Akshay Kulkarni, P. V. S. Raju, Raghavendra Ashrit, Archana Sagalgile, Bhupendra Bahadur Singh, Jagdish Prasad
The advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.
{"title":"Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance","authors":"Akshay Kulkarni, P. V. S. Raju, Raghavendra Ashrit, Archana Sagalgile, Bhupendra Bahadur Singh, Jagdish Prasad","doi":"10.1002/qj.4757","DOIUrl":"https://doi.org/10.1002/qj.4757","url":null,"abstract":"The advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"21 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059629","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}
Federico Grazzini, Joshua Dorrington, Christian M. Grams, George C. Craig, Linus Magnusson, Frederic Vitart
The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.
{"title":"Improving forecasts of precipitation extremes over northern and central Italy using machine learning","authors":"Federico Grazzini, Joshua Dorrington, Christian M. Grams, George C. Craig, Linus Magnusson, Frederic Vitart","doi":"10.1002/qj.4755","DOIUrl":"https://doi.org/10.1002/qj.4755","url":null,"abstract":"The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"216 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061867","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}
This study examines the effect of surface moisture flux on fog formation, as it is an essential factor of water vapor distribution that supports fog formation. A one‐way nested large‐eddy simulation embedded in the mesoscale community Weather Research and Forecasting model is used to examine the effect of surface moisture flux on a cold fog event over the Heber Valley on January 16, 2015. Results indicate that large‐eddy simulation successfully reproduces the fog over the mountainous valley, with turbulent mixing of the fog aloft in the valley downward. However, the simulated fog is too dense and has higher humidity, a larger mean surface moisture flux, more extensive liquid water content, and longer duration relative to the observations. The sensitivity of fog simulations to surface moisture flux is then examined. Results indicate that reduction of surface moisture flux leads to fog with a shorter duration and a lower height extension than the original simulation, as the decrease in surface moisture flux impairs water vapor transport from the surface. Consequently, the lower humidity combined with the cold air helps the model reproduce a realistic thin fog close to the observations. The outcomes of this study illustrate that a minor change in moisture flux can have a significant impact on the formation and evolution of fog events over complex terrain, even during the winter when moisture flux is typically very weak.
{"title":"Effects of surface moisture flux on the formation and evolution of cold fog over complex terrain with large‐eddy simulation","authors":"Xin Li, Zhaoxia Pu","doi":"10.1002/qj.4748","DOIUrl":"https://doi.org/10.1002/qj.4748","url":null,"abstract":"This study examines the effect of surface moisture flux on fog formation, as it is an essential factor of water vapor distribution that supports fog formation. A one‐way nested large‐eddy simulation embedded in the mesoscale community Weather Research and Forecasting model is used to examine the effect of surface moisture flux on a cold fog event over the Heber Valley on January 16, 2015. Results indicate that large‐eddy simulation successfully reproduces the fog over the mountainous valley, with turbulent mixing of the fog aloft in the valley downward. However, the simulated fog is too dense and has higher humidity, a larger mean surface moisture flux, more extensive liquid water content, and longer duration relative to the observations. The sensitivity of fog simulations to surface moisture flux is then examined. Results indicate that reduction of surface moisture flux leads to fog with a shorter duration and a lower height extension than the original simulation, as the decrease in surface moisture flux impairs water vapor transport from the surface. Consequently, the lower humidity combined with the cold air helps the model reproduce a realistic thin fog close to the observations. The outcomes of this study illustrate that a minor change in moisture flux can have a significant impact on the formation and evolution of fog events over complex terrain, even during the winter when moisture flux is typically very weak.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"18 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935422","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}
Ting Lin, Thomas Spengler, Anna Rutgersson, Lichuan Wu
Sea spray, originating from wave breaking under high wind conditions, can significantly affect turbulent heat fluxes at the air–sea interface. Even though polar lows (PLs) can become extreme weather features with gale‐force wind, the impact of sea spray on their development has rarely been investigated and is not considered in operational forecast models. In this study, the impact of sea spray on the development of two PLs over the Barents Sea is studied based on sensitivity experiments with an atmosphere–wave coupled model, where the spray‐mediated heat fluxes are parameterized. The results show that the impact of sea‐spray‐mediated heat fluxes on PL development is sensitive to the surface wind speed. In the case of the stronger PL, the higher surface wind speed results in significantly higher spray‐mediated heat fluxes. Consequently, these spray‐mediated heat fluxes intensify the convection and diabatic heating of the PL, resulting in its intensification. In comparison, the case with a weaker PL experiences less sea spray production and lower spray‐mediated heat fluxes due to its weaker surface wind speeds. Overall, we find that spray‐mediated sensible heat fluxes play an important role in the development of PLs, while the latent heat fluxes induced by sea spray have a relatively minor impact.
{"title":"Impact of sea spray‐mediated heat fluxes on polar low development","authors":"Ting Lin, Thomas Spengler, Anna Rutgersson, Lichuan Wu","doi":"10.1002/qj.4746","DOIUrl":"https://doi.org/10.1002/qj.4746","url":null,"abstract":"Sea spray, originating from wave breaking under high wind conditions, can significantly affect turbulent heat fluxes at the air–sea interface. Even though polar lows (PLs) can become extreme weather features with gale‐force wind, the impact of sea spray on their development has rarely been investigated and is not considered in operational forecast models. In this study, the impact of sea spray on the development of two PLs over the Barents Sea is studied based on sensitivity experiments with an atmosphere–wave coupled model, where the spray‐mediated heat fluxes are parameterized. The results show that the impact of sea‐spray‐mediated heat fluxes on PL development is sensitive to the surface wind speed. In the case of the stronger PL, the higher surface wind speed results in significantly higher spray‐mediated heat fluxes. Consequently, these spray‐mediated heat fluxes intensify the convection and diabatic heating of the PL, resulting in its intensification. In comparison, the case with a weaker PL experiences less sea spray production and lower spray‐mediated heat fluxes due to its weaker surface wind speeds. Overall, we find that spray‐mediated sensible heat fluxes play an important role in the development of PLs, while the latent heat fluxes induced by sea spray have a relatively minor impact.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"111 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934985","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}
Maximiliano A. Sacco, Manuel Pulido, Juan J. Ruiz, Pierre Tandeo
Quantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather prediction and data assimilation systems. Ensemble‐based data assimilation systems incorporate state‐dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work, a machine‐learning method is presented based on convolutional neural networks that estimates the state‐dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heteroscedastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman‐like analysis update and the machine‐learning‐based estimation of a state‐dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof‐of‐concept. The promising results show that the machine‐learning method is able to predict precise values of the forecast covariance matrix in relatively high‐dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter, outperforming it when the ensembles are relatively small.
{"title":"On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation","authors":"Maximiliano A. Sacco, Manuel Pulido, Juan J. Ruiz, Pierre Tandeo","doi":"10.1002/qj.4743","DOIUrl":"https://doi.org/10.1002/qj.4743","url":null,"abstract":"Quantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather prediction and data assimilation systems. Ensemble‐based data assimilation systems incorporate state‐dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work, a machine‐learning method is presented based on convolutional neural networks that estimates the state‐dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heteroscedastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman‐like analysis update and the machine‐learning‐based estimation of a state‐dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof‐of‐concept. The promising results show that the machine‐learning method is able to predict precise values of the forecast covariance matrix in relatively high‐dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter, outperforming it when the ensembles are relatively small.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"343 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941781","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 European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022.
{"title":"Assessment for operational assimilation of horizontal line of sight winds from the European Space Agency's Aeolus at the Met Office","authors":"Gemma Halloran, Mary Forsythe","doi":"10.1002/qj.4739","DOIUrl":"https://doi.org/10.1002/qj.4739","url":null,"abstract":"The European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"32 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935074","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}
Cold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase.
{"title":"Factors influencing subseasonal predictability of northern Eurasian cold spells","authors":"Irina Statnaia, Alexey Karpechko","doi":"10.1002/qj.4744","DOIUrl":"https://doi.org/10.1002/qj.4744","url":null,"abstract":"Cold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"69 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935424","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}
Dehai Luo, Binhe Luo, Wenqi Zhang, Wenqin Zhuo, Ian Simmonds, Yao Yao
In the mid–high latitude atmosphere, the instability of planetary waves characterizes enhanced planetary wave activity or amplified large‐scale waviness leading to increased regional weather extremes. In this paper, a nonlinear Schrödinger equation is derived to describe the evolution of planetary waves. Then the consequences of Arctic amplification (AA)‐induced meridional background potential vorticity (PVy) changes on the modulational instability of planetary waves are examined. It is found that the modulational instability of uniform planetary wave trains mainly results from the presence of high‐order dispersion and nonlinearity, even though such an instability depends on the amplitude, vertical structure and zonal wavenumber of uniform planetary waves and the atmospheric stratification. Because the nonlinearity and high‐order dispersion depend on the magnitude of PVy, the modulational instability of planetary waves is significantly influenced by the variation of PVy associated with AA. It is also revealed that stronger modulational instability of planetary waves tends to occur in the smaller PVy region or in higher latitudes due to both stronger nonlinearity and weaker high‐order dispersion for fixed background and planetary wave parameters, which is conducive to more intense large‐scale waviness. However, because AA can reduce PVy in the mid–high latitudes mainly in the lower troposphere via reductions of winter zonal winds and meridional temperature gradients, the reduced PVy under AA can significantly enhance the modulational instability. Thus, the role of AA is to amplify planetary wave activity in mid–high latitudes through strengthening the modulational instability of planetary waves due to reduced PVy, which further enhances large‐scale waviness.
在中高纬度大气层中，行星波的不稳定性表现为行星波活动增强或大尺度波性放大，从而导致区域极端天气增加。本文推导了一个非线性薛定谔方程来描述行星波的演变。然后研究了北极放大（AA）引起的子午线背景势涡度（PVy）变化对行星波调制不稳定性的影响。研究发现，均匀行星波列的调制不稳定性主要源于高阶色散和非线性的存在，尽管这种不稳定性取决于均匀行星波的振幅、垂直结构和带状波数以及大气分层。由于非线性和高阶色散取决于 PVy 的大小，行星波的调制不稳定性受到与 AA 有关的 PVy 变化的显著影响。研究还发现，在背景和行星波参数固定的情况下，由于较强的非线性和较弱的高阶色散，行星波较强的调制不稳定性往往发生在 PVy 较小的区域或较高纬度地区，这有利于产生更强烈的大尺度波浪。然而，由于 AA 主要通过减少冬季带风和经向温度梯度来降低中高纬度对流层低层的 PVy，因此在 AA 作用下降低的 PVy 可以显著增强调制不稳定性。因此，AA 的作用是通过减少 PVy 来加强行星波的调制不稳定性，从而放大中高纬度地区的行星波活动，进一步增强大尺度波浪性。
{"title":"Arctic amplification‐induced intensification of planetary wave modulational instability: A simplified theory of enhanced large‐scale waviness","authors":"Dehai Luo, Binhe Luo, Wenqi Zhang, Wenqin Zhuo, Ian Simmonds, Yao Yao","doi":"10.1002/qj.4740","DOIUrl":"https://doi.org/10.1002/qj.4740","url":null,"abstract":"In the mid–high latitude atmosphere, the instability of planetary waves characterizes enhanced planetary wave activity or amplified large‐scale waviness leading to increased regional weather extremes. In this paper, a nonlinear Schrödinger equation is derived to describe the evolution of planetary waves. Then the consequences of Arctic amplification (AA)‐induced meridional background potential vorticity (PV<jats:sub><jats:italic>y</jats:italic></jats:sub>) changes on the modulational instability of planetary waves are examined. It is found that the modulational instability of uniform planetary wave trains mainly results from the presence of high‐order dispersion and nonlinearity, even though such an instability depends on the amplitude, vertical structure and zonal wavenumber of uniform planetary waves and the atmospheric stratification. Because the nonlinearity and high‐order dispersion depend on the magnitude of PV<jats:sub><jats:italic>y</jats:italic></jats:sub>, the modulational instability of planetary waves is significantly influenced by the variation of PV<jats:sub><jats:italic>y</jats:italic></jats:sub> associated with AA. It is also revealed that stronger modulational instability of planetary waves tends to occur in the smaller PV<jats:sub><jats:italic>y</jats:italic></jats:sub> region or in higher latitudes due to both stronger nonlinearity and weaker high‐order dispersion for fixed background and planetary wave parameters, which is conducive to more intense large‐scale waviness. However, because AA can reduce PV<jats:sub><jats:italic>y</jats:italic></jats:sub> in the mid–high latitudes mainly in the lower troposphere via reductions of winter zonal winds and meridional temperature gradients, the reduced PV<jats:sub><jats:italic>y</jats:italic></jats:sub> under AA can significantly enhance the modulational instability. Thus, the role of AA is to amplify planetary wave activity in mid–high latitudes through strengthening the modulational instability of planetary waves due to reduced PV<jats:sub><jats:italic>y</jats:italic></jats:sub>, which further enhances large‐scale waviness.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"111 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935068","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}
Hamza Ruzayqat, Alexandros Beskos, Dan Crisan, Ajay Jasra, Nikolas Kantas
We consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and provably convergent computational methodology from Berzuini et al. (1997, Journal of the American Statistical Association, 92, 1403–1412); Centanniand Minozzo (2006, Journal of the American Statistical Association, 101, 1582–1597); Martin et al. (2013, Annals of the Institute of Statistical Mathematics, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about – dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals , , respectively.
{"title":"Sequential Markov chain Monte Carlo for Lagrangian data assimilation with applications to unknown data locations","authors":"Hamza Ruzayqat, Alexandros Beskos, Dan Crisan, Ajay Jasra, Nikolas Kantas","doi":"10.1002/qj.4716","DOIUrl":"https://doi.org/10.1002/qj.4716","url":null,"abstract":"We consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and <jats:italic>provably convergent</jats:italic> computational methodology from Berzuini <jats:italic>et al</jats:italic>. (1997, <jats:italic>Journal of the American Statistical Association</jats:italic>, 92, 1403–1412); Centanniand Minozzo (2006, <jats:italic>Journal of the American Statistical Association</jats:italic>, 101, 1582–1597); Martin <jats:italic>et al</jats:italic>. (2013, <jats:italic>Annals of the Institute of Statistical Mathematics</jats:italic>, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about – dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals , , respectively.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"23 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941783","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}
Sándor István Mahó, Sergiy Vasylkevych, Nedjeljka Žagar
The equatorial mixed Rossby–gravity wave (MRGW) is an important contributor to tropical variability. Its excitation mechanism capable of explaining the observed MRGW variance peak at synoptic scales in the troposphere remains elusive. This study investigates wave–mean flow interactions as a generation process for the MRGWs using the TIGAR model, which employs Hough harmonics as the basis of spectral expansion on the sphere, thereby representing MRGWs as prognostic variables. Idealized numerical simulations reveal the interactions between waves emanating from a symmetric tropical heat source and an asymmetric subtropical zonal jet as an excitation mechanism for the MRGWs. The excited MRGWs have variance spectra resembling the observed MRGWs in the tropical troposphere. The mixed Rossby–gravity energy spectrum has a maximum at zonal wavenumbers –5 also in the case of an asymmetric forcing that generates MRGWs across large scales. Effects of wave–wave interactions appear of little importance for the MRGW growth compared with wave–mean flow interactions. Application of the zonal‐mean zonal wind profiles from ERA5 reaffirms the importance of the asymmetry of the zonal mean flow.
{"title":"Excitation of mixed Rossby–gravity waves by wave–mean flow interactions on the sphere","authors":"Sándor István Mahó, Sergiy Vasylkevych, Nedjeljka Žagar","doi":"10.1002/qj.4742","DOIUrl":"https://doi.org/10.1002/qj.4742","url":null,"abstract":"The equatorial mixed Rossby–gravity wave (MRGW) is an important contributor to tropical variability. Its excitation mechanism capable of explaining the observed MRGW variance peak at synoptic scales in the troposphere remains elusive. This study investigates wave–mean flow interactions as a generation process for the MRGWs using the TIGAR model, which employs Hough harmonics as the basis of spectral expansion on the sphere, thereby representing MRGWs as prognostic variables. Idealized numerical simulations reveal the interactions between waves emanating from a symmetric tropical heat source and an asymmetric subtropical zonal jet as an excitation mechanism for the MRGWs. The excited MRGWs have variance spectra resembling the observed MRGWs in the tropical troposphere. The mixed Rossby–gravity energy spectrum has a maximum at zonal wavenumbers –5 also in the case of an asymmetric forcing that generates MRGWs across large scales. Effects of wave–wave interactions appear of little importance for the MRGW growth compared with wave–mean flow interactions. Application of the zonal‐mean zonal wind profiles from ERA5 reaffirms the importance of the asymmetry of the zonal mean flow.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"20 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882158","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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