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Abstract
New operational tools for monitoring flash flooding based on radar quantitative precipitation estimates (QPEs) have become available to U.S. National Weather Service forecasters. Herman and Schumacher examined QPE exceedance thresholds for several tools and compared them to each other, to flash flood reports (FFRs), and to flash flood warnings. The Next Generation Radar network has been updated with dual-polarization capabilities since the publication of Herman and Schumacher, which has changed the characteristics of the derived QPEs. Updated thresholds on Multi-Radar Multi-Sensor version 12 products that are associated to FFRs are provided and thus can be used as guidance by the operational forecasting community and other end-users of the products.
Abstract
New operational tools for monitoring flash flooding based on radar quantitative precipitation estimates (QPEs) have become available to U.S. National Weather Service forecasters. Herman and Schumacher examined QPE exceedance thresholds for several tools and compared them to each other, to flash flood reports (FFRs), and to flash flood warnings. The Next Generation Radar network has been updated with dual-polarization capabilities since the publication of Herman and Schumacher, which has changed the characteristics of the derived QPEs. Updated thresholds on Multi-Radar Multi-Sensor version 12 products that are associated to FFRs are provided and thus can be used as guidance by the operational forecasting community and other end-users of the products.
Abstract
A sudden increase in temperature during the nighttime hours accompanies the passages of some cold fronts. In some cold front–associated warming events, the temperature can rise by as much as 10°C and can last from a few minutes to several hours. Previous studies suggest that these events are due to the downward transport of warmer air by the strong and gusty winds associated with the cold-frontal passages. In this study, a climatology of nocturnal warming events associated with cold fronts was created using 6 yr of Oklahoma Mesonetwork (Mesonet) data from 2003 to 2008. Nocturnal warming events associated with cold-frontal passages occurred surprisingly frequently across Oklahoma. Of the cold fronts observed in this study, 91.5% produced at least one warming event at an Oklahoma Mesonet station. The winter months accounted for the most events (37.9%), and the summer months accounted for the fewest (3.8%). When normalized by the monthly number of cold-frontal passages, the winter months still had the most number of warming events. The number of warming events increased rapidly from 2300 to 0200 UTC; thereafter, the number of events gradually decreased. A spatial analysis revealed that the frequency of warming events decreased markedly from west to east across the state. In contrast, the average magnitude of the warming increased from west to east. In contrast to control periods (associated with cold-frontal passages without nocturnal warming events), warming events were associated with weaker initial winds and stronger initial temperature inversions. Moreover, the nocturnal temperature inversion weakened more during warming events than during control periods and the surface wind speeds increased more during warming events than during control periods. These results are consistent with previous studies that suggest the warming events are due to the “mixing out” of the nocturnal temperature inversion.
Abstract
A sudden increase in temperature during the nighttime hours accompanies the passages of some cold fronts. In some cold front–associated warming events, the temperature can rise by as much as 10°C and can last from a few minutes to several hours. Previous studies suggest that these events are due to the downward transport of warmer air by the strong and gusty winds associated with the cold-frontal passages. In this study, a climatology of nocturnal warming events associated with cold fronts was created using 6 yr of Oklahoma Mesonetwork (Mesonet) data from 2003 to 2008. Nocturnal warming events associated with cold-frontal passages occurred surprisingly frequently across Oklahoma. Of the cold fronts observed in this study, 91.5% produced at least one warming event at an Oklahoma Mesonet station. The winter months accounted for the most events (37.9%), and the summer months accounted for the fewest (3.8%). When normalized by the monthly number of cold-frontal passages, the winter months still had the most number of warming events. The number of warming events increased rapidly from 2300 to 0200 UTC; thereafter, the number of events gradually decreased. A spatial analysis revealed that the frequency of warming events decreased markedly from west to east across the state. In contrast, the average magnitude of the warming increased from west to east. In contrast to control periods (associated with cold-frontal passages without nocturnal warming events), warming events were associated with weaker initial winds and stronger initial temperature inversions. Moreover, the nocturnal temperature inversion weakened more during warming events than during control periods and the surface wind speeds increased more during warming events than during control periods. These results are consistent with previous studies that suggest the warming events are due to the “mixing out” of the nocturnal temperature inversion.
Abstract
A major goal in quantitative precipitation estimation and forecasting is the ability to provide accurate initial conditions for the purposes of hydrologic modeling. The accuracy of a streamflow prediction system is dependent upon how well the initial hydrometeorological states are characterized. A methodology is developed to objectively and quantitatively evaluate the skill of several different precipitation algorithms at the scale of application—a watershed. Thousands of hydrologic simulations are performed in an ensemble fashion, enabling an exploration of the model parameter space. Probabilistic statistics are then utilized to compare the relative skill of hydrologic simulations produced from the different precipitation inputs to the observed streamflow. The primary focus of this study is to demonstrate a methodology to evaluate precipitation algorithms that can be used to supplement traditional radar–rain gauge analyses. This approach is appropriate for the evaluation of precipitation estimates or forecasts that are intended to serve as inputs to hydrologic models.
Abstract
A major goal in quantitative precipitation estimation and forecasting is the ability to provide accurate initial conditions for the purposes of hydrologic modeling. The accuracy of a streamflow prediction system is dependent upon how well the initial hydrometeorological states are characterized. A methodology is developed to objectively and quantitatively evaluate the skill of several different precipitation algorithms at the scale of application—a watershed. Thousands of hydrologic simulations are performed in an ensemble fashion, enabling an exploration of the model parameter space. Probabilistic statistics are then utilized to compare the relative skill of hydrologic simulations produced from the different precipitation inputs to the observed streamflow. The primary focus of this study is to demonstrate a methodology to evaluate precipitation algorithms that can be used to supplement traditional radar–rain gauge analyses. This approach is appropriate for the evaluation of precipitation estimates or forecasts that are intended to serve as inputs to hydrologic models.
Abstract
During stratiform precipitation, hydrometeors within the melting layer increase backscatter to radar. This layer can persist at a nearly constant height for hours and can lead to serious radar-based overestimates in accumulated surface rainfall. Sophisticated precipitation algorithms of the present and near future are beginning to identify regions where there is contaminated reflectivity in order to make corrections to the data. An automated algorithm that operates on full-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity data (i.e., archive level II) to identify the height and depth of the bright band for every volume scan has been developed. Results from the algorithm are compared with 0°C heights from nearby radiosonde observations and from model analyses for three different regions in the United States. In addition, reflectivity observations from an independent, vertically pointing radar situated in complex terrain are compared with results from the brightband algorithm operating on WSR-88D data. The output from the brightband algorithm matches observations well. A case is presented to show how the radar-observed brightband heights can be used to identify regions in precipitation products where radar is sampling within the melting layer and therefore may be subject to overestimation. Improved monitoring of the bright band, because of the comparatively high temporal resolution of the radar observations, results from application of the algorithm. The algorithm output can provide guidance to forecasters who are using radar-based quantitative precipitation estimates to issue advisories and warnings. Moreover, the melting-layer observations can be used with a digital elevation model to map the approximate rain–snow line.
Abstract
During stratiform precipitation, hydrometeors within the melting layer increase backscatter to radar. This layer can persist at a nearly constant height for hours and can lead to serious radar-based overestimates in accumulated surface rainfall. Sophisticated precipitation algorithms of the present and near future are beginning to identify regions where there is contaminated reflectivity in order to make corrections to the data. An automated algorithm that operates on full-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity data (i.e., archive level II) to identify the height and depth of the bright band for every volume scan has been developed. Results from the algorithm are compared with 0°C heights from nearby radiosonde observations and from model analyses for three different regions in the United States. In addition, reflectivity observations from an independent, vertically pointing radar situated in complex terrain are compared with results from the brightband algorithm operating on WSR-88D data. The output from the brightband algorithm matches observations well. A case is presented to show how the radar-observed brightband heights can be used to identify regions in precipitation products where radar is sampling within the melting layer and therefore may be subject to overestimation. Improved monitoring of the bright band, because of the comparatively high temporal resolution of the radar observations, results from application of the algorithm. The algorithm output can provide guidance to forecasters who are using radar-based quantitative precipitation estimates to issue advisories and warnings. Moreover, the melting-layer observations can be used with a digital elevation model to map the approximate rain–snow line.
Abstract
Uncertainty in quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the neighboring pixel ensemble technique (NPET) was devised and implemented as a postprocessing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixel’s physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the U.S. National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context.
Significance Statement
This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is an algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in quantitative precipitation forecasts (QPFs) without the requirement of multiple hydrologic model runs. NPET is capable of this feat through probabilistic assimilation of a priori QPF displacement information and its uncertainty. The application of NPET with the Flooded Locations and Simulated Hydrographs (FLASH) project shows the technique could be beneficial for flash flood warning operations in the U.S. National Weather Service (NWS). It is envisioned that the application of NPET with Warn-on-Forecast System (WoFS)-forced FLASH outputs will further enhance the quality of flash flood forecasts that support NWS warning operations.
Abstract
Uncertainty in quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the neighboring pixel ensemble technique (NPET) was devised and implemented as a postprocessing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixel’s physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the U.S. National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context.
Significance Statement
This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is an algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in quantitative precipitation forecasts (QPFs) without the requirement of multiple hydrologic model runs. NPET is capable of this feat through probabilistic assimilation of a priori QPF displacement information and its uncertainty. The application of NPET with the Flooded Locations and Simulated Hydrographs (FLASH) project shows the technique could be beneficial for flash flood warning operations in the U.S. National Weather Service (NWS). It is envisioned that the application of NPET with Warn-on-Forecast System (WoFS)-forced FLASH outputs will further enhance the quality of flash flood forecasts that support NWS warning operations.
Abstract
A major limitation of improved radar-based rainfall estimation is accurate calibration of radar reflectivity. In this paper, the authors fully automate a polarimetric method that uses the consistency between radar reflectivity, differential reflectivity, and the path integral of specific differential phase to calibrate reflectivity. Complete instructions are provided such that this study can serve as a guide for agencies that are upgrading their radars with polarimetric capabilities and require accurate calibration. The method is demonstrated using data from Météo-France’s operational C-band polarimetric radar. Daily averages of the calibration of radar reflectivity are shown to vary by less than 0.2 dB. In addition to achieving successful calibration, a sensitivity test is also conducted to examine the impacts of using different models relating raindrop oblateness to diameter. It turns out that this study highlights the suitability of the raindrop shape models themselves. Evidence is shown supporting the notion that there is a unique model that relates drop oblateness to diameter in midlatitudes.
Abstract
A major limitation of improved radar-based rainfall estimation is accurate calibration of radar reflectivity. In this paper, the authors fully automate a polarimetric method that uses the consistency between radar reflectivity, differential reflectivity, and the path integral of specific differential phase to calibrate reflectivity. Complete instructions are provided such that this study can serve as a guide for agencies that are upgrading their radars with polarimetric capabilities and require accurate calibration. The method is demonstrated using data from Météo-France’s operational C-band polarimetric radar. Daily averages of the calibration of radar reflectivity are shown to vary by less than 0.2 dB. In addition to achieving successful calibration, a sensitivity test is also conducted to examine the impacts of using different models relating raindrop oblateness to diameter. It turns out that this study highlights the suitability of the raindrop shape models themselves. Evidence is shown supporting the notion that there is a unique model that relates drop oblateness to diameter in midlatitudes.
Abstract
This paper investigates the circumstances of 1,075 fatalities from flash flooding recorded from 1996 to 2014 across the United States. This study provides insights into the situations of the fatality events as determined by the victims’ profile and activity and the spatiotemporal context of the flooding. A reclassification of the individual fatality circumstance (i.e., location and/or activity) is performed to explore statistically the timing, the duration, and location of the flash flood event and the age and gender of the victims. In agreement with other studies, more than 60% of the reported fatalities were related to vehicles involving mainly males. A geospatial analysis indicated these were most common in southern states. Further, 21% of fatalities occurred outdoors, typically in neighborhoods near streams, where the victims were exhibiting high-risk-taking behavior, such as cleaning out drains and even playing in the floodwaters. Human vulnerability varies dynamically on a subdaily basis and depends on social and natural factors of the flash flood. For example, most campsite-related fatalities were associated with very fast-responding flash flood events (less than 5-h duration), occurred more commonly after midnight, and impacted younger females and males alike. On the other hand, fatalities related to inundation of permanent buildings were most commonly associated with longer-duration events and impacted the elderly. Situational rather than generic examination of vulnerability is required to realistically capture risky cases during short-fuse flood events.
The circumstances in which people perished in flash floods suggest that situational rather than generic examination of vulnerability is required to realistically capture risky cases during short-fuse flood events.
Abstract
This paper investigates the circumstances of 1,075 fatalities from flash flooding recorded from 1996 to 2014 across the United States. This study provides insights into the situations of the fatality events as determined by the victims’ profile and activity and the spatiotemporal context of the flooding. A reclassification of the individual fatality circumstance (i.e., location and/or activity) is performed to explore statistically the timing, the duration, and location of the flash flood event and the age and gender of the victims. In agreement with other studies, more than 60% of the reported fatalities were related to vehicles involving mainly males. A geospatial analysis indicated these were most common in southern states. Further, 21% of fatalities occurred outdoors, typically in neighborhoods near streams, where the victims were exhibiting high-risk-taking behavior, such as cleaning out drains and even playing in the floodwaters. Human vulnerability varies dynamically on a subdaily basis and depends on social and natural factors of the flash flood. For example, most campsite-related fatalities were associated with very fast-responding flash flood events (less than 5-h duration), occurred more commonly after midnight, and impacted younger females and males alike. On the other hand, fatalities related to inundation of permanent buildings were most commonly associated with longer-duration events and impacted the elderly. Situational rather than generic examination of vulnerability is required to realistically capture risky cases during short-fuse flood events.
The circumstances in which people perished in flash floods suggest that situational rather than generic examination of vulnerability is required to realistically capture risky cases during short-fuse flood events.
Abstract
Radar measurement uncertainties associated with storm top, cloud top, and other height measurements are well recognized; however, the authors feel the resulting impacts on the trends of storm features are not as well documented or understood by some users of the WSR-88D system. Detailed examination of radar-measured life cycles of thunderstorms occurring in Arizona indicates substantial limitations in the WSR-88D’s capability to depict certain aspects of storm-height attribute evolution (i.e., life cycle) accurately. These inherent limitations are illustrated using a vertical reflectivity structure model for the life cycle of a simple, “single-pulse” thunderstorm. The life cycle of this simple storm is “scanned” at varying ranges and translation speeds. The results show that radar-determined trends are often substantially different from those of the model storm and that in extreme cases the radar-detected storm and the model storm can have trends in storm-top height of opposite sign. Caution is clearly required by both the operational and research users of some products derived from operational WSR-88D data.
Abstract
Radar measurement uncertainties associated with storm top, cloud top, and other height measurements are well recognized; however, the authors feel the resulting impacts on the trends of storm features are not as well documented or understood by some users of the WSR-88D system. Detailed examination of radar-measured life cycles of thunderstorms occurring in Arizona indicates substantial limitations in the WSR-88D’s capability to depict certain aspects of storm-height attribute evolution (i.e., life cycle) accurately. These inherent limitations are illustrated using a vertical reflectivity structure model for the life cycle of a simple, “single-pulse” thunderstorm. The life cycle of this simple storm is “scanned” at varying ranges and translation speeds. The results show that radar-determined trends are often substantially different from those of the model storm and that in extreme cases the radar-detected storm and the model storm can have trends in storm-top height of opposite sign. Caution is clearly required by both the operational and research users of some products derived from operational WSR-88D data.
Abstract
The French operational radar network is being upgraded and expanded from 2002 to 2006 by Meteo-France in partnership with the French Ministry of the Environment. A detailed examination of the quality of the raw polarimetric variables is reported here. The analysis procedures determine the precision of the measurements and quantify errors resulting from miscalibration, near-radome interference, and noise effects. Correction methods to remove biases resulting from effective noise powers in the horizontal and vertical channels, radar miscalibration, and the system offset in differential propagation phase measurements are presented and evaluated. Filtering methods were also required in order to remove azimuthal dependencies discovered with fields of differential reflectivity and differential propagation phase. The developed data quality analysis procedures may be useful to the agencies that are in the process of upgrading their radar networks with dual-polarization capabilities.
Abstract
The French operational radar network is being upgraded and expanded from 2002 to 2006 by Meteo-France in partnership with the French Ministry of the Environment. A detailed examination of the quality of the raw polarimetric variables is reported here. The analysis procedures determine the precision of the measurements and quantify errors resulting from miscalibration, near-radome interference, and noise effects. Correction methods to remove biases resulting from effective noise powers in the horizontal and vertical channels, radar miscalibration, and the system offset in differential propagation phase measurements are presented and evaluated. Filtering methods were also required in order to remove azimuthal dependencies discovered with fields of differential reflectivity and differential propagation phase. The developed data quality analysis procedures may be useful to the agencies that are in the process of upgrading their radar networks with dual-polarization capabilities.
Abstract
As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land–coast–ocean continuum in the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V06B product. It is examined over three coastal regions of the United States—the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, and ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range [25%–39%]), followed by morphing ([20%–34%]), morphing+IR ([17%–27%]) and IR ([11%–16%]) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10%–53%]). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land–coast–ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.
Abstract
As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land–coast–ocean continuum in the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V06B product. It is examined over three coastal regions of the United States—the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, and ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range [25%–39%]), followed by morphing ([20%–34%]), morphing+IR ([17%–27%]) and IR ([11%–16%]) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10%–53%]). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land–coast–ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.
