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Abstract
Participatory modeling is commonly applied in climate change adaptation research to integrate stakeholder knowledge, beliefs, values, and norms into modeling processes. However, participation is not neutral, and current climate change adaptation research is tailored toward those with sufficient resources to adapt, as opposed to those most in need of adaptation. These are commonly marginalized stakeholder groups who remain on the social, economic, and political periphery, driving their vulnerability to climate change impacts. This paper presents the concept of autonomy in the context of multilevel governance for climate change adaptation before examining past participatory modeling approaches, illustrating the lack of application as an emancipatory tool for increasing the autonomy of marginalized stakeholder groups. Therefore, a list of 10 necessary conditions is presented for conducting participatory modeling for increasing the autonomy of marginalized stakeholder groups, strengthening multilevel governance for climate change adaptation. These theoretical foundations are intended to guide public policy and increase the societal impact of participatory modeling.
Significance Statement
If you’re a scholar who studies the social impacts of climate change and you aren’t somehow an activist what are you really?—Professor Kian Goh, University of California, Los Angeles
Abstract
Participatory modeling is commonly applied in climate change adaptation research to integrate stakeholder knowledge, beliefs, values, and norms into modeling processes. However, participation is not neutral, and current climate change adaptation research is tailored toward those with sufficient resources to adapt, as opposed to those most in need of adaptation. These are commonly marginalized stakeholder groups who remain on the social, economic, and political periphery, driving their vulnerability to climate change impacts. This paper presents the concept of autonomy in the context of multilevel governance for climate change adaptation before examining past participatory modeling approaches, illustrating the lack of application as an emancipatory tool for increasing the autonomy of marginalized stakeholder groups. Therefore, a list of 10 necessary conditions is presented for conducting participatory modeling for increasing the autonomy of marginalized stakeholder groups, strengthening multilevel governance for climate change adaptation. These theoretical foundations are intended to guide public policy and increase the societal impact of participatory modeling.
Significance Statement
If you’re a scholar who studies the social impacts of climate change and you aren’t somehow an activist what are you really?—Professor Kian Goh, University of California, Los Angeles
Abstract
This study provides a very clear picture of the microphysics and flow field in a convective storm in the Rondonia region of Brazil through a synthesis of observations from two unique radars, measurements of the surface drop size distribution (DSD), and particle types and sizes from an aircraft penetration. The primary findings are 1) the growth of rain by the collision–coalescence–breakup (CCB) process to equilibrium drop size distributions entirely below the 0°C level; 2) the subsequent growth of larger ice particles (graupel and hail) at subfreezing temperatures; 3) the paucity of lightning activity during the former process, and the increased lightning frequency during the latter; 4) the occurrence of strong downdrafts and a downburst during the latter phase of the storm resulting from cooling by melting and evaporation; 5) the occurrence of turbulence along the main streamlines of the storm; and 6) the confirmation of the large drops reached during the CCB growth by polarimetric radar observations. These interpretations have been made possible by estimating the updraft magnitude using the “lower bound” of the Doppler spectrum at vertical incidence, and identifying the “balance level” at which particles are supported for growth. The combination of these methods shows where raindrops are supported for extended periods to allow their growth to equilibrium drop size distributions, while smaller drops ascend and large ones descend. A hypothesis worthy of pursuit is the control of the storm motion by the winds at the balance level, which is the effective precipitation generating level. Above the 0°C level the balance level separates the small ascending ice crystals from the large descending graupel and hail. Collisions between the two cause electrical charging, while gravity and the updrafts separate the charges to cause lightning. Below the 0°C level, large downward velocities (caused by the above-mentioned cooling) in excess of the terminal fall speeds of raindrops represent the downbursts, which are manifested in the surface winds.
Abstract
This study provides a very clear picture of the microphysics and flow field in a convective storm in the Rondonia region of Brazil through a synthesis of observations from two unique radars, measurements of the surface drop size distribution (DSD), and particle types and sizes from an aircraft penetration. The primary findings are 1) the growth of rain by the collision–coalescence–breakup (CCB) process to equilibrium drop size distributions entirely below the 0°C level; 2) the subsequent growth of larger ice particles (graupel and hail) at subfreezing temperatures; 3) the paucity of lightning activity during the former process, and the increased lightning frequency during the latter; 4) the occurrence of strong downdrafts and a downburst during the latter phase of the storm resulting from cooling by melting and evaporation; 5) the occurrence of turbulence along the main streamlines of the storm; and 6) the confirmation of the large drops reached during the CCB growth by polarimetric radar observations. These interpretations have been made possible by estimating the updraft magnitude using the “lower bound” of the Doppler spectrum at vertical incidence, and identifying the “balance level” at which particles are supported for growth. The combination of these methods shows where raindrops are supported for extended periods to allow their growth to equilibrium drop size distributions, while smaller drops ascend and large ones descend. A hypothesis worthy of pursuit is the control of the storm motion by the winds at the balance level, which is the effective precipitation generating level. Above the 0°C level the balance level separates the small ascending ice crystals from the large descending graupel and hail. Collisions between the two cause electrical charging, while gravity and the updrafts separate the charges to cause lightning. Below the 0°C level, large downward velocities (caused by the above-mentioned cooling) in excess of the terminal fall speeds of raindrops represent the downbursts, which are manifested in the surface winds.
Abstract
A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.
Abstract
A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.
Abstract
A unique set of Doppler and polarimetric radar observations were made of a microburst-producing storm in Amazonia during the Tropical Rainfall Measuring Mission (TRMM) Large-Scale Biosphere–Atmosphere (LBA) field experiment. The key features are high reflectivity (50 dBZ) and modest size hail (up to 0.8 mm) in high liquid water concentrations (>4 g m−3) at the 5-km 0°C level, melting near the 3-km level as evidenced by the Doppler spectrum width on the profiler radar (PR), by differential polarization on the S-band dual-polarized radar (S-POL), and a sharp downward acceleration from 2.8 to 1.6 km to a peak downdraft of 11 m s−1, followed by a weak microburst of 15 m s−1 at the surface. The latter features closely match the initial conditions and results of the Srivastava numerical model of a microburst produced by melting hail. It is suggested that only modest size hail in large concentrations that melt aloft can produce wet microbursts. The narrower the distribution of hail particle sizes, the more confined will be the layer of melting and negative buoyancy, and the more intense the microburst. It is hypothesized that the timing of the conditions leading to the microburst is determined by the occurrence of an updraft of proper magnitude in the layer in which supercooled water accounts for the growth of hail or graupel.
Abstract
A unique set of Doppler and polarimetric radar observations were made of a microburst-producing storm in Amazonia during the Tropical Rainfall Measuring Mission (TRMM) Large-Scale Biosphere–Atmosphere (LBA) field experiment. The key features are high reflectivity (50 dBZ) and modest size hail (up to 0.8 mm) in high liquid water concentrations (>4 g m−3) at the 5-km 0°C level, melting near the 3-km level as evidenced by the Doppler spectrum width on the profiler radar (PR), by differential polarization on the S-band dual-polarized radar (S-POL), and a sharp downward acceleration from 2.8 to 1.6 km to a peak downdraft of 11 m s−1, followed by a weak microburst of 15 m s−1 at the surface. The latter features closely match the initial conditions and results of the Srivastava numerical model of a microburst produced by melting hail. It is suggested that only modest size hail in large concentrations that melt aloft can produce wet microbursts. The narrower the distribution of hail particle sizes, the more confined will be the layer of melting and negative buoyancy, and the more intense the microburst. It is hypothesized that the timing of the conditions leading to the microburst is determined by the occurrence of an updraft of proper magnitude in the layer in which supercooled water accounts for the growth of hail or graupel.
Abstract
The dynamical control of the eddy-induced transport is investigated in a series of idealized eddy-resolving experiments. When there is an active eddy field, the eddy-induced transport is found to correlate with isopycnic gradients of potential vorticity, rather than gradients of layer thickness. For any unforced layers, the eddy transfer leads to a homogenization of potential vorticity and a vanishing of the eddy-induced transport in the final steady state.
Abstract
The dynamical control of the eddy-induced transport is investigated in a series of idealized eddy-resolving experiments. When there is an active eddy field, the eddy-induced transport is found to correlate with isopycnic gradients of potential vorticity, rather than gradients of layer thickness. For any unforced layers, the eddy transfer leads to a homogenization of potential vorticity and a vanishing of the eddy-induced transport in the final steady state.
Abstract
Meteotsunamis are shallow-water waves that, despite often being small (~0.3 m), can cause damage, injuries, and fatalities due to relatively strong currents (>1 m s−1). Previous case studies, modeling, and localized climatologies have indicated that dangerous meteotsunamis can occur across northwest Europe. Using 71 tide gauges across northwest Europe between 2010 and 2017, a regional climatology was made to understand the typical sizes, times, and atmospheric systems that generate meteotsunamis. A total of 349 meteotsunamis (54.0 meteotsunamis per year) were identified with 0.27–0.40-m median wave heights. The largest waves (~1 m high) were measured in France and the Republic of Ireland. Most meteotsunamis were identified in winter (43%–59%), and the fewest identified meteotsunamis occurred in either spring or summer (0%–15%). There was a weak diurnal signal, with most meteotsunami identifications between 1200 and 1859 UTC (30%) and the fewest between 0000 and 0659 UTC (23%). Radar-derived precipitation was used to identify and classify the morphologies of mesoscale precipitating weather systems occurring within 6 h of each meteotsunami. Most mesoscale atmospheric systems were quasi-linear systems (46%) or open-cellular convection (33%), with some nonlinear clusters (17%) and a few isolated cells (4%). These systems occurred under westerly geostrophic flow, with Proudman resonance possible in 43 out of 45 selected meteotsunamis. Because most meteotsunamis occur on cold winter days, with precipitation, and in large tides, wintertime meteotsunamis may be missed by eyewitnesses, helping to explain why previous observationally based case studies of meteotsunamis are documented predominantly in summer.
Abstract
Meteotsunamis are shallow-water waves that, despite often being small (~0.3 m), can cause damage, injuries, and fatalities due to relatively strong currents (>1 m s−1). Previous case studies, modeling, and localized climatologies have indicated that dangerous meteotsunamis can occur across northwest Europe. Using 71 tide gauges across northwest Europe between 2010 and 2017, a regional climatology was made to understand the typical sizes, times, and atmospheric systems that generate meteotsunamis. A total of 349 meteotsunamis (54.0 meteotsunamis per year) were identified with 0.27–0.40-m median wave heights. The largest waves (~1 m high) were measured in France and the Republic of Ireland. Most meteotsunamis were identified in winter (43%–59%), and the fewest identified meteotsunamis occurred in either spring or summer (0%–15%). There was a weak diurnal signal, with most meteotsunami identifications between 1200 and 1859 UTC (30%) and the fewest between 0000 and 0659 UTC (23%). Radar-derived precipitation was used to identify and classify the morphologies of mesoscale precipitating weather systems occurring within 6 h of each meteotsunami. Most mesoscale atmospheric systems were quasi-linear systems (46%) or open-cellular convection (33%), with some nonlinear clusters (17%) and a few isolated cells (4%). These systems occurred under westerly geostrophic flow, with Proudman resonance possible in 43 out of 45 selected meteotsunamis. Because most meteotsunamis occur on cold winter days, with precipitation, and in large tides, wintertime meteotsunamis may be missed by eyewitnesses, helping to explain why previous observationally based case studies of meteotsunamis are documented predominantly in summer.
Abstract
Temperature time series for stations in western North Carolina are used to evaluate the potential for an urban signal in the local temperature trend, and to compare a homogeneous temperature record from a mountain-top station to two versions of the lower-tropospheric, satellite-derived temperatures from the Microwave Sounding Unit (MSU). Results regarding the urban signal are in agreement with the conclusion from previous investigations that after a location is urbanized, the local temperature trend is consistent with trends derived from surrounding, more rural stations. With respect to the mountain top and lower-tropospheric temperature comparison, the magnitudes of the two MSU-derived trends for the western North Carolina area are closer to the average annual minimum temperature trend than to the annual average maximum temperature trend.
Abstract
Temperature time series for stations in western North Carolina are used to evaluate the potential for an urban signal in the local temperature trend, and to compare a homogeneous temperature record from a mountain-top station to two versions of the lower-tropospheric, satellite-derived temperatures from the Microwave Sounding Unit (MSU). Results regarding the urban signal are in agreement with the conclusion from previous investigations that after a location is urbanized, the local temperature trend is consistent with trends derived from surrounding, more rural stations. With respect to the mountain top and lower-tropospheric temperature comparison, the magnitudes of the two MSU-derived trends for the western North Carolina area are closer to the average annual minimum temperature trend than to the annual average maximum temperature trend.
Abstract
On the morning of 23 June 2016, a 0.70-m meteotsunami was observed in the English Channel between the United Kingdom and France. This wave was measured by several tide gauges and coincided with a heavily precipitating convective system producing 10 m s−1 wind speeds at the 10-m level and 1–2.5-hPa surface pressure anomalies. A combination of precipitation rate cross correlations and NCEP–NCAR Reanalysis 1 data showed that the convective system moved northeastward at 19 ± 2 m s−1. To model the meteotsunami, the finite element model Telemac was forced with an ensemble of prescribed pressure forcings, covering observational uncertainty. Ensembles simulated the observed wave period and arrival times within minutes and wave heights within tens of centimeters. A directly forced wave and a secondary coastal wave were simulated, and these amplified as they propagated. Proudman resonance was responsible for the wave amplification, and the coastal wave resulted from strong refraction of the primary wave. The main generating mechanism was the atmospheric pressure anomaly with wind stress playing a secondary role, increasing the first wave peak by 16% on average. Certain tidal conditions reduced modeled wave heights by up to 56%, by shifting the location where Proudman resonance occurred. This shift was mainly from tidal currents rather than tidal elevation directly affecting shallow-water wave speed. An improved understanding of meteotsunami return periods and generation mechanisms would be aided by tide gauge measurements sampled at less than 15-min intervals.
Abstract
On the morning of 23 June 2016, a 0.70-m meteotsunami was observed in the English Channel between the United Kingdom and France. This wave was measured by several tide gauges and coincided with a heavily precipitating convective system producing 10 m s−1 wind speeds at the 10-m level and 1–2.5-hPa surface pressure anomalies. A combination of precipitation rate cross correlations and NCEP–NCAR Reanalysis 1 data showed that the convective system moved northeastward at 19 ± 2 m s−1. To model the meteotsunami, the finite element model Telemac was forced with an ensemble of prescribed pressure forcings, covering observational uncertainty. Ensembles simulated the observed wave period and arrival times within minutes and wave heights within tens of centimeters. A directly forced wave and a secondary coastal wave were simulated, and these amplified as they propagated. Proudman resonance was responsible for the wave amplification, and the coastal wave resulted from strong refraction of the primary wave. The main generating mechanism was the atmospheric pressure anomaly with wind stress playing a secondary role, increasing the first wave peak by 16% on average. Certain tidal conditions reduced modeled wave heights by up to 56%, by shifting the location where Proudman resonance occurred. This shift was mainly from tidal currents rather than tidal elevation directly affecting shallow-water wave speed. An improved understanding of meteotsunami return periods and generation mechanisms would be aided by tide gauge measurements sampled at less than 15-min intervals.
Abstract
Doppler radar profilers are widely used for routine measurement of wind, especially in the lower troposphere. The same profilers with minor modifications are useful tools for precipitation research. Specifically, the profilers are now increasingly being used to explore the structure of precipitating cloud systems and to provide calibration and validation of other instruments used in precipitation research, including scanning radars and active and passive satellite-borne sensors. A vertically directed profiler is capable of resolving the vertical structure of precipitating cloud systems that pass overhead. Standard profiler measurements include reflectivity, reflectivity-weighted Doppler velocity, and spectral width. This paper presents profiler observations of precipitating cloud systems observed during Tropical Rainfall Measuring Mission (TRMM) Ground Validation field campaigns. The observations show similarities and differences between convective systems observed in Florida; Brazil; and Kwajalein, Republic of the Marshall Islands. In addition, it is shown how a profiler can be calibrated using a collocated Joss–Waldvogel disdrometer, how the profiler can then be used to calibrate a scanning radar, and how the profiler may be used to retrieve drop size distributions.
Abstract
Doppler radar profilers are widely used for routine measurement of wind, especially in the lower troposphere. The same profilers with minor modifications are useful tools for precipitation research. Specifically, the profilers are now increasingly being used to explore the structure of precipitating cloud systems and to provide calibration and validation of other instruments used in precipitation research, including scanning radars and active and passive satellite-borne sensors. A vertically directed profiler is capable of resolving the vertical structure of precipitating cloud systems that pass overhead. Standard profiler measurements include reflectivity, reflectivity-weighted Doppler velocity, and spectral width. This paper presents profiler observations of precipitating cloud systems observed during Tropical Rainfall Measuring Mission (TRMM) Ground Validation field campaigns. The observations show similarities and differences between convective systems observed in Florida; Brazil; and Kwajalein, Republic of the Marshall Islands. In addition, it is shown how a profiler can be calibrated using a collocated Joss–Waldvogel disdrometer, how the profiler can then be used to calibrate a scanning radar, and how the profiler may be used to retrieve drop size distributions.
Abstract
The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.
Abstract
The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.
