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Table 4.5. Initial cloud base and environmental aerosol distributions.

in Linear Eddy Modeling Of Entrainment And Mixing In Cumulus Clouds
by Chwen-wei Su

Table 3.1 lists the cloud base conditions used in the simulations: the ensemble

in Linear Eddy Modeling Of Entrainment And Mixing In Cumulus Clouds
by Chwen-wei Su

Table 3: veri cation of surface parameters. t2m = 2 meter temperature, td2 = 2 meter dew point temperature, msl = surface pressure, win = 10 meter wind speed,low = cloud cover in oktas (low clouds), clb= signi cant cloud base cl2 = cloud cover in oktas, pre = total precip- itation, rms= root mean square error, bias = mean (systematic) error, mae = mean absolute error, num= number of cases.

in Tests With Separated Tables for Water Vapor Saturation Pressure Over Ice and Water.
by Karl-ivar Ivarsson

Table 3: Offset times for ight segments for RF01-RF03. RL denotes remote sensing leg; CB, cloud base leg; SC, subcloud leg; CT, cloud top leg; SP, special pattern (which varied from ight to ight); SF, surface ux leg. For the pro les we label full pro les (FP), cloud pro les (CP) and inversion pro les (IP).

in Dynamics and Chemistry of Marine Stratocumulus -- DYCOMS-II
by Bjorn Stevens Donald, Donald H. Lenschow, Gabor Vali, Hermann Gerber, J. -l. Brenguier, C. Bretherton S, T. Campos, S. Chai, I. Faloona, D. Friesen, S. Haimov, K. Laursen, D. Lilly K, S. Loehrer M, Szymon Malinowski P, B. Morely, J. Szumowski, H. Takagi, D. C. Thorton, M. Tschudi, Twohy C, M. Wetzel, M. C. Zanten
"... In PAGE 36: ... The radar was operated during all ights. The radar legs (RL in Table3 ) own not far above cloud top yielded full coverage from the ocean surface to the top of the cloud. Flight segments in and below the cloud layer provided partial views.... ..."

Table 2: veri cation of surface parameters. t2m = 2 meter temperature, td2 = 2 meter dew point temperature, msl = surface pressure, win = 10 meter wind speed,low = cloud cover in oktas (low clouds), clb= signi cant cloud base in meters, cl2 = cloud cover in oktas, pre = total precipitation, rms= root mean square error, bias = mean (systematic) error, mae = mean absolute error and num= number of cases. 30 and 36 hours forecast:

in Tests With Separated Tables for Water Vapor Saturation Pressure Over Ice and Water.
by Karl-ivar Ivarsson
"... In PAGE 10: ...ig. 9: RMS error and bias for wind-speed from the postprocessed 36 hour forecasts. 4.3 Results for near surface parameters The results for the near surface parameters, or direct output model parameters, (DMO) are tabulated in Table2 . Signi cant cloud base is normally not post processed.... ..."

Table 2: Summary of DYCOMS-II ights, July 2001. State variables are estimated from subcloud legs, SSTs are estimated from radiometric measurements during low altitude (a142a152a145a76a151a131a151 m) surface legs. Cloud base taken from average LCL for all legs within PBL. The cloud top liquid water a127 a130a154a153 a155a157a156 a9 estimated from soundings. Flight latitude and longitude is at the center of ight region for middle four hours on target.

in Dynamics and Chemistry of Marine Stratocumulus -- DYCOMS-II
by Bjorn Stevens Donald, Donald H. Lenschow, Gabor Vali, Hermann Gerber, J. -l. Brenguier, C. Bretherton S, T. Campos, S. Chai, I. Faloona, D. Friesen, S. Haimov, K. Laursen, D. Lilly K, S. Loehrer M, Szymon Malinowski P, B. Morely, J. Szumowski, H. Takagi, D. C. Thorton, M. Tschudi, Twohy C, M. Wetzel, M. C. Zanten
"... In PAGE 23: ... Here CW denotes a clockwise circle, CCW a counter clockwise circle. Leg Duration Heading Description RL1 30 min CW Radar/Lidar and Dropsonde Leg at 3km CB 60 min CW-CCW Level ux leg just above cloud base (CB) SC 60 min CCW-CW Level ux leg in subcloud (SC) layer RL2 30 min CW Radar/Lidar leg just above lidar dead-zone CT 60 min CW-CCW Level ux leg just below cloud top (CT) SP 30 min CCW Special (SP) Leg, generally porpoising at cloud top SF 60 min CCW-CW Surface (SF) Leg at lowest safe ight level RL3 30 min CCW Radar/Lidar and Dropsonde Leg at 3km A basic summary of the conditions encountered on each ight is included in Table2 . Because the time on target approached seven hours, and the ight legs generally drifted with the mean wind (e.... ..."

Table 2. Classification accuracies of surface normal and point cloud representations for different patch resolutions. First column denotes the number of patches over the facial surfaces and the second column shows the average number of 3D points in each patch.

in Selection and Extraction of Patch Descriptors For 3D Face Recognition
by Berk Gökberk, Lale Akarun 2005
"... In PAGE 7: ... From coarse to fine scale, we have extracted different face segmentations where the numbers of patches used are : 4, 9, 16, 25, 34, 45, 60, 72, 88, 105, 124, 145, 166, 183, 211, 230, 260, 243 and 207. Table2 displays the classification accuracies of surface normal-based and point cloud-based patch descriptors on different patch resolutions. The first col- umn shows the number of local patches formed over the face region and the sec- ond column shows the average number of 3D points at each local patch.... In PAGE 7: ... Figure 4 graphically displays the recognition rates found in Table 2. It is evident by analyzing Table2 that significant dimensionality reduction is... ..."
Cited by 2

Table1: Synoptic Code for Reporting Height of Lowest Cloud

in ADAPTIVE WAVELET NEURAL NETWORK FOR PREDICTION OF HOURLY NOX AND NO2 CONCENTRATIONS
by R G. Ingalls, M. D. Rossetti, J. S. Smith, B. A. Peters
"... In PAGE 5: ... The meteorological variables used in this work are similar to that used by Gardner(1999). They are Low cloud amount (LOW): oktas; Base of lowest cloud (BASE): synoptic code shown in Table1 ; Visibility (VIS): synoptic code shown in Table 2; Dry bulb tempera- ture(DRY): 0C ; Vapour pressure(VP): mbar; Wind speed(WS): ms-1. Instead of the emission factors, the net- work is given two additional time of day inputs consisting of the sine and cosine of the time of day normalized be- tween 0 and 24 h(Gardner, 1998).... ..."

Table 1: Distinct levels of tricks

in Tricks Generate Patterns
by Amnon Eden, Amiram Yehudai

Table 1. Enterprise Architecture as a Cross layer View of Aggregate Artifacts

in Article Essential Layers,  Artifacts,  and Dependencies of Enterprise Architecture
by Robert Winter, Ronny Fischer
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