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- Author or Editor: Rabindra Palikonda x
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Abstract
Widespread persistent contrails over the western Great Lakes during 9 October 2000 were examined using commercial flight data, coincident meteorological data, and satellite remote sensing data from several platforms. The data were analyzed to determine the atmospheric conditions under which the contrails formed and to measure several physical properties of the contrails, including areal coverage, spreading rates, fall speeds, and optical properties. Most of the contrails were located between 10.6 and 11.8 km in atmospheric conditions consistent with a modified form of the Appleman contrail formation theory. However, the Rapid Update Cycle-2 analyses have a dry bias in the upper-tropospheric relative humidity with respect to ice (RHI), as indicated by persistent contrail generation during the outbreak where RHI ≥ 85%. The model analyses show that synoptic-scale vertical velocities affect the formation of persistent contrails. Areal coverage by linear contrails peaked at 30 000 km2, but the maximum contrail-generated cirrus coverage was over twice as large. Contrail spreading rates averaged around 2.7 km h−1, and the contrails were visible in the 4-km Geostationary Operational Environmental Satellite (GOES) imagery approximately 1 h after formation. Contrail fall speed estimates were between 0.00 and 0.045 m s−1 based on observed contrail advection rates. Optical depth measurements ranged from 0.1 to 0.6, with consistent differences between remote sensing methods. Contrail formation density was roughly correlated with air traffic density after the effects of competing cloud coverage, humidity, and vertical velocity were considered. Improved tropospheric humidity measurements are needed for realistic simulations of contrail and cirrus development.
Abstract
Widespread persistent contrails over the western Great Lakes during 9 October 2000 were examined using commercial flight data, coincident meteorological data, and satellite remote sensing data from several platforms. The data were analyzed to determine the atmospheric conditions under which the contrails formed and to measure several physical properties of the contrails, including areal coverage, spreading rates, fall speeds, and optical properties. Most of the contrails were located between 10.6 and 11.8 km in atmospheric conditions consistent with a modified form of the Appleman contrail formation theory. However, the Rapid Update Cycle-2 analyses have a dry bias in the upper-tropospheric relative humidity with respect to ice (RHI), as indicated by persistent contrail generation during the outbreak where RHI ≥ 85%. The model analyses show that synoptic-scale vertical velocities affect the formation of persistent contrails. Areal coverage by linear contrails peaked at 30 000 km2, but the maximum contrail-generated cirrus coverage was over twice as large. Contrail spreading rates averaged around 2.7 km h−1, and the contrails were visible in the 4-km Geostationary Operational Environmental Satellite (GOES) imagery approximately 1 h after formation. Contrail fall speed estimates were between 0.00 and 0.045 m s−1 based on observed contrail advection rates. Optical depth measurements ranged from 0.1 to 0.6, with consistent differences between remote sensing methods. Contrail formation density was roughly correlated with air traffic density after the effects of competing cloud coverage, humidity, and vertical velocity were considered. Improved tropospheric humidity measurements are needed for realistic simulations of contrail and cirrus development.
Abstract
Rising global air traffic and its associated contrails have the potential for affecting climate via radiative forcing. Current estimates of contrail climate effects are based on coverage by linear contrails that do not account for spreading and, therefore, represent the minimum impact. The maximum radiative impact is estimated by assuming that long-term trends in cirrus coverage are due entirely to air traffic in areas where humidity is relatively constant. Surface observations from 1971 to 1995 show that cirrus increased significantly over the northern oceans and the United States while decreasing over other land areas except over western Europe where cirrus coverage was relatively constant. The surface observations are consistent with satellite-derived trends over most areas. Land cirrus trends are positively correlated with upper-tropospheric (300 hPa) humidity (UTH), derived from the National Centers for Environmental Prediction (NCEP) analyses, except over the United States and western Europe where air traffic is heaviest. Over oceans, the cirrus trends are negatively correlated with the NCEP relative humidity suggesting some large uncertainties in the maritime UTH. The NCEP UTH decreased dramatically over Europe while remaining relatively steady over the United States, thereby permitting an assessment of the cirrus–contrail relationship over the United States. Seasonal cirrus changes over the United States are generally consistent with the annual cycle of contrail coverage and frequency lending additional evidence to the role of contrails in the observed trend. It is concluded that the U.S. cirrus trends are most likely due to air traffic. The cirrus increase is a factor of 1.8 greater than that expected from current estimates of linear contrail coverage suggesting that a spreading factor of the same magnitude can be used to estimate the maximum effect of the contrails. From the U.S. results and using mean contrail optical depths of 0.15 and 0.25, the maximum contrail–cirrus global radiative forcing is estimated to be 0.006–0.025 W m−2 depending on the radiative forcing model. Using results from a general circulation model simulation of contrails, the cirrus trends over the United States are estimated to cause a tropospheric warming of 0.2°–0.3°C decade−1, a range that includes the observed tropospheric temperature trend of 0.27°C decade−1 between 1975 and 1994. The magnitude of the estimated surface temperature change and the seasonal variations of the estimated temperature trends are also in good agreement with the corresponding observations.
Abstract
Rising global air traffic and its associated contrails have the potential for affecting climate via radiative forcing. Current estimates of contrail climate effects are based on coverage by linear contrails that do not account for spreading and, therefore, represent the minimum impact. The maximum radiative impact is estimated by assuming that long-term trends in cirrus coverage are due entirely to air traffic in areas where humidity is relatively constant. Surface observations from 1971 to 1995 show that cirrus increased significantly over the northern oceans and the United States while decreasing over other land areas except over western Europe where cirrus coverage was relatively constant. The surface observations are consistent with satellite-derived trends over most areas. Land cirrus trends are positively correlated with upper-tropospheric (300 hPa) humidity (UTH), derived from the National Centers for Environmental Prediction (NCEP) analyses, except over the United States and western Europe where air traffic is heaviest. Over oceans, the cirrus trends are negatively correlated with the NCEP relative humidity suggesting some large uncertainties in the maritime UTH. The NCEP UTH decreased dramatically over Europe while remaining relatively steady over the United States, thereby permitting an assessment of the cirrus–contrail relationship over the United States. Seasonal cirrus changes over the United States are generally consistent with the annual cycle of contrail coverage and frequency lending additional evidence to the role of contrails in the observed trend. It is concluded that the U.S. cirrus trends are most likely due to air traffic. The cirrus increase is a factor of 1.8 greater than that expected from current estimates of linear contrail coverage suggesting that a spreading factor of the same magnitude can be used to estimate the maximum effect of the contrails. From the U.S. results and using mean contrail optical depths of 0.15 and 0.25, the maximum contrail–cirrus global radiative forcing is estimated to be 0.006–0.025 W m−2 depending on the radiative forcing model. Using results from a general circulation model simulation of contrails, the cirrus trends over the United States are estimated to cause a tropospheric warming of 0.2°–0.3°C decade−1, a range that includes the observed tropospheric temperature trend of 0.27°C decade−1 between 1975 and 1994. The magnitude of the estimated surface temperature change and the seasonal variations of the estimated temperature trends are also in good agreement with the corresponding observations.
Abstract
The mean structure and diurnal cycle of southeast (SE) Atlantic boundary layer clouds are described with satellite observations and multiscale modeling framework (MMF) simulations during austral spring (September–November). Hourly resolution cloud fraction (CF) and cloud-top height (H T ) are retrieved from Meteosat-9 radiances using modified Clouds and the Earth’s Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms, whereas liquid water path (LWP) is from the University of Wisconsin microwave satellite climatology. The MMF simulations use a 2D cloud-resolving model (CRM) that contains an advanced third-order turbulence closure to explicitly simulate cloud physical processes in every grid column of a general circulation model. The model accurately reproduces the marine stratocumulus spatial extent and cloud cover. The mean cloud cover spatial variability in the model is primarily explained by the boundary layer decoupling strength, whereas a boundary layer shoaling accounts for a coastal decrease in CF. Moreover, the core of the stratocumulus cloud deck is concomitant with the location of the strongest temperature inversion. Although the model reproduces the observed westward boundary layer deepening and the spatial variability of LWP, it overestimates LWP by 50%. Diurnal cycles of H T , CF, and LWP from satellites and the model have the same phase, with maxima during the early morning and minima near 1500 local solar time, which suggests that the diurnal cycle is driven primarily by solar heating. Comparisons with the SE Pacific cloud deck indicate that the observed amplitude of the diurnal cycle is modest over the SE Atlantic, with a shallower boundary layer as well. The model qualitatively reproduces these interregime differences.
Abstract
The mean structure and diurnal cycle of southeast (SE) Atlantic boundary layer clouds are described with satellite observations and multiscale modeling framework (MMF) simulations during austral spring (September–November). Hourly resolution cloud fraction (CF) and cloud-top height (H T ) are retrieved from Meteosat-9 radiances using modified Clouds and the Earth’s Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms, whereas liquid water path (LWP) is from the University of Wisconsin microwave satellite climatology. The MMF simulations use a 2D cloud-resolving model (CRM) that contains an advanced third-order turbulence closure to explicitly simulate cloud physical processes in every grid column of a general circulation model. The model accurately reproduces the marine stratocumulus spatial extent and cloud cover. The mean cloud cover spatial variability in the model is primarily explained by the boundary layer decoupling strength, whereas a boundary layer shoaling accounts for a coastal decrease in CF. Moreover, the core of the stratocumulus cloud deck is concomitant with the location of the strongest temperature inversion. Although the model reproduces the observed westward boundary layer deepening and the spatial variability of LWP, it overestimates LWP by 50%. Diurnal cycles of H T , CF, and LWP from satellites and the model have the same phase, with maxima during the early morning and minima near 1500 local solar time, which suggests that the diurnal cycle is driven primarily by solar heating. Comparisons with the SE Pacific cloud deck indicate that the observed amplitude of the diurnal cycle is modest over the SE Atlantic, with a shallower boundary layer as well. The model qualitatively reproduces these interregime differences.
Abstract
Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH).
Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.
Abstract
Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH).
Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.
Abstract
Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.
Abstract
Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.
Abstract
An algorithm is developed to determine the flight icing threat to aircraft utilizing quantitative information on clouds derived from meteorological satellite data as input. Algorithm inputs include the satellite-derived cloud-top temperature, thermodynamic phase, water path, and effective droplet size. The icing-top and -base altitude boundaries are estimated from the satellite-derived cloud-top and -base altitudes using the freezing level obtained from numerical weather analyses or a lapse-rate approach. The product is available at the nominal resolution of the satellite pixel. Aircraft pilot reports (PIREPs) over the United States and southern Canada provide direct observations of icing and are used extensively in the algorithm development and validation on the basis of correlations with Geostationary Operational Environmental Satellite imager data. Verification studies using PIREPs, Tropospheric Airborne Meteorological Data Reporting, and NASA Icing Remote Sensing System data indicate that the satellite algorithm performs reasonably well, particularly during the daytime. The algorithm is currently being run routinely using data taken from a variety of satellites across the globe and is providing useful information on icing conditions at high spatial and temporal resolutions that are unavailable from any other source.
Abstract
An algorithm is developed to determine the flight icing threat to aircraft utilizing quantitative information on clouds derived from meteorological satellite data as input. Algorithm inputs include the satellite-derived cloud-top temperature, thermodynamic phase, water path, and effective droplet size. The icing-top and -base altitude boundaries are estimated from the satellite-derived cloud-top and -base altitudes using the freezing level obtained from numerical weather analyses or a lapse-rate approach. The product is available at the nominal resolution of the satellite pixel. Aircraft pilot reports (PIREPs) over the United States and southern Canada provide direct observations of icing and are used extensively in the algorithm development and validation on the basis of correlations with Geostationary Operational Environmental Satellite imager data. Verification studies using PIREPs, Tropospheric Airborne Meteorological Data Reporting, and NASA Icing Remote Sensing System data indicate that the satellite algorithm performs reasonably well, particularly during the daytime. The algorithm is currently being run routinely using data taken from a variety of satellites across the globe and is providing useful information on icing conditions at high spatial and temporal resolutions that are unavailable from any other source.
Abstract
Forecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.
Abstract
Forecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.
Abstract
This research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not ongoing at the beginning of the radar and satellite data assimilation period. For more complex cases containing significant amounts of ongoing convection, only assimilating clear-sky satellite retrievals in place of clear-air reflectivity resulted in spurious regions of light precipitation compared to the radar-only experiments. The analyzed tornadic storms in these experiments are sometimes too weak and quickly diminished in intensity in the forecasts. The lessons learned as part of these experiments should lead to improved iterations of the NEWS-e system, building on the modestly successful results described here.
Abstract
This research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not ongoing at the beginning of the radar and satellite data assimilation period. For more complex cases containing significant amounts of ongoing convection, only assimilating clear-sky satellite retrievals in place of clear-air reflectivity resulted in spurious regions of light precipitation compared to the radar-only experiments. The analyzed tornadic storms in these experiments are sometimes too weak and quickly diminished in intensity in the forecasts. The lessons learned as part of these experiments should lead to improved iterations of the NEWS-e system, building on the modestly successful results described here.
Abstract
Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.
Abstract
Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.
Abstract
Flight data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r = 0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (−31% averaged over all cases), MBL depth (+560 m), estimated inversion strength (EIS; −2.2 K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc–Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics.
Abstract
Flight data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r = 0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (−31% averaged over all cases), MBL depth (+560 m), estimated inversion strength (EIS; −2.2 K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc–Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics.
