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Volume 33 Issue 1

Jan.  2016

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2016 >  33(1): 73-84

Verification and Correction of Cloud Base and Top Height Retrievals from Ka-band Cloud Radar in Boseong, Korea

  • 1.

    Numerical Data Application Division, National Institute of Meteorological Sciences, KMA, Republic of Korea

  • 2.

    Applied Meteorology Research Division, National Institute of Meteorological Sciences, KMA, Republic of Korea

  • 3.

    Observation Research Division, National Institute of Meteorological Sciences, KMA, Republic of Korea

  • 4.

    National Institute of Meteorological Sciences, KMA, Republic of Korea


doi: 10.1007/s00376-015-5058-y

  • In this study, cloud base height (CBH) and cloud top height (CTH) observed by the Ka-band (33.44 GHz) cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013 (September-November) were verified and corrected. For comparative verification, CBH and CTH were obtained using a ceilometer (CL51) and the Communication, Ocean and Meteorological Satellite (COMS). During rainfall, the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops, and this difference increased with rainfall intensity. During dry periods, however, the CBH and CTH observed by the cloud radar, ceilometer, and COMS were similar. Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS. In cases of rainfall or missing cloud radar data, the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data. These corrected cloud data were used to classify cloud types, which revealed that low clouds occurred most frequently.
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  • Ahlgrimm M., R. Forbes, 2014: Improving the representation of low clouds and drizzle in the ECMWF model based on ARM observations from the Azores. Mon. Wea. Rev., 142, 668- 685.10.1175/MWR-D-13-00153.1c53e9fdf-33ee-4370-988d-02ad176a50dec2d516365a45b95bc22319957c04e3abhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F266383218_Improving_the_representation_of_low_clouds_and_drizzle_in_the_ECMWF_model_based_on_ARM_observations_from_the_Azoresrefpaperuri:(508c63b60f224b6804f3d75207189375)http://www.researchgate.net/publication/266383218_Improving_the_representation_of_low_clouds_and_drizzle_in_the_ECMWF_model_based_on_ARM_observations_from_the_AzoresAbstract In this study, the representation of marine boundary layer clouds is investigated in the ECMWF model using observations from the Atmospheric Radiation Measurement (ARM) mobile facility deployment to Graciosa Island in the North Atlantic. Systematic errors in the occurrence of clouds, liquid water path, precipitation, and surface radiation are assessed in the operational model for a 19-month-long period. Boundary layer clouds were the most frequently observed cloud type but were underestimated by 10% in the model. Systematic but partially compensating surface radiation errors exist and can be linked to opposing cloud cover and liquid water path errors in broken (shallow cumulus) and overcast (stratocumulus) low-cloud regimes, consistent with previously reported results from the continental ARM Southern Great Plains (SGP) site. Occurrence of precipitation is overestimated by a factor of 1.5 at cloud base and by a factor of 2 at the surface, suggesting deficiencies in both the warm-rain formation and subcloud evaporation parameterizations. A single-column version of the ECMWF model is used to test combined changes to the parameterizations of boundary layer, autoconversion/accretion, and rain evaporation processes at Graciosa. Low-cloud occurrence, liquid water path, radiation biases, and precipitation occurrence are all significantly improved when compared to the ARM observations. Initial results from the modified parameterizations in the full model show improvement in the global top-of-the-atmosphere shortwave radiation, suggesting the reduced errors in the comparison at Graciosa are more widely applicable to boundary layer cloud around the globe.
    Aydin K., J. Singh, 2004: Cloud ice crystal classification using a 95-GHz polarimetric radar. J. Atmos. Oceanic Technol., 21, 1679- 1688.10.1175/JTECH1671.1a7b0a4cf-6625-46e6-a476-1558c8e8e6053d8c5b82a3154860c2302a39ca8a4f83http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249604509_Cloud_Ice_Crystal_Classification_Using_a_95GHz_Polarimetric_Radarrefpaperuri:(c612c557d246bba531ea3db3d0183c34)http://www.researchgate.net/publication/249604509_Cloud_Ice_Crystal_Classification_Using_a_95GHz_Polarimetric_RadarAbstract Two algorithms are presented for ice crystal classification using 95-GHz polarimetric radar observables and air temperature ( T ). Both are based on a fuzzy logic scheme. Ice crystals are classified as columnar crystals (CC), planar crystals (PC), mixtures of PC and small- to medium-sized aggregates and/or lightly to moderately rimed PC (PSAR), medium- to large-sized aggregates of PC, or densely rimed PC, or graupel-like snow or small lumpy graupel (PLARG), and graupel larger than about 2 mm (G). The 1D algorithm makes use of Z h , Z dr , LDR hv , and T, while the 2D algorithm incorporates the three radar observables in pairs, ( Z dr , Z h ), (LDR hv , Z h ), and ( Z dr , LDR hv ), plus the temperature T. The range of values for each observable or pair of observables is derived from extensive modeling studies conducted earlier. The algorithms are tested using side-looking radar measurements from an aircraft, which was also equipped with particle probes producing simultaneous and nearly collocated shadow images of cloud ice crystals. The classification results from both algorithms agreed very well with the particle images. The two algorithms were in agreement by 89% in one case and 97% in the remaining three cases considered here. The most effective observable in the 1D algorithm was Z dr , and in the 2D algorithm the pair ( Z dr , Z h ). LDR hv had negligible effect in the 1D classification algorithm for the cases considered here. The temperature T was mainly effective in separating columnar crystals from the rest. The advantage of the 2D algorithm over the 1D algorithm was that it significantly reduced the dependence on T in two out of the four cases.
    Brown P. R. A., A. J. Illingworth, A. J. Heymsfield, G. M. McFarquhar, K. A. Browning, and M. Gosset, 1995: The role of spaceborne millimeter-wave radar in the global monitoring of ice cloud. J. Appl. Meteor., 34, 2346- 2366.10.1175/1520-0450(1995)034<2346:TROSMW>2.0.CO;29378f3f6-641e-49ee-82b0-8c278aef5eef812dc4ad605d83370e04908c4ec3e3dehttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249606617_The_Role_of_Spaceborne_Millimeter-Wave_Radar_in_the_Global_Monitoring_of_Ice_Cloudrefpaperuri:(15521550e8c4860534cc1892db9a8f76)http://www.researchgate.net/publication/249606617_The_Role_of_Spaceborne_Millimeter-Wave_Radar_in_the_Global_Monitoring_of_Ice_CloudAbstract The purpose of this paper is to assess the potential of a spaceborne 94-GHz radar for providing useful measurements of the vertical distribution and water content of ice clouds on a global scale. Calculations of longwave (LW) fluxes for a number of model ice clouds are performed. These are used to determine the minimum cloud optical depth that will cause changes in the outgoing longwave radiation or flux divergence within a cloud layer greatear than 10 W m 612 , and in surface downward LW flux greater than 5 W m 612 , compared to the clear-sky value. These optical depth values are used as the definition of a “radiatively significant” cloud. Different “thresholds of radiative significance” are calculated for each of the three radiation parameters and also for tropical and midlatitude cirrus clouds. Extensive observational datasets of ice crystal size spectra from midlatitude and tropical cirrus are then used to assess the capability of a radar to meet these measurement requirements. A radar with a threshold of 6130 dBZ should detect 99% (92%) of “radiatively significant” clouds in the midlatitudes (Tropics). This detection efficiency may be reduced significantly for tropical clouds at very low temperatures (6180°C). The LW flux calculations are also used to establish the required accuracy within which the optical depth should be known in order to estimate LW fluxes or flux divergence to within specified limits of accuracy. Accuracy requirements are also expressed in terms of ice water content (IWC) because of the need to validate cloud parameterization schemes in general circulation models (GCMs). Estimates of IWC derived using radar alone and also using additional information to define the mean crystal size are considered. With crystal size information available, the IWC for samples with a horizontal scale of 1022 km may be obtained with a bias of less than 8%. For IWC larger than 0.01 g m 613 , the random error is in the range +50% to 6135%, whereas for a value of 0.001 g m 613 the random error increases to between +80% and 6145%. This level of accuracy also represents the best that may be achieved for estimates of the cloud optical depth and meets the requirements derived from LW flux calculations. In the absence of independent particle size information, the random error is within the range +85% to 6155% for IWC greater than 0.01 g m 613 . For the same IWC range, the estimated bias is few than ±15%. This accuracy is sufficient to provide useful constraints on GCM cloud parameteriation schemes.
    Clothiaux E. E., M. A. Miller, B. A. Albrecht, T. P. Ackerman, J. Verlinde, D. M. Babb, R. M. Peters, and W. J. Syrett, 1995: An evaluation of a 94-GHz radar for remote sensing of cloud properties. J. Atmos. Oceanic Technol., 12, 201- 229.10.1175/1520-0426(1995)012<0201:AEOAGR>2.0.CO;2f69d3e54-69a3-4c34-b3ff-7d99943203633e1b853e1211287158ccc0a9805968fchttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F234351192_An_Evaluation_of_a_94-GHz_Radar_for_Remote_Sensing_of_Cloud_Propertiesrefpaperuri:(0009d67011de61962c899e0e7836add1)http://www.researchgate.net/publication/234351192_An_Evaluation_of_a_94-GHz_Radar_for_Remote_Sensing_of_Cloud_PropertiesAbstract The performance of a 94-GHz radar is evaluated for a variety of cloud conditions. Descriptions of the radar hardware, signal processing, and calibration provide an overview of the radar's capabilities. An important component of the signal processing is the application of two cloud-mask schemes to the data to provide objective estimates of cloud boundaries and to detect significant returns that would otherwise be discarded if a simple threshold method for delectability was applied to the return power. Realistic profiles of atmospheric pressure, temperature, and water vapor are used in a radiative transfer model to address clear-sky attenuation. A physically relevant study of beam extinction and backscattering by clouds is attempted by modeling cloud drop size distributions with a gamma distribution over a range of number concentrations, particle mean diameters, and distribution shape factors; cloud liquid water contents and mean drop size diameters reported in the literature are analyzed in this context. Results of observations of a number of cloud structures, including marine strato- cumulus, cirrus, and stratus and cirrus associated with a midlatitude cyclone are described.
    Clothiaux E. E., T. P. Ackerman, G. G. 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
    Fuller W. H., M. T. Osborn, and W. M. Hunt, 1988: 48-inch lidar aerosol measurements taken at the Langley research center-May 1974 to December 1987. NASA Reference Publication, 1209.e979fe1ed0f23f9bde5f80cb983c9c99http://www.researchgate.net/publication/24332087_The_48-inch_lidar_aerosol_measurements_taken_at_the_Langley_Research_Centerhttp://www.researchgate.net/publication/24332087_The_48-inch_lidar_aerosol_measurements_taken_at_the_Langley_Research_CenterABSTRACT This report presents lidar data taken between July 1991 and December 1992 using a ground-based 48-inch lidar instrument at the Langley Research Center in Hampton, Virginia. Seventy lidar profiles (approximately one per week) were obtained during this period, which began less than 1 month after the eruption of the Mount Pinatubo volcano in the Philippines. Plots of backscattering ratio as a function of altitude are presented for each data set along with tables containing numerical values of the backscattering ratio and backscattering coefficient versus altitude. The enhanced aerosol backscattering seen in the profiles highlights the influence of the Mount Pinatubo eruption on the stratospheric aerosol loading over Hampton. The long-term record of the profiles gives a picture of the evolution of the aerosol cloud, which reached maximum loading approximately 8 months after the eruption and then started to decrease gradually. NASA RP-1209 discusses 48-inch lidar aerosol measurements taken at the Langley Research Center from May 1974 to December 1987.
    Hogan R. J., A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 126, 2903- 2009.10.1002/qj.497126569145362ae66-515d-4197-8be2-3cbacf4b685182df4376a5f3eb4ca38f4ec135f8a8f5http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.49712656914%2Ffullrefpaperuri:(b84f82ee3eb58252dd750f91d3c03385)http://onlinelibrary.wiley.com/doi/10.1002/qj.49712656914/fullABSTRACT The predictions of general-circulation models (GCMs) are sensitive to the assumed cloud overlap within a vertical column of model grid boxes, but until now no reliable observations of the degree of cloud overlap have been available. In this note we derive the overlap characteristics of clouds from 71 days of high vertical resolution 94 GHz cloud radar data in the UK. It is found that, contrary to the assumption made in most models, vertically continuous clouds tend not to be maximally overlapped. Rather, the overlap of clouds at two levels tends to fall rapidly as their vertical separation is increased, and for levels more than 4 km apart, overlap is essentially random. A simple inverse-exponential expression for the degree of overlap as a function of level separation is proposed that could, once results become available from a variety of other locations and seasons, be implemented in current GCMs with relatively little difficulty.
    Hollars S., Q. Fu, J. Comstock, and T. Ackerman, 2004: Comparison of cloud-top height retrievals from ground-based 35 GHz MMCR and GMS-5 satellite observations at ARM TWP Manus site. Atmospheric Research, 72, 169- 186.10.1016/j.atmosres.2004.03.0152de48cb1-a935-4e52-b94b-943de3e4398d465ca84566f471a8c1b0820a7c9e45f5http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809504000717refpaperuri:(5aefb6091a1178c156f0f2b27070857d)http://www.sciencedirect.com/science/article/pii/S0169809504000717Retrievals of cloud-top heights from the ARM 35 GHz Millimeter Wave Cloud Radar (MMCR) located on Manus Island are compared to those from the GMS-5 satellite as a means to evaluate the accuracy of both MMCR and GMS-5 retrievals, as well as to ascertain their limitations. Comparisons are carried out for retrievals of both single-layer and multilayer clouds as seen by radar, but only for satellite-detected clouds with 100% amount within a 0.3 x 0.3degrees domain centered at the ARM site of one cloud type (i.e., low, middle, or high). Mean differences, with 95% confidence limits, between radar- and satellite-retrieved cloud-top heights (i.e., radar-retrieved cloud-top heights-satellite-retrieved cloud-top heights) are 0.3 +/- 0.3 km for single-layer clouds and -0.7 +/- 0.3 km for multilayer clouds. The study reveals that for thick clouds (i.e., cloud base less than or equal to 1 km and cloud thickness greater than or equal to 10 km), which are representative of convective towers with no/light precipitation as well as thick anvil clouds, retrievals from the MMCR agree well with those from satellite with mean differences of 0.0 +/- 0.4 and -0.2 +/- 0.3 km for single-layer and multilayer clouds, respectively. For clouds of lesser thickness, mean cloud-top heights derived from satellite are lower than those derived from radar by as much as 2.0 km. It is also shown that for convective clouds with heavy precipitation, MMCR retrievals underestimate the cloud-top heights significantly. (C) 2004 Elsevier B.V. All rights reserved.
    Illingworth, A. J., Coauthors, 2007: Cloudnet. Bull. Amer. Meteor. Soc., 88, 883- 898.10.1175/BAMS-88-6-88328bcb2b3-8c5b-45f5-9511-667d44cdd16266db9286b9e93d844da1ede0ec238ef4http://www.researchgate.net/publication/216674059_Cloudnet_-_Continuous_evaluation_of_cloud_profiles_in_seven_operational_models_using_ground-based_observations?ev=prf_cithttp://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.
    Kent G. S., S. K. Schaffner, 1988: Analysis of atmospheric dynamics and radiative properties for understanding weather and climate, task 1, 10 \upmum backscatter modeling. STC Tech. Rep. 2175, prepared for NASA under contract NAS1-18252.
    Kneifel S., M. Maahn, G. Peters, and C. Simmer, 2011: Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar). Meteor. Atmos. Phys., 113, 75- 87.10.1007/s00703-011-0142-zd8ceb000-1f03-4ab2-b995-1a5265e773ea0c30fa55869ceac7e10b7a9c15ccb933http%3A%2F%2Fwww.springerlink.com%2Fcontent%2Fb2100mqrr7033570%2Frefpaperuri:(73b288e359ef988816824806a093352d)http://www.springerlink.com/content/b2100mqrr7033570/Quantifying snowfall intensity especially under arctic conditions is a challenge because wind and snow drift deteriorate estimates obtained from both ground-based gauges and disdrometers. Ground-based remote sensing with active instruments might be a solution because they can measure well above drifting snow and do not suffer from flow distortions by the instrument. Clear disadvantages are, however, the dependency of e.g. radar returns on snow habit which might lead to similar large uncertainties. Moreover, high sensitivity radars are still far too costly to operate in a network and under harsh conditions. In this paper we compare returns from a low-cost, low-power vertically pointing FM-CW radar (Micro Rain Radar, MRR) operating at 24.1 GHz with returns from a 35.5 GHz cloud radar (MIRA36) for dry snowfall during a 6-month observation period at an Alpine station (Environmental Research Station Schneefernerhaus, UFS) at 2,650 m height above sea level. The goal was to quantify the potential and limitations of the MRR in relation to what is achievable by a cloud radar. The operational MRR procedures to derive standard radar variables like effective reflectivity factor ( Z) or the mean Doppler velocity ( W) had to be modified for snowfall since the MRR was originally designed for rain observations. Since the radar returns from snowfall are weaker than from comparable rainfall, the behavior of the MRR close to its detection threshold has been analyzed and a method is proposed to quantify the noise level of the MRR based on clear sky observations. By converting the resulting MRR- Z into 35.5 GHz equivalent Z values, a remaining difference below 1 dBz with slightly higher values close to the noise threshold could be obtained. Due to the much higher sensitivity of MIRA36, the transition of the MRR from the true signal to noise can be observed, which agrees well with the independent clear sky noise estimate. The mean Doppler velocity differences between both radars are below 0.3 ms. The distribution of Z values from MIRA36 are finally used to estimate the uncertainty of retrieved snowfall and snow accumulation with the MRR. At UFS low snowfall rates missed by the MRR are negligible when comparing snow accumulation, which were mainly caused by intensities between 0.1 and 0.8 mm h. The MRR overestimates the total snow accumulation by about 7%. This error is much smaller than the error caused by uncertain Z-snowfall rate relations, which would affect the MIRA36 estimated to a similar degree.
    Kobayashi F., T. Takano, and T. Takamura, 2011: Isolated cumulonimbus initiation observed by 95-GHz FM-CW radar, X-band radar, and photogrammetry in the Kanto region, Japan. SOLA, 7, 125- 128.10.2151/sola.2011-032b44895cb-1497-4588-950e-bc4cbebb66f6f923923b9c53a142b56acc763ef087e6http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F130004448546refpaperuri:(91487b4b30eb13bbc4d226c76f4097a4)http://ci.nii.ac.jp/naid/130004448546Simultaneous observations of cumulonimbi using the 95-GHz FM-CW cloud radar, the X-band radar, and photogrammetry were carried out during the summer of 2010 in the Kanto region, Japan. An isolated cumulonimbus developed above the cloud radar site after 16:00 Japan Standard Time (JST) on 24 July 2010. A continuous generation of turrets was observed and a total of four turrets formed. The growth rates of the turrets were quite different. The first radar echo of the X-band radar was detected at 3 km above ground level (AGL), three minutes after turret 1 reached its maximum height. Turret 2, generated above the cloud radar, reached 10 km AGL and had a growth rate of 8 m s-1. The cloud radar detected echoes approximately two minutes after the generation of turret 2. The intermittent echo pattern observed by the cloud radar denotes fine structures in the cumulonimbus.
    Kollias P., E. E. Clothiaux, M. A. Miller, B. A. Albrecht, G. L. Stephens, and T. P. Ackerman, 2007a: Millimeter-wavelength radars: New frontier in atmospheric cloud and precipitation research. Bull. Amer. Meteor. Soc., 88, 1608- 1624.10.1175/BAMS-88-10-1608f55fe536-0798-476d-a82d-c99ca20600884ec07a674f0409ee1a21a372702eb0bbhttp://www.researchgate.net/publication/228666652_Millimeter-Wavelength_Radars_New_Frontier_in_Atmospheric_Cloud_and_Precipitation_Researchhttp://www.researchgate.net/publication/228666652_Millimeter-Wavelength_Radars_New_Frontier_in_Atmospheric_Cloud_and_Precipitation_ResearchAbstract During the past 20 yr there has been substantial progress on the development and application of millimeter-wavelength (3.2 and 8.6 mm, corresponding to frequencies of 94 and 35 GHz) radars in atmospheric cloud research, boosted by continuous advancements in radar technology and the need to better understand clouds and their role in the Earth's climate. Applications of millimeter-wavelength radars range from detailed cloud and precipitation process studies to long-term monitoring activities that strive to improve our understanding of cloud processes over a wide range of spatial and temporal scales. These activities are the result of a long period of successful research, starting from the 1980s, in which research tools and sophisticated retrieval techniques were developed, tested, and evaluated in field experiments. This paper presents a cohesive, chronological overview of millimeter-wavelength radar advancements during this period and describes the potential of new applications of millimeter-wavelength radars on sophisticated platforms and the benefits of both lower- and higher-frequency radars for cloud and precipitation research.
    Kollias P., G. Tselioudis, and B. A. Albrecht, 2007b: Cloud climatology at the Southern Great Plains and the layer structure, drizzle, and atmospheric modes of continental stratus. J. Geophys. Res., 112,D09116, doi: 10.1029/2006JD007307.10.1029/2006JD00730789c6a496-d9f5-4365-a775-22064b9c7afb678e1fe7495c0cfefa1a243a2780e740http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006JD007307%2Fabstractrefpaperuri:(3ff07ffc8d97f7141ff4cec778d5904b)http://onlinelibrary.wiley.com/doi/10.1029/2006JD007307/abstract[1] Long-term (6.5 years) cloud observations from the Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) climate research facility in Oklahoma are used to develop detailed cloud climatology. Clouds are classified with respect to their altitude (low, middle, and high), vertical development, and the presence of multilayer clouds. Single-layered cirrus, middle or low clouds were observed a total of 23% of the time the MilliMeter Cloud Radar (MMCR) was operating, and multilayer clouds were observed 20.5% of the time. Boundary layer clouds exhibit the strongest seasonal variability because of continental stratus associated with midlatitude frontal systems. Cirrus clouds are the most frequently observed cloud type and exhibit strong seasonal variability in cloud base height (higher cloud base during the summer months) and relatively constant cloud fraction. The majority of middle-level clouds are shallow with vertical extent less than 1 km. No strong seasonal cycle in the fractional coverage of multilayer clouds is observed. Continental stratus clouds exhibit strong seasonal variability with maximum occurrence during the cold seasons. Nondrizzling stratus clouds exhibit a bimodal seasonal variability with maximum occurrences in the fall and spring, while drizzling stratus occur most frequently in the winter. Thermodynamic and dynamic variables from soundings and the European Centre for Medium-Range Weather Forecasts Model (ECMWF) analyses at the SGP site illustrate an interesting coupling between strong large-scale forcing and the formation of single-layered (no other cloud layer is present) continental stratus clouds. Single-layered stratus clouds (drizzling and nondrizzling) exhibit a strong correlation with positive at 500 mbar and strong northerly flow.
    Mace G. G., C. Jakob, and K. P. Moran, 1998: Validation of hydrometeor occurrence predicted by the ECMWF model using millimeter wave radar data. Geophys. Res. Lett., 25, 1645- 1648.10.1029/98GL00845b629ac84-2cae-4f9e-8540-b5f92bea020a7b4aaf2a74da68641b7e0df8026a46b9http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F98GL00845%2Fabstractrefpaperuri:(e45e5d101945904f1e5928742ab97599)http://onlinelibrary.wiley.com/doi/10.1029/98GL00845/abstractABSTRACT Validation of hydrometeor prediction by global models is an important issue as it pertains to the accuracy of climate predictions. In this study we use data from a continuously operating millimeter wave radar at a research site in north central Oklahoma, USA to validate output from the operational ECMWF forecast model. We demonstrate that the ECMWF model shows good overall skill at predicting the vertical distribution of the clouds and precipitation that occurred over this site during winter 1997. However, we also show that the model tended to predict the onset of deep cloud events too soon, made the layers too deep and predicted dissipation somewhat later than observed.
    METRI/KMA, 2009: Development of meteorological data processing system of communication, ocean and meteorological satellite. 846 pp.
    Moran K. P., B. E. Martner, M. J. Post, R. A. Kropfli, D. C. Welsh, and K. B. Widener, 1998: An unattended cloud-profiling radar for use in climate research. Bull. Amer. Meteor. Soc., 79, 443- 455.10.1175/1520-0477(1998)079<0443:AUCPRF>2.0.CO;2b2c09796-6864-47a8-934f-588f1d9f42e888b4ba46aabc493aeeb31cb98bc0e7f8http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F253533024_An_Unattended_Cloud-Profiling_Radar_for_Use_in_Climate_Researchrefpaperuri:(07b869bf0142341037a9298275b42b44)http://www.researchgate.net/publication/253533024_An_Unattended_Cloud-Profiling_Radar_for_Use_in_Climate_ResearchPresents a study utilizing a millimeter-wave cloud radar (MMCR) for observations in climate research. Description of MMCR; Application of the radar; Indication of the study; Discussion on findings.
    O'Connor, E. J., R. J. Hogan, A. J. Illingworth, 2005: Retrieving stratocumulus drizzle parameters using Doppler radar and lidar. J. Appl. Meteor., 44, 14- 27.10.1175/JAM-2181.13e760ea2-2294-49ff-a86d-a416ff02f36ac077a264840d35e2bc0332ee4e263f7bhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F43155443_Retrieving_stratocumulus_drizzle_parameters_using_Doppler_radar_and_lidarrefpaperuri:(dc07849fac8f15301074072fa8296e82)http://www.researchgate.net/publication/43155443_Retrieving_stratocumulus_drizzle_parameters_using_Doppler_radar_and_lidarABSTRACT Stratocumulus is one of the most common cloud types globally, with a profound effect on the earth's radiation budget, and the drizzle process is fundamental in understanding the evolution of these boundary layer clouds. In this paper a combination of 94-GHz Doppler radar and backscatter lidar is used to investigate the microphysical properties of drizzle falling below the base of stratocumulus clouds. The ratio of the radar to lidar backscatter power is proportional to the fourth power of mean size, and so potentially it can provide an accurate size estimate. Information about the shape of the drop size distribution is then inferred from the Doppler spectral width. The algorithm estimates vertical profiles of drizzle parameters such as liquid water content, liquid water flux, and vertical air velocity, assuming that the drizzle size spectrum may be represented by a gamma distribution. The depletion time scale of cloud liquid water through the drizzle process can be estimated when the liquid water path of the cloud is available from microwave radiometers, and observations suggest that this time scale varies from a few days in light drizzle to a few hours in strong drizzle events. Radar and lidar observations from Chilbolton (in southern England) and aircraft size spectra taken during the Atlantic Stratocumulus Transition Experiment have both been used to derive the following power-law relationship between liquid water flux (LWF) (g m-2 s-1) and radar reflectivity (Z) (mm6 m-3): LWF = 0.0093Z0.69. This relation is valid for frequencies up to 94 GHz and therefore would allow a forthcoming spaceborne radar to measure liquid water flux around the globe to within a factor of 2 for values of Z above -20 dBZ.
    Oh S. B., H. Y. Won, J. C. Ha, and K. Y. Chung, 2014: Comparison of cloud top height observed by a Ka-band cloud radar and COMS. Atmosphere, 24, 39- 48. (in Korean)
    Sakurai N., K. Iwanami, T. Maesaka, S. I. Suzuki, S. Shimizu, R. Misumi, D. S. Kim, and M. Maki, 2012: Case study of misoscale convective echo behavior associated with cumulonimbus development observed by Ka-band Doppler radar in the Kanto region, Japan.SOLA, 8, 107- 110.10.2151/sola.2012-027f64a5987-e609-44e5-801e-6b2a16fc464df0e78beb12d27ba436ded05f0f5d4cdfhttp://www.researchgate.net/publication/273668341_Case_Study_of_Misoscale_Convective_Echo_Behavior_Associated_with_Cumulonimbus_Development_Observed_by_Ka-band_Doppler_Radar_in_the_Kanto_Region_Japanhttp://www.researchgate.net/publication/273668341_Case_Study_of_Misoscale_Convective_Echo_Behavior_Associated_with_Cumulonimbus_Development_Observed_by_Ka-band_Doppler_Radar_in_the_Kanto_Region_JapanSimultaneous observations of cumulonimbi by a Ka-band Doppler radar (KaDR) and an X-band polarimetric Doppler radar (MP-X) were performed during the summer of 2011 in the Kanto region, Japan to study the process of cumulonimbus initiation and development. A cumulonimbus developed up to 12 km above sea level (ASL) in the mountainous western part of the Kanto region on the morning of 18 August 2011, and its initiation and development were observed by the two radars. A misoscale convective echo which was newly detected in an RHI or PPI scan and developed vertically (RHI scan) or spatially (PPI scan) was labeled as a 'new misoscale convective echo' (NMCE). In the developing stage (DS), NMCEs occurred one after another, and the echo top height and maximum reflectivity of each individual echo gradually increased. In the first half of the DS, the NMCEs appeared between 2 and 5 km ASL. In contrast, in the second half of the DS, the NMCEs' appearance height stepped up to between 5 and 12 km ASL. These results suggest that the ascent of NMCE appearance height is one of the key factors in the prediction of deep convection, which later causes localized heavy rainfall.
    Stokes G. M., S. E. Schwartz, 1994: The Atmospheric Radiation Measurement (ARM) Program: Programmatic Background and Design of the Cloud and Radiation Test Bed. Bull. Amer. Meteor. Soc., 75, 1201- 1221.10.1175/1520-0477(1994)0752.0.CO;2f38e199a-2545-497d-9dbf-1886808898fdcb559e9e853bfdaee1ca96431bd47f95http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F230889816_The_Atmospheric_Radiation_Measurement_%28ARM%29_Program_Programmatic_background_design_of_the_cloud_and_radiation_test_bedrefpaperuri:(dda3011ad4949adc7c4553aa7d98341a)http://www.researchgate.net/publication/230889816_The_Atmospheric_Radiation_Measurement_(ARM)_Program_Programmatic_background_design_of_the_cloud_and_radiation_test_bedThe Atmospheric Radiation Measurement (ARM) Program, supported by the U.S. Department of Energy, is a major new program of atmospheric measurement and modeling. The program is intended to improve the understanding of processes that affect atmospheric radiation and the description of these processes in climate models. An accurate description of atmospheric radiation and its interaction with clouds and cloud processes is necessary to improve the performance of and confidence in models used to study and predict climate change. The ARM Program will employ five (this paper was prepared prior to a decision to limit the number of primary measurement sites to three) highly instrumented primary measurement sites for up to 10 years at land and ocean locations, from the Tropics to the Arctic, and will conduct observations for shorter periods at additional sites and in specialized campaigns. Quantities to be measured at these sites include longwave and shortwave radiation, the spatial and temporal distribution of clouds, water vapor, temperature, and other radiation-influencing quantities. There will be further observations of meterological variables that influence these quantities, including wind velocity, precipitation rate, surface moisture, temperature, and fluxes of sensible and latent heat. These data will be used for the prospective testing of more models of varying complexity, ranging from detailed process models to the highly parameterized description of these processes for use in general circulation models of the earth's atmosphere. This article reviews the scientific background of the ARM Program, describes the design of the program, and presents its status and plans. 31 refs., 4 figs., 5 tabs. less
    Syrett W. J., B. A. Albrecht, and E. E. Clothiaux, 1995: Vertical cloud structure in a midlatitude cyclone from a 94-GHz radar. Mon. Wea. Rev., 123, 3393- 3407.10.1175/1520-0493(1995)123<3393:VCSIAM>2.0.CO;2ac23d3d9-fc5f-4129-86f8-0581701b4ef898d341bfc86e5b93af3e01943815fbe5http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249620705_Vertical_Cloud_Structure_in_a_Midlatitude_Cyclone_from_a_94GHz_Radarrefpaperuri:(f6ece916aabc0396addfccb5ff9e7045)http://www.researchgate.net/publication/249620705_Vertical_Cloud_Structure_in_a_Midlatitude_Cyclone_from_a_94GHz_RadarThe vertical structure of clouds associated with a developing midlatitude cyclone was studied using a 94-GHz cloud radar accompanied by a host of other surface-based instruments and rawinsondes. A thickening cirrostratus deck was observed as the storm approached. As the storm drew near, low-level moisture advection increased, and a drizzle-producing stratus deck quickly developed. A rather lengthy period of light to occasionally moderate rain accompanied the passage of the storm. When the storm pulled away to the northeast and the rain ended, the character of the stratus deck changed markedly, with no drizzle production evident. Other cloud features observed included generating cells and their resultant fallstreaks and an eye that apparently accompanied the passage of a negatively tilted upper-level trough, as evidenced by measurements from a 50-MHz wind profiler located near the cloud radar. Rawinsonde measurements showed that the cloud radar also traced the descent of the melting layer. Satellite observations indicated that attenuation often limited the ability of the radar to detect cloud top when precipitation was occurring. As a result, the radar-reported cloud tops were 2-5 km lower than those indicated from the satellite cloud-top temperatures during the heaviest precipitation. During very light precipitation and precipitation-free periods, the satellite brightness temperatures yielded slight underestimates of cloud-top height. In spite of the attenuation, the cloud radar revealed many detailed structures of the clouds of a fairly typical midlatitude cyclone and captured the entire 3-day event.
    Widener K. B., J. B. Mead, 2004: W-band ARM cloud radar-Specifications and design. Proc. 14th ARM Science Team Meeting, Albuquerque, NM, Department of Energy/Office of Science. [Available online at http://www.arm.gov/\!publications/proceedings/conf14/.]9cd5acec0f409b68561aadbc23234eb1http://www.researchgate.net/publication/228875580_W-Band_ARM_cloud_radarSpecifications_and_designhttp://www.researchgate.net/publication/228875580_W-Band_ARM_cloud_radarSpecifications_and_designABSTRACT The Atmospheric Radiation Measurement (ARM) Program and ProSensing, Inc. have teamed to develop and deploy the W-band ARM Cloud Radar (WACR) at the SGP central facility. The WACR will be co-located with the ARM millimeter wave cloud radar (MMCR) with planned operation to begin in early 2005. This radar will complement the measurements of the MMCR and will aid in filtering out insect contamination in the data. In this poster we present the design goals, expected performance characteristics, and the detailed design for the WACR.
    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,D12124, doi: 10.1029/2009JD012800.10.1029/2009JD012800ed9e337e-9a37-40e2-8418-f666e3840320c8b860c8be7908a849c132a0a20e0c82http://onlinelibrary.wiley.com/doi/10.1029/2009JD012800/pdfhttp://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.
    Yum S. S., S. N. Oh, J. Y. Kim, C. K. Kim, and J. C. Nam, 2004: Measurements of cloud droplet size spectra using a forward scattering spectrometer probe (FSSP) in the Korean peninsula, Journal of the Korean Meteorological Society, 40, 623- 631 (in Korean).0fa11215-a0a2-40b2-a795-ba4cf296b49c4d7d26b4b9eddda3a8520606b41c699ehttp://www.dbpia.co.kr/Article/773654http://www.dbpia.co.kr/Article/773654Forward Scattering Spectrometer Probe (FSSP) is a basic and essential instrument for in-situ measurements of cloud droplet (diameter ＜ 50 97) size distributions. As a first step towards understanding microphysical characteristics of Korean clouds for the project, Weather Modification Technique in Korea, one of the Research and Development on Meteorology and Seismology funded by the Korea Meteorological Administration, an FSSP system is imported in 2003. Measurement of cloud droplet spectra started in November 2003 at Daegwallyeong Weather Station (elevation above sea level of 840 m), which becomes the first time in Korean history to make an in-situ measurement of cloud microphysics. There are four size range settings for the FSSP and each of them is attempted. The usual size range setting of range 0 (2-47 97) for aircraft cloud droplet measurements seems to be too large for the small cloud droplets at the near cloud base altitudes such as Daegwallyeong Weather Station and lead to under-counting of activated but small (diameter ＜ 2 97) cloud droplets. Range 2 (1-16 97) is used for the temporary measurement at Anmyeon Korea Global Atmospheric Watch Observatory (KGAWO) (elevation above sea level of about 55 m) and seems to be an appropriate size range setting for the cloud/fog droplets at this place. The measured peak concentration of about 800 cm-3 is somewhat higher than typical peak cloud droplet concentration of continental clouds measured in some other places in the world.
    Zhong L. Z., L. P. Liu, S. Feng, R. Ge, and Z. Zhang, 2011: A 35-GHz polarimetric Doppler radar and its application for observing clouds associated with typhoon Nuri. Adv. Atmos. Sci.,28, 945-956, doi: 10.1007/s00376-010-0073-5.
    Zhong L. Z., L. P. Liu, M. Deng, and X. Zhou, 2012: Retrieving microphysical properties and air motion of cirrus clouds based on the Doppler moments method using cloud radar. Adv. Atmos. Sci.,29, 611-622, doi: 10.1007/s00376-011-0112-x.10.1007/s00376-011-0112-xe1c30e0c-2e87-44c1-b589-241f6be9f3014f09c8d8a52082e6325324d95a542bd3http%3A%2F%2Fwww.springerlink.com%2Fopenurl.asp%3Fid%3Ddoi%3A10.1007%2Fs00376-011-0112-xrefpaperuri:(328adec6aea48fce0cf73d4577b5ef1e)http://d.wanfangdata.com.cn/Periodical_dqkxjz-e201203017.aspxRadar parameters including radar reflectivity, Doppler velocity, and Doppler spectrum width were obtained from Doppler spectrum moments. The Doppler spectrum moment is the convolution of both the particle spectrum and the mean air vertical motion. Unlike strong precipitation, the motion of particles in cirrus clouds is quite close to the air motion around them. In this study, a method of Doppler moments was developed and used to retrieve cirrus cloud microphysical properties such as the mean air vertical velocity, mass-weighted diameter, effective particle size, and ice content. Ice content values were retrieved using both the Doppler spectrum method and classic Z -IWC (radar reflectivity-ice water content) relationships; however, the former is a more reasonable method.
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    • Manuscript History

    Manuscript received: 17 February 2015
    Manuscript revised: 12 June 2015
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

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    Verification and Correction of Cloud Base and Top Height Retrievals from Ka-band Cloud Radar in Boseong, Korea

    • 1. Numerical Data Application Division, National Institute of Meteorological Sciences, KMA, Republic of Korea
    • 2. Applied Meteorology Research Division, National Institute of Meteorological Sciences, KMA, Republic of Korea
    • 3. Observation Research Division, National Institute of Meteorological Sciences, KMA, Republic of Korea
    • 4. National Institute of Meteorological Sciences, KMA, Republic of Korea
    • Manuscript received: 2015-02-17
    • Manuscript revised: 2015-06-12

    Abstract: In this study, cloud base height (CBH) and cloud top height (CTH) observed by the Ka-band (33.44 GHz) cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013 (September-November) were verified and corrected. For comparative verification, CBH and CTH were obtained using a ceilometer (CL51) and the Communication, Ocean and Meteorological Satellite (COMS). During rainfall, the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops, and this difference increased with rainfall intensity. During dry periods, however, the CBH and CTH observed by the cloud radar, ceilometer, and COMS were similar. Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS. In cases of rainfall or missing cloud radar data, the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data. These corrected cloud data were used to classify cloud types, which revealed that low clouds occurred most frequently.

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    1. Introduction

    • Clouds are important in influencing the energy balance, weather, and climate because they absorb and reflect radiant energy from the Sun and Earth's surface. By identifying the mechanisms of cloud formation and development, and obtaining information on meteorological phenomena in advance, the ability to predict high-impact weather events could be improved significantly. Understanding the microphysical processes of clouds has particular importance for the prediction of the development of precipitation and the estimation of its amount. For these purposes, quantitative and detailed observations of clouds are necessary, but the spatial characteristics of clouds impose many constraints on obtaining such data.

      Many studies have observed clouds using diverse equipment. In the case of satellites and ceilometers, the upper and lower boundaries of clouds are detected, which makes it difficult to identify their internal characteristics and to collect three-dimensional cloud data (Zhong et al., 2011). The method of obtaining measurements of meteorological parameters and cloud-particle shapes by direct sampling of clouds using aircraft provides reliable data on the microphysical processes and thermodynamic structure of clouds, but it is costly and only provides instantaneous data (Aydin and Singh, 2004; Yum et al., 2004). Therefore, the observation of clouds using a radar system is more effective in obtaining three-dimensional and continuous data on atmospheric particles. Generally, rainfall radars are designed specifically for observations of precipitation particles and thus, they are limited for measuring cloud particles that are relatively smaller (Sakurai et al., 2012).

      To detect smaller hydrometeors, cloud radars may be used. Since these radars use shorter wavelengths than precipitation radars, they are referred to as short-wavelength millimeter wave radar. Rayleigh scattering occurs when the particle size is significantly smaller than the wavelength and scattering strength is proportional to the biquadrate of the wave. As a result, cloud radars have high sensitivities for cloud size hydrometeors (Moran et al., 1998, Kollias et al., 2007a). They typically have high spatial resolution due to the narrow beam width and small sidelobes.

      Previous studies using cloud radar data include analyses of the mechanisms of cloud formation and development prior to the development of precipitation phenomena (Kobayashi et al., 2011; Sakurai et al., 2012), and cloud climatology based on long-term data (Kollias et al., 2007b). Furthermore, research has been conducted to improve numerical model predictions by verifying and improving cloud radar data or through parameterization and data assimilation (Mace et al., 1998; Hogan and Illingworth, 2000; Ahlgrimm and Forbes, 2014). Moreover, other studies have considered the microphysical characteristics of clouds such as the liquid water content and size distribution of rain droplets (O'Connor et al., 2005; Zhong et al., 2012) and the classification of ice crystal forms in clouds (Aydin and Singh, 2004). Such studies using cloud radar have been conducted widely throughout the world and a network has been formed through the Atmospheric Radiation Measurement (ARM) program (Stokes and Schwartz, 1994). In the ARM program, non-precipitation and weakly precipitating clouds have been observed since 1996 using a vertically pointing Ka-band millimeter-wave cloud radar (Moran et al., 1998) and a W-band ARM cloud radar (Widener and Mead, 2004). (Xi et al., 2010) obtained cloud fraction data using millimeter-wave cloud radar (MMCR), light detection and ranging (LiDAR) and ceilometer data observed at the North Slope of Alaska ARM site for 10 years from 1998 to 2008 and observed their influence on radiative forcing. In Europe, the Cloudnet program has utilized observations by ground-based remote sensing instruments (cloud radar, ceilometer, and microwave radiometer) to study clouds for about 15 years (Illingworth et al., 2007).

      The National Institute of Meteorological Sciences of Korea installed a Ka-band cloud radar system at the National Center for Intensive Observation of Severe Weather (NCIO) at Boseong in April 2013. It is expected that analysis of the microphysical characteristics of clouds based on the cloud radar data will promote understanding of cloud processes and improve numerical model predictions. However, before such research can be performed, verification and quality control of the cloud radar data must be completed.

      The results of comparative analyses of reflectivity, liquid water content, and cloud height, obtained in previous studies from cloud radar and other instruments (e.g., satellites, LiDAR, and micro rain radar), have shown that highly diverse and good quality data can be obtained by linking and combining multiple sources (Syrett et al., 1995; Hollars et al., 2004; O'Connor et al., 2005; Kneifel et al., 2011).

      In particular, cloud base height (CBH) and cloud top height (CTH) are important parameters in the formation and development processes of clouds, and it is therefore necessary to compare these data to check whether cloud radars are effective in detecting cloud boundaries. (Clothiaux et al., 2000) objectively determined the hydrometeor height distribution using active remote sensing at the Cloud and Radiation Testbed ARM site in Oklahoma and at the Tropical West Pacific site in Darwin, Australia. Cloud boundaries were determined from the returned radar signal using the cloud mask algorithm by (Clothiaux et al., 1995). A LiDAR and ceilometer were utilized to detect optically thin clouds and to aid with clutter removal. To evaluate the accuracy of ARM MMCR and GMS-5 satellite data over Manus Island, (Hollars et al., 2004) compared the cloud top heights calculated from each piece of equipment according to the type of cloud and precipitation. (Oh et al., 2014) performed a comparative analysis of CTHs observed by cloud radar and sensors onboard the Communication, Ocean, and Meteorological Satellite (COMS). They established that cloud radar was useful in detecting the CTH in the absence of precipitation. However, during periods of rainfall, the CTHs obtained by the cloud radar tended to be lower than reality and thus, the expectation was that the radar-derived CTHs could be corrected using COMS data. However, because that study focused solely on the upper boundary of the cloud and analyzed only one case, it was determined that additional analyses were required.

      This study was conducted to verify and correct CBHs and CTHs obtained by the cloud radar at Boseong NCIO in the fall (September-November) of 2013. For comparative verification, CBHs and CTHs observed by a ceilometer and COMS were used, and the effectiveness of the cloud radar data was examined by consideration of the occurrence of precipitation, rainfall rate, and cloud thickness and density. Based on these results, a method for the correction of radar-derived CBH and CTH is proposed and, additionally, the characteristics of the occurrence of cloud types examined.

      • Data and method

      2.1. Ka-band cloud radar

    • The Ka-band cloud radar used in this study is installed at the Boseong NCIO and operated by the National Institute of Meteorological Sciences of Korea. Boseong NCIO is located on the southern coast of Korea (34.76°N, 127.21°E), and equipped with a variety of meteorological observational instruments (ceilometers, optical rain gauge, micro rain radar, particle size velocity disdrometer, global navigation satellite system, and wind profiler) in addition to the cloud radar. The cloud radar transmits 33.44 GHz pulses in the Ka-band, and it is used for observations of precipitable clouds, non-precipitable clouds, and low precipitation. By transmitting horizontal waves and receiving both horizontal and vertical waves, the cloud radar produces reflectivity, radial velocity, spectrum width, linear depolarization ratio, and signal-to-noise ratio data. It is designed to observe clouds of up to 15- km in height with a resolution of 15 m. Additional details of its characteristics are provided in Table 1.

      Clouds were defined using the co-polar vertical reflectivity obtained by the cloud radar from September to November 2013 (Fig. 1a). Overall, 6.27% of the cloud radar data were missing (12.64% in September, 2.96% in October, and 3.33% in November) because of a variety of reasons including the suspension of observations (from 7 to 8 October 2013) because of strong winds. To eliminate ground clutter, noise, and non-cloud echoes, clouds were defined as echoes with reflectivity values of greater than -30 dBZ and thicknesses of greater than 1.5 km. The reflectivity threshold of -30 dBZ has been reported previously as the minimum value of radar reflectivity for cirrus clouds (Brown et al., 1995), and the thickness threshold of 1.5 km was determined based on the average thickness of cirrus clouds observed by LiDAR (Fuller et al., 1988; Kent and Schaffner, 1988). In addition, to remove non-meteorological echo, like that generated by insects, which appears with reflectivity values lower than -30 dBZ and at heights of less than 2 km, the hydrometeor boundaries were determined using the threshold of reflectivity of -30 dBZ and a signal to noise ratio (SNR) of 5 dB. Although not shown in this paper, when these thresholds were set to a reflectivity lower than -30 dBZ and an SNR lower than 5 dB, it was hard to detect the boundaries accurately due to the influence of the noise generated on the ground: and when it was set to values greater than the thresholds, the top height was estimated lower and the base height greater. The CBHs and CTHs were defined as the lowest and highest altitudes of the clouds, respectively (Fig. 1b). Multi-layer clouds were considered as single entities and cloud thicknesses were defined as the difference between the CBH and CTH.

      Figure 1.  (a) Time-height cross section of reflectivity (units: dBZ) observed by cloud radar from September to November 2013 and (b) cloud base (crosses) and top (circles) heights (units: km) observed by cloud radar (CR; blue) and ceilometer-COMS (Ceil; red). The gray and green shading indicates missing values and rainfall cases, respectively. MDS means minimum detectable signal.

      Figure 2.  (Continued.)

      • 2.2. Ceilometer and COMS

    • For comparative analysis, clouds were defined using a ceilometer and COMS from September to November 2013. The CBHs were observed using a Vaisala CL51 ceilometer, which uses LiDAR technology to transmit pulsed waves vertically and receive backscattered signals reflected by cloud drops. This ceilometer has a range of 13 km with a 10-m resolution for cloud detection. The CTHs were obtained using the COMS meteorological data processing system, which simultaneously employs single-channel and radiation-ratio methods (METRI/KMA, 2009). The single-channel method calculates the cloud top temperature by converting the brightness temperature of COMS to cloud top pressure. When this method is employed, the cloud top pressures of semi-transparent clouds are calculated to be higher than their actual values. Therefore, this is corrected using the radiation-ratio method. The radiation-ratio method involves applying the brightness temperatures of the water vapor (6.75 μm) and infrared-1 channels to obtain the cloud top pressure. The temporal resolution of the CTHs observed by COMS is 15 min and the spatial resolution is 4 km. In this study, the data at 00 min were extracted and used and were analyzed using the grid data (34.76°N, 127.21°E) nearest to Boseong Center. The results of these two methods were compared to select the optimum cloud top pressure, from which CTHs were calculated using the hypsometric equation.

      In this study, targets for which CBHs and CTHs were observed by both the ceilometer and COMS were defined as clouds. Furthermore, cases for which the cloud radar data contained missing values were excluded from further analysis. The CBHs and CTHs obtained using the ceilometer-COMS data were compared with the cloud radar data.

    3. Comparative verification of cloud radar data

      3.1. Comparison of cloud base and top height formations

    • 3.1.1. Average cloud base and top heights

      Table 2 shows the CBHs, CTHs, and cloud thicknesses observed by the cloud radar and ceilometer-COMS. The difference between the average CBH and CTH based on the observations from the cloud radar (363 cases) and ceilometer-COMS (510 cases), showed that the cloud-radar-derived CBH and CTH were higher by 0.75 and 0.36 km, respectively, and that the radar-derived average cloud thickness was 0.39 km smaller. However, for the 285 cases in which the clouds were observed simultaneously by the cloud radar and ceilometer-COMS, it was found that the radar-derived CBH and CTH was 0.11 km higher and 0.73 km lower, respectively, and that the radar-derived cloud thickness was 0.84 km smaller. These cases showed only slight differences between the data obtained by the cloud radar and ceilometer-COMS. Conversely, when the CBH and CTH were observed by either the cloud radar or ceilometer-COMS, the differences in the data were relatively more significant. For instance, observations by the cloud radar (78 cases) showed average CBHs and CTHs of 5.04 and 7.54 km, respectively, indicating mainly high clouds with thicknesses of about 2.5 km. However, observations by ceilometer-COMS (225 cases) showed average CBHs and CTHs of 2.13 and 5.38 km, respectively, indicating mainly low clouds with thicknesses of 3.25 km. Although the average CBHs and CTHs were similar when cloud observations were made concurrently by the cloud radar and ceilometer-COMS, there were differences between the frequencies of occurrence determined for varying altitudes. The frequency of occurrence of CBH decreased gradually from the surface to the altitude of 10 km in the ceilometer observations, whereas the frequency of occurrence was concentrated below the altitude of 1 km in the cloud radar observations (Fig. 2a). Excluding the fact that the frequency of occurrence of clouds with top heights of 2-3 km was higher in the cloud radar observations, similar distributions were observed at most altitudes (Fig. 2b).

      3.1.2. Precipitation events

      In order to analyze the reason for the differences between the cloud radar and ceilometer-COMS data, the CBHs and CTHs were compared based on whether precipitation had been present (Fig. 3). The cases in which rainfall was detected by the micro rain radar at the Boseong NCIO were defined as precipitation cases (125 cases), and the remainder defined as non-precipitation cases (160 cases). The CBHs obtained by the cloud radar were either similar to or higher than the values obtained by the ceilometer in non-precipitation cases (Fig. 3a). However, in precipitation cases, the CBHs observed by the cloud radar were similar to ground level, and for this reason, the frequency of low CBH was shown to be high in the cloud radar data, as shown in Fig. 2a. For the precipitation cases, the CTHs derived by cloud radar were similar to or lower than observed by COMS (Fig. 3b).

      Figure 3.  Frequency of occurrence of cloud (a) base and (b) top heights observed by cloud radar (black) and ceilometer-COMS (gray).

      Figure 4.  Scatter plots of cloud (a) base and (b) top heights (km) observed by cloud radar and ceilometer-COMS. The crosses and circles indicate non-precipitation and precipitation cases, respectively.

      Figure 5.  Time-series of hourly rainfall rate (R; units: mm h-1) observed by MRR (upper panels) and time-height cross sections of reflectivity (units: dBZ) and cloud base (triangles) and top (circles) height (lower panels) observed by cloud radar (CR) and ceilometer-COMS (Ceil) pn (a) 5-6 September 2013 and (b) 24 November 2013.

      Figure 6.  Scatter plots and box plots of rainfall rate (units: mm h-1) observed by MRR versus differences of cloud (a) base and (b) top heights (units: km) between cloud radar (CR) and ceilometer-COMS (Ceil). Boxes denote the 25th and 75th percentile positions, and the lines inside the box show the median; the whiskers denote the 10th and 90th percentile; outliers are indicated by the 5th and 95th percentile positions.

      These results can be explained based on the observational characteristics of cloud radar. First, cloud radar transmits signals from the ground, and is thus influenced by meteorological phenomena occurring in the lower atmosphere. Figure 4a shows that similar CBHs were observed by the cloud radar and ceilometer before the occurrence of precipitation. However, in the precipitation cases, cloud radar reflectivity was even observed at the surface due to the raindrops. The bottom boundaries of the cloud radar reflectivity that appeared during rainfall can be explained by the hydrometeor boundaries, which have a different meaning to the CTHs. Therefore, during rainfall, the CTHs of the cloud radar need to be compensated using other ground observation data. Another characteristic of the cloud radar is that it uses short-wavelength signals to detect small cloud particles and therefore, signal attenuation occurs due to the large raindrops. This phenomenon was confirmed by the fact that the CTHs observed by the cloud radar were lower than observed by COMS in the precipitation cases (Fig. 4b).

      The impact of precipitation could change depending on rainfall intensity. The differences between the CBHs and CTHs obtained by the cloud radar and ceilometer-COMS and the micro rain radar at 200 m at varying rainfall rates were examined (Fig. 5). The results showed that at higher rainfall rates, CTHs observed by the cloud radar were lower than COMS (Fig. 5b). Generally, the CBHs observed by the cloud radar were lower than the ceilometer, but the difference decreased as the rainfall rate increased (Fig. 5a). This can be attributed to the ceilometer also being a ground-based instrument. In cases of heavy precipitation (>30 mm h-1), the CBHs observed by the ceilometer were close to the ground, as was the case for the cloud radar.

      3.1.3. Cloud thickness and density

      Even in the non-precipitation cases, there were differences between the CBHs and CTHs observed by the cloud radar and ceilometer-COMS (Fig. 3). In order to determine the cause, the differences between the CBHs and CTHs observed by the cloud radar and ceilometer-COMS were examined according to cloud thickness (Fig. 6). Cloud thickness was calculated using the cloud radar data. When the cloud was thick, the CBH and CTH values were relatively similar between the cloud radar and ceilometer-COMS, but there were significant differences for thin cloud.

      Figure 7.  Scatter plots and box plots of cloud thickness (units: km) versus differences of (a) CBHs and (b) CTHs (units: km) between cloud radar (CR) and ceilometer-COMS (Ceil). Boxes denote the 25th and 75th percentile positions, and the lines inside the box show the median; the whiskers denote the 10th and 90th percentile; outliers are indicated by the 5th and 95th percentile positions.

      Figure 8.  Time-height cross sections of reflectivity (units: dBZ) and cloud base (triangles) and top (circles) height observed by cloud radar (CR) and ceilometer-COMS (Ceil) on (a) 5 (∼1500 UTC) October 2013 and (b) 6 (1600 UTC) to 7 (0300 UTC) October 2013.

      The cloud radar observations of thin and high clouds showed higher sensitivity (Fig. 7a). The reason for this can be conjectured based on the observational characteristics of COMS: in the cases of thin and high clouds, the energy emitted from below the cloud is observed by the satellite, which can lead to a higher brightness temperature in the infrared channel than the actual cloud top temperature. However, even with thin clouds, similar CBHs and CTHs were observed by the cloud radar and ceilometer-COMS in some cases (Fig. 6). Although the clouds were thin in such cases, the cloud radar reflectivity was greater than 0 dBZ (Fig. 7b). The reflectivity of the radar is a log of the ratio of the number of water droplets with diameter of 1 mm to the unit volume (1 m3); therefore, it can be said that it provides information on the density of the cloud particles. Even in the case of thin clouds, if the cloud density is high, they will be observed effectively by ceilometer-COMS.

      Figure 9.  Occurrence counts of cloud types observed by (a) cloud radar and (b) ceilometer-COMS.

      Figure 10.  Time-height cross section of reflectivity (units: dBZ) and cloud base and top height using corrected cloud radar data. The gray and green shading indicates missing values and rainfall cases, respectively.

      • 3.2. Comparison of cloud types

    • CBH and CTH data from the cloud radar and ceilometer-COMS were used to classify the cloud types observed at Boseong NCIO in the fall of 2013, based on the classification method of (Kollias et al., 2007b) (Table 3). Using this method, clouds were classified as high, middle, and low depending on their CBHs and CTHs. Additionally, low clouds were subdivided into non-precipitable and precipitable clouds, and then the precipitable clouds subdivided further into shallow and deep precipitable clouds according to their CTHs. The frequency of occurrence during the entire analysis period of clouds observed by the cloud radar was the highest for low clouds (49.59%), followed by middle clouds (31.68%), and high clouds (18.73%), as shown in Fig. 8a. With respect to the monthly data, the aforementioned frequency pattern was also observed in September and November, whereas the frequency of occurrence of low clouds was lower than the other two types in October. Similar to the cloud radar data, the ceilometer-COMS data revealed that the frequency of occurrence was highest for low clouds (61.37%), followed by middle clouds (31.18%), and high clouds (7.45%), as shown in Fig. 8b. However, significant differences were found regarding the sub-classifications of low clouds. For instance, deep precipitable clouds were observed mainly by the cloud radar (Fig. 8a), whereas non-precipitable clouds were observed mainly by ceilometer-COMS (Fig. 8b).

      Cases of precipitable and non-precipitable cloud types were also examined (not shown). In the case of precipitable clouds, low clouds were observed mostly by the cloud radar (92.8%) and ceilometer-COMS (76.8%). However, different frequencies of cloud type were observed in the cases of non-precipitable clouds between the cloud radar (middle 46.25% > low 30.63% > high 23.13%) and ceilometer-COMS (low 43.75% > middle 41.25% > high 15.00%). The results of sub-classifying the low clouds showed that, in the event of precipitation, deep precipitable clouds (97.41%) were observed mainly by the cloud radar, whereas the frequency of non-precipitable clouds (94.79%) was highest in the ceilometer-COMS observations. In the event of non-precipitation, the cloud radar did not observe any one particular cloud type more frequently, while non-precipitable clouds (100%) were still observed with high frequency by the ceilometer-COMS.

      This could be explained by the fact that, in the event of precipitation, the CBH is observed to be close to ground level by the cloud radar, whereas the CBH observed by ceilometer-COMS is greater than 200 m. However, the CBH observed by the cloud radar is lower than the actual height because of the influence of the precipitation and thus, there is a need for a new set of cloud classification criteria for cases in which the CBH values require correction based on ceilometer-COMS data.

    4. Cloud radar data correction and characteristic analysis

    • Although the cloud radar made high-sensitivity observations in the absence of precipitation, data obtained during the occurrence of precipitation were unreliable. Thus, in cases of precipitation or missing cloud radar data, the CBH and CTH values obtained from the cloud radar were corrected using ceilometer-COMS data (Fig. 9).

      Using the corrected CBH and CTH data, the cloud types were re-classified. Similar to the pre-correction cloud classification results, the cloud types in decreasing order of frequency of occurrence were: low clouds (54.55%) > middle clouds (32.59%) > high clouds (12.86%) (Fig. 10). Data obtained in October showed that the frequency of occurrence of low clouds increased following the correction, which was attributed to the occurrence of low clouds mainly under conditions of strong winds that resulted in missing data values. The result of sub-classifying the low clouds (not shown) was similar to the result obtained from ceilometer-COMS (Fig. 8b). This was thought to be because low clouds occurred most frequently during precipitation events, which meant that cloud radar data were substituted by ceilometer-COMS data. In such cases, the reference value for the CBH (200 m) in the sub-classification of low clouds was changed appropriately to the corrected data. Based on the corrected data, the average CBH of 1.27 km was used during precipitation events for the sub-classification of low clouds, and the results are shown in Fig. 10. The cloud type with the highest frequency of occurrence was deep precipitable clouds, followed by non-precipitable clouds, and shallow precipitable clouds.

      Figure 11.  As in Fig. 8, but using corrected cloud radar data.

    5. Summary and conclusions

    • In this study, the CBHs and CTHs observed by the Ka-band cloud radar at the Boseong NCIO in the fall of 2013 (September-November) were verified and corrected. For the purposes of this study, a cloud was defined as cases in which the cloud radar observed reflectivity values of greater than -30 dBZ and with a thickness of 1.5 km. For comparison, cases in which the CBH observed by the ceilometer and the CTH observed by COMS occurred concurrently were defined as a cloud.

      First, the CBH and CTH data obtained by the cloud radar and ceilometer-COMS were compared. In cases of precipitation, the CBHs and CTHs observed by the cloud radar tended to be lower than the actual heights. The reason for this could be explained by the observational characteristics of the cloud radar. Cloud radar is a ground-based observation system, which is affected by meteorological phenomena occurring in the lower levels of the atmosphere. Of particular note, as the cloud radar uses a millimeter-wavelength signal to detect the small cloud particles, signal attenuation occurs in the presence of raindrops. For this reason, the radar-derived CBHs were observed to be closer to the ground, while the CTHs were observed to be lower than the actual heights. In the absence of precipitation, the CBHs and CTHs observed by the cloud radar and ceilometer-COMS were similar. Thin or low-density clouds were observed more effectively by the cloud radar compared with ceilometer-COMS.

      The result of classifying the cloud types observed by the cloud radar and ceilometer-COMS showed that the frequency of occurrence was highest for low clouds, followed by middle clouds, and high clouds. Sub-classification of low clouds occurring in precipitation cases showed that deep precipitable clouds were observed mainly by the cloud radar, whereas non-precipitable clouds were observed mainly by ceilometer-COMS. The cloud radar data obtained during the occurrence of precipitation could not be considered reliable. Thus, it was deemed necessary to correct the cloud radar data using the ceilometer-COMS data and to establish new criteria for the sub-classification of cloud in such cases.

      Based on these results, for cases of precipitation or missing data, the cloud radar data were corrected using the ceilometer-COMS data and re-classified using the new reference value. The reference value for the CBH (200 m) was changed to 1.27 km for the sub-classification in cases of precipitation.

      The results of this study show that cloud radar could effectively provide a description of cloud boundaries in the absence of precipitation.However, cloud radar data are deemed unreliable in the presence of precipitation. In such cases, it is proposed that the radar data be corrected using data obtained from other observational systems such as a ceilometer or satellite. It is expected that future research involving analyses of the liquid water content and rain-rate estimation, with a focus on the microphysical characteristics of clouds, will contribute to the understanding of the mechanisms and characteristics of cloud formation.

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