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Sandra Starkweather and Taneil Uttal
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Sara Morris and Taneil Uttal

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Creation of metadata (data about data) takes many forms and has many standards, much of which are designed to provide information for computer algorithms to find, access, and distribute data rather than for how humans might ingest data information. The humans (engineers, technicians, operators, scientists, data managers) that are increasingly tasked with being the providers of standard scientific metadata by the data science community also have a critical need for a different kind of metadata: metadata that can be used in the field (often offline) that provide a detailed visual map of the pathway taken by the electronic signal from a measuring device to a finalized, quality controlled geophysical variable. Datagrams presented here have been developed to fill this requirement and are a user-friendly, information-rich, graphical format that outline, record, and detail the critical information and steps involved with origin, collection, dataflow, processing, and archiving of data. Datagrams are designed to provide critical information across engineering, maintenance, data processing, and scientific teams that might speak different languages but are all required to process and maintain the data or instrument. The essential components of datagrams developed for instruments operating at remote Arctic stations are described here, but of course the concept is applicable to any type of observing protocol in any location.

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Taneil Uttal and Robert A. Kropfli

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When observing clouds with radars, there are a number of design parameters, such as transmitted power, antenna size, and wavelength, that can affect the detection threshold. In making calculations of radar thresholds, also known as minimum sensitivities, it is usually assumed that the radar pulse volume is completely filled with targets. In this paper, the issue of partial beam filling, which results, for instance, if a cloud is thin with respect to the pulse length, or measurements are being made near cloud edges, is investigated. This study pursues this question by using measurements of radar reflectivities made with a 35-GHz, surface-based radar with 37.5-m pulse lengths, and computing how reflectivity statistics would be affected if the same clouds and/or precipitation had been observed with a radar with a 450-m pulse length. In a dataset measured during winter over a midcontinental site, partial beamfilling degraded the percentage of clouds detected by about 22% if it was assumed that the minimum detection threshold was −30 dBZ. In a second dataset collected during summer over a summertime subtropical site that was dominated by thin, boundary layer stratus, partial beam filling degraded the percentage of clouds detected by 38%, again assuming a minimum detection threshold of −30 dBZ. This study provides a preliminary indication of how radar reflectivity statistics from a spaceborne cloud radar may be impacted by design constraints, which would mandate a pulse length of around 500 m and a minimum detection threshold of around −30 dBZ.

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Kenneth Sassen, David O'C. Starr, and Taneil Uttal

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The structure and composition of three basic cirrus cloud types are examined through coordinated aircraft and ground-based polarization lidar and radar measurements. The cloud systems consist of a multilayered orographic cirrus, a 6-km deep cirrostratus, and a group of fibrous cirrus bands at the tropopause. The data reveal the presence of mesoscale generating regions with horizontal dimensions ranging from ∼15 km in narrow cloud bands up to ∼100 km in cirrostratus. These generating regions appear to be composed of complexes of much smaller convective structures, presumably on the ∼1-km scale of cirrus uncinus cells, and so are termed Mesoscale Uncinus Complexes (MUC). Accumulations of ice particles within cirrus, commonly referred to as precipitation trails, are associated with generating regions at or near cloud tops, but are also created by the local production of ice crystals within embedded convective impulses. Supercooled cloud droplets large enough to be detected by aircraft probes (≳5 μm diameter) were sampled in embedded convective cells near cloud base at temperatures ranging from −21° to −36°C. Ice particle nucleation at colder temperatures is assumed to involve the homogeneous freezing of haze particles too small to be detected by the aircraft probes employed, although they appear to have been detected by the polarization lidar technique under some conditions. Average ice mass contents are temperature dependent in a manner consistent with the conversion of a relatively small amount of excess water vapor (corresponding to ice supersaturations of a few percent) to ice mass.

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Taneil Uttal, Robert M. Rauber, and Lewis O. Grant

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The phase distribution of the water mass of a cold orographic cloud into vapor, liquid, and ice is calculated from measurements made from an instrumented aircraft. The vapor values are calculated from thermodynamic measurements, and the liquid is measured directly with a Johnson-Williams hot-wire device. Ice mass is calculated from particle size spectra obtained with a two-dimensional optical array cloud probe (2-D probe) and a knowledge of crystal habit based on decelerator measurements and cloud temperatures. Maximum vapor mass in the cloud is 2.0 g m−3, which is comparable with maximum ice mass in the cloud of 1.5 G m−3. Maximum liquid mass is approximately one order of magnitude lower at 0.15 g m−3 and appears to be a small remainder between the vapor and the ice as they compete for the major portion of the cloud water mass. In the cloud upwind of the mountain, liquid + vapor + ice is nearly constant, suggesting that precipitation does not deplete the water mass at the levels studied by the aircraft. Maxima in both ice and liquid mass appear just over the windward crest of the mountain, indicating a strong orographic effect on condensation of vapor to liquid and growth of ice by vapor diffusion and riming. The distribution of crystal habits also suggests a significant downdraft exists just downwind of the mountain.

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Junhong Wang, William B. Rossow, Taneil Uttal, and Margaret Rozendaal

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The macroscale cloud vertical structure (CVS), including cloud-base and -top heights and layer thickness, and characteristics of multilayered clouds, is studied at Porto Santo Island during the Atlantic Stratocumulus Transition Experiment (ASTEX) by using rawinsonde, radar, ceilometer, and satellite data. The comparisons of CVS parameters obtained from four different approaches show that 1) by using the method developed by Wang and Rossow rawinsonde observations (raob’s) can sample all low clouds and determine their boundaries accurately, but oversample low clouds by about 10%, mistaking clear moist layers for clouds; 2) cloud-base heights less than 200 m in the radar data are ambiguous, but can be replaced by the values measured by ceilometer; and 3) the practical limit on the accuracy of marine boundary layer cloud-top heights retrieved from satellites appears to be about 150–300 m mainly due to errors in specifying the atmospheric temperature and humidity in the inversion layer above the cloud. The vertical distribution of clouds at Porto Santo during ASTEX is dominated by low clouds below 3 km, a cloud-free layer between 3 and 4 km, and ∼20% high clouds with a peak occurrence around 7–8 km. Low clouds have mean base and top heights of 1.0 km and 1.4 km, respectively, and occur as single layers 90% of the time. For double-layered low clouds, the tops of the uppermost layers and the bases of the lowermost layers have similar distributions as those of single-layered clouds. The temporal variations of low clouds during ASTEX are apparently dominated by advecting mesoscale (20–200 km) horizontal variations. Coherent time variations are predominately synoptic (timescale 4.5–6.8 days) and diurnal variability. On the diurnal timescale, all cloud properties show maxima in the early morning (around 0530 LST) decreasing to minima in the late afternoon. Diurnal variations appear to be altered when high clouds are present above low clouds. The general characteristics of CVS in three ASTEX and the First ISCCP Regional Experiment (FIRE87) regions derived from a 20-yr rawinsonde dataset are also presented. The results suggest that CVS characteristics obtained from data collected at Porto Santo during ASTEX (June 1992) are not representative of other marine stratiform cloud regions.

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Matthew D. Shupe, Sergey Y. Matrosov, and Taneil Uttal

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Arctic mixed-phase cloud macro- and microphysical properties are derived from a year of radar, lidar, microwave radiometer, and radiosonde observations made as part of the Surface Heat Budget of the Arctic Ocean (SHEBA) Program in the Beaufort Sea in 1997–98. Mixed-phase clouds occurred 41% of the time and were most frequent in the spring and fall transition seasons. These clouds often consisted of a shallow, cloud-top liquid layer from which ice particles formed and fell, although deep, multilayered mixed-phase cloud scenes were also observed. On average, individual cloud layers persisted for 12 h, while some mixed-phase cloud systems lasted for many days. Ninety percent of the observed mixed-phase clouds were 0.5–3 km thick, had a cloud base of 0–2 km, and resided at a temperature of −25° to −5°C. Under the assumption that the relatively large ice crystals dominate the radar signal, ice properties were retrieved from these clouds using radar reflectivity measurements. The annual average ice particle mean diameter, ice water content, and ice water path were 93 μm, 0.027 g m−3, and 42 g m−2, respectively. These values are all larger than those found in single-phase ice clouds at SHEBA. Vertically resolved cloud liquid properties were not retrieved; however, the annual average, microwave radiometer–derived liquid water path (LWP) in mixed-phase clouds was 61 g m−2. This value is larger than the average LWP observed in single-phase liquid clouds because the liquid water layers in the mixed-phase clouds tended to be thicker than those in all-liquid clouds. Although mixed-phase clouds were observed down to temperatures of about −40°C, the liquid fraction (ratio of LWP to total condensed water path) increased on average from zero at −24°C to one at −14°C. The observations show a range of ∼25°C at any given liquid fraction and a phase transition relationship that may change moderately with season.

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Matthew D. Shupe, Taneil Uttal, and Sergey Y. Matrosov

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An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997–98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%–72%. In all-ice clouds, ice particle mean sizes D mean can be retrieved with an uncertainty of 26%–46% while retrieved ice water contents (IWC) have an uncertainty of 62%–100%. In general, radar-only, regionally tuned empirical power-law retrievals were best suited among the tested retrieval algorithms for operational cloud monitoring at SHEBA because of their wide applicability, ease of use, and reasonable statistical accuracy. More complex multisensor techniques provided a moderate improvement in accuracy for specific case studies and were useful for deriving location-specific coefficients for the empirical retrievals but were not as frequently applicable as the single sensor methods because of various limitations. During the yearlong SHEBA program, all-liquid clouds were identified 19% of the time and were characterized by an annual average droplet Re of 6.5 μm, LWC of 0.10 g m−3, and liquid water path of 45 g m−2. All-ice clouds were identified 38% of the time with an annual average particle D mean of 73 μm, IWC of 0.014 g m−3, and ice water path of 30 g m−2.

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Sergey Y. Matrosov, Taneil Uttal, and Duane A. Hazen

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The performance of radar reflectivity (Z e)–based relations for retrievals of marine stratiform cloud liquid water content (LWC) is evaluated by comparing liquid water path (LWP) estimates from microwave radiometers with vertically integrated LWC values retrieved from radar measurements. Based on a measurement dataset from a research vessel in the tropical eastern Pacific Ocean, it is shown that reflectivity thresholding allows minimizing of the influence of drizzle drops present in marine stratiform clouds to the extent that LWP estimates from a ground-/shipborne radar can have uncertainties that might be acceptable for different applications. The accuracies of Z e-based retrievals depend on the thresholding level Z et, and they are generally better than a factor of 2 for Z et ≲ −15 dBZ. These accuracies typically improve when Z et is lowered; however, the amount of cloud profiles that pass thresholding diminishes as Z et is decreased from about 50% for a −15-dbZ threshold to only about 10% for a −25-dBZ threshold. Different thresholding strategies are considered. Ancillary information on cloud-base heights can improve LWP estimates from reflectivities. The ship-based dataset was used to simulate measurements from prospective 94-GHz spaceborne cloud radar (CloudSat). CloudSat measurements would, on average, detect about 75% of warm marine stratiform clouds, though many clouds with negligible presence of drizzle will be missed. Because of sensitivity and resolution issues for the spaceborne radar, reflectivity-based estimates of LWP are generally biased toward high values and have higher uncertainties when compared with the ground-based radar, for the same Z et.

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Michael J. Pavolonis, Andrew K. Heidinger, and Taneil Uttal

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Three multispectral algorithms for determining the cloud type of previously identified cloudy pixels during the daytime, using satellite imager data, are presented. Two algorithms were developed for use with 0.65-, 1.6-/3.75-, 10.8-, and 12.0-μm data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites. The AVHRR algorithms are identical except for the near-infrared data that are used. One algorithm uses AVHRR channel 3a (1.6 μm) reflectances, and the other uses AVHRR channel 3b (3.75 μm) reflectance estimates. Both of these algorithms are necessary because the AVHRRs on NOAA-15 through NOAA-17 have the capability to transmit either channel 3a or 3b data during the day, whereas all of the other AVHRRs on NOAA-7 through NOAA-14 can only transmit channel 3b data. The two AVHRR cloud-typing schemes are used operationally in NOAA’s extended Clouds from AVHRR (CLAVR)-x processing system. The third algorithm utilizes additional spectral bands in the 1.38- and 8.5-μm regions of the spectrum that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible–Infrared Imaging Radiometer Suite (VIIRS). The VIIRS will eventually replace the AVHRR on board the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), which is currently scheduled to be launched in 2009. Five cloud-type categories are employed: warm liquid water, supercooled water–mixed phase, opaque ice, nonopaque high ice (cirrus), and cloud overlap (multiple cloud layers). Each algorithm was qualitatively evaluated through scene analysis and then validated against inferences of cloud type that were derived from ground-based observations of clouds at the three primary Atmospheric Radiation Measurement (ARM) Program sites to help to assess the potential continuity of a combined AVHRR channel 3a–AVHRR channel 3b–VIIRS cloud-type climatology. In this paper, “validation” is strictly defined as comparisons with ground-based estimates that are completely independent of the satellite retrievals. It was determined that the two AVHRR algorithms produce nearly identical results except for certain thin clouds and cloud edges. The AVHRR 3a algorithm tends to incorrectly classify the thin edges of some low- and midlevel clouds as cirrus and opaque ice more often than the AVHRR 3b algorithm. The additional techniques implemented in the VIIRS algorithm result in a significant improvement in the identification of cirrus clouds, cloud overlap, and overall phase identification of thin clouds, as compared with the capabilities of the AVHRR algorithms presented in this paper.

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