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Abstract
Satellite multisensor precipitation products (SMPPs) have a variety of potential uses but suffer from relatively poor accuracy due to systematic biases and random errors in precipitation occurrence and magnitude. The censored, shifted gamma distribution (CSGD) is used here to characterize the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis (TMPA), a commonly used SMPP, and to compare it against the rain gauge–based North American Land Data Assimilation System phase 2 (NLDAS-2) reference precipitation dataset across the conterminous United States. The CSGD describes both the occurrence and the magnitude of precipitation. Climatological CSGD characterization reveals significant regional differences between TMPA and NLDAS-2 in terms of magnitude and probability of occurrence. A flexible CSGD-based error modeling framework is also used to quantify errors in TMPA relative to NLDAS-2. The framework can model conditional bias as either a linear or nonlinear function of satellite precipitation rate and can produce a “conditional CSGD” describing the distribution of “true” precipitation based on a satellite observation. The framework is also used to “merge” TMPA with atmospheric variables from version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) to reduce SMPP errors. Despite the coarse resolution of MERRA-2, this merging offers robust reductions in random error due to the better performance of numerical models in resolving stratiform precipitation. Improvements in the near-real-time version of TMPA are relatively greater than for the higher-latency research version.
Abstract
Satellite multisensor precipitation products (SMPPs) have a variety of potential uses but suffer from relatively poor accuracy due to systematic biases and random errors in precipitation occurrence and magnitude. The censored, shifted gamma distribution (CSGD) is used here to characterize the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis (TMPA), a commonly used SMPP, and to compare it against the rain gauge–based North American Land Data Assimilation System phase 2 (NLDAS-2) reference precipitation dataset across the conterminous United States. The CSGD describes both the occurrence and the magnitude of precipitation. Climatological CSGD characterization reveals significant regional differences between TMPA and NLDAS-2 in terms of magnitude and probability of occurrence. A flexible CSGD-based error modeling framework is also used to quantify errors in TMPA relative to NLDAS-2. The framework can model conditional bias as either a linear or nonlinear function of satellite precipitation rate and can produce a “conditional CSGD” describing the distribution of “true” precipitation based on a satellite observation. The framework is also used to “merge” TMPA with atmospheric variables from version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) to reduce SMPP errors. Despite the coarse resolution of MERRA-2, this merging offers robust reductions in random error due to the better performance of numerical models in resolving stratiform precipitation. Improvements in the near-real-time version of TMPA are relatively greater than for the higher-latency research version.
Abstract
Diagnostic equations relating the zonally averaged overturning circulation to north–south density variations are derived and used to determine a new closure scheme for use in zonally averaged ocean models. The presentation clarifies the dynamical link between vorticity dissipation in the western boundary layer and the meridional overturning circulation and provides insight into the relevance and limitations of zonally averaged models. The new closure scheme is substantially different from that introduced by Wright and Stocker. In particular, it includes nonlocal effects, which are potentially important. The model formulation results in a solution with three free parameters. A parameter representing the damping associated with meridional velocity gradients has very little effect on the solution and could be set to zero. The other two are used to tune the model to agree with results of a general circulation model. Good agreement is achieved for values consistent with expectations. The closure scheme is implemented in the model of Wright and Stocker, and the sensitivity to the new model parameters is examined. The derivation and model tests give additional support for the use of zonally averaged models in studies of the climate system.
Abstract
Diagnostic equations relating the zonally averaged overturning circulation to north–south density variations are derived and used to determine a new closure scheme for use in zonally averaged ocean models. The presentation clarifies the dynamical link between vorticity dissipation in the western boundary layer and the meridional overturning circulation and provides insight into the relevance and limitations of zonally averaged models. The new closure scheme is substantially different from that introduced by Wright and Stocker. In particular, it includes nonlocal effects, which are potentially important. The model formulation results in a solution with three free parameters. A parameter representing the damping associated with meridional velocity gradients has very little effect on the solution and could be set to zero. The other two are used to tune the model to agree with results of a general circulation model. Good agreement is achieved for values consistent with expectations. The closure scheme is implemented in the model of Wright and Stocker, and the sensitivity to the new model parameters is examined. The derivation and model tests give additional support for the use of zonally averaged models in studies of the climate system.
Abstract
Intensification of extreme rainfall due to climate change means that federally published rainfall metrics such as the “100-yr storm” are outdated throughout much of the United States. Given their central role in a wide range of infrastructure designs and risk management decisions, updating these metrics to reflect recent and future changes is essential to protect communities. There have been considerable advances in recent years in data collection, statistical methods, and climate modeling that can now be brought to bear on the problem. Scientists must take a lead in this updating process, which should be open, inclusive, and leverage recent scientific advances.
Abstract
Intensification of extreme rainfall due to climate change means that federally published rainfall metrics such as the “100-yr storm” are outdated throughout much of the United States. Given their central role in a wide range of infrastructure designs and risk management decisions, updating these metrics to reflect recent and future changes is essential to protect communities. There have been considerable advances in recent years in data collection, statistical methods, and climate modeling that can now be brought to bear on the problem. Scientists must take a lead in this updating process, which should be open, inclusive, and leverage recent scientific advances.
Abstract
The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.
Abstract
The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.
Abstract
Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty.
Abstract
Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty.
Abstract
The authors examine the hydroclimatology, hydrometeorology, and hydrology of extreme floods through analyses that center on the June 2008 flooding in Iowa. The most striking feature of the June 2008 flooding was the flood peak of the Cedar River at Cedar Rapids (3964 m3 s−1), which was almost twice the previous maximum from a record of 110 years. The spatial extent of extreme flooding was exceptional, with more U.S. Geological Survey stream gauging stations reporting record flood peaks than in any other year. The 2008 flooding was produced by a sequence of organized thunderstorm systems over a period of two weeks. The authors examine clustering and seasonality of flooding in the Iowa study region and link these properties to features of the June 2008 flood event. They examine the environment of heavy rainfall in Iowa during June 2008 through analyses of composite rainfall fields (15-min time interval and 1-km spatial resolution) developed with the Hydro-NEXRAD system and simulations using the Weather Research and Forecasting Model (WRF). Water balance analyses of extreme flood response, based on rainfall and discharge observations from basins with extreme flooding, suggest that antecedent soil moisture plays a diminishing role in flood response as the return interval increases. Rainfall structure and evolution play a critical and poorly understood role in determining the scaling of flood response. As in other extreme flood studies, analyses of the Iowa flood data suggest that measurement errors can be significant for record discharge estimates.
Abstract
The authors examine the hydroclimatology, hydrometeorology, and hydrology of extreme floods through analyses that center on the June 2008 flooding in Iowa. The most striking feature of the June 2008 flooding was the flood peak of the Cedar River at Cedar Rapids (3964 m3 s−1), which was almost twice the previous maximum from a record of 110 years. The spatial extent of extreme flooding was exceptional, with more U.S. Geological Survey stream gauging stations reporting record flood peaks than in any other year. The 2008 flooding was produced by a sequence of organized thunderstorm systems over a period of two weeks. The authors examine clustering and seasonality of flooding in the Iowa study region and link these properties to features of the June 2008 flood event. They examine the environment of heavy rainfall in Iowa during June 2008 through analyses of composite rainfall fields (15-min time interval and 1-km spatial resolution) developed with the Hydro-NEXRAD system and simulations using the Weather Research and Forecasting Model (WRF). Water balance analyses of extreme flood response, based on rainfall and discharge observations from basins with extreme flooding, suggest that antecedent soil moisture plays a diminishing role in flood response as the return interval increases. Rainfall structure and evolution play a critical and poorly understood role in determining the scaling of flood response. As in other extreme flood studies, analyses of the Iowa flood data suggest that measurement errors can be significant for record discharge estimates.
Abstract
Recent tropical cyclones (TCs) have highlighted the hazards that TC rainfall poses to human life and property. These hazards are not adequately conveyed by the commonly used Saffir–Simpson scale. Additionally, while recurrence intervals (or, their inverse, annual exceedance probabilities) are sometimes used in the popular media to convey the magnitude and likelihood of extreme rainfall and floods, these concepts are often misunderstood by the public and have important statistical limitations. We introduce an alternative metric—the extreme rain multiplier (ERM), which expresses TC rainfall as a multiple of the climatologically derived 2-yr rainfall value. ERM allows individuals to connect (“anchor,” in cognitive psychology terms) the magnitude of a TC rainfall event to the magnitude of rain events that are more typically experienced in their area. A retrospective analysis of ERM values for TCs from 1948 to 2017 demonstrates the utility of the metric as a hazard quantification and communication tool. Hurricane Harvey (2017) had the highest ERM value during this period, underlining the storm’s extreme nature. ERM correctly identifies damaging historical TC rainfall events that would have been classified as “weak” using wind-based metrics. The analysis also reveals that the distribution of ERM maxima is similar throughout the eastern and southern United States, allowing for both the accurate identification of locally extreme rainfall events and the development of regional-scale (rather than local-scale) recurrence interval estimates for extreme TC rainfall. Last, an analysis of precipitation forecast data for Hurricane Florence (2018) demonstrates ERM’s ability to characterize Florence’s extreme rainfall hazard in the days preceding landfall.
Abstract
Recent tropical cyclones (TCs) have highlighted the hazards that TC rainfall poses to human life and property. These hazards are not adequately conveyed by the commonly used Saffir–Simpson scale. Additionally, while recurrence intervals (or, their inverse, annual exceedance probabilities) are sometimes used in the popular media to convey the magnitude and likelihood of extreme rainfall and floods, these concepts are often misunderstood by the public and have important statistical limitations. We introduce an alternative metric—the extreme rain multiplier (ERM), which expresses TC rainfall as a multiple of the climatologically derived 2-yr rainfall value. ERM allows individuals to connect (“anchor,” in cognitive psychology terms) the magnitude of a TC rainfall event to the magnitude of rain events that are more typically experienced in their area. A retrospective analysis of ERM values for TCs from 1948 to 2017 demonstrates the utility of the metric as a hazard quantification and communication tool. Hurricane Harvey (2017) had the highest ERM value during this period, underlining the storm’s extreme nature. ERM correctly identifies damaging historical TC rainfall events that would have been classified as “weak” using wind-based metrics. The analysis also reveals that the distribution of ERM maxima is similar throughout the eastern and southern United States, allowing for both the accurate identification of locally extreme rainfall events and the development of regional-scale (rather than local-scale) recurrence interval estimates for extreme TC rainfall. Last, an analysis of precipitation forecast data for Hurricane Florence (2018) demonstrates ERM’s ability to characterize Florence’s extreme rainfall hazard in the days preceding landfall.
Abstract
The authors examine the hydroclimatology, hydrometeorology, and hydrology of flooding in the Milwaukee metropolitan region of the upper midwestern United States. The objectives of this study are 1) to assess nonstationarities in flood frequency associated with urban transformation of land surface properties and climate change and 2) to examine how spatial heterogeneity in land surface properties and heavy rainfall climatology interact to determine floods in urbanizing areas. The authors focus on the Menomonee River basin, which drains much of the urban core of Milwaukee, and the adjacent Cedar Creek basin, where agricultural land use dominates. Results are based on analyses of bias-corrected, high-resolution (1-km2 spatial resolution and 15-min time resolution) radar rainfall fields that are developed using the Hydro-NEXRAD system, rainfall observations from a network of 21 rain gauges in the Milwaukee metropolitan region, and discharge observations from 11 U.S. Geological Survey stream gauging stations. Both annual flood peak magnitudes and annual peaks over threshold flood counts have increased for the Menomonee River basin during the past five decades, and these trends are accompanied by a transition of flood events dominated by snowmelt (March–April floods) to a regime in which warm season thunderstorms are the dominant flood-producing agents. The frequency of heavy rainfall events has increased significantly. The spatial distribution of rainfall for flood-producing storms in the Milwaukee study region exhibits striking spatial heterogeneity, with a maximum in the central portion of the Menomonee River basin. Storm event hydrologic response is determined by the interactions of spatial patterns of urbanization and rainfall distribution in the Menomonee River basin.
Abstract
The authors examine the hydroclimatology, hydrometeorology, and hydrology of flooding in the Milwaukee metropolitan region of the upper midwestern United States. The objectives of this study are 1) to assess nonstationarities in flood frequency associated with urban transformation of land surface properties and climate change and 2) to examine how spatial heterogeneity in land surface properties and heavy rainfall climatology interact to determine floods in urbanizing areas. The authors focus on the Menomonee River basin, which drains much of the urban core of Milwaukee, and the adjacent Cedar Creek basin, where agricultural land use dominates. Results are based on analyses of bias-corrected, high-resolution (1-km2 spatial resolution and 15-min time resolution) radar rainfall fields that are developed using the Hydro-NEXRAD system, rainfall observations from a network of 21 rain gauges in the Milwaukee metropolitan region, and discharge observations from 11 U.S. Geological Survey stream gauging stations. Both annual flood peak magnitudes and annual peaks over threshold flood counts have increased for the Menomonee River basin during the past five decades, and these trends are accompanied by a transition of flood events dominated by snowmelt (March–April floods) to a regime in which warm season thunderstorms are the dominant flood-producing agents. The frequency of heavy rainfall events has increased significantly. The spatial distribution of rainfall for flood-producing storms in the Milwaukee study region exhibits striking spatial heterogeneity, with a maximum in the central portion of the Menomonee River basin. Storm event hydrologic response is determined by the interactions of spatial patterns of urbanization and rainfall distribution in the Menomonee River basin.
Abstract
The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.
Abstract
The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.
Abstract
The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.
Abstract
The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.
