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

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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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Ronald Stenz, Xiquan Dong, Baike Xi, and Robert J. Kuligowski

Abstract

Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs). As DCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithm biases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicating Q2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3–4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.

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

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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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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, Zhe Feng, and Jiwen Fan

Abstract

Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An accurate representation of MCSs’ rainfall is therefore crucial in understanding their impact on the climate system. The V06B Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) half-hourly precipitation final product is a useful tool to study the precipitation characteristics of MCSs because of its global coverage and fine spatiotemporal resolutions. However, errors and uncertainties in IMERG should be quantified before applying it to hydrology and climate applications. This study evaluates IMERG performance on capturing and detecting MCSs’ precipitation in the central and eastern United States during a 3-yr study period against the radar-based Stage IV product. The tracked MCSs are divided into four seasons and are analyzed separately for both datasets. IMERG shows a wet bias in total precipitation but a dry bias in hourly mean precipitation during all seasons due to the false classification of nonprecipitating pixels as precipitating. These false alarm events are possibly caused by evaporation under the cloud base or the misrepresentation of MCS cold anvil regions as precipitating clouds by the algorithm. IMERG agrees reasonably well with Stage IV in terms of the seasonal spatial distribution and diurnal cycle of MCSs precipitation. A relative humidity (RH)-based correction has been applied to the IMERG precipitation product, which helps reduce the number of false alarm pixels and improves the overall performance of IMERG with respect to Stage IV.

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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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Xiquan Dong, Baike Xi, Aaron Kennedy, Patrick Minnis, and Robert Wood

Abstract

A 19-month record of total and single-layered low (<3 km), middle (3–6 km), and high (>6 km) cloud fractions (CFs) and the single-layered marine boundary layer (MBL) cloud macrophysical and microphysical properties was generated from ground-based measurements at the Atmospheric Radiation Measurement Program (ARM) Azores site between June 2009 and December 2010. This is the most comprehensive dataset of marine cloud fraction and MBL cloud properties. The annual means of total CF and single-layered low, middle, and high CFs derived from ARM radar and lidar observations are 0.702, 0.271, 0.01, and 0.106, respectively. Greater total and single-layered high (>6 km) CFs occurred during the winter, whereas single-layered low (<3 km) CFs were more prominent during summer. Diurnal cycles for both total and low CFs were stronger during summer than during winter. The CFs are bimodally distributed in the vertical with a lower peak at ~1 km and a higher peak between 8 and 11 km during all seasons, except summer when only the low peak occurs. Persistent high pressure and dry conditions produce more single-layered MBL clouds and fewer total clouds during summer, whereas the low pressure and moist air masses during winter generate more total and multilayered clouds, and deep frontal clouds associated with midlatitude cyclones.

The seasonal variations of cloud heights and thickness are also associated with the seasonal synoptic patterns. The MBL cloud layer is low, warm, and thin with large liquid water path (LWP) and liquid water content (LWC) during summer, whereas during winter it is higher, colder, and thicker with reduced LWP and LWC. The cloud LWP and LWC values are greater at night than during daytime. The monthly mean daytime cloud droplet effective radius r e values are nearly constant, while the daytime droplet number concentration N d basically follows the LWC variation. There is a strong correlation between cloud condensation nuclei (CCN) concentration N CCN and N d during January–May, probably due to the frequent low pressure systems because upward motion brings more surface CCN to cloud base (well-mixed boundary layer). During summer and autumn, the correlation between N d and N CCN is not as strong as that during January–May because downward motion from high pressure systems is predominant. Compared to the compiled aircraft in situ measurements during the Atlantic Stratocumulus Transition Experiment (ASTEX), the cloud microphysical retrievals in this study agree well with historical aircraft data. Different air mass sources over the ARM Azores site have significant impacts on the cloud microphysical properties and surface CCN as demonstrated by great variability in N CCN and cloud microphysical properties during some months.

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