Atmospheric Research最新文献

{"title":"Robust Typhoon Rainfall forecasting based on machine learning and Bayesian model averaging","authors":"Yifan Chen ,&nbsp;Jingwen Zhang ,&nbsp;Zejun Li ,&nbsp;Pan Liu ,&nbsp;Lei Guo ,&nbsp;Kairong Lin ,&nbsp;Mingzhong Xiao ,&nbsp;Xiaohong Chen","doi":"10.1016/j.atmosres.2025.108216","DOIUrl":"10.1016/j.atmosres.2025.108216","url":null,"abstract":"<div><div>Typhoon-induced heavy rainfall can lead to severe flooding, causing significant damage to social systems. Although machine learning (ML) offers an efficient approach for typhoon rainfall forecasting, individual models often exhibit considerable uncertainty. To address this, this study proposes a robust typhoon rainfall forecasting model based on Bayesian Model Averaging (BMA) with four ML models including Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors Regression (KNN), and eXtreme Gradient Boosting (XGBoost) across 0–6 h lead time. The model incorporates three types of features within the typhoon impact area (radius of 400 km): typhoon characteristics, grid spatial attributes and meteorological characteristics. Typhoon characteristics of each grid are dynamically weighted to reflect the weakening typhoon impacts with increasing distance from the typhoon center. Based on these features, multiple scenarios consisting of two experiments (Input Unchanged (IU) and Rolling Forecast (RO)) paired with various input designs (ALL: all features, SHAP3: 3 most critical variables, and LAG4: 4 lagged rainfall) are designed to explore the performance of all models. Case study of 28 typhoons affecting Guangdong Province from 2020 to 2023 clearly demonstrated that the weighted typhoon wind speed contributed the most in typhoon rainfall forecasting, and the BMA approach significantly enhanced the forecast accuracy. Leveraging sufficient effective information as model inputs could significantly improve the predictive performance of individual models in long-term forecasting. This method could provide flexible and suitable scenario options for data-rich and data-scarce regions, supporting early disaster warning during typhoon-prone seasons.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"325 ","pages":"Article 108216"},"PeriodicalIF":4.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variation and clustered routes of atmospheric speciated mercury transported from the East and West Channels of Taiwan Island to South China Sea","authors":"Mei-Yun Xiao ,&nbsp;Wen-Hsi Cheng ,&nbsp;Yu-Lun Tseng ,&nbsp;Po-Hsuan Yen ,&nbsp;Chung-Shin Yuan ,&nbsp;Jia-Yi Zhao ,&nbsp;Ming-Shiou Jeng","doi":"10.1016/j.atmosres.2025.108209","DOIUrl":"10.1016/j.atmosres.2025.108209","url":null,"abstract":"<div><div>This study investigated the spatiotemporal variation and long-range transport characteristics of atmospheric speciated mercury (ASM) along the West and East Channels surrounding Taiwan Island toward the South China Sea (SCS) in East Asia. Field sampling was conducted at three remote island sites, and potential sources of ASM were examined using backward trajectory clustering and regional fire maps. Seasonally, ASM concentrations followed the order: spring &gt; winter &gt; fall &gt; summer, with higher ASM levels observed in the western waters of Taiwan Island than those in the eastern waters. Among the mercury species, gaseous elemental mercury (GEM) was predominant, and followed by particulate-bound mercury (PBM) and gaseous oxidized mercury (GOM), with total gaseous mercury (TGM = GEM+GOM) accounting for 92.1–98.4 % of ASM. Clustered trajectory analysis indicated that Asian Northeastern Monsoons (ANMs) may transport ASMs originating from continental sources in North and East Asia to the island sites via long-range transport. Slash-and-burn activities in the Indochina Peninsula and mainland China during spring and winter contributed to elevated ASM levels. The West Channel exhibited a pronounced “channel effect,” amplifying industrial emissions across the Taiwan Strait and resulting in higher ASM levels at the Penghu Islands. Conversely, the East Channel received diluted air masses through long-range transport from the Korean Peninsula and North China via Green Island and the Bashi Channel, leading to relatively lower ASM levels at the Dongsha Island. Overall, this study highlights the importance of seasonal circulation patterns and regional combustion sources in shaping mercury distribution in the marine boundary layer of East Asia.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"325 ","pages":"Article 108209"},"PeriodicalIF":4.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benefit for inversion of long-term satellite daily temperature based on multi-machine learning algorithms","authors":"Xiaochen Zhu ,&nbsp;Guanjie Jiao ,&nbsp;Qiangyu Li ,&nbsp;Rangjian Qiu","doi":"10.1016/j.atmosres.2025.108217","DOIUrl":"10.1016/j.atmosres.2025.108217","url":null,"abstract":"<div><div>The Advanced Very High-Resolution Radiometer (AVHRR), a satellite sensor has been in orbit for over 40 years, providing remote sensing images before 2000 and there is considerable room for improvement in the accuracy of current air temperature (T_a) inversions based on AVHRR to obtain accurate long-term T_a data before 2000. Here, we aim to estimate daily average (T_ave), maximum (T_max), and minimum (T_min) air temperatures at a resolution of 5 km for the Chinese region during the period 1983–2000. We developed a satellite-retrieval daily temperature extrapolation method based on machine learning (ML) combining multiple sources of big data, i.e., leveraging comprehensive and gap-free land surface temperature data from remote sensing along with other relevant variables from reanalysis data, topography and auxiliary data of local temperature, to generate extended time series of high-resolution T_a data. Quality validation results indicate that the ML can enhance the accuracy of T_a inversion with average error range of various ML methods being 0.995–1.606 °C, 1.316–1.971 °C and 1.396–1.980 °C for T_ave, T_max, and T_min, respectively, which is better than the 2.297 ± 1.704 °C, 3.294 ± 2.016 °C and 2.873 ± 1.666 °C of ERA5. Integrated ML method outperforms individual algorithms, yielding a high correlation coefficient of 0.96 and a robust mean error of 1 °C. The spatial distribution of newly daily T_a data from the multi-ML nationwide and local region is homogeneous with ERA5, indicating high physical consistency, and has higher resolution of 5 km. These updated temperature data can be beneficial in better revealing intricate structural attributes on a regional scale, as well as exploring urban heat islands.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"325 ","pages":"Article 108217"},"PeriodicalIF":4.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extreme wind speed estimation in thunderstorm gales utilizing dual-polarization weather radar hydrometeor classification products","authors":"Yinglian Guo,&nbsp;Yanjiao Xiao,&nbsp;Jue Wang,&nbsp;Zhimin Zhou","doi":"10.1016/j.atmosres.2025.108202","DOIUrl":"10.1016/j.atmosres.2025.108202","url":null,"abstract":"<div><div>Severe thunderstorm gales are the predominant meteorological phenomena associated with convective storms. Accurately estimating the extreme wind speed of thunderstorm gales (ESTG) is crucial for both forecasting services and disaster investigations related to severe convective weather. This paper proposes a method for estimating the ESTG based on dual-polarization weather radar hydrometeors classification products (EESonHC). This method utilizes changes in hydrometeors, combines these changes with the atmospheric vertical momentum equation, and estimates the ESTG by approximating the average vertical acceleration of storm cells. Experimental tests were conducted using cases of thunderstorm gales in both strong and weak wind shear environments, demonstrating that this estimation method achieves an accuracy rate as high as 75 % for extreme thunderstorm gales exceeding 25 m/s, with an average error in estimated wind speed of approximately 0.5 m/s. The primary issues include: 1. When multiple storm cells are in close vicinity, accurately isolating the vertical motion acceleration pairs for each individual cell becomes challenging, thereby complicating wind speed estimation; 2. Overestimating ice-phase hydrometeors in HCL could lead to underestimating wind speed or make it impossible to estimate accurately.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"324 ","pages":"Article 108202"},"PeriodicalIF":4.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stacking machine learning model for precipitable water vapor vertical adjustment using GNSS networks and radio occultation data","authors":"Jiaqi Shi ,&nbsp;Min Li ,&nbsp;Andrea K. Steiner ,&nbsp;Wenwen Li ,&nbsp;Minghao Zhang ,&nbsp;Yongzhao Fan ,&nbsp;Wenliang Gao ,&nbsp;Kefei Zhang","doi":"10.1016/j.atmosres.2025.108212","DOIUrl":"10.1016/j.atmosres.2025.108212","url":null,"abstract":"<div><div>This study presents a stacking machine learning (SML) model for vertical adjustment of precipitable water vapor (PWV), addressing missing water vapor information near the surface in radio occultation (RO) profiles and enhancing the accuracy of PWV estimation from RO data. The model is trained and validated using more than 1500 ground-based Global Navigation Satellite System (GNSS) stations and more than 320,000 RO profiles of the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) for two regions of the Northern hemisphere from January 2020 to December 2023. Results show that in the North American region, the SML model reduces the root-mean-square error (RMSE) of PWV estimates by 58.05 %, 36.99 %, and 33.05 % compared to conventional linear, exponential, and global PWV vertical adjustment (GPWV-H) models, respectively. In the region of China and Southeast Asia, the RMSE of PWV estimates is reduced by more than 42.9 %. External validation reveals that the SML-adjusted RO-PWV is in close agreement with PWV estimated from radiosondes and other RO products. Notably, the SML model outperforms conventional models across various latitudes and longitudes, making it well-suited for complex terrain and different climatic conditions. This study also examines the SML model performance for different climate types and extreme weather and proposes incorporating these factors in future work to improve model adaptability. Overall, the SML model excels in PWV vertical adjustment, providing a high-accuracy, fast solution for global PWV estimation, water vapor monitoring and weather forecasting.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"325 ","pages":"Article 108212"},"PeriodicalIF":4.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing of Arctic marine fog using ship-based ceilometer","authors":"Jin Ye ,&nbsp;Lei Liu ,&nbsp;Yuan Shang ,&nbsp;Jinfeng Ding ,&nbsp;Hailing Xie","doi":"10.1016/j.atmosres.2025.108204","DOIUrl":"10.1016/j.atmosres.2025.108204","url":null,"abstract":"<div><div>Arctic marine fog is a severe disastrous weather characterized by drastically reduced visibility, and its occurrence often poses great challenges to navigation and aviation. Accurate measurement of the characteristics of the Arctic marine fog plays a significant role in improving the accuracy of marine fog forecasts in the Arctic region. The Vaisala CL31 ceilometer, with its high sensitivity to liquid water content (LWC), is an effective means of detecting and obtaining the vertical structure of marine fog. In this work, a method to detect fog and retrieve fog thickness based on the ceilometer's backscatter coefficient profile has been proposed. The dataset collected at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from September 2019 to September 2020 is analyzed. Results indicate that compared with fog detection results of present weather detector (PWD22), the average deviation of marine fog occurrence probability of the proposed algorithm is within 5 %. Meanwhile, Arctic marine fog has obvious seasonal cycle, with the probability of high-concentration large-particle Arctic marine fog reaching up to 40 % in summer, accompanied by the thickness basically below 300 m. The distribution of marine fog thickness in summer is more concentrated and can last longer, with an average duration of about 6.24 h. In addition, there is an obvious correlation between the marine fog and the atmospheric boundary layer, with the correlation coefficient (CC) between the boundary layer height and the marine fog thickness being 0.7. However, the marine fog is generally shallower than boundary layer height in most cases.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"324 ","pages":"Article 108204"},"PeriodicalIF":4.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing air quality forecasting with OSSE: Assessing the assimilation of aerosol species data via multi-resolution observation network design in the WRF-Chem and 3DVAR frameworks","authors":"Xiao Chen ,&nbsp;Wei You ,&nbsp;Ran Duan ,&nbsp;Mingzhen Gu ,&nbsp;Zengliang Zang ,&nbsp;Jiali Luo","doi":"10.1016/j.atmosres.2025.108213","DOIUrl":"10.1016/j.atmosres.2025.108213","url":null,"abstract":"<div><div>This study investigates the impact of aerosol component data assimilation on air quality forecasting using the WRF-Chem model coupled with a 3DVAR framework and Observation System Simulation Experiments(OSSE). Four observation network configurations(27 km, 100 km, 270 km, and full-density 2016 stations) were tested to evaluate how spatial resolution and station density influence the simulation of six aerosol components(SO₄²⁻, NH₄⁺, NO₃⁻, BC, OC, OIN). Results demonstrate that assimilating aerosol speciation data significantly outperforms total PM₂.₅/PM₁₀ assimilation, reducing initial field RMSE by 38.2 % and enhancing 48-h forecast correlation(CORR) by 0.15–0.18. The DA_270km experiment, with only 119 stations, achieved accuracy comparable to the full-density DA experiment, highlighting the critical role of spatial representativeness over station count. High-resolution networks(e.g., DA_27km) exhibited suboptimal performance, particularly for carbonaceous aerosols(BC, OC), where RMSE increased by 25–30 % due to “super-resolution assimilation” effects and amplified subgrid parameterization errors. Mechanistically, traditional 3DVAR frameworks showed limitations in handling ultra-dense networks, including redundancy in observational data and scale mismatches between model grids and observation density. Chemically inert components like OIN showed minimal assimilation impacts(&lt;5 % variance), emphasizing the need for refined emission inventories. These findings advocate for optimized observation networks(50–100 km resolution) balancing spatial coverage, model resolution, and cost-effectiveness, prioritizing aerosol speciation data to advance air quality forecasting and emission control strategies.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"325 ","pages":"Article 108213"},"PeriodicalIF":4.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transition of dominant cloud microphysical processes for increasing lightning preceding downbursts in multi-cell convective clouds","authors":"Makoto Kondo ,&nbsp;Yousuke Sato","doi":"10.1016/j.atmosres.2025.108203","DOIUrl":"10.1016/j.atmosres.2025.108203","url":null,"abstract":"<div><div>Based on the results of numerical simulations, a possible mechanism for the occurrence of a rapid increase in lightning frequency preceding a downburst was investigated according to cloud microphysical processes in mixed-phase areas. To elucidate the mechanism, idealized experiments were conducted targeting multi-cell convective clouds using a meteorological model coupled with a bulk lightning model, which explicitly calculates riming, charge separation via riming electrification, and lightning discharge. The model well reproduced a rapid increase in the lightning flash rate in multi-cell convective clouds approximately 15 min before a downburst. In a convective cell during increasing flash rate, solid hydrometeors were supplied to the convective area and riming electrification occurred actively. In contrast, in a convective cell that caused a downburst, riming occurred actively because of the supply of a large amount of supercooled water from the lower layers. A convective cell suitable for riming electrification or graupel growth by riming occurred when the convection was or was not tilted, respectively. The transition from tilted convection suitable for riming electrification to upright convection suitable for active riming growth is critical for the occurrence of the rapid increase of lightning preceding downbursts.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"324 ","pages":"Article 108203"},"PeriodicalIF":4.5,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainties in the estimation of Thornthwaite's Aridity and Moisture Indices in Greece over the last century using ground and gridded datasets","authors":"Ioannis X. Tsiros ,&nbsp;Nikolaos D. Proutsos ,&nbsp;Stefanos P. Stefanidis","doi":"10.1016/j.atmosres.2025.108200","DOIUrl":"10.1016/j.atmosres.2025.108200","url":null,"abstract":"<div><div>During the last decades several gridded climatic datasets have been developed offering thus long-term, continuous, and spatially uniform records of key hydrometeorological parameters. Few studies, however, have rigorously evaluated the accuracy and reliability of these datasets, especially in regions characterized by complex topography and sparse observational data, such as the Mediterranean. Moreover, even fewer have explored how uncertainties inherent in these datasets affect derived climatic indices. To address this gap, the present study deals with the application of ground station measurements and a gridded dataset (CRU_TS 4.04) to estimate the aridity index (AI) and Thornthwaite's Moisture index (Im) over the Greek peninsula for three different consecutive climatic periods. Ground station data estimations are compared against the outcomes of the respective gridded datasets. Results show that the application of gridded data led to AI values increase in many areas, altering the aridity classification from semi-arid (SA) to sub-humid (SH) or humid (H) categories. Further, the application of gridded data led to both underestimations and overestimations of the Im, depending on the site: more dry conditions along the western coast (in most cases) and the eastern Aegean islands (with significant differences in some of the islands) and more humid conditions over the northwestern mainland and the southern part along with the Aegean islands. These results suggest that while gridded datasets always offer an alternative, careful consideration is required when they are used in regions characterized by complex topography (heterogenous terrain, site continentality and extensive land-sea contrast) and also temporal climatic variability.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"324 ","pages":"Article 108200"},"PeriodicalIF":4.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A time-invariant bias correction strategy for improving CLM5.0 evapotranspiration simulation by random forest method for mainland China","authors":"Lanhui Zhang ,&nbsp;Zhilan Wang ,&nbsp;Xuliang Bai ,&nbsp;Haixin Zhang ,&nbsp;Yu Liu","doi":"10.1016/j.atmosres.2025.108196","DOIUrl":"10.1016/j.atmosres.2025.108196","url":null,"abstract":"<div><div>Reliable and accurate estimation of large-scale evapotranspiration (ET) is fundamental for research in Earth System Science, however, large-scale ET estimation remains a challenge. Bias correction of the land surface model (LSM) simulated ET is a popular approach for providing large-scale, long-term estimations. Previous correction methods often ignore stochastic errors, making them unsuitable for complex conditions. Therefore, this study evaluated the performance of the Community Land Model 5.0 (CLM 5.0) in simulating ET based on the Global Land Evaporation Amsterdam Model (GLEAM) product and in situ observations across mainland China, analyzed the main factors influencing model performance, developed a time-variant bias correction strategy using the random forest (RF) method. Results show that precipitation is crucial in influencing the performance and uncertainty of CLM 5.0 simulations since it determines whether ET is limited by energy or water. The CLM 5.0 performed worse but with lower uncertainty in water-limited regions due to soil water stress, while it performed better but with higher uncertainty in energy-limited regions. Future research should focus on refining the depth limits of dry surface layer (DSL) parameterization to improve model performance in water-limited regions. Furthermore, the CLM 5.0 overestimated ET in the Northern China (NC) region but underestimated ET in the other seven regions. The overestimation is attributed to the model's overestimation of leaf area index and the underestimation of GLEAM data in farmland. After bias correction, the national average correlation coefficients (<em>R</em>) increased by 0.102, root mean square errors (<em>RMSE</em>) decreased by 0.178 mm/d, absolute mean bias (<em>BIAS</em>) values decreased by 0.006 mm/d, Kling-Gupta efficiency (<em>KGE</em>) values increased by 0.154, and uncertainty coefficients with 95 % confidence level (<em>U</em>_<em>95%</em>) decreased by 0.195 mm/d. The performance of bias corrected simulations is significantly improved. The trends of both overestimation and underestimation of ET simulations by CLM 5.0 at a regional scale have also been alleviated. The time-invariant bias correction strategy proposed in this study demonstrates more reliable performance compared to the previous studies that applied monthly scaling factors. This advancement is essential for estimating more reliable large-scale ET under complex conditions.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"324 ","pages":"Article 108196"},"PeriodicalIF":4.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}