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James B. Elsner, Laura E. Michaels, Kelsey N. Scheitlin, and Ian J. Elsner

Abstract

Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.

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Gerald L Potter, George J. Huffman, David T. Bolvin, Michael G. Bosilovich, Judy Hertz, and Laura E. Carriere

ABSTRACT

We introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from MERRA-2 and from GPCP One-Degree Daily (1DD) precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land–ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.

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Mary C. Barth, Christopher A. Cantrell, William H. Brune, Steven A. Rutledge, James H. Crawford, Heidi Huntrieser, Lawrence D. Carey, Donald MacGorman, Morris Weisman, Kenneth E. Pickering, Eric Bruning, Bruce Anderson, Eric Apel, Michael Biggerstaff, Teresa Campos, Pedro Campuzano-Jost, Ronald Cohen, John Crounse, Douglas A. Day, Glenn Diskin, Frank Flocke, Alan Fried, Charity Garland, Brian Heikes, Shawn Honomichl, Rebecca Hornbrook, L. Gregory Huey, Jose L. Jimenez, Timothy Lang, Michael Lichtenstern, Tomas Mikoviny, Benjamin Nault, Daniel O’Sullivan, Laura L. Pan, Jeff Peischl, Ilana Pollack, Dirk Richter, Daniel Riemer, Thomas Ryerson, Hans Schlager, Jason St. Clair, James Walega, Petter Weibring, Andrew Weinheimer, Paul Wennberg, Armin Wisthaler, Paul J. Wooldridge, and Conrad Ziegler

Abstract

The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.

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Charles O. Stanier, R. Bradley Pierce, Maryam Abdi-Oskouei, Zachariah E. Adelman, Jay Al-Saadi, Hariprasad D. Alwe, Timothy H. Bertram, Gregory R. Carmichael, Megan B. Christiansen, Patricia A. Cleary, Alan C. Czarnetzki, Angela F. Dickens, Marta A. Fuoco, Dagen D. Hughes, Joseph P. Hupy, Scott J. Janz, Laura M. Judd, Donna Kenski, Matthew G. Kowalewski, Russell W. Long, Dylan B. Millet, Gordon Novak, Behrooz Roozitalab, Stephanie L. Shaw, Elizabeth A. Stone, James Szykman, Lukas Valin, Michael Vermeuel, Timothy J. Wagner, Andrew R. Whitehill, and David J. Williams

Abstract

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

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Brian J. Butterworth, Ankur R. Desai, Philip A. Townsend, Grant W. Petty, Christian G. Andresen, Timothy H. Bertram, Eric L. Kruger, James K. Mineau, Erik R. Olson, Sreenath Paleri, Rosalyn A. Pertzborn, Claire Pettersen, Paul C. Stoy, Jonathan E. Thom, Michael P. Vermeuel, Timothy J. Wagner, Daniel B. Wright, Ting Zheng, Stefan Metzger, Mark D. Schwartz, Trevor J. Iglinski, Matthias Mauder, Johannes Speidel, Hannes Vogelmann, Luise Wanner, Travis J. Augustine, William O. J. Brown, Steven P. Oncley, Michael Buban, Temple R. Lee, Patricia Cleary, David J. Durden, Christopher R. Florian, Kathleen Lantz, Laura D. Riihimaki, Joseph Sedlar, Tilden P. Meyers, David M. Plummer, Eliceo Ruiz Guzman, Elizabeth N. Smith, Matthias Sühring, David D. Turner, Zhien Wang, Loren D. White, and James M. Wilczak

Abstract

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

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Randall M. Dole, J. Ryan Spackman, Matthew Newman, Gilbert P. Compo, Catherine A. Smith, Leslie M. Hartten, Joseph J. Barsugli, Robert S. Webb, Martin P. Hoerling, Robert Cifelli, Klaus Wolter, Christopher D. Barnet, Maria Gehne, Ronald Gelaro, George N. Kiladis, Scott Abbott, Elena Akish, John Albers, John M. Brown, Christopher J. Cox, Lisa Darby, Gijs de Boer, Barbara DeLuisi, Juliana Dias, Jason Dunion, Jon Eischeid, Christopher Fairall, Antonia Gambacorta, Brian K. Gorton, Andrew Hoell, Janet Intrieri, Darren Jackson, Paul E. Johnston, Richard Lataitis, Kelly M. Mahoney, Katherine McCaffrey, H. Alex McColl, Michael J. Mueller, Donald Murray, Paul J. Neiman, William Otto, Ola Persson, Xiao-Wei Quan, Imtiaz Rangwala, Andrea J. Ray, David Reynolds, Emily Riley Dellaripa, Karen Rosenlof, Naoko Sakaeda, Prashant D. Sardeshmukh, Laura C. Slivinski, Lesley Smith, Amy Solomon, Dustin Swales, Stefan Tulich, Allen White, Gary Wick, Matthew G. Winterkorn, Daniel E. Wolfe, and Robert Zamora

Abstract

Forecasts by mid-2015 for a strong El Niño during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Niño Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Niño.

The overarching ENRR goal was to determine the atmospheric response to El Niño and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Niño to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Niño. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Niño and its impacts.

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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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William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager
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M. Ades, R. Adler, Rob Allan, R. P. Allan, J. Anderson, Anthony Argüez, C. Arosio, J. A. Augustine, C. Azorin-Molina, J. Barichivich, J. Barnes, H. E. Beck, Andreas Becker, Nicolas Bellouin, Angela Benedetti, David I. Berry, Stephen Blenkinsop, Olivier. Bock, Michael G. Bosilovich, Olivier. Boucher, S. A. Buehler, Laura. Carrea, Hanne H. Christiansen, F. Chouza, John R. Christy, E.-S. Chung, Melanie Coldewey-Egbers, Gil P. Compo, Owen R. Cooper, Curt Covey, A. Crotwell, Sean M. Davis, Elvira de Eyto, Richard A. M de Jeu, B.V. VanderSat, Curtis L. DeGasperi, Doug Degenstein, Larry Di Girolamo, Martin T. Dokulil, Markus G. Donat, Wouter A. Dorigo, Imke Durre, Geoff S. Dutton, G. Duveiller, James W. Elkins, Vitali E. Fioletov, Johannes Flemming, Michael J. Foster, Richard A. Frey, Stacey M. Frith, Lucien Froidevaux, J. Garforth, S. K. Gupta, Leopold Haimberger, Brad D. Hall, Ian Harris, Andrew K Heidinger, D. L. Hemming, Shu-peng (Ben) Ho, Daan Hubert, Dale F. Hurst, I. Hüser, Antje Inness, K. Isaksen, Viju John, Philip D. Jones, J. W. Kaiser, S. Kelly, S. Khaykin, R. Kidd, Hyungiun Kim, Z. Kipling, B. M. Kraemer, D. P. Kratz, R. S. La Fuente, Xin Lan, Kathleen O. Lantz, T. Leblanc, Bailing Li, Norman G Loeb, Craig S. Long, Diego Loyola, Wlodzimierz Marszelewski, B. Martens, Linda May, Michael Mayer, M. F. McCabe, Tim R. McVicar, Carl A. Mears, W. Paul Menzel, Christopher J. Merchant, Ben R. Miller, Diego G. Miralles, Stephen A. Montzka, Colin Morice, Jens Mühle, R. Myneni, Julien P. Nicolas, Jeannette Noetzli, Tim J. Osborn, T. Park, A. Pasik, Andrew M. Paterson, Mauri S. Pelto, S. Perkins-Kirkpatrick, G. Pétron, C. Phillips, Bernard Pinty, S. Po-Chedley, L. Polvani, W. Preimesberger, M. Pulkkanen, W. J. Randel, Samuel Rémy, L. Ricciardulli, A. D. Richardson, L. Rieger, David A. Robinson, Matthew Rodell, Karen H. Rosenlof, Chris Roth, A. Rozanov, James A. Rusak, O. Rusanovskaya, T. Rutishäuser, Ahira Sánchez-Lugo, P. Sawaengphokhai, T. Scanlon, Verena Schenzinger, S. Geoffey Schladow, R. W Schlegel, Eawag Schmid, Martin, H. B. Selkirk, S. Sharma, Lei Shi, S. V. Shimaraeva, E. A. Silow, Adrian J. Simmons, C. A. Smith, Sharon L Smith, B. J. Soden, Viktoria Sofieva, T. H. Sparks, Paul W. Stackhouse Jr., Wolfgang Steinbrecht, Dimitri A. Streletskiy, G. Taha, Hagen Telg, S. J. Thackeray, M. A. Timofeyev, Kleareti Tourpali, Mari R. Tye, Ronald J. van der A, Robin, VanderSat B.V. van der Schalie, Gerard van der SchrierW. Paul, Guido R. van der Werf, Piet Verburg, Jean-Paul Vernier, Holger Vömel, Russell S. Vose, Ray Wang, Shohei G. Watanabe, Mark Weber, Gesa A. Weyhenmeyer, David Wiese, Anne C. Wilber, Jeanette D. Wild, Takmeng Wong, R. Iestyn Woolway, Xungang Yin, Lin Zhao, Guanguo Zhao, Xinjia Zhou, Jerry R. Ziemke, and Markus Ziese
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