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Xiquan Dong
,
Baike Xi
, and
Patrick Minnis

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.

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Xiquan Dong
,
Baike Xi
, and
Peng Wu

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.

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Zhe Feng
,
Xiquan Dong
, and
Baike Xi

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.

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Peng Wu
,
Xiquan Dong
, and
Baike Xi

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.

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Xiquan Dong
,
Patrick Minnis
, and
Baike Xi

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.

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Wenjun Cui
,
Xiquan Dong
,
Baike Xi
, and
Ronald Stenz

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.

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Wenjun Cui
,
Xiquan Dong
,
Baike Xi
, and
Aaron Kennedy

Abstract

Atmospheric reanalyses have been used in many studies to investigate the variabilities and trends of precipitation because of their global coverage and long record; however, their results must be properly analyzed and their uncertainties must be understood. In this study, precipitation estimates from five global reanalyses [ERA-Interim; MERRA, version 2 (MERRA2); JRA-55; CFSR; and 20CR, version 2c (20CRv2c)] and one regional reanalysis (NARR) are compared against the CPC Unified Gauge-Based Analysis (CPCUGA) and GPCP over the contiguous United States (CONUS) during the period 1980–2013. Reanalyses capture the variability of the precipitation distribution over the CONUS as observed in CPCUGA and GPCP, but large regional and seasonal differences exist. Compared with CPCUGA, global reanalyses generally overestimate the precipitation over the western part of the country throughout the year and over the northeastern CONUS during the fall and winter seasons. These issues may be associated with the difficulties models have in accurately simulating precipitation over complex terrain and during snowfall events. Furthermore, systematic errors found in five global reanalyses suggest that their physical processes in modeling precipitation need to be improved. Even though negative biases exist in NARR, its spatial variability is similar to both CPCUGA and GPCP; this is anticipated because it assimilates observed precipitation, unlike the global reanalyses. Based on CPCUGA, there is an average decreasing trend of −1.38 mm yr−1 over the CONUS, which varies depending on the region with only the north-central to northeastern parts of the country having positive trends. Although all reanalyses exhibit similar interannual variation as observed in CPCUGA, their estimated precipitation trends, both linear and spatial trends, are distinct from CPCUGA.

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Wenjun Cui
,
Xiquan Dong
,
Baike Xi
, and
Zhe Feng

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.

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Xiquan Dong
,
Xiaojian Zheng
,
Baike Xi
, and
Shaocheng Xie

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.

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Jingjing Tian
,
Xiquan Dong
,
Baike Xi
, and
Zhe Feng

Abstract

In this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

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