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- Author or Editor: Katja Friedrich x
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Abstract
Horizontal wind vector fields can be measured in real time by a bistatic Doppler radar network and can be applied directly for hazard warnings and weather surveillance. Most applications, however, especially for meteorological research and operational meteorology, require quality-controlled wind fields. Therefore, a quality-control scheme is developed that includes algorithms to determine the data quality. The algorithms are applied through a decision criterion, and the quality of wind measurement is weighted with values ranging from 1 to 0. The results of each weighting algorithm are merged to an average quality index field, which represents the confidence of each horizontal wind measurement. This averaged field is available together with the measured horizontal wind vector field for further applications. This idea is applicable for all kinds of spatial wind field measurements and is applied in the paper for horizontal wind fields measured for monostatic dual- and bistatic dual- and/or multiple-Doppler radar measurements. Wind synthesis and quality control of three-dimensional wind fields are presented for two frontal passages with stratiform precipitation and for a convective situation.
Abstract
Horizontal wind vector fields can be measured in real time by a bistatic Doppler radar network and can be applied directly for hazard warnings and weather surveillance. Most applications, however, especially for meteorological research and operational meteorology, require quality-controlled wind fields. Therefore, a quality-control scheme is developed that includes algorithms to determine the data quality. The algorithms are applied through a decision criterion, and the quality of wind measurement is weighted with values ranging from 1 to 0. The results of each weighting algorithm are merged to an average quality index field, which represents the confidence of each horizontal wind measurement. This averaged field is available together with the measured horizontal wind vector field for further applications. This idea is applicable for all kinds of spatial wind field measurements and is applied in the paper for horizontal wind fields measured for monostatic dual- and bistatic dual- and/or multiple-Doppler radar measurements. Wind synthesis and quality control of three-dimensional wind fields are presented for two frontal passages with stratiform precipitation and for a convective situation.
Abstract
The object of this paper was to develop an automated dealiasing scheme that dealiases Doppler velocities measured by a bistatic Doppler radar network. The particular network consists of the C-band polarimetric diversity Doppler radar, POLDIRAD, and three passive receivers located at remote sites. The wind components, independent but measured simultaneously, are then merged to a horizontal wind vector field. In order to dealias these independent wind components separately, the real-time four-dimensional Doppler dealiasing scheme (4DD) developed by James and Houze was modified. In altering 4DD, the main difficulties arose from dealiasing bistatically measured Doppler velocities, the spatial data inhomogeneity, and to a lesser extent, from the small spatial coverage of bistatic data due to the limited size of the bistatic antenna's aperture. Furthermore, an internal dealiasing algorithm was added to 4DD that uses the full wind vector information to optimize dealising of small isolated cells. Because the determination of microphysical and dynamical parameters requires alternating or fixed polarization bases, respectively, two different scanning strategies are developed to determine these parameters effectively during both slowly and rapidly evolving weather events. An example is presented of dealiasing monostatically and bistatically measured Doppler velocities which were acquired using both scanning modes to observe a downburst-producing thunderstorm.
Abstract
The object of this paper was to develop an automated dealiasing scheme that dealiases Doppler velocities measured by a bistatic Doppler radar network. The particular network consists of the C-band polarimetric diversity Doppler radar, POLDIRAD, and three passive receivers located at remote sites. The wind components, independent but measured simultaneously, are then merged to a horizontal wind vector field. In order to dealias these independent wind components separately, the real-time four-dimensional Doppler dealiasing scheme (4DD) developed by James and Houze was modified. In altering 4DD, the main difficulties arose from dealiasing bistatically measured Doppler velocities, the spatial data inhomogeneity, and to a lesser extent, from the small spatial coverage of bistatic data due to the limited size of the bistatic antenna's aperture. Furthermore, an internal dealiasing algorithm was added to 4DD that uses the full wind vector information to optimize dealising of small isolated cells. Because the determination of microphysical and dynamical parameters requires alternating or fixed polarization bases, respectively, two different scanning strategies are developed to determine these parameters effectively during both slowly and rapidly evolving weather events. An example is presented of dealiasing monostatically and bistatically measured Doppler velocities which were acquired using both scanning modes to observe a downburst-producing thunderstorm.
Abstract
By installing and linking additional receivers to a monostatic Doppler radar, several wind components can be measured and combined into a wind vector field. Such a bistatic Doppler radar network was developed in 1993 by the National Center for Atmospheric Research and has been in operation at different research departments. Since then, the accuracy of wind vectors has been investigated mainly based on theoretical examinations. Observational analysis of the accuracy has been limited to comparisons of dual-Doppler-derived wind vectors always including the monostatic Doppler radar. Intercomparisons to independent wind measurements have not yet been accomplished. In order to become an alternative to monostatic multiple–Doppler applications, the reliability of wind vector fields has to be also proven by observational analysis. In this paper wind vectors measured by a bistatic Doppler radar network are evaluated by 1) internally comparing results of bistatic receivers; 2) comparing with independent wind measurements observed by a second Doppler radar; and 3) comparing with in situ flight measurements achieved with a research aircraft during stratiform precipitation events. Investigations show how reliable bistatically measured wind fields are and how they can contribute highly to research studies, weather surveillance, and forecasting. As a result of the intercomparison, the instrumentation error of the bistatic receivers can be assumed to be within 1 m s−1. Differences between bistatic Doppler radar and independent measurements range mainly between 2 and 3 m s−1.
Abstract
By installing and linking additional receivers to a monostatic Doppler radar, several wind components can be measured and combined into a wind vector field. Such a bistatic Doppler radar network was developed in 1993 by the National Center for Atmospheric Research and has been in operation at different research departments. Since then, the accuracy of wind vectors has been investigated mainly based on theoretical examinations. Observational analysis of the accuracy has been limited to comparisons of dual-Doppler-derived wind vectors always including the monostatic Doppler radar. Intercomparisons to independent wind measurements have not yet been accomplished. In order to become an alternative to monostatic multiple–Doppler applications, the reliability of wind vector fields has to be also proven by observational analysis. In this paper wind vectors measured by a bistatic Doppler radar network are evaluated by 1) internally comparing results of bistatic receivers; 2) comparing with independent wind measurements observed by a second Doppler radar; and 3) comparing with in situ flight measurements achieved with a research aircraft during stratiform precipitation events. Investigations show how reliable bistatically measured wind fields are and how they can contribute highly to research studies, weather surveillance, and forecasting. As a result of the intercomparison, the instrumentation error of the bistatic receivers can be assumed to be within 1 m s−1. Differences between bistatic Doppler radar and independent measurements range mainly between 2 and 3 m s−1.
Abstract
Radar-observed vertical structure of precipitation as defined by contoured frequency by altitude diagrams (CFADs) is related to dynamic and thermodynamic environmental parameters. CFADs from 559 storms occurring over the years 2004–11 in the vicinity of Locarno, Switzerland, combined with Interim ECMWF Re-Analysis (ERA-Interim) data show that the radar-observed vertical structure of precipitation correlates with synoptic pattern (as defined by 1000- and 500-hPa geopotential heights), integrated water vapor flux, atmospheric stability, and vertical profiles of temperature, moisture, and wind. Following the analysis of vertical structure and environmental parameters, a generalized linear model (GLM) is developed for radar-observed vertical structure as a function of data from ERA-Interim. The GLM provides expected values for the vertical extent and magnitude of radar reflectivity and predicts storm vertical structure type with 79% overall accuracy. The relationships found between environmental parameters and storm vertical structure underscore the importance of including both dynamic and thermodynamic variables when evaluating climate change effects on precipitation. In addition, the ability of the GLM to reproduce storm types shows the potential for using GLMs as a link between lower-resolution global model data and high-resolution precipitation observations.
Abstract
Radar-observed vertical structure of precipitation as defined by contoured frequency by altitude diagrams (CFADs) is related to dynamic and thermodynamic environmental parameters. CFADs from 559 storms occurring over the years 2004–11 in the vicinity of Locarno, Switzerland, combined with Interim ECMWF Re-Analysis (ERA-Interim) data show that the radar-observed vertical structure of precipitation correlates with synoptic pattern (as defined by 1000- and 500-hPa geopotential heights), integrated water vapor flux, atmospheric stability, and vertical profiles of temperature, moisture, and wind. Following the analysis of vertical structure and environmental parameters, a generalized linear model (GLM) is developed for radar-observed vertical structure as a function of data from ERA-Interim. The GLM provides expected values for the vertical extent and magnitude of radar reflectivity and predicts storm vertical structure type with 79% overall accuracy. The relationships found between environmental parameters and storm vertical structure underscore the importance of including both dynamic and thermodynamic variables when evaluating climate change effects on precipitation. In addition, the ability of the GLM to reproduce storm types shows the potential for using GLMs as a link between lower-resolution global model data and high-resolution precipitation observations.
Abstract
Operational radar data reveal that precipitation systems occurring on the southern side of the Alps near Locarno, Switzerland, follow seasonal patterns of vertical reflectivity structure. Storms occurring in summer are more convective than winter season storms as indicated by more frequent observation of reflectivity at higher altitudes during summer. Individual precipitation events occurring year-round are classified by comparison to average seasonal vertical reflectivity structure. Seasonal classification of individual storms reveals a transition between winter- and summer-type storms during spring and fall that follows changes in average daily surface temperature. In addition to distinct vertical structure, summer- and winter-type storms have differences in duration, intensity, and interval between storms. Although summer- and winter-type storms result in a similar amount of total precipitation, summer-type storms have shorter duration, and therefore greater intensity. The dependence of storm types on temperature has implications for intensification of the hydrologic cycle due to climate change. Warmer winter, spring, or fall surface temperatures may affect average precipitation intensity by increasing the number of days per year that experience more intense convective precipitation while decreasing the probability of less intense stratiform precipitation.
Abstract
Operational radar data reveal that precipitation systems occurring on the southern side of the Alps near Locarno, Switzerland, follow seasonal patterns of vertical reflectivity structure. Storms occurring in summer are more convective than winter season storms as indicated by more frequent observation of reflectivity at higher altitudes during summer. Individual precipitation events occurring year-round are classified by comparison to average seasonal vertical reflectivity structure. Seasonal classification of individual storms reveals a transition between winter- and summer-type storms during spring and fall that follows changes in average daily surface temperature. In addition to distinct vertical structure, summer- and winter-type storms have differences in duration, intensity, and interval between storms. Although summer- and winter-type storms result in a similar amount of total precipitation, summer-type storms have shorter duration, and therefore greater intensity. The dependence of storm types on temperature has implications for intensification of the hydrologic cycle due to climate change. Warmer winter, spring, or fall surface temperatures may affect average precipitation intensity by increasing the number of days per year that experience more intense convective precipitation while decreasing the probability of less intense stratiform precipitation.
Abstract
Projections of twenty-first-century precipitation for seven Swiss river basins are generated by linking high-resolution (2 km × 2 km) radar-estimated precipitation observations to a global climate model (GCM) via synoptic weather patterns. The use of synoptic patterns characterizes the effect of changes in large-scale circulation, or dynamic effects, on precipitation. In each basin observed total daily precipitation received during advective synoptic patterns is shown to be dependent on the basin’s general topographic aspect. Across all basins convective synoptic patterns follow the same trend in total daily precipitation with cyclonic patterns consistently producing a larger amount of precipitation than anticyclonic patterns. Identification of synoptic patterns from a GCM for the twenty-first century [Community Climate System Model, version 3.0, (CCSM3)] shows increasing frequency of anticyclonic synoptic patterns, decreasing frequency of cyclonic patterns, and constant frequency of advective patterns over Switzerland. When coupled with observed radar-estimated precipitation for each synoptic pattern, the changes in synoptic pattern frequencies result in an approximately 10%–15% decrease in decadal precipitation over the course of the twenty-first century for seven Swiss river basins. The study results also show an insignificant change in the future (twenty-first century) probability of exceeding the current (2000–08) 95th quantile of total precipitation. The lack of a trend in exceeding the 95th quantile of precipitation in combination with a decreasing trend in total precipitation provides evidence that dynamic effects will not result in increased frequency of heavy precipitation events, but that heavy precipitation will account for a greater proportion of total precipitation in Swiss river basins by the end of the twenty-first century.
Abstract
Projections of twenty-first-century precipitation for seven Swiss river basins are generated by linking high-resolution (2 km × 2 km) radar-estimated precipitation observations to a global climate model (GCM) via synoptic weather patterns. The use of synoptic patterns characterizes the effect of changes in large-scale circulation, or dynamic effects, on precipitation. In each basin observed total daily precipitation received during advective synoptic patterns is shown to be dependent on the basin’s general topographic aspect. Across all basins convective synoptic patterns follow the same trend in total daily precipitation with cyclonic patterns consistently producing a larger amount of precipitation than anticyclonic patterns. Identification of synoptic patterns from a GCM for the twenty-first century [Community Climate System Model, version 3.0, (CCSM3)] shows increasing frequency of anticyclonic synoptic patterns, decreasing frequency of cyclonic patterns, and constant frequency of advective patterns over Switzerland. When coupled with observed radar-estimated precipitation for each synoptic pattern, the changes in synoptic pattern frequencies result in an approximately 10%–15% decrease in decadal precipitation over the course of the twenty-first century for seven Swiss river basins. The study results also show an insignificant change in the future (twenty-first century) probability of exceeding the current (2000–08) 95th quantile of total precipitation. The lack of a trend in exceeding the 95th quantile of precipitation in combination with a decreasing trend in total precipitation provides evidence that dynamic effects will not result in increased frequency of heavy precipitation events, but that heavy precipitation will account for a greater proportion of total precipitation in Swiss river basins by the end of the twenty-first century.
Abstract
Over the last few years the use of weather radar data has become a fundamental part of various applications like rain-rate estimation, nowcasting of severe weather events, and assimilation into numerical weather prediction models. The increasing demand for radar data necessitates an automated, flexible, and modular quality control. In this paper a quality control procedure is developed for radar reflectivity factors, polarimetric parameters, and Doppler velocity. It consists of several modules that can be extended, modified, and omitted depending on the user requirement, weather situation, and radar characteristics. Data quality is quantified on a pixel-by-pixel basis and encoded into a quality-index field that can be easily interpreted by a nontrained end user or an automated scheme that generates radar products. The quality-index algorithms detect and quantify the influence of beam broadening, the height of the first radar echo, ground clutter contamination, return from non-weather-related objects, and attenuation of electromagnetic energy by hydrometeors on the quality of the radar measurement. The quality-index field is transferred together with the radar data to the end user who chooses the amount of data and the level of quality used for further processing. The calculation of quality-index fields is based on data measured by the polarimetric C-band Doppler radar (POLDIRAD) located in the Alpine foreland in southern Germany.
Abstract
Over the last few years the use of weather radar data has become a fundamental part of various applications like rain-rate estimation, nowcasting of severe weather events, and assimilation into numerical weather prediction models. The increasing demand for radar data necessitates an automated, flexible, and modular quality control. In this paper a quality control procedure is developed for radar reflectivity factors, polarimetric parameters, and Doppler velocity. It consists of several modules that can be extended, modified, and omitted depending on the user requirement, weather situation, and radar characteristics. Data quality is quantified on a pixel-by-pixel basis and encoded into a quality-index field that can be easily interpreted by a nontrained end user or an automated scheme that generates radar products. The quality-index algorithms detect and quantify the influence of beam broadening, the height of the first radar echo, ground clutter contamination, return from non-weather-related objects, and attenuation of electromagnetic energy by hydrometeors on the quality of the radar measurement. The quality-index field is transferred together with the radar data to the end user who chooses the amount of data and the level of quality used for further processing. The calculation of quality-index fields is based on data measured by the polarimetric C-band Doppler radar (POLDIRAD) located in the Alpine foreland in southern Germany.
Abstract
A 9-yr (2000–08) analysis of precipitation characteristics for the central and western European Alps has been generated from ground-based operational weather radar data provided by the Swiss radar network. The radar-based precipitation analysis focuses on the relationship between synoptic-scale weather patterns and mesoscale precipitation distribution over complex alpine terrain. The analysis divides the Alps into six regions (each approximately 200 × 200 km2 in size)—one on the northern side, two each on the western and southern sides of the Alps, and one in the Massif Central—representing various orographic aspects and localized climates within the radar coverage area. For each region, estimated precipitation rate derived from radar data is analyzed on a seasonal basis for total daily precipitation and frequency of high-precipitation-rate events. The summer season has the highest total daily precipitation for all regions in the study, whereas median values of daily precipitation in winter are less than one-half of median daily precipitation for summer. For all regions, high-precipitation-rate events occur most frequently in the summer. Daily synoptic-scale weather patterns are associated with total daily precipitation and frequency of high precipitation rate to show that an advective synoptic-scale pattern with southerly midtropospheric flow results in the highest median and 90th-quantile values for total daily precipitation and that a convective synoptic-scale pattern results in elevated frequency of extreme-precipitation-rate events.
Abstract
A 9-yr (2000–08) analysis of precipitation characteristics for the central and western European Alps has been generated from ground-based operational weather radar data provided by the Swiss radar network. The radar-based precipitation analysis focuses on the relationship between synoptic-scale weather patterns and mesoscale precipitation distribution over complex alpine terrain. The analysis divides the Alps into six regions (each approximately 200 × 200 km2 in size)—one on the northern side, two each on the western and southern sides of the Alps, and one in the Massif Central—representing various orographic aspects and localized climates within the radar coverage area. For each region, estimated precipitation rate derived from radar data is analyzed on a seasonal basis for total daily precipitation and frequency of high-precipitation-rate events. The summer season has the highest total daily precipitation for all regions in the study, whereas median values of daily precipitation in winter are less than one-half of median daily precipitation for summer. For all regions, high-precipitation-rate events occur most frequently in the summer. Daily synoptic-scale weather patterns are associated with total daily precipitation and frequency of high precipitation rate to show that an advective synoptic-scale pattern with southerly midtropospheric flow results in the highest median and 90th-quantile values for total daily precipitation and that a convective synoptic-scale pattern results in elevated frequency of extreme-precipitation-rate events.
Abstract
The influence of ground clutter contamination on the estimation of polarimetric radar parameters, horizontal reflectivity (Zh ), differential reflectivity (Z dr), correlation coefficient (ρ hυ ), and differential propagation phase (ϕ dp) was examined. This study aims to derive the critical level of ground clutter contamination for Zh , Z dr, ρ hυ , and ϕ dp at which ground clutter influence exceeds predefined precision thresholds. Reference data with minimal ground clutter contamination consist of eight precipitation fields measured during three rain events characterized by stratiform and convective precipitation. Data were collected at an elevation angle of 0.8° by the Météo-France operational, polarimetric Doppler C-band weather radar located in Trappes, France, ∼30 km southwest of Paris. Nine different ground clutter signatures, ranging from point targets to more complex signatures typical for mountain ranges or urban obstacles, were added to the precipitation fields. This is done at the level of raw in-phase and quadrature component data in the two polarimetric channels. For each ground clutter signature, 30 simulations were conducted in which the mean reflectivity of ground clutter within the resolution volume varied between being 30 dB higher to 30 dB lower than the mean reflectivity of precipitation. Differences in Zh, Z dr, ρυ , and ϕ dp between simulation and reference were shown as a function of ratio between ground clutter and precipitation intensities.
As a result of this study, horizontal reflectivity showed the lowest sensitivity to ground clutter contamination. Furthermore, a precision of 1.7 dBZ in Zh is achieved on average when the precipitation and ground clutter intensities are equal. Requiring a precision of 0.2 dB in Z dr and 3° in ϕ dp, the reflectivity of precipitation needs to be on average ∼5.5 and ∼6 dB, respectively, higher compared to the reflectivity of ground clutter. The analysis also indicates that the highest sensitivity to the nine clutter signatures was derived for ρ hυ . To meet a predefined precision threshold of 0.02, reflectivity of precipitation needs to be ∼13.5 dB higher than the reflectivity of ground clutter.
Abstract
The influence of ground clutter contamination on the estimation of polarimetric radar parameters, horizontal reflectivity (Zh ), differential reflectivity (Z dr), correlation coefficient (ρ hυ ), and differential propagation phase (ϕ dp) was examined. This study aims to derive the critical level of ground clutter contamination for Zh , Z dr, ρ hυ , and ϕ dp at which ground clutter influence exceeds predefined precision thresholds. Reference data with minimal ground clutter contamination consist of eight precipitation fields measured during three rain events characterized by stratiform and convective precipitation. Data were collected at an elevation angle of 0.8° by the Météo-France operational, polarimetric Doppler C-band weather radar located in Trappes, France, ∼30 km southwest of Paris. Nine different ground clutter signatures, ranging from point targets to more complex signatures typical for mountain ranges or urban obstacles, were added to the precipitation fields. This is done at the level of raw in-phase and quadrature component data in the two polarimetric channels. For each ground clutter signature, 30 simulations were conducted in which the mean reflectivity of ground clutter within the resolution volume varied between being 30 dB higher to 30 dB lower than the mean reflectivity of precipitation. Differences in Zh, Z dr, ρυ , and ϕ dp between simulation and reference were shown as a function of ratio between ground clutter and precipitation intensities.
As a result of this study, horizontal reflectivity showed the lowest sensitivity to ground clutter contamination. Furthermore, a precision of 1.7 dBZ in Zh is achieved on average when the precipitation and ground clutter intensities are equal. Requiring a precision of 0.2 dB in Z dr and 3° in ϕ dp, the reflectivity of precipitation needs to be on average ∼5.5 and ∼6 dB, respectively, higher compared to the reflectivity of ground clutter. The analysis also indicates that the highest sensitivity to the nine clutter signatures was derived for ρ hυ . To meet a predefined precision threshold of 0.02, reflectivity of precipitation needs to be ∼13.5 dB higher than the reflectivity of ground clutter.
Abstract
Warm season heavy rainfall in Minnesota can lead to flooding with serious impacts on life and infrastructure. Situated in a transition zone between humid eastern and semiarid western conditions in the United States, Minnesota experiences large spatial variability in precipitation. Previous research has often lacked spatiotemporal detail important for heavy rainfall analysis for Minnesota. This research used Stage-IV hourly precipitation data with 4-km grid spacing during May–September 2004–20 to analyze Minnesota spatial, seasonal, and event-based characteristics. Rain event frequency, accumulation, hours, and intensities were compared for all rain events (>2.5 mm) and heavy rain events (>36 mm). For all rain events, results showed the highest regional median monthly rain event frequency (>6 events) in June and the lowest (<5 events) in September. Median monthly accumulations were largest (∼75 mm) in June, followed by July and August. Monthly total rain event hours at a point peaked around 20 h in May in southeastern Minnesota. Smaller event accumulations occurred more frequently than larger accumulations, and event mean intensities were higher in summertime (June–August) than in May and September for rain events and heavy rain events. Heavy rain event region-based analyses showed monthly peaks for frequency in July–August, accumulation in July, and event hours in June–July and September. Median heavy rain event durations were shorter during June–August than in May and September. Monthly heavy rain event accumulation as a percent of all rain event accumulation was greatest in September (24%). These results establish a foundation for future research into precipitation patterns and trends.
Significance Statement
Climate analysis has indicated that Minnesota is in a region where increases in heavy rainfall are anticipated for the future. Heavy rainfall in Minnesota has led to flooding with severe adverse impacts. This study addresses a gap in information about heavy precipitation in Minnesota and provides heavy rainfall analyses useful for climate-related planning. Stage-IV hourly precipitation data for the warm season (May–September) during 2004–20 enabled the identification of rain events and heavy rain events, as well as their characteristic frequency, rainfall accumulation, duration, and intensity. The results help establish a baseline for past and future analyses of precipitation patterns and trends. They also build a foundation for future research investigating the weather patterns that lead to heavy rainfall.
Abstract
Warm season heavy rainfall in Minnesota can lead to flooding with serious impacts on life and infrastructure. Situated in a transition zone between humid eastern and semiarid western conditions in the United States, Minnesota experiences large spatial variability in precipitation. Previous research has often lacked spatiotemporal detail important for heavy rainfall analysis for Minnesota. This research used Stage-IV hourly precipitation data with 4-km grid spacing during May–September 2004–20 to analyze Minnesota spatial, seasonal, and event-based characteristics. Rain event frequency, accumulation, hours, and intensities were compared for all rain events (>2.5 mm) and heavy rain events (>36 mm). For all rain events, results showed the highest regional median monthly rain event frequency (>6 events) in June and the lowest (<5 events) in September. Median monthly accumulations were largest (∼75 mm) in June, followed by July and August. Monthly total rain event hours at a point peaked around 20 h in May in southeastern Minnesota. Smaller event accumulations occurred more frequently than larger accumulations, and event mean intensities were higher in summertime (June–August) than in May and September for rain events and heavy rain events. Heavy rain event region-based analyses showed monthly peaks for frequency in July–August, accumulation in July, and event hours in June–July and September. Median heavy rain event durations were shorter during June–August than in May and September. Monthly heavy rain event accumulation as a percent of all rain event accumulation was greatest in September (24%). These results establish a foundation for future research into precipitation patterns and trends.
Significance Statement
Climate analysis has indicated that Minnesota is in a region where increases in heavy rainfall are anticipated for the future. Heavy rainfall in Minnesota has led to flooding with severe adverse impacts. This study addresses a gap in information about heavy precipitation in Minnesota and provides heavy rainfall analyses useful for climate-related planning. Stage-IV hourly precipitation data for the warm season (May–September) during 2004–20 enabled the identification of rain events and heavy rain events, as well as their characteristic frequency, rainfall accumulation, duration, and intensity. The results help establish a baseline for past and future analyses of precipitation patterns and trends. They also build a foundation for future research investigating the weather patterns that lead to heavy rainfall.
