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TRUVACE RECORD VERSION
record: TRV-2026-2492
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:42:21.165034Z
status: published
lens: p_space
sector: climate
headline: Regional Evaluation of Pangu-Weather Driven by Multiple NWP Initial Fields over Shandong Province, China
dek: Accurate weather forecasting is essential for decision-making in agriculture, transportation, disaster prevention, and energy management. In recent years, data-driven artificial intelligence (AI) models have shown considerable potential for medium-range weather forecasting by substantially improving computational efficiency while maintaining competitive forecast skill. This study evaluates the regional performance of Pangu-Weather initialized with three global numerical weather prediction (NWP) systems—ECMWF-IFS…
gain_title: (none)
problem_title: In the same Shandong test, Pangu-Weather runs started from CMA and NCEP data had larger errors than ECMWF forecasts at several longer lead times over coastal stations and at 500–700 hPa.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In the same Shandong test, Pangu-Weather runs started from CMA and NCEP data had larger errors than ECMWF forecasts at several longer lead times over coastal stations and at 500–700 hPa.
problem_evidence: CMA_PG and NCEP_PG have larger errors than EC at several longer lead times over coastal stations and at 500–700 hPa
quick_read: A peer-reviewed evaluation tested the AI model Pangu-Weather over Shandong Province, China, from August to December 2024. Started from ECMWF-IFS fields, it cut mean 2 m temperature error by about 13.2% compared with ECMWF operational forecasts, improving at 38 of 40 lead times, with a maximum gain of 28.3%.

Surface forecasts were checked against 123 national meteorological stations and upper-air forecasts against three radiosonde stations. Wind-speed error at 10 m fell at every lead time, by 5.7% on average and up to 10.6%. Specific-humidity forecasts improved in all three configurations, by 8.6–12.8% on average, with a peak of 33.7% in one comparison.

Results were not uniform. Runs started from CMA-GFS and NCEP-GFS fields had larger errors than ECMWF at several longer lead times over coastal stations and at 500–700 hPa, and upper-air temperature and wind gains varied by level, station and lead time. The authors say the model could serve as a regional forecasting aid, with performance depending on the initial fields, variable, location and lead time.
limitation: Performance depended on the initial fields, variable, location and lead time, and upper-air temperature and wind gains varied by pressure level, station and lead time.
tag: Evidence-backed problem
key_points: The test covered August to December 2024 over Shandong Province, China. | Pangu-Weather was started from three global systems: ECMWF-IFS, CMA-GFS and NCEP-GFS. | Surface forecasts were verified against 123 national meteorological stations; upper-air forecasts against three radiosonde stations. | The ECMWF-initialized run cut 2 m temperature error by about 13.2%, improving at 38 of 40 lead times. | Its 10 m wind-speed error was lower at every lead time, by 5.7% on average and 10.6% at most. | Specific-humidity forecasts improved in all three configurations, by 8.6–12.8% on average. | CMA- and NCEP-initialized runs were worse than ECMWF at several longer lead times over coastal stations and at 500–700 hPa.
rundown: The study assessed how Pangu-Weather, a data-driven AI model for medium-range forecasting, performs at regional scale when fed different operational starting fields. It compared runs from ECMWF-IFS, CMA-GFS and NCEP-GFS with ECMWF operational forecasts over Shandong Province.

The ECMWF-initialized run (EC_PG) gave the most robust overall performance. Its mean 2 m temperature error was about 13.2% lower than ECMWF operational forecasts, with a maximum improvement of 28.3%. Its 10 m wind-speed error was lower at every lead time, with a mean improvement of 5.7% and a maximum of 10.6%.

Upper-air results were mixed. Specific-humidity forecasts generally improved across three stations and three pressure levels, by 8.6–12.8% on average and up to 33.7% in one comparison. Temperature and wind improvements depended on pressure level, station and lead time, and the CMA- and NCEP-started runs trailed ECMWF at some longer lead times.
sources:
- peer_reviewed | Atmosphere | https://doi.org/10.3390/atmos17111087 | 2026-11-04
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