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Abstract
Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (SCF) are analyzed to determine the monthly and hourly variations of cloud fraction and radiative forcing between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layered low (0–3 km), middle (3–6 km), and high clouds (>6 km) using ARM SCF ground-based paired lidar–radar measurements. Shortwave (SW) and longwave (LW) fluxes are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements with uncertainties of ∼10 W m−2. The annual averages of total and single-layered low-, middle-, and high-cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Both total- and low-cloud amounts peak during January and February and reach a minimum during July and August; high clouds occur more frequently than other types of clouds with a peak in summer. The average annual downwelling surface SW fluxes for total and low clouds (151 and 138 W m−2, respectively) are less than those under middle and high clouds (188 and 201 W m−2, respectively), but the downwelling LW fluxes (349 and 356 W m−2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 W m−2). Low clouds produce the largest LW warming (55 W m−2) and SW cooling (−91 W m−2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest LW warming (17 W m−2) and SW cooling (−37 W m−2) effects at the surface. All-sky SW cloud radiative forcing (CRF) decreases and LW CRF increases with increasing cloud fraction with mean slopes of −0.984 and 0.616 W m−2 %−1, respectively. Over the entire diurnal cycle, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux. The calculated CRFs do not appear to be significantly affected by uncertainties in data sampling and clear-sky screening. Traditionally, cloud radiative forcing includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the ARM SCF, changes in humidity and surface albedo between clear and cloudy conditions offset ∼20% of the NET radiative forcing caused by the cloud hydrometeors alone. Variations in water vapor, on average, account for 10% and 83% of the SW and LW CRFs, respectively, in total cloud cover conditions. The error analysis further reveals that the cloud hydrometeors dominate the SW CRF, while water vapor changes are most important for LW flux changes in cloudy skies. Similar studies over other locales are encouraged where water and surface albedo changes from clear to cloudy conditions may be much different than observed over the ARM SCF.
Abstract
Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (SCF) are analyzed to determine the monthly and hourly variations of cloud fraction and radiative forcing between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layered low (0–3 km), middle (3–6 km), and high clouds (>6 km) using ARM SCF ground-based paired lidar–radar measurements. Shortwave (SW) and longwave (LW) fluxes are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements with uncertainties of ∼10 W m−2. The annual averages of total and single-layered low-, middle-, and high-cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Both total- and low-cloud amounts peak during January and February and reach a minimum during July and August; high clouds occur more frequently than other types of clouds with a peak in summer. The average annual downwelling surface SW fluxes for total and low clouds (151 and 138 W m−2, respectively) are less than those under middle and high clouds (188 and 201 W m−2, respectively), but the downwelling LW fluxes (349 and 356 W m−2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 W m−2). Low clouds produce the largest LW warming (55 W m−2) and SW cooling (−91 W m−2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest LW warming (17 W m−2) and SW cooling (−37 W m−2) effects at the surface. All-sky SW cloud radiative forcing (CRF) decreases and LW CRF increases with increasing cloud fraction with mean slopes of −0.984 and 0.616 W m−2 %−1, respectively. Over the entire diurnal cycle, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux. The calculated CRFs do not appear to be significantly affected by uncertainties in data sampling and clear-sky screening. Traditionally, cloud radiative forcing includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the ARM SCF, changes in humidity and surface albedo between clear and cloudy conditions offset ∼20% of the NET radiative forcing caused by the cloud hydrometeors alone. Variations in water vapor, on average, account for 10% and 83% of the SW and LW CRFs, respectively, in total cloud cover conditions. The error analysis further reveals that the cloud hydrometeors dominate the SW CRF, while water vapor changes are most important for LW flux changes in cloudy skies. Similar studies over other locales are encouraged where water and surface albedo changes from clear to cloudy conditions may be much different than observed over the ARM SCF.
Abstract
A record of single-layer and overcast low cloud (stratus) properties has been generated using approximately 4000 h of data collected from January 1997 to December 2002 at the Atmospheric Radiation Measurement (ARM) Southern Great Plains Central Facility (SCF). The cloud properties include liquid-phase and liquid-dominant mixed-phase low cloud macrophysical, microphysical, and radiative properties including cloud-base and -top heights and temperatures, and cloud physical thickness derived from a ground-based radar and lidar pair, and rawinsonde sounding; cloud liquid water path (LWP) and content (LWC), and cloud-droplet effective radius (re ) and number concentration (N) derived from the macrophysical properties and radiometer data; and cloud optical depth (τ), effective solar transmission (γ), and cloud/top-of-atmosphere albedos (R cldy/R TOA) derived from Eppley precision spectral pyranometer measurements. The cloud properties were analyzed in terms of their seasonal, monthly, and hourly variations. In general, more stratus clouds occur during winter and spring than in summer. Cloud-layer altitudes and physical thicknesses were higher and greater in summer than in winter with averaged physical thicknesses of 0.85 and 0.73 km for day and night, respectively. The seasonal variations of LWP, LWC, N, τ, R cldy, and R TOA basically follow the same pattern with maxima and minima during winter and summer, respectively. There is no significant variation in mean re , however, despite a summertime peak in aerosol loading. Although a considerable degree of variability exists, the 6-yr average values of LWP, LWC, re , N, τ, γ, R cldy, and R TOA are 151 gm−2 (138), 0.245 gm−3 (0.268), 8.7 μm (8.5), 213 cm−3 (238), 26.8 (24.8), 0.331, 0.672, and 0.563 for daytime (nighttime). A new conceptual model of midlatitude continental low clouds at the ARM SGP site has been developed from this study. The low stratus cloud amount monotonically increases from midnight to early morning (0930 LT), and remains large until around local noon, then declines until 1930 LT when it levels off for the remainder of the night. In the morning, the stratus cloud layer is low, warm, and thick with less LWC, while in the afternoon it is high, cold, and thin with more LWC. Future parts of this series will consider other cloud types and cloud radiative forcing at the ARM SCF.
Abstract
A record of single-layer and overcast low cloud (stratus) properties has been generated using approximately 4000 h of data collected from January 1997 to December 2002 at the Atmospheric Radiation Measurement (ARM) Southern Great Plains Central Facility (SCF). The cloud properties include liquid-phase and liquid-dominant mixed-phase low cloud macrophysical, microphysical, and radiative properties including cloud-base and -top heights and temperatures, and cloud physical thickness derived from a ground-based radar and lidar pair, and rawinsonde sounding; cloud liquid water path (LWP) and content (LWC), and cloud-droplet effective radius (re ) and number concentration (N) derived from the macrophysical properties and radiometer data; and cloud optical depth (τ), effective solar transmission (γ), and cloud/top-of-atmosphere albedos (R cldy/R TOA) derived from Eppley precision spectral pyranometer measurements. The cloud properties were analyzed in terms of their seasonal, monthly, and hourly variations. In general, more stratus clouds occur during winter and spring than in summer. Cloud-layer altitudes and physical thicknesses were higher and greater in summer than in winter with averaged physical thicknesses of 0.85 and 0.73 km for day and night, respectively. The seasonal variations of LWP, LWC, N, τ, R cldy, and R TOA basically follow the same pattern with maxima and minima during winter and summer, respectively. There is no significant variation in mean re , however, despite a summertime peak in aerosol loading. Although a considerable degree of variability exists, the 6-yr average values of LWP, LWC, re , N, τ, γ, R cldy, and R TOA are 151 gm−2 (138), 0.245 gm−3 (0.268), 8.7 μm (8.5), 213 cm−3 (238), 26.8 (24.8), 0.331, 0.672, and 0.563 for daytime (nighttime). A new conceptual model of midlatitude continental low clouds at the ARM SGP site has been developed from this study. The low stratus cloud amount monotonically increases from midnight to early morning (0930 LT), and remains large until around local noon, then declines until 1930 LT when it levels off for the remainder of the night. In the morning, the stratus cloud layer is low, warm, and thick with less LWC, while in the afternoon it is high, cold, and thin with more LWC. Future parts of this series will consider other cloud types and cloud radiative forcing at the ARM SCF.
Abstract
In this study, more than 4 years of ground-based observations and retrievals were collected and analyzed to investigate the seasonal and diurnal variations of single-layered MBL (with three subsets: nondrizzling, virga, and rain) cloud and drizzle properties, as well as their vertical and horizontal variations. The annual mean drizzle frequency was ~55%, with ~70% in winter and ~45% in summer. The cloud-top (cloud-base) height for rain clouds was the highest (lowest), resulting in the deepest cloud layer, i.e., 0.8 km, which is 4 (2) times that of nondrizzling (virga) clouds. The retrieved cloud-droplet effective radii r c were the largest (smallest) for rain (nondrizzling) clouds, and the nighttime values were greater than the daytime values. Drizzle number concentration N d and liquid water content LWC d were three orders and one order lower, respectively, than their cloud counterparts. The r c and LWC c increased from the cloud base to z i ≈ 0.75 by condensational growth, while drizzle median radii r d increased from the cloud top downward the cloud base by collision–coalescence. The adiabaticity values monotonically increased from the cloud top to the cloud base with maxima of ~0.7 (0.3) for nondrizzling (rain) clouds. The drizzling process decreases the adiabaticity by 0.25 to 0.4, and the cloud-top entrainment mixing impacts as deep as upper 40% of the cloud layers. Cloud and drizzle homogeneities decreased with increased horizontal sampling lengths. Cloud homogeneity increases with increasing cloud fraction. These results can serve as baselines for studying MBL cloud-to-rain conversion and growth processes over the Azores.
Abstract
In this study, more than 4 years of ground-based observations and retrievals were collected and analyzed to investigate the seasonal and diurnal variations of single-layered MBL (with three subsets: nondrizzling, virga, and rain) cloud and drizzle properties, as well as their vertical and horizontal variations. The annual mean drizzle frequency was ~55%, with ~70% in winter and ~45% in summer. The cloud-top (cloud-base) height for rain clouds was the highest (lowest), resulting in the deepest cloud layer, i.e., 0.8 km, which is 4 (2) times that of nondrizzling (virga) clouds. The retrieved cloud-droplet effective radii r c were the largest (smallest) for rain (nondrizzling) clouds, and the nighttime values were greater than the daytime values. Drizzle number concentration N d and liquid water content LWC d were three orders and one order lower, respectively, than their cloud counterparts. The r c and LWC c increased from the cloud base to z i ≈ 0.75 by condensational growth, while drizzle median radii r d increased from the cloud top downward the cloud base by collision–coalescence. The adiabaticity values monotonically increased from the cloud top to the cloud base with maxima of ~0.7 (0.3) for nondrizzling (rain) clouds. The drizzling process decreases the adiabaticity by 0.25 to 0.4, and the cloud-top entrainment mixing impacts as deep as upper 40% of the cloud layers. Cloud and drizzle homogeneities decreased with increased horizontal sampling lengths. Cloud homogeneity increases with increasing cloud fraction. These results can serve as baselines for studying MBL cloud-to-rain conversion and growth processes over the Azores.
Abstract
A new method has been developed to retrieve the nighttime marine boundary layer (MBL) cloud microphysical properties, which provides a complete 19-month dataset to investigate the diurnal variation of MBL cloud microphysical properties at the Azores. Compared to the corresponding daytime results presented in the authors' previous study over the Azores region, all nighttime monthly means of cloud liquid water path (LWP) exceed their daytime counterparts with an annual-mean LWP of 140 g m−2, which is ~30.9 g m−2 larger than daytime. Because the MBL clouds are primarily driven by convective instabilities caused by cloud-top longwave (LW) radiative cooling, more MBL clouds are well mixed and coupled with the surface during the night; thus, its cloud layer is deeper and its LWP is higher. During the day, the cloud layer is warmed by the absorption of solar radiation and partially offsets the cloud-top LW cooling, which makes the cloud layer thinner with less LWP. The seasonal and diurnal variations of cloud LWC and optical depth basically follow the variation of LWP. There are, however, no significant day–night differences and diurnal variations in cloud-droplet effective radius (r e ), number concentration (N d ), and corresponding surface measured cloud condensation nuclei (CCN) number concentration (N CCN) (at supersaturation S = 0.2%). Surface N CCN increases from around sunrise (0300–0600 LT) to late afternoon, which strongly correlates with surface wind speed (r = 0.76) from 0300 to 1900 LT. The trend in hourly-mean N d is consistent with N CCN variation from 0000 to 0900 LT but not for afternoon and evening with an averaged ratio (N d /N CCN) of 0.35 during the entire study period.
Abstract
A new method has been developed to retrieve the nighttime marine boundary layer (MBL) cloud microphysical properties, which provides a complete 19-month dataset to investigate the diurnal variation of MBL cloud microphysical properties at the Azores. Compared to the corresponding daytime results presented in the authors' previous study over the Azores region, all nighttime monthly means of cloud liquid water path (LWP) exceed their daytime counterparts with an annual-mean LWP of 140 g m−2, which is ~30.9 g m−2 larger than daytime. Because the MBL clouds are primarily driven by convective instabilities caused by cloud-top longwave (LW) radiative cooling, more MBL clouds are well mixed and coupled with the surface during the night; thus, its cloud layer is deeper and its LWP is higher. During the day, the cloud layer is warmed by the absorption of solar radiation and partially offsets the cloud-top LW cooling, which makes the cloud layer thinner with less LWP. The seasonal and diurnal variations of cloud LWC and optical depth basically follow the variation of LWP. There are, however, no significant day–night differences and diurnal variations in cloud-droplet effective radius (r e ), number concentration (N d ), and corresponding surface measured cloud condensation nuclei (CCN) number concentration (N CCN) (at supersaturation S = 0.2%). Surface N CCN increases from around sunrise (0300–0600 LT) to late afternoon, which strongly correlates with surface wind speed (r = 0.76) from 0300 to 1900 LT. The trend in hourly-mean N d is consistent with N CCN variation from 0000 to 0900 LT but not for afternoon and evening with an averaged ratio (N d /N CCN) of 0.35 during the entire study period.
Abstract
The microwave radiometer–derived cloud liquid water path (LWP) and a profile of radar reflectivity are used to derive a profile of cloud liquid water content (LWC). Two methods (M1 and M2) have been developed for inferring the profile of cloud-droplet effective radius (r e ) in liquid phase or liquid dominant mixed phase stratocumulus clouds. The M1-inferred r e profile is proportional to a previously derived layer-mean r e and to the ratio of the radar reflectivity to the integrated radar reflectivity. This algorithm is independent of the radar calibration and is applicable to overcast low-level stratus clouds that occur during the day because it is dependent on solar transmission observations. In order to extend the retrieval algorithm to a wider range of conditions, a second method is described that uses an empirical relationship between effective radius and radar reflectivity based on theory and the results of M1. Sensitivity studies show that the surface-retrieved r e is more sensitive to the variation of radar reflectivity when the radar reflectivity is large, and the uncertainties of retrieved r e related to the assumed vertically constant cloud-droplet number concentration and shape of the size distribution are about 9% and 2%, respectively. For validation, a total of 10 h of aircraft data and 36 h of surface data were collected over the Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site during the March 2000 cloud intensive observational period (IOP). More detailed comparisons in two cases quantify the agreement between the aircraft data and the surface retrievals. When the temporal averages of the two datasets increase from 1 min to 30 min, the means and standard deviations of differences between the two datasets decrease from −2.5% ± 84% to 1.3% ± 42.6% and their corresponding correlation coefficients increase from 0.47 to 0.8 for LWC; and decrease from −4.8% ± 36.4% to −3.3% ± 22.5% with increased coefficients from 0.64 to 0.94 for r e (both M1 and M2). The agreement between the aircraft and surface data in the 30-min averages suggests that the two platforms are capable of characterizing the cloud microphysics over this temporal scale. On average, the surface retrievals are unbiased relative to the aircraft in situ measurements. However, when only the 1-min averaged aircraft data within 3 km of the surface site were selected, the means and standard deviations of differences between the two datasets are larger (23.4% ± 113% for LWC and 28.3% ± 60.7% for r e ) and their correlation coefficients are smaller (0.32 for LWC and 0.3 for r e ) than those from all 1-min samples. This result suggests that restricting the comparison to the samples better matched in space and time between the surface and aircraft data does not result in a better comparison.
Abstract
The microwave radiometer–derived cloud liquid water path (LWP) and a profile of radar reflectivity are used to derive a profile of cloud liquid water content (LWC). Two methods (M1 and M2) have been developed for inferring the profile of cloud-droplet effective radius (r e ) in liquid phase or liquid dominant mixed phase stratocumulus clouds. The M1-inferred r e profile is proportional to a previously derived layer-mean r e and to the ratio of the radar reflectivity to the integrated radar reflectivity. This algorithm is independent of the radar calibration and is applicable to overcast low-level stratus clouds that occur during the day because it is dependent on solar transmission observations. In order to extend the retrieval algorithm to a wider range of conditions, a second method is described that uses an empirical relationship between effective radius and radar reflectivity based on theory and the results of M1. Sensitivity studies show that the surface-retrieved r e is more sensitive to the variation of radar reflectivity when the radar reflectivity is large, and the uncertainties of retrieved r e related to the assumed vertically constant cloud-droplet number concentration and shape of the size distribution are about 9% and 2%, respectively. For validation, a total of 10 h of aircraft data and 36 h of surface data were collected over the Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site during the March 2000 cloud intensive observational period (IOP). More detailed comparisons in two cases quantify the agreement between the aircraft data and the surface retrievals. When the temporal averages of the two datasets increase from 1 min to 30 min, the means and standard deviations of differences between the two datasets decrease from −2.5% ± 84% to 1.3% ± 42.6% and their corresponding correlation coefficients increase from 0.47 to 0.8 for LWC; and decrease from −4.8% ± 36.4% to −3.3% ± 22.5% with increased coefficients from 0.64 to 0.94 for r e (both M1 and M2). The agreement between the aircraft and surface data in the 30-min averages suggests that the two platforms are capable of characterizing the cloud microphysics over this temporal scale. On average, the surface retrievals are unbiased relative to the aircraft in situ measurements. However, when only the 1-min averaged aircraft data within 3 km of the surface site were selected, the means and standard deviations of differences between the two datasets are larger (23.4% ± 113% for LWC and 28.3% ± 60.7% for r e ) and their correlation coefficients are smaller (0.32 for LWC and 0.3 for r e ) than those from all 1-min samples. This result suggests that restricting the comparison to the samples better matched in space and time between the surface and aircraft data does not result in a better comparison.
Abstract
A decade of collocated Atmospheric Radiation Measurement Program (ARM) 35-GHz Millimeter Cloud Radar (MMCR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data over the ARM Southern Great Plains (SGP) site have been collected during the period of 1997–2006. A total of 28 winter and 45 summer deep convective system (DCS) cases over the ARM SGP site have been selected for this study during the 10-yr period. For the winter cases, the MMCR reflectivity, on average, is only 0.2 dB lower than that of the WSR-88D, with a correlation coefficient of 0.85. This result indicates that the MMCR signals have not been attenuated for ice-phase convective clouds, and the MMCR reflectivity measurements agree well with the WSR-88D, regardless of their vastly different characteristics. For the summer nonprecipitating convective clouds, however, the MMCR reflectivity, on average, is 10.6 dB lower than the WSR-88D measurement, and the average differences between the two radar reflectivities are nearly constant with height above cloud base. Three lookup tables with Mie calculations have been generated for correcting the MMCR signal attenuation. After applying attenuation correction for the MMCR reflectivity measurements, the averaged difference between the two radars has been reduced to 9.1 dB. Within the common sensitivity range (−10 to 20 dBZ), the mean differences for the uncorrected and corrected MMCR reflectivities have been reduced to 6.2 and 5.3 dB, respectively. The corrected MMCR reflectivities were then merged with the WSR-88D data to fill in the gaps during the heavy precipitation periods. This merged dataset provides a more complete radar reflectivity profile for studying convective systems associated with heavier precipitation than the original MMCR dataset. It also provides the intensity, duration, and frequency of the convective systems as they propagate over the ARM SGP for climate modelers. Eventually, it will be possible to improve understanding of the cloud-precipitation processes, and evaluate GCM predictions using the long-term merged dataset, which could not have been done with either the MMCR or the WSR-88D dataset alone.
Abstract
A decade of collocated Atmospheric Radiation Measurement Program (ARM) 35-GHz Millimeter Cloud Radar (MMCR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data over the ARM Southern Great Plains (SGP) site have been collected during the period of 1997–2006. A total of 28 winter and 45 summer deep convective system (DCS) cases over the ARM SGP site have been selected for this study during the 10-yr period. For the winter cases, the MMCR reflectivity, on average, is only 0.2 dB lower than that of the WSR-88D, with a correlation coefficient of 0.85. This result indicates that the MMCR signals have not been attenuated for ice-phase convective clouds, and the MMCR reflectivity measurements agree well with the WSR-88D, regardless of their vastly different characteristics. For the summer nonprecipitating convective clouds, however, the MMCR reflectivity, on average, is 10.6 dB lower than the WSR-88D measurement, and the average differences between the two radar reflectivities are nearly constant with height above cloud base. Three lookup tables with Mie calculations have been generated for correcting the MMCR signal attenuation. After applying attenuation correction for the MMCR reflectivity measurements, the averaged difference between the two radars has been reduced to 9.1 dB. Within the common sensitivity range (−10 to 20 dBZ), the mean differences for the uncorrected and corrected MMCR reflectivities have been reduced to 6.2 and 5.3 dB, respectively. The corrected MMCR reflectivities were then merged with the WSR-88D data to fill in the gaps during the heavy precipitation periods. This merged dataset provides a more complete radar reflectivity profile for studying convective systems associated with heavier precipitation than the original MMCR dataset. It also provides the intensity, duration, and frequency of the convective systems as they propagate over the ARM SGP for climate modelers. Eventually, it will be possible to improve understanding of the cloud-precipitation processes, and evaluate GCM predictions using the long-term merged dataset, which could not have been done with either the MMCR or the WSR-88D dataset alone.
Abstract
A record of single-layer and overcast low-level Arctic stratus cloud properties has been generated using data collected from May to September 2000 at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) (71.3°N, 156.6°W) site near Barrow, Alaska. The record includes liquid-phase and liquid dominant mixed-phase Arctic stratus macrophysical, microphysical, and radiative properties, as well as surface radiation budget and cloud radiative forcing. The macrophysical properties consist of cloud fractions, cloud-base/top heights and temperatures, and cloud thickness derived from a ground-based radar and lidar pair, and rawinsonde sounding. The microphysical properties include cloud liquid water path and content, and cloud-droplet effective radius and number concentration obtained from microwave radiometer brightness temperature measurements, and the new cloud parameterization. The radiative properties contain cloud optical depth, effective solar transmission, and surface/cloud/top-of-atmosphere albedos derived from the new cloud parameterization and standard Epply precision spectral pyranometers. The shortwave, longwave, and net cloud radiative forcings at the surface are inferred from measurements by standard Epply precision spectral pyranometers and pyrgeometers. There are approximately 300 h and more than 3600 samples (5-min resolution) of single-layer and overcast low-level stratus during the study period. The 10-day averaged total and low-level cloud (Z top < 3 km) fractions are 0.87 and 0.55, and low-level cloud-base and -top heights are around 0.4 and 0.8 km. The cloud-droplet effective radii and number concentrations in the spring are similar to midlatitude continental stratus cloud microphysical properties, and in the summer they are similar to midlatitude marine stratus clouds. The total cloud fractions in this study show good agreement with the satellite and surface results compiled from data collected during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Cloud Experiment (ACE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) (∼77°N, 165°W) field experiments in 1998. The cloud microphysics derived from this study are similar, in general, to those collected in past field programs, although these comparisons are based on data collected at different locations and years. At the ARM NSA site, the summer cooling period is much longer (2–3 months vs 1–2 weeks), and the summer cooling magnitude is much larger (−100 W m−2 vs −5 W m−2) than at the SHEBA ship under the conditions of all skies at the SHEBA and overcast low-level stratus clouds at the NSA site.
Abstract
A record of single-layer and overcast low-level Arctic stratus cloud properties has been generated using data collected from May to September 2000 at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) (71.3°N, 156.6°W) site near Barrow, Alaska. The record includes liquid-phase and liquid dominant mixed-phase Arctic stratus macrophysical, microphysical, and radiative properties, as well as surface radiation budget and cloud radiative forcing. The macrophysical properties consist of cloud fractions, cloud-base/top heights and temperatures, and cloud thickness derived from a ground-based radar and lidar pair, and rawinsonde sounding. The microphysical properties include cloud liquid water path and content, and cloud-droplet effective radius and number concentration obtained from microwave radiometer brightness temperature measurements, and the new cloud parameterization. The radiative properties contain cloud optical depth, effective solar transmission, and surface/cloud/top-of-atmosphere albedos derived from the new cloud parameterization and standard Epply precision spectral pyranometers. The shortwave, longwave, and net cloud radiative forcings at the surface are inferred from measurements by standard Epply precision spectral pyranometers and pyrgeometers. There are approximately 300 h and more than 3600 samples (5-min resolution) of single-layer and overcast low-level stratus during the study period. The 10-day averaged total and low-level cloud (Z top < 3 km) fractions are 0.87 and 0.55, and low-level cloud-base and -top heights are around 0.4 and 0.8 km. The cloud-droplet effective radii and number concentrations in the spring are similar to midlatitude continental stratus cloud microphysical properties, and in the summer they are similar to midlatitude marine stratus clouds. The total cloud fractions in this study show good agreement with the satellite and surface results compiled from data collected during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Cloud Experiment (ACE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) (∼77°N, 165°W) field experiments in 1998. The cloud microphysics derived from this study are similar, in general, to those collected in past field programs, although these comparisons are based on data collected at different locations and years. At the ARM NSA site, the summer cooling period is much longer (2–3 months vs 1–2 weeks), and the summer cooling magnitude is much larger (−100 W m−2 vs −5 W m−2) than at the SHEBA ship under the conditions of all skies at the SHEBA and overcast low-level stratus clouds at the NSA site.
Abstract
More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CF T ), and single-layered low (CF L ) and high (CF H ) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CF T , CF L , and CF H are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CF T and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CF T (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.
Abstract
More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CF T ), and single-layered low (CF L ) and high (CF H ) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CF T , CF L , and CF H are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CF T and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CF T (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.
Abstract
This study uses machine-learning methods, specifically the random-forests (RF) method, on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004–16. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, along with reanalysis environmental information from a 5-yr manually identified nonlinear mode and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, of which one-half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (March–October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus they have higher potential for producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.
Abstract
This study uses machine-learning methods, specifically the random-forests (RF) method, on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004–16. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, along with reanalysis environmental information from a 5-yr manually identified nonlinear mode and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, of which one-half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (March–October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus they have higher potential for producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.
Abstract
This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.
Abstract
This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.
