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Record W4387376222 · doi:10.5194/gmd-2023-193

Global variable-resolution simulations of extreme precipitation over Henan, China in 2021

2023· preprint· en· W4387376222 on OpenAlexaff
Zijun Liu, Li Dong, Huiling Yuan, Xingrong Li, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, Yafei Wang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMinistry of Education and Child Care
FundersShenzhen Science and Technology Innovation ProgramSouthern University of Science and TechnologyEuropean Centre for Medium-Range Weather ForecastsNational Natural Science Foundation of China
KeywordsMesoscale meteorologyPrecipitationMeteorologySuiteClimatologyEnvironmental scienceClimate modelConvectionGeologyClimate changeGeography

Abstract

fetched live from OpenAlex

Abstract. A historic rainstorm occurred over Henan, China in July 2021 ("7.20" extreme precipitation event), resulting in significant human casualties and socio-economic losses. A global variable-resolution model (MPAS-Atmosphere v7.3) was employed to simulate this extreme precipitation event, by bridging the hydrostatic and non-hydrostatic scales together. A series of simulations have been done at both quasi-uniform (60 km and 15 km) and variable-resolution meshes (60–15 km and 60–3 km). For the 48-hour peak precipitation duration (07/20–07/22), the 60–3 km variable-resolution simulation coupled with the scale-aware convection-permitting parameterization scheme suite stands out predominately among other simulation experiments as it reproduces this extreme precipitation event most accurately, in terms of both the intensity and location of the peak precipitation. At 15-km resolution, the 60–15 km variable-resolution simulation achieves comparable forecasting skills as the 15-km quasi-uniform simulation, but at a much reduced computing cost. In addition, at 15-km resolution, we found that the default mesoscale suite generally outperforms the convection-permitting suite at 15-km resolution as simulations coupled with convection-permitting suite missed the 3rd peak of this extreme precipitation event while the mesoscale suite did not. This implies that, when the resolution of the refined region is coarser than the cloud-resolving scale, the convection-permitting parameterization scheme suite does not necessarily work better than the default mesoscale suite, but once the refined mesh is close to the cloud-resolving scale, the convection-permitting suite becomes scale aware such that it can intelligently distinguish the convective precipitation and grid-scale precipitation, respectively. Finally, it is found that the large-scale wind field plays a vital role in affecting extreme precipitation simulations since it primarily influences the transport of the water vapor flux thereby altering the prediction of the precise peak precipitation location.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.273
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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