Quarterly Journal of the Royal Meteorological Society最新文献

{"title":"Post‐processing output from ensembles with and without parametrised convection, to create accurate, blended, high‐fidelity rainfall forecasts","authors":"Estíbaliz Gascón, Andrea Montani, Tim D. Hewson","doi":"10.1002/qj.4753","DOIUrl":"https://doi.org/10.1002/qj.4753","url":null,"abstract":"Flash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address, one can try to use output from convection‐parametrising (global) ensembles, post‐processed to forecast at point‐scale, or convection‐resolving limited area ensembles. The new methodology described here combines both. We apply “ecPoint‐rainfall” post‐processing to the ECMWF global ensemble. Alongside we use 2.2 km COSMO LAM ensemble output (centred on Italy), and also post‐process that, using a scale‐selective neighbourhood approach to compensate for insufficient members and to preserve consistently forecast local details. The two resulting scale‐compatible components then undergo lead‐time‐weighted blending, to create the final probabilistic 6 h rainfall forecasts. Product creation for forecasters, in this way, constituted the “Italy Flash Flood use case” within the EU‐funded MISTRAL project; real‐time delivery of open access products is ongoing. One year of verification shows that, of the five components (2 raw, 2 post‐processed and blended), ecPoint is the most skilful. The post‐processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two typical heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint elevated the ECMWF maxima and highlighted best the most affected areas and merged products seemed to be the most skilful of all. Even though our LAM post‐processing does not include (or arguably need) bias‐correction, this study still provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection‐permitting resolution.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"172 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059874","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}

{"title":"Sampling and misspecification errors in the estimation of observation‐error covariance matrices using observation‐minus‐background and observation‐minus‐analysis statistics","authors":"Guannan Hu, Sarah L. Dance","doi":"10.1002/qj.4750","DOIUrl":"https://doi.org/10.1002/qj.4750","url":null,"abstract":"Specification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"49 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059644","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Irrigation strongly influences near‐surface conditions and induces breeze circulation: Observational and model‐based evidence","authors":"Tanguy Lunel, Aaron A. Boone, Patrick Le Moigne","doi":"10.1002/qj.4736","DOIUrl":"https://doi.org/10.1002/qj.4736","url":null,"abstract":"Irrigation is becoming increasingly common in agriculture and is essential to meet the growing demand for food. Studies of the impact of irrigated areas on local meteorology reveal a strong influence on near‐surface conditions, although the extent of this influence varies considerably between locations. In addition, though theoretical evidence suggests that irrigation can create breeze‐like atmospheric boundary‐layer circulations, observational evidence is still lacking. This study investigates the effects of irrigation on the surface and atmospheric boundary layer in the Ebro basin, an intensively irrigated area with a semi‐arid climate in northeastern Spain. Observational data from the international field campaign Land Surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment are analysed together with coupled surface–atmosphere model output to better understand and quantify the impact of irrigation on the lower atmosphere. A simple parametrization of irrigation is shown to improve the accuracy of the model. Results demonstrate that irrigation increases the average latent heat flux by over 200 Wm, reduces air temperature by 4.7°C, and increases specific humidity by 50% at 2 m during the day over the irrigated region of the domain. Moreover, irrigation limits convection and strongly stabilizes the atmospheric boundary layer. Notably, the study provides evidence for an irrigation‐induced breeze from the irrigated area to the semi‐arid area. These findings highlight the importance of considering irrigation in numerical models for weather forecasting, climate modelling and sustainable agricultural planning.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"23 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840085","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}

{"title":"Improving subseasonal forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models","authors":"Meriem Krouma, Damien Specq, Linus Magnusson, Constantin Ardilouze, Lauriane Batté, Pascal Yiou","doi":"10.1002/qj.4733","DOIUrl":"https://doi.org/10.1002/qj.4733","url":null,"abstract":"We propose a forecasting tool for precipitation based on analogues of circulation defined from 5‐day hindcasts and a stochastic weather generator that we call “HC–SWG.” In this study, we aim to improve the forecast of European precipitation for subseasonal lead times (from 2 to 4 weeks) using the HC–SWG. We designed the HC–SWG to generate an ensemble precipitation forecast from the European Centre of Medium‐range Weather Forecasts (ECMWF) and Centre National de la Recherche Météorologique (CNRM) subseasonal‐to‐seasonal ensemble reforecasts. We define analogues from 5‐day ensemble reforecast of Z500 from the ECMWF (11 members) and CNRM (10 members) models. Then, we generate a 100‐member ensemble for precipitation over Europe. We evaluate the skill of the ensemble forecast using probabilistic skill scores such as the continuous ranked probability skill score (CRPSS) and receiver operating characteristic curve. We obtain reasonable forecast skill scores within 35 days for different locations in Europe. The CRPSS shows positive improvement with respect to climatology and persistence at the station level. The HC–SWG shows a capacity to distinguish between events and non‐events of precipitation within 15 days at the different stations. We compare the HC–SWG forecast with other precipitation forecasts to further confirm the benefits of our method. We found that the HC–SWG shows improvement against the ECMWF precipitation forecast until 25 days.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"12 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840083","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}