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Abstract
Hail and damaging winds are two threats associated with intense and severe thunderstorms that traverse the Midwest and Great Plains during the primary growing season. In certain severe thunderstorm events, large swaths of agricultural crops are impacted, allowing the damage to be viewed from multiple satellite remote sensing platforms. Previous studies have focused on analyzing individual hail and wind damage swaths (HWDSs) using satellite remote sensing, but these swaths have never been officially archived or documented. This lack of documentation has made it difficult to analyze the spatial extent and temporal frequency of HWDSs from year to year. This study utilizes daily true color imagery from MODIS aboard NASA’s Terra and Aqua satellites and daily local storm reports from the Storm Prediction Center to build a database of HWDSs occurring in the months of May–August, for years 2000–20. This database identified 1646 HWDSs in 12 states throughout the Midwest and Great Plains, confirmed through a combination of archived severe weather warnings, radar information, and official storm reports. For each entry in the HWDS database, a geospatial outline is provided along with the most likely date of first visible damage from MODIS imagery as well as the physical characteristics and time of occurrence estimated from available warnings. This study also provides a summary of the radar characteristics for a portion of the database. This database will further the understanding of severe weather damage by hail and wind to agriculture to help understand the frequency of these events and assist in mapping the impacted areas.
Significance Statement
Hail and wind damage swaths (HWDSs) frequently occur during the primary growing season throughout the Midwest and Great Plains but are not yet officially documented or tracked like other severe weather impacts (e.g., tornadoes and derechos). This study describes the creation of a 21-yr HWDS event database using archived daily storm reports and daily true color satellite imagery. Once the database was completed and underwent quality checks, the research team identified spatial and temporal trends from the confirmed swaths.
Abstract
Hail and damaging winds are two threats associated with intense and severe thunderstorms that traverse the Midwest and Great Plains during the primary growing season. In certain severe thunderstorm events, large swaths of agricultural crops are impacted, allowing the damage to be viewed from multiple satellite remote sensing platforms. Previous studies have focused on analyzing individual hail and wind damage swaths (HWDSs) using satellite remote sensing, but these swaths have never been officially archived or documented. This lack of documentation has made it difficult to analyze the spatial extent and temporal frequency of HWDSs from year to year. This study utilizes daily true color imagery from MODIS aboard NASA’s Terra and Aqua satellites and daily local storm reports from the Storm Prediction Center to build a database of HWDSs occurring in the months of May–August, for years 2000–20. This database identified 1646 HWDSs in 12 states throughout the Midwest and Great Plains, confirmed through a combination of archived severe weather warnings, radar information, and official storm reports. For each entry in the HWDS database, a geospatial outline is provided along with the most likely date of first visible damage from MODIS imagery as well as the physical characteristics and time of occurrence estimated from available warnings. This study also provides a summary of the radar characteristics for a portion of the database. This database will further the understanding of severe weather damage by hail and wind to agriculture to help understand the frequency of these events and assist in mapping the impacted areas.
Significance Statement
Hail and wind damage swaths (HWDSs) frequently occur during the primary growing season throughout the Midwest and Great Plains but are not yet officially documented or tracked like other severe weather impacts (e.g., tornadoes and derechos). This study describes the creation of a 21-yr HWDS event database using archived daily storm reports and daily true color satellite imagery. Once the database was completed and underwent quality checks, the research team identified spatial and temporal trends from the confirmed swaths.
Abstract
When testing hypotheses about which of two competing models is better, say A and B, the difference is often not significant. An alternative, complementary approach, is to measure how often model A is better than model B regardless of how slight or large the difference. The hypothesis concerns whether or not the percentage of time that model A is better than model B is larger than 50%. One generalized test statistic that can be used is the power-divergence test, which encompasses many familiar goodness-of-fit test statistics, such as the loglikelihood-ratio and Pearson X
2 tests. Theoretical results justify using the
Abstract
When testing hypotheses about which of two competing models is better, say A and B, the difference is often not significant. An alternative, complementary approach, is to measure how often model A is better than model B regardless of how slight or large the difference. The hypothesis concerns whether or not the percentage of time that model A is better than model B is larger than 50%. One generalized test statistic that can be used is the power-divergence test, which encompasses many familiar goodness-of-fit test statistics, such as the loglikelihood-ratio and Pearson X
2 tests. Theoretical results justify using the
Abstract
The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.
Abstract
The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.
Abstract
The roles of chaos seeding and multiple perturbations, including model perturbations and topographic perturbations, in convection-permitting ensemble forecasting, are assessed. Six comparison experiments were conducted for 14 heavy rainfall events over southern China. Chaos seeding was run as a benchmark experiment to compare their effects to the intended perturbations. The results first reveal the chaos seeding phenomenon. That is, the tiny and local perturbations of the skin soil moisture propagate into the whole analysis domain within an hour and expand to every prognostic variable, and the perturbations derived from chaos seeding develop when moist convection is active. Second, the chaos seeding has a statistically significant difference from our intended perturbations for the ensemble spread magnitudes of precipitation and the spread–skill relationships and probabilistic forecast skills of dynamical variables. Additionally, for the probabilistic forecasts of precipitation, initial and lateral boundary perturbations and model perturbations can yield statistically larger FSS and AROC scores than chaos seeding; topographic perturbations can only improve FSS and AROC scores a little. The different performances may be related to the different degrees of the real dynamical influence of our intended perturbations. Finally, model perturbations can increase the ensemble spreads of precipitation, and improve FSS and AROC scores of precipitation and the consistency of mid- and low-level dynamical variables. However, the effects of topographic perturbations are small.
Abstract
The roles of chaos seeding and multiple perturbations, including model perturbations and topographic perturbations, in convection-permitting ensemble forecasting, are assessed. Six comparison experiments were conducted for 14 heavy rainfall events over southern China. Chaos seeding was run as a benchmark experiment to compare their effects to the intended perturbations. The results first reveal the chaos seeding phenomenon. That is, the tiny and local perturbations of the skin soil moisture propagate into the whole analysis domain within an hour and expand to every prognostic variable, and the perturbations derived from chaos seeding develop when moist convection is active. Second, the chaos seeding has a statistically significant difference from our intended perturbations for the ensemble spread magnitudes of precipitation and the spread–skill relationships and probabilistic forecast skills of dynamical variables. Additionally, for the probabilistic forecasts of precipitation, initial and lateral boundary perturbations and model perturbations can yield statistically larger FSS and AROC scores than chaos seeding; topographic perturbations can only improve FSS and AROC scores a little. The different performances may be related to the different degrees of the real dynamical influence of our intended perturbations. Finally, model perturbations can increase the ensemble spreads of precipitation, and improve FSS and AROC scores of precipitation and the consistency of mid- and low-level dynamical variables. However, the effects of topographic perturbations are small.
Abstract
Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts.
Significance Statement
Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.
Abstract
Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts.
Significance Statement
Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.
Abstract
Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the Sichuan basin (SCB) has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, and intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional differences over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expand westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB has small difference in terms of the location; hence, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offers a promising way forward for improvements of model physical processes.
Abstract
Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the Sichuan basin (SCB) has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, and intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional differences over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expand westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB has small difference in terms of the location; hence, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offers a promising way forward for improvements of model physical processes.
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.
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.
Abstract
A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States.
Significance Statement
Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States.
Abstract
A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States.
Significance Statement
Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States.
Abstract
A severe derecho impacted the Midwestern United States on 10 August 2020, causing over $12 billion (U.S. dollars) in damage, and producing peak winds estimated at 63 m s−1, with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell–Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13- and 25-km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s−1 surface wind gusts.
Abstract
A severe derecho impacted the Midwestern United States on 10 August 2020, causing over $12 billion (U.S. dollars) in damage, and producing peak winds estimated at 63 m s−1, with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell–Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13- and 25-km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s−1 surface wind gusts.
Abstract
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
Abstract
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
