Heatwaves are known to arise from the interplay between large-scale climate variability, synoptic weather patterns and regional to local scale surface processes. While recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study, we consider a wide range of possible predictor variables from the ERA5 reanalysis, and ask, how much information on heatwave occurrence in Europe can be learned from each of them. To simplify the problem, we first adapt the recently developed logistic principal component analysis to the task of compressing large binary heatwave fields to a small number of interpretable principal components. The relationships between heatwaves and various climate variables can then be learned by a neural network. Starting from the simple notion that the importance of a variable is given by its impact on the performance of our statistical model, we arrive naturally at the definition of Shapley values. Classic results of game theory show that this is the only fair way of distributing the overall success of a model among its inputs. We find a non linear model that explains 70 % of reduced heatwave variability. The biggest individual contribution (27 % of the 70 %) comes from upper level geopotential, top level soil moisture is in second place (15 %). Beyond this decomposition, Shapley interaction values enable us to quantify overlapping information and positive synergies between all pairs of predictors.
{"title":"Explaining heatwaves with machine learning","authors":"Sebastian Buschow, Jan Keller, Sabrina Wahl","doi":"10.1002/qj.4642","DOIUrl":"https://doi.org/10.1002/qj.4642","url":null,"abstract":"Heatwaves are known to arise from the interplay between large-scale climate variability, synoptic weather patterns and regional to local scale surface processes. While recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study, we consider a wide range of possible predictor variables from the ERA5 reanalysis, and ask, how much information on heatwave occurrence in Europe <i>can be learned</i>\u0000from each of them. To simplify the problem, we first adapt the recently developed logistic principal component analysis to the task of compressing large binary heatwave fields to a small number of interpretable principal components. The relationships between heatwaves and various climate variables can then be learned by a neural network. Starting from the simple notion that the importance of a variable is given by its impact on the performance of our statistical model, we arrive naturally at the definition of Shapley values. Classic results of game theory show that this is the only fair way of distributing the overall success of a model among its inputs. We find a non linear model that explains 70 % of reduced heatwave variability. The biggest individual contribution (27 % of the 70 %) comes from upper level geopotential, top level soil moisture is in second place (15 %). Beyond this decomposition, Shapley interaction values enable us to quantify overlapping information and positive synergies between all pairs of predictors.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"13 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138824602","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}
Andreas Rauchöcker, Alexander Rudolph, Ivana Stiperski, Manuela Lehner
A field campaign in a valley near Seefeld, Austria, well-known for the frequent occurrence of cold-air pools, was conducted to identify the processes leading to the formation and erosion of the cold-air pool. Here we focus on a case study in January 2020 that featured cold-air pool formation interrupted by a wind disturbance. Simulations with the Weather Research and Forecasting Model (WRF) were performed at a horizontal grid spacing of 40 m and compared to measurement results. The model was able to reproduce the intense cooling in the beginning of the night and the cold-air pool erosion in the middle of the night caused by the wind disturbance, but stronger winds than observed prevented the cold-air pool from fully reestablishing in the model after the disturbance. The dominant cooling processes were longwave radiative heat loss and turbulent exchange, both of which are parameterized and cool the air locally. Advection was the most important warming contribution during the cold-air pool disturbances, especially its cross-valley and vertical components. Due to numerical constraints and the shallow nature of the cold-air pool, its extent was limited to the lowest model level. Further improvements to the cold-air pool's representation in the model would require a finer grid resolution.
{"title":"Cold-Air Pool Development in a small Alpine Valley","authors":"Andreas Rauchöcker, Alexander Rudolph, Ivana Stiperski, Manuela Lehner","doi":"10.1002/qj.4644","DOIUrl":"https://doi.org/10.1002/qj.4644","url":null,"abstract":"A field campaign in a valley near Seefeld, Austria, well-known for the frequent occurrence of cold-air pools, was conducted to identify the processes leading to the formation and erosion of the cold-air pool. Here we focus on a case study in January 2020 that featured cold-air pool formation interrupted by a wind disturbance. Simulations with the Weather Research and Forecasting Model (WRF) were performed at a horizontal grid spacing of 40 m and compared to measurement results. The model was able to reproduce the intense cooling in the beginning of the night and the cold-air pool erosion in the middle of the night caused by the wind disturbance, but stronger winds than observed prevented the cold-air pool from fully reestablishing in the model after the disturbance. The dominant cooling processes were longwave radiative heat loss and turbulent exchange, both of which are parameterized and cool the air locally. Advection was the most important warming contribution during the cold-air pool disturbances, especially its cross-valley and vertical components. Due to numerical constraints and the shallow nature of the cold-air pool, its extent was limited to the lowest model level. Further improvements to the cold-air pool's representation in the model would require a finer grid resolution.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"22 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138824601","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}
Reliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parameterization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU-Net) for the maximum and minimum air temperature (Tmax and Tmin, respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 km to 1.5 km over South Korea through the fusion of deep learning (i.e., U-Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU-Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression (SVR)-based statistical downscaling model at the seen and unseen stations for forecasting the next-day Tmax and Tmin, respectively, DeU-Net showed the highest spatial correlation and the lowest RMSE in all cases. In a qualitative evaluation, DeU-Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to seven days indicated that the proposed DeU-Net is a better downscaling approach than SVR, regardless of the forecast lead time. These results demonstrate that bias-corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting.
{"title":"A new statistical downscaling approach for short-term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation","authors":"Dongjin Cho, Jungho Im, Sihun Jung","doi":"10.1002/qj.4643","DOIUrl":"https://doi.org/10.1002/qj.4643","url":null,"abstract":"Reliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parameterization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU-Net) for the maximum and minimum air temperature (Tmax and Tmin, respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 km to 1.5 km over South Korea through the fusion of deep learning (i.e., U-Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU-Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression (SVR)-based statistical downscaling model at the seen and unseen stations for forecasting the next-day Tmax and Tmin, respectively, DeU-Net showed the highest spatial correlation and the lowest RMSE in all cases. In a qualitative evaluation, DeU-Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to seven days indicated that the proposed DeU-Net is a better downscaling approach than SVR, regardless of the forecast lead time. These results demonstrate that bias-corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"55 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138824359","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}
Jaume Ramon, Llorenç Lledó, Christopher A. T. Ferro, Francisco J. Doblas-Reyes
The probabilistic skill of seasonal prediction systems is often inferred using reanalysis data, assuming these benchmark observations to be error-free. However, an increasing number of studies report non-negligible levels of uncertainty affecting reanalysis observations, especially when it comes to variables like precipitation or wind speed. We consider different possibilities to account for such error in forecast quality assessment, either exploiting the newly produced ensemble reanalyses (e.g. ERA5-EDA) or applying methodologies that use scores that take observational uncertainty into account. We illustrate the benefits of employing ensemble reanalyses over traditional reanalyses, and show how the true skill can be approximated, whatever the observational reference. We ultimately emphasise the perils and quantify the error committed when the observational reference, either reanalysis or point dataset, is selected arbitrarily for verifying a seasonal prediction system.
{"title":"Uncertainties in the observational reference: implications in skill assessment and model ranking of seasonal predictions","authors":"Jaume Ramon, Llorenç Lledó, Christopher A. T. Ferro, Francisco J. Doblas-Reyes","doi":"10.1002/qj.4628","DOIUrl":"https://doi.org/10.1002/qj.4628","url":null,"abstract":"The probabilistic skill of seasonal prediction systems is often inferred using reanalysis data, assuming these benchmark observations to be error-free. However, an increasing number of studies report non-negligible levels of uncertainty affecting reanalysis observations, especially when it comes to variables like precipitation or wind speed. We consider different possibilities to account for such error in forecast quality assessment, either exploiting the newly produced ensemble reanalyses (e.g. ERA5-EDA) or applying methodologies that use scores that take observational uncertainty into account. We illustrate the benefits of employing ensemble reanalyses over traditional reanalyses, and show how the true skill can be approximated, whatever the observational reference. We ultimately emphasise the perils and quantify the error committed when the observational reference, either reanalysis or point dataset, is selected arbitrarily for verifying a seasonal prediction system.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"85 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138692139","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 seeks to characterize the development of atmospheric fronts during the extratropical transition (ET) of tropical cyclones (TCs) as a function of their evolution during ET. Composite histograms indicate that the magnitude of the lower atmospheric frontogenesis and average sea surface temperature (SST) is different based on the nature of the TC's structural change during ET. We find that the development of cold and warm fronts evolves as expected from conceptual models of extratropical cyclones. Composites of these fronts relative to the completion of ET show that azimuth, storm motion, and deep-layer shear all appear to have equal influence on the frontal positions. TCs that have more fronts at the time of ET onset complete ET more quickly, suggesting that pre-existing fronts before ET begins may contribute to a shorter ET duration. The orientations of fronts at ET completion in the North Atlantic and West Pacific align with the climatological distributions of the SSTs associated with the western boundary currents in each of those basins. These results provide a perspective on the locations of frontal development within TCs undergoing ET.
本研究旨在描述热带气旋（TC）外热带过渡（ET）期间大气锋面的发展特征，作为其在 ET 期间演变的函数。复合直方图表明，根据热带气旋在 ET 期间结构变化的性质，低层大气锋面生成和平均海面温度（SST）的幅度是不同的。我们发现，冷锋和暖锋的发展正如外热带气旋概念模型所预期的那样。这些锋面相对于 ET 完成时的复合显示，方位角、风暴运动和深层切变对锋面位置的影响似乎相同。在 ET 开始时锋面较多的热带气旋完成 ET 的速度更快，这表明在 ET 开始前已经存在的锋面可能有助于缩短 ET 持续时间。北大西洋和西太平洋 ET 结束时锋面的方向与这两个海盆中与西部边界流相关的海温的气候学分布一致。这些结果为研究经历 ET 的热带气旋中锋面的发展位置提供了一个视角。
{"title":"Development of Frontal Boundaries During the Extratropical Transition of Tropical Cyclones","authors":"Evan Jones, Rhys Parfitt, Allison A. Wing","doi":"10.1002/qj.4633","DOIUrl":"https://doi.org/10.1002/qj.4633","url":null,"abstract":"This study seeks to characterize the development of atmospheric fronts during the extratropical transition (ET) of tropical cyclones (TCs) as a function of their evolution during ET. Composite histograms indicate that the magnitude of the lower atmospheric frontogenesis and average sea surface temperature (SST) is different based on the nature of the TC's structural change during ET. We find that the development of cold and warm fronts evolves as expected from conceptual models of extratropical cyclones. Composites of these fronts relative to the completion of ET show that azimuth, storm motion, and deep-layer shear all appear to have equal influence on the frontal positions. TCs that have more fronts at the time of ET onset complete ET more quickly, suggesting that pre-existing fronts before ET begins may contribute to a shorter ET duration. The orientations of fronts at ET completion in the North Atlantic and West Pacific align with the climatological distributions of the SSTs associated with the western boundary currents in each of those basins. These results provide a perspective on the locations of frontal development within TCs undergoing ET.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"8 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573690","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 Chinese second ocean dynamic environment satellite Haiyang-2B (HY-2B), was successfully launched on 25 October 2018, carrying a Scanning Microwave Radiometer (SMR) to provide the information of ocean and atmosphere. For the first time, this study investigated the impact of radiance data from the HY-2B SMR in the Four Dimensional Variational (4DVar) Data Assimilation system of the Global Forecast System developed by the China Meteorology Administration (CMA-GFS). Prior to the radiance assimilation, we evaluated the data quality and developed suitable quality control (QC) procedures for the HY-2B SMR observations. In addition, the cloud liquid water path (CLWP) and ocean wind speed (OWS) were introduced as new bias correction (BC) predictors to remove the systematic bias originating from the forward operator in the CMA-GFS 4DVar system. The assimilation effects of SMR observations were evaluated through one-month cycling experiments. Verification with independent observations and reanalysis data has demonstrated that assimilating SMR clear-sky radiance can greatly reduce the analysis errors for temperature and humidity, while indirectly improve the quality of wind analysis. Furthermore, significant improvements in geopotential height forecasts were found for days 1-5 in the lower troposphere of the tropical region.
{"title":"Impact of HY-2B SMR Radiance Assimilation on CMA Global Medium Range Weather Forecasts","authors":"Li Zeting, Wei Han","doi":"10.1002/qj.4630","DOIUrl":"https://doi.org/10.1002/qj.4630","url":null,"abstract":"The Chinese second ocean dynamic environment satellite Haiyang-2B (HY-2B), was successfully launched on 25 October 2018, carrying a Scanning Microwave Radiometer (SMR) to provide the information of ocean and atmosphere. For the first time, this study investigated the impact of radiance data from the HY-2B SMR in the Four Dimensional Variational (4DVar) Data Assimilation system of the Global Forecast System developed by the China Meteorology Administration (CMA-GFS). Prior to the radiance assimilation, we evaluated the data quality and developed suitable quality control (QC) procedures for the HY-2B SMR observations. In addition, the cloud liquid water path (CLWP) and ocean wind speed (OWS) were introduced as new bias correction (BC) predictors to remove the systematic bias originating from the forward operator in the CMA-GFS 4DVar system. The assimilation effects of SMR observations were evaluated through one-month cycling experiments. Verification with independent observations and reanalysis data has demonstrated that assimilating SMR clear-sky radiance can greatly reduce the analysis errors for temperature and humidity, while indirectly improve the quality of wind analysis. Furthermore, significant improvements in geopotential height forecasts were found for days 1-5 in the lower troposphere of the tropical region.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"49 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138574345","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}
Roja Chaluvadi, Hamza Varikoden, Milind Mujumdar, S. T. Ingle
The present study examined the interrelationship between the Indian summer monsoon (ISM) rainfall, west Pacific subtropical high (WPSH) and Tropical cyclone (TC) activity over the western north Pacific (WNP) Ocean during the peak monsoon season in 1951-2019. When WNP TCs are inactive (active), there is a noticeable 10° westward (eastward) shift is observed along with intensification (weakening) of WPSH. The WPSH and WNP TC activities are divided into four categories: Eastward shift of WPSH with active TCs (EA) and inactive TC (EI), westward shift of WPSH with active TCs (WA) and inactive TCs (WI) in order to investigate the combined influence of WPSH and WNP TCs on ISMR during NonENSO, El Niño, and La Niña. In EA NonENSO cases, a surplus (deficit) rainfall was noticed over central (southern peninsular) India as a result of moisture convergence (low CAPE, abnormal moisture divergence and downdraft); however, the rainfall is above normal over the Indian subcontinent. During EI NonENSO case, an excess (deficit) regional rainfall activity was observed over eastcentral India and parts of north India (southern peninsular India) through regional changes in atmospheric circulations and mid-tropospheric vertical velocity. In contrast, WI (WA) NonENSO cases, the majority of India (southern peninsular and northeast India) experiences excessive precipitation while east-central India (east and west central India) undergoes deficit rainfall. This pattern is consistent with the spatial distribution of moisture flux convergence, CAPE and mid-tropospheric vertical velocity. In the case of NonENSO, the westward shift of WPSH associated with inactive TCs is one of the favourable conditions for ISMR. In general, the effect of eastward (westward) shift of WPSH along with more (less) TC during NonENSO case is favourable to ISMR. On the other hand, rainfall patterns during ENSO are significantly associated with the circulation changes teleconnected to the El Niño and La Niña conditions.
{"title":"The combined influence of west pacific subtropical high and tropical cyclones over the northwest Pacific on Indian summer monsoon rainfall","authors":"Roja Chaluvadi, Hamza Varikoden, Milind Mujumdar, S. T. Ingle","doi":"10.1002/qj.4640","DOIUrl":"https://doi.org/10.1002/qj.4640","url":null,"abstract":"The present study examined the interrelationship between the Indian summer monsoon (ISM) rainfall, west Pacific subtropical high (WPSH) and Tropical cyclone (TC) activity over the western north Pacific (WNP) Ocean during the peak monsoon season in 1951-2019. When WNP TCs are inactive (active), there is a noticeable 10° westward (eastward) shift is observed along with intensification (weakening) of WPSH. The WPSH and WNP TC activities are divided into four categories: Eastward shift of WPSH with active TCs (EA) and inactive TC (EI), westward shift of WPSH with active TCs (WA) and inactive TCs (WI) in order to investigate the combined influence of WPSH and WNP TCs on ISMR during NonENSO, El Niño, and La Niña. In EA NonENSO cases, a surplus (deficit) rainfall was noticed over central (southern peninsular) India as a result of moisture convergence (low CAPE, abnormal moisture divergence and downdraft); however, the rainfall is above normal over the Indian subcontinent. During EI NonENSO case, an excess (deficit) regional rainfall activity was observed over eastcentral India and parts of north India (southern peninsular India) through regional changes in atmospheric circulations and mid-tropospheric vertical velocity. In contrast, WI (WA) NonENSO cases, the majority of India (southern peninsular and northeast India) experiences excessive precipitation while east-central India (east and west central India) undergoes deficit rainfall. This pattern is consistent with the spatial distribution of moisture flux convergence, CAPE and mid-tropospheric vertical velocity. In the case of NonENSO, the westward shift of WPSH associated with inactive TCs is one of the favourable conditions for ISMR. In general, the effect of eastward (westward) shift of WPSH along with more (less) TC during NonENSO case is favourable to ISMR. On the other hand, rainfall patterns during ENSO are significantly associated with the circulation changes teleconnected to the El Niño and La Niña conditions.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"7 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573697","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}
By the end of 2021, the renewable energy share of the global electricity capacity reached 38.3% and the new installations are dominated by wind and solar energy, showing global increases of 12.7% and 18.5%, respectively. However, both wind and photovoltaic energy sources are highly volatile making planning difficult for grid operators, so accurate forecasts of the corresponding weather variables are essential for reliable electricity predictions. The most advanced approach in weather prediction is the ensemble method, which opens the door for probabilistic forecasting; though ensemble forecast are often underdispersive and subject to systematic bias. Hence, they require some form of statistical post-processing, where parametric models provide full predictive distributions of the weather variables at hand. We propose a general two-step machine learning-based approach to calibrating ensemble weather forecasts, where in the first step improved point forecasts are generated, which are then together with various ensemble statistics serve as input features of the neural network estimating the parameters of the predictive distribution. In two case studies based of 100m wind speed and global horizontal irradiance forecasts of the operational ensemble prediction system of the Hungarian Meteorological Service, the predictive performance of this novel method is compared with the forecast skill of the raw ensemble and the state-of-the-art parametric approaches. Both case studies confirm that at least up to 48h statistical post-processing substantially improves the predictive performance of the raw ensemble for all considered forecast horizons. The investigated variants of the proposed two-step method outperform in skill their competitors and the suggested new approach is well applicable for different weather quantities and for a fair range of predictive distributions.
{"title":"A two-step machine learning approach to statistical post-processing of weather forecasts for power generation","authors":"Ágnes Baran, Sándor Baran","doi":"10.1002/qj.4635","DOIUrl":"https://doi.org/10.1002/qj.4635","url":null,"abstract":"By the end of 2021, the renewable energy share of the global electricity capacity reached 38.3% and the new installations are dominated by wind and solar energy, showing global increases of 12.7% and 18.5%, respectively. However, both wind and photovoltaic energy sources are highly volatile making planning difficult for grid operators, so accurate forecasts of the corresponding weather variables are essential for reliable electricity predictions. The most advanced approach in weather prediction is the ensemble method, which opens the door for probabilistic forecasting; though ensemble forecast are often underdispersive and subject to systematic bias. Hence, they require some form of statistical post-processing, where parametric models provide full predictive distributions of the weather variables at hand. We propose a general two-step machine learning-based approach to calibrating ensemble weather forecasts, where in the first step improved point forecasts are generated, which are then together with various ensemble statistics serve as input features of the neural network estimating the parameters of the predictive distribution. In two case studies based of 100m wind speed and global horizontal irradiance forecasts of the operational ensemble prediction system of the Hungarian Meteorological Service, the predictive performance of this novel method is compared with the forecast skill of the raw ensemble and the state-of-the-art parametric approaches. Both case studies confirm that at least up to 48h statistical post-processing substantially improves the predictive performance of the raw ensemble for all considered forecast horizons. The investigated variants of the proposed two-step method outperform in skill their competitors and the suggested new approach is well applicable for different weather quantities and for a fair range of predictive distributions.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"9 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573692","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}
Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to sub-seasonal numerical weather prediction might therefore hinder exploiting the potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 20 years (2000 - 2020) of sub-seasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. The reforecasts reproduce the climatology of cyclone frequency and life cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis, size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest regional deficit of cyclones of more than 30% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones reach too high intensities during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime might partly but not exclusively explain this intensity bias. While the cyclone bias patterns often appear in lead time weeks 1-2, their magnitudes typically grow further at sub-seasonal lead times, in some cases up to weeks 5-6. Most of the dynamical sources of these biases thus likely appear in the early medium range, but sources on longer timescales probably contribute to the biases' further increase with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in sub-seasonal weather forecasts. Given the considerable biases during summer, when sub-seasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.
{"title":"Northern Hemisphere extratropical cyclone biases in ECMWF sub-seasonal forecasts","authors":"Dominik Büeler, Michael Sprenger, Heini Wernli","doi":"10.1002/qj.4638","DOIUrl":"https://doi.org/10.1002/qj.4638","url":null,"abstract":"Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to sub-seasonal numerical weather prediction might therefore hinder exploiting the potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 20 years (2000 - 2020) of sub-seasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. The reforecasts reproduce the climatology of cyclone frequency and life cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis, size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest regional deficit of cyclones of more than 30% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones reach too high intensities during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime might partly but not exclusively explain this intensity bias. While the cyclone bias patterns often appear in lead time weeks 1-2, their magnitudes typically grow further at sub-seasonal lead times, in some cases up to weeks 5-6. Most of the dynamical sources of these biases thus likely appear in the early medium range, but sources on longer timescales probably contribute to the biases' further increase with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in sub-seasonal weather forecasts. Given the considerable biases during summer, when sub-seasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"2 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573565","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}
Probabilistic forecasts in oceanographic applications, such as drift trajectory forecasts for search-and-rescue operations, face challenges due to high-dimensional complex models and sparse spatial observations. We discuss localisation strategies for assimilating sparse point observations and compare the implicit equal-weights particle filter and a localised version of the ensemble-transform Kalman filter. First, we verify these methods thoroughly against the analytic Kalman filter solution for a linear advection diffusion model. We then use a non-linear simplified ocean model to do state estimation and drift prediction. The methods are rigorously compared using a wide range of metrics and skill scores. Our findings indicate that both methods succeed in approximating the Kalman filter reference for linear models of moderate dimensions, even for small ensemble sizes. However, in high-dimensional settings with a non-linear model, we discover that the outcomes are significantly influenced by the dependence of the ensemble Kalman filter on relaxation and the particle filter's sensitivity to the chosen model error covariance structure. Upon proper relaxation and localisation parametrisation, the ensemble Kalman filter version outperforms the particle filter in our experiments.
{"title":"Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data","authors":"Florian Beiser, Håavard Heitlo Holm, Jo Eidsvik","doi":"10.1002/qj.4637","DOIUrl":"https://doi.org/10.1002/qj.4637","url":null,"abstract":"Probabilistic forecasts in oceanographic applications, such as drift trajectory forecasts for search-and-rescue operations, face challenges due to high-dimensional complex models and sparse spatial observations. We discuss localisation strategies for assimilating sparse point observations and compare the implicit equal-weights particle filter and a localised version of the ensemble-transform Kalman filter. First, we verify these methods thoroughly against the analytic Kalman filter solution for a linear advection diffusion model. We then use a non-linear simplified ocean model to do state estimation and drift prediction. The methods are rigorously compared using a wide range of metrics and skill scores. Our findings indicate that both methods succeed in approximating the Kalman filter reference for linear models of moderate dimensions, even for small ensemble sizes. However, in high-dimensional settings with a non-linear model, we discover that the outcomes are significantly influenced by the dependence of the ensemble Kalman filter on relaxation and the particle filter's sensitivity to the chosen model error covariance structure. Upon proper relaxation and localisation parametrisation, the ensemble Kalman filter version outperforms the particle filter in our experiments.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":"14 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573870","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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