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Lucas Craig, Arash Moharreri, David C. Rogers, Bruce Anderson, and Suresh Dhaniyala

Abstract

Interaction of liquid cloud droplets and ice particles with aircraft aerosol inlets can result in the generation of a large number of secondary particles and contaminate aerosol measurements. Recent studies have shown that a sampler designed with a perpendicular subsampling tube located within a flow-through conduit (i.e., a flow-restriction inlet) was best suited for in-cloud sampling. Analysis of field data obtained from different flow-restriction inlets shows that their critical cloud droplet breakup diameters are strongly dependent on design details and operating conditions. Using computational fluid dynamics (CFD) simulations, in-cloud sampling performance of a selected inlet can be predicted reasonably accurately for known operating conditions. To understand the relation between inlet design parameters and its sampling performance, however, CFD calculations are impractical. Here, using a simple, representative one-dimensional velocity profile and a validated empirical droplet breakup criteria, a parametric study is conducted to understand the relationship between different inlet design features and operating conditions on its critical breakup diameters. The results of this study suggest that an optimal inlet for in-cloud aerosol sampling should have a combination of a restriction nozzle at the aft end of the flow-through conduit to minimize wall-impaction shatter artifacts and a blunt leading edge to minimize shatter artifact generation from the aerodynamic breakup of cloud droplets. Inlets for in-cloud aerosol sampling from aircraft will, therefore, differ significantly in design from those used for clear-air aerosol sampling.

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Glen S. Romine, Craig S. Schwartz, Chris Snyder, Jeff L. Anderson, and Morris L. Weisman

Abstract

During the spring 2011 season, a real-time continuously cycled ensemble data assimilation system using the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed toolkit provided initial and boundary conditions for deterministic convection-permitting forecasts, also using WRF, over the eastern two-thirds of the conterminous United States (CONUS). In this study the authors evaluate the mesoscale assimilation system and the convection-permitting forecasts, at 15- and 3-km grid spacing, respectively. Experiments employing different physics options within the continuously cycled ensemble data assimilation system are shown to lead to differences in the mean mesoscale analysis characteristics. Convection-permitting forecasts with a fixed model configuration are initialized from these physics-varied analyses, as well as control runs from 0.5° Global Forecast System (GFS) analysis. Systematic bias in the analysis background influences the analysis fit to observations, and when this analysis initializes convection-permitting forecasts, the forecast skill is degraded as bias in the analysis background increases. Moreover, differences in mean error characteristics associated with each physical parameterization suite lead to unique errors of spatial, temporal, and intensity aspects of convection-permitting rainfall forecasts. Observation bias by platform type is also shown to impact the analysis quality.

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Michael K. Tippett, Jeffrey L. Anderson, Craig H. Bishop, Thomas M. Hamill, and Jeffrey S. Whitaker

Abstract

Ensemble data assimilation methods assimilate observations using state-space estimation methods and low-rank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be performed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter analysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.

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Glen S. Romine, Craig S. Schwartz, Judith Berner, Kathryn R. Fossell, Chris Snyder, Jeff L. Anderson, and Morris L. Weisman

Abstract

Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established.

Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral boundary conditions; or, model error representation using either 2) stochastic kinetic energy backscatter or 3) stochastically perturbed parameterization tendencies. Forecasts are evaluated against stage IV accumulated precipitation analyses and radiosonde observations. Perturbed ensemble forecasts are also compared to the control forecast to assess the relative impact from adding forecast perturbations. For precipitation forecasts, all perturbation approaches improve ensemble reliability relative to the control CPEFS. Deterministic ensemble member forecast skill, verified against radiosonde observations, decreases when forecast perturbations are added, while ensemble mean forecasts remain similarly skillful to the control.

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K. Dieter Klaes, Jörg Ackermann, Craig Anderson, Yago Andres, Thomas August, Régis Borde, Bojan Bojkov, Leonid Butenko, Alessandra Cacciari, Dorothée Coppens, Marc Crapeau, Stephanie Guedj, Olivier Hautecoeur, Tim Hultberg, Rüdiger Lang, Stefanie Linow, Christian Marquardt, Rosemarie Munro, Carlo Pettirossi, Gabriele Poli, Francesca Ticconi, Olivier Vandermarcq, Mayte Vasquez, and Margarita Vazquez-Navarro

Abstract

After successful launch in November 2018 and successful commissioning of Metop-C, all three satellites of the EUMETSAT Polar System (EPS) are in orbit together and operational. EPS is part of the Initial Joint Polar System (IJPS) with the US (NOAA) and provides the service in the mid-morning orbit. The Metop satellites carry a mission payload of sounding and imaging instruments, which allow provision of support to operational meteorology and climate monitoring which are the main mission objectives for EPS. Applications include Numerical Weather Prediction, atmospheric composition monitoring, and marine meteorology. Climate monitoring is supported through the generation of long time series through the program duration of 20+ years. The payload was developed and contributed by partners, including NOAA, CNES, and ESA. EUMETSAT and ESA developed the space segment in cooperation. The system has proven its value since the first satellite Metop-A, with enhanced products at high reliability for atmospheric sounding, delivered a very strong positive impact on NWP and results beyond expectations for atmospheric composition and chemistry applications. Having multiple satellites in orbit - now three, has enabled enhanced and additional products with increased impact, like atmospheric motion vector products at latitudes not accessible to geostationary observations or increased probability of radio-occultations and hence atmospheric soundings with the GRAS instruments. The paper gives an overview on the system, the embarked payload and discusses the benefits of generated products for applications and services. The conclusions point to the follow-on system, currently under development and assuring continuity for another 20+ years.

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K. Dieter Klaes, Jörg Ackermann, Craig Anderson, Yago Andres, Thomas August, Régis Borde, Bojan Bojkov, Leonid Butenko, Alessandra Cacciari, Dorothée Coppens, Marc Crapeau, Stephanie Guedj, Olivier Hautecoeur, Tim Hultberg, Rüdiger Lang, Stefanie Linow, Christian Marquardt, Rosemarie Munro, Carlo Pettirossi, Gabriele Poli, Francesca Ticconi, Olivier Vandermarcq, Mayte Vasquez, and Margarita Vazquez-Navarro

Abstract

After successful launch in November 2018 and successful commissioning of Metop-C, all three satellites of the EUMETSAT Polar System (EPS) are in orbit together and operational. EPS is part of the Initial Joint Polar System (IJPS) with the United States (NOAA) and provides the service in the midmorning orbit. The Metop satellites carry a mission payload of sounding and imaging instruments, which allow provision of support to operational meteorology and climate monitoring, which are the main mission objectives for EPS. Applications include numerical weather prediction, atmospheric composition monitoring, and marine meteorology. Climate monitoring is supported through the generation of long time series through the program duration of 20+ years. The payload was developed and contributed by partners, including NOAA, CNES, and ESA. EUMETSAT and ESA developed the space segment in cooperation. The system has proven its value since the first satellite Metop-A, with enhanced products at high reliability for atmospheric sounding, delivered a very strong positive impact on NWP and results beyond expectations for atmospheric composition and chemistry applications. Having multiple satellites in orbit—now three—has enabled enhanced and additional products with increased impact, like atmospheric motion vector products at latitudes not accessible to geostationary observations or increased probability of radio occultations and hence atmospheric soundings with the Global Navigation Satellite System (GNSS) Radio-Occultation Atmospheric Sounder (GRAS) instruments. The paper gives an overview of the system and the embarked payload and discusses the benefits of generated products for applications and services. The conclusions point to the follow-on system, currently under development and assuring continuity for another 20+ years.

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Jielun Sun, Steven P. Oncley, Sean P. Burns, Britton B. Stephens, Donald H. Lenschow, Teresa Campos, Russell K. Monson, David S. Schimel, William J. Sacks, Stephan F. J. De Wekker, Chun-Ta Lai, Brian Lamb, Dennis Ojima, Patrick Z. Ellsworth, Leonel S. L. Sternberg, Sharon Zhong, Craig Clements, David J. P. Moore, Dean E. Anderson, Andrew S. Watt, Jia Hu, Mark Tschudi, Steven Aulenbach, Eugene Allwine, and Teresa Coons

A significant fraction of Earth consists of mountainous terrain. However, the question of how to monitor the surface–atmosphere carbon exchange over complex terrain has not been fully explored. This article reports on studies by a team of investigators from U.S. universities and research institutes who carried out a multiscale and multidisciplinary field and modeling investigation of the CO2 exchange between ecosystems and the atmosphere and of CO2 transport over complex mountainous terrain in the Rocky Mountain region of Colorado. The goals of the field campaign, which included ground and airborne in situ and remote-sensing measurements, were to characterize unique features of the local CO2 exchange and to find effective methods to measure regional ecosystem–atmosphere CO2 exchange over complex terrain. The modeling effort included atmospheric and ecological numerical modeling and data assimilation to investigate regional CO2 transport and biological processes involved in ecosystem–atmosphere carbon exchange. In this report, we document our approaches, demonstrate some preliminary results, and discuss principal patterns and conclusions concerning ecosystem–atmosphere carbon exchange over complex terrain and its relation to past studies that have considered these processes over much simpler terrain.

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Faisal Hossain, Margaret Srinivasan, Craig Peterson, Alice Andral, Ed Beighley, Eric Anderson, Rashied Amini, Charon Birkett, David Bjerklie, Cheryl Ann Blain, Selma Cherchali, Cédric H. David, Bradley Doorn, Jorge Escurra, Lee-Lueng Fu, Chris Frans, John Fulton, Subhrendu Gangopadhay, Subimal Ghosh, Colin Gleason, Marielle Gosset, Jessica Hausman, Gregg Jacobs, John Jones, Yasir Kaheil, Benoit Laignel, Patrick Le Moigne, Li Li, Fabien Lefèvre, Robert Mason, Amita Mehta, Abhijit Mukherjee, Anthony Nguy-Robertson, Sophie Ricci, Adrien Paris, Tamlin Pavelsky, Nicolas Picot, Guy Schumann, Sudhir Shrestha, Pierre-Yves Le Traon, and Eric Trehubenko
Open access
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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