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Claude-Morel
and
Ranjit M. Passi

efficiency, and(iii) simplicity of implementation. Its implementation is demonstrated on a set of pressure datg collectedby CLASS.1. Introduction The National Center for Atmospheric Research hasdeveloped a radiosonde system named Cross-ChainLORAN Atmospheric Sounding System (CLASS). Itis planned to employ this new system in operationalfield projects where the data will be distributed to theresearch community in real time for comparison andpooling with other systems to perform real-time analyses and

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William J. Koshak
and
Richard J. Solakiewicz

1. Introduction The Geostationary Operational Environmental Satellite-R series (GOES-R) is due to launch in early 2016 as of this writing. In preparation for this launch and in order to optimize return on investment, many ongoing research activities are being carried out to explore and exploit the full information content of the GOES-R instrument data. One instrument on GOES-R is the Geostationary Lightning Mapper (GLM) described in Goodman et al. (2013) . The GLM will map the locations and

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Sergey Y. Matrosov
,
Andrew J. Heymsfield
,
Robert A. Kropfli
,
Brooks E. Martner
,
Roger F. Reinking
,
Jack B. Snider
,
Paivi Piironen
, and
Edwin W. Eloranta

. So D e is the size averaged throughout the cloud layer, which at 2111 UTC was about 3.5 km thick. The satellite retrievals of Ou et al. (1995) show that values of D e about 60 μ m are present around Coffeyville at 2111 UTC. However, according to the satellite data map, the Coffeyville hub was located within a region of high horizontal gradient of D e . To compare size information from different sources, it should be expressed in the same terms as was done for comparisons shown in Fig. 2

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Georg J. Mayr
,
Johannes Vergeiner
, and
Alexander Gohm

. Verification Radio soundings launched a few kilometers away from the downstream endpoint of the automobile's traverse during the field phase of MAP allows one to compare slantwise reduction with vertical reduction and estimate the error from partly neglecting horizontal changes in the vertical temperature profile. This comparison was particularly exacting for two reasons. The comparison location is farthest—both vertically (∼800 m) and horizontally (∼30 km)—away from the reduction level. And often there

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Larry W. O’Neill
,
Dudley B. Chelton
,
Ernesto Rodríguez
,
Roger Samelson
, and
Alexander Wineteer

statistical comparisons are somewhat less flattering than portrayed by the snapshot in Fig. 4 . As shown in Table 1 , the domainwide RMS error is 6.7 cm, the bias is −1.3 cm, and the reconstruction skill is 0.633. Figure 5 shows maps of the RMS error, bias, and model skill computed from the hourly output over the 43-day simulation. The reconstruction errors are smallest and the skill is close to unity along the periphery of the domain as expected given the imposed SSHA boundary conditions. Away from

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Leo O Lai
and
Jed O. Kaplan

, its 10% bound tolerance will equate to 2°C, in other words, no single interpolated value within the interval can exceed 2°C of the original interval mean. 3. Results and discussion a. Accuracy and comparison with other methods In this section we compare our newly proposed interpolation method with existing mean-preserving smoothing algorithms including the polynomial method with mixed boundary conditions described by D03 and the recursive algebraic method of RM01 . We evaluate each

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Peter C. Chu
,
Robin T. Tokmakian
,
Chenwu Fan
, and
L. Charles Sun

. Oceanographers have constructed to include inhomogeneities and anisotropies associated with the presence of topography, and to reflect in a way the adaptation of the ocean fields to the topography. Utilization of ocean topography may change the weighting operation, in (1) , into a mathematical operator, , that maps the innovation (at the observational points) directly onto the grid points, where could be different in (1) and (5) when vertical interpolation is involved. The difference, Δ c = c

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M. F. Larsen
and
J. Röttger

.g., Handbook for MAP, Bowhill andEdwards 1983, 1984, 1986) and in diverse journalsthat may not be readily accessible to the meteorologicalcommunity. Hocking et al. (1989) have provided themost recent publication on the interpretation, reliability, and accuracy of parameters deduced fromspaced antenna measurements. In this article, we willdescribe the technique and outline some of the potentialadvantages and disadvantages of the method. We willalso describe the handful of comparisons of both thespaced

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G. D. Nastrom
,
W. L. Clark
,
K. S. Gage
,
T. E. VanZandt
,
J. M. Warnock
,
R. Creasey
, and
P. M. Pauley

radiosonde data and National Meteorological Center (NMC) analyses. Vertical motions were computed for all days inMarch-April and September-October 1990. The radiosonde data are available at 0000 and 1200 UTCdaily. The sounding times with largest computed upward and largest downward motion at 500 hPa (about5.6 km above sea level) in the spring and fall wereselected for presentation here. The operational surfacesynoptic weather maps for these four cases, given inFig. 1, will be discussed in detail as

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W. J. Koshak

well as p g ( x ) and p c ( x ), are probability density functions (pdfs); the terms “distribution” and “density” are used interchangeably. Hence, the population mean and variance of the mixture distribution are Here, the population mean and variance of the ground and cloud flashes are, respectively, Obtaining the results in (2) are straightforward, and the second result in (2) requires a little algebra. 3. Bayesian inference of model parameters a. The MAP solution As discussed above, the

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