Alison Cobb, Daniel Steinhoff, R. Weihs, L. Delle Monache, L. DeHaan, David Reynolds, Forest Cannon, B. Kawzenuk, Caroline Papadopolous, F. M. Ralph
This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) model, tailored for the prediction of extreme hydrometeorological events over the Western U.S. (West-WRF) spanning 34 cool seasons (1 December to 31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering Western North America and the Eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast compared to GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin-scale, the reforecast can improve MAP compared to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coastal Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high resolution (< 25 km) regional models. The applications of this high-resolution multi-year reforecast include process-based studies, assessment of model performance, and machine learning applications.
{"title":"West-WRF 34-Year Reforecast: Description and Validation","authors":"Alison Cobb, Daniel Steinhoff, R. Weihs, L. Delle Monache, L. DeHaan, David Reynolds, Forest Cannon, B. Kawzenuk, Caroline Papadopolous, F. M. Ralph","doi":"10.1175/jhm-d-22-0235.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0235.1","url":null,"abstract":"\u0000This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) model, tailored for the prediction of extreme hydrometeorological events over the Western U.S. (West-WRF) spanning 34 cool seasons (1 December to 31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering Western North America and the Eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast compared to GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin-scale, the reforecast can improve MAP compared to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coastal Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high resolution (< 25 km) regional models. The applications of this high-resolution multi-year reforecast include process-based studies, assessment of model performance, and machine learning applications.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"29 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82082061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pengcheng Zhao, Quan J. Wang, Wenyan Wu, Qichun Yang
Abstract Postprocessing forecast precipitation fields from numerical weather prediction models aims to produce ensemble forecasts that are of high quality at each grid cell and, importantly, are spatially structured in an appropriate manner. A conventional approach, the gridcell-by-gridcell postprocessing, typically consists of two steps: 1) perform statistical calibration separately at individual grid cells to generate unbiased, skillful, and reliable ensemble forecasts and 2) employ ensemble reordering to link ensemble members of all grid cells according to certain templates to form spatially structured ensemble forecasts. However, ensemble reordering techniques are generally problematic in practical use. For example, the well-known Schaake shuffle is often criticized for not considering real physical atmospheric conditions. In this context, a fundamentally new approach, namely, spatial-mode-based calibration (SMoC), has recently been developed for postprocessing forecast precipitation fields with inbuilt spatial structures, thereby eliminating the need for ensemble reordering. SMoC was tested on 1-day-ahead forecasts of heavy precipitation events and was found to produce ensemble forecasts with appropriate spatial structures. In this paper, we extend SMoC to calibrate forecasts of light and no precipitation events and forecasts at long lead times. We also compare SMoC with the gridcell-by-gridcell postprocessing. Results based on multiple evaluation metrics show that SMoC performs well in calibrating both forecasts of light and no precipitation events and forecasts at long lead times. Compared with the gridcell-by-gridcell postprocessing, SMoC produces ensemble forecasts with similar forecast skill, improved forecast reliability, and clearly better spatial structures. In addition, SMoC is computationally far more efficient.
{"title":"Spatial-Mode-Based Calibration (SMoC) of Forecast Precipitation Fields with Spatially Correlated Structures: An Extended Evaluation and Comparison with Gridcell-by-Gridcell Postprocessing","authors":"Pengcheng Zhao, Quan J. Wang, Wenyan Wu, Qichun Yang","doi":"10.1175/jhm-d-23-0023.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0023.1","url":null,"abstract":"Abstract Postprocessing forecast precipitation fields from numerical weather prediction models aims to produce ensemble forecasts that are of high quality at each grid cell and, importantly, are spatially structured in an appropriate manner. A conventional approach, the gridcell-by-gridcell postprocessing, typically consists of two steps: 1) perform statistical calibration separately at individual grid cells to generate unbiased, skillful, and reliable ensemble forecasts and 2) employ ensemble reordering to link ensemble members of all grid cells according to certain templates to form spatially structured ensemble forecasts. However, ensemble reordering techniques are generally problematic in practical use. For example, the well-known Schaake shuffle is often criticized for not considering real physical atmospheric conditions. In this context, a fundamentally new approach, namely, spatial-mode-based calibration (SMoC), has recently been developed for postprocessing forecast precipitation fields with inbuilt spatial structures, thereby eliminating the need for ensemble reordering. SMoC was tested on 1-day-ahead forecasts of heavy precipitation events and was found to produce ensemble forecasts with appropriate spatial structures. In this paper, we extend SMoC to calibrate forecasts of light and no precipitation events and forecasts at long lead times. We also compare SMoC with the gridcell-by-gridcell postprocessing. Results based on multiple evaluation metrics show that SMoC performs well in calibrating both forecasts of light and no precipitation events and forecasts at long lead times. Compared with the gridcell-by-gridcell postprocessing, SMoC produces ensemble forecasts with similar forecast skill, improved forecast reliability, and clearly better spatial structures. In addition, SMoC is computationally far more efficient.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Equity, Inclusion, and Justice: An Opportunity for Action for AMS Publications Stakeholders","authors":"_ _","doi":"10.1175/jhm-d-23-0131.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0131.1","url":null,"abstract":"","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135150982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yueli Chen, Minghu Ding, Guo Zhang, Ying Wang, Jianduo Li
Abstract Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial–temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB.
{"title":"Evaluation of ERA5 Reanalysis Precipitation Data in the Yarlung Zangbo River Basin of the Tibetan Plateau","authors":"Yueli Chen, Minghu Ding, Guo Zhang, Ying Wang, Jianduo Li","doi":"10.1175/jhm-d-22-0229.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0229.1","url":null,"abstract":"Abstract Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial–temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The three-dimensional (3D) structure of precipitation systems is highly dependent on the hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar/Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes inter-cluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes.
{"title":"Precipitation Vertical Structure Characterization - a Feature-based approach","authors":"M. Arulraj, V. Petković, R. Ferraro, H. Meng","doi":"10.1175/jhm-d-23-0034.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0034.1","url":null,"abstract":"\u0000The three-dimensional (3D) structure of precipitation systems is highly dependent on the hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar/Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes inter-cluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"297 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82863692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hong Wang, F. Sun, Tingting Wang, Yao Feng, Fa Liu, Wenbin Liu
Pan evaporation (Epan) serves as a monitorable method for estimating potential evaporation evapotranspiration and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, monthly Epan was calculated over China during 1951-2021, resulting in an average R2 of 0.93±0.045 and RMSE of 21.48±6.06 mm month−1. The trend of Epan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of Epan trends, the Epan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of Epan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted Epan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in Epan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in Epan. These results show the complex and dynamic nature of Epan and underscore the need for continued monitoring and in-depth analysis of its drivers.
{"title":"On the Pattern and Attribution of Pan Evaporation over China (1951-2021)","authors":"Hong Wang, F. Sun, Tingting Wang, Yao Feng, Fa Liu, Wenbin Liu","doi":"10.1175/jhm-d-23-0066.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0066.1","url":null,"abstract":"\u0000Pan evaporation (Epan) serves as a monitorable method for estimating potential evaporation evapotranspiration and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, monthly Epan was calculated over China during 1951-2021, resulting in an average R2 of 0.93±0.045 and RMSE of 21.48±6.06 mm month−1. The trend of Epan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of Epan trends, the Epan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of Epan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted Epan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in Epan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in Epan. These results show the complex and dynamic nature of Epan and underscore the need for continued monitoring and in-depth analysis of its drivers.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"51 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84645803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of sub-grid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EF centered on the analyzed value. Coupling sensitivity exists as both positive (cloudiness increases with EF) and negative (clouds increase with decreasing EF) feedback regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. This study provides a framework intermediate complexity, lying between parcel theory and high-resolution coupled land-atmosphere modeling, and therefore isolates the relevant first-order processes in land-atmosphere interactions.
{"title":"A novel method for diagnosing land-atmosphere coupling sensitivity in a single-column model","authors":"F. M. Hay-Chapman, P. Dirmeyer","doi":"10.1175/jhm-d-22-0237.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0237.1","url":null,"abstract":"\u0000The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of sub-grid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EF centered on the analyzed value. Coupling sensitivity exists as both positive (cloudiness increases with EF) and negative (clouds increase with decreasing EF) feedback regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. This study provides a framework intermediate complexity, lying between parcel theory and high-resolution coupled land-atmosphere modeling, and therefore isolates the relevant first-order processes in land-atmosphere interactions.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"1 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89546051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yongjie Huang, M. Xue, Xiao‐Ming Hu, E. Martin, H. Novoa, R. McPherson, A. Perez, Isaac Yanqui Morales
Regional climate dynamical downscaling at convection-permitting resolutions is now practical and has potential to significantly improve over coarser-resolution simulations, but the former is not necessarily free of systematic biases. Evaluation and optimization of model configurations are therefore important. Twelve simulations at a grid spacing of 3 km using the WRF model with different microphysics, planetary boundary layer (PBL), and land surface model (LSM) schemes are performed over the Peruvian Central Andes during austral summer, a region with particularly complex terrain. The simulated precipitation is evaluated using rain-gauge data and three gridded precipitation datasets. All simulations correctly capture four precipitation hotspots associated with prevailing winds and terrain features along the east slope of Andes, though they generally overestimate the precipitation intensity. The simulation using Thompson microphysics, ACM2 PBL and Noah LSM schemes has the smallest bias. The simulated precipitation is most sensitive to PBL, secondly sensitive to microphysics and least sensitive to LSM schemes. The simulated precipitation is generally stronger in simulations using YSU than MYNN and ACM2 schemes. All simulations successfully capture the diurnal precipitation peak time mainly in the afternoon over the Peruvian Central Andes and in the early morning along its east slope. However, there are significant differences over the western Amazon Basin, where the precipitation peak occurs primarily in the late afternoon. Simulations using YSU exhibit a 4–8-hour delay in the precipitation peak over the western Amazon Basin, consistent with their stronger and more persistent low-level jets. These results provide guidance on the optimal configuration of dynamical downscaling of global climate projections for the Peruvian Central Andes.
{"title":"Convection-Permitting Simulations of Precipitation over the Peruvian Central Andes: Strong Sensitivity to Planetary Boundary Layer Parameterization","authors":"Yongjie Huang, M. Xue, Xiao‐Ming Hu, E. Martin, H. Novoa, R. McPherson, A. Perez, Isaac Yanqui Morales","doi":"10.1175/jhm-d-22-0173.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0173.1","url":null,"abstract":"\u0000Regional climate dynamical downscaling at convection-permitting resolutions is now practical and has potential to significantly improve over coarser-resolution simulations, but the former is not necessarily free of systematic biases. Evaluation and optimization of model configurations are therefore important. Twelve simulations at a grid spacing of 3 km using the WRF model with different microphysics, planetary boundary layer (PBL), and land surface model (LSM) schemes are performed over the Peruvian Central Andes during austral summer, a region with particularly complex terrain. The simulated precipitation is evaluated using rain-gauge data and three gridded precipitation datasets. All simulations correctly capture four precipitation hotspots associated with prevailing winds and terrain features along the east slope of Andes, though they generally overestimate the precipitation intensity. The simulation using Thompson microphysics, ACM2 PBL and Noah LSM schemes has the smallest bias. The simulated precipitation is most sensitive to PBL, secondly sensitive to microphysics and least sensitive to LSM schemes. The simulated precipitation is generally stronger in simulations using YSU than MYNN and ACM2 schemes. All simulations successfully capture the diurnal precipitation peak time mainly in the afternoon over the Peruvian Central Andes and in the early morning along its east slope. However, there are significant differences over the western Amazon Basin, where the precipitation peak occurs primarily in the late afternoon. Simulations using YSU exhibit a 4–8-hour delay in the precipitation peak over the western Amazon Basin, consistent with their stronger and more persistent low-level jets. These results provide guidance on the optimal configuration of dynamical downscaling of global climate projections for the Peruvian Central Andes.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"30 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89373188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in Artificial Intelligence (AI) and Explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley Additive Explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based Evaporative Stress Index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere-ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.
{"title":"Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI)","authors":"Jacob Mardian, C. Champagne, B. Bonsal, A. Berg","doi":"10.1175/jhm-d-23-0036.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0036.1","url":null,"abstract":"\u0000Recent advances in Artificial Intelligence (AI) and Explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley Additive Explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based Evaporative Stress Index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere-ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"10 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84108143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Changes in surface water and energy balance can influence weather through interactions between the land and lower atmosphere. In convecting atmospheres, increases in convective available potential energy (CAPE) at the base of the column are driven by surface turbulent fluxes and can lead to precipitation. Using two global satellite data sets, we analyze the impact of surface energy balance partitioning on convective development by tracking CAPE over soil moisture drydowns (interstorms) during the summer, when land-atmosphere coupling is strongest. Our results show that the sign and magnitude of CAPE development during summertime drydowns depends on regional hydroclimate and initial soil moisture content. On average, CAPE increases between precipitation events over humid regions (e.g., the Eastern United States) and decreases slightly over arid regions (e.g., the Western United States). The soil moisture content at the start of a drydown was found to only impact CAPE evolution over arid regions, leading to greater decreases in CAPE when initial soil moisture content was high. The effect of these factors on CAPE can be explained by their influence principally on surface evaporation, demonstrating the importance of evaporative controls on CAPE and providing a basis for understanding the soil moisture-precipitation relationship, as well as land-atmosphere interaction as a whole.
{"title":"Land Surface Influence on Convective Available Potential Energy (CAPE) Change During Interstorms","authors":"Lily N. Zhang, D. S. Short Gianotti, D. Entekhabi","doi":"10.1175/jhm-d-22-0191.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0191.1","url":null,"abstract":"\u0000Changes in surface water and energy balance can influence weather through interactions between the land and lower atmosphere. In convecting atmospheres, increases in convective available potential energy (CAPE) at the base of the column are driven by surface turbulent fluxes and can lead to precipitation. Using two global satellite data sets, we analyze the impact of surface energy balance partitioning on convective development by tracking CAPE over soil moisture drydowns (interstorms) during the summer, when land-atmosphere coupling is strongest. Our results show that the sign and magnitude of CAPE development during summertime drydowns depends on regional hydroclimate and initial soil moisture content. On average, CAPE increases between precipitation events over humid regions (e.g., the Eastern United States) and decreases slightly over arid regions (e.g., the Western United States). The soil moisture content at the start of a drydown was found to only impact CAPE evolution over arid regions, leading to greater decreases in CAPE when initial soil moisture content was high. The effect of these factors on CAPE can be explained by their influence principally on surface evaporation, demonstrating the importance of evaporative controls on CAPE and providing a basis for understanding the soil moisture-precipitation relationship, as well as land-atmosphere interaction as a whole.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"112 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72377493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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