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Traditional methods rely on idealized assumptions and control area selection, with limited applicability under complex terrain conditions. To address this problem, this paper proposes physics\u2010informed simple video prediction (SimVP) (PiSim), a hybrid physics\u2010informed disentangled framework designed to disentangle precipitation evolution. By employing the advection\u2013diffusion equation (ADE) as an inductive bias within the latent space, we transform spatial comparative evaluation into a precise spatiotemporal sequence prediction task. The method adopts a dual\u2010branch architecture: The physics branch explicitly models deterministic macroscopic motion (advection and diffusion), whereas the data\u2010driven branch learns complex nonlinear residuals, effectively compensating for microphysical processes and local variations. Experimental results on the Hubei Province Swan radar dataset demonstrate that PiSim achieves a 5.5% improvement in MSE compared to the SimVP baseline, with pronounced advantages in heavy precipitation forecasting. Evaluation of 10 typical artificial precipitation enhancement operations shows hourly net rainfall increments ranging from 0.16 to 4.60\u2009mm, which are highly consistent with historical records, validating the method\u2019s effectiveness.<\/jats:p>","DOI":"10.1155\/int\/4159977","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T21:28:21Z","timestamp":1773005301000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Physics\u2010Informed Deep Learning Method for Quantitative Evaluation of Artificial Precipitation Enhancement Effects"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2527-7420","authenticated-orcid":false,"given":"Renfeng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9895-0195","authenticated-orcid":false,"given":"Ziheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0161-1550","authenticated-orcid":false,"given":"Zhengteng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6122-0784","authenticated-orcid":false,"given":"Chi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5091-3452","authenticated-orcid":false,"given":"Dingdong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1080\/10962247.2017.1401017"},{"key":"e_1_2_11_2_2","first-page":"53","article-title":"Evaluation of Cloud Seeding Techniques for Precipitation Enhancement","volume":"1","author":"Essien M.","year":"2023","journal-title":"Global Journal of Climate Studies"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0477(2001)082<0903:acaogs>2.3.co;2"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0477(1999)080<0805:arocse>2.0.co;2"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1175\/bams-d-18-0160.1"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1175\/2010jas3496.1"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1175\/2011jamc2659.1"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1175\/jamc-d-13-0128.1"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018ea000424"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/atmos12081013"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2024.107457"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2024.106091"},{"key":"e_1_2_11_13_2","article-title":"Convolutional LSTM Network: a Machine Learning Approach for Precipitation Nowcasting","volume":"28","author":"Shi X.","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_14_2","article-title":"PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTM","volume":"30","author":"Wang Y.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/jstars.2024.3365612"},{"key":"e_1_2_11_16_2","article-title":"Attention is all you Need","volume":"30","author":"Vaswani A.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102293"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00317"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/w15081585"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09647-x"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3227717"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01239"},{"key":"e_1_2_11_23_2","unstructured":"DosovitskiyA. 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