Simon Ageet, Andreas H. Fink, Marlon Maranan, Benedikt Schulz
Abstract Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over Equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–2018. 24-hour rainfall accumulations are analysed from short to medium-range time scales. Additionally, 48- and 120-hour rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rain seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead-time. There is an improvement of up to 30% in Brier score/continuous rank probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead-time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid-points with positive Brier skill score (continuous rank probability score) by an average of 81% (91%) for lead-times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatio-temporal variation in skill and benefit of postprocessing in EEA.
{"title":"Predictability of Rainfall over Equatorial East Africa in the ECMWF Ensemble Reforecasts on short to medium-range time scales","authors":"Simon Ageet, Andreas H. Fink, Marlon Maranan, Benedikt Schulz","doi":"10.1175/waf-d-23-0093.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0093.1","url":null,"abstract":"Abstract Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over Equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–2018. 24-hour rainfall accumulations are analysed from short to medium-range time scales. Additionally, 48- and 120-hour rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rain seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead-time. There is an improvement of up to 30% in Brier score/continuous rank probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead-time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid-points with positive Brier skill score (continuous rank probability score) by an average of 81% (91%) for lead-times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatio-temporal variation in skill and benefit of postprocessing in EEA.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885066","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}
Stanley B. Trier, David A. Ahijevych, Dereka Carroll-Smith, George H. Bryan, Roger Edwards
Abstract Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within United States landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data from 1995 through 2021. For 72 cases of LTCs with wide ranging TC intensites at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m to 700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m to 700-hPa and 10-m to 500-hPa BWDs (∼20m s −1 ) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s −1 ). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s −1 ) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west-to-east oriented baroclinic zone (i.e., north-to-south temperature gradient) that enhances mesoscale ascent on the LTC’s east side.
利用1995年至2021年27年的再分析数据，研究了热带气旋龙卷风(tct)的空间格局及其与美国登陆热带气旋(LTCs)中尺度预测模式的关系。在72个大范围TC强度的LTCs中，当它们的组合分别在地面相对坐标、TC航向相对坐标和环境剪切相对坐标上构建时，白天的TCT频率最大值分别出现在东北、右前和右下剪切象限。TCT最大值位于10 ~ 700 hpa体风差(BWD)最大值附近，并受到TC环流的增强。这一代理体垂直切变在大约最低的3公里是最大TCT频率的最佳预测之一。相对于其他时间，下午最大TCT频率的位置从LTC中心向外移动约100公里，朝向更大的MLCAPE值。含有最强LTCs的复合材料具有最强的10-m ~ 700 hpa和10-m ~ 500 hpa BWDs (~ 20m s−1)，ttc的最大频率接近。相应的复合材料含有较弱的LTCs，但仍有许多TCTs，其整体垂直剪切值小了约20%(约16 m s−1)。其他在登陆时平均LTC强度同样较弱，但很少或没有ttc的情况下，其最大体积垂直剪切量额外降低了约20%(约12 m s - 1)， MLCAPE也较小。发生在内陆的TCT环境与其他环境的区别在于具有较强的西风切变和西向东的斜压带(即南北温度梯度)，该斜压带增强了LTC东侧的中尺度上升。
{"title":"Composite Mesoscale Environmental Conditions Influencing Tornado Frequencies in Landfalling Tropical Cyclones","authors":"Stanley B. Trier, David A. Ahijevych, Dereka Carroll-Smith, George H. Bryan, Roger Edwards","doi":"10.1175/waf-d-22-0227.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0227.1","url":null,"abstract":"Abstract Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within United States landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data from 1995 through 2021. For 72 cases of LTCs with wide ranging TC intensites at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m to 700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m to 700-hPa and 10-m to 500-hPa BWDs (∼20m s −1 ) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s −1 ). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s −1 ) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west-to-east oriented baroclinic zone (i.e., north-to-south temperature gradient) that enhances mesoscale ascent on the LTC’s east side.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136097623","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 This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts to objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts to surface observations under certain warm sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.
{"title":"Regional and Seasonal Biases in Convection-Allowing Model Forecasts of Near-Surface Temperature and Moisture","authors":"Andrew R. Wade, Israel L. Jirak, Anthony W. Lyza","doi":"10.1175/waf-d-23-0120.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0120.1","url":null,"abstract":"Abstract This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts to objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts to surface observations under certain warm sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136352576","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 Fog is a phenomenon that exerts significant impacts on transportation, aviation, air quality, agriculture, and even water resources. While data-driven machine learning algorithms have shown promising performance in capturing non-linear fog events at point locations, their applicability to different areas and time periods is questionable. This study addresses this issue by examining five decision-tree-based classifiers in a South Korean region, where diverse fog formation mechanisms are at play. The five machine learning algorithms were trained at point locations, and tested with other point locations for time periods independent of the training processes. Using the ensemble classifiers and high-resolution atmospheric reanalysis data, we also attempted to establish fog occurrence maps in a regional area. Results showed that machine learning models trained on the local datasets exhibited superior performance in mountainous areas, where radiative cooling predominantly contributes to fog formation, compared to inland and coastal regions. As the fog generation mechanisms diversified, the tree-based ensemble models appeared to encounter challenges in delineating their decision boundaries. When they were trained with the reanalysis data, their predictive skills were significantly decreased, resulting in high false alarm rates. This prompted the need for post-processing techniques to rectify overestimated fog frequency. While post-processing may ameliorate overestimation, caution is needed to interpret the resultant fog frequency estimates, especially in regions with more diverse fog generation mechanisms. The spatial upscaling of machine-learning-based fog prediction models poses challenges owing to the intricate interplay of various fog formation mechanisms, data imbalances, and potential inaccuracies in reanalysis data.
{"title":"Performance of decision-tree-based ensemble classifiers in predicting fog frequency in ungauged areas","authors":"Daeha Kim, Eunhee Kim, Eunji Kim","doi":"10.1175/waf-d-23-0024.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0024.1","url":null,"abstract":"Abstract Fog is a phenomenon that exerts significant impacts on transportation, aviation, air quality, agriculture, and even water resources. While data-driven machine learning algorithms have shown promising performance in capturing non-linear fog events at point locations, their applicability to different areas and time periods is questionable. This study addresses this issue by examining five decision-tree-based classifiers in a South Korean region, where diverse fog formation mechanisms are at play. The five machine learning algorithms were trained at point locations, and tested with other point locations for time periods independent of the training processes. Using the ensemble classifiers and high-resolution atmospheric reanalysis data, we also attempted to establish fog occurrence maps in a regional area. Results showed that machine learning models trained on the local datasets exhibited superior performance in mountainous areas, where radiative cooling predominantly contributes to fog formation, compared to inland and coastal regions. As the fog generation mechanisms diversified, the tree-based ensemble models appeared to encounter challenges in delineating their decision boundaries. When they were trained with the reanalysis data, their predictive skills were significantly decreased, resulting in high false alarm rates. This prompted the need for post-processing techniques to rectify overestimated fog frequency. While post-processing may ameliorate overestimation, caution is needed to interpret the resultant fog frequency estimates, especially in regions with more diverse fog generation mechanisms. The spatial upscaling of machine-learning-based fog prediction models poses challenges owing to the intricate interplay of various fog formation mechanisms, data imbalances, and potential inaccuracies in reanalysis data.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093957","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}
Laurel L. DeHaan, Anna M. Wilson, Brian Kawzenuk, Minghua Zheng, Luca Delle Monache, Xingren Wu, David A. Lavers, Bruce Ingleby, Vijay Tallapragada, Florian Pappenberger, F. Martin Ralph
Abstract Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an Intensive Observation Period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.
{"title":"Impacts of Dropsonde Observations on Forecasts of Atmospheric Rivers and Associated Precipitation in the NCEP GFS and ECMWF IFS models","authors":"Laurel L. DeHaan, Anna M. Wilson, Brian Kawzenuk, Minghua Zheng, Luca Delle Monache, Xingren Wu, David A. Lavers, Bruce Ingleby, Vijay Tallapragada, Florian Pappenberger, F. Martin Ralph","doi":"10.1175/waf-d-23-0025.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0025.1","url":null,"abstract":"Abstract Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an Intensive Observation Period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094263","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}
Leah Cicon, Johannes Gemmrich, Benoit Pouliot, Natacha Bernier
Abstract Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir Index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth controlled linear superposition. Recent studies have shown that the bandwidth parameter crest-trough correlation, r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the Northeast Pacific. This work further demonstrates that crest-trough correlation can be forecast by a regional WAVEWATCHIII ® wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r . Semi-logarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm October 21-22, 2021.
{"title":"A probabilistic prediction of rogue waves from a WAVEWATCH III® model for the Northeast Pacific","authors":"Leah Cicon, Johannes Gemmrich, Benoit Pouliot, Natacha Bernier","doi":"10.1175/waf-d-23-0074.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0074.1","url":null,"abstract":"Abstract Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir Index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth controlled linear superposition. Recent studies have shown that the bandwidth parameter crest-trough correlation, r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the Northeast Pacific. This work further demonstrates that crest-trough correlation can be forecast by a regional WAVEWATCHIII ® wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r . Semi-logarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm October 21-22, 2021.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135481901","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}
Anna del Moral Méndez, Tammy M. Weckwerth, Rita D. Roberts, James W. Wilson
Abstract East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline and 5.4 million people subsist on its fishing industry. However, more than 1,000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-year “HIGH impact Weather lAke sYstem” (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new Standard Operation Procedures (SOP) for severe weather surveillance and warning.
{"title":"Towards improved short-term forecasting for Lake Victoria Basin: Part I – A radar-based convective mode analysis","authors":"Anna del Moral Méndez, Tammy M. Weckwerth, Rita D. Roberts, James W. Wilson","doi":"10.1175/waf-d-23-0039.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0039.1","url":null,"abstract":"Abstract East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline and 5.4 million people subsist on its fishing industry. However, more than 1,000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-year “HIGH impact Weather lAke sYstem” (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new Standard Operation Procedures (SOP) for severe weather surveillance and warning.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135644920","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 This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.
{"title":"Improved seasonal forecast skill of pan-Arctic and regional sea ice extent in CanSIPS version 2","authors":"Joseph Martin, Adam Monahan, Michael Sigmond","doi":"10.1175/waf-d-22-0193.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0193.1","url":null,"abstract":"Abstract This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136080083","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 orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.
{"title":"Using the Orthogonal Conditional Nonlinear Optimal Perturbations Approach to Address the Uncertainties of Tropical Cyclone Track Forecasts Generated by the WRF Model","authors":"Han Zhang, Wansuo Duan, Yichi Zhang","doi":"10.1175/waf-d-22-0175.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0175.1","url":null,"abstract":"Abstract The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135274828","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}
C. R. Sampson, J. A. Knaff, C. J. Slocum, M. J. Onderlinde, A. Brammer, M. Frost, B. Strahl
Abstract Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.
{"title":"Deterministic Rapid Intensity Forecast Guidance for the Joint Typhoon Warning Center’s Area of Responsibility","authors":"C. R. Sampson, J. A. Knaff, C. J. Slocum, M. J. Onderlinde, A. Brammer, M. Frost, B. Strahl","doi":"10.1175/waf-d-23-0084.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0084.1","url":null,"abstract":"Abstract Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246749","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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