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Daniel S. Wilks and Allan H. Murphy

Abstract

The economic value of current and hypothetically improved seasonal precipitation forecasts is estimated for a regionally important haying/pasturing problem in western Oregon by modeling and analyzing the problem in a decision-analytic framework. Although current forecasts are found to be of relatively little value in this decision-making problem, moderate increases in the quality of the forecasts would lead to substantial increases in their value. The quality/value relationship is sensitive to changes in various economic parameters, including the decision maker's attitude toward risk.

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Allan H. Murphy and Daniel S. Wilks

Abstract

The traditional approach to forecast verification consists of computing one, or at most very few, quantities from a set of forecasts and verifying observations. However, this approach necessarily discards a large portion of the information regarding forecast quality that is contained in a set of forecasts and observations. Theoretically sound alternative verification approaches exist, but these often involve computation and examination of many quantities in order to obtain a complete description of forecast quality and, thus, pose difficulties in interpretation. This paper proposes and illustrates an intermediate approach to forecast verification, in which the multifaceted nature of forecast quality is recognized but the description of forecast quality is encapsulated in a much smaller number of parameters. These parameters are derived from statistical models fit to verification datasets. Forecasting performance as characterized by the statistical models can then be assessed in a relatively complete manner. In addition, the fitted statistical models provide a mechanism for smoothing sampling variations in particular finite samples of forecasts and observations.

This approach to forecast verification is illustrated by evaluating and comparing selected samples of probability of precipitation (PoP) forecasts and the matching binary observations. A linear regression model is fit to the conditional distributions of the observations given the forecasts and a beta distribution is fit to the frequencies of use of the allowable probabilities. Taken together, these two models describe the joint distribution of forecasts and observations, and reduce a 21-dimensional verification problem to 4 dimensions (two parameters each for the regression and beta models). Performance of the selected PoP forecasts is evaluated and compared across forecast type, location, and lead time in terms of these four parameters (and simple functions of the parameters), and selected graphical displays are explored as a means of obtaining relatively transparent views of forecasting performance within this approach to verification.

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Laurie Yung, Nicky Phear, Alayna DuPont, Jess Montag, and Daniel Murphy

Abstract

Agricultural producers may be particularly vulnerable to climate impacts, such as drought. To better understand how ranchers respond to ongoing drought and the relationship between climate change beliefs and drought adaptation, in-depth interviews with working ranchers were conducted. Ranchers described drought conditions as unprecedented and detailed the interacting impacts of drought and nonclimatic stressors. They viewed adaptation as critical and employed a wide range of responses to drought, but lack of financial resources, risks associated with change, local social norms, and optimism about future moisture created barriers to change. Most ranchers attributed drought to natural cycles and were skeptical about anthropogenic climate change. Many ranchers likened current drought conditions to past droughts, concluding that conditions would return to “normal.” A belief in natural cycles provided a sense of hope for some ranchers but felt immutable to others, reducing their sense of agency and efficacy. Taken together, climate skepticism, optimism about future conditions, lack of financial resources, and a limited sense of agency might be reducing investments in long-term adaptation. However, the relationship between climate change beliefs and adaptation action was not entirely clear, since the handful of ranchers adapting in anticipation of long-term drought were skeptical or uncertain about anthropogenic climate change. Further, most ranchers characterized adaptation as an individual endeavor and resisted government involvement in drought adaptation. In the context of climate skepticism and antigovernment sentiment, strategies to scale up adaptation efforts beyond the household will only succeed to the extent that they build on local norms and ideologies.

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Allan H. Murphy, Wu-ron Hsu, Robert L. Winkler, and Daniel S. Wilks

Abstract

This paper summarizes the results of an experiment in which National Weather Service forecasters formulated probabilistic quantitative precipitation forecasts (QPFs) during a 17-month period in 1981–82. These forecasts expressed the likelihood that certain threshold amounts of precipitation would be equaled or exceeded in 12-hour periods at four locations in Texas. The forecasters had no previous experience in quantifying the uncertainty in such forecasts, but they did receive feedback regarding their collective performance at the end of the first year of the experiment. In the evaluation of the experimental results, particular attention is focused on three issues: 1) the reliability and skill of the subjective QPFs; 2) the effects of feedback and experience on the quality of these forecasts; and 3) the relative performance of the subjective probabilistic QPFs and objective probabilistic QPFs produced by the model output statistics system.

The subjective probabilistic QPFs possess positive skill, although they exhibit considerable overforecasting for larger precipitation amounts. Moreover, the feedback provided to the forecasters evidently contributed to modest increases in the reliability and skill of their forecasts. In this regard, the quality of the subjective and objective QPFs is generally comparable in the first year of the experiment. However, after the receipt of the feedback, the skill of the subjective forecasts exceeded the skill of the objective forecasts. These results are considered to be encouraging regarding the ability of forecasters to formulate reliable and skillful probabilistic QPFS, but more extensive experiments should be undertaken to investigate this and related issues in greater detail.

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Clark Evans, Heather M. Archambault, Jason M. Cordeira, Cody Fritz, Thomas J. Galarneau Jr., Saska Gjorgjievska, Kyle S. Griffin, Alexandria Johnson, William A. Komaromi, Sarah Monette, Paytsar Muradyan, Brian Murphy, Michael Riemer, John Sears, Daniel Stern, Brian Tang, and Segayle Thompson

The Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) field experiment successfully gathered data from four developing and four decaying/nondeveloping tropical disturbances over the tropical North Atlantic basin between 15 August and 30 September 2010. The invaluable roles played by early career scientists (ECSs) throughout the campaign helped make possible the successful execution of the field program's mission to investigate tropical cyclone formation. ECSs provided critical meteorological information— often obtained from novel ECS-created products—during daily weather briefings that were used by the principal investigators in making mission planning decisions. Once a Gulfstream V (G-V) flight mission was underway, ECSs provided nowcasting support, relaying information that helped the mission scientists to steer clear of potential areas of turbulence aloft. Data from these missions, including dropsonde and GPS water vapor profiler data, were continually obtained, processed, and quality-controlled by ECSs. The dropsonde data provided National Hurricane Center forecasters and PREDICT mission scientists with real-time information regarding the characteristics of tropical disturbances. These data and others will serve as the basis for multiple ECS-led research topics over the years to come and are expected to provide new insights into the tropical cyclone formation process. PREDICT also provided invaluable educational and professional development experiences for ECSs, including the opportunity to critically evaluate observational evidence for tropical cyclone development theories and networking opportunities with their peers and established scientists in the field.

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Ralph A. Kahn, Tim A. Berkoff, Charles Brock, Gao Chen, Richard A. Ferrare, Steven Ghan, Thomas F. Hansico, Dean A. Hegg, J. Vanderlei Martins, Cameron S. McNaughton, Daniel M. Murphy, John A. Ogren, Joyce E. Penner, Peter Pilewskie, John H. Seinfeld, and Douglas R. Worsnop

Abstract

A modest operational program of systematic aircraft measurements can resolve key satellite aerosol data record limitations. Satellite observations provide frequent global aerosol amount maps but offer only loose aerosol property constraints needed for climate and air quality applications. We define and illustrate the feasibility of flying an aircraft payload to measure key aerosol optical, microphysical, and chemical properties in situ. The flight program could characterize major aerosol airmass types statistically, at a level of detail unobtainable from space. It would 1) enhance satellite aerosol retrieval products with better climatology assumptions and 2) improve translation between satellite-retrieved optical properties and species-specific aerosol mass and size simulated in climate models to assess aerosol forcing, its anthropogenic components, and other environmental impacts. As such, Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM) could add value to data records representing several decades of aerosol observations from space; improve aerosol constraints on climate modeling; help interrelate remote sensing, in situ, and modeling aerosol-type definitions; and contribute to future satellite aerosol missions. Fifteen required variables are identified and four payload options of increasing ambition are defined to constrain these quantities. “Option C” could meet all the SAM-CAAM objectives with about 20 instruments, most of which have flown before, but never routinely several times per week, and never as a group. Aircraft integration and approaches to data handling, payload support, and logistical considerations for a long-term, operational mission are discussed. SAM-CAAM is feasible because, for most aerosol sources and specified seasons, particle properties tend to be repeatable, even if aerosol loading varies.

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A. Gannet Hallar, Steven S. Brown, Erik Crosman, Kelley C. Barsanti, Christopher D. Cappa, Ian Faloona, Jerome Fast, Heather A. Holmes, John Horel, John Lin, Ann Middlebrook, Logan Mitchell, Jennifer Murphy, Caroline C. Womack, Viney Aneja, Munkhbayar Baasandorj, Roya Bahreini, Robert Banta, Casey Bray, Alan Brewer, Dana Caulton, Joost de Gouw, Stephan F.J. De Wekker, Delphine K. Farmer, Cassandra J. Gaston, Sebastian Hoch, Francesca Hopkins, Nakul N. Karle, James T. Kelly, Kerry Kelly, Neil Lareau, Keding Lu, Roy L. Mauldin III, Derek V. Mallia, Randal Martin, Daniel L. Mendoza, Holly J. Oldroyd, Yelena Pichugina, Kerri A. Pratt, Pablo E. Saide, Philip J. Silva, William Simpson, Britton B. Stephens, Jochen Stutz, and Amy Sullivan

Abstract

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical–meteorological interactions that drive high-pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in western U.S. basins. Approximately 120 people participated, representing 50 institutions and five countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological–chemical linkages outlined here, nor to validate complex processes within coupled atmosphere–chemistry models.

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Chelsea R. Thompson, Steven C. Wofsy, Michael J. Prather, Paul A. Newman, Thomas F. Hanisco, Thomas B. Ryerson, David W. Fahey, Eric C. Apel, Charles A. Brock, William H. Brune, Karl Froyd, Joseph M. Katich, Julie M. Nicely, Jeff Peischl, Eric Ray, Patrick R. Veres, Siyuan Wang, Hannah M. Allen, Elizabeth Asher, Huisheng Bian, Donald Blake, Ilann Bourgeois, John Budney, T. Paul Bui, Amy Butler, Pedro Campuzano-Jost, Cecilia Chang, Mian Chin, RóISíN Commane, Gus Correa, John D. Crounse, Bruce Daube, Jack E. Dibb, Joshua P. Digangi, Glenn S. Diskin, Maximilian Dollner, James W. Elkins, Arlene M. Fiore, Clare M. Flynn, Hao Guo, Samuel R. Hall, Reem A. Hannun, Alan Hills, Eric J. Hintsa, Alma Hodzic, Rebecca S. Hornbrook, L. Greg Huey, Jose L. Jimenez, Ralph F. Keeling, Michelle J. Kim, Agnieszka Kupc, Forrest Lacey, Leslie R. Lait, Jean-Francois Lamarque, Junhua Liu, Kathryn Mckain, Simone Meinardi, David O. Miller, Stephen A. Montzka, Fred L. Moore, Eric J. Morgan, Daniel M. Murphy, Lee T. Murray, Benjamin A. Nault, J. Andrew Neuman, Louis Nguyen, Yenny Gonzalez, Andrew Rollins, Karen Rosenlof, Maryann Sargent, Gregory Schill, Joshua P. Schwarz, Jason M. St. Clair, Stephen D. Steenrod, Britton B. Stephens, Susan E. Strahan, Sarah A. Strode, Colm Sweeney, Alexander B. Thames, Kirk Ullmann, Nicholas Wagner, Rodney Weber, Bernadett Weinzierl, Paul O. Wennberg, Christina J. Williamson, Glenn M. Wolfe, and Linghan Zeng

Abstract

This article provides an overview of the NASA Atmospheric Tomography (ATom) mission and a summary of selected scientific findings to date. ATom was an airborne measurements and modeling campaign aimed at characterizing the composition and chemistry of the troposphere over the most remote regions of the Pacific, Southern, Atlantic, and Arctic Oceans, and examining the impact of anthropogenic and natural emissions on a global scale. These remote regions dominate global chemical reactivity and are exceptionally important for global air quality and climate. ATom data provide the in situ measurements needed to understand the range of chemical species and their reactions, and to test satellite remote sensing observations and global models over large regions of the remote atmosphere. Lack of data in these regions, particularly over the oceans, has limited our understanding of how atmospheric composition is changing in response to shifting anthropogenic emissions and physical climate change. ATom was designed as a global-scale tomographic sampling mission with extensive geographic and seasonal coverage, tropospheric vertical profiling, and detailed speciation of reactive compounds and pollution tracers. ATom flew the NASA DC-8 research aircraft over four seasons to collect a comprehensive suite of measurements of gases, aerosols, and radical species from the remote troposphere and lower stratosphere on four global circuits from 2016 to 2018. Flights maintained near-continuous vertical profiling of 0.15 – 13 km altitudes on long meridional transects of the Pacific and Atlantic Ocean basins. Analysis and modeling of ATom data have led to the significant early findings highlighted here.

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