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Matthew J. Bunkers, James R. Miller Jr., and Arthur T. DeGaetano

Abstract

Spatially homogeneous climate regions were developed from long-term monthly temperature and precipitation data for a subset of the U.S. Northern Plains. Climate regions were initially defined using the “best” of three agglomerative and hierarchical clustering methodologies, then the clusters were objectively modified using a “pseudohierarchical” iterative improvement technique. Under the premise of hierarchical cluster analysis, once an object has been assigned to a cluster, it cannot later he reassigned to a different cluster, even if it is statistically desirable. The objective modification technique used herein is employed to compensate for this problem.

Principal component analysis (PCA) was used to reduce a 147-station dataset, consisting of 24 climatic variables averaged over the 1931–1990 period, to three orthogonal components. The new standardized mars, which explain 93% of the original dataset variance, were then subjected to the Ward's, average linkage, and complete linkage clustering methods. The average linkage method produced the most representative statistical results in identifying the climate regions. An iterative improvement technique was then utilized to test “border station” membership and to modify the climate region houses. Fifteen climate regions resulted from the clustering (with two single-station clusters in the Black Hills alone), although they age just one possible partitioning of the data. The within-cluster variability is generally the same for the 15 climate regions and the corresponding 21 National Climatic Data Center (NCM) climate divisions. However, since data within-cluster variability tends to decrease with increasing cluster number, this result favors the new climate regions. Additionally, the new climate regions am shown to be superior to the NCDC climate, divisions in wont of between-cluster variability. These results suggest that the NCDC climate divisions could be redefined, improving their climatic homogeneity.

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Matthew J. Bunkers, James R. Miller Jr., and Arthur T. DeGaetano

Abstract

Monthly total precipitation and mean temperature data records extending from the late nineteenth century to 1990 were collected for 147 stations in South Dakota, North Dakota, and portions of adjacent states and provinces. This region, defined as the Northern Plains region (NPR), was examined for patterns associated with the warm phase (ENSO) and the cold phase (LNSO) of the Southern Oscillation to elucidate some of the debate concerning a signal in this area. Based on a correlation analysis, the NPR was treated as having one spatial degree of freedom.

Using Monte Carlo simulations of the Student's t-test statistic, four seasons with significant changes in mean precipitation or temperature during either ENSO or LNSO were identified. A highly significant signal was evident during the ENSO April to October season for precipitation, where the mean precipitation increased 7.21 cm for the 23 events studied. Here 20 of these 23 ENSO events exhibited precipitation above the median value, and 14 of the 23 events were in the upper quartile. In contrast, a strong signal for decreased LNSO precipitation was noted where May to August precipitation averaged 3.91 cm lower during the 17 events, with similar significance values. Complementing the enhanced ENSO warm season precipitation, the August to October ten-iperatme decreased by 2.17°C, with a significant number of events in both the lowest half and lowest quartile. Finally, temperature averaged 4.67°C cooler during LNSO winters. These results will be useful for limited-season prediction of precipitation and temperature tendencies across the NPR.

It is interesting to note that the initial ENSO years did not reveal a significant temperature increase during the NPR winter, which is in contrast to similar studies. However, by slightly modifying the years that were classified as ENSO years, a significant winter temperature response was indicated. This suggests that there is a tendency for warmer NPR winters during ENSO; however, this was not statistically significant.

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J. R. Miller Jr., E. I. Boyd, R. A. Schleusener, and A. S. Dennis

Abstract

Four seasons of hail data were gathered on a randomized cloud seeding project aimed at reducing hail damage and increasing rainfall in western North Dakota. Hail on seed days was generally less severe than on no-seed days. Statistical tests of data from passive hail indicators do not permit rejection of the null hypothesis at the 90% confidence level, but application of rank tests to crop-hail insurance loss data indicates that the seeding reduced crop damage from hail.Post-analyses of related data indicate that 1) the ratio of rainfall amount to hail energy was greater for seed days than no-seed days, and 2) radar characteristics of seeded storms differ from those of unseeded storms. In addition, case studies of 34 storms indicate that damaging hail was usually suppressed when their updraft areas were seeded continuously.

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A. S. Dennis, J. R. Miller Jr., D. E. Cain, and R. L. Schwaller

Abstract

Rainfall data collected at 67 gages in a 2750 mi2 target area during a four-year randomized cloud seeding experiment in North Dakota have been stratified in a variety of ways and subjected to several kinds of statistical tests. Some stratifications related to cloud model predictions were possible for only the last two years when a rawinsonde station was operated as part of the project. Monte Carlo experiments simulating 500 reruns of the four-year experiment have been used to establish significance levels for the tests within each data stratification.

The analysis provides significant evidence that seeding convective clouds on a determinate set of days leads to 1) an increase in the frequency of rainfall events at the individual target gages, 2) an increase in the average rainfall recorded per rainfall event, and 3) an increase in total rainfall on the target. The set of days to which this evidence applies is those days with dynamic seedability; that is, days for which a cloud model predicted an increase in cloud top height under the influence of silver iodide seeding. Rainfall observations on days when the cloud model predicted no increase in cloud height show no significant differences between seed and no-seed days.

The possibility of bias has been checked by comparing the frequencies of wet and dry days and the averages of several meteorological variables for seed and no-seed days within each stratification, by cross-checking the stratifications, and by comparing rainfall on seed and no-seed days over an area of roughly 50,000 square miles surrounding the target area. There is no obvious bias to account for the significant differences between seed and no-seed days with dynamic seedability.

It is tentatively concluded that dynamic effects, including rainfall increases, were produced by light to moderate silver iodide seeding from below cloud base. The potential rainfall increase resulting from seeding below selected clouds on days with dynamic seedability is estimated at one inch per growing season.

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Paul W. Mielke Jr., Kenneth J. Berry, Arnett S. Dennis, Paul L. Smith, James R. Miller Jr., and Bernard A. Silverman

Abstract

Results of statistical analyses for HIPLEX-1, a randomized cloud seeding experiment, are presented. The analyses are based principally on multi-response permutation procedures (MRPP) as specified before the HIPLEX-1 experiment was initiated. Even though the sample sizes are very small, due in part to the premature termination of this experiment, the three primary response variables measured in the first five minutes following treatment indicate pronounced differences in the development of ice crystals between nonseeded and seeded events. However, the response variables measured more than five minutes after treatment generally do not indicate obvious differences in the subsequent development of precipitation between nonseeded and seeded events. This lack of difference is a possible consequence of 1) lack of a seeding effect, 2) inadequacies in the physical hypothesis, or 3) the small sample sizes. Consequently, only the initial steps in the HIPLEX-1 physical hypothesis could be confirmed in this evaluation of the experiment.

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G. L. Stephens, R. G. Ellingson, J. Vitko Jr., W. Bolton, T. P. Tooman, F. P. J. Valero, P. Minnis, P. Pilewskie, G. S. Phipps, S. Sekelsky, J. R. Carswell, S. D. Miller, A. Benedetti, R. B. McCoy, R. F. McCoy Jr., A. Lederbuhr, and R. Bambha

The U.S. Department of Energy has established an unmanned aerospace vehicle (UAV) measurement program. The purpose of this paper is to describe the evolution of the program since its inception, review the progress of the program, summarize the measurement capabilities developed under the program, illustrate key results from the various UAV campaigns carried out to date, and provide a sense of the future direction of the program. The Atmospheric Radiation Measurement (ARM)–UAV program has demonstrated how measurements from unmanned aircraft platforms operating under the various constraints imposed by different science experiments can contribute to our understanding of cloud and radiative processes. The program was first introduced in 1991 and has evolved in the form of four phases of activity each culminating in one or more flight campaigns. A total of 8 flight campaigns produced over 140 h of science flights using three different UAV platforms. The UAV platforms and their capabilities are described as are the various phases of the program development. Examples of data collected from various campaigns highlight the powerful nature of the observing system developed under the auspices of the ARM–UAV program and confirm the viability of the UAV platform for the kinds of research of interest to ARM and the clouds and radiation community as a whole. The specific examples include applications of the data in the study of radiative transfer through clouds, the evaluation of cloud parameterizations, and the development and evaluation of cloud remote sensing methods. A number of notable and novel achievements of the program are also highlighted.

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Paul L. Smith, Arnett S. Dennis, Bernard A. Silverman, Arlin B. Super, Edmond W. Holroyd III, William A. Cooper, Paul W. Mielke Jr., Kenneth J. Berry, Harold D. Orville, and James R. Miller Jr.

Abstract

The design and conduct of HIPLEX-1, a randomized seeding experiment carried out on small cumulus congestus clouds in eastern Montana, are outlined. The seeding agent was dry ice, introduced in an effort to produce microphysical effects, especially the earlier formation of precipitation in the seeded clouds. The earlier formation was expected to increase both the probability and the amount of precipitation from those small clouds with short lifetimes. The experimental unit selection procedure, treatment and randomization procedures, the physical hypothesis, measurement procedures and the response variables defined for the experiment are discussed. Procedures used to calculate the response variables from aircraft and radar measurements are summarized and the values of those variables for the 20 HIPLEX-1 test cases from 1979 and 1980 are tabulated.

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Jerald A. Brotzge, J. Wang, C. D. Thorncroft, E. Joseph, N. Bain, N. Bassill, N. Farruggio, J. M. Freedman, K. Hemker Jr., D. Johnston, E. Kane, S. McKim, S. D. Miller, J. R. Minder, P. Naple, S. Perez, James J. Schwab, M. J. Schwab, and J. Sicker

Abstract

The New York State Mesonet (NYSM) is a network of 126 standard environmental monitoring stations deployed statewide with an average spacing of 27 km. The primary goal of the NYSM is to provide high-quality weather data at high spatial and temporal scales to improve atmospheric monitoring and prediction, especially for extreme weather events. As compared with other statewide networks, the NYSM faced considerable deployment obstacles with New York’s complex terrain, forests, and very rural and urban areas; its wide range of weather extremes; and its harsh winter conditions. To overcome these challenges, the NYSM adopted a number of innovations unique among statewide monitoring systems, including 1) strict adherence to international siting standards and metadata documentation; 2) a hardened system design to facilitate continued operations during extreme, high-impact weather; 3) a station design optimized to monitor winter weather conditions; and 4) a camera installed at every site to aid situational awareness. The network was completed in spring of 2018 and provides data and products to a variety of sectors including weather monitoring and forecasting, emergency management, agriculture, transportation, utilities, and education. This paper focuses on the standard network of the NYSM and reviews the network siting, site configuration, sensors, site communications and power, network operations and maintenance, data quality control, and dissemination. A few example analyses are shown that highlight the benefits of the NYSM.

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John S. Kain, Michael C. Coniglio, James Correia, Adam J. Clark, Patrick T. Marsh, Conrad L. Ziegler, Valliappa Lakshmanan, Stuart D. Miller Jr., Scott R. Dembek, Steven J. Weiss, Fanyou Kong, Ming Xue, Ryan A. Sobash, Andrew R. Dean, Israel L. Jirak, and Christopher J. Melick

The 2011 Spring Forecasting Experiment in the NOAA Hazardous Weather Testbed (HWT) featured a significant component on convection initiation (CI). As in previous HWT experiments, the CI study was a collaborative effort between forecasters and researchers, with equal emphasis on experimental forecasting strategies and evaluation of prototype model guidance products. The overarching goal of the CI effort was to identify the primary challenges of the CI forecasting problem and to establish a framework for additional studies and possible routine forecasting of CI. This study confirms that convection-allowing models with grid spacing ~4 km represent many aspects of the formation and development of deep convection clouds explicitly and with predictive utility. Further, it shows that automated algorithms can skillfully identify the CI process during model integration. However, it also reveals that automated detection of individual convection cells, by itself, provides inadequate guidance for the disruptive potential of deep convection activity. Thus, future work on the CI forecasting problem should be couched in terms of convection-event prediction rather than detection and prediction of individual convection cells.

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Robert J. H. Dunn, F. Aldred, Nadine Gobron, John B. Miller, Kate M. Willett, M. Ades, Robert Adler, Richard, P. Allan, Rob Allan, J. Anderson, Anthony Argüez, C. Arosio, John A. Augustine, C. Azorin-Molina, J. Barichivich, H. E. Beck, Andreas Becker, Nicolas Bellouin, Angela Benedetti, David I. Berry, Stephen Blenkinsop, Olivier Bock, X. Bodin, Michael G. Bosilovich, Olivier Boucher, S. A. Buehler, B. Calmettes, Laura Carrea, Laura Castia, Hanne H. Christiansen, John R. Christy, E.-S. Chung, Melanie Coldewey-Egbers, Owen R. Cooper, Richard C. Cornes, Curt Covey, J.-F. Cretaux, M. Crotwell, Sean M. Davis, Richard A. M. de Jeu, Doug Degenstein, R. Delaloye, Larry Di Girolamo, Markus G. Donat, Wouter A. Dorigo, Imke Durre, Geoff S. Dutton, Gregory Duveiller, James W. Elkins, Vitali E. Fioletov, Johannes Flemming, Michael J. Foster, Stacey M. Frith, Lucien Froidevaux, J. Garforth, Matthew Gentry, S. K. Gupta, S. Hahn, Leopold Haimberger, Brad D. Hall, Ian Harris, D. L. Hemming, M. Hirschi, Shu-pen (Ben) Ho, F. Hrbacek, Daan Hubert, Dale F. Hurst, Antje Inness, K. Isaksen, Viju O. John, Philip D. Jones, Robert Junod, J. W. Kaiser, V. Kaufmann, A. Kellerer-Pirklbauer, Elizabeth C. Kent, R. Kidd, Hyungjun Kim, Z. Kipling, A. Koppa, B. M. Kraemer, D. P. Kratz, Xin Lan, Kathleen O. Lantz, D. Lavers, Norman G. Loeb, Diego Loyola, R. Madelon, Michael Mayer, M. F. McCabe, Tim R. McVicar, Carl A. Mears, Christopher J. Merchant, Diego G. Miralles, L. Moesinger, Stephen A. Montzka, Colin Morice, L. Mösinger, Jens Mühle, Julien P. Nicolas, Jeannette Noetzli, Ben Noll, J. O’Keefe, Tim J. Osborn, T. Park, A. J. Pasik, C. Pellet, Maury S. Pelto, S. E. Perkins-Kirkpatrick, G. Petron, Coda Phillips, S. Po-Chedley, L. Polvani, W. Preimesberger, D. G. Rains, W. J. Randel, Nick A. Rayner, Samuel Rémy, L. Ricciardulli, A. D. Richardson, David A. Robinson, Matthew Rodell, N. J. Rodríguez-Fernández, K.H. Rosenlof, C. Roth, A. Rozanov, T. Rutishäuser, Ahira Sánchez-Lugo, P. Sawaengphokhai, T. Scanlon, Verena Schenzinger, R. W. Schlegel, S. Sharma, Lei Shi, Adrian J. Simmons, Carolina Siso, Sharon L. Smith, B. J. Soden, Viktoria Sofieva, T. H. Sparks, Paul W. Stackhouse Jr., Wolfgang Steinbrecht, Martin Stengel, Dimitri A. Streletskiy, Sunny Sun-Mack, P. Tans, S. J. Thackeray, E. Thibert, D. Tokuda, Kleareti Tourpali, Mari R. Tye, Ronald van der A, Robin van der Schalie, Gerard van der Schrier, M. van der Vliet, Guido R. van der Werf, A. Vance, Jean-Paul Vernier, Isaac J. Vimont, Holger Vömel, Russell S. Vose, Ray Wang, Markus Weber, David Wiese, Anne C. Wilber, Jeanette D. Wild, Takmeng Wong, R. Iestyn Woolway, Xinjia Zhou, Xungang Yin, Guangyu Zhao, Lin Zhao, Jerry R. Ziemke, Markus Ziese, and R. M. Zotta
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