Pub Date : 2022-11-30DOI: 10.3390/meteorology1040029
L. Thalheimer, Dorothy Heinrich, K. Haustein, Roop K. Singh
Populations around the world have already experienced the increasing severity of extreme weather causing disaster displacement. Anthropogenic climate change can intensify these impacts. Extreme event attribution studies center around the question of whether impactful extreme events could have occurred in a pre-industrial climate. Here, we argue that the next step for attribution science is to focus on those most vulnerable populations to future extremes and impacts from climate change. Up until now, the vulnerability dimension has not been systematically addressed in attribution studies, yet it would add urgently needed context, given the vast differences in adaptive capacity. We propose three integrative points to cascade disaster displacement linked to anthropogenic climate change.
{"title":"Integrating a Disaster Displacement Dimension in Climate Change Attribution","authors":"L. Thalheimer, Dorothy Heinrich, K. Haustein, Roop K. Singh","doi":"10.3390/meteorology1040029","DOIUrl":"https://doi.org/10.3390/meteorology1040029","url":null,"abstract":"Populations around the world have already experienced the increasing severity of extreme weather causing disaster displacement. Anthropogenic climate change can intensify these impacts. Extreme event attribution studies center around the question of whether impactful extreme events could have occurred in a pre-industrial climate. Here, we argue that the next step for attribution science is to focus on those most vulnerable populations to future extremes and impacts from climate change. Up until now, the vulnerability dimension has not been systematically addressed in attribution studies, yet it would add urgently needed context, given the vast differences in adaptive capacity. We propose three integrative points to cascade disaster displacement linked to anthropogenic climate change.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84945530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-07DOI: 10.3390/meteorology1040028
E. Kristóf
In this study, a pattern detection method is applied on the RCP4.5 and RCP8.5 simulation outputs of seven GCMs—disseminated by the Coupled Model Intercomparison Project Phase 5 (CMIP5)—to determine whether atmospheric teleconnection patterns detected in the ERA-20C reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will be observable in the future projections of the CMIP5 GCMs. The pattern detection technique—which combines the negative extrema method and receiver operating characteristic (ROC) curve analysis—is used on the geopotential height field at the 500 hPa pressure level in wintertime, in the Northern Hemisphere. It was found that teleconnections obtained from the ERA-20C reanalysis dataset for the period of 1976–2005 remain observable in the majority of the GCM outputs under the RCP4.5 and RCP8.5 scenarios for the periods of 2006–2035, 2021–2050, and 2071–2100. The results imply that atmospheric internal variability is the major factor that controls the teleconnections rather than the impact of radiative forcing.
{"title":"Evaluation of Future Simulations of the CMIP5 GCMs Concerning Boreal Wintertime Atmospheric Teleconnection Patterns","authors":"E. Kristóf","doi":"10.3390/meteorology1040028","DOIUrl":"https://doi.org/10.3390/meteorology1040028","url":null,"abstract":"In this study, a pattern detection method is applied on the RCP4.5 and RCP8.5 simulation outputs of seven GCMs—disseminated by the Coupled Model Intercomparison Project Phase 5 (CMIP5)—to determine whether atmospheric teleconnection patterns detected in the ERA-20C reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will be observable in the future projections of the CMIP5 GCMs. The pattern detection technique—which combines the negative extrema method and receiver operating characteristic (ROC) curve analysis—is used on the geopotential height field at the 500 hPa pressure level in wintertime, in the Northern Hemisphere. It was found that teleconnections obtained from the ERA-20C reanalysis dataset for the period of 1976–2005 remain observable in the majority of the GCM outputs under the RCP4.5 and RCP8.5 scenarios for the periods of 2006–2035, 2021–2050, and 2071–2100. The results imply that atmospheric internal variability is the major factor that controls the teleconnections rather than the impact of radiative forcing.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79551365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-10DOI: 10.3390/meteorology1040027
S. Lovejoy
Since the first climate models in the 1970s, algorithms and computer speeds have increased by a factor of ≈1017 allowing the simulation of more and more processes at finer and finer resolutions. Yet, the spread of the members of the multi-model ensemble (MME) of the Climate Model Intercomparison Project (CMIP) used in last year’s 6th IPCC Assessment Report was larger than ever: model uncertainty, in the sense of MME uncertainty, has increased. Even if the holy grail is still kilometric scale models, bigger may not be better. Why model structures that live for ≈15 min only to average them over factors of several hundred thousand in order to produce decadal climate projections? In this commentary, I argue that alongside the development of “seamless” (unique) weather-climate models that chase ever smaller—and mostly irrelevant—details, the community should seriously invest in the development of stochastic macroweather models. Such models exploit the statistical laws that are obeyed at scales longer than the lifetimes of planetary scale structures, beyond the deterministic prediction limit (≈10 days). I argue that the conventional General Circulation Models and these new macroweather models are complementary in the same way that statistical mechanics and continuum mechanics are equally valid with the method of choice determined by the application. Candidates for stochastic macroweather models are now emerging, those based on the Fractional Energy Balance Equation (FEBE) are particularly promising. The FEBE is an update and generalization of the classical Budyko–Sellers energy balance models, it respects the symmetries of scaling and energy conservation and it already allows for both state-of-the-art monthly and seasonal, interannual temperature forecasts and multidecadal projections. I demonstrate this with 21st century FEBE climate projections for global mean temperatures. Overall, the projections agree with the CMIP5 and CMIP6 multi-model ensembles and the FEBE parametric uncertainty is about half of the MME structural uncertainty. Without the FEBE, uncertainties are so large that climate policies (mitigation) are largely decoupled from climate consequences (warming) allowing policy makers too much “wiggle room”. The lower FEBE uncertainties will help overcome the current “uncertainty crisis”. Both model types are complementary, a fact demonstrated by showing that CMIP global mean temperatures can be accurately projected using such stochastic macroweather models (validating both approaches). Unsurprisingly, they can therefore be combined to produce an optimum hybrid model in which the two model types are used as copredictors: when combined, the various uncertainties are reduced even further.
{"title":"The Future of Climate Modelling: Weather Details, Macroweather Stochastics—Or Both?","authors":"S. Lovejoy","doi":"10.3390/meteorology1040027","DOIUrl":"https://doi.org/10.3390/meteorology1040027","url":null,"abstract":"Since the first climate models in the 1970s, algorithms and computer speeds have increased by a factor of ≈1017 allowing the simulation of more and more processes at finer and finer resolutions. Yet, the spread of the members of the multi-model ensemble (MME) of the Climate Model Intercomparison Project (CMIP) used in last year’s 6th IPCC Assessment Report was larger than ever: model uncertainty, in the sense of MME uncertainty, has increased. Even if the holy grail is still kilometric scale models, bigger may not be better. Why model structures that live for ≈15 min only to average them over factors of several hundred thousand in order to produce decadal climate projections? In this commentary, I argue that alongside the development of “seamless” (unique) weather-climate models that chase ever smaller—and mostly irrelevant—details, the community should seriously invest in the development of stochastic macroweather models. Such models exploit the statistical laws that are obeyed at scales longer than the lifetimes of planetary scale structures, beyond the deterministic prediction limit (≈10 days). I argue that the conventional General Circulation Models and these new macroweather models are complementary in the same way that statistical mechanics and continuum mechanics are equally valid with the method of choice determined by the application. Candidates for stochastic macroweather models are now emerging, those based on the Fractional Energy Balance Equation (FEBE) are particularly promising. The FEBE is an update and generalization of the classical Budyko–Sellers energy balance models, it respects the symmetries of scaling and energy conservation and it already allows for both state-of-the-art monthly and seasonal, interannual temperature forecasts and multidecadal projections. I demonstrate this with 21st century FEBE climate projections for global mean temperatures. Overall, the projections agree with the CMIP5 and CMIP6 multi-model ensembles and the FEBE parametric uncertainty is about half of the MME structural uncertainty. Without the FEBE, uncertainties are so large that climate policies (mitigation) are largely decoupled from climate consequences (warming) allowing policy makers too much “wiggle room”. The lower FEBE uncertainties will help overcome the current “uncertainty crisis”. Both model types are complementary, a fact demonstrated by showing that CMIP global mean temperatures can be accurately projected using such stochastic macroweather models (validating both approaches). Unsurprisingly, they can therefore be combined to produce an optimum hybrid model in which the two model types are used as copredictors: when combined, the various uncertainties are reduced even further.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82798413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-10DOI: 10.3390/meteorology1040026
J. Dudhia
At multi-kilometer grid scales, numerical weather prediction models represent surface-based convective eddies as a completely sub-grid one-dimensional vertical mixing and transport process. At tens of meters grid scales, large-eddy simulation models, explicitly resolve all the primary three-dimensional eddies associated with boundary-layer transport from the surface and entrainment at the top. Between these scales, at hundreds of meters grid size, is a so-called grey zone in which the primary transport is neither entirely sub-grid nor resolved, where explicit large-eddy models and sub-grid boundary-layer parameterization models fail in different ways that are outlined in this review article. This article also reviews various approaches that have been taken to span this gap in the proper representation of eddy transports in the sub-kilometer grid range using scale-aware approaches. Introduction of moisture with condensation in the eddies expands this problem to that of handling shallow convection, but similarities between dry and cloud-topped convective boundary layers can lead to some unified views of the processes that need to be represented in convective boundary-layers which will be briefly addressed here.
{"title":"Challenges in Sub-Kilometer Grid Modeling of the Convective Planetary Boundary Layer","authors":"J. Dudhia","doi":"10.3390/meteorology1040026","DOIUrl":"https://doi.org/10.3390/meteorology1040026","url":null,"abstract":"At multi-kilometer grid scales, numerical weather prediction models represent surface-based convective eddies as a completely sub-grid one-dimensional vertical mixing and transport process. At tens of meters grid scales, large-eddy simulation models, explicitly resolve all the primary three-dimensional eddies associated with boundary-layer transport from the surface and entrainment at the top. Between these scales, at hundreds of meters grid size, is a so-called grey zone in which the primary transport is neither entirely sub-grid nor resolved, where explicit large-eddy models and sub-grid boundary-layer parameterization models fail in different ways that are outlined in this review article. This article also reviews various approaches that have been taken to span this gap in the proper representation of eddy transports in the sub-kilometer grid range using scale-aware approaches. Introduction of moisture with condensation in the eddies expands this problem to that of handling shallow convection, but similarities between dry and cloud-topped convective boundary layers can lead to some unified views of the processes that need to be represented in convective boundary-layers which will be briefly addressed here.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84125617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-09DOI: 10.3390/meteorology1040025
J. Lee, Huqiang Zhang, D. Barker, Song Chen, Raj Kumar, B. W. An, K. Sharma, Krishnamoorthy Chandramouli
Singapore is a tiny city-state located in maritime Southeast Asia. Weather systems such as localized thunderstorms, squalls, and monsoon surges bring extreme rainfall to Singapore, influencing the day-to-day conduct of stakeholders in many sectors. Numerical weather prediction models can provide forecast guidance, but existing global models struggle to capture the development and evolution of the small-scale and transient weather systems impacting the region. To address this, Singapore has collaborated with international partners and developed regional numerical weather prediction systems. Steady progress has been made, bringing added value to stakeholders. In recent years, complex earth system and ultra high-resolution urban models have also been developed to meet increasingly diverse stakeholder needs. However, further advancement of weather prediction for Singapore is often hindered by existing challenges, such as the lack of data, limited understanding of underlying processes, and geographical complexities. These may be viewed as opportunities, but are not trivial to address. There are also other opportunities that have remained relatively unexplored over Singapore and the region, such as the integration of earth system models, uncertainty estimation and machine learning methods. These are perhaps key research directions that Singapore should embark on to continue ensuring value for stakeholders.
{"title":"Weather Prediction for Singapore—Progress, Challenges, and Opportunities","authors":"J. Lee, Huqiang Zhang, D. Barker, Song Chen, Raj Kumar, B. W. An, K. Sharma, Krishnamoorthy Chandramouli","doi":"10.3390/meteorology1040025","DOIUrl":"https://doi.org/10.3390/meteorology1040025","url":null,"abstract":"Singapore is a tiny city-state located in maritime Southeast Asia. Weather systems such as localized thunderstorms, squalls, and monsoon surges bring extreme rainfall to Singapore, influencing the day-to-day conduct of stakeholders in many sectors. Numerical weather prediction models can provide forecast guidance, but existing global models struggle to capture the development and evolution of the small-scale and transient weather systems impacting the region. To address this, Singapore has collaborated with international partners and developed regional numerical weather prediction systems. Steady progress has been made, bringing added value to stakeholders. In recent years, complex earth system and ultra high-resolution urban models have also been developed to meet increasingly diverse stakeholder needs. However, further advancement of weather prediction for Singapore is often hindered by existing challenges, such as the lack of data, limited understanding of underlying processes, and geographical complexities. These may be viewed as opportunities, but are not trivial to address. There are also other opportunities that have remained relatively unexplored over Singapore and the region, such as the integration of earth system models, uncertainty estimation and machine learning methods. These are perhaps key research directions that Singapore should embark on to continue ensuring value for stakeholders.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86401125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-28DOI: 10.3390/meteorology1040024
I. Jankov, Z. Toth, Jie Feng
Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve.
{"title":"Initial-Value vs. Model-Induced Forecast Error: A New Perspective","authors":"I. Jankov, Z. Toth, Jie Feng","doi":"10.3390/meteorology1040024","DOIUrl":"https://doi.org/10.3390/meteorology1040024","url":null,"abstract":"Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85694524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-27DOI: 10.3390/meteorology1040023
P. Lynch
A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.
{"title":"A Lagrange–Laplace Integration Scheme for Weather Prediction and Climate Modelling","authors":"P. Lynch","doi":"10.3390/meteorology1040023","DOIUrl":"https://doi.org/10.3390/meteorology1040023","url":null,"abstract":"A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91091098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.3390/meteorology1040022
H. D. Behr
This study characterizes the spatiotemporal solar radiation and air temperature patterns and their dependence on the general atmospheric circulation characterized by the North Atlantic Oscillation (NAO) Index in Germany from 1991 to 2015. Germany was selected as the study area because it can be subdivided into three climatologically different regions: the North German lowlands are under the maritime influence of the North and Baltic Seas. Several low mountain ranges dominate Germany’s center. In the south, the highest low mountain ranges and the Alps govern solar radiation and air temperature differently. Solar radiation and air temperature patterns were studied in the context of the NAO index using daily values from satellite and ground measurements. The most significant long-term solar radiation increase was observed in spring, mainly due to seasonal changes in cloud cover. Air temperature shows a noticeable increase in spring and autumn. Solar radiation and air temperature were significantly correlated in spring and autumn, with correlation coefficient values up to 0.93. In addition, a significant dependence of solar radiation and air temperature on the NAO index was revealed, with correlation coefficient values greater than 0.66. The results obtained are important not only for studies on the climate of the study area but also for photovoltaic system operators to design their systems. They need to be massively expanded to support Germany’s climate neutrality ambitions until 2045.
{"title":"Trends and Interdependence of Solar Radiation and Air Temperature—A Case Study from Germany","authors":"H. D. Behr","doi":"10.3390/meteorology1040022","DOIUrl":"https://doi.org/10.3390/meteorology1040022","url":null,"abstract":"This study characterizes the spatiotemporal solar radiation and air temperature patterns and their dependence on the general atmospheric circulation characterized by the North Atlantic Oscillation (NAO) Index in Germany from 1991 to 2015. Germany was selected as the study area because it can be subdivided into three climatologically different regions: the North German lowlands are under the maritime influence of the North and Baltic Seas. Several low mountain ranges dominate Germany’s center. In the south, the highest low mountain ranges and the Alps govern solar radiation and air temperature differently. Solar radiation and air temperature patterns were studied in the context of the NAO index using daily values from satellite and ground measurements. The most significant long-term solar radiation increase was observed in spring, mainly due to seasonal changes in cloud cover. Air temperature shows a noticeable increase in spring and autumn. Solar radiation and air temperature were significantly correlated in spring and autumn, with correlation coefficient values up to 0.93. In addition, a significant dependence of solar radiation and air temperature on the NAO index was revealed, with correlation coefficient values greater than 0.66. The results obtained are important not only for studies on the climate of the study area but also for photovoltaic system operators to design their systems. They need to be massively expanded to support Germany’s climate neutrality ambitions until 2045.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80319711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.3390/meteorology1030021
C. Sampson, J. Cummings, J. Knaff, M. DeMaria, Efren A. Serra
The upper ocean provides a source of thermal energy for tropical cyclone development and maintenance through a series of complex interactions. In this work, we develop a seventeen-year dataset of upper ocean thermal field metrics for use in tropical cyclone studies and development of tropical cyclone intensity prediction models. These metrics include the surface temperature, two different measures of vertically integrated heat content, and four different measures of vertically averaged temperature. Some metrics have been used to study upper-ocean energy response to tropical cyclone passage, while others have been employed to improve operational tropical cyclone intensity prediction models. The vertically integrated ocean heat content has been used to improve tropical cyclone intensity forecasts at U.S. tropical cyclone forecast centers and is an integral part of several operational intensity forecast models. A static 2005–2021 dataset that includes all twelve metrics described within is available on the Naval Research Laboratory web server, and a subset of six metrics have been produced in real-time at Fleet Numerical Meteorology and Oceanography Center and provided to the public via the GODAE server since 2021.
{"title":"An Upper Ocean Thermal Field Metrics Dataset","authors":"C. Sampson, J. Cummings, J. Knaff, M. DeMaria, Efren A. Serra","doi":"10.3390/meteorology1030021","DOIUrl":"https://doi.org/10.3390/meteorology1030021","url":null,"abstract":"The upper ocean provides a source of thermal energy for tropical cyclone development and maintenance through a series of complex interactions. In this work, we develop a seventeen-year dataset of upper ocean thermal field metrics for use in tropical cyclone studies and development of tropical cyclone intensity prediction models. These metrics include the surface temperature, two different measures of vertically integrated heat content, and four different measures of vertically averaged temperature. Some metrics have been used to study upper-ocean energy response to tropical cyclone passage, while others have been employed to improve operational tropical cyclone intensity prediction models. The vertically integrated ocean heat content has been used to improve tropical cyclone intensity forecasts at U.S. tropical cyclone forecast centers and is an integral part of several operational intensity forecast models. A static 2005–2021 dataset that includes all twelve metrics described within is available on the Naval Research Laboratory web server, and a subset of six metrics have been produced in real-time at Fleet Numerical Meteorology and Oceanography Center and provided to the public via the GODAE server since 2021.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90870054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.3390/meteorology1030020
E. Bucchignani
In the last few decades there has been increasing interest in the commercial usage of the stratosphere, especially for Earth observation systems. Stratospheric platforms allow Earth monitoring at a regional scale with persistency toward a limited area. For this reason, accurate meteorological forecasts are needed in order to guarantee stationarity. The main aim of this work is to provide a review of wind prediction techniques in the stratosphere, achieved by the most popular global models, such as ECMWF IFS, NCEP GFS and ICON. Then, the capabilities of the COSMO limited area model to reproduce the wind speed in the stratosphere are evaluated considering a model configuration with very high resolution (about 1 km) over a domain located in Southern Italy, assuming the radio sounding data at Pratica di Mare airport as the reference. Vertical profiles were analyzed for selected days, highlighting good performances, though improvements can be achieved by adopting a fifth-order interpolation of the model data. Finally, monthly wind speed time series for selected heights were post-processed by means of fast Fourier transform, revealing the existence of main frequencies and the presence of a scaling regime and a power law of the form f−β over a broad range of time scales, in the Fourier space. The exponent spectral β is close to the exact 5/3 Kolmogorov value for all the datasets.
在过去的几十年里，人们对平流层的商业用途越来越感兴趣，特别是对地球观测系统。平流层平台允许在区域尺度上对有限区域进行持续的地球监测。因此，需要准确的气象预报来保证平稳性。本研究的主要目的是回顾目前最流行的全球模式(如ECMWF IFS、NCEP GFS和ICON)所实现的平流层风预报技术。然后，以Pratica di Mare机场的无线电探测数据为参考，在意大利南部一个区域以非常高分辨率(约1 km)的模式配置，评估了COSMO有限区域模型再现平流层风速的能力。对选定日期的垂直剖面进行了分析，突出了良好的性能，尽管可以通过对模型数据采用五阶插值来实现改进。最后，通过快速傅立叶变换对选定高度的月风速时间序列进行后处理，揭示了主要频率的存在，以及在广泛的时间尺度范围内傅立叶空间中f−β形式的标度制度和幂律的存在。所有数据集的指数谱β都接近精确的5/3 Kolmogorov值。
{"title":"Wind Predictions in the Lower Stratosphere: State of the Art and Application of the COSMO Limited Area Model","authors":"E. Bucchignani","doi":"10.3390/meteorology1030020","DOIUrl":"https://doi.org/10.3390/meteorology1030020","url":null,"abstract":"In the last few decades there has been increasing interest in the commercial usage of the stratosphere, especially for Earth observation systems. Stratospheric platforms allow Earth monitoring at a regional scale with persistency toward a limited area. For this reason, accurate meteorological forecasts are needed in order to guarantee stationarity. The main aim of this work is to provide a review of wind prediction techniques in the stratosphere, achieved by the most popular global models, such as ECMWF IFS, NCEP GFS and ICON. Then, the capabilities of the COSMO limited area model to reproduce the wind speed in the stratosphere are evaluated considering a model configuration with very high resolution (about 1 km) over a domain located in Southern Italy, assuming the radio sounding data at Pratica di Mare airport as the reference. Vertical profiles were analyzed for selected days, highlighting good performances, though improvements can be achieved by adopting a fifth-order interpolation of the model data. Finally, monthly wind speed time series for selected heights were post-processed by means of fast Fourier transform, revealing the existence of main frequencies and the presence of a scaling regime and a power law of the form f−β over a broad range of time scales, in the Fourier space. The exponent spectral β is close to the exact 5/3 Kolmogorov value for all the datasets.","PeriodicalId":100061,"journal":{"name":"Agricultural Meteorology","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82536550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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