J. Alessandri, N. Pinardi, I. Federico, A. Valentini
We developed a storm surge Ensemble Prediction System (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a Lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components.
{"title":"Storm Surge Ensemble Prediction System for Lagoons and Transitional Environments","authors":"J. Alessandri, N. Pinardi, I. Federico, A. Valentini","doi":"10.1175/waf-d-23-0040.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0040.1","url":null,"abstract":"\u0000We developed a storm surge Ensemble Prediction System (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a Lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42535180","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}
Weiguo Wang, Jongil Han, Fanglin Yang, Johnathan Steffen, Bin Liu, Zhan Zhang, A. Mehra, V. Tallapragada
A modification to the mixing length formulation in a planetary boundary-layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational global forecast system performs well in terms of the reduced root mean square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed.
{"title":"Improving the intensity forecast of Tropical Cyclones in Hurricane Analysis and Forecast System","authors":"Weiguo Wang, Jongil Han, Fanglin Yang, Johnathan Steffen, Bin Liu, Zhan Zhang, A. Mehra, V. Tallapragada","doi":"10.1175/waf-d-23-0041.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0041.1","url":null,"abstract":"\u0000A modification to the mixing length formulation in a planetary boundary-layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational global forecast system performs well in terms of the reduced root mean square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43146396","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. Bertossa, P. Hitchcock, A. Degaetano, R. Plougonven
A previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for 2-meter temperature exhibit properties of univariate bimodality, in some locations occurring in over 30% of forecasts. This study introduces a novel methodology to identify ‘bimodal events’, meteorological events which trigger the development of spatially and temporally correlated bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also indicates when Gaussian interpretations of forecasts are detrimental. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean, and one in the North Atlantic. It is found that bimodal events in each region appear to be triggered by synoptic processes interacting with geographically specific processes: in South America bimodality is often related to Andes blocking events, in the Southern Ocean bimodality is often related to an atmospheric Rossby wave interacting with sea ice, and in the North Atlantic bimodality is often connected to the displacement of a persistent subtropical high. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions.
{"title":"Coherent Bimodal Events in Ensemble Forecasts of 2-m Temperature","authors":"C. Bertossa, P. Hitchcock, A. Degaetano, R. Plougonven","doi":"10.1175/waf-d-23-0053.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0053.1","url":null,"abstract":"\u0000A previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for 2-meter temperature exhibit properties of univariate bimodality, in some locations occurring in over 30% of forecasts. This study introduces a novel methodology to identify ‘bimodal events’, meteorological events which trigger the development of spatially and temporally correlated bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also indicates when Gaussian interpretations of forecasts are detrimental. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean, and one in the North Atlantic. It is found that bimodal events in each region appear to be triggered by synoptic processes interacting with geographically specific processes: in South America bimodality is often related to Andes blocking events, in the Southern Ocean bimodality is often related to an atmospheric Rossby wave interacting with sea ice, and in the North Atlantic bimodality is often connected to the displacement of a persistent subtropical high. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45848551","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}
Regional understanding of severe surface winds produced by convective processes (severe convective winds: SCWs) is important for decision making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and non-severe events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between clusters suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types.
{"title":"Long-term observational characteristics of different severe convective wind types around Australia","authors":"Andrew Brown, A. Dowdy, T. Lane, S. Hitchcock","doi":"10.1175/waf-d-23-0069.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0069.1","url":null,"abstract":"\u0000Regional understanding of severe surface winds produced by convective processes (severe convective winds: SCWs) is important for decision making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and non-severe events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between clusters suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64708251","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}
Jiafen Hu, Jidong Gao, Chengsi Liu, Guifu Zhang, P. Heinselman, Jacob T. Carlin
Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF model’s hydrometeor and water vapor mixing ratio variables in this study. Three 3-D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo-water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qv) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-hour forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo-water vapor with power-transformed qv as an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments.
{"title":"Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data","authors":"Jiafen Hu, Jidong Gao, Chengsi Liu, Guifu Zhang, P. Heinselman, Jacob T. Carlin","doi":"10.1175/waf-d-22-0158.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0158.1","url":null,"abstract":"\u0000Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF model’s hydrometeor and water vapor mixing ratio variables in this study. Three 3-D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo-water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qv) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-hour forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo-water vapor with power-transformed qv as an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44937379","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 object-based verification procedure described in a recent paper (Duda and Turner 2021) was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast-observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced over-forecasting bias for medium and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely over-forecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts.
{"title":"Using Object-Based Verification to Assess Improvements in Forecasts of Convective Storms Between Operational HRRR Versions 3 and 4","authors":"Jeffrey D. Duda, David D. Turner","doi":"10.1175/waf-d-22-0181.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0181.1","url":null,"abstract":"\u0000The object-based verification procedure described in a recent paper (Duda and Turner 2021) was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast-observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced over-forecasting bias for medium and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely over-forecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43648116","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":"Masthead","authors":"","doi":"10.1175/waf-388masthead","DOIUrl":"https://doi.org/10.1175/waf-388masthead","url":null,"abstract":"","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47649770","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}
Omon A. Obarein, Cameron C. Lee, Erik T. Smith, Scott C. Sheridan
Abstract Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced and, consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea level pressure (MSLP) data from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979 to 2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, the relative skill of four different forecasting methods over a 90-day lead time was studied: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP), 3) a simple climatology forecast, and 4) a simple persistence forecast. As expected, forecast skill of both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10–12 days when using correlation, and at 7–10 days when using the root-mean-square error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8–11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical or circulation pattern–based forecasting at 1–2-week lead times.
{"title":"Evaluating Medium-Range Forecast Performance of Regional-Scale Circulation Patterns","authors":"Omon A. Obarein, Cameron C. Lee, Erik T. Smith, Scott C. Sheridan","doi":"10.1175/waf-d-22-0149.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0149.1","url":null,"abstract":"Abstract Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced and, consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea level pressure (MSLP) data from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979 to 2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, the relative skill of four different forecasting methods over a 90-day lead time was studied: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP), 3) a simple climatology forecast, and 4) a simple persistence forecast. As expected, forecast skill of both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10–12 days when using correlation, and at 7–10 days when using the root-mean-square error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8–11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical or circulation pattern–based forecasting at 1–2-week lead times.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135568399","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}
J. L. García-Franco, Chia-ying Lee, S. Camargo, M. Tippett, Daehyun Kim, A. Molod, Y. Lim
This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal-to-Seasonal (S2S) Project. The global distribution of precipitation in S2S models shows relevant biases in the multi-model mean ensemble which are characterized by wet biases in total precipitation (P) and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of total P biases in basins such as the Southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence (TCF). The S2S models simulate too few TCs in the Atlantic and Western North Pacific and too many TCs in the Southern Hemisphere and Eastern North Pacific. At the storm-scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300-500 km). An analysis of the mean TCP for each TC at each grid-point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and Western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total P.
{"title":"Climatology of tropical cyclone precipitation in the S2S models","authors":"J. L. García-Franco, Chia-ying Lee, S. Camargo, M. Tippett, Daehyun Kim, A. Molod, Y. Lim","doi":"10.1175/waf-d-23-0029.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0029.1","url":null,"abstract":"This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal-to-Seasonal (S2S) Project. The global distribution of precipitation in S2S models shows relevant biases in the multi-model mean ensemble which are characterized by wet biases in total precipitation (P) and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of total P biases in basins such as the Southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence (TCF). The S2S models simulate too few TCs in the Atlantic and Western North Pacific and too many TCs in the Southern Hemisphere and Eastern North Pacific. At the storm-scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300-500 km). An analysis of the mean TCP for each TC at each grid-point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and Western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total P.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45239961","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}
Paul D. Mykolajtchuk, K. C. Eure, D. Stensrud, Yunji Zhang, S. Greybush, M. Kumjian
On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the pre-convective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome.
{"title":"Diagnosing Factors Leading to an Incorrect Supercell Thunderstorm Forecast","authors":"Paul D. Mykolajtchuk, K. C. Eure, D. Stensrud, Yunji Zhang, S. Greybush, M. Kumjian","doi":"10.1175/waf-d-23-0010.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0010.1","url":null,"abstract":"\u0000On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the pre-convective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47147693","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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