Stéphane Vannitsem, Carlos A. Pires, David Docquier
The information entropy budget and the rate of information transfer between variables is studied in the context of a nonlinear reduced‐order atmospheric model. The key ingredients of the dynamics are present in this model; namely, the baroclinic instability, the instability related to the presence of an orography, the dissipation related to the surface friction, and the large‐scale meridional imbalance of energy. For the parameters chosen, the solutions of this system display a chaotic dynamics reminiscent of the large‐scale atmospheric dynamics in the extratropics. The detailed information entropy budget analysis of this system reveals that the linear rotation terms play a minor role in the generation of uncertainties compared with the orography and the surface friction. Additionally, the dominant contribution comes from the nonlinear advection terms, and their decomposition in synergetic (covariability) and single (impact of each single variable on the target one) components reveals that for some variables the covariability dominates the information transfer. The estimation of the rate of information transfer based on time series is also discussed, and an extension of the Liang's approach to nonlinear observables is proposed.
{"title":"Causal dependencies and Shannon entropy budget: Analysis of a reduced‐order atmospheric model","authors":"Stéphane Vannitsem, Carlos A. Pires, David Docquier","doi":"10.1002/qj.4805","DOIUrl":"https://doi.org/10.1002/qj.4805","url":null,"abstract":"The information entropy budget and the rate of information transfer between variables is studied in the context of a nonlinear reduced‐order atmospheric model. The key ingredients of the dynamics are present in this model; namely, the baroclinic instability, the instability related to the presence of an orography, the dissipation related to the surface friction, and the large‐scale meridional imbalance of energy. For the parameters chosen, the solutions of this system display a chaotic dynamics reminiscent of the large‐scale atmospheric dynamics in the extratropics. The detailed information entropy budget analysis of this system reveals that the linear rotation terms play a minor role in the generation of uncertainties compared with the orography and the surface friction. Additionally, the dominant contribution comes from the nonlinear advection terms, and their decomposition in synergetic (covariability) and single (impact of each single variable on the target one) components reveals that for some variables the covariability dominates the information transfer. The estimation of the rate of information transfer based on time series is also discussed, and an extension of the Liang's approach to nonlinear observables is proposed.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"45 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781355","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}
Sivareddy Sanikommu, Naila Raboudi, Mohamad El Gharamti, Peng Zhan, Bilel Hadri, Ibrahim Hoteit
Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA.
{"title":"Insights from very‐large‐ensemble data assimilation experiments with a high‐resolution general circulation model of the Red Sea","authors":"Sivareddy Sanikommu, Naila Raboudi, Mohamad El Gharamti, Peng Zhan, Bilel Hadri, Ibrahim Hoteit","doi":"10.1002/qj.4813","DOIUrl":"https://doi.org/10.1002/qj.4813","url":null,"abstract":"Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"66 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781356","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}
Ensemble forecast post‐processing is a necessary step in producing accurate probabilistic forecasts. Many post‐processing methods operate by estimating the parameters of a predetermined probability distribution; others operate on a per‐lead‐time or per‐station basis. All of the aforementioned factors either limit the expressive power of the methods in question or require additional models, one for each lead time and station. We propose a novel, neural network‐based method that produces forecasts for all lead times jointly and requires a single model for all stations. We incorporate normalizing spline flows as flexible parametric distribution estimators, which enables us to model complex forecast distributions. Furthermore, we demonstrate the effectiveness of our method in the context of the EUPPBench benchmark, where we conduct 2‐m temperature forecast post‐processing for stations in a subregion of Europe. We show that our novel method exhibits state‐of‐the‐art performance on the benchmark, improving upon other well‐performing entries. Additionally, by providing a detailed comparison of three variants of our novel post‐processing method, we elucidate the reasons why our method outperforms per‐lead‐time‐based approaches and approaches with distributional assumptions.
{"title":"Ensemble weather forecast post‐processing with a flexible probabilistic neural network approach","authors":"Peter Mlakar, Janko Merše, Jana Faganeli Pucer","doi":"10.1002/qj.4809","DOIUrl":"https://doi.org/10.1002/qj.4809","url":null,"abstract":"Ensemble forecast post‐processing is a necessary step in producing accurate probabilistic forecasts. Many post‐processing methods operate by estimating the parameters of a predetermined probability distribution; others operate on a per‐lead‐time or per‐station basis. All of the aforementioned factors either limit the expressive power of the methods in question or require additional models, one for each lead time and station. We propose a novel, neural network‐based method that produces forecasts for all lead times jointly and requires a single model for all stations. We incorporate normalizing spline flows as flexible parametric distribution estimators, which enables us to model complex forecast distributions. Furthermore, we demonstrate the effectiveness of our method in the context of the EUPPBench benchmark, where we conduct 2‐m temperature forecast post‐processing for stations in a subregion of Europe. We show that our novel method exhibits state‐of‐the‐art performance on the benchmark, improving upon other well‐performing entries. Additionally, by providing a detailed comparison of three variants of our novel post‐processing method, we elucidate the reasons why our method outperforms per‐lead‐time‐based approaches and approaches with distributional assumptions.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"66 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785774","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}
Model ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble.
{"title":"Estimating the gain of increasing the ensemble size from analytical considerations","authors":"Bo Christiansen","doi":"10.1002/qj.4815","DOIUrl":"https://doi.org/10.1002/qj.4815","url":null,"abstract":"Model ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"83 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781357","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}
Aleena M. Jaison, Lesley J. Gray, Scott Osprey, Anne K. Smith, Rolando R. Garcia
The representation of the semi‐annual oscillation (SAO) in climate models shows a common easterly bias of several tens of metres per second compared to observations. These biases could be due to deficiencies in eastward tropical wave forcing, the position or strength of the climatological summertime jet or the strength/timing of the Brewer–Dobson circulation. This motivates further analysis of the momentum budget of the upper stratosphere within models and a more detailed comparison with reanalyses to determine the origin of the bias. In this study, the transformed Eulerian mean momentum equation is used to evaluate the different forcing terms that contribute to the SAO in the MERRA2 reanalysis dataset. This is then compared with the equivalent analysis using data from a climate simulation of the Whole Atmosphere Community Climate Model (WACCM). The comparison shows that WACCM underestimates eastward forcing by both resolved and parameterised waves at equatorial latitudes when compared with MERRA2 and also has a weaker tropical upwelling above 1 hPa.
{"title":"A momentum budget study of the semi‐annual oscillation in the Whole Atmosphere Community Climate Model","authors":"Aleena M. Jaison, Lesley J. Gray, Scott Osprey, Anne K. Smith, Rolando R. Garcia","doi":"10.1002/qj.4782","DOIUrl":"https://doi.org/10.1002/qj.4782","url":null,"abstract":"The representation of the semi‐annual oscillation (SAO) in climate models shows a common easterly bias of several tens of metres per second compared to observations. These biases could be due to deficiencies in eastward tropical wave forcing, the position or strength of the climatological summertime jet or the strength/timing of the Brewer–Dobson circulation. This motivates further analysis of the momentum budget of the upper stratosphere within models and a more detailed comparison with reanalyses to determine the origin of the bias. In this study, the transformed Eulerian mean momentum equation is used to evaluate the different forcing terms that contribute to the SAO in the MERRA2 reanalysis dataset. This is then compared with the equivalent analysis using data from a climate simulation of the Whole Atmosphere Community Climate Model (WACCM). The comparison shows that WACCM underestimates eastward forcing by both resolved and parameterised waves at equatorial latitudes when compared with MERRA2 and also has a weaker tropical upwelling above 1 hPa.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"1 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781359","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}
Georgios A. Efstathiou, Robert S. Plant, Fotini Katopodes Chow
We examine the ability of two dynamic turbulence closure models to simulate the diurnal development of convection and the transition from dry to shallow cumuli and then to deep convection. The dynamic models are compared with the conventional Smagorinsky scheme at a range of cloud‐resolving and grey‐zone resolutions. The dynamic schemes include the Lagrangian‐averaged, scale‐dependent dynamic Smagorinsky model and a Lagrangian‐averaged, dynamic mixed model. The conventional Smagorinsky model fails to reproduce the shallow convection stage beyond the large‐eddy simulation regime, continuously building up the convective available potential energy that eventually leads to an unrealistic deep convection phase. The dynamic Smagorinsky model significantly improves the representation of shallow and deep convection; however, it exhibits issues similar to the conventional scheme at coarser resolutions. In contrast, the dynamic mixed model closely follows the large‐eddy simulation results across the range of sub‐kilometre simulations. This is achieved by the combined effect of an adaptive length scale and the inclusion of the Leonard terms, which can produce counter‐gradient fluxes through the backscatter of energy from the subgrid to the resolved scales and enable appropriate non‐local contributions. A further sensitivity test on the inclusion of the Leonard terms on all hydrometeor fluxes reveals the strong interaction between turbulent transport and microphysics and the possible need for further optimisation of the dynamic mixed model coefficients together with the microphysical representation.
{"title":"Grey‐zone simulations of shallow‐to‐deep convection transition using dynamic subgrid‐scale turbulence models","authors":"Georgios A. Efstathiou, Robert S. Plant, Fotini Katopodes Chow","doi":"10.1002/qj.4817","DOIUrl":"https://doi.org/10.1002/qj.4817","url":null,"abstract":"We examine the ability of two dynamic turbulence closure models to simulate the diurnal development of convection and the transition from dry to shallow cumuli and then to deep convection. The dynamic models are compared with the conventional Smagorinsky scheme at a range of cloud‐resolving and grey‐zone resolutions. The dynamic schemes include the Lagrangian‐averaged, scale‐dependent dynamic Smagorinsky model and a Lagrangian‐averaged, dynamic mixed model. The conventional Smagorinsky model fails to reproduce the shallow convection stage beyond the large‐eddy simulation regime, continuously building up the convective available potential energy that eventually leads to an unrealistic deep convection phase. The dynamic Smagorinsky model significantly improves the representation of shallow and deep convection; however, it exhibits issues similar to the conventional scheme at coarser resolutions. In contrast, the dynamic mixed model closely follows the large‐eddy simulation results across the range of sub‐kilometre simulations. This is achieved by the combined effect of an adaptive length scale and the inclusion of the Leonard terms, which can produce counter‐gradient fluxes through the backscatter of energy from the subgrid to the resolved scales and enable appropriate non‐local contributions. A further sensitivity test on the inclusion of the Leonard terms on all hydrometeor fluxes reveals the strong interaction between turbulent transport and microphysics and the possible need for further optimisation of the dynamic mixed model coefficients together with the microphysical representation.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"41 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781358","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}
Most numerical weather prediction (NWP) models have a significant bias in predicting supercooled liquid water (SLW). For this reason, icing risk diagnostic tools do not use supercooled liquid water forecast by the models as an input parameter, but rather temperature and humidity, which are forecast better than SLW. The main objective of this study is to improve the SLW representation in the microphysical scheme ICE3 (Three Ice categories) used in the operational Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) model. For this purpose, several parametrizations of the microphysical processes were evaluated to find a better representation of SLW in the Mesoscale Non‐Hydrostatic model (MESO‐NH), which also uses ICE3. Elements of the microphysical scheme of the HARMONIE‐AROME model and work carried out by the Centre National de Recherches Météorologiques (CNRM) have been tested and compared with the current scheme. After a preliminary study, three parametrizations of the microphysical scheme were selected, in which the processes of ice initiation, snow and graupel collection of cloud droplets, condensation, Bergeron–Findeisen, and saturation adjustment were modified. Then, MESO‐NH simulations were performed and compared with observations from the In‐Cloud ICing and Large‐drop Experiment (ICICLE) airborne campaign. Three case studies were used with different icing weather conditions such as freezing rain, freezing drizzle, lake effect, etc. The results show a better representation of SLW with a greater presence of cloud droplets for colder temperatures up to 30 C. However, the liquid water content remains underestimated and the ice mass is overestimated. The ice initiation and cloud droplet collection by snow and graupel play a major role in the SLW representation. Parametrizations with restrictive ice initiation criteria reduce cloud droplet consumption and provide better agreement with observations. The results are promising and need to be investigated further with more cases and in the operational model AROME.
大多数数值天气预报（NWP）模式在预测过冷液态水（SLW）时存在明显偏差。因此，结冰风险诊断工具不使用模型预测的过冷液态水作为输入参数，而是使用温度和湿度作为输入参数，而温度和湿度的预测效果要好于过冷液态水。本研究的主要目的是改进运行中的 "AROME"（Applications de la Recherche à l'Opérationnel à Méso-Echelle）模式所使用的微物理方案 ICE3（三冰类别）中的过冷液态水表示。为此，对微观物理过程的几种参数化进行了评估，以便在同样使用 ICE3 的中尺度非静水模型（MESO-NH）中更好地表示 SLW。对 HARMONIE-AROME 模式的微观物理方案和国家气象研究中心（CNRM）的工作进行了测试，并与当前方案进行了比较。经过初步研究，选择了微物理方案的三个参数，其中对冰的形成过程、云滴的雪和岩浆收集过程、凝结过程、Bergeron-Findeisen 过程和饱和度调整过程进行了修改。然后，进行了 MESO-NH 模拟，并与云内结冰和大滴实验（ICICLE）机载活动的观测结果进行了比较。三个案例研究使用了不同的结冰天气条件，如冻雨、冻细雨、湖泊效应等。结果表明，在温度高达 30 C 的低温条件下，SLW 的代表性更好，云滴的存在更多。冰的形成以及雪和冰砾的云滴收集在 SLW 表征中起着重要作用。采用限制性起冰标准进行参数化可以减少云滴消耗，并与观测结果更加吻合。这些结果很有希望，需要在更多的案例和 AROME 运行模式中进一步研究。
{"title":"Improving supercooled liquid water representation in the microphysical scheme ICE3","authors":"Rémi Dupont, Claire Taymans, Benoît Vié","doi":"10.1002/qj.4806","DOIUrl":"https://doi.org/10.1002/qj.4806","url":null,"abstract":"Most numerical weather prediction (NWP) models have a significant bias in predicting supercooled liquid water (SLW). For this reason, icing risk diagnostic tools do not use supercooled liquid water forecast by the models as an input parameter, but rather temperature and humidity, which are forecast better than SLW. The main objective of this study is to improve the SLW representation in the microphysical scheme ICE3 (Three Ice categories) used in the operational Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) model. For this purpose, several parametrizations of the microphysical processes were evaluated to find a better representation of SLW in the Mesoscale Non‐Hydrostatic model (MESO‐NH), which also uses ICE3. Elements of the microphysical scheme of the HARMONIE‐AROME model and work carried out by the Centre National de Recherches Météorologiques (CNRM) have been tested and compared with the current scheme. After a preliminary study, three parametrizations of the microphysical scheme were selected, in which the processes of ice initiation, snow and graupel collection of cloud droplets, condensation, Bergeron–Findeisen, and saturation adjustment were modified. Then, MESO‐NH simulations were performed and compared with observations from the In‐Cloud ICing and Large‐drop Experiment (ICICLE) airborne campaign. Three case studies were used with different icing weather conditions such as freezing rain, freezing drizzle, lake effect, etc. The results show a better representation of SLW with a greater presence of cloud droplets for colder temperatures up to 30 C. However, the liquid water content remains underestimated and the ice mass is overestimated. The ice initiation and cloud droplet collection by snow and graupel play a major role in the SLW representation. Parametrizations with restrictive ice initiation criteria reduce cloud droplet consumption and provide better agreement with observations. The results are promising and need to be investigated further with more cases and in the operational model AROME.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"12 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742470","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}
Cameron R. Henderson, Michael J. Reeder, Teresa J. Parker, Julian F. Quinting, Christian Jakob
Heatwaves in southeastern Australia have characteristic weather patterns that are well understood but are outnumbered by days with similar synoptic‐scale patterns that are not heatwaves. Accordingly, the aim of this study is to identify the key differences between heatwave and non‐heatwave days from 40 years of reanalysis data. A synoptic climatology of seven weather states was constructed by ‐means cluster analysis. Four of these states account for more than 80% of heatwave days across south‐and‐central eastern Australia. Moreover, the spatial maxima in the frequency of the heatwave days are distinct and geographically separated. Heatwave days have a stronger upper anticyclone or ridge that has propagated further equatorward in comparison with non‐heatwave days. The air upstream of the ridge is more humid on heatwave days, whereas downstream of the ridge the air is much drier. These dry anomalies are co‐located with midtropospheric subsidence and the moist anomalies with ascent, and their respective spatial distributions are consistent with regions of adiabatic warming and latent heating identified in recent studies of southeast Australian heatwaves. The corresponding vertical motion on non‐heatwave days is weaker and shifted further poleward. Southeast Queensland heatwave days exhibit increased baroclinicity over the Australian Subtropics and reduced rainfall over Queensland. Further south and west, heatwave days are associated with more amplified Rossby waves and increased rainfall over the Australian Tropics. Anticyclonic Rossby wave breaking is greatly enhanced on heatwave days south of 30°S. For every day in each of these four weather states, the 3‐day‐mean maximum temperature in the region of peak heatwave day frequency is positively correlated with 500 hPa geopotential height anomalies on the equatorward flank of the cluster‐mean upper ridge. These findings underline the importance of equatorward Rossby wave propagation in the dynamics of southeast Australian heatwaves.
{"title":"Summer Heatwaves in Southeastern Australia","authors":"Cameron R. Henderson, Michael J. Reeder, Teresa J. Parker, Julian F. Quinting, Christian Jakob","doi":"10.1002/qj.4816","DOIUrl":"https://doi.org/10.1002/qj.4816","url":null,"abstract":"Heatwaves in southeastern Australia have characteristic weather patterns that are well understood but are outnumbered by days with similar synoptic‐scale patterns that are not heatwaves. Accordingly, the aim of this study is to identify the key differences between heatwave and non‐heatwave days from 40 years of reanalysis data. A synoptic climatology of seven weather states was constructed by ‐means cluster analysis. Four of these states account for more than 80% of heatwave days across south‐and‐central eastern Australia. Moreover, the spatial maxima in the frequency of the heatwave days are distinct and geographically separated. Heatwave days have a stronger upper anticyclone or ridge that has propagated further equatorward in comparison with non‐heatwave days. The air upstream of the ridge is more humid on heatwave days, whereas downstream of the ridge the air is much drier. These dry anomalies are co‐located with midtropospheric subsidence and the moist anomalies with ascent, and their respective spatial distributions are consistent with regions of adiabatic warming and latent heating identified in recent studies of southeast Australian heatwaves. The corresponding vertical motion on non‐heatwave days is weaker and shifted further poleward. Southeast Queensland heatwave days exhibit increased baroclinicity over the Australian Subtropics and reduced rainfall over Queensland. Further south and west, heatwave days are associated with more amplified Rossby waves and increased rainfall over the Australian Tropics. Anticyclonic Rossby wave breaking is greatly enhanced on heatwave days south of 30°S. For every day in each of these four weather states, the 3‐day‐mean maximum temperature in the region of peak heatwave day frequency is positively correlated with 500 hPa geopotential height anomalies on the equatorward flank of the cluster‐mean upper ridge. These findings underline the importance of equatorward Rossby wave propagation in the dynamics of southeast Australian heatwaves.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"21 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742325","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}
Eliza Karlowska, Adrian J. Matthews, Benjamin G. M. Webber, Tim Graham, Prince Xavier
Diurnal warm layers develop in the upper ocean on sunny days with low surface wind speeds. They rectify intraseasonal sea‐surface temperatures (SSTs), potentially impacting intraseasonal weather patterns such as the Madden–Julian Oscillation (MJO). Here we analyse 15‐lead‐day forecast composites of coupled ocean–atmosphere and atmosphere‐only numerical weather prediction (NWP) models of the UK Met Office to reveal that the presence of diurnal warming of SST (dSST) leads to a faster MJO propagation in the coupled model compared with the atmosphere‐only model. To test the feedback between the MJO and the dSST, we designed a set of experiments with instantaneous vertical mixing over the top 5 or of the ocean component of the coupled model. Weaker dSST in the mixing experiments leads to a slower MJO over 15 lead days. The dSST produces a increase in the MJO phase speed between the coupled and the atmosphere‐only model. An additional increase is found for other coupling effects, unrelated to the dSST. A two‐way feedback manifests in the coupled model over the 15 lead days of the forecast between the MJO and the dSST. The MJO regime dictates the strength of the dSST and the dSST rectifies the intraseasonal anomalies of SST in the coupled model. Stronger dSST in the coupled model leads to stronger intraseasonal anomalies of SST. The MJO convection responds to these SSTs on a seven‐lead‐day timescale, and feeds back into the SST anomalies within the next three lead days. Overall, this study demonstrates the importance of high vertical resolution in the upper ocean for predicting the eastward propagation of the MJO in an NWP setting, which is potentially impactful for seasonal predictions and climate projections, should this feedback be unrepresented in the models.
{"title":"Two‐way feedback between the Madden–Julian Oscillation and diurnal warm layers in a coupled ocean–atmosphere model","authors":"Eliza Karlowska, Adrian J. Matthews, Benjamin G. M. Webber, Tim Graham, Prince Xavier","doi":"10.1002/qj.4807","DOIUrl":"https://doi.org/10.1002/qj.4807","url":null,"abstract":"Diurnal warm layers develop in the upper ocean on sunny days with low surface wind speeds. They rectify intraseasonal sea‐surface temperatures (SSTs), potentially impacting intraseasonal weather patterns such as the Madden–Julian Oscillation (MJO). Here we analyse 15‐lead‐day forecast composites of coupled ocean–atmosphere and atmosphere‐only numerical weather prediction (NWP) models of the UK Met Office to reveal that the presence of diurnal warming of SST (dSST) leads to a faster MJO propagation in the coupled model compared with the atmosphere‐only model. To test the feedback between the MJO and the dSST, we designed a set of experiments with instantaneous vertical mixing over the top 5 or of the ocean component of the coupled model. Weaker dSST in the mixing experiments leads to a slower MJO over 15 lead days. The dSST produces a increase in the MJO phase speed between the coupled and the atmosphere‐only model. An additional increase is found for other coupling effects, unrelated to the dSST. A two‐way feedback manifests in the coupled model over the 15 lead days of the forecast between the MJO and the dSST. The MJO regime dictates the strength of the dSST and the dSST rectifies the intraseasonal anomalies of SST in the coupled model. Stronger dSST in the coupled model leads to stronger intraseasonal anomalies of SST. The MJO convection responds to these SSTs on a seven‐lead‐day timescale, and feeds back into the SST anomalies within the next three lead days. Overall, this study demonstrates the importance of high vertical resolution in the upper ocean for predicting the eastward propagation of the MJO in an NWP setting, which is potentially impactful for seasonal predictions and climate projections, should this feedback be unrepresented in the models.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"85 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742324","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 work presented in this article studies the impact of cloud condensation nuclei (CCN) activation for fog forecasting and improves its parameterization in the LIMA (Liquid, Ice, Multiple Aerosols) two‐moment microphysical scheme, building upon the Local And Non‐local Fog EXperiment (LANFEX) field campaign observations, specifically the intensive observation period (IOP) 1 and the DEMISTIFY intercomparison. Large‐eddy simulations were performed with the Meso‐NH model, first using a prognostic supersaturation allowing us to compute the number of activated CCN at each time step, and then with the usual saturation adjustment hypothesis and a diagnostic maximum supersaturation. The prognostic supersaturation method provided very good results, similar to those from earlier simulations of this case using a bin scheme, and was thus used as a reference. In contrast, the diagnostic maximum supersaturation method strongly overestimated droplet numbers and produced a too‐thick fog. Thus, improvements to the maximum supersaturation diagnostic were proposed, by (1) revising the temperature tendency and (2) accounting for pre‐existing cloud droplets in the activation parameterization. These improvements resulted in a simulation in good agreement with observations and the reference simulation, and are promising for use in numerical weather prediction systems with a lower resolution and/or a longer time step.
{"title":"Importance of CCN activation for fog forecasting and its representation in the two‐moment microphysical scheme LIMA","authors":"B. Vié, L. Ducongé, C. Lac, T. Bergot, J. Price","doi":"10.1002/qj.4812","DOIUrl":"https://doi.org/10.1002/qj.4812","url":null,"abstract":"The work presented in this article studies the impact of cloud condensation nuclei (CCN) activation for fog forecasting and improves its parameterization in the LIMA (Liquid, Ice, Multiple Aerosols) two‐moment microphysical scheme, building upon the Local And Non‐local Fog EXperiment (LANFEX) field campaign observations, specifically the intensive observation period (IOP) 1 and the DEMISTIFY intercomparison. Large‐eddy simulations were performed with the Meso‐NH model, first using a prognostic supersaturation allowing us to compute the number of activated CCN at each time step, and then with the usual saturation adjustment hypothesis and a diagnostic maximum supersaturation. The prognostic supersaturation method provided very good results, similar to those from earlier simulations of this case using a bin scheme, and was thus used as a reference. In contrast, the diagnostic maximum supersaturation method strongly overestimated droplet numbers and produced a too‐thick fog. Thus, improvements to the maximum supersaturation diagnostic were proposed, by (1) revising the temperature tendency and (2) accounting for pre‐existing cloud droplets in the activation parameterization. These improvements resulted in a simulation in good agreement with observations and the reference simulation, and are promising for use in numerical weather prediction systems with a lower resolution and/or a longer time step.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"23 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746104","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：