**Overall Verdict:** Strong Accept — The submission is complete, professionally representative, securely evaluable, and reachable end to end with no material defects.

**Completeness Check:**
- Instruction: Complete — Clearly defines the scientific objective, causal split policy, six required artifacts, schemas, metric equations, release gates, and deterministic replay requirements.
- Inputs: Complete — The public archive contains the stated training, validation, and test-context arrays with finite, bounded values, correct dimensions, chronological timestamps, coordinates, thresholds, provenance, and no test future.
- Toolchain: Complete — CPython 3.12.13 and NumPy 2.3.5 are precisely pinned in a frozen, open-source, CPU-only environment suitable for one-machine execution.
- Reference output: Complete — Protected test truth, an evaluation contract, full provenance, and six coherent expert artifacts are supplied separately. Independent metric recomputation exactly matched the visible claims, confirmed all release gates, and showed the reference forecast outperforming persistence on the protected metrics.
- Rubric: Complete — The evaluator implements the stated weighted scoring, passing threshold, mandatory gates, artifact safety checks, exact replay, visible-claim verification, hidden-quality comparisons, and infrastructure-error handling.

**File and Task Reachability:**
The attachment set is `reachable`, and the end-to-end task is `reachable`. Both declared archives were downloaded at their manifest sizes, passed integrity checks, and contained only unencrypted regular files with safe paths, modest expansion, and no links or device entries. Inspected content included both attachment manifests; `NEXRAD_PROVENANCE.json`, `OUTPUT_CONTRACT.json`, `README_DATA.md`, `nexrad_public.npz`, all four runtime declarations, and `nowcast_starter.py`; plus the ALE readme, originality and provenance notes, task readme, task card, task hook, scorer, full provenance, evaluation contract, `nexrad_hidden.npz`, and all six expert-output files. No attachment content remained unreviewed. NumPy members and standalone arrays were inspected for headers, member inventories, types, shapes, ranges, finiteness, timestamps, and solver/reference separation. The review environment lacked the declared Python runtime, so submitted programs were not executed; static source inspection and independent array and metric parsing covered the material contracts. A contributor can use the staged public archive and pinned NumPy environment to select a causal method, generate the six artifacts, and receive objective evaluation after protected truth is loaded and deleted before isolated replay. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**
`public_benchmark` — The protected future is deterministically derived from publicly distributed NOAA observations identifiable by station and event chronology. The risk is materially mitigated because the solver is fully offline, protected-source identifiers are omitted from solver-visible provenance, and hidden arrays are absent until evaluation. Live public-source availability was not externally verified under the review’s no-network boundary.

**Recommended Changes:**
The submission provides unusually thorough provenance binding, causal isolation, replay security, and objective scientific verification. No changes are required.