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- Author or Editor: Heike Kalesse x
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Abstract
Ice cloud properties are influenced by cloud-scale vertical air motion. Dynamical properties of ice clouds can be determined via Doppler measurements from ground-based, profiling cloud radars. Here, the decomposition of the Doppler velocities into reflectivity-weighted particle velocity Vt and vertical air motion w is described. The methodology is applied to high clouds observations from 35-GHz profiling millimeter wavelength radars at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) climate research facility in Oklahoma (January 1997–December 2010) and the ARM Tropical Western Pacific (TWP) site in Manus (July 1999–December 2010). The Doppler velocity measurements are used to detect gravity waves (GW), whose correlation with high cloud macrophysical properties is investigated. Cloud turbulence is studied in the absence and presence of GW. High clouds are less turbulent when GW are observed. Probability density functions of Vt , w, and high cloud macrophysical properties for the two cloud subsets (with and without GW) are presented. Air-density-corrected Vt for high clouds for which GW (no GW) were detected amounted to hourly means and standard deviations of 0.89 ± 0.52 m s−1 (0.8 ± 0.48 m s−1) and 1.03 ± 0.41 m s−1 (0.86 ± 0.49 m s−1) at SGP and Manus, respectively. The error of w at one standard deviation was estimated as 0.15 m s−1. Hourly means of w averaged around 0 m s−1 with standard deviations of ±0.27 (SGP) and ±0.29 m s−1 (Manus) for high clouds without GW and ±0.22 m s−1 (both sites) for high clouds with GW. The midlatitude site showed stronger seasonality in detected high cloud properties.
Abstract
Ice cloud properties are influenced by cloud-scale vertical air motion. Dynamical properties of ice clouds can be determined via Doppler measurements from ground-based, profiling cloud radars. Here, the decomposition of the Doppler velocities into reflectivity-weighted particle velocity Vt and vertical air motion w is described. The methodology is applied to high clouds observations from 35-GHz profiling millimeter wavelength radars at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) climate research facility in Oklahoma (January 1997–December 2010) and the ARM Tropical Western Pacific (TWP) site in Manus (July 1999–December 2010). The Doppler velocity measurements are used to detect gravity waves (GW), whose correlation with high cloud macrophysical properties is investigated. Cloud turbulence is studied in the absence and presence of GW. High clouds are less turbulent when GW are observed. Probability density functions of Vt , w, and high cloud macrophysical properties for the two cloud subsets (with and without GW) are presented. Air-density-corrected Vt for high clouds for which GW (no GW) were detected amounted to hourly means and standard deviations of 0.89 ± 0.52 m s−1 (0.8 ± 0.48 m s−1) and 1.03 ± 0.41 m s−1 (0.86 ± 0.49 m s−1) at SGP and Manus, respectively. The error of w at one standard deviation was estimated as 0.15 m s−1. Hourly means of w averaged around 0 m s−1 with standard deviations of ±0.27 (SGP) and ±0.29 m s−1 (Manus) for high clouds without GW and ±0.22 m s−1 (both sites) for high clouds with GW. The midlatitude site showed stronger seasonality in detected high cloud properties.
Abstract
Atmospheric models with typical resolution in the tenths of kilometers cannot resolve the dynamics of air parcel ascent, which varies on scales ranging from tens to hundreds of meters. Small-scale wind fluctuations are thus characterized by a subgrid distribution of vertical wind velocity, W, with standard deviation σW . The parameterization of σW is fundamental to the representation of aerosol-cloud interactions, yet it is poorly constrained. Using a novel deep learning technique, this work develops a new parameterization for σW merging data from global storm-resolving model simulations, high-frequency retrievals of W, and climate reanalysis products. The parameterization reproduces the observed statistics of σW and leverages learned physical relations from the model simulations to guide extrapolation beyond the observed domain. Incorporating observational data during the training phase was found to be critical for its performance. The parameterization can be applied online within large-scale atmospheric models, or offline using output from weather forecasting and reanalysis products.
Abstract
Atmospheric models with typical resolution in the tenths of kilometers cannot resolve the dynamics of air parcel ascent, which varies on scales ranging from tens to hundreds of meters. Small-scale wind fluctuations are thus characterized by a subgrid distribution of vertical wind velocity, W, with standard deviation σW . The parameterization of σW is fundamental to the representation of aerosol-cloud interactions, yet it is poorly constrained. Using a novel deep learning technique, this work develops a new parameterization for σW merging data from global storm-resolving model simulations, high-frequency retrievals of W, and climate reanalysis products. The parameterization reproduces the observed statistics of σW and leverages learned physical relations from the model simulations to guide extrapolation beyond the observed domain. Incorporating observational data during the training phase was found to be critical for its performance. The parameterization can be applied online within large-scale atmospheric models, or offline using output from weather forecasting and reanalysis products.
Abstract
Observations from the Atmospheric Radiation Measurement Program (ARM) site at Manus Island in the western Pacific and (re)analysis products are used to investigate moistening by shallow cumulus clouds and by the circulation in large-scale convective events. Large-scale convective events are defined as rainfall anomalies larger than one standard deviation for a minimum of three consecutive days over a 10° × 10° domain centered at Manus. These events are categorized into two groups: Madden–Julian oscillation (MJO) events, with eastward propagation, and non-MJO events, without propagation. Shallow cumulus clouds are identified as continuous time–height echoes from 1-min cloud radar observations with their tops below the freezing level and their bases within the boundary layer. Daily moistening tendencies of shallow clouds, estimated from differences between their mean liquid water content and precipitation over their presumed life spans, and those of physical processes and advection from (re)analysis products are compared with local moistening tendencies from soundings. Increases in low-level moisture before rainfall peaks of MJO and non-MJO events are evident in both observations and reanalyses. Before and after the rainfall peaks of these events, precipitating and nonprecipitating shallow clouds exist all the time, but their occurrence fluctuates randomly. Their contributions to moisture tendencies through evaporation of condensed water are evident. These clouds provide perpetual background moistening to the lower troposphere but do not cause the observed increase in low-level moisture leading to rainfall peaks. Such moisture increase is mainly caused by anomalous nonlinear zonal advection.
Abstract
Observations from the Atmospheric Radiation Measurement Program (ARM) site at Manus Island in the western Pacific and (re)analysis products are used to investigate moistening by shallow cumulus clouds and by the circulation in large-scale convective events. Large-scale convective events are defined as rainfall anomalies larger than one standard deviation for a minimum of three consecutive days over a 10° × 10° domain centered at Manus. These events are categorized into two groups: Madden–Julian oscillation (MJO) events, with eastward propagation, and non-MJO events, without propagation. Shallow cumulus clouds are identified as continuous time–height echoes from 1-min cloud radar observations with their tops below the freezing level and their bases within the boundary layer. Daily moistening tendencies of shallow clouds, estimated from differences between their mean liquid water content and precipitation over their presumed life spans, and those of physical processes and advection from (re)analysis products are compared with local moistening tendencies from soundings. Increases in low-level moisture before rainfall peaks of MJO and non-MJO events are evident in both observations and reanalyses. Before and after the rainfall peaks of these events, precipitating and nonprecipitating shallow clouds exist all the time, but their occurrence fluctuates randomly. Their contributions to moisture tendencies through evaporation of condensed water are evident. These clouds provide perpetual background moistening to the lower troposphere but do not cause the observed increase in low-level moisture leading to rainfall peaks. Such moisture increase is mainly caused by anomalous nonlinear zonal advection.
Abstract
Understanding phase transitions in mixed-phase clouds is of great importance because the hydrometeor phase controls the lifetime and radiative effects of clouds. In high latitudes, these cloud radiative effects have a crucial impact on the surface energy budget and thus on the evolution of the ice cover. For a springtime low-level mixed-phase stratiform cloud case from Barrow, Alaska, a unique combination of instruments and retrieval methods is combined with multiple modeling perspectives to determine key processes that control cloud phase partitioning. The interplay of local cloud-scale versus large-scale processes is considered. Rapid changes in phase partitioning were found to be caused by several main factors. Major influences were the large-scale advection of different air masses with different aerosol concentrations and humidity content, cloud-scale processes such as a change in the thermodynamical coupling state, and local-scale dynamics influencing the residence time of ice particles. Other factors such as radiative shielding by a cirrus and the influence of the solar cycle were found to only play a minor role for the specific case study (11–12 March 2013). For an even better understanding of cloud phase transitions, observations of key aerosol parameters such as profiles of cloud condensation nucleus and ice nucleus concentration are desirable.
Abstract
Understanding phase transitions in mixed-phase clouds is of great importance because the hydrometeor phase controls the lifetime and radiative effects of clouds. In high latitudes, these cloud radiative effects have a crucial impact on the surface energy budget and thus on the evolution of the ice cover. For a springtime low-level mixed-phase stratiform cloud case from Barrow, Alaska, a unique combination of instruments and retrieval methods is combined with multiple modeling perspectives to determine key processes that control cloud phase partitioning. The interplay of local cloud-scale versus large-scale processes is considered. Rapid changes in phase partitioning were found to be caused by several main factors. Major influences were the large-scale advection of different air masses with different aerosol concentrations and humidity content, cloud-scale processes such as a change in the thermodynamical coupling state, and local-scale dynamics influencing the residence time of ice particles. Other factors such as radiative shielding by a cirrus and the influence of the solar cycle were found to only play a minor role for the specific case study (11–12 March 2013). For an even better understanding of cloud phase transitions, observations of key aerosol parameters such as profiles of cloud condensation nucleus and ice nucleus concentration are desirable.
Abstract
In this study, methods of convective/stratiform precipitation classification and surface rain-rate estimation based on the Atmospheric Radiation Measurement Program (ARM) cloud radar measurements were developed and evaluated. Simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two scanning precipitation radars [NCAR S-band/Ka-band Dual Polarization, Dual Wavelength Doppler Radar (S-PolKa) and Texas A&M University Shared Mobile Atmospheric Research and Teaching Radar (SMART-R)], and surface precipitation during the Dynamics of the Madden–Julian Oscillation/ARM MJO Investigation Experiment (DYNAMO/AMIE) field campaign were used. The motivation of this study is to apply the unique long-term ARM cloud radar observations without accompanying precipitation radars to the study of cloud life cycle and precipitation features under different weather and climate regimes. The resulting convective/stratiform classification from KAZR was evaluated against precipitation radars. Precipitation occurrence and classified convective/stratiform rain fractions from KAZR compared favorably to the collocated SMART-R and S-PolKa observations. Both KAZR and S-PolKa radars observed about 5% precipitation occurrence. The convective (stratiform) precipitation fraction is about 18% (82%). Collocated disdrometer observations of two days showed an increased number concentration of small and large raindrops in convective rain relative to dominant small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also showed distinct structures for convective and stratiform rain. These evidences indicate that the method produces physically consistent results for the two types of rain. A new KAZR-based, two-parameter [the gradient of accumulative radar reflectivity Z e (GAZ) below 1 km and near-surface Z e ] rain-rate estimation procedure was developed for both convective and stratiform rain. This estimate was compared with the exponential Z–R (reflectivity–rain rate) relation. The relative difference between the estimated and surface-measured rainfall rates showed that the two-parameter relation can improve rainfall estimation relative to the Z–R relation.
Abstract
In this study, methods of convective/stratiform precipitation classification and surface rain-rate estimation based on the Atmospheric Radiation Measurement Program (ARM) cloud radar measurements were developed and evaluated. Simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two scanning precipitation radars [NCAR S-band/Ka-band Dual Polarization, Dual Wavelength Doppler Radar (S-PolKa) and Texas A&M University Shared Mobile Atmospheric Research and Teaching Radar (SMART-R)], and surface precipitation during the Dynamics of the Madden–Julian Oscillation/ARM MJO Investigation Experiment (DYNAMO/AMIE) field campaign were used. The motivation of this study is to apply the unique long-term ARM cloud radar observations without accompanying precipitation radars to the study of cloud life cycle and precipitation features under different weather and climate regimes. The resulting convective/stratiform classification from KAZR was evaluated against precipitation radars. Precipitation occurrence and classified convective/stratiform rain fractions from KAZR compared favorably to the collocated SMART-R and S-PolKa observations. Both KAZR and S-PolKa radars observed about 5% precipitation occurrence. The convective (stratiform) precipitation fraction is about 18% (82%). Collocated disdrometer observations of two days showed an increased number concentration of small and large raindrops in convective rain relative to dominant small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also showed distinct structures for convective and stratiform rain. These evidences indicate that the method produces physically consistent results for the two types of rain. A new KAZR-based, two-parameter [the gradient of accumulative radar reflectivity Z e (GAZ) below 1 km and near-surface Z e ] rain-rate estimation procedure was developed for both convective and stratiform rain. This estimate was compared with the exponential Z–R (reflectivity–rain rate) relation. The relative difference between the estimated and surface-measured rainfall rates showed that the two-parameter relation can improve rainfall estimation relative to the Z–R relation.
Abstract
The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) had a special observing period (SOP) that ran from 16 November 2018 to 15 February 2019, a period chosen to span the austral warm season months of greatest operational activity in the Antarctic. Some 2,200 additional radiosondes were launched during the 3-month SOP, roughly doubling the routine program, and the network of drifting buoys in the Southern Ocean was enhanced. An evaluation of global model forecasts during the SOP and using its data has confirmed that extratropical Southern Hemisphere forecast skill lags behind that in the Northern Hemisphere with the contrast being greatest between the southern and northern polar regions. Reflecting the application of the SOP data, early results from observing system experiments show that the additional radiosondes yield the greatest forecast improvement for deep cyclones near the Antarctic coast. The SOP data have been applied to provide insights on an atmospheric river event during the YOPP-SH SOP that presented a challenging forecast and that impacted southern South America and the Antarctic Peninsula. YOPP-SH data have also been applied in determinations that seasonal predictions by coupled atmosphere–ocean–sea ice models struggle to capture the spatial and temporal characteristics of the Antarctic sea ice minimum. Education, outreach, and communication activities have supported the YOPP-SH SOP efforts. Based on the success of this Antarctic summer YOPP-SH SOP, a winter YOPP-SH SOP is being organized to support explorations of Antarctic atmospheric predictability in the austral cold season when the southern sea ice cover is rapidly expanding.
Abstract
The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) had a special observing period (SOP) that ran from 16 November 2018 to 15 February 2019, a period chosen to span the austral warm season months of greatest operational activity in the Antarctic. Some 2,200 additional radiosondes were launched during the 3-month SOP, roughly doubling the routine program, and the network of drifting buoys in the Southern Ocean was enhanced. An evaluation of global model forecasts during the SOP and using its data has confirmed that extratropical Southern Hemisphere forecast skill lags behind that in the Northern Hemisphere with the contrast being greatest between the southern and northern polar regions. Reflecting the application of the SOP data, early results from observing system experiments show that the additional radiosondes yield the greatest forecast improvement for deep cyclones near the Antarctic coast. The SOP data have been applied to provide insights on an atmospheric river event during the YOPP-SH SOP that presented a challenging forecast and that impacted southern South America and the Antarctic Peninsula. YOPP-SH data have also been applied in determinations that seasonal predictions by coupled atmosphere–ocean–sea ice models struggle to capture the spatial and temporal characteristics of the Antarctic sea ice minimum. Education, outreach, and communication activities have supported the YOPP-SH SOP efforts. Based on the success of this Antarctic summer YOPP-SH SOP, a winter YOPP-SH SOP is being organized to support explorations of Antarctic atmospheric predictability in the austral cold season when the southern sea ice cover is rapidly expanding.
