Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Abstract
Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada Mountains. Rain-on-snow (ROS) events are the common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981-2019. Observations across a 500 m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a 1-dimensional, physically-based snow model, initiated with the Richards Equation and calibrated with co-located snow pillow observations. We compare observed ‘historical’ and ‘scenario’ ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include: cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g/cm 3) and colder (>0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding.
Abstract
Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada Mountains. Rain-on-snow (ROS) events are the common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981-2019. Observations across a 500 m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a 1-dimensional, physically-based snow model, initiated with the Richards Equation and calibrated with co-located snow pillow observations. We compare observed ‘historical’ and ‘scenario’ ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include: cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g/cm 3) and colder (>0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding.
Abstract
Fire emissions from the Maritime Continent (MC) over the western tropical Pacific are strongly influenced by El Niño–Southern Oscillation (ENSO), posing various climate effect to the Earth system. In this study, we show that the historical biomass burning emissions of black carbon (BCbb) aerosol in the dry season from the MC are strengthened in El Niño years due to the dry conditions. The Eastern-Pacific type of El Niño exerts a stronger modulation in BCbb emissions over the MC region than the Central-Pacific type of El Niño. Based on simulations using the fully coupled Community Earth System Model (CESM), the impacts of increased BCbb emissions on ENSO variability and frequency are also investigated in this study. With BCbb emissions from the MC scaled up by a factor of 10, which enables the identification of climate response from the internal variability, the increased BCbb heats the local atmosphere and changes land-sea thermal contrast, which suppresses the westward transport of the eastern Pacific surface water. It leads to an increase of sea surface temperature in the eastern tropical Pacific, which further enhances ENSO variability and increases the frequency of extreme El Niño and La Niña events. This study highlights the potential role of BCbb emissions on extreme ENSO frequency and this role may be increasingly important in the warming future with higher wildfire risks.
Abstract
Fire emissions from the Maritime Continent (MC) over the western tropical Pacific are strongly influenced by El Niño–Southern Oscillation (ENSO), posing various climate effect to the Earth system. In this study, we show that the historical biomass burning emissions of black carbon (BCbb) aerosol in the dry season from the MC are strengthened in El Niño years due to the dry conditions. The Eastern-Pacific type of El Niño exerts a stronger modulation in BCbb emissions over the MC region than the Central-Pacific type of El Niño. Based on simulations using the fully coupled Community Earth System Model (CESM), the impacts of increased BCbb emissions on ENSO variability and frequency are also investigated in this study. With BCbb emissions from the MC scaled up by a factor of 10, which enables the identification of climate response from the internal variability, the increased BCbb heats the local atmosphere and changes land-sea thermal contrast, which suppresses the westward transport of the eastern Pacific surface water. It leads to an increase of sea surface temperature in the eastern tropical Pacific, which further enhances ENSO variability and increases the frequency of extreme El Niño and La Niña events. This study highlights the potential role of BCbb emissions on extreme ENSO frequency and this role may be increasingly important in the warming future with higher wildfire risks.
Abstract
Common disaster phase models provide a useful heuristic for understanding how disasters evolve, but they do not adequately characterize the transitions between phases, such as the forecast and warning phase of predictable disasters. In this study, we use tweets posted by professional sources of meteorological information in Florida during Hurricane Irma (2017) to understand how visual risk communication evolves during this transition. We identify four sub-phases of the forecast and warning phase: the hypothetical threat, actualized threat, looming threat, and impact sub-phases. Each sub-phase is denoted by changes in the kinds of visual risk information disseminated by professional sources and retransmitted by the public, which are often driven by new information provided by the U.S. National Weather Service. Additionally, we use regression analysis to understand the impact of tweet timing, content, risk visualization and other factors on tweet retransmission across Irma’s forecast and warning phase. We find that Cone, Satellite, and Spaghetti Plots imagery are retweeted more, while Watch/Warning imagery is retweeted less. In addition, manually generated tweets are retweeted more than automated tweets. These results highlight several information needs to incorporate into the current NWS hurricane forecast visualization suite, such as uncertainty and hazard-specific information at longer lead-times, and the importance of investigating the effectiveness of different social media posting strategies. Our results also demonstrate the roles and responsibilities that professional sources engage in during these sub-phases, which builds understanding of disasters by contextualizing the sub-phases along the transition from long-term preparedness to post-event response and recovery.
Abstract
Common disaster phase models provide a useful heuristic for understanding how disasters evolve, but they do not adequately characterize the transitions between phases, such as the forecast and warning phase of predictable disasters. In this study, we use tweets posted by professional sources of meteorological information in Florida during Hurricane Irma (2017) to understand how visual risk communication evolves during this transition. We identify four sub-phases of the forecast and warning phase: the hypothetical threat, actualized threat, looming threat, and impact sub-phases. Each sub-phase is denoted by changes in the kinds of visual risk information disseminated by professional sources and retransmitted by the public, which are often driven by new information provided by the U.S. National Weather Service. Additionally, we use regression analysis to understand the impact of tweet timing, content, risk visualization and other factors on tweet retransmission across Irma’s forecast and warning phase. We find that Cone, Satellite, and Spaghetti Plots imagery are retweeted more, while Watch/Warning imagery is retweeted less. In addition, manually generated tweets are retweeted more than automated tweets. These results highlight several information needs to incorporate into the current NWS hurricane forecast visualization suite, such as uncertainty and hazard-specific information at longer lead-times, and the importance of investigating the effectiveness of different social media posting strategies. Our results also demonstrate the roles and responsibilities that professional sources engage in during these sub-phases, which builds understanding of disasters by contextualizing the sub-phases along the transition from long-term preparedness to post-event response and recovery.
Corrections for Geostationary Cloud Liquid Water Path Using Microwave ImageryAbstract
Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
Significance Statement
Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.
Abstract
Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
Significance Statement
Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.
Abstract
This study identifies that cold surges over the South China Sea (SCS) have experienced a significant change on decadal time scales. The results indicate that cold surges occur more frequently after the early 2000s than before and are at least partially explained by changes in circulation patterns. Both the negative phase of the Scandinavian (SCA) pattern and the cold phase of Interdecadal Pacific Oscillation (IPO) can induce increased cold surges and the IPO effect dominates in recent decades. When the IPO shifts to its cold phase, low-level cyclones are induced over the western North Pacific through a Gill response. The northeasterlies along the northwest flank of the cyclones further lead to intensified cold surges over the SCS. The above processes can be reproduced in coupled models, suggesting a robust connection between IPO and cold surges. The present findings highlight the role of tropical forcing and bring an insight into understanding of the future climate variability and change over East Asia during boreal winter.
Abstract
This study identifies that cold surges over the South China Sea (SCS) have experienced a significant change on decadal time scales. The results indicate that cold surges occur more frequently after the early 2000s than before and are at least partially explained by changes in circulation patterns. Both the negative phase of the Scandinavian (SCA) pattern and the cold phase of Interdecadal Pacific Oscillation (IPO) can induce increased cold surges and the IPO effect dominates in recent decades. When the IPO shifts to its cold phase, low-level cyclones are induced over the western North Pacific through a Gill response. The northeasterlies along the northwest flank of the cyclones further lead to intensified cold surges over the SCS. The above processes can be reproduced in coupled models, suggesting a robust connection between IPO and cold surges. The present findings highlight the role of tropical forcing and bring an insight into understanding of the future climate variability and change over East Asia during boreal winter.
Abstract
The spectrum of kinetic energy of vertical motions (VKE) is less well understood compared to the kinetic energy spectrum of horizontal motions (HKE). One challenge that has limited progress in describing the VKE spectrum is a lack of a unified approach to the decomposition of vertical velocities associated with the Rossby motions and inertia-gravity (IG) wave flows. This paper presents such a unified approach using a linear Rossby-IG vertical velocity normal-mode decomposition appropriate for a spherical, hydrostatic atmosphere.
New theoretical developments show that for every zonal wavenumber k, the limit VKE is proportional to the total mechanical energy and to the square of the frequency of the normal mode. The theory predicts a VKE ∝ k −5 and a VKE ∝ k 1/3 power law for the Rossby and IG waves, assuming a k −3 and a k −5/3 power law for the Rossby and IG HKE spectra, respectively. The Kelvin and mixed Rossby-gravity wave VKE spectra are predicted to follow k −1 and k −5 power laws, respectively. The VKE spectra for ERA5 analyses from August 2018 show that the Rossby VKE spectra approximately follow the predicted a k −5 power law. The expected k 1/3 power law for the gravity wave VKE spectrum is found only in the SH midlatitude stratosphere for k ≈ 10−60. The inertial range IG VKE spectra in the tropical and midlatitude troposphere reflect a mixture of ageostrophic and convection-coupled dynamics and have slopes between −1 and −1/3, likely associated with too steep IG HKE spectra. The forcing by quasi-geostrophic ageostrophic motions is seen as an IG VKE peak at synoptic scales in the SH upper troposphere which gradually moves to planetary scales in the stratosphere.
Abstract
The spectrum of kinetic energy of vertical motions (VKE) is less well understood compared to the kinetic energy spectrum of horizontal motions (HKE). One challenge that has limited progress in describing the VKE spectrum is a lack of a unified approach to the decomposition of vertical velocities associated with the Rossby motions and inertia-gravity (IG) wave flows. This paper presents such a unified approach using a linear Rossby-IG vertical velocity normal-mode decomposition appropriate for a spherical, hydrostatic atmosphere.
New theoretical developments show that for every zonal wavenumber k, the limit VKE is proportional to the total mechanical energy and to the square of the frequency of the normal mode. The theory predicts a VKE ∝ k −5 and a VKE ∝ k 1/3 power law for the Rossby and IG waves, assuming a k −3 and a k −5/3 power law for the Rossby and IG HKE spectra, respectively. The Kelvin and mixed Rossby-gravity wave VKE spectra are predicted to follow k −1 and k −5 power laws, respectively. The VKE spectra for ERA5 analyses from August 2018 show that the Rossby VKE spectra approximately follow the predicted a k −5 power law. The expected k 1/3 power law for the gravity wave VKE spectrum is found only in the SH midlatitude stratosphere for k ≈ 10−60. The inertial range IG VKE spectra in the tropical and midlatitude troposphere reflect a mixture of ageostrophic and convection-coupled dynamics and have slopes between −1 and −1/3, likely associated with too steep IG HKE spectra. The forcing by quasi-geostrophic ageostrophic motions is seen as an IG VKE peak at synoptic scales in the SH upper troposphere which gradually moves to planetary scales in the stratosphere.
Abstract
A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers select cooling center locations in response to extreme heat.
Abstract
A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers select cooling center locations in response to extreme heat.
