224}{1.7268} & \ms{47.3871}{0.3153} & \ms{74.9666}{3.2381} \\ |
| Aurora & \ms{57.2752}{1.7248} & \ms{47.4368}{0.3149} & \ms{75.0755}{3.1074} \\ |
| ClimaX & \ms{57.2828}{1.7239} & \ms{47.4407}{0.3140} & \ms{75.1012}{3.0777} \\ |
| StormCast & \ms{56.6512}{1.7517} & \ms{46.7914}{0.3281} & \ms{74.0794}{3.4707} \\ |
| DLWP & \ms{57.0075}{1.7359} & \ms{47.1971}{0.3198} & \ms{74.4936}{3.3826} \\ |
| FCN & \ms{57.0582}{1.7339} & \ms{47.2401}{0.3187} & \ms{74.6431}{3.1982} \\ |
| FengWu & \ms{57.0158}{1.7357} & \ms{47.1957}{0.3194} & \ms{74.5652}{3.2871} \\ |
| FuXi & \ms{56.9622}{1.7371} & \ms{47.1508}{0.3201} & \ms{74.3278}{3.4435} \\ |
| Pangu-Weather & \ms{57.1282}{1.7307} & \ms{47.3050}{0.3170} & \ms{74.6830}{3.2375} \\ |
| AlphaEarth & \ms{48.0665}{0.7904} & \ms{35.6088}{0.7341} & \ms{66.7613}{3.9235} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{4pt} |
| \caption{Extreme-heat exceedance precision and recall for the \(F_1\) row.} |
| \label{tab:app_heat_pr} |
| \begin{tabular}{lcc} |
| \toprule |
| \rowcolor{boxbg} |
| Backbone & Precision & Recall \\ |
| \midrule |
| \model{} & \ms{0.9767}{0.0117} & \ms{0.9330}{0.0299} \\ |
| Prithvi-WxC & \ms{0.8260}{0.0030} & \ms{0.9173}{0.0033} \\ |
| Aurora & \ms{0.5920}{0.0347} & \ms{0.0517}{0.0020} \\ |
| ClimaX & \ms{0.7397}{0.0099} & \ms{0.7994}{0.0051} \\ |
| StormCast & \ms{0.8840}{0.0237} & \ms{0.9320}{0.0165} \\ |
| DLWP & \ms{0.9429}{0.0085} & \ms{0.8899}{0.0167} \\ |
| FCN & \ms{0.9408}{0.0097} & \ms{0.9111}{0.0127} \\ |
| FengWu & \ms{0.3808}{0.2719} & \ms{0.0266}{0.0267} \\ |
| FuXi & \ms{0.3262}{0.1262} & \ms{0.1810}{0.0481} \\ |
| Pangu-Weather & \ms{0.1159}{0.0743} & \ms{0.0112}{0.0032} \\ |
| AlphaEarth & \ms{0.9824}{0.0040} & \ms{0.9278}{0.0178} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \begin{table}[H] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{4pt} |
| \renewcommand{\arraystretch}{1.12} |
| \caption{Seed robustness summary for stochastic task checks.} |
| \label{tab:app_seed_robustness} |
| \resizebox{\linewidth}{!}{ |
| \begin{tabular}{lcll} |
| \toprule |
| \rowcolor{boxbg} |
| Check & Seeds & Primary value & Additional values \\ |
| \midrule |
| Final burned area & 5 & log-RMSE \ms{1.1649}{0.0139} & log-MAE \ms{1.0355}{0.0114}; Spearman \ms{0.5884}{0.0338} \\ |
| Smoke PM\(_{2.5}\) & 5 & RMSE \ms{4.4684}{0.0060} & MAE \ms{2.4127}{0.0016}; \(r\) \ms{0.6360}{0.0013} \\ |
| Extreme heat & 5 & RMSE-C \ms{0.2179}{0.0048} & MAE-C \ms{0.1787}{0.0020}; exceed. \(F_1\) \ms{0.9541}{0.0164} \\ |
| Fire spread & 5 & exact \(F_1\) \ms{37.98}{1.33} & spatial \(F_1\) \ms{82.20}{0.32}; AP \ms{32.73}{0.60} \\ |
| \bottomrule |
| \end{tabular} |
| } |
| \end{table} |
| \FloatBarrier |
|
|
| \end{document} |
|
|
