doi:10.1007/s00376-015-5032-8

Bouniol D., F. Couvreux, P. H. Kamsu-Tamo, M. Leplay, F. Guichard, F. Favot, and E. J. O'Connor, 2012: Diurnal and seasonal cycles of cloud occurrences,types, and radiative impact over West Africa. J. Appl. Meteor. Climatol., 51, 534-553, doi: 10.1175/JAMC-D-11-051.1.10.1175/JAMC-D-11-051.1

c7262364-58ee-43b4-9743-9c66d8c867bb

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http://www.researchgate.net/publication/258658961_Diurnal_and_Seasonal_Cycles_of_Cloud_Occurrences_Types_and_Radiative_Impact_over_West_Africa

http://www.researchgate.net/publication/258658961_Diurnal_and_Seasonal_Cycles_of_Cloud_Occurrences_Types_and_Radiative_Impact_over_West_AfricaAbstract This study focuses on the occurrence and type of clouds observed in West Africa, a subject that has been neither much documented nor quantified. It takes advantage of data collected above Niamey, Niger, in 2006 with the Atmospheric Radiation Measurement (ARM) Mobile Facility. A survey of cloud characteristics inferred from ground measurements is presented with a focus on their seasonal evolution and diurnal cycle. Four types of clouds are distinguished: high-level clouds, deep convective clouds, shallow convective clouds, and midlevel clouds. A frequent occurrence of the latter clouds located at the top of the Saharan air layer is highlighted. High-level clouds are ubiquitous throughout the period whereas shallow convective clouds are mainly noticeable during the core of the monsoon. The diurnal cycle of each cloud category and its seasonal evolution are investigated. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) data are used to demonstrate that these four cloud types (in addition to stratocumulus clouds over the ocean) are not a particularity of the Niamey region and that midlevel clouds are present over the Sahara during most of the monsoon season. Moreover, using complementary datasets, the radiative impact of each type of clouds at the surface level has been quantified in the short- and longwave domains. Midlevel clouds and anvil clouds have the largest impact, respectively, in longwave (about 15 W m 612 ) and shortwave (about 150 W m 612 ) radiation. Furthermore, midlevel clouds exert a strong radiative forcing during the spring at a time when the other cloud types are less numerous.

Chen C., W. R. Cotton, 1987: The physics of the marine stratocumulus-capped mixed layer. J. Atmos. Sci., 44, 2951- 2977.10.1175/1520-0469(1987)0442.0.CO;2

716b5425-e452-42b0-877c-58a6d93719a3

23696e69233ee2809f5a80feef9d2531

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249608974_The_Physics_of_the_Marine_Stratocumulus-Capped_Mixed_Layer

refpaperuri:(5b9ddf13e30623417a1f541076ac3b66)

http://www.researchgate.net/publication/249608974_The_Physics_of_the_Marine_Stratocumulus-Capped_Mixed_LayerAbstract In order to simulate the stratocumulus-capped mixed layer, a one-dimensional stratocumulus model is developed. This model consists of five major points: 1) a one-dimensional (1D) option of the CSU Cloud/Mesoscale Model, 2) a partially diagnostic higher-order turbulence model, 3) an atmospheric radiation model, 4) a partial condensation parameterization, and 5) the drizzle process. This model is tested against the observed structure of the marine stratocumulus layer reported by Brost et al. In this paper we also investigate the interactions among the following physical processes: atmospheric radiation, cloud microphysics, vertical wind shear, turbulent mixing, large-scale divergence, the sea surface temperature and the presence of high-level clouds above the capping inversion. The model simulated fields were found to be in generally good agreement with observations, although the amount of cloud liquid water predicted was too large. This may have been a result of employing a wind profile that exhibits somewhat weaker shear than observed, since the sensitivity experiment with an unbalanced wind similar to that observed produced liquid water contents similar to the observed values. It is also found that drizzle precipitation greatly alters the liquid water content of the cloud and the rate of radiative cooling. This then feeds back into the turbulence structure of the cloud. For the case with large-scale subsidence and the presence of high-level clouds above the capping inversion, the effect of cloud top radiative cooling is found to become less important. Longer time integrations (up to 6 hours) revealed a 15 to 20 min periodicity in cloud top entrainment. The length of the period of oscillation was regulated by the magnitude of shear and the presence of drizzle. Complete removal of shear and drizzle processes resulted in the elimination of sporadic entrainment. Finally, sensitivity experiments were also conducted to examine the role of shortwave radiation. It is found that the influence of shortwave radiation on the cloud layer varies with the intensity of overlying large-scale subsidence and the moisture content of the airmass overlying the capping inversion.

Chernykh I. V., R. E. Eskridge, 1996: Determination of cloud amount and level from radiosonde soundings. J. Appl. Meteor., 35, 1362- 1369.10.1175/1520-0450(1996)0352.0.CO;2

a8b31ee2-1417-4c4f-bb88-147d308cfbad

2db3c57b37ce61386c4baeee8fc2275c

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F255894200_Determination_of_cloud_amount_and_level_from_radiosonde_soundings

refpaperuri:(99328687049f58d24823a861d1fcb8c8)

http://www.researchgate.net/publication/255894200_Determination_of_cloud_amount_and_level_from_radiosonde_soundingsReports that a method for predicting cloud amounts developed in the former Soviet Union is supplemented with a new method for determining the base and tops of clouds. Criteria for predicting a cloud layer; Result of analyzing radiosonde data; Relationship between cloud amount dewpoint depression within a predicted cloud layer.

Chernykh I. V., O. A. Alduchov, and R. E. Eskridge, 2000: Trends in low and high cloud boundaries and errors in height determination of cloud boundaries. Bull. Amer. Meteor. Soc., 82, 1941- 1947.10.1175/1520-0477(2001)0822.3.CO;2

e088bded-7b2d-4e2b-ae56-ca8213279f03

888f7affdf90010fd994e4c831cbfede

http://onlinelibrary.wiley.com/resolve/reference/ADS?id=2001BAMS...82.1941C

http://onlinelibrary.wiley.com/resolve/reference/ADS?id=2001BAMS...82.1941CABSTRACT Clouds are important to climate and climate trends. To determine trends in cloud-base heights and cloud-top heights, the Comprehensive Aerological Reference Data Set (CARDS) and the method of Chernykh and Eskridge are used to diagnose cloud base, top, and amount. Trends in time series of cloud bases and tops at 795 radiosonde stations from 1964 to 1998 are presented. It was found that trends in cloud-base height and cloud-top height are seasonally dependent and a function of cloud cover amount. There was a small increase in multilayer cloudiness in all seasons. Geographical distributions of decadal changes of cloud bases and tops were spatially nonuniform and depended upon the season. To estimate the errors made in calculating the heights of cloud boundaries, an analysis was made of the response of the thermistors and hygristors. Thermistors and hygristors are linear sensors of the first order. From this it is shown that the distance between calculated inflection points (cloud boundaries) of observed and true values is exactly equal to the time constant of the sensor times the balloon speed. More accurate cloud boundaries can be determined using this finding.

Clothiaux E. E., T. P. Ackerman, G. C. Mace, K. P. Moran, R. T. Marchand , M. A. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39, 645- 665.3e1820d5-5cc4-4be6-8979-f1394315131f/s?wd=paperuri%3A%283c45307a9ef203f333cce4c6fe06c847%29&filter=sc_long_sign&sc_ks_para=q%3D2000%3A%20Objective%20determination%20of%20cloud%20heights%20and%20radar%20reflectivities%20using%20a%20combination%20of%20active%20remote%20sensors%20at%20the%20ARM%20CART%20sites&tn=SE_baiduxueshu_c1gjeupa&ie=utf-8

Cotton W. R., R. A. Anthes, 1989: Storm and Cloud Dynamics. Academic Press, San Diego, USA, 883 pp./s?wd=paperuri%3A%28f500a3b722d9704922e22c3053529441%29&filter=sc_long_sign&sc_ks_para=q%3DSTORM%20AND%20CLOUD%20DYNAMICS&tn=SE_baiduxueshu_c1gjeupa&ie=utf-8

Craven J. P., R. E. Jewell, and H. E. Brooks, 2002: Comparison between observed convective cloud-base heights and lifting condensation level for two different lifted parcels. Wea.Forecasting, 17, 885- 890.10.1175/1520-0434(2002)017<0885:CBOCCB>2.0.CO;2

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237256629_Comparison_between_Observed_Convective_Cloud-Base_Heights_and_Lifting_Condensation_Level_for_Two_Different_Lifted_Parcels%2Ffile%2F0deec528bd8787cdf0000000.pdf

refpaperuri:(7c8fcea34dd00efe02efdbd5eee675ad)

http://www.researchgate.net/publication/237256629_Comparison_between_Observed_Convective_Cloud-Base_Heights_and_Lifting_Condensation_Level_for_Two_Different_Lifted_Parcels/file/0deec528bd8787cdf0000000.pdfApproximately 400 Automated Surface Observing System (ASOS) observations of convective cloud-base heights at 2300 UTC were collected from April through August of 2001. These observations were compared with lifting condensation level (LCL) heights above ground level determined by 0000 UTC rawinsonde soundings from collocated upper-air sites. The LCL heights were calculated using both surface-based parcels (SBLCL) and mean-layer parcels (MLLCL sing mean temperature and dewpoint in lowest 100 hPa). The results show that the mean error for the MLLCL heights was substantially less than for SBLCL heights, with SBLCL heights consistently lower than observed cloud bases. These findings suggest that the mean-layer parcel is likely more representative of the actual parcel associated with convective cloud development, which has implications for calculations of thermodynamic parameters such as convective available potential energy (CAPE) and convective inhibition. In addition, the median value of surface-based CAPE (SBCAPE) was more than 2 times that of the mean-layer CAPE (MLCAPE). Thus, caution is advised when considering surface-based thermodynamic indices, despite the assumed presence of a well-mixed afternoon boundary layer.

Del Genio, A. D., A. B. Wolf, M. S. Yao, 2005: Evaluation of regional cloud feedbacks using single-column models. J. Geophys. Res., 110,D15S13, doi: 10.1029/2004JD005011.10.1029/2004JD005011

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004JD005011%2Fcitedby

refpaperuri:(7010341b68cfd6c38f7018ba8ecd3d4b)

http://onlinelibrary.wiley.com/doi/10.1029/2004JD005011/citedby[1] Cloud feedbacks in a warmer climate have not yet been constrained by models or observations. We present an approach that combines a general circulation model (GCM), single-column model (SCM), satellite and surface remote sensing data, and analysis product to infer regional cloud feedbacks and evaluate model simulations of them. The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) continuous forcing product, derived from a mesoscale analysis constrained by top-of-atmosphere and surface data, provides long-term advective forcing that links the models to the data. We drive an SCM with the continuous forcing for 10 cold season months in which synoptic forcing dominates the meteorology. Cloud feedbacks in midlatitude winter are primarily responses to changes in dynamical forcing. Thus we select times when observed advective forcing anomalies resemble doubled CO 2 advective forcing changes in the parent GCM. For these times we construct cloud type anomaly histograms in the International Satellite Cloud Climatology Project and Active Remotely Sensed Cloud Locations data sets and simulated versions of these histograms in the SCM. Comparison of the SCM subset to GCM doubled CO 2 cloud type changes tells us how relevant the selected times are to the GCM's cloud feedbacks, while comparisons of the SCM to the data tell us how well the model performs in these situations. The data suggest that in midlatitude winter, high thick clouds should increase while cirrus and low clouds decrease in upwelling regimes in a climate warming. Downwelling regime cloud feedbacks are dominated by changes in low clouds but are not as well constrained by the data.

Doswell III, C. A., E. N. Rasmussen, 1994: The effect of neglecting the virtual temperature correction on CAPE calculations. Wea.Forecasting, 9, 625- 629.10.1175/1520-0434(1994)0092.0.CO;2

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F255874994_The_effect_of_neglecting_the_virtual_temperature_correction_on_CAPE_calculations

refpaperuri:(bcab018363d9dac4faaf406ccecb4a78)

http://www.researchgate.net/publication/255874994_The_effect_of_neglecting_the_virtual_temperature_correction_on_CAPE_calculationsAbstract A simple theoretical analysis of the impact of neglecting the virtual correction on calculation of CAPE is made. This theory suggests that while ignoring the virtual correction does not introduce much error for large CAPE values, the relative error can become substantial for small CAPE. A test of the theory is done by finding the error made by ignoring the virtual correction to CAPE for all the soundings in 1992 having positive CAPE (when the correction is made). Results of this empirical test confirm that the relative error made in ignoring the correction increases with decreasing CAPE. A number of other “corrections” to CAPE might be considered. In a discussion of the issues associated with the results of the analysis, it is recommended that CAPE calculations should include the virtual correction but that other complications should be avoided for most purposes, especially when making comparisons of CAPE values. A standardized CAPE calculation also is recommended.

Espy J. P., 1841: The Philosophy of Storms. C. C. Little and J. Brown, Boston, USA, 552 pp.

Fan X. H., H. B. Chen, X. G. Xia, Z. Q. Li, and M. Cribb, 2010: Aerosol optical properties from the Atmospheric Radiation Measurement Mobile Facility at Shouxian, China. J. Geophys. Res., 115,D00K33, doi: 10.1029/2010JD014650.

Haeffelin, M., Coauthors, 2005: SIRTA, a ground-based atmospheric observatory for cloud and aerosol research. Ann. Geophys., 23, 253- 275.10.5194/angeo-23-253-2005

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2f385d357cb2f7be0df1986679a28c0a

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F29620371_SIRTA_a_ground-based_atmospheric_observatory_for_cloud_and_aerosol_research%3Fev%3Dprf_cit

refpaperuri:(91d99fa02247d8c2510a8f9231526dab)

http://www.researchgate.net/publication/29620371_SIRTA_a_ground-based_atmospheric_observatory_for_cloud_and_aerosol_research?ev=prf_citGround-based remote sensing observatories have a crucial role to play in providing data to improve our understanding of atmospheric processes, to test the performance of atmospheric models, and to develop new methods for future space-borne observations. Institut Pierre Simon Laplace, a French research institute in environmental sciences, created the Site Instrumental de Recherche par tection Atmosphrique (SIRTA), an atmospheric observatory with these goals in mind. Today SIRTA, located 20km south of Paris, operates a suite a state-of-the-art active and passive remote sensing instruments dedicated to routine monitoring of cloud and aerosol properties, and key atmospheric parameters. Detailed description of the state of the atmospheric column is progressively archived and made accessible to the scientific community. This paper describes the SIRTA infrastructure and database, and provides an overview of the scientific research associated with the observatory. Researchers using SIRTA data conduct research on atmospheric processes involving complex interactions between clouds, aerosols and radiative and dynamic processes in the atmospheric column. Atmospheric modellers working with SIRTA observations develop new methods to test their models and innovative analyses to improve parametric representations of sub-grid processes that must be accounted for in the model. SIRTA provides the means to develop data interpretation tools for future active remote sensing missions in space (e.g. CloudSat and CALIPSO). SIRTA observation and research activities take place in networks of atmospheric observatories that allow scientists to access consistent data sets from diverse regions on the globe.

Illingworth, A. J., Coauthors, 2007: Cloudnet-continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Amer. Meteor. Soc.,88, 883-898, doi: 10.1175/BAMS-88-6-883.10.1175/BAMS-88-6-883

28bcb2b3-8c5b-45f5-9511-667d44cdd162

66db9286b9e93d844da1ede0ec238ef4

http://www.researchgate.net/publication/216674059_Cloudnet_-_Continuous_evaluation_of_cloud_profiles_in_seven_operational_models_using_ground-based_observations?ev=prf_cit

http://www.researchgate.net/publication/216674059_Cloudnet_-_Continuous_evaluation_of_cloud_profiles_in_seven_operational_models_using_ground-based_observations?ev=prf_citAbstract The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasieal time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Mto-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

Intergovernmental Panel on Climate Change (IPCC), 2007: The Physical Science Basis. S. Solomon et al., Eds. Cambridge Univ. Press, Cambridge, U. K., 996 pp.

Intergovernmental Panel on Climate Change (IPCC), 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Summary for Policymakers. Stocker et al., Eds., 33 pp. [Available online at http://www.climatechange2013.org/.]

d2bc0988f8b7d9451492d18d1a70103e

http://www.researchgate.net/publication/242691805_Intergovernmental_Panel_on_Climate_Change_Fifth_Assessment_Report

http://www.researchgate.net/publication/242691805_Intergovernmental_Panel_on_Climate_Change_Fifth_Assessment_ReportFifth Assessment ReportThe Intergovernmental Panel on Climate Change (IPCC) publishes Assessment Reports every six to seven years, with the IPCC First Assessment report published in 1990. The Fifth Assessment Report is being published in stages across 2013 and 2014.Each of the three Working Groups contributes to the development of Assessment Reports:

Kalesse H., P. Kollias, 2013: Climatology of high cloud dynamics using profiling ARM Doppler radar observations. J.Climate, 26, 6340- 6359.10.1175/JCLI-D-12-00695.1

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F275468026_Climatology_of_High_Cloud_Dynamics_Using_Profiling_ARM_Doppler_Radar_Observations

refpaperuri:(f3342d2f0f9a3d6d5c29a16e6f78ebf2)

http://www.researchgate.net/publication/275468026_Climatology_of_High_Cloud_Dynamics_Using_Profiling_ARM_Doppler_Radar_ObservationsAbstract Ice cloud properties are influenced by cloud-scale vertical air motion. Dynamical properties of ice clouds can be determined via Doppler measurements from ground-based, profiling cloud radars. Here, the decomposition of the Doppler velocities into reflectivity-weighted particle velocity V t and vertical air motion w is described. The methodology is applied to high clouds observations from 35-GHz profiling millimeter wavelength radars at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) climate research facility in Oklahoma (January 1997–December 2010) and the ARM Tropical Western Pacific (TWP) site in Manus (July 1999–December 2010). The Doppler velocity measurements are used to detect gravity waves (GW), whose correlation with high cloud macrophysical properties is investigated. Cloud turbulence is studied in the absence and presence of GW. High clouds are less turbulent when GW are observed. Probability density functions of V t , w , and high cloud macrophysical properties for the two cloud subsets (with and without GW) are presented. Air-density-corrected V t for high clouds for which GW (no GW) were detected amounted to hourly means and standard deviations of 0.89 ± 0.52 m s 611 (0.8 ± 0.48 m s 611 ) and 1.03 ± 0.41 m s 611 (0.86 ± 0.49 m s 611 ) at SGP and Manus, respectively. The error of w at one standard deviation was estimated as 0.15 m s 611 . Hourly means of w averaged around 0 m s 611 with standard deviations of ±0.27 (SGP) and ±0.29 m s 611 (Manus) for high clouds without GW and ±0.22 m s 611 (both sites) for high clouds with GW. The midlatitude site showed stronger seasonality in detected high cloud properties.

Kollias P., M. A. Miller, K. L. Johnson, M. P. Jensen, and D. T. Troyan, 2009: Cloud, thermodynamic, and precipitation in West Africa during 2006. J. Geophys. Res., 114,D00E08, doi: 10.1029/2008JD010641.

Kunnen R. P. J., C. Siewert, M. Meinke, W. Schrder, and K. D. Beheng, 2013: Numerically determined geometric collision kernels in spatially evolving isotropic turbulence relevant for droplets in clouds. Atmospheric Research, 127, 8- 21.10.1016/j.atmosres.2013.02.003

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WOS:000319099800002

d1f199c2607cc316146bf6e6f685d1cc

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809513000549

refpaperuri:(6f61b5eb8d9ea2e1fe4756222fdc5185)

http://www.sciencedirect.com/science/article/pii/S0169809513000549The collision probability of cloud droplets in a turbulent flow has been investigated using direct numerical simulation. A novel simulation method is used in which synthetic turbulence is generated at the inlet and is transported through the flow domain with a mean carrier flow. For the dispersed phase a Lagrangian point particle model is applied. Collision statistics have been gathered for ten droplet sizes ranging from 5 to 50 tun in different statistic volumes in the turbulent flows with dissipation rates between 30 and 250 cm(2) s(-3) and Taylor-scale Reynolds numbers between 16.4 and 22.4. It is found that turbulence enhances the collision probability by factors up to 1.66 relative to gravitational settling. The resulting geometric collision kernel is decomposed into its primary contributions: the radial distribution function (RDF) and the mean radial relative velocity. The RDF quantifying the preferential droplet concentration reaches values up to 8.6, while a random distribution corresponds to I. The mean radial relative velocity is enhanced by factors up to 1.18 relative to gravitational settling. The findings are in good quantitative agreement with results from other studies reported in the literature. (C) 2013 Elsevier B.V. All rights reserved.

Li Z. Q., M. Cribb, F. L. Chang, A. Trishchenko, and Y. Luo, 2005: Natural variability and sampling errors in solar radiation measurements for model validation over the Atmospheric Radiation Measurement Southern Great Plains region. J. Geophys. Res., 110,D15S19, doi: 10.1029/2004JD005028.10.1029/2004JD005028

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004JD005028%2Fcitedby

refpaperuri:(9b4a99c40adcd268af93d57e77c4a7da)

http://onlinelibrary.wiley.com/doi/10.1029/2004JD005028/citedbyABSTRACT [1] Ground-based radiation measurements are frequently used for validating the performance of a model in simulating clouds. Such important questions are often raised as: (1) How well do the measurements represent model grid mean values?; (2) How much of model-observation differences can be attributed to inherent sampling errors?; and (3) What scale does modeling need to be performed in order to capture the cloud variation? We attempt to address these questions using surface solar irradiance data retrieved from the Geostationary Operational Environmental Satellite (GOES) and measured at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The satellite retrievals are used to mimic ground measurements with various spatial densities and temporal frequencies, from which the sampling errors of the ground observations are quantified and characterized. Most of the differences between point-specific measurements and area-mean satellite retrievals originate from ground sampling errors. We quantify these errors for different months, model grid sizes, and integration intervals. In March 2000, for example, the sampling error is 16 W m2 for instantaneous irradiances averaged over an area of 10 × 10 km2. It increases to 46 and 64 W m2 if the model grid size is enlarged to 200 × 200 km2 and 400 × 400 km2, respectively. The sampling uncertainties decrease rapidly as the time-averaging interval increases up to 24 hours and then level off to a relatively small and stable value. Averaging over periods greater than 5 days reduces the error to a magnitude of less than 15 W m2 over all grid sizes. The sampling error also decreases as the number of ground stations increases inside a grid, but the most substantial reduction occurs as the number of ground sites increases from 1 to 2 or 3 for a grid size of 200 × 200 km2. This means that for computing grid-mean surface solar irradiance, there is no need for an overly dense network of observation stations.

Li, Z. Q., Coauthors, 2011: East Asian studies of tropospheric aerosols and their impact on regional climate (EAST-AIRC): An overview. J. Geophys. Res., 116,D00K34, doi: 10.1029/2010JD015257.10.1029/2010JD015257

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http://onlinelibrary.wiley.com/doi/10.1029/2010JD015257/abstract

http://onlinelibrary.wiley.com/doi/10.1029/2010JD015257/abstract[1] As the most populated region of the world, Asia is a major source of aerosols with potential large impact over vast downstream areas. Papers published in this special section describe the variety of aerosols observed in China and their effects and interactions with the regional climate as part of the East Asian Study of Tropospheric Aerosols and their Impact on Regional Climate (EAST-AIRC). The majority of the papers are based on analyses of observations made under three field projects, namely, the Atmospheric Radiation Measurements (ARM) Mobile Facility mission in China (AMF-China), the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE), and the Atmospheric Aerosols of China and their Climate Effects (AACCE). The former two are U.S.-China collaborative projects, and the latter is a part of the China's National Basic Research program (or often referred to as 973 project). Routine meteorological data of China are also employed in some studies. The wealth of general and specialized measurements lead to extensive and close-up investigations of the optical, physical, and chemical properties of anthropogenic, natural, and mixed aerosols; their sources, formation, and transport mechanisms; horizontal, vertical, and temporal variations; direct and indirect effects; and interactions with the East Asian monsoon system. Particular efforts are made to advance our understanding of the mixing and interaction between dust and anthropogenic pollutants during transport. Several modeling studies were carried out to simulate aerosol impact on radiation budget, temperature, precipitation, wind and atmospheric circulation, fog, etc. In addition, impacts of the Asian monsoon system on aerosol loading are also simulated.

Mace G. G., S. Benson, 2008: The vertical structure of cloud occurrence and radiative forcing at the SGP ARM site as revealed by 8 years of continuous data. J. Climate,21, 2591-2610, doi: 10.1175/2007JCLI1987.1.10.1175/2007JCLI1987.1

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F240687055_The_Vertical_Structure_of_Cloud_Occurrence_and_Radiative_Forcing_at_the_SGP_ARM_Site_as_Revealed_by_8_Years_of_Continuous_Data

refpaperuri:(dd64f3a1df76822d35eb4e4fb44e35de)

http://www.researchgate.net/publication/240687055_The_Vertical_Structure_of_Cloud_Occurrence_and_Radiative_Forcing_at_the_SGP_ARM_Site_as_Revealed_by_8_Years_of_Continuous_DataAbstract Data collected at the Atmospheric Radiation Measurement (ARM) Program ground sites allow for the description of the atmospheric thermodynamic state, cloud occurrence, and cloud properties. This information allows for the derivation of estimates of the effects of clouds on the radiation budget of the surface and atmosphere. Herein 8 yr of continuous data collected at the ARM Southern Great Plains (SGP) Climate Research Facility (ACRF) are analyzed, and the influence of clouds on the radiative flux divergence of solar and infrared energy on annual, seasonal, and monthly time scales is documented. Given the uncertainties in derived cloud microphysical properties that result in calculated radiant flux errors, it is demonstrated that the ability to quantitatively resolve all but the largest heating and cooling influences by clouds is marginal for averaging periods less than 1 month. Concentrating on seasonal and monthly averages, it is found that the net column-integrated radiative effect of clouds on the atmosphere is nearly neutral at this middle-latitude location. However, a net heating of the upper troposphere by upper-tropospheric clouds and a cooling of the lower troposphere by boundary layer clouds is documented. The balance evolves over the course of an annual cycle as the troposphere deepens in summer and boundary layer clouds become less frequent relative to upper-tropospheric clouds. Although the top-of-atmosphere IR radiative effect is nearly invariant through the annual cycle, the seasonally varying heating profile is determined largely by the convergence of IR flux because solar heating is offset by IR cooling within the column.

Manzato A., 2007. Sounding-derived indices for neural network based short-term thunderstorm and rainfall forecasts. Atmospheric Research, 83, 349-365.10.1016/j.atmosres.2005.10.021

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cda8f8a2dacfc5baa2082810cff38f93

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809506001335

refpaperuri:(adf62445ad83a1cc4299912d9869b7bc)

http://www.sciencedirect.com/science/article/pii/S0169809506001335ABSTRACT A neural network-based scheme to do a multivariate analysis for forecasting the occurrence and intensity of a meteo event is presented. Many sounding-derived indices are combined together to build a short-term forecast of thunderstorm and rainfall events, in the plain of the Friuli Venezia Giulia region (hereafter FVG, NE Italy).For thunderstorm forecasting, sounding, lightning strikes and mesonet station data (rain and wind) from April to November of the years 1995–2002 have been used to train and validate the artificial neural network (hereafter ANN), while the 2003 and 2004 data have been used as an independent test sample. Two kind of ANNs have been developed: the first is a “classification model” ANN and is built for forecasting the thunderstorm occurrence. If this first ANN predicts convective activity, then a second ANN, built as a “regression model”, is used for forecasting the thunderstorm intensity, as defined in a previous article.The classification performances are evaluated with the ROC diagram and some indices derived from the Table of Contingency (like KSS, FAR, Odds Ratio). The regression performances are evaluated using the Mean Square Error and the linear cross correlation coefficient R.A similar approach is applied to the problem of 6 h rainfall forecast in the Friuli Venezia Giulia plain, but in this second case the data cover the period from 1992 to 2004. Also the forecasts of binary events (defined as the occurrence of 5, 20 or 40 mm of maximum rain), made by classification and regression ANN, were compared. Particular emphasis is given to the sounding-derived indices which are chosen in the first places by the predictor forward selection algorithm.

Minnis P., Y. H. Yi, J. P. Huang, and J. K. Ayers, 2005: Relationships between radiosonde and RUC-2 meteorological conditions and cloud occurrence determined from ARM data. J. Geophys. Res., 110,D23, doi: 10.1029/2005JD006005.10.1029/2005JD006005

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228653451_Relationships_between_radiosonde_and_RUC-2_meteorological_conditions_and_cloud_occurrence_determined_from_ARM_data._J_Geophys_Res_110

refpaperuri:(a15e84af292beff26dd89b7845e65dee)

http://www.researchgate.net/publication/228653451_Relationships_between_radiosonde_and_RUC-2_meteorological_conditions_and_cloud_occurrence_determined_from_ARM_data._J_Geophys_Res_110ABSTRACT 1] Relationships between modeled and measured meteorological state parameters and cloudy and cloud-free conditions are examined using data taken over the ARM (Atmospheric Radiation Measurement) Southern Great Plains Central Facility between 1 March 2000 and 28 February 2001. Cloud vertical layering was determined from the Active Remotely Sensed Cloud Location product based on the ARM active sensor measurements. Both temperature and relative humidity (RH) observations from balloon-borne Vaisala RS80-15LH radiosonde (SONDE) and the Rapid Update Cycle (RUC) 40-km resolution model are highly correlated, but the SONDE RHs generally exceed those from RUC. Inside cloudy layers, the RH from SONDE is 2–14% higher than the RH from RUC at all pressure levels. Although the layer mean RH within clouds is much greater than the layer mean RH outside clouds or in clear skies, RH thresholds chosen as a function of temperature can more accurately diagnose cloud occurrence for either data set than a fixed RH threshold. For overcast clouds (cloud amount greater than or equal to 90%), it was found that the 50% probability RH threshold for diagnosing a cloud, within a given upper tropospheric layer, is roughly 90% for the SONDE and 80% for RUC data. For partial cloud cover (cloud amount is less than 90%), the SONDE RH thresholds are close to those for RUC at a given probability in upper tropospheric layers. Cloud probability was found to be only minimally dependent on vertical velocity. In the upper troposphere, SONDE ice-supersaturated air occurred in 8 and 35% of the clear and cloudy layers, respectively. The RH was distributed exponentially in the ice supersaturated layers as found in previous studies. The occurrence of high-altitude, ice-supersaturated layers in the RUC data was roughly half of that in the SONDE data. Optimal thresholds were derived as functions of temperature to define the best RH thresholds for accurately determining the mean cloud cover. For warm clouds the typical SONDE threshold exceeds 87%, while the RH thresholds for cold clouds are typically less than 80% and greater than 90% with respect to liquid and ice water, respectively. Preliminary comparisons with satellite data suggest that the relationships between cloudiness and RH and T determined here could be useful for improving the characterization of cloud vertical structure from satellite data by providing information about low-level clouds that were obscured by high-level clouds viewed by the satellite. The results have potential for improving computations of atmospheric heating rate profiles and estimates of aircraft icing conditions. Similar analyses are recommended for later versions of the RUC analyses and forecasts. Citation: Minnis, P., Y. Yi, J. Huang, and K. Ayers (2005), Relationships between radiosonde and RUC-2 meteorological conditions and cloud occurrence determined from ARM data, J. Geophys. Res., 110, D23204, doi:10.1029/2005JD006005.

Naud C., J. P. Muller, and E. E. Clothiaux, 2003: Comparison between active sensor and radiosonde cloud boundaries over the ARM Southern Great Plains site. J. Geophys. Res., 108,D44140, doi: 10.1029/2002JD002887.10.1029/2002JD002887

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2002JD002887%2Fabstract

refpaperuri:(a9ce40012ebb99a8415eea2b7e53d20f)

http://onlinelibrary.wiley.com/doi/10.1029/2002JD002887/abstract[1] In order to test the strengths and limitations of cloud boundary retrievals from radiosonde profiles, 4 years of radar, lidar, and ceilometer data collected at the Atmospheric Radiation Measurements Southern Great Plains site from November 1996 through October 2000 are used to assess the retrievals of Wang and Rossow [1995] and Chernykh and Eskridge [1996] . The lidar and ceilometer data yield lowest-level cloud base heights that are, on average, within approximately 125 m of each other when both systems detect a cloud. These quantities are used to assess the accuracy of coincident cloud base heights obtained from radar and the two radiosonde-based methods applied to 200 m resolution profiles obtained at the same site. The lidar/ceilometer and radar cloud base heights agree by 0.156 0.423 km for 85.27% of the observations, while the agreement between the lidar/ceilometer and radiosonde-derived heights is at best 0.044 0.559 km for 74.60% of all cases. Agreement between radar- and radiosonde-derived cloud boundaries is better for cloud base height than for cloud top height, being at best 0.018 -0.641 km for 70.91% of the cloud base heights and 0.348 - 0.729 km for 68.27% of the cloud top heights. The disagreements between radar- and radiosonde-derived boundaries are mainly caused by broken cloud situations when it is difficult to verify that drifting radiosondes and fixed active sensors are observing the same clouds. In the case of the radar the presence of clutter (e.g., vegetal particles or insects) can affect the measurements from the surface up to approximately 3–5 km, preventing comparisons with radiosonde-derived boundaries. Overall, Wang and Rossow [1995] tend to classify moist layers that are not clouds as clouds and both radiosonde techniques report high cloud top heights that are higher than the corresponding heights from radar.

Poore K. D., J. H. Wang, and W. B. Rossow, 1995: Cloud layer thicknesses from a combination of surface and upper-air observations. J.Climate, 8, 550- 568.10.1175/1520-0442(1995)0082.0.CO;2

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82a7c706efe16cd984bf4efb5a8c4409

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F23929162_Cloud_layer_thicknesses_from_a_combination_of_surface_and_upper-air_observations

refpaperuri:(4c724a1b43fcfd037a22cf7afab19ba8)

http://www.researchgate.net/publication/23929162_Cloud_layer_thicknesses_from_a_combination_of_surface_and_upper-air_observationsAbstract Cloud layer thicknesses are derived from base and top altitudes by combining 14 years (1975-1988) of surface and upper-air observations at 63 sites in the Northern Hemisphere. Rawinsonde observations are employed to determine the locations of cloud-layer top and base by testing for dewpoint temperature depressions below some threshold value. Surface observations serve as quality cheeks on the rawinsonde-determined cloud properties and provide cloud amount and cloud-type information. The dataset provides layer-cloud amount, cloud type, high, middle, or low height classes, cloud-top heights, base heights and layer thicknesses, covering a range of latitudes from 0 to 80N. All data comes from land sites: 34 are located in continental interiors, 14 are near coasts, and 15 are on islands. The uncertainties in the derived cloud properties are discussed. For clouds classified by low-, mid-, and high-top altitudes, there are strong latitudinal and seasonal variations in the layer thickness only for high clouds. High-cloud layer thickness increases with latitude and exhibits different seasonal variations in different latitude zones: in summer, high-cloud layer thickness is a maximum in the Tropics but a minimum at high latitudes. For clouds classified into three types by base altitude or into six standard morphological types, latitudinal and seasonal variations in layer thickness are very small. The thickness of the clear surface layer decreases with latitude and reaches a summer minimum in the Tropics and summer maximum at higher latitudes over land, but does not vary much over the ocean. Tropical clouds occur in three base-altitude groups and the layer thickness of each group increases linearly with top altitude. Extratropical clouds exhibit two groups, one with layer thickness proportional to their cloud-top altitude and one with small (1000 m) layer thickness independent of cloud-top altitude.

Protat, A., Coauthors, 2014: Reconciling ground-based and space-based estimates of the frequency of occurrence and radiative effect of clouds around Darwin, Australia. J. Appl. Meteor. Climatol., 53, 456-478, doi: 10.1175/JAMC-D-13-072.1.10.1175/JAMC-D-13-072.1

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http://www.researchgate.net/publication/262988541_Reconciling_Ground-Based_and_Space-Based_Estimates_of_the_Frequency_of_Occurrence_and_Radiative_Effect_of_Clouds_around_Darwin_Australia?ev=auth_pub

http://www.researchgate.net/publication/262988541_Reconciling_Ground-Based_and_Space-Based_Estimates_of_the_Frequency_of_Occurrence_and_Radiative_Effect_of_Clouds_around_Darwin_Australia?ev=auth_pubThe objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar-lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar-Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar-lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day0903’1 for the shortwave and 0.8 K day0903’1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud-radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model-observation differences.

Rickenbach T., R. Nieto Ferreira, N. Guy, and E. Williams, 2009: Radar-observed squall line propagation and the diurnal cycle of convection in Niamey, Niger, during the 2006 African Monsoon and Multidisciplinary Analysis Intensive Observing Period. J. Geophys. Res., 114,D03107, doi: 10.1029/2008JD 010871.10.1029/2008JD010871

2c3faa66-5c99-4ed9-91a3-77ffab35695a

fd4a7358523a1238793e935b350c1754

http://onlinelibrary.wiley.com/doi/10.1029/2008JD010871/pdf

http://onlinelibrary.wiley.com/doi/10.1029/2008JD010871/pdf[1] Surface radar observations near Niamey, Niger, during the African MonsoonMultidisciplinary Analyses (AMMA) campaign in 2006 documented the structure,motion, and precipitation of cloud systems during the monsoon season. These uniqueobservations for that part of the Sahel were combined with satellite rain estimates and infrared satellite imagery to study the diurnal cycle of rainfall in Niamey, Niger. Thisstudy confirms the bimodal structure of the diurnal rainfall cycle in Niamey duringAMMA, seen by previous studies of West African rainfall. Radar analysis of squallline mesoscale convective systems (SLMCS) and non-MCS isolated convection clearly demonstrated that the nocturnal maximum was associated with the observed arrivaltime of westward propagating SLMCS. Satellite imagery suggested that these SLMCS formed in elevated terrain to the east of Niamey the prior afternoon. Radar observations showed that local isolated convection produced the smaller afternoon maximum. Early in the monsoon season, locally generated convection produced an afternoon diurnalrainfall maximum that was delayed by several hours compared to midseason when African easterly wave (AEW) activity was much greater. We suggest that the observed greatermean convective inhibition early in the season, perhaps tied to the absence of large-scale forcing from AEW, played a role in the delayed initiation time.

Riihimaki L. D., S. A. McFarlane, and J. M. Comstock, 2012: Climatology and formation of tropical midlevel clouds at the Darwin ARM Site. J. Climate,25, 6835-6850, doi: 10.1175/ JCLI-D-11-00599.1.10.1175/JCLI-D-11-00599.1

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F255811707_Climatology_and_Formation_of_Tropical_Midlevel_Clouds_at_the_Darwin_ARM_Site

refpaperuri:(426254144fbd0aac6a713cdffbe3a5bb)

http://www.researchgate.net/publication/255811707_Climatology_and_Formation_of_Tropical_Midlevel_Clouds_at_the_Darwin_ARM_SiteAbstract A 4-yr climatology of midlevel clouds is presented from vertically pointing cloud lidar and radar measurements at the Atmospheric Radiation Measurement Program (ARM) site at Darwin, Australia. Few studies exist of tropical midlevel clouds using a dataset of this length. Seventy percent of clouds with top heights between 4 and 8 km are less than 2 km thick. These thin layer clouds have a peak in cloud-top temperature around the melting level (0°C) and also a second peak around 6112.5°C. The diurnal frequency of thin clouds is highest during the night and reaches a minimum around noon, consistent with variation caused by solar heating. Using a 1.5-yr subset of the observations, the authors found that thin clouds have a high probability of containing supercooled liquid water at low temperatures: ~20% of clouds at 6130°C, ~50% of clouds at 6120°C, and ~65% of clouds at 6110°C contain supercooled liquid water. The authors hypothesize that thin midlevel clouds formed at the melting level are formed differently during active and break monsoon periods and test this over three monsoon seasons. A greater frequency of thin midlevel clouds are likely formed by increased condensation following the latent cooling of melting during active monsoon periods when stratiform precipitation is most frequent. This is supported by the high percentage (65%) of midlevel clouds with preceding stratiform precipitation and the high frequency of stable layers slightly warmer than 0°C. In the break monsoon, a distinct peak in the frequency of stable layers at 0°C matches the peak in thin midlevel cloudiness, consistent with detrainment from convection.

Sassen K., Z. E. Wang, 2012: The clouds of the middle troposphere: composition,radiative impact, and global distribution. Surveys in Geophysics, 33, 677-691, doi: 10.1007/ s10712-011-9163-x.10.1007/s10712-011-9163-x

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http://link.springer.com/10.1007/s10712-011-9163-x

http://link.springer.com/10.1007/s10712-011-9163-xAbstract<br/>The clouds of the middle troposphere span the temperature range where both ice and liquid water in a supercooled state can exist. However, because one phase tends to dominate, of the two midlevel cloud types, altostratus are deep ice-dominated, while altocumulus are shallow water-dominated, mixed-phase clouds with ice crystal virga typically trailing below. Multiple remote sensor examples of these cloud types are given to illustrate their main features, and the radiative consequences of the different cloud microphysical compositions are discussed. Spaceborne radar and lidar measurements using the CloudSat and CALIPSO satellites are analyzed to determine the global distributions of cloud frequencies and heights of these clouds. It is found that together these little-studied clouds cover ~25% of the Earth’s surface, which is about one-third of the total cloud cover, and thus represent a significant contribution to the planet’s energy balance.<br/>

Sherwood S. C., S. Bony, and J. L. Dufresne, 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature,505, 37-42, doi: 10.1038/nature12829.10.1038/nature12829

24380952

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http%3A%2F%2Fmed.wanfangdata.com.cn%2FPaper%2FDetail%2FPeriodicalPaper_PM24380952

refpaperuri:(855db0042e019bae05dd462d740399e4)

http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM24380952Equilibrium climate sensitivity refers to the ultimate change in global mean temperature in response to a change in external forcing. Despite decades of research attempting to narrow uncertainties, equilibrium climate sensitivity estimates from climate models still span roughly 1.5 to 5 degrees Celsius for a doubling of atmospheric carbon dioxide concentration, precluding accurate projections of future climate. The spread arises largely from differences in the feedback from low clouds, for reasons not yet understood. Here we show that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming.

Sobel A. H., S. E. Yuter, C. S. Bretherton, and G. N. Kiladis, 2004: Large-scale meteorology and deep convection during TRMM KWAJEX. Mon. Wea. Rev., 132, 422- 444.10.1175/1520-0493(2004)1322.0.CO;2

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http://www.researchgate.net/publication/230891965_Large-scale_meteorology_and_deep_convection_during_TRMM_KWAJEX

http://www.researchgate.net/publication/230891965_Large-scale_meteorology_and_deep_convection_during_TRMM_KWAJEXAbstract An overview of the large-scale behavior of the atmosphere during the Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX) is presented. Sounding and ground radar data collected during KWAJEX, and several routinely available datasets including the Geostationary Meteorological Satellite (GMS), NOAA outgoing longwave radiation (OLR), the Special Sensor Microwave Imager (SSM/I), and ECMWF operational analyses are used. One focus is on the dynamical characterization of synoptic-scale systems in the western/central tropical Pacific during KWAJEX, particularly those that produced the largest rainfall at Kwajalein. Another is the local relationships observed on daily time scales among various thermodynamic variables and areal average rain rate. These relationships provide evidence regarding the degree and kind of local thermodynamic control of convection. Although convection in the Marshall Islands and surrounding regions often appears chaotic when viewed in satellite imagery, the largest rain events at Kwajalein during the experiment were clearly associated with large-scale envelopes of convection, which propagated coherently over several days and thousands of kilometers, had clear signals in the lower-level large-scale wind field, and are classifiable in terms of known wave modes. Spectral filtering identifies mixed Rossby ravity (MRG) and Kelvin waves prominently in the OLR data. ropical depression ype disturbances are also evident. In some cases multiple wave types may be associated with a single event. Three brief case studies involving different wave types are presented. Daily-mean sounding data averaged over the five sounding sites show evidence of shallow convective adjustment, in that near-surface moist static energy variations correlate closely with lower-tropospheric temperature. Evidence of thermodynamic control of deep convection on daily time scales is weaker. Upper-tropospheric temperature is weakly correlated with near-surface moist static energy. There are correlations of relative humidity (RH) with deep convection. Significant area-averaged rainfall occurs only above a lower-tropospheric RH threshold of near 80%. Above this threshold there is a weak but significant correlation of further lower-tropospheric RH increases with enhanced rain rate. Upper-tropospheric RH increases more consistently with rain rate. Lag correlations suggest that higher lower-tropospheric RH favors subsequent convection while higher upper-tropospheric RH is a result of previous or current convection. Convective available potential energy and surface wind speed have weak negative and positive relationships to rain rate, respectively. A strong relationship between surface wind speed (a proxy for latent heat flux) and rain rate has been recently observed in the eastern Pacific. It is suggested that in the KWAJEX region, this relationship is weaker because there are strong zonal gradients of vertically integrated water vapor. The strongest surface winds tend to be easterlies, so that strong surface fluxes are accompanied by strong dry-air advection from the east of Kwajalein. These two effects are of opposite sign in the moist static energy budget, reducing the tendency for strong surface fluxes to promote rainfall.

Stephens G. L., 2005: Cloud feedbacks in the climate system: A critical review. J.Climate, 18, 237- 273.10.1175/JCLI-3243.1

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228622086_Cloud_feedbacks_in_the_climate_system_A_critical_review

refpaperuri:(33eb4566813785f3db5f8eb0943a2db3)

http://www.researchgate.net/publication/228622086_Cloud_feedbacks_in_the_climate_system_A_critical_reviewThis paper offers a critical review of the topic of cloud–climate feedbacks and exposes some of the underlying reasons for the inherent lack of understanding of these feedbacks and why progress might be expected on this important climate problem in the coming decade. Although many processes and related parameters come under the influence of clouds, it is argued that atmospheric processes fundamentally govern the cloud feedbacks via the relationship between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. It is also shown how perturbations to the atmospheric radiation budget that are induced by cloud changes in response to climate forcing dictate the eventual response of the global-mean hydrological cycle of the climate model to climate forcing. This suggests that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet’s hydrological cycle to climate radiative forcings. The paper provides a brief overview of the effects of clouds on the radiation budget of the earth–atmosphere system and a review of cloud feedbacks as they have been defined in simple systems, one being a system in radiative–convective equilibrium (RCE) and others relating to simple feedback ideas that regulate tropical SSTs. The systems perspective is reviewed as it has served as the basis for most feedback analyses. What emerges is the importance of being clear about the definition of the system. It is shown how different assumptions about the system produce very different conclusions about the magnitude and sign of feedbacks. Much more diligence is called for in terms of defining the system and justifying assumptions. In principle, there is also neither any theoretical basis to justify the system that defines feedbacks in terms of global–time-mean changes in surface temperature nor is there any compelling empirical evidence to do so. The lack of maturity of feedback analysis methods also suggests that progress in understanding climate feedback will require development of alternative methods of analysis. It has been argued that, in view of the complex nature of the climate system, and the cumbersome problems encountered in diagnosing feedbacks, understanding cloud feedback will be gleaned neither from observations nor proved from simple theoretical argument alone. The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations. Models provide the tool for diagnosing processes and quantifying feedbacks while observations provide the essential test of the model’s credibility in representing these processes. While GCM climate and NWP models represent the most complete description of all the interactions between the processes that presumably establish the main cloud feedbacks, the weak link in the use of these models lies in the cloud parameterization imbedded in them. Aspects of these parameterizations remain worrisome, containing levels of empiricism and assumptions that are hard to evaluate with current global observations. Clearly observationally based methods for evaluating cloud parameterizations are an important element in the road map to progress. Although progress in understanding the cloud feedback problem has been slow and confused by past analysis, there are legitimate reasons outlined in the paper that give hope for real progress in the future.

Tao W. K., J. P. Chen, Z. Q. Li, C. Wang, and C. D. Zhang, 2012: Impact of aerosols on convective clouds and precipitation. Rev. Geophys. , 50,RG2001, doi:10.1029/2011RG000369.10.1029/2011RG000369

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011RG000369%2Fpdf

refpaperuri:(3a0e464ebd5dd88cfae5e579c24e16f9)

http://onlinelibrary.wiley.com/doi/10.1029/2011RG000369/pdf[1] Aerosols are a critical factor in the atmospheric hydrological cycle and radiation budget. As a major agent for clouds to form and a significant attenuator of solar radiation, aerosols affect climate in several ways. Current research suggests that aerosol effects on clouds could further extend to precipitation, both through the formation of cloud particles and by exerting persistent radiative forcing on the climate system that disturbs dynamics. However, the various mechanisms behind these effects, in particular, the ones connected to precipitation, are not yet well understood. The atmospheric and climate communities have long been working to gain a better grasp of these critical effects and hence to reduce the significant uncertainties in climate prediction resulting from such a lack of adequate knowledge. Here we review past efforts and summarize our current understanding of the effect of aerosols on convective precipitation processes from theoretical analysis of microphysics, observational evidence, and a range of numerical model simulations. In addition, the discrepancies between results simulated by models, as well as those between simulations and observations, are presented. Specifically, this paper addresses the following topics: (1) fundamental theories of aerosol effects on microphysics and precipitation processes, (2) observational evidence of the effect of aerosols on precipitation processes, (3) signatures of the aerosol impact on precipitation from large-scale analyses, (4) results from cloud-resolving model simulations, and (5) results from large-scale numerical model simulations. Finally, several future research directions for gaining a better understanding of aerosol-cloud-precipitation interactions are suggested.

Trenberth K., J. T. Fasullo, and J. Kiehl, 2009: Earth's global energy budget. Bull. Amer. Meteor. Soc.,90, 311-324, doi: 10.1175/2008BAMS2634.1.10.1175/2008BAMS2634.1

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2a508f3936c540bebebefe765440103a

http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20093117158.html

refpaperuri:(cce9e305298175c12cdc88d042f4f2eb)

http://www.cabdirect.org/abstracts/20093117158.htmlAn update is provided on the Earth's global annual mean energy budget in the light of new observations and analyses. In 1997, Kiehl and Trenberth provided a review of past estimates and performed a number of radiative computations to better establish the role of clouds and various greenhouse gases in the overall radiative energy flows, with top-of-atmosphere (TOA) values constrained by Earth Radiation Budget Experiment values from 1985 to 1989, when the TOA values were approximately in balance. The Clouds and the Earth's Radiant Energy System (CERES) measurements from March 2000 to May 2004 are used at TOA but adjusted to an estimated imbalance from the enhanced greenhouse effect of 0.9 W m-2. Revised estimates of surface turbulent fluxes are made based on various sources. The partitioning of solar radiation in the atmosphere is based in part on the International Satellite Cloud Climatology Project (ISCCP) FD computations that utilize the global ISCCP cloud data every 3 h, and also accounts for increased atmospheric absorption by water vapor and aerosols. Surface upward longwave radiation is adjusted to account for spatial and temporal variability. A lack of closure in the energy balance at the surface is accommodated by making modest changes to surface fluxes, with the downward longwave radiation as the main residual to ensure a balance. Values are also presented for the land and ocean domains that include a net transport of energy from ocean to land of 2.2 petawatts (PW) of which 3.2 PW is from moisture (latent energy) transport, while net dry static energy transport is from land to ocean. Evaluations of atmospheric reanalyses reveal substantial biases. INSET: SPATIAL AND TEMPORAL SAMPLING.

Wang J. H., W. B. Rossow, 1995: Determination of cloud vertical structure from upper-air observations. J. Appl. Meteor., 34, 2243- 2258.10.1175/1520-0450(1995)034<2243:DOCVSF>2.0.CO;2

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8a52fbbd42e4410c68040dd27ed82d31

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F255798344_Determination_of_cloud_vertical_structure_from_upper-air_observations

refpaperuri:(c57173756686fd1ba871c29bc95d2042)

http://www.researchgate.net/publication/255798344_Determination_of_cloud_vertical_structure_from_upper-air_observationsA method is described to use rawinsonde data to estimate cloud vertical structure, including cloud-top and cloud-base heights, cloud-layer thickness, and the characteristics of multilayered clouds. Cloud-layer base and top locations are identified based on: maximum relative cloud humidity at least 87%, minimum relative humidity at least 84%, and relative humidity jumps exceeding 3% at cloud-layer top and base (relative humidity is with respect to liquid water at temperature greater than or equal to 0{degrees}C and with respect to ice at less than 0{degrees}C). The analysis method is tested at 30 ocean sites by comparing with cloud properties derived from independent data. Comparison of layer-cloud frequencies of occurrence with surface observations shows that rawinsonde observations (RAOBS) usually detect the same number of cloud layers for low and middle clouds as the surface observers, but disagree more for high-level clouds. There is good agreement between the seasonal variations of RAOBS top pressure of the highest cloud and that from the International Satellite Cloud Climate Project (ISCCP) data. RAOBS top pressures of low and middle clouds agree better with ISCCP, but RAOBS often fail to detect very high and thin clouds. The frequency of multilayered clouds is qualitatively consistent with surface observation more» estimates. In cloudy soundings, multilayered clouds occur 56% of the time and are predominately two layered. Multilayered clouds are most frequent ({approx}70%) in the Tropics and least frequent at subtropical eastern Pacific stations. The frequency of multilayered clouds is higher in summer than in winter at low-latitude stations, but the opposite variation appears at subtropical stations. Frequency distributions of cloud top, cloud base, and cloud-layer thickness and cloud occurrence as a function of height are presented. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer. 31 refs., 25 refs., 2 tabs. 芦less

Wang J. H., W. B. Rossow, T. Uttal, and M. Rozendaal, 1999: Variability of cloud vertical structure during ASTEX observed from a combination of rawinsonde, radar, ceilometer, and satellite. Mon. Wea. Rev., 127, 2482- 2502.10.1175/1520-0493(1999)127<2484:VOCVSD>2.0.CO;2

5bc60d75-1674-49a6-b1cb-0c768421d113

d7c095506ff26c13b8e41b3deabcb7fd

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F254805865_Variability_of_Cloud_Vertical_Structure_during_ASTEX_Observed_from_a_Combination_of_Rawinsonde_Radar_Ceilometer_and_Satellite

refpaperuri:(88371a5d8ca1805527c027e90ee66217)

http://www.researchgate.net/publication/254805865_Variability_of_Cloud_Vertical_Structure_during_ASTEX_Observed_from_a_Combination_of_Rawinsonde_Radar_Ceilometer_and_SatelliteProvides information on a study which examined the macroscale cloud vertical structure (CVS) and characteristics of multilayered clouds at Porto Santo Island during the Atlantic Stratocumulus Transition Experiment. Data and analysis method of the study; Comparison of rawinsonde-derived cloud boundaries with radar and ceilometer data; Comparison of satellite-derived cloud-top locations with radar data; Variability of CVS.

Wang J. H., W. B. Rossow, and Y. C. Zhang, 2000: Cloud vertical structure and its variations from a 20-year global rawinsonde dataset. J.Climate, 13, 3041- 3056.10.1175/1520-0442(2000)0132.0.CO;2

14032a83-09d3-4181-b9a4-58ffd1926a4d

f269831cc2ee3928fd6260a2df0e70d0

http://www.researchgate.net/publication/237431107_Cloud_Vertical_Structure_and_Its_Variations_from_a_20Yr_Global_Rawinsonde_Dataset

http://www.researchgate.net/publication/237431107_Cloud_Vertical_Structure_and_Its_Variations_from_a_20Yr_Global_Rawinsonde_DatasetAbstract A global cloud vertical structure (CVS) climatic dataset is created by applying an analysis method to a 20-yr collection of twice-daily rawinsonde humidity profiles to estimate the height of cloud layers. The CVS dataset gives the vertical distribution of cloud layers for single and multilayered clouds, as well as the top and base heights and layer thicknesses of each layer, together with the original rawinsonde profiles of temperature, humidity, and winds. The average values are cloud-top height = 4.0 km above mean sea level (MSL), cloud-base height = 2.4 km MSL, cloud-layer thickness = 1.6 km, and separation distance between consecutive layers = 2.2 km. Multilayered clouds occur 42% of the time and are predominately two-layered. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer (below 2-km height MSL). Clouds over the ocean occur more frequently at lower levels and are more often formed in multiple layers than over land. Latitudinal variations of CVS also show maxima and minima that correspond to the locations of the intertropical convergence zone, the summer monsoons, the subtropical subsidence zones, and the midlatitude storm zones. Multilayered clouds exist most frequently in the Tropics and least frequently in the subtropics; there are more multilayered clouds in summer than in winter. Cloud layers are thicker in winter than in summer at mid- and high latitudes, but are thinner in winter in Southeast Asia.

Xi B. K., X. Q. Dong, P. Minnis, and M. M. Khaiyer, 2010: A 10 year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SGP Site. J. Geophys. Res., 115,D12, doi: 10.1029/2009JD012800.10.1029/2009JD012800

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c8b860c8be7908a849c132a0a20e0c82

http://onlinelibrary.wiley.com/doi/10.1029/2009JD012800/pdf

http://onlinelibrary.wiley.com/doi/10.1029/2009JD012800/pdfABSTRACT Analysis of one decade of radar-lidar and Geostationary Operational Environmental Satellite (GOES) observations at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site reveals that there is excellent agreement in the long-term mean cloud fractions (CFs) derived from the surface and GOES data, and the CF is independent of temporal resolution and spatial scales for grid boxes of size 0.5 to 2.5. When computed over a a 0.5 h (4 h) period, cloud frequency of occurrence (FREQ) and amount when present (AWP) derived from the point surface data agree very well with the same quantities determined from GOES for a 0.5 (2.5) region centered on the DOE ARM SGP site. The values of FREQ (AWP) derived from the radar-lidar observations at a given altitude increase (decrease) as the averaging period increases from 5 min to 6 h. Similarly, CF at a given altitude increases as the vertical resolution increases from 90 to 1000 m. The profiles of CF have distinct bimodal vertical distributions, with a lower peak between 1 and 2 km and a higher one between 8 and 11 km. The 10 year mean total CF, 46.9%, varies seasonally from a summer minimum of 39.8% to a maximum of 54.6% during the winter. The annual mean CF is 1%-2% less than that from previous studies, &tilde;48%-49%, because fewer clouds occurred during 2005 and 2006, especially during winter. The differences in single- and multilayered CFs between this study and an earlier analysis can be explained by the different temporal resolutions used in the two studies, where single-layered CFs decrease but multilayered CFs increase from a 5 min resolution to a 1 h resolution. The vertical distribution of nighttime GOES high cloud tops agrees well with surface observations, but during the daytime, fewer high clouds are retrieved by the GOES analysis than seen from the surface observations. The FREQs for both daytime and nighttime GOES low cloud tops are significantly higher than surface observations, but the CFs are in good agreement.

Zhang J. Q., H. B. Chen, Z. Q. Li, X. H. Fan, L. Peng, Y. Yu, and M. Cribb, 2010: Analysis of cloud layer structure in Shouxian, China using RS92 radiosonde aided by 95 GHz cloud radar. J. Geophys. Res., 115,D7, doi: 10.1029/2010JD 014030.10.1029/2010JD014030

53bac809-322e-4017-a899-4bb551517c83

2dd85b2bb06402ea3f8eb34bdad05de5

http://onlinelibrary.wiley.com/doi/10.1029/2010JD014030/suppinfo

http://onlinelibrary.wiley.com/doi/10.1029/2010JD014030/suppinfo[1] The Atmospheric Radiation Measurement Mobile Facility (AMF) was deployed in Shouxian, Anhui Province, China from 14 May to 28 December 2008. Radiosonde data obtained during the AMF campaign are used to analyze cloud vertical structure over this area by taking advantage of the first direct measurements of cloud vertical layers from the 95 GHz radar. Single-layer, two-layer, and three-layer clouds account for 28.0%, 25.8%, and 13.9% of all cloud configurations, respectively. Low, middle, high and deep convective clouds account for 20.1%, 19.3%, 59.5%, and 1.1% of all clouds observed at the site, respectively. The average cloud base height, cloud top height, and cloud thickness for all clouds are 5912, 7639, and 1727 m, respectively. Maximum cloud top height and cloud thickness occurred at 1330 local standard time (LST) for single-layer clouds and the uppermost layer of multiple layers of cloud. For lower layer clouds in multiple-layer cloud systems, maximum cloud top height and cloud thickness occurred at 1930 LST. Diurnal variations in the thickness of upper level clouds are larger than those of lower level clouds. Multilayer clouds occurred more frequently in the summer. The absolute differences in cloud base heights from radiosonde and micropulse lidar/ceilometer comparisons are less than 500 m for 77.1%/68.4% of the cases analyzed.

Zhang J. Q., Z. Q. Li, H. B. Chen, and M. Cribb, 2013: Validation of a radiosonde-based cloud layer detection method against a ground-based remote sensing method at multiple ARM sites. J. Geophys. Res. ,118, 846-858, doi:10.1029/2012JD 018515.10.1029/2012JD018515

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http://www.researchgate.net/publication/258796765_Validation_of_a_radiosonde-based_cloud_layer_detection_method_against_a_ground-based_remote_sensing_method_at_multiple_ARM_sites

http://www.researchgate.net/publication/258796765_Validation_of_a_radiosonde-based_cloud_layer_detection_method_against_a_ground-based_remote_sensing_method_at_multiple_ARM_sitesABSTRACT

Zhang J. Q., Z. Q. Li, H. B. Chen, H. Yoo, and M. Cribb, 2014: Cloud vertical distribution from radiosonde,remote sensing, and model simulations. Climate Dyn., 43, 1129 -1140, doi: 10.1007/s00382-014-2142-4.10.1007/s00382-014-2142-4

1ffe7075-81c3-4c33-bb43-c78c73aea7ab

758c94beaf57bd75ea04fd5d95ec3127

http://link.springer.com/article/10.1007/s00382-014-2142-4

http://link.springer.com/article/10.1007/s00382-014-2142-4Knowledge of cloud vertical structure is important for meteorological and climate studies due to the impact of clouds on both the Earth radiation budget and atmospheric adiabatic heating. Yet it is among the most difficult quantities to observe. In this study, we develop a long-term (10 years) radiosonde-based cloud profile product over the Southern Great Plains and along with ground-based and space-borne remote sensing products, use it to evaluate cloud layer distributions simulated by the National Centers for Environmental Prediction global forecast system (GFS) model. The primary objective of this study is to identify advantages and limitations associated with different cloud layer detection methods and model simulations. Cloud occurrence frequencies are evaluated on monthly, annual, and seasonal scales. Cloud vertical distributions from all datasets are bimodal with a lower peak located in the boundary layer and an upper peak located in the high troposphere. In general, radiosonde low-level cloud retrievals bear close resemblance to the ground-based remote sensing product in terms of their variability and gross spatial patterns. The ground-based remote sensing approach tends to underestimate high clouds relative to the radiosonde-based estimation and satellite products which tend to underestimate low clouds. As such, caution must be exercised to use any single product. Overall, the GFS model simulates less low-level and more high-level clouds than observations. In terms of total cloud cover, GFS model simulations agree fairly well with the ground-based remote sensing product. A large wet bias is revealed in GFS-simulated relative humidity fields at high levels in the atmosphere.

Zhang, M. H., Coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J. Geophys. Res., 110,D15, doi: 10.1029/2004JD005021.10.1029/2004JD005021

0fcc1f526ffd21f64857031eba7d0012

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004JD005021%2Fpdf

http://onlinelibrary.wiley.com/doi/10.1029/2004JD005021/pdf[1] To assess the current status of climate models in simulating clouds, basic cloud climatologies from ten atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are also, however, large uncertainties in satellite high thin clouds to effectively constrain the models. The majority of models only simulated 30–40% of middle-top clouds in the ISCCP and CERES data sets. Half of the models underestimated low clouds, while none overestimated them at a statistically significant level. When stratified in the optical thickness ranges, the majority of the models simulated optically thick clouds more than twice the satellite observations. Most models, however, underestimated optically intermediate and thin clouds. Compensations of these clouds biases are used to explain the simulated longwave and shortwave cloud radiative forcing at the top of the atmosphere. Seasonal sensitivities of clouds are also analyzed to compare with observations. Models are shown to simulate seasonal variations better for high clouds than for low clouds. Latitudinal distribution of the seasonal variations correlate with satellite measurements at >0.9, 0.6–0.9, and 0.2–0.7 levels for high, middle, and low clouds, respectively. The seasonal sensitivities of cloud types are found to strongly depend on the basic cloud climatology in the models. Models that systematically underestimate middle clouds also underestimate seasonal variations, while those that overestimate optically thick clouds also overestimate their seasonal sensitivities. Possible causes of the systematic cloud biases in the models are discussed.

Zhang Y. Y., S. A. Klein, 2010: Mechanisms affecting the transition from shallow to deep convection over land: Inferences from observations of the diurnal cycle collected at the ARM Southern Great Plains site. J. Atmos. Sci. , 67, 2943- 2959.10.1175/2010JAS3366.1

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249610668_Mechanisms_Affecting_the_Transition_from_Shallow_to_Deep_Convection_over_Land_Inferences_from_Observations_of_the_Diurnal_Cycle_Collected_at_the_ARM_Southern_Great_Plains_Site

refpaperuri:(b2eacaf716b2da48501e5f0d7a28dbc0)

http://www.researchgate.net/publication/249610668_Mechanisms_Affecting_the_Transition_from_Shallow_to_Deep_Convection_over_Land_Inferences_from_Observations_of_the_Diurnal_Cycle_Collected_at_the_ARM_Southern_Great_Plains_SiteAbstract Summertime observations for 11 yr from the Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site are used to investigate mechanisms controlling the transition from shallow to deep convection over land. It is found that a more humid environment immediately above the boundary layer is present before the start of late afternoon heavy precipitation events. The higher moisture content is brought by wind from the south. Greater boundary layer inhomogeneity in moist static energy, temperature, moisture, and horizontal wind before precipitation begins is correlated to larger rain rates at the initial stage of precipitation. In an examination of afternoon rain statistics, higher relative humidity above the boundary layer is correlated to an earlier onset and longer duration of afternoon precipitation events, whereas greater boundary layer inhomogeneity and atmospheric instability in the 2鈥4-km layer above the surface are positively correlated to the total rain amount and the maximum rain rate. Although other interpretations may be possible, these observations are consistent with theories for the transition from shallow to deep convection that emphasize the role of a moist lower free troposphere and boundary layer inhomogeneity.

Zhao C., Coauthors, 2011: ARM Cloud Retrieval Ensemble Data Set (ACRED). DOE ARM technical report, DOE/SC-ARM-TR-099, Dep. of Energy, Washington, D. C., 28 pp. [Available online at http://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-099.pdf.]10.2172/1024213

e4622d6e55ce5dffb5e788093e7c50dd

http://dx.doi.org/10.2172/1024213

http://dx.doi.org/10.2172/1024213This document describes a new Atmospheric Radiation Measurement (ARM) data set, the ARM Cloud Retrieval Ensemble Data Set (ACRED), which is created by assembling nine existing ground-based cloud retrievals of ARM measurements from different cloud retrieval algorithms. The current version of ACRED includes an hourly average of nine ground-based retrievals with vertical resolution of 45 m for 512 layers. The techniques used for the nine cloud retrievals are briefly described in this document. This document also outlines the ACRED data availability, variables, and the nine retrieval products. Technical details about the generation of ACRED, such as the methods used for time average and vertical re-grid, are also provided.

Zhao, C. F., Coauthors, 2012: Toward understanding of differences in current cloud retrievals of ARM ground-based measurements. J. Geophys. Res., 117,D10206, doi: 10.1029/2011 JD016792.10.1029/2011JD016792

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011JD016792%2Fpdf

refpaperuri:(179226b3690435fd04f6249658ca251c)

http://onlinelibrary.wiley.com/doi/10.1029/2011JD016792/pdfAccurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval more» theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes. 芦less

Zhao C. F., Y. Z. Wang, Q. Q. Wang, Z. Q. Li, Z. E. Wang, and D. Liu, 2014: A new cloud and aerosol layer detection method based on micropulse lidar measurements. J. Geophy. Res.,119, 6788-6802, doi: 10.1002/2014JD021760.10.1002/2014JD021760

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JD021760%2Fabstract

refpaperuri:(9b9a3f1d3bb51cb6dd35596365a0eb15)

http://onlinelibrary.wiley.com/doi/10.1002/2014JD021760/abstractAbstract This paper introduces a new algorithm to detect aerosols and clouds based on micropulse lidar measurements. A semidiscretization processing technique is first used to inhibit the impact of increasing noise with distance. The value distribution equalization method which reduces the magnitude of signal variations with distance is then introduced. Combined with empirical threshold values, we determine if the signal waves indicate clouds or aerosols. This method can separate clouds and aerosols with high accuracy, although differentiation between aerosols and clouds are subject to more uncertainties depending on the thresholds selected. Compared with the existing Atmospheric Radiation Measurement program lidar-based cloud product, the new method appears more reliable and detects more clouds with high bases. The algorithm is applied to a year of observations at both the U.S. Southern Great Plains (SGP) and China Taihu sites. At the SGP site, the cloud frequency shows a clear seasonal variation with maximum values in winter and spring and shows bimodal vertical distributions with maximum occurrences at around 3–665km and 8–1265km. The annual averaged cloud frequency is about 50%. The dominant clouds are stratiform in winter and convective in summer. By contrast, the cloud frequency at the Taihu site shows no clear seasonal variation and the maximum occurrence is at around 165km. The annual averaged cloud frequency is about 15% higher than that at the SGP site. A seasonal analysis of cloud base occurrence frequency suggests that stratiform clouds dominate at the Taihu site.