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Carrie E. Lang, Jessica M. McDonald, Lauriana Gaudet, Dylan Doeblin, Erin A. Jones, and Neil F. Laird

Abstract

Lake-effect storms (LES) produce substantial snowfall in the vicinity of the downwind shores of the Great Lakes. These storms may take many forms; one type of LES event, lake to lake (L2L), occurs when LES clouds/snowbands develop over an upstream lake (e.g., Lake Huron), extend across an intervening landmass, and continue over a downstream lake (e.g., Lake Ontario). The current study examined LES snowfall in the vicinity of Lake Ontario and the atmospheric conditions during Lake Huron-to-Lake Ontario L2L days as compared with LES days on which an L2L connection was not present [i.e., only Lake Ontario (OLO)] for the cold seasons (October–March) from 2003/04 through 2013/14. Analyses of snowfall demonstrate that, on average, significantly greater LES snowfall totals occur downstream of Lake Ontario on L2L days than on OLO days. The difference in mean snowfall between L2L and OLO days approaches 200% in some areas near the Tug Hill Plateau and central New York State. Analyses of atmospheric conditions found more-favorable LES environments on L2L days relative to OLO days that included greater instability over the upwind lake, more near-surface moisture available, faster wind speeds, and larger surface heat fluxes over the upstream lake. Last, despite significant snowfalls on L2L days, their average contribution to the annual accumulated LES snowfall in the vicinity of Lake Ontario was found to be small (i.e., 25%–30%) because of the relatively infrequent occurrence of L2L days.

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Cynthia E. Bluteau, Rolf G. Lueck, Gregory N. Ivey, Nicole L. Jones, Jeffrey W. Book, and Ana E. Rice

Abstract

Ocean mixing has historically been estimated using Osborn’s model by measuring the rate of dissipation of turbulent kinetic energy ϵ and the background density stratification N while assuming a value of the flux Richardson number . A constant is typically assumed, despite mounting field, laboratory, and modeling evidence that varies. This challenge can be overcome by estimating the turbulent diffusivity of heat using the Osborn–Cox model. This model, however, requires measuring the rate of dissipation of thermal variance χ, which has historically been challenging, particularly in energetic flows because the high wavenumbers of the temperature gradient spectra are unresolved with current technology. To overcome this difficulty, a method is described that determines χ by spectral fitting to the inertial-convective (IC) subrange of the temperature gradient spectra. While this concept has been exploited for moored time series, particularly near the bottom boundary, it has yet to be adapted to vertical microstructure profilers such as gliders, and autonomous and ship-based vertical profilers from which there are the most measurements. By using the IC subrange, χ, and hence , can be estimated even in very energetic events—precisely the conditions requiring more field observations. During less energetic periods, the temperature gradient spectra can also be integrated to obtain χ. By combining these two techniques, microstrucure profiles at a field site known for its very energetic internal waves are analyzed. This study demonstrates that the spectral fitting approach resolves intense mixing events with . By equating the Osborn and Osborn–Cox models, indirect estimates for can also be obtained.

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Jerome A. Smith, Paola Cessi, Ilker Fer, Gregory Foltz, Baylor Fox-Kemper, Karen Heywood, Nicole Jones, Jody Klymak, and Joseph LaCasce
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A. Wiacek, J. R. Taylor, K. Strong, R. Saari, T. E. Kerzenmacher, N. B. Jones, and D. W. T. Griffith

Abstract

The authors describe the optical design of a high-resolution Fourier Transform Spectrometer (FTS), which serves as the primary instrument at the University of Toronto Atmospheric Observatory (TAO). The FTS is dedicated to ground-based infrared solar absorption atmospheric measurements from Toronto, Ontario, Canada. Instrument performance is discussed in terms of instrumental line shape (ILS) and phase error and modulation efficiency as a function of optical path difference. Typical measurement parameters are presented together with retrieval parameters used to derive total and partial column concentrations of ozone. Retrievals at TAO employ the optimal estimation method (OEM), and some impacts of the necessary a priori constraints are examined. In March 2004, after participating in a retrieval algorithm user intercomparison exercise, the TAO FTS was granted the status of a Complementary Observation Station within the international community of high-resolution FTS users in the Network for the Detection of Atmospheric Composition and Change (NDACC). During this exercise, average differences between total columns retrieved from the same spectra by different users were below 2.1% for O3, HCl, and N2O in the blind phase, and below 1% in the open phase, when all retrieval constraints were identical. Finally, a 2.5-yr time series of monthly mean stratospheric ozone columns agrees within 3% with those retrieved from Optical Spectrograph and Infrared Imager System (OSIRIS) measurements on board the Odin satellite, which is within the errors of both measurement platforms.

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Victoria A. Bell, Nicola Gedney, Alison L. Kay, Roderick N. B. Smith, Richard G. Jones, and Robert J. Moore

Abstract

River basin managers concerned with maintaining water supplies and mitigating flood risk in the face of climate change are taking outputs from climate models and using them in hydrological models for assessment purposes. While precipitation is the main output used, evaporation is attracting increasing attention because of its significance to the water balance of river basins. Climate models provide estimates of actual evaporation that are consistent with their simplified land surface schemes but do not naturally provide the estimates of potential evaporation (PE) commonly required as input to hydrological models. There are clear advantages in using PE estimates controlled by atmospheric forcings when using stand-alone hydrological models with integral soil-moisture accounting schemes. The atmosphere–land decoupling approximation that PE provides can prove to be of further benefit if it is possible to account for the effect of different, or changing, land cover on PE outside of the climate model. The methods explored here estimate Penman–Monteith PE from vegetated surfaces using outputs from climate models that have an embedded land surface scheme. The land surface scheme enables an examination of the dependence of canopy stomatal resistance on atmospheric composition, and the sensitivity of PE estimates to the choice of canopy resistance values under current and changing climates is demonstrated. The conclusions have practical value for climate change impact studies relating to flood, drought, and water management applications.

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Montgomery L. Flora, Patrick S. Skinner, Corey K. Potvin, Anthony E. Reinhart, Thomas A. Jones, Nusrat Yussouf, and Kent H. Knopfmeier

Abstract

An object-based verification method for short-term, storm-scale probabilistic forecasts was developed and applied to mesocyclone guidance produced by the experimental Warn-on-Forecast System (WoFS) in 63 cases from 2017 to 2018. The probabilistic mesocyclone guidance was generated by calculating gridscale ensemble probabilities from WoFS forecasts of updraft helicity (UH) in layers 2–5 km (midlevel) and 0–2 km (low-level) above ground level (AGL) aggregated over 60-min periods. The resulting ensemble probability swaths are associated with individual thunderstorms and treated as objects with a single, representative probability value prescribed. A mesocyclone probability object, conceptually, is a region bounded by the ensemble forecast envelope of a mesocyclone track for a given thunderstorm over 1 h. The mesocyclone probability objects were matched against rotation track objects in Multi-Radar Multi-Sensor data using the total interest score, but with the maximum displacement varied between 0, 9, 15, and 30 km. Forecast accuracy and reliability were assessed at four different forecast lead time periods: 0–60, 30–90, 60–120, and 90–150 min. In the 0–60-min forecast period, the low-level UH probabilistic forecasts had a POD, FAR, and CSI of 0.46, 0.45, and 0.31, respectively, with a probability threshold of 22.2% (the threshold of maximum CSI). In the 90–150-min forecast period, the POD and CSI dropped to 0.39 and 0.27 while FAR remained relatively unchanged. Forecast probabilities > 60% overpredicted the likelihood of observed mesocyclones in the 0–60-min period; however, reliability improved when allowing larger maximum displacements for object matching and at longer lead times.

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Thomas A. Jones, Patrick Skinner, Nusrat Yussouf, Kent Knopfmeier, Anthony Reinhart, Xuguang Wang, Kristopher Bedka, William Smith Jr., and Rabindra Palikonda

Abstract

The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-μm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.

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A. Manderson, M. D. Rayson, E. Cripps, M. Girolami, J. P. Gosling, M. Hodkiewicz, G. N. Ivey, and N. L. Jones

Abstract

We present a statistical method for reconstructing continuous background density profiles that embeds incomplete measurements and a physically intuitive density stratification model within a Bayesian hierarchal framework. A double hyperbolic tangent function is used as a parametric density stratification model that captures various pycnocline structures in the upper ocean and offers insight into several density profile characteristics (e.g., pycnocline depth). The posterior distribution is used to quantify uncertainty and is estimated using recent advances in Markov chain Monte Carlo sampling. Temporally evolving posterior distributions of density profile characteristics, isopycnal heights, and nonlinear ocean process models for internal gravity waves are presented as examples of how uncertainty propagates through models dependent on the density stratification. The results show 0.95 posterior interval widths that ranged from 2.5% to 4% of the expected values for the linear internal wave phase speed and 15%–40% for the nonlinear internal wave steepening parameter. The data, collected over a year from a through-the-column mooring, and code, implemented in the software package Stan, accompany the article.

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John Turner, Steve R. Colwell, Gareth J. Marshall, Tom A. Lachlan-Cope, Andrew M. Carleton, Phil D. Jones, Victor Lagun, Phil A. Reid, and Svetlana Iagovkina

Abstract

A new dataset of monthly and annual mean near-surface climate data (temperature, surface and mean sea level pressure, and wind speed) for the Antarctic region has been created using historical observations [Scientific Committee on Antarctic Research (SCAR) Reference Antarctic Data for Environmental Research (READER)]. Where possible, 6-hourly surface synoptic and automatic weather station observations were used to compute the means. The ability to quality control the data at the level of individual observations has produced a more accurate series of monthly means than was available previously. At the time of writing, the mean data are available on the Internet (http://www.antarctica.ac.uk/met/programs-hosted.html). Data for 43 surface-staffed stations and 61 automatic weather stations are included in the database. Here, mean temperature, pressure, and wind speed data for 19 occupied stations with long records are provided.

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Bharat Rastogi, A. Park Williams, Douglas T. Fischer, Sam F. Iacobellis, Kathryn McEachern, Leila Carvalho, Charles Jones, Sara A. Baguskas, and Christopher J. Still

Abstract

The presence of low-lying stratocumulus clouds and fog has been known to modify biophysical and ecological properties in coastal California where forests are frequently shaded by low-lying clouds or immersed in fog during otherwise warm and dry summer months. Summer fog and stratus can ameliorate summer drought stress and enhance soil water budgets and often have different spatial and temporal patterns. Here, this study uses remote sensing datasets to characterize the spatial and temporal patterns of cloud cover over California’s northern Channel Islands. The authors found marine stratus to be persistent from May to September across the years 2001–12. Stratus clouds were both most frequent and had the greatest spatial extent in July. Clouds typically formed in the evening and dissipated by the following early afternoon. This study presents a novel method to downscale satellite imagery using atmospheric observations and discriminate patterns of fog from those of stratus and help explain patterns of fog deposition previously studied on the islands. The outcomes of this study contribute significantly to the ability to quantify the occurrence of coastal fog at biologically meaningful spatial and temporal scales that can improve the understanding of cloud–ecosystem interactions, species distributions, and coastal ecohydrology.

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