Journal of Advances in Modeling Earth Systems最新文献

{"title":"Sensitivity of Self-Aggregation and the Key Role of the Free Convection Distance","authors":"A. Casallas,&nbsp;A. M. Tompkins,&nbsp;C. Muller,&nbsp;G. Thompson","doi":"10.1029/2024MS004791","DOIUrl":"https://doi.org/10.1029/2024MS004791","url":null,"abstract":"<p>Recently, Biagioli and Tompkins (2023, https://doi.org/10.1029/2022ms003231) used a simple stochastic model to derive a dimensionless parameter to predict convective self aggregation (SA) development, which was based on the derivation of the maximum free convective distance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mfenced>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation> $left({d}_{mathit{clr}}right)$</annotation>\u0000 </semantics></math> expected in the pre-aggregated, random state. Our goal is to test and further investigate this hypothesis, namely that <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${d}_{mathit{clr}}$</annotation>\u0000 </semantics></math> can predict SA occurrence, using an ensemble of 24 distinct combinations of horizontal mixing, planetary boundary layer (PBL), and microphysical parameterizations. We conclude that the key impact of parameterization schemes on SA is through their control of the number of convective cores and their relative spacing, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${d}_{mathit{clr}}$</annotation>\u0000 </semantics></math>, which itself is impacted by cold-pool (CP) properties and mean updraft core size. SA is more likely when the convective core count is small, while CPs modify convective spacing via suppression in their interiors and triggering by gust-front convergence and collisions. Each parameterization scheme emphasizes a different mechanism. Subgrid-scale horizontal turbulent mixing mainly affects SA through the determination of convective core size and thus spacing. The sensitivity to the microphysics is mainly through rain evaporation and the subsequent impact on CPs, while perturbations to the ice cloud microphysics have a limited effect. Non-local PBL mixing schemes promote SA primarily by increasing convective inhibition through inversion entrainment and altering low cloud amounts, leading to fewer convective cores and larger <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${d}_{mathit{clr}}$</annotation>\u0000 </semantics></math>.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Downward Convective Moisture Transport Dominated by a Few Overshooting Clouds in Marine and Continental Shallow Convection","authors":"Heng Xiao,&nbsp;Adam Varble,&nbsp;Colleen Kaul,&nbsp;Johannes Mülmenstädt","doi":"10.1029/2024MS004489","DOIUrl":"https://doi.org/10.1029/2024MS004489","url":null,"abstract":"<p>In a previous study (Xiao et al., 2023, https://doi.org/10.1029/2022ms003526), we found that ignoring the moist convective downdrafts associated with overshooting clouds in parameterizations can lead to significant biases in the simulated depth and liquid water content of a shallow cloud layer. In this study, we seek to better quantify the properties of the clouds responsible for these moist downdrafts to help improve shallow convection parameterizations. We apply a 3-D cloud-tracking algorithm to large-eddy simulations (LESs) of marine and continental shallow convection. We find that top 1% and 2% of the tracked cloud population ranked by lifetime-mean cloud-base mass flux can explain 90%–95% of the total downward moisture transport in the upper cloud layer whereas top 10%–20% is required to explain 90%–95% of the total upward moisture transport near mean cloud base. The vertical structure of the clouds in the top 1% and 2% (the overshooting “deep mode”) is also distinctively different from that of the rest of the cloud population (the “shallow mode”). Shallow convection parameterizations need to capture accurately the properties and convective transports of the clouds in both the deep and shallow modes. To do that, our results suggest that mass-flux parameterizations need to (a) accurately predict the size and number of the deep-mode clouds and (b) explicitly represent overshooting cloud updrafts and associated moist downdrafts.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thank You to Our 2024 Peer Reviewers","authors":"Stephen M. Griffies,&nbsp;Jiwen Fan,&nbsp;Yuanyuan Huang,&nbsp;Natasha MacBean,&nbsp;Tapio Schneider","doi":"10.1029/2025MS005079","DOIUrl":"https://doi.org/10.1029/2025MS005079","url":null,"abstract":"<p>The editors of <i>Journal of Advances in Modeling Earth Systems</i> thank the 1,001 reviewers who provided 1,593 reviews during 2024. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics","authors":"Yushu Xia,&nbsp;Jonathan Sanderman,&nbsp;Jennifer D. Watts,&nbsp;Megan B. Machmuller,&nbsp;Andrew L. Mullen,&nbsp;Charlotte Rivard,&nbsp;Arthur Endsley,&nbsp;Haydee Hernandez,&nbsp;John Kimball,&nbsp;Stephanie A. Ewing,&nbsp;Marcy Litvak,&nbsp;Tomer Duman,&nbsp;Praveena Krishnan,&nbsp;Tilden Meyers,&nbsp;Nathaniel A. Brunsell,&nbsp;Binayak Mohanty,&nbsp;Heping Liu,&nbsp;Zhongming Gao,&nbsp;Jiquan Chen,&nbsp;Michael Abraha,&nbsp;Russell L. Scott,&nbsp;Gerald N. Flerchinger,&nbsp;Patrick E. Clark,&nbsp;Paul C. Stoy,&nbsp;Anam M. Khan,&nbsp;E. N. Jack Brookshire,&nbsp;Quan Zhang,&nbsp;David R. Cook,&nbsp;Thomas Thienelt,&nbsp;Bhaskar Mitra,&nbsp;Marguerite Mauritz-Tozer,&nbsp;Craig E. Tweedie,&nbsp;Margaret S. Torn,&nbsp;Dave Billesbach","doi":"10.1029/2024MS004342","DOIUrl":"https://doi.org/10.1029/2024MS004342","url":null,"abstract":"<p>Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (<i>R</i>² &gt; 0.6, RMSE &lt;390 g C m⁻²) relative to net ecosystem exchange of CO₂ (NEE) (<i>R</i>² &gt; 0.4, RMSE &lt;180 g C m⁻²). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with <i>R</i>² = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmark Framework for Global River Models","authors":"Xudong Zhou,&nbsp;Dai Yamazaki,&nbsp;Menaka Revel,&nbsp;Gang Zhao,&nbsp;Prakat Modi","doi":"10.1029/2024MS004379","DOIUrl":"https://doi.org/10.1029/2024MS004379","url":null,"abstract":"<p>Global River Models (GRMs), which simulate river flow and flood processes, have rapidly developed in recent decades. However, these advancements necessitate meaningful and standardized quality assessments and comparisons against a suitable set of observational variables using appropriate metrics, a requirement currently lacking within GRM communities. This study proposes implementing a benchmark system designed to facilitate the assessment of river models and enable comparisons against established benchmarks. The benchmark system incorporates satellite remote sensing data complementing in situ data, including water surface elevation and inundation extent information, with necessary preprocessing. Consequently, this evaluation system encompasses a larger geographical area than traditional methods relying solely on in-situ river discharge measurements for GRMs. A set of evaluation and comparison metrics has been developed, including a quantile-based comparison metric that allows for a comprehensive analysis of multiple simulation outputs. The test application of this benchmark system to a global river model (CaMa-Flood), utilizing diverse runoff inputs, illustrates that incorporating bias-corrected runoff data leads to improved model performance across various observational variables and performance metrics. The current iteration of the benchmark system is suitable for global-scale assessments and can effectively evaluate the impact of model development and facilitate intercomparisons among different models. The source codes are accessible from https://doi.org/10.5281/zenodo.10903210.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forest Carbon Modeling Improved Through Hierarchical Assimilation of Pool-Based Measurements","authors":"Yu Zhou,&nbsp;Christopher A. Williams","doi":"10.1029/2024MS004622","DOIUrl":"https://doi.org/10.1029/2024MS004622","url":null,"abstract":"<p>Accurate assessment of forest carbon dynamics is a critical element of appraising forest-based Natural Climate Solutions. National forest inventory and analysis (FIA) data provide valuable pool-based estimates of carbon stocks, but have been underutilized to inform carbon cycle modeling for forest carbon dynamics with stand development. This study introduces a hierarchical data assimilation (HDA) framework to optimize modeling parameters by incrementally assimilating measured carbon pool data into the model. We found that most carbon stocks could be reproduced by constrained parameters after each HDA step. Using aboveground live biomass (AGB) alone in HDA was able to reproduce the AGB trajectories but introduced biases in estimating the downstream dead biomass and soil carbon pools. Assimilating dead biomass measurements narrowed the posterior space of parameter solutions and improved consistency between measured and modeled carbon dynamics. The HDA framework also reduced uncertainties on modeled carbon fluxes. Young stands were found to release less carbon when the model was informed by dead biomass compared to simulations guided by aboveground biomass alone. The remaining mismatches between modeled and FIA pool estimates could be attributed to wide uncertainty in some FIA estimates, differing definitions of functional carbon pools, and structural rigidity in the model. Together, this study underscores the importance of pool-based measurements in forest carbon modeling, which improves the model-observation fit and reduces process-model uncertainty.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Deep Learning in Ensemble Streamflow Forecasting: Exploring the Predictive Value of Explicit Snowpack Information","authors":"Parthkumar Modi,&nbsp;Keith Jennings,&nbsp;Joseph Kasprzyk,&nbsp;Eric Small,&nbsp;Cameron Wobus,&nbsp;Ben Livneh","doi":"10.1029/2024MS004582","DOIUrl":"https://doi.org/10.1029/2024MS004582","url":null,"abstract":"<p>The Ensemble Streamflow Prediction (ESP) framework combines a probabilistic forecast structure with process-based models for water supply predictions. However, process-based models require computationally intensive parameter estimation, increasing uncertainties and limiting usability. Motivated by the strong performance of deep learning models, we seek to assess whether the Long Short-Term Memory (LSTM) model can provide skillful forecasts and replace process-based models within the ESP framework. Given challenges in <i>implicitly</i> capturing snowpack dynamics within LSTMs for streamflow prediction, we also evaluated the added skill of <i>explicitly</i> incorporating snowpack information to improve hydrologic memory representation. LSTM-ESPs were evaluated under four different scenarios: one excluding snow and three including snow with varied snowpack representations. The LSTM models were trained using information from 664 GAGES-II basins during WY1983–2000. During a testing period, WY2001–2010, 80% of basins exhibited Nash-Sutcliffe Efficiency (NSE) above 0.5 with a median NSE of around 0.70, indicating satisfactory utility in simulating seasonal water supply. LSTM-ESP forecasts were then tested during WY2011–2020 over 76 western US basins with operational Natural Resources Conservation Services (NRCS) forecasts. A key finding is that in high snow regions, LSTM-ESP forecasts using simplified ablation assumptions performed worse than those excluding snow, highlighting that snow data do not consistently improve LSTM-ESP performance. However, LSTM-ESP forecasts that explicitly incorporated past years' snow accumulation and ablation performed comparably to NRCS forecasts and better than forecasts excluding snow entirely. Overall, integrating deep learning within an ESP framework shows promise and highlights important considerations for including snowpack information in forecasting.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing Long-Standing Surface Ozone Overestimation in Earth System Modeling by High-Resolution Simulation and Dry Deposition Improvement","authors":"Yang Gao,&nbsp;Wenbin Kou,&nbsp;Wenxuan Cheng,&nbsp;Xiuwen Guo,&nbsp;Binglin Qu,&nbsp;Yubing Wu,&nbsp;Shaoqing Zhang,&nbsp;Hong Liao,&nbsp;Deliang Chen,&nbsp;L. Ruby Leung,&nbsp;Oliver Wild,&nbsp;Junxi Zhang,&nbsp;Guangxing Lin,&nbsp;Hang Su,&nbsp;Yafang Cheng,&nbsp;Ulrich Pöschl,&nbsp;Andrea Pozzer,&nbsp;Leiming Zhang,&nbsp;Jean-Francois Lamarque,&nbsp;Alex B. Guenther,&nbsp;Guy Brasseur,&nbsp;Zhao Liu,&nbsp;Haitian Lu,&nbsp;Chenlin Li,&nbsp;Bin Zhao,&nbsp;Shuxiao Wang,&nbsp;Xin Huang,&nbsp;Jingshan Pan,&nbsp;Guangliang Liu,&nbsp;Xin Liu,&nbsp;Haipeng Lin,&nbsp;Yuanhong Zhao,&nbsp;Chun Zhao,&nbsp;Junlei Meng,&nbsp;Xiaohong Yao,&nbsp;Huiwang Gao,&nbsp;Lixin Wu","doi":"10.1029/2023MS004192","DOIUrl":"https://doi.org/10.1029/2023MS004192","url":null,"abstract":"<p>The overestimation of surface ozone concentration in low-resolution global atmospheric chemistry and climate models has been a long-standing issue. We first update the ozone dry deposition scheme in both high- (0.25°) and low-resolution (1°) Community Earth System Model (CESM) version 1.3 runs, by adding the effects of leaf area index and correcting the sunlit and shaded fractions of stomatal resistances. With this update, 5-year-long summer simulations (2015–2019) using the low-resolution CESM still exhibit substantial ozone overestimation (by 6.0–16.2 ppbv) over the U.S., Europe, eastern China, and ozone pollution hotspots. The ozone dry deposition scheme is further improved by adjusting the leaf cuticle conductance, reducing the mean ozone bias by 19%, and increasing the model resolution further reduces the ozone overestimation by 43%. We elucidate the mechanism by which model grid spacing influences simulated ozone, revealing distinctive pathways in urban versus rural areas. In rural areas, grid spacing mainly affects daytime ozone levels, where additional NO_x emissions from nearby urban areas result in an ozone boost and overestimation in low-resolution simulations. In contrast, over urban areas, daytime ozone overestimation follows a similar mechanism due to the influence of volatile organic compounds from surrounding rural areas. However, nighttime ozone overestimation is closely linked to weakened NO titration owing to the redistribution of urban NO_x to rural areas. Additionally, stratosphere-troposphere exchange may also contribute to reducing ozone bias in high-resolution simulations, warranting further investigation. This optimized high-resolution CESM may enhance understanding of ozone formation mechanisms, sources, and changes in a warming climate.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water Mass Transformation Budgets in Finite-Volume Generalized Vertical Coordinate Ocean Models","authors":"Henri F. Drake,&nbsp;Shanice Bailey,&nbsp;Raphael Dussin,&nbsp;Stephen M. Griffies,&nbsp;John Krasting,&nbsp;Graeme MacGilchrist,&nbsp;Geoffrey Stanley,&nbsp;Jan-Erik Tesdal,&nbsp;Jan D. Zika","doi":"10.1029/2024MS004383","DOIUrl":"https://doi.org/10.1029/2024MS004383","url":null,"abstract":"<p>Water Mass Transformation (WMT) theory provides conceptual tools that in principle enable innovative analyses of numerical ocean models; in practice, however, these methods can be challenging to implement and interpret, and therefore remain under-utilized. Our aim is to demonstrate the feasibility of diagnosing all terms in the water mass budget and to exemplify their usefulness for scientific inquiry and model development by quantitatively relating water mass changes, overturning circulations, boundary fluxes, and interior mixing. We begin with a pedagogical derivation of key results of classical WMT theory. We then describe best practices for diagnosing each of the water mass budget terms from the output of Finite-Volume Generalized Vertical Coordinate (FV-GVC) ocean models, including the identification of a non-negligible remainder term as the spurious numerical mixing due to advection scheme discretization errors. We illustrate key aspects of the methodology through the analysis of a polygonal region of the Greater Baltic Sea in a regional demonstration simulation using the Modular Ocean Model v6 (MOM6). We verify the convergence of our WMT diagnostics by brute-force, comparing time-averaged (“offline”) diagnostics on various vertical grids to timestep-averaged (“online”) diagnostics on the native model grid. Finally, we briefly describe a stack of xarray-enabled Python packages for evaluating WMT budgets in FV-GVC models (culminating in the new <span>xwmb</span> package), which is intended to be model-agnostic and available for community use and development.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model","authors":"Bobby Antonio,&nbsp;Andrew T. T. McRae,&nbsp;David MacLeod,&nbsp;Fenwick C. Cooper,&nbsp;John Marsham,&nbsp;Laurence Aitchison,&nbsp;Tim N. Palmer,&nbsp;Peter A. G. Watson","doi":"10.1029/2024MS004796","DOIUrl":"https://doi.org/10.1029/2024MS004796","url":null,"abstract":"<p>Existing weather models are known to have poor skill at forecasting rainfall over East Africa. Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning (ML)-based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6–18 hr lead times, at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.1</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $0.1{}^{circ}$</annotation>\u0000 </semantics></math> resolution. We combine the cGAN predictions with a novel neighborhood version of quantile mapping, to integrate the strengths of ML and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves predictions up to the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>99</mn>\u0000 <mo>.</mo>\u0000 <msup>\u0000 <mn>9</mn>\u0000 <mtext>th</mtext>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> $99.{9}^{text{th}}$</annotation>\u0000 </semantics></math> percentile <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 <mn>10</mn>\u0000 <mtext>mm</mtext>\u0000 <mo>/</mo>\u0000 <mtext>hr</mtext>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation> $(sim 10text{mm}/text{hr})$</annotation>\u0000 </semantics></math>. This improvement extends to the March–May 2018 season, which had extremely high rainfall, indicating that the approach has some ability to generalize to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterized by a mixture of under- and over-dispersion. Overall our results demonstrate how the strengths of ML and conventional postprocessing methods can be combined, and illuminate what benefits ML approaches can bring to this region.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}