Abstract The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters ( D m ) on the SETP are the smallest among six sites. The generalized intercept parameter ( N w ) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)’s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied.
{"title":"Comparisons of Rainfall Microphysical Characteristics Between the southeastern Tibetan Plateau and Low-altitude Areas","authors":"Xin Xu, Xuelong Chen, Dianbin Cao, Yajing Liu, Luhan Li, Yaoming Ma","doi":"10.1175/jamc-d-23-0046.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0046.1","url":null,"abstract":"Abstract The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters ( D m ) on the SETP are the smallest among six sites. The generalized intercept parameter ( N w ) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)’s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"63 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134996583","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}
Pub Date : 2023-11-01DOI: 10.1175/jamc-d-23-0072.1
Troy P. Wixson, Daniel Cooley
Abstract Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change.
{"title":"Attribution of Seasonal Wildfire Risk to Changes in Climate: A Statistical Extremes Approach","authors":"Troy P. Wixson, Daniel Cooley","doi":"10.1175/jamc-d-23-0072.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0072.1","url":null,"abstract":"Abstract Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"76 48","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135062559","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}
Pub Date : 2023-11-01DOI: 10.1175/jamc-d-23-0104.1
Jacob Coburn, Sara C. Pryor
Abstract Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.
{"title":"Projecting Future Energy Production from Operating Wind Farms in North America. Part III: Variability","authors":"Jacob Coburn, Sara C. Pryor","doi":"10.1175/jamc-d-23-0104.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0104.1","url":null,"abstract":"Abstract Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"55 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410158","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}
Pub Date : 2023-11-01DOI: 10.1175/jamc-d-23-0042.1
Zachary J. Suriano, Gina R. Henderson, Julia Arthur, Kricket Harper, Daniel J. Leathers
Abstract Extreme snow ablation can greatly impact regional hydrology, affecting streamflow, soil moisture, and groundwater supplies. Relatively little is known about the climatology of extreme ablation events in the eastern United States, and the causal atmospheric forcing mechanisms behind such events. Studying the Susquehanna River basin over a 50-yr period, here we evaluate the variability of extreme ablation and river discharge events in conjunction with a synoptic classification and global-scale teleconnection pattern analysis. Results indicate that an average of 4.2 extreme ablation events occurred within the basin per year, where some 88% of those events resulted in an increase in river discharge when evaluated at a 3-day lag. Both extreme ablation and extreme discharge events occurred most frequently during instances of southerly synoptic-scale flow, accounting for 35.7% and 35.8% of events, respectively. However, extreme ablation was also regularly observed during high pressure overhead and rain-on-snow synoptic weather types. The largest magnitude of snow ablation per extreme event occurred during occasions of rain-on-snow, where a basinwide, areal-weighted 5.7 cm of snow depth was lost, approximately 23% larger than the average extreme event. Interannually, southerly flow synoptic weather types were more frequent during winter seasons when the Arctic and North Atlantic Oscillations were positively phased. Approximately 30% of the variance in rain-on-snow weather type frequency was explained by the Pacific–North American pattern. Evaluating the pathway of physical forcing mechanisms from regional events up through global patterns allows for improved understanding of the processes resulting in extreme ablation and discharge across the Susquehanna basin. Significance Statement The purpose of this study is to better understand how certain weather patterns are related to extreme snowmelt and streamflow events and what causes those weather patterns to vary with time. This is valuable information for informing hazard preparation and resource management within the basin. We found that weather patterns with southerly winds were the most frequent patterns responsible for extreme melt and streamflow, and those patterns occurred more often when the Arctic and North Atlantic Oscillations were in their “positive” configuration. Future work should consider the potential for these patterns, and related impacts, to change over time.
{"title":"Atmospheric Drivers Associated with Extreme Snow Ablation and Discharge Events in the Susquehanna River Basin: A Climatology","authors":"Zachary J. Suriano, Gina R. Henderson, Julia Arthur, Kricket Harper, Daniel J. Leathers","doi":"10.1175/jamc-d-23-0042.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0042.1","url":null,"abstract":"Abstract Extreme snow ablation can greatly impact regional hydrology, affecting streamflow, soil moisture, and groundwater supplies. Relatively little is known about the climatology of extreme ablation events in the eastern United States, and the causal atmospheric forcing mechanisms behind such events. Studying the Susquehanna River basin over a 50-yr period, here we evaluate the variability of extreme ablation and river discharge events in conjunction with a synoptic classification and global-scale teleconnection pattern analysis. Results indicate that an average of 4.2 extreme ablation events occurred within the basin per year, where some 88% of those events resulted in an increase in river discharge when evaluated at a 3-day lag. Both extreme ablation and extreme discharge events occurred most frequently during instances of southerly synoptic-scale flow, accounting for 35.7% and 35.8% of events, respectively. However, extreme ablation was also regularly observed during high pressure overhead and rain-on-snow synoptic weather types. The largest magnitude of snow ablation per extreme event occurred during occasions of rain-on-snow, where a basinwide, areal-weighted 5.7 cm of snow depth was lost, approximately 23% larger than the average extreme event. Interannually, southerly flow synoptic weather types were more frequent during winter seasons when the Arctic and North Atlantic Oscillations were positively phased. Approximately 30% of the variance in rain-on-snow weather type frequency was explained by the Pacific–North American pattern. Evaluating the pathway of physical forcing mechanisms from regional events up through global patterns allows for improved understanding of the processes resulting in extreme ablation and discharge across the Susquehanna basin. Significance Statement The purpose of this study is to better understand how certain weather patterns are related to extreme snowmelt and streamflow events and what causes those weather patterns to vary with time. This is valuable information for informing hazard preparation and resource management within the basin. We found that weather patterns with southerly winds were the most frequent patterns responsible for extreme melt and streamflow, and those patterns occurred more often when the Arctic and North Atlantic Oscillations were in their “positive” configuration. Future work should consider the potential for these patterns, and related impacts, to change over time.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"66 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136372505","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}
Pub Date : 2023-11-01DOI: 10.1175/jamc-d-22-0144.1
Felix Erdmann, Dieter R. Poelman
Abstract Rapid increases in the flash rate (FR) of a thunderstorm, so-called lightning jumps (LJs), have potential for nowcasting applications and to increase lead times for severe weather warnings. To date, there are some automated LJ algorithms that were developed and tuned for ground-based lightning locating systems. This study addresses the optimization of an automated LJ algorithm for the Geostationary Lightning Mapper (GLM) lightning observations from space. The widely used σ -LJ algorithm is used in its original form and in an adapted calculation including the footprint area of the storm cell (FRarea LJ algorithm). In addition, a new relative increase level (RIL) LJ algorithm is introduced. All algorithms are tested in different configurations, and detected LJs are verified against National Centers for Environmental Information severe weather reports. Overall, the FRarea algorithm with an activation FR threshold of 15 flashes per minute and a σ -level threshold of 1.0–1.5 as well as the RIL algorithm with FR threshold of 15 flashes per minute and RIL threshold of 1.1 are recommended. These algorithms scored the best critical success index (CSI) of ∼0.5, with a probability of detection of 0.6–0.7 and a false alarm ratio of ∼0.4. For daytime warm-season thunderstorms, the CSI can exceed 0.5, reaching 0.67 for storms observed during three consecutive days in April 2021. The CSI is generally lower at night and in winter.
{"title":"Automated Lightning Jump (LJ) detection from geostationary satellite data","authors":"Felix Erdmann, Dieter R. Poelman","doi":"10.1175/jamc-d-22-0144.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0144.1","url":null,"abstract":"Abstract Rapid increases in the flash rate (FR) of a thunderstorm, so-called lightning jumps (LJs), have potential for nowcasting applications and to increase lead times for severe weather warnings. To date, there are some automated LJ algorithms that were developed and tuned for ground-based lightning locating systems. This study addresses the optimization of an automated LJ algorithm for the Geostationary Lightning Mapper (GLM) lightning observations from space. The widely used σ -LJ algorithm is used in its original form and in an adapted calculation including the footprint area of the storm cell (FRarea LJ algorithm). In addition, a new relative increase level (RIL) LJ algorithm is introduced. All algorithms are tested in different configurations, and detected LJs are verified against National Centers for Environmental Information severe weather reports. Overall, the FRarea algorithm with an activation FR threshold of 15 flashes per minute and a σ -level threshold of 1.0–1.5 as well as the RIL algorithm with FR threshold of 15 flashes per minute and RIL threshold of 1.1 are recommended. These algorithms scored the best critical success index (CSI) of ∼0.5, with a probability of detection of 0.6–0.7 and a false alarm ratio of ∼0.4. For daytime warm-season thunderstorms, the CSI can exceed 0.5, reaching 0.67 for storms observed during three consecutive days in April 2021. The CSI is generally lower at night and in winter.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"79 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136372502","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}
Pub Date : 2023-11-01DOI: 10.1175/jamc-d-23-0059.1
Ronald D. Leeper, Michael A. Palecki, Matthew Watts, Howard Diamond
Abstract Remotely sensed soil moisture observations provide an opportunity to monitor hydrological conditions from droughts to floods. The European Space Agency’s (ESA) Climate Change Initiative has released both Combined and Passive datasets, which include multiple satellites’ measurements of soil moisture conditions since the 1980s. In this study, both volumetric soil moisture and soil moisture standardized anomalies from the U.S. Climate Reference Network (USCRN) were compared with ESA’s Combined and Passive datasets. Results from this study indicate the importance of using standardized anomalies over volumetric soil moisture conditions as satellite datasets were unable to capture the frequency of conditions observed at the extreme ends of the volumetric distribution. Overall, the Combined dataset had slightly lower measures of soil moisture anomaly errors for all regions; although these differences were not statistically significant. Both satellite datasets were able to detect the evolution from worsening to amelioration of the 2012 drought across the central United States and 2019 flood over the upper Missouri River basin. While the ESA datasets were not able to detect the magnitude of the extremes, the ESA standardized datasets were able to detect the interannual variability of extreme wet and dry day counts for most climate regions. These results suggest that remotely sensed standardized soil moisture can be included in hydrological monitoring systems and combined with in situ measures to detect the magnitude of extreme conditions. Significance Statement This study examines how well soil moisture extremes, wet or dry, can be detected from space using one of the lengthiest remotely sensed soil moisture datasets. Comparisons with high-quality station data from the U.S. Climate Reference Network revealed the satellite datasets could capture the frequency of extreme conditions important for climate monitoring, but often missed the absolute magnitudes of the extremes. Future research should focus on how to combine satellite and station data to improve the detection of extreme values important for monitoring.
{"title":"On the Detection of Remotely Sensed Soil Moisture Extremes","authors":"Ronald D. Leeper, Michael A. Palecki, Matthew Watts, Howard Diamond","doi":"10.1175/jamc-d-23-0059.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0059.1","url":null,"abstract":"Abstract Remotely sensed soil moisture observations provide an opportunity to monitor hydrological conditions from droughts to floods. The European Space Agency’s (ESA) Climate Change Initiative has released both Combined and Passive datasets, which include multiple satellites’ measurements of soil moisture conditions since the 1980s. In this study, both volumetric soil moisture and soil moisture standardized anomalies from the U.S. Climate Reference Network (USCRN) were compared with ESA’s Combined and Passive datasets. Results from this study indicate the importance of using standardized anomalies over volumetric soil moisture conditions as satellite datasets were unable to capture the frequency of conditions observed at the extreme ends of the volumetric distribution. Overall, the Combined dataset had slightly lower measures of soil moisture anomaly errors for all regions; although these differences were not statistically significant. Both satellite datasets were able to detect the evolution from worsening to amelioration of the 2012 drought across the central United States and 2019 flood over the upper Missouri River basin. While the ESA datasets were not able to detect the magnitude of the extremes, the ESA standardized datasets were able to detect the interannual variability of extreme wet and dry day counts for most climate regions. These results suggest that remotely sensed standardized soil moisture can be included in hydrological monitoring systems and combined with in situ measures to detect the magnitude of extreme conditions. Significance Statement This study examines how well soil moisture extremes, wet or dry, can be detected from space using one of the lengthiest remotely sensed soil moisture datasets. Comparisons with high-quality station data from the U.S. Climate Reference Network revealed the satellite datasets could capture the frequency of extreme conditions important for climate monitoring, but often missed the absolute magnitudes of the extremes. Future research should focus on how to combine satellite and station data to improve the detection of extreme values important for monitoring.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"47 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136371336","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}
Abstract To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.
{"title":"Impacts of Multiscale Components of Initial Perturbations on Error Growth Characteristics and Ensemble Forecasting Skill","authors":"Jingzhuo Wang, Jing Chen, Hanbin Zhang, Ruoyun Ma, Fajing Chen","doi":"10.1175/jamc-d-23-0108.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0108.1","url":null,"abstract":"Abstract To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"38 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135615866","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}
Pub Date : 2023-10-31DOI: 10.1175/jamc-d-23-0087.1
Stephen Jewson
Abstract We use a simple risk model for U.S. hurricane wind and surge economic damage to investigate the impact of projected changes in the frequencies of hurricanes of different intensities due to climate change. For average annual damage we find that changes in the frequency of category 4 storms dominate. For distributions of annual damage we find that changes in the frequency of category 4 storms again dominate for all except the shortest return periods. Sensitivity tests show that accounting for landfall, uncertainties and correlations leads to increases in damage estimates. When we propagate the distributions of uncertain frequency changes to give a best estimate of the changes in damage, the changes are moderate. When we pick individual scenarios from within the distributions of frequency changes, we find a significant probability of much larger changes in damage. The inputs on which our study depends are highly uncertain, and our methods are approximate, leading to high levels of uncertainty in our results. Also, the damage changes we consider are only part of the total possible change in hurricane damage due to climate change. Total damage change estimates would also need to include changes due to other factors, including possible changes in genesis, tracks, size, forward-speed, sea-level rise, rainfall and exposure. Nevertheless, we believe that our results give important new insights into U.S. hurricane risk under climate change.
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Pub Date : 2023-10-26DOI: 10.1175/jamc-d-22-0202.1
Mathieu Lachapelle, Hadleigh D. Thompson, Nicolas R. Leroux, Julie M. Thériault
This study aims to characterize the shapes and fall speeds of ice pellets formed in various atmospheric conditions and to investigate the possibility to use a laser-optical disdrometer to distinguish between ice pellets and other types of precipitation. To do so, four ice pellet events were documented using manual observations, macro photography, and laser-optical disdrometer data. First, various ice pellet fall speeds, and shapes, including spherical, bulged, fractured, and irregular particles, were associated with distinct atmospheric conditions. A higher fraction of bulged and fractured ice pellets was observed when solid precipitation was completely melted aloft while more irregular particles were observed during partial melting. These characteristics affected the diameter-fall speed relations measured. Second, the measurements of particles’ fall speed and diameter show that ice pellets could be differentiated from rain or freezing rain. Ice pellets larger than 1.5 mm tend to fall > 0.5 m s−1 slower than raindrops of the same size. Additionally, the fall speed of a small fraction of ice pellets was < 2 m s−1 regardless of their size, compared to a fall speed > 3 m s−1 for ice pellets with diameter > 1.5 mm. Video analysis suggests that these slower particles could be ice pellets passing through the laser-optical disdrometer after colliding with the head of the instrument. Overall, these findings contribute to a better understanding of the microphysics of ice pellets and their measurement using a laser-optical disdrometer.
本研究旨在表征在不同大气条件下形成的冰球的形状和下落速度，并探讨使用激光光学衍射仪区分冰球和其他类型降水的可能性。为此，使用人工观测、微距摄影和激光衍射仪数据记录了四次冰粒事件。首先，不同的冰粒下落速度和形状，包括球形、凸起、破碎和不规则颗粒，与不同的大气条件有关。当固体降水在高空完全融化时，观察到凸起和破裂的冰球比例较高，而在部分融化时观察到更多不规则颗粒。这些特性影响了所测的直径-下落速度关系。其次，对颗粒下落速度和直径的测量表明，冰颗粒可以与雨或冻雨区分开来。大于1.5 mm的冰球容易落下;比相同大小的雨滴慢0.5 m s−1。此外，一小部分冰球的下落速度为<2米s−1，无论大小，与下落速度>直径>的冰球3 m s−1;1.5毫米。视频分析表明，这些速度较慢的粒子可能是冰粒，在与仪器头部碰撞后穿过激光光学衍射仪。总的来说，这些发现有助于更好地理解冰球的微物理学以及使用激光光学分差仪对其进行测量。
{"title":"Measuring ice pellets and refrozen wet snow using a laser-optical disdrometer","authors":"Mathieu Lachapelle, Hadleigh D. Thompson, Nicolas R. Leroux, Julie M. Thériault","doi":"10.1175/jamc-d-22-0202.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0202.1","url":null,"abstract":"This study aims to characterize the shapes and fall speeds of ice pellets formed in various atmospheric conditions and to investigate the possibility to use a laser-optical disdrometer to distinguish between ice pellets and other types of precipitation. To do so, four ice pellet events were documented using manual observations, macro photography, and laser-optical disdrometer data. First, various ice pellet fall speeds, and shapes, including spherical, bulged, fractured, and irregular particles, were associated with distinct atmospheric conditions. A higher fraction of bulged and fractured ice pellets was observed when solid precipitation was completely melted aloft while more irregular particles were observed during partial melting. These characteristics affected the diameter-fall speed relations measured. Second, the measurements of particles’ fall speed and diameter show that ice pellets could be differentiated from rain or freezing rain. Ice pellets larger than 1.5 mm tend to fall > 0.5 m s−1 slower than raindrops of the same size. Additionally, the fall speed of a small fraction of ice pellets was < 2 m s−1 regardless of their size, compared to a fall speed > 3 m s−1 for ice pellets with diameter > 1.5 mm. Video analysis suggests that these slower particles could be ice pellets passing through the laser-optical disdrometer after colliding with the head of the instrument. Overall, these findings contribute to a better understanding of the microphysics of ice pellets and their measurement using a laser-optical disdrometer.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909149","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}
Pub Date : 2023-10-26DOI: 10.1175/jamc-d-23-0097.1
Zachary J. Suriano, Charles Loewy, Jamie Uz
Abstract Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledge the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948-2021, focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or mid-latitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain’s synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15-25% of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1-93.5% by SWT. Synoptic climatological investigations provide value insights into the unique weather systems that generate hydroclimatic variability.
{"title":"Synoptic Climatology of Central United States Snowfall","authors":"Zachary J. Suriano, Charles Loewy, Jamie Uz","doi":"10.1175/jamc-d-23-0097.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0097.1","url":null,"abstract":"Abstract Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledge the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948-2021, focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or mid-latitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain’s synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15-25% of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1-93.5% by SWT. Synoptic climatological investigations provide value insights into the unique weather systems that generate hydroclimatic variability.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908445","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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