Joshua Dorrington, Christian Grams, Federico Grazzini, Linus Magnusson, Frederic Vitart
A number of studies have investigated the large-scale drivers and upstream-precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote in both time and space from the impacted region. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building story-lines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high-impact weather, and providing early warning of their possible development. However, operational applications of this developing knowledge-base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large-scale precursors of categorical weather events in an automated fashion, and for reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional- to national-scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large-scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how for more continental-scale applications the regionally-specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales and regions.
{"title":"Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall","authors":"Joshua Dorrington, Christian Grams, Federico Grazzini, Linus Magnusson, Frederic Vitart","doi":"10.1002/qj.4622","DOIUrl":"https://doi.org/10.1002/qj.4622","url":null,"abstract":"A number of studies have investigated the large-scale drivers and upstream-precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote in both time and space from the impacted region. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building story-lines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high-impact weather, and providing early warning of their possible development. However, operational applications of this developing knowledge-base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large-scale precursors of categorical weather events in an automated fashion, and for reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional- to national-scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large-scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how for more continental-scale applications the regionally-specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales and regions.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"281 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532481","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}
Dipanjan Dey, Ruth Geen, F. Hugo Lambert, Shubhi Agrawal, Geoffrey Vallis, Robert Marsh, Nikolaos Skliris, Kristofer Döös
The East Asian Summer Monsoon (EASM) rainfall provides water security and socio-economic benefit for over 20% of the global population. However, the sources of this rainfall and how it is carried to the East Asian landmass is still uncertain. To address this, atmospheric water sources and pathways associated with the East Asian summer rainfall are identified and quantified in the present study using atmospheric water trajectories, calculated with a novel Lagrangian framework. Evaporated water from the East Asian landmass is found to be the major contributor to East Asian rainfall, amounting to local recycling. The results further indicated that the South Indian Ocean is a major non-local source for rainfall over southern East Asia during June to August. The role of the South Indian Ocean as a source of atmospheric water is one of the major findings of the study and would help in better understanding and predicting the East Asian summer rainfall. Evaporated waters from the Pacific Ocean (particularly the far-west Pacific Ocean) dominate the non-local contribution to precipitation over northern East Asia during June to September, and over southern East Asian rainfall during September. The spatial structure of the East Asian rainfall is reported to be determined by the atmospheric waters that are evaporated and transported from the non-local sources. The role of the North Indian Ocean and the South Asian landmass as a source of water for East Asian precipitation is minimal and restricted to southern East Asia. The cross-equatorial Somali jet and equatorial trade winds associated with the western North Pacific Subtropical High are important pathways for East Asian precipitation sourced over the South Indian Ocean and the Pacific Ocean respectively. In contrast, minor roles are attributed to the Bay of Bengal as a source, and mid-latitude westerlies as a transport pathway, for East Asian precipitation.
{"title":"Identification of the atmospheric water sources and pathways responsible for the East Asian summer monsoon rainfall","authors":"Dipanjan Dey, Ruth Geen, F. Hugo Lambert, Shubhi Agrawal, Geoffrey Vallis, Robert Marsh, Nikolaos Skliris, Kristofer Döös","doi":"10.1002/qj.4621","DOIUrl":"https://doi.org/10.1002/qj.4621","url":null,"abstract":"The East Asian Summer Monsoon (EASM) rainfall provides water security and socio-economic benefit for over 20% of the global population. However, the sources of this rainfall and how it is carried to the East Asian landmass is still uncertain. To address this, atmospheric water sources and pathways associated with the East Asian summer rainfall are identified and quantified in the present study using atmospheric water trajectories, calculated with a novel Lagrangian framework. Evaporated water from the East Asian landmass is found to be the major contributor to East Asian rainfall, amounting to local recycling. The results further indicated that the South Indian Ocean is a major non-local source for rainfall over southern East Asia during June to August. The role of the South Indian Ocean as a source of atmospheric water is one of the major findings of the study and would help in better understanding and predicting the East Asian summer rainfall. Evaporated waters from the Pacific Ocean (particularly the far-west Pacific Ocean) dominate the non-local contribution to precipitation over northern East Asia during June to September, and over southern East Asian rainfall during September. The spatial structure of the East Asian rainfall is reported to be determined by the atmospheric waters that are evaporated and transported from the non-local sources. The role of the North Indian Ocean and the South Asian landmass as a source of water for East Asian precipitation is minimal and restricted to southern East Asia. The cross-equatorial Somali jet and equatorial trade winds associated with the western North Pacific Subtropical High are important pathways for East Asian precipitation sourced over the South Indian Ocean and the Pacific Ocean respectively. In contrast, minor roles are attributed to the Bay of Bengal as a source, and mid-latitude westerlies as a transport pathway, for East Asian precipitation.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"1155 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532463","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}
K. B. R. R. Hari Prasad, V. S. Prasad, M. Sateesh, K. Amar Jyothi
The accurate prediction of high-impact weather systems using cloud-resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three-dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI-3DVAR) and nudging in the same modelling system on short-range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model-simulated rainfall is evaluated by using the Integrated Multi-Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area-averaged three-hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre-forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period.
{"title":"The impact of Indian radar and lightning data assimilation on the short-range forecasts of heavy rainfall events","authors":"K. B. R. R. Hari Prasad, V. S. Prasad, M. Sateesh, K. Amar Jyothi","doi":"10.1002/qj.4623","DOIUrl":"https://doi.org/10.1002/qj.4623","url":null,"abstract":"The accurate prediction of high-impact weather systems using cloud-resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three-dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI-3DVAR) and nudging in the same modelling system on short-range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model-simulated rainfall is evaluated by using the Integrated Multi-Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area-averaged three-hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre-forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"28 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532431","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}
Jerry M. Straka, Paul D. Williams, Katharine M. Kanak
Atmospheric numerical models play a crucial role in operational weather forecasting, as well as improving our understanding of atmospheric dynamics via research studies. Maximising their accuracy is of paramount importance. Use of >O7 flux schemes in atmospheric models is largely undocumented, with no studies considering O3–17 fluxes with formal accuracy-preserving high order interpolation, pressure gradient / divergence, and subgrid-scale (SGS) turbulent fluxes. Higher order numerical approximations can reduce truncation, amplitude and phase errors, and potentially improve model accuracy and effective resolution.
{"title":"Nonlinear diffusion-limited 2D colliding plume simulations with very high order numerical approximations","authors":"Jerry M. Straka, Paul D. Williams, Katharine M. Kanak","doi":"10.1002/qj.4616","DOIUrl":"https://doi.org/10.1002/qj.4616","url":null,"abstract":"Atmospheric numerical models play a crucial role in operational weather forecasting, as well as improving our understanding of atmospheric dynamics via research studies. Maximising their accuracy is of paramount importance. Use of >O7 flux schemes in atmospheric models is largely undocumented, with no studies considering O3–17 fluxes with formal accuracy-preserving high order interpolation, pressure gradient / divergence, and subgrid-scale (SGS) turbulent fluxes. Higher order numerical approximations can reduce truncation, amplitude and phase errors, and potentially improve model accuracy and effective resolution.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"53 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532466","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}
Michaela Schütz, Adrian Schütz, Jörg Bendix, Boris Thies
Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog-influencing processes. Machine-Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non-linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving-average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden-Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting.
{"title":"Improving classification-based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data","authors":"Michaela Schütz, Adrian Schütz, Jörg Bendix, Boris Thies","doi":"10.1002/qj.4619","DOIUrl":"https://doi.org/10.1002/qj.4619","url":null,"abstract":"Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog-influencing processes. Machine-Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non-linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving-average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden-Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"82 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532465","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 reports on the analysis of the results from a 20 km grid spacing, Regional Coupled ocean–atmosphere Model (RCM) integration over the Western Pacific Warm Pool (WP2). The RCM was integrated over a 20-year period (1986–2005) using reanalysis boundary conditions for the atmosphere and the ocean. This is a first-of-a-kind study with an RCM at 20 km over the WP2. The RCM simulation shows reasonable fidelity of the mean state and of the Madden–Julian Oscillation (MJO). We utilize this successful integration of the RCM to understand a well-known observed feature of MJOs in the WP2 to be of the strongest amplitude during the December–March period of the year. Our analysis of the model integration reveals that the recharge of moist static energy (MSE) prior to peak MJO convection and its discharge during and after the convection explains the MJO in the simulation. The recharge/discharge of the MSE is shown to be largely dictated by horizontal advection, which is stemmed to a small extent by column-integrated radiative heating and surface evaporation. This balance of forces in the evolution of the MSE anomalies and their corresponding variations with sea-surface temperature (SST) anomalies at MJO time-scales in the WP2 is strongest in the December–March period in the RCM simulation.
{"title":"Characterizing the Madden–Julian Oscillation in the western Pacific Ocean from a regional coupled ocean–atmosphere model simulation","authors":"Vasubandhu Misra, C. B. Jayasankar","doi":"10.1002/qj.4620","DOIUrl":"https://doi.org/10.1002/qj.4620","url":null,"abstract":"This study reports on the analysis of the results from a 20 km grid spacing, Regional Coupled ocean–atmosphere Model (RCM) integration over the Western Pacific Warm Pool (WP2). The RCM was integrated over a 20-year period (1986–2005) using reanalysis boundary conditions for the atmosphere and the ocean. This is a first-of-a-kind study with an RCM at 20 km over the WP2. The RCM simulation shows reasonable fidelity of the mean state and of the Madden–Julian Oscillation (MJO). We utilize this successful integration of the RCM to understand a well-known observed feature of MJOs in the WP2 to be of the strongest amplitude during the December–March period of the year. Our analysis of the model integration reveals that the recharge of moist static energy (MSE) prior to peak MJO convection and its discharge during and after the convection explains the MJO in the simulation. The recharge/discharge of the MSE is shown to be largely dictated by horizontal advection, which is stemmed to a small extent by column-integrated radiative heating and surface evaporation. This balance of forces in the evolution of the MSE anomalies and their corresponding variations with sea-surface temperature (SST) anomalies at MJO time-scales in the WP2 is strongest in the December–March period in the RCM simulation.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"222 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532435","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}
Ashar A. Aslam, Juliane Schwendike, Simon C. Peatman, Cathryn E. Birch, Massimo A. Bollasina, Paul Barrett
Patterns in extreme precipitation across the Maritime Continent in southeast Asia are known to be modulated by many processes, from large-scale modes of variability such as the Madden–Julian oscillation, to finer-scale mechanisms such as the diurnal cycle. Transient mid-level dry air intrusions are an example of a feature not extensively studied over the Maritime Continent, which has the potential to influence rainfall patterns. Here, we show that these dry air intrusions originate from upper level disturbances along the subtropical jet. Mid-level cyclonic circulation anomalies northwest of Australia from December to February (DJF) intensify westerlies in the southern Maritime Continent, advecting dry air eastward. In contrast, mid-level anticyclonic circulation anomalies northwest of Australia from June to August (JJA) intensify southern Maritime Continent easterlies, advecting dry air westward. The resultant transport direction of associated air parcels is also dependent on the seasonal low-level monsoon circulation. Dry air intrusions are important in influencing low-level wind and rainfall patterns, suppressing rainfall over seas near the southern Maritime Continent in both seasons, as well as over southern Maritime Continent islands in DJF and the Indian Ocean in JJA. In both seasons there is enhanced rainfall to the east of the intrusion, where there is moist return flow to the extratropics. This study highlights the importance of synoptic-scale extratropical features in influencing meteorological patterns in the Tropics.
{"title":"Mid-level dry air intrusions over the southern Maritime Continent","authors":"Ashar A. Aslam, Juliane Schwendike, Simon C. Peatman, Cathryn E. Birch, Massimo A. Bollasina, Paul Barrett","doi":"10.1002/qj.4618","DOIUrl":"https://doi.org/10.1002/qj.4618","url":null,"abstract":"Patterns in extreme precipitation across the Maritime Continent in southeast Asia are known to be modulated by many processes, from large-scale modes of variability such as the Madden–Julian oscillation, to finer-scale mechanisms such as the diurnal cycle. Transient mid-level dry air intrusions are an example of a feature not extensively studied over the Maritime Continent, which has the potential to influence rainfall patterns. Here, we show that these dry air intrusions originate from upper level disturbances along the subtropical jet. Mid-level cyclonic circulation anomalies northwest of Australia from December to February (DJF) intensify westerlies in the southern Maritime Continent, advecting dry air eastward. In contrast, mid-level anticyclonic circulation anomalies northwest of Australia from June to August (JJA) intensify southern Maritime Continent easterlies, advecting dry air westward. The resultant transport direction of associated air parcels is also dependent on the seasonal low-level monsoon circulation. Dry air intrusions are important in influencing low-level wind and rainfall patterns, suppressing rainfall over seas near the southern Maritime Continent in both seasons, as well as over southern Maritime Continent islands in DJF and the Indian Ocean in JJA. In both seasons there is enhanced rainfall to the east of the intrusion, where there is moist return flow to the extratropics. This study highlights the importance of synoptic-scale extratropical features in influencing meteorological patterns in the Tropics.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"79 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Radiosonde measurements of relative humidity (RH) are the main source of uncertainty in precipitable water vapor (PWV) calculation from pressure, temperature, and RH/dewpoint (PTU) data. This paper presents a formula expressing PWV in terms of pressure and temperature as functions of geopotential height (GPH), thereby allowing the PWV to be determined: 1) without any moisture‐related calculations other than those involved in measuring GPH (in radiosondes with a pressor sensor) or pressure (otherwise); 2) without relying on humidity measurements by using GPS‐based GPH according to the gravity field, provided that pressure is directly measured. The numerical instability associated with random data errors or deviations from hydrostatic equilibrium makes the second approach unfeasible on short time scales, revealing discrepancies between the PTU‐ and GPS‐based GPHs; however, the estimation of long‐term average PWV above a location is not hindered. The estimation of PWV without humidity data was tested using high‐resolution data from 62 upper‐air stations operated by the NOAA National Weather Service. The seasonal mean {DJF, MAM, JJA, SON} PWV from the surface to 300‐hPa calculated from PT and GPS data over the period 2016–2018, after rejecting individual estimates inconsistent with the 0–100% RH range, showed a mean bias error of {‐0.1, +0.1, ‐1.4, ‐0.9} kg m ‐2 relative to the PTU‐based values across the stations, and a RMSE ranging from 2.4 (DJF) to 3.2 (JJA) kg m –2 . By restricting the analysis to observations with above‐average matching between the PTU‐ and GPS‐based GPH, the bias magnitude and RMSE reduced respectively to less than 0.5 and 1 kg m –2 in all seasons. The results indicate that evaluating the long‐term agreement between the two PWV calculation methods at different sites could be useful in detecting systematic observation errors in GPS radiosonde systems using a pressure sensor. This article is protected by copyright. All rights reserved.
无线电探空仪测量相对湿度(RH)是根据压力、温度和RH/露点(PTU)数据计算可降水量(PWV)的主要不确定性来源。本文提出了一个以压力和温度作为位势高度(GPH)函数来表示PWV的公式，从而可以确定PWV: 1)除了测量GPH(在带有压力传感器的无线电探空仪中)或压力(否则)所涉及的计算外，不需要任何与水分相关的计算;2)在直接测量压力的情况下，不依赖于根据重力场使用基于GPS的GPH测量湿度。与随机数据误差或流体静力平衡偏差相关的数值不稳定性使得第二种方法在短时间尺度上不可行，这揭示了基于PTU和GPS的GPHs之间的差异;但是，对某一地点上方的长期平均PWV的估计不会受到阻碍。利用NOAA国家气象局运营的62个高空站点的高分辨率数据，对没有湿度数据的PWV估计进行了测试。2016-2018年期间，利用PT和GPS数据计算的地表至300 hPa的季节平均{DJF, MAM, JJA, SON} PWV在剔除与0-100% RH范围不一致的个别估计后，相对于各台站基于PTU的值，平均偏差为{‐0.1，+0.1，‐1.4，‐0.9}kg m‐2,RMSE范围为2.4 (DJF)至3.2 (JJA) kg m‐2。通过将分析限制在PTU -和GPS - GPH匹配高于平均水平的观测值，偏差幅度和RMSE在所有季节分别减小到小于0.5和1 kg m -2。结果表明，评估两种PWV计算方法在不同地点的长期一致性可能有助于检测使用压力传感器的GPS无线电探空系统的系统观测误差。这篇文章受版权保护。版权所有。
{"title":"Determining precipitable water vapor from upper‐air temperature, pressure and geopotential height","authors":"António P. Ferreira, Luis Gimeno","doi":"10.1002/qj.4609","DOIUrl":"https://doi.org/10.1002/qj.4609","url":null,"abstract":"Abstract Radiosonde measurements of relative humidity (RH) are the main source of uncertainty in precipitable water vapor (PWV) calculation from pressure, temperature, and RH/dewpoint (PTU) data. This paper presents a formula expressing PWV in terms of pressure and temperature as functions of geopotential height (GPH), thereby allowing the PWV to be determined: 1) without any moisture‐related calculations other than those involved in measuring GPH (in radiosondes with a pressor sensor) or pressure (otherwise); 2) without relying on humidity measurements by using GPS‐based GPH according to the gravity field, provided that pressure is directly measured. The numerical instability associated with random data errors or deviations from hydrostatic equilibrium makes the second approach unfeasible on short time scales, revealing discrepancies between the PTU‐ and GPS‐based GPHs; however, the estimation of long‐term average PWV above a location is not hindered. The estimation of PWV without humidity data was tested using high‐resolution data from 62 upper‐air stations operated by the NOAA National Weather Service. The seasonal mean {DJF, MAM, JJA, SON} PWV from the surface to 300‐hPa calculated from PT and GPS data over the period 2016–2018, after rejecting individual estimates inconsistent with the 0–100% RH range, showed a mean bias error of {‐0.1, +0.1, ‐1.4, ‐0.9} kg m ‐2 relative to the PTU‐based values across the stations, and a RMSE ranging from 2.4 (DJF) to 3.2 (JJA) kg m –2 . By restricting the analysis to observations with above‐average matching between the PTU‐ and GPS‐based GPH, the bias magnitude and RMSE reduced respectively to less than 0.5 and 1 kg m –2 in all seasons. The results indicate that evaluating the long‐term agreement between the two PWV calculation methods at different sites could be useful in detecting systematic observation errors in GPS radiosonde systems using a pressure sensor. This article is protected by copyright. All rights reserved.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"6 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The global data assimilation (DA) system at Météo‐France is currently based on a 4D‐Var formulation relying on wavelet‐based 3D background‐error covariances. These covariances are specified at the beginning of the DA window and are evolved implicitly in the DA window through tangent linear and adjoint model integrations. Further research and development steps on data assimilation at Météo‐France are conducted in the framework of the Object‐Oriented Prediction System (OOPS), which is developed in collaboration with the European Centre for Medium‐Range Weather Forecasts (ECMWF). For instance, 3D background‐error covariances can be made hybrid through a linear combination between wavelet‐based covariances and ensemble‐based covariances that are filtered through spatial localisation. This allows covariances to be made more anisotropic in a flow‐dependent way, and implementation of this hybridation in the OOPS framework is shown to have general positive impacts on the forecast quality. This 3D‐hybrid approach can also be extended to a 4D‐hybrid approach in the OOPS framework: this relies on a linear combination between 4D ensemble covariances on the one hand and 4D linearly propagated covariances on the other hand, corresponding to initial covariances that are evolved more explicitly by tangent linear and adjoint versions of the model. This provides a unifying framework for implementations of DA schemes that correspond to 4DEnVar, 4D‐Var, and new 4D‐hybrid formulations. This is thus considered as a novel way to combine the respective attractive features of 4D‐Var and 4DEnVar approaches, leading in particular to a new 4D‐hybrid formulation of 4DEnVar. Its properties and implementation in the OOPS framework are presented, and first experimental results show that this new formulation of 4DEnVar is competitive with 4D‐Var, in relation with the improved hybridisation.
{"title":"Formulation and use of 3D‐hybrid and 4D‐hybrid ensemble covariances in the Météo‐France global data assimilation system","authors":"Loïk Berre, Etienne Arbogast","doi":"10.1002/qj.4603","DOIUrl":"https://doi.org/10.1002/qj.4603","url":null,"abstract":"Abstract The global data assimilation (DA) system at Météo‐France is currently based on a 4D‐Var formulation relying on wavelet‐based 3D background‐error covariances. These covariances are specified at the beginning of the DA window and are evolved implicitly in the DA window through tangent linear and adjoint model integrations. Further research and development steps on data assimilation at Météo‐France are conducted in the framework of the Object‐Oriented Prediction System (OOPS), which is developed in collaboration with the European Centre for Medium‐Range Weather Forecasts (ECMWF). For instance, 3D background‐error covariances can be made hybrid through a linear combination between wavelet‐based covariances and ensemble‐based covariances that are filtered through spatial localisation. This allows covariances to be made more anisotropic in a flow‐dependent way, and implementation of this hybridation in the OOPS framework is shown to have general positive impacts on the forecast quality. This 3D‐hybrid approach can also be extended to a 4D‐hybrid approach in the OOPS framework: this relies on a linear combination between 4D ensemble covariances on the one hand and 4D linearly propagated covariances on the other hand, corresponding to initial covariances that are evolved more explicitly by tangent linear and adjoint versions of the model. This provides a unifying framework for implementations of DA schemes that correspond to 4DEnVar, 4D‐Var, and new 4D‐hybrid formulations. This is thus considered as a novel way to combine the respective attractive features of 4D‐Var and 4DEnVar approaches, leading in particular to a new 4D‐hybrid formulation of 4DEnVar. Its properties and implementation in the OOPS framework are presented, and first experimental results show that this new formulation of 4DEnVar is competitive with 4D‐Var, in relation with the improved hybridisation.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"66 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135087365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The European Space Agency's Aeolus mission, launched in August 2018, provides the first global horizontal line‐of‐sight wind profile measurements. Previous studies have shown that Aeolus winds in global forecast systems improve the overall forecast skill, especially in the upper tropospheric tropics and in other data‐sparse regions. In this study, we use a series of observing system experiments with the latest version of the reprocessed Aeolus wind product (2B11) to better characterize the locations and drivers of improved skill from Aeolus with Environment and Climate Change Canada's Global Deterministic Prediction System. Observing system experiments that test the impact of Aeolus winds and the impact of all operational wind observations are carried out, covering the period summer 2019 and winter 2019–2020. Assimilation of operational winds improves the tropospheric wind forecast over the Tropics by a reduction of 8% in the forecast error, and adding the Aeolus winds to the assimilations results in an extra 0.7–0.9%. Aeolus wind impacts are improvements are 0.7–0.9% for the Arctic, and 0.4–0.6% over the Northern and Southern Hemisphere extratropics. The scale dependence of these impacts is investigated using spatial spectra (spherical harmonic decomposition). The improvement is quantified using the difference of the 250 hPa kinetic energy forecast error spectra between experiments. The operational winds largely improve the forecast of planetary scale to intermediate scale for spherical wave numbers between 1 and 20 in the short‐range forecasts. The operational wind impact decreases as the forecast lead time increases. On the other hand, the impact of Aeolus is mostly seen in the intermediate to large scale range with a peak around spherical wave number 8. The Aeolus ‐related improvement around this wave number increases with forecast lead time. This analysis suggests that Aeolus winds provide estimates of the wind state that are valuable and complementary to that provided from current operational winds.
{"title":"Scale‐dependent impact of Aeolus winds on a global forecast system","authors":"Chih‐Chun Chou, Paul J. Kushner","doi":"10.1002/qj.4601","DOIUrl":"https://doi.org/10.1002/qj.4601","url":null,"abstract":"Abstract The European Space Agency's Aeolus mission, launched in August 2018, provides the first global horizontal line‐of‐sight wind profile measurements. Previous studies have shown that Aeolus winds in global forecast systems improve the overall forecast skill, especially in the upper tropospheric tropics and in other data‐sparse regions. In this study, we use a series of observing system experiments with the latest version of the reprocessed Aeolus wind product (2B11) to better characterize the locations and drivers of improved skill from Aeolus with Environment and Climate Change Canada's Global Deterministic Prediction System. Observing system experiments that test the impact of Aeolus winds and the impact of all operational wind observations are carried out, covering the period summer 2019 and winter 2019–2020. Assimilation of operational winds improves the tropospheric wind forecast over the Tropics by a reduction of 8% in the forecast error, and adding the Aeolus winds to the assimilations results in an extra 0.7–0.9%. Aeolus wind impacts are improvements are 0.7–0.9% for the Arctic, and 0.4–0.6% over the Northern and Southern Hemisphere extratropics. The scale dependence of these impacts is investigated using spatial spectra (spherical harmonic decomposition). The improvement is quantified using the difference of the 250 hPa kinetic energy forecast error spectra between experiments. The operational winds largely improve the forecast of planetary scale to intermediate scale for spherical wave numbers between 1 and 20 in the short‐range forecasts. The operational wind impact decreases as the forecast lead time increases. On the other hand, the impact of Aeolus is mostly seen in the intermediate to large scale range with a peak around spherical wave number 8. The Aeolus ‐related improvement around this wave number increases with forecast lead time. This analysis suggests that Aeolus winds provide estimates of the wind state that are valuable and complementary to that provided from current operational winds.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"72 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135088042","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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