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To assess if current radar-based liquid cloud microphysical retrievals of the Atmospheric Radiation Measurement (ARM) program can provide useful constraints for modeling studies, this paper presents intercomparison results of three cloud products at the Southern Great Plains (SGP) site: the ARM MICROBASE, University of Utah (UU), and University of North Dakota (UND) products over the nine-year period from 1998 to 2006. The probability density and spatial autocorrelation functions of the three cloud Liquid Water Content (LWC) retrievals appear to be consistent with each other, while large differences are found in the droplet effective radius retrievals. The differences in the vertical distribution of both cloud LWC and droplet effective radius retrievals are found to be alarmingly large, with the relative difference between nine-year mean cloud LWC retrievals ranging from 20% at low altitudes to 100% at high altitudes. Nevertheless, the spread in LWC retrievals is much smaller than that in cloud simulations by climate and cloud resolving models. The MICROBASE effective radius ranges from 2.0 at high altitudes to 6.0 μm at low altitudes and the UU and UND droplet effective radius is 6 μm larger. Further analysis through a suite of retrieval experiments shows that the difference between MICROBASE and UU LWC retrievals stems primarily from the partition total Liquid Water path (LWP) into supercooled and warm liquid, and from the input cloud boundaries and LWP. The large differences between MICROBASE and UU droplet effective radius retrievals are mainly due to rain/drizzle contamination and the assumptions of cloud droplet concentration used in the retrieval algorithms. The large discrepancy between different products suggests caution in model evaluation with these observational products, and calls for improved retrievals in general.

In Part I of this study, the NASA GISS Coupled Model Intercomparison Project (CMIP5) and post-CMIP5 (herein called C5 and P5, respectively) simulated cloud properties were assessed utilizing multiple satellite observations, with a particular focus on the southern midlatitudes (SMLs). This study applies the knowledge gained from Part I of this series to evaluate the modeled TOA radiation budgets and cloud radiative effects (CREs) globally using CERES EBAF (CE) satellite observations and the impact of regional cloud properties and water vapor on the TOA radiation budgets. Comparisons revealed that the P5- and C5-simulated global means of clear-sky and all-sky outgoing longwave radiation (OLR) match well with CE observations, while biases are observed regionally. Negative biases are found in both P5- and C5-simulated clear-sky OLR. P5-simulated all-sky albedo slightly increased over the SMLs due to the increase in low-level cloud fraction from the new planetary boundary layer (PBL) scheme. Shortwave, longwave, and net CRE are quantitatively analyzed as well. Regions of strong large-scale atmospheric upwelling/downwelling motion are also defined to compare regional differences across multiple cloud and radiative variables. In general, the P5 and C5 simulations agree with the observations better over the downwelling regime than over the upwelling regime. Comparing the results herein with the cloud property comparisons presented in Part I, the modeled TOA radiation budgets and CREs agree well with the CE observations. These results, combined with results in Part I, have quantitatively estimated how much improvement is found in the P5-simulated cloud and radiative properties, particularly over the SMLs and tropics, due to the implementation of the new PBL and convection schemes.

Reanalyses have proven to be convenient tools for studying the Arctic climate system, but their uncertainties should first be identified. In this study, five reanalyses (JRA-55, 20CRv2c, CFSR, ERA-Interim, and MERRA-2) are compared with NASA CERES–MODIS (CM)-derived cloud fractions (CFs), cloud water paths (CWPs), top-of-atmosphere (TOA) and surface longwave (LW) and shortwave (SW) radiative fluxes over the Arctic (70°–90°N) over the period of 2000–12, and CloudSat–CALIPSO (CC)-derived CFs from 2006 to 2010. The monthly mean CFs in all reanalyses except JRA-55 are close to or slightly higher than the CC-derived CFs from May to September. However, wintertime CF cannot be confidently evaluated until instrument simulators are implemented in reanalysis products. The comparison between CM and CC CFs indicates that CM-derived CFs are reliable in summer but not in winter. Although the reanalysis CWPs follow the general seasonal variations of CM CWPs, their annual means are only half or even less than the CM-retrieved CWPs (126 g m−2). The annual mean differences in TOA and surface SW and LW fluxes between CERES EBAF and reanalyses are less than 6 W m−2 for TOA radiative fluxes and 16 W m−2 for surface radiative fluxes. All reanalyses show positive biases along the northern and eastern coasts of Greenland as a result of model elevation biases or possible CM clear-sky retrieval issues. The correlations between the reanalyses and CERES satellite retrievals indicate that all five reanalyses estimate radiative fluxes better than cloud properties, and MERRA-2 and JRA-55 exhibit comparatively higher correlations for Arctic cloud and radiation properties.

An intercomparison study of a midlatitude mesoscale squall line is performed using the Weather Research and Forecasting (WRF) model at 1 km horizontal grid spacing with eight different cloud microphysics schemes to investigate processes that contribute to the large variability in simulated cloud and precipitation properties. All simulations tend to produce a wider area of high radar reflectivity (Z_e > 45 dBZ) than observed but a much narrower stratiform area. The magnitude of the virtual potential temperature drop associated with the gust front passage is similar in simulations and observations, while the pressure rise and peak wind speed are smaller than observed, possibly suggesting that simulated cold pools are shallower than observed. Most of the microphysics schemes overestimate vertical velocity and Ze in convective updrafts as compared with observational retrievals. Simulated precipitation rates and updraft velocities have significant variability across the eight schemes, even in this strongly dynamically driven system. Differences in simulated updraft velocity correlate well with differences in simulated buoyancy and low-level vertical perturbation pressure gradient, which appears related to cold pool intensity that is controlled by the evaporation rate. Simulations with stronger updrafts have a more optimal convective state, with stronger cold pools, ambient low-level vertical wind shear, and rear-inflow jets. Updraft velocity variability between schemes is mainly controlled by differences in simulated ice-related processes, which impact the overall latent heating rate, whereas surface rainfall variability increases in no-ice simulations mainly because of scheme differences in collision-coalescence parameterizations.

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 (re), number concentration (Nd), and corresponding surface measured cloud condensation nuclei (CCN) number concentration (NCCN) (at supersaturation S = 0.2%). Surface NCCN 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 Nd is consistent with NCCN variation from 0000 to 0900 LT but not for afternoon and evening with an averaged ratio (Nd/NCCN) of 0.35 during the entire study period.

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.

A large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds and how they interact with incoming solar and outgoing longwave radiation. In this study, the simulated total cloud fraction (CF), cloud water path (CWP), top of the atmosphere (TOA) radiation budgets and cloud radiative forcings (CRFs) from 28 CMIP5 AMIP models are evaluated and compared with multiple satellite observations from CERES, MODIS, ISCCP, CloudSat, and CALIPSO. The multimodel ensemble mean CF (57.6 %) is, on average, underestimated by nearly 8 % (between 65°N/S) when compared to CERES–MODIS (CM) and ISCCP results while an even larger negative bias (17.1 %) exists compared to the CloudSat/CALIPSO results. CWP bias is similar in comparison to the CF results, with a negative bias of 16.1 gm−2 compared to CM. The model simulated and CERES EBAF observed TOA reflected SW and OLR fluxes on average differ by 1.8 and −0.9 Wm−2, respectively. The averaged SW, LW, and net CRFs from CERES EBAF are −50.1, 27.6, and −22.5 Wm−2, respectively, indicating a net cooling effect of clouds on the TOA radiation budget. The differences in SW and LW CRFs between observations and the multimodel ensemble means are only −1.3 and −1.6 Wm−2, respectively, resulting in a larger net cooling effect of 2.9 Wm−2 in the model simulations. A further investigation of cloud properties and CRFs reveals that the GCM biases in atmospheric upwelling (15°S–15°N) regimes are much less than in their downwelling (15°–45°N/S) counterparts over the oceans. Sensitivity studies have shown that the magnitude of SW cloud radiative cooling increases significantly with increasing CF at similar rates (~−1.25 Wm−2 %−1) in both regimes. The LW cloud radiative warming increases with increasing CF but is regime dependent, suggested by the different slopes over the upwelling and downwelling regimes (0.81 and 0.22 Wm−2 %−1, respectively). Through a comprehensive error analysis, we found that CF is a primary modulator of warming (or cooling) in the atmosphere. The comparisons and statistical results from this study may provide helpful insight for improving GCM simulations of clouds and TOA radiation budgets in future versions of CMIP.

A 19-month record of total and single-layered low (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 (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 re values are nearly constant, while the daytime droplet number concentration Nd basically follows the LWC variation. There is a strong correlation between cloud condensation nuclei (CCN) concentration NCCN and Nd 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 Nd and NCCN 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 NCCN and cloud microphysical properties during some months.

In this study, an atmospheric river (AR) detection algorithm is developed to investigate the downstream modulation of the eastern North Pacific ARs by another weather extreme, known as the East Asian cold surge (EACS), in both reanalysis data and high-resolution global model simulations. It is shown that following the peak of an EACS, atmospheric disturbances of intermediate frequency (IF; 10–30 day period) are excited downstream. This leads to the formation of a persistent cyclonic circulation anomaly over the eastern North Pacific that dramatically enhances the AR occurrence probability and the surface precipitation over the western U.S. between 30°N and 50°N. A diagnosis of the local geopotential height tendency further confirms the essential role of IF disturbances in establishing the observed persistent anomaly. This downstream modulation effect is then examined in the two simulations of the National Center for Atmospheric Research Community Climate System Model version 4 with different horizontal resolutions (T85 and T341) for the same period (1979–2005). The connection between EACS and AR is much better captured by the T341 version of the model, mainly due to a better representation of the scale interaction and the characteristics of IF atmospheric disturbances in the higher-resolution model. The findings here suggest that faithful representations of scale interaction in a global model are critical for modeling and predicting the occurrences of hydrological extremes in the western U.S. and for understanding their potential future changes.

The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site has a rich history of actively sensed cloud observations. Fourteen years (1997–2010) of observations from the Millimeter Cloud Radar (MMCR), Micropulse Lidar (MPL), and Belfort/Vaisala Ceilometers are used to understand how instrument selection and sampling impacts estimates of Cloud Fraction (CF) at this location. Although all instruments should be used in combination for the best estimates of CF, instrument downtime limits available samples and increases observational errors, demanding that users make sacrifices when calculating CF at longer intervals relevant to climate studies. Selection of MMCR or MMCR + MPL cloud masks changes very little in the overall understanding of total CF. Addition of the MPL increases the 14-year average CF by 9 %, mainly through an increase in optically thin high clouds year-round, and mid-level clouds during the summer months. Splitting the period into two equal 7-year periods reveals negligible change in MMCR + MPL CF. For the MMCR, however, CF deceases by 6.1 %. This sudden change in CF occurs around the time the radar was upgraded, suggesting that this decrease is tied to hardware sensitivity or scanning strategy changes. Users must be cognizant of this and other issues when calculating CF from the variety of observations available at the ARM SGP site.

Asian aerosols are among the most complex yet widely studied components of the atmosphere not only due to their seasonal variability but also their effects on climate change. Four Aerosol Robotic Network (AERONET) sites have been selected to represent aerosol properties dominated by pollution (Taihu), mixed complex particle types (Xianghe), desert-urban (SACOL), and biomass (Mukdahan) in East Asia during the 2001–2010 period. The volume size distribution, aerosol optical depth (τ and τabs), Ångström exponent (α and αabs), and the single scattering co-albedo (ωoabs) parameters over the four selected sites have been used to (a) illustrate seasonal changes in aerosol size and composition and (b) discern the absorptive characteristics of black carbon (BC), organic carbon (OC), mineral dust particles, and mixtures. A strongly absorbing mineral dust influence is seen at the Xianghe, Taihu, and SACOL sites during the spring months (MAM), as given by coarse mode dominance, mean α440–870 < 1, and mean αabs440–870 > 1.5. There is a shift towards weakly absorbing pollution (sulfate) and biomass (OC) aerosol dominance in the summer (JJA) and autumn (SON) months, as given by a strong fine mode influence, α440–870 > 1, and αabs440–870 < 1.5. A winter season (DJF) shift toward strongly fine mode, absorbing particles (BC and OC) is observed at Xianghe and Taihu (αabs440–870 > 1 and αabs440–870 > 1.5). At Mukdahan, a strong fine mode influence is evident year round, with weakly and strongly absorbing biomass particles dominant in the autumn and winter months, respectively, while particles exhibit variable absorption during the spring season. A classification method using α440–870 and ωoabs440 is developed in order to infer the seasonal physico-chemical properties of the aerosol types, such as fine and coarse mode, weak and strong absorption, at the four selected Asian sites.

Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here we derive hygroscopic aerosol size distribution input profiles from ground- based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of the aerosol inputs in mesoscale model simulations of the only well-observed case study that produced extensive stratiform outflow, on 20 May 2011. At well-sampled elevations between −10 and −23 °C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near ∼ 400 μm in randomly oriented maximum dimension (Dmax). The ice mass at −23 °C is primarily in a closely collocated mode, whereas a mass mode near Dmax ∼ 1000 μm becomes dominant with decreasing elevation to the −10 °C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak Dmax by a factor of 3–5 and underpredict ice number concentration by a factor of 4–10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that the microphysics pathways associated with deep tropical convection outflow also occurred in the 20 May MC3E case, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

This study presents newly developed algorithms for retrieving ice cloud microphysical properties (ice water content (IWC) and median mass diameter (Dm)) for the stratiform rain and thick anvil regions of Deep Convective Systems (DCSs) using Next-Generation Radar (NEXRAD) reflectivity and empirical relationships from aircraft in situ measurements. A typical DCS case (20 May 2011) during the Midlatitude Continental Convective Clouds Experiment (MC3E) is selected as an example to demonstrate the 4-D retrievals. The vertical distributions of retrieved IWC are compared with previous studies and cloud-resolving model simulations. The statistics from six selected cases during MC3E show that the aircraft in situ derived IWC and Dm are 0.47 ± 0.29 g m-3 and 2.02 ± 1.3 mm, while the mean values of retrievals have a positive bias of 0.19 g m-3 (40%) and negative bias of 0.41 mm (20%), respectively. To evaluate the new retrieval algorithms, IWC and Dm are retrieved for other DCSs observed during the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) using NEXRAD reflectivity and compared with aircraft in situ measurements. During BAMEX, a total of 63 1-min collocated aircraft and radar samples are available for comparisons, and the averages of radar retrieved and aircraft in situ measured IWC values are 1.52 g m-3 and 1.25 g m-3 with a correlation of 0.55, and their averaged Dm values are 2.08 and 1.77 mm. In general, the new retrieval algorithms are suitable for continental DCSs during BAMEX, especially within stratiform rain and thick anvil regions.

With continual advancements in data assimilation systems, new observing systems, and improvements in model parameterizations, several new atmospheric reanalysis datasets have recently become available. Before using these new reanalyses it is important to assess the strengths and underlying biases contained in each dataset. A study has been performed to evaluate and compare cloud fractions (CFs) and surface radiative fluxes in several of these latest reanalyses over the Arctic using 15 years (1994–2008) of high-quality Baseline Surface Radiation Network (BSRN) observations from Barrow (BAR) and Ny-Alesund (NYA) surface stations. The five reanalyses being evaluated in this study are (i) NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA), (ii) NCEP's Climate Forecast System Reanalysis (CFSR), (iii) NOAA's Twentieth Century Reanalysis Project (20CR), (iv) ECMWF's Interim Reanalysis (ERA-I), and (v) NCEP–Department of Energy (DOE)'s Reanalysis II (R2). All of the reanalyses show considerable bias in reanalyzed CF during the year, especially in winter. The large CF biases have been reflected in the surface radiation fields, as monthly biases in shortwave (SW) and longwave (LW) fluxes are more than 90 (June) and 60 W m−2 (March), respectively, in some reanalyses. ERA-I and CFSR performed the best in reanalyzing surface downwelling fluxes with annual mean biases less than 4.7 (SW) and 3.4 W m−2 (LW) over both Arctic sites. Even when producing the observed CF, radiation flux errors were found to exist in the reanalyses suggesting that they may not always be dependent on CF errors but rather on variations of more complex cloud properties, water vapor content, or aerosol loading within the reanalyses.

Atmospheric states from the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the North American Regional Reanalysis (NARR) are compared with data from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, including the ARM continuous forcing product and Cloud Modeling Best Estimate (CMBE) soundings, during the period 1999–2001 to understand their validity for single-column model (SCM) and cloud-resolving model (CRM) forcing datasets. Cloud fraction, precipitation, and radiation information are also compared to determine what errors exist within these reanalyses. For the atmospheric state, ARM continuous forcing and the reanalyses have good agreement with the CMBE sounding information, with biases generally within 0.5 K for temperature, 0.5 m s−1 for wind, and 5% for relative humidity. Larger disagreements occur in the upper troposphere (p < 300 hPa) for temperature, humidity, and zonal wind, and in the boundary layer (p > 800 hPa) for meridional wind and humidity. In these regions, larger errors may exist in derived forcing products. Significant differences exist for vertical pressure velocity, with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. While reanalysis-based forcing appears to be feasible for the majority of the year at this location, it may have limited usage during the late spring and early summer, when convection is common at the ARM SGP site. Both NARR and MERRA capture the seasonal variation of cloud fractions (CFs) observed by ARM radar–lidar and Geostationary Operational Environmental Satellite (GOES) with high correlations (0.92–0.78) but with negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows better agreement for both shortwave (SW) and longwave (LW) fluxes except for LW-down (due to a negative bias in water vapor): NARR has significant positive bias for SW-down and negative bias for LW-down under clear-sky and all-sky conditions. The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited period, and more comparisons at different locations and longer periods are needed.

Three years of surface and Geostationary Operational Environmental Satellite (GOES) data from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to evaluate the NASA GISS Single Column Model (SCM) simulated clouds from January 1999 to December 2001. The GOES-derived total cloud fractions for both 0.5° and 2.5° grid boxes are in excellent agreement with surface observations, suggesting that ARM point observations can represent large areal observations. Low (6 km) levels of cloud fractions, however, have negative biases as compared to the ARM results due to multilayer cloud scenes that can either mask lower cloud layers or cause misidentifications of cloud tops. Compared to the ARM observations, the SCM simulated most midlevel clouds, overestimated low clouds (4%), and underestimated total and high clouds by 7% and 15%, respectively. To examine the dependence of the modeled high and low clouds on the large-scale synoptic patterns, variables such as relative humidity (RH) and vertical pressure velocity (omega) from North American Regional Reanalysis (NARR) data are included. The successfully modeled and missed high clouds are primarily associated with a trough and ridge upstream of the ARM SGP, respectively. The PDFs of observed high and low occurrence as a function of RH reveal that high clouds have a Gaussian-like distribution with mode RH values of ∼40%–50%, whereas low clouds have a gammalike distribution with the highest cloud probability occurring at RH ∼75%–85%. The PDFs of modeled low clouds are similar to those observed; however, for high clouds the PDFs are shifted toward higher values of RH. This results in a negative bias for the modeled high clouds because many of the observed clouds occur at RH values below the SCM-specified stratiform parameterization threshold RH of 60%. Despite many similarities between PDFs derived from the NARR and ARM forcing datasets for RH and omega, differences do exist. This warrants further investigation of the forcing and reanalysis datasets.

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.

The Mesoscale Heavy Rainfall Observing System (MHROS), supported by the Institute of Heavy Rain (IHR), Chinese Meteorology Administration, is one of the major systems to observe mesoscale convective systems (MCSs) over the middle region of the Yangtze River in China. The IHR MHROS consists of mobile C-POL and X-POL precipitation radars, millimeter wavelength cloud radar (MMCR), fixed S-band precipitation radars, GPS network, microwave radiometers, radio soundings, wind profiler radars and disdrometers. The atmospheric variables observed or retrieved by these instruments include the profiles of atmospheric temperature, moisture, wind speed and direction, vertical structures of MCS clouds and precipitation, atmospheric water vapor, and cloud liquid water. These quality controlled observations and retrievals have been used in mesoscale numerical weather prediction to improve the accuracy of weather forecasting and MCS research since 2007. These long-term observations have provided the most comprehensive datasets for researchers to investigate the formation-dissipation processes of MCSs and for modelers to improve their simulations of MCSs. As the first paper of a series, we briefly introduce the IHR MHROS and describe the specifications of its major instruments. Then, we provide an integrative analysis of the IHR MHROS observations for a heavy rain case on 3–5 July 2014 as well as the application of IHR MHROS observations in improving the model simulations. In a series of papers, we will tentatively answer several key scientific questions related to the MCS and Meiyu frontal systems over the middle region of the Yangtze River using the IHR MHROS observations.

Atmospheric profiles of temperature (T), vapor density (ρv), and relative humidity (RH) retrieved from ground-based microwave radiometer (MWR) measurements are compared with radiosonde soundings at Wuhan, China. The MWR retrievals were averaged in the ±30 min period centered at sounding times of 00 and 12 UTC. A total of 403 and 760 profiles under clear and cloudy skies were selected. Based on the comparisons, temperature profiles have better consistency than the ρv and RH profiles, lower levels are better than upper levels, and the cloudy are better than the clear-sky profiles. Three cloud types (low, middle, and high) were identified by matching the infrared radiation thermometer-detected cloud base temperature to the MWR-retrieved temperature-height profiles. Temperature profile under high cloud has the highest correlation coefficient (R) and the lowest bias and RMS, but under low cloud is in the opposite direction. The ρv profile under middle cloud has the highest R and the lowest bias but under high cloud has the lowest R, the largest bias, and RMS. Based on the radiosonde soundings, both clear and cloudy wind speeds and drifting distances increase with height but increase much faster under clear than cloudy above 4 km. The increased wind speeds and drifting distances with height have resulted in decreased correlation coefficient and increased temperature biases and RMSs with height for both clear and cloudy skies. The differences in R, bias, and RMS between clear and cloudy skies are primarily resulted from their wind speeds and drifting distances.

The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21-month (April 2009–December 2010) comprehensive dataset documenting clouds, aerosols, and precipitation using the Atmospheric Radiation Measurement Program (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols, and precipitation in the marine boundary layer.\ud \ud Graciosa Island straddles the boundary between the subtropics and midlatitudes in the northeast Atlantic Ocean and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1 to 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back-trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging.\ud \ud The data from Graciosa are being compared with short-range forecasts made with a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.

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 (Rcldy/RTOA) 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, Rcldy, and RTOA 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, Rcldy, and RTOA 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.

Anthropogenic aerosols enhance cloud reflectivity by increasing the number concentration of cloud droplets, leading to a cooling effect on climate known as the indirect aerosol effect. Observational support for this effect is based mainly on evidence that aerosol number concentrations are connected with droplet concentrations, but it has been difficult to determine the impact of these indirect effects on radiative forcing1,2,3. Here we provide observational evidence for a substantial alteration of radiative fluxes due to the indirect aerosol effect. We examine the effect of aerosols on cloud optical properties using measurements of aerosol and cloud properties at two North American sites that span polluted and clean conditions—a continental site in Oklahoma with high aerosol concentrations, and an Arctic site in Alaska with low aerosol concentrations. We determine the cloud optical depth required to fit the observed shortwave downward surface radiation. We then use a cloud parcel model to simulate the cloud optical depth from observed aerosol properties due to the indirect aerosol effect. From the good agreement between the simulated indirect aerosol effect and observed surface radiation, we conclude that the indirect aerosol effect has a significant influence on radiative fluxes.

NASA Earth and Space Science Fellowship program; NASA CERES project [NNX14AP84G]; NOAA MAPP grant at the University of North Dakota [NA13OAR4310105]; Jet Propulsion Laboratory, California Institute of Technology; NASA

Author(s): Dong, X; Xi, B; Qiu, S; Minnis, P; Sun-Mack, S; Rose, F | Abstract: Retrievals of cloud microphysical properties based on passive satellite imagery are especially difficult over snow-covered surfaces because of the bright and cold surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth’s Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfcg0.3), respectively, were selected from Terra and Aqua satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (t) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured surface albedos were adjusted (63.6% and 80% of the ARM surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10Wm-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.

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-...

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 (re) in liquid phase or liquid dominant mixed phase stratocumulus clouds. The M1-inferred re profile is proportional to a previously derived layer-mean re 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 re is more sensitive to the variation of radar reflectivity when the radar reflectivity is large, and the uncertainties of retrieved re 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 22.5% 6 84% to 1.3% 6 42.6% and their corresponding correlation coefficients increase from 0.47 to 0.8 for LWC; and decrease from 24.8% 6 36.4% to 23.3% 6 22.5% with increased coefficients from 0.64 to 0.94 for re (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% 6 113% for LWC and 28.3% 6 60.7% for re) and their correlation coefficients are smaller (0.32 for LWC and 0.3 for re) 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.

Low-level stratus cloud microphysical properties derived from surface and Geostationary Operational Environmental Satellite (GOES) data during the March 2000 cloud intensive observational period (IOP) at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site are compared with aircraft in situ measurements. For the surface retrievals, the cloud droplet effective radius and optical depth are retrieved from a d2-stream radiative transfer model with the input of ground-based measurements, and the cloud liquid water path (LWP) is retrieved from ground-based microwave-radiometer-measured brightness temperature. The satellite results, retrieved from GOES visible, solar-infrared, and infrared radiances, are averaged in a 0.5 8 3 0.58 box centered on the ARM SGP site. The forward scattering spectrometer probe (FSSP) on the University of North Dakota Citation aircraft provided in situ measurements of the cloud microphysical properties. During the IOP, four low-level stratus cases were intensively observed by the ground- and satellite-based remote sensors and aircraft in situ instruments resulting in a total of 10 h of simultaneous data from the three platforms. In spite of the large differences in temporal and spatial resolution between surface, GOES, and aircraft, the surface retrievals have excellent agreement with the aircraft data overall for the entire 10-h period, and the GOES results agree reasonably well with the surface and aircraft data and have similar trends and magnitudes except for the GOES-derived effective radii, which are typically larger than the surface- and aircraft-derived values. The means and standard deviations of the differences between the surface and aircraft effective radius, LWP, and optical depth are 24% 6 20.1%, 21% 6 31.2%, and 8% 6 29.3%, respectively; while their correlation coefficients are 0.78, 0.92, and 0.89, respectively, during the 10-h period. The differences and correlations between the GOES-8 and aircraft results are of a similar magnitude, except for the droplet sizes. The averaged GOES-derived effective radius is 23% or 1.8 mm greater than the corresponding aircraft values, resulting in a much smaller correlation coefficient of 0.18. Additional surface‐satellite datasets were analyzed for time periods when the aircraft was unavailable. When these additional results are combined with the retrievals from the four in situ cases, the means and standard deviations of the differences between the satellite-derived cloud droplet effective radius, LWP, and optical depth and their surface-based counterparts are 16% 6 31.2%, 4% 6 31.6%, and 26% 6 39.9%, respectively. The corresponding correlation coefficients are 0.24, 0.88, and 0.73. The frequency distributions of the two datasets are very similar indicating that the satellite retrieval method should be able to produce reliable statistics of boundary layer cloud properties for use in climate and cloud process models.

A new method to retrieve cloud liquid water path using 23.8 and 31.4 GHz microwave radiometer brightness temperature measurements is developed. This method does not depend on climatological estimates of either the mean radiating temperature of the atmosphere Tmr or the mean cloud liquid water temperature Tcloud. Rather, Tmr is estimated from surface temperature and relative humidity measurements, while Tcloud is estimated using millimeter-wave cloud radar data, together with atmospheric temperature profiles obtained from either radiosonde or rapid update cycle (RUC) model output. Simulations demonstrate that the new retrieval method significantly reduces the biases in the liquid water path estimates that are apparent in a site-specific retrieval based on monthly stratified, local climatology. An analysis of the liquid water path estimates produced by the two retrievals over four case study days illustrates trends and retrieval performances consistent with the model simulations.

A 25-month database of the macrophysical, microphysical, and radiative properties of isolated and overcast low-level stratus clouds has been generated using a newly developed parameterization and surface measurements from the Atmospheric Radiation Measurement central facility in Oklahoma. The database (5-min resolution) includes two parts: measurements and retrievals. The former consist of cloud base and top heights, layer-mean temperature, cloud liquid water path, and solar transmission ratio measured by a ground-based lidar/ceilometer and radar pair, radiosondes, a microwave radiometer, and a standard Eppley precision spectral pyranometer, respectively. The retrievals include the cloud-droplet effective radius and number concentration and broadband shortwave optical depth and cloud and top-of-atmosphere albedos. Stratus without any overlying mid or high-level clouds occurred most frequently during winter and least often during summer. Mean cloud-layer altitudes and geometric thicknesses were higher and greater, respectively, in summer than in winter. Both quantities are positively correlated with the cloud-layer mean temperature. Mean cloud-droplet effective radii range from 8.1 μm in winter to 9.7 μm during summer, while cloud-droplet number concentrations during winter are nearly twice those in summer. Since cloud liquid water paths are almost the same in both seasons, cloud optical depth is higher during the winter, leading to greater cloud albedos and lower cloud transmittances.

In earlier work a δ2-stream radiative transfer model was used in conjunction with ground-based measurements to retrieve the rnicrophysical and shortwave radiative properties of boundary layer stratus. The effective radii output by the retrieval scheme for optically thick clouds have now been parameterized as a function of the cloud liquid water path, the transmission ratio, and the cosine of the solar zenith angle. The parameterization enables estimation of stratus cloud microphysical properties using ground-based measurements that are readily available at a number of locations. To validate the retrieved and parameterized cloud microphysics, ∼5 hours of data were analyzed and compared to collocated in situ measurements made by a Forward Scattering Spectrometer Probe. On average, the retrieved values of the cloud droplet effective radius and the cloud droplet number concentration differed from the corresponding aircraft measurements by 7% and 27%, respectively, while the parameterized values differed from the aircraft measurements by 15% and 32%, respectively. Averaging all of the data to 30-min resolution significantly reduced the differences between the aircraft data and the retrieved and parameterized results, suggesting that both the aircraft and the ground-based data are capable of characterizing the cloud microphysics over this temporal scale. The parameterizations of the stratus shortwave radiative properties were generally within 5% of Slingo's four-band, model-derived parameterization when absorption above cloud top was incorporated into the Slingo parameterization.

Aerosol optical depth (AOD), Angstrom Exponent (AE), and single scattering albedo (SSA) simulated by a new aerosol-coupled version of Nonhydrostatic ICosahedral Atmospheric Model (NICAM) have been compared with corresponding AERONET retrievals over a total of 196 sites during the 2006–2008 period. The temporal and spatial distributions of the modeled AODs and AEs match those of the AERONET retrievals reasonably well. For the 3-year mean AODs and AEs for all sites show the correlations between model and AERONET of 0.753 and 0.735, respectively, and 82.1% of the modeled AODs agree within a factor of two with the retrieved AODs. The primary model deficiency is an underestimation of fine mode aerosol AOD and a corresponding underestimation of AE over pollution region. Compared to the retrievals, the model underestimates the global 3-year mean AOD and AE by 0.022 (10.5%) and 0.329 (31.2%), respectively. The probability distribution function (PDF) of the modeled AODs is comparable to that of the retrieved ones, however, the model overestimates the occurrence frequencies of small AEs and SSAs.

Two methods for retrieving the microphysical and radiative properties of marine and continental boundary layer stratiform clouds from ground-based measurements are implemented. The first method uses measurements of the cloud liquid water path and the cloud nadir radiance at 1 μm to infer the cloud optical depth, cloud droplet effective radius, and cloud droplet concentration. In the second method a

Biomass burning aerosols can have a large impact on atmospheric processes as well as human health. During the 2012 warm season, a large outbreak of wildfires originating from the intermountain and Pacific states provided many opportunities to observe the physical and chemical properties of biomass smoke aerosols. Six biomass burning smoke plumes (26 June–15 September) have been observed by the newly installed Grand Forks, North Dakota, AERONET site (47.91° N, 97.32° W) and are selected for this study. To identify the source regions, HYSPLIT backward trajectory model data and satellite imagery are used to track these events. The volume size distribution and spectral aerosol optical depth (AOD) dependence showed the relative influences of fine and coarse mode particles. Case II (4 July) had the strongest fine mode influence as evidenced by a strong spectral AOD dependence while Case VI (15 September) had the strongest coarse mode influence with the weakest spectral dependence. The spectral dependences of absorption aerosol optical depth (AAOD) and single scattering co-albedo (ωoabs) illustrated the varying absorption of the smoke plumes by inferring the relative contributions of strongly and weakly absorbing carbonaceous species. More specifically, the AAOD parameter is primarily influenced by aerosol particle size while ωoabs is more dependent on aerosol composition. The AAOD spectral dependences for Cases I (26 June), III (31 July), and VI were weaker than those from Cases II, IV (28 August), and V (30 August). However, the spectral ωoabs dependences were different in that the smoke particles in Cases III and VI had the strongest absorption while Cases I, II, IV, and V had moderate to weakly absorbing particles. In addition, a weak correlation was found between plume transport time and particle absorption where strongly absorbing carbon was converted to weakly absorbing carbon.

Deep Convective Systems (DCSs) consist of intense convective cores (CC), large stratiform rain (SR) regions, and extensive non-precipitating anvil clouds (AC). This study focuses on the evolution of these three components and the factors that affect convective AC production. An automated satellite tracking method is used in conjunction with a recently developed multi-sensor hybrid classification to analyze the evolution of DCS structure in a Lagrangian framework over the central United States. Composite analysis from 4221 tracked DCSs during two warm seasons (May-August, 2010-2011) shows that maximum system size correlates with lifetime, and longer-lived DCSs have more extensive SR and AC. Maximum SR and AC area lag behind peak convective intensity and the lag increases linearly from approximately 1-hour for short-lived systems to more than 3-hours for long-lived ones. The increased lag, which depends on the convective environment, suggests that changes in the overall diabatic heating structure associated with the transition from CC to SR and AC could prolong the system lifetime by sustaining stratiform cloud development. Longer-lasting systems are associated with up to 60% higher mid-tropospheric relative humidity and up to 40% stronger middle to upper tropospheric wind shear. Regression analysis shows that the areal coverage of thick AC is strongly correlated with the size of CC, updraft strength, and SR area. Ambient upper tropospheric wind speed and wind shear also play an important role for convective AC production where for systems with large AC (radius greater than 120-km) they are 24% and 20% higher, respectively, than those with small AC (radius=20 km).

Ongoing urbanization and industrialization in East Asia have generated a wide variety of aerosols in the atmosphere and have consequently added more uncertainty when evaluating global climate change. To classify different types of aerosols and investigate their physical and chemical properties, four AErosol RObotic NETwork (AERONET) sites have been selected to represent aerosol properties dominated by mixed complex particle types (Xianghe and Taihu), desert-urban (SACOL), and biomass (Mukdahan) over East Asia during the 2001–2010 period. The volume size distribution, aerosol optical depth [τ (λ) and τabs(λ)], Ångström exponent (α and αabs), and the single scattering co-albedo [ωoabs(λ)] and α(ωoabs) parameters over the four selected sites have been analyzed. These parameters are used to (a) investigate the aerosol properties and their seasonal variations over the four selected sites, (b) discern the different absorptive characteristics of BC, OC, and mineral dust particles using αabs440-870 and α (ωoabs440-870), and (c) develop an aerosol clustering method involving α440-870 and ωoabs440. A strong mineral dust influence is seen at the Xianghe, Taihu, and SACOL sites during the spring months (MAM) as given by coarse mode size distribution dominance, declining α440-870, and elevated αabs440-870 and α (ωoabs440-870) values. A weakly absorbing pollution (OC and biomass) aerosol dominance is seen in the summer (JJA) and autumn (SON) months as given by a strong fine mode influence, increasing α440-870, and declining αabs440-870 and α (ωoabs440-870) values. A winter season (DJF) shift toward strongly absorbing BC particles is observed at Xianghe and Taihu (elevated α440-870, increase in αabs440-870 and α(ωoabs440-870)). At Mukdahan, a fine mode biomass particle influence is observed year round as given by the volume size distribution, elevated α440-870 (higher than the other sites), low αabs440-870 and negative α (ωoabs440-870) values indicating weakly absorbing OC particles. The α(ωoabs) parameter is also shown to have less overlap in values than αabs in discerning influences from OC, BC, biomass and mineral dust particles. The clustering method using α440-870 and ωoabs440 illustrates four groups of aerosols: Cluster I – fine mode, weakly absorbing pollution particles, Cluster II – fine mode, strongly absorbing pollution particles, Cluster III – coarse mode, strongly absorbing mineral dust particles, and Cluster IV – biomass particles with similar characteristics as Cluster II but less absorbing. This method has shown that aerosol mixtures are both seasonal and regional combinations of particles that were either locally generated or transported from other source regions and should be implemented over other AERONET sites in the future.

[1] A new hybrid classification algorithm to objectively identify Deep Convective Systems (DCSs) in radar and satellite observations has been developed. This algorithm can classify the convective cores (CC), stratiform rain (SR) area and nonprecipitating anvil cloud (AC) from the identified DCSs through an integrative analysis of ground-based scanning radar and geostationary satellite data over the Southern Great Plains. In developing the algorithm, AC is delineated into transitional, thick, and thin components. While there are distinct physical/dynamical differences among these subcategories, their top-of-atmosphere (TOA) radiative fluxes are not significantly different. Therefore, these anvil subcategories are grouped as total anvil, and the radiative impact of each DCS component on the TOA radiation budget is quantitatively estimated. We found that more DCSs occurred during late afternoon, producing peak AC fraction right after sunset. AC covers 3 times the area of SR and almost an order of magnitude larger than CC. The average outgoing longwave (LW) irradiances are almost identical for CC and SR, while slightly higher for AC. Compared to the clear-sky average, the reflected shortwave (SW) fluxes for the three DCS components are greater by a factor of 2–3 and create a strong cooling effect at TOA. The calculated SW and LW cloud radiative forcing (CRF) of AC contribute up to 31% of total NET CRF, while CC and SR contribute only 4 and 11%, respectively. The hybrid classification further lays the groundwork for studying the life cycle of DCS and improvements in geostationary satellite IR-based precipitation retrievals.

Hydrological years 2006 (HY06, 10/2005-09/2006) and 2007 (HY07, 10/2006-09/2007) provide a unique opportunity to examine hydrological extremes in the central US because there are no other examples of two such highly contrasting precipitation extremes occurring in consecutive years at the Southern Great Plains (SGP) in recorded history. The HY06 annual precipitation in the state of Oklahoma, as observed by the Oklahoma Mesonet, is around 61% of the normal (92.84 cm, based on the 1921-2008 climatology), which results in HY06 the second-driest year in the record. In particular, the total precipitation during the winter of 2005-06 is only 27% of the normal, and this winter ranks as the driest season. On the other hand, the HY07 annual precipitation amount is 121% of the normal and HY07 ranks as the seventh-wettest year for the entire state and the wettest year for the central region of the state. Summer 2007 is the second-wettest season for the state. Large-scale dynamics play a key role in these extreme events. During the extreme dry period (10/2005-02/2006), a dipole pattern in the 500-hPa GH anomaly existed where an anomalous high was over the southwestern U.S. region and an anomalous low was over the Great Lakes. This pattern is associated with inhibited moisture transport from the Gulf of Mexico and strong sinking motion over the SGP, both contributing to the extreme dryness. The precipitation deficit over the SGP during the extreme dry period is clearly linked to significantly suppressed cyclonic activity over the southwestern U.S., which shows robust relationship with the Western Pacific (WP) teleconnection pattern. The precipitation events during the extreme wet period (May-July 2007) were initially generated by active synoptic weather patterns, linked with moisture transport from the Gulf of Mexico by the northward low level jet, and enhanced by the mesoscale convective systems. Although the drought and pluvial conditions are dominated by large-scale dynamic patterns, we have demonstrated that the two positive feedback processes during the extreme dry and wet periods found in this study play a key role to maintain and reinforce the length and severity of existing drought and flood events. For example, during the extreme dry period, with less clouds, LWP, PWV, precipitation, and thinner Cu cloud thickness, more net radiation was absorbed and used to evaporate water from the ground. The evaporated moisture, however, was removed by low-level divergence. Thus, with less precipitation and removed atmospheric moisture, more absorbed incoming solar radiation was used to increase surface temperature and to make the ground drier.

[1] Asian dust events occur frequently during the boreal spring season. Their optical properties have been analyzed by using a combination of source region (ground-based and satellite) and remote Pacific Ocean (aircraft) measurements during the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) field campaign which lasted from 7 April to 15 May 2006. A strong dust event originating from the Gobi Desert and passing over the Xianghe surface site on 17 April 2006 has been extensively analyzed. The surface averaged aerosol optical depth (AOD) values increased from 0.17 (clear sky) to 4.0 (strong dust), and the Angstrom exponent (α) dropped from 1.26 (clear sky) to below 0.1. Its total downwelling SW flux over the Xianghe site (thousands of kilometers away from the dust source region) is only 46% of the clear-sky value with almost no direct transmission and nearly double the diffuse SW clear-sky value. This event was also captured 6 days later by satellite observations as well as the UND/NASA DC-8 aircraft over the eastern Pacific Ocean. The DC-8 measurements in the remote Pacific region further classified the plumes into dust dominant, pollution dominant, and a mixture of dust and pollution events. HYSPLIT backward trajectories not only verified the origins of each case we selected but also showed (1) two possible origins for the dust: the Gobi and Taklimakan deserts; and (2) pollution: urban areas in eastern China, Japan, and other industrialized cities east of the two deserts. Based on the averaged satellite retrieved AOD data (0.5° × 0.5° grid box), declining AOD values with respect to longitude demonstrated the evolution of the transpacific transport pathway of Asian dust and pollution over the period of the field campaign.