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L. D. Williams, R. G. Barry, and J. T. Andrews

Abstract

The variation over uneven terrain of the daily total of incident shortwave (global) radiation under cloudless conditions may be estimated by existing methods for calculating direct and diffuse solar radiation on a slope. A computer program for performing these calculations, incorporating a technique to determine when the direct rays of the sun are screened by the horizon at each point, is described. The adequacy of the approximation for diffuse radiation is considered by comparison with published data. Computations for an area of east Baffin Island, Northwest Territories, Canada, demonstrate that the occurrence of glaciers there is influenced both by elevation and by solar radiation. The potential of such computations as an aid in selecting station sites for climatological studies is also discussed.

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Paul D. Williams, Thomas W. N. Haine, and Peter L. Read

Abstract

This paper describes laboratory observations of inertia–gravity waves emitted from balanced fluid flow. In a rotating two-layer annulus experiment, the wavelength of the inertia–gravity waves is very close to the deformation radius. Their amplitude varies linearly with Rossby number in the range 0.05–0.14, at constant Burger number (or rotational Froude number). This linear scaling challenges the notion, suggested by several dynamical theories, that inertia–gravity waves generated by balanced motion will be exponentially small. It is estimated that the balanced flow leaks roughly 1% of its energy each rotation period into the inertia–gravity waves at the peak of their generation.

The findings of this study imply an inevitable emission of inertia–gravity waves at Rossby numbers similar to those of the large-scale atmospheric and oceanic flow. Extrapolation of the results suggests that inertia–gravity waves might make a significant contribution to the energy budgets of the atmosphere and ocean. In particular, emission of inertia–gravity waves from mesoscale eddies may be an important source of energy for deep interior mixing in the ocean.

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Alexei Lyapustin, D. L. Williams, B. Markham, J. Irons, B. Holben, and Y. Wang

Abstract

Because the land surface reflectance varies spatially, the atmospheric radiative transfer over land in clear-sky conditions is essentially three-dimensional. This is manifested through horizontal radiative fluxes that blur satellite images. It is important that the atmospheric blurring systematically increases the apparent brightness of the dark pixels. As a consequence, there are systematic biases in the satellite products of aerosol optical thickness and surface albedo over dark targets based on 1D theory, which may have a negative impact on climate research. Below, a new dark target method is presented for unbiased simultaneous retrieval of the aerosol model and optical thickness over land from Landsat Enhanced Thematic Mapper Plus (ETM+) data, based on 3D radiative transfer theory. The method automatically selects an aerosol model from a large set of candidate models using a statistical approach of the probability distribution function. The dark target method of aerosol retrieval in the blue and red bands relies on prediction of the surface reflectance in these bands from the shortwave infrared region (2.1–2.2 μm) based on the linear regression. In the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm, the regression coefficients are constants, whereas different studies indicate that they have seasonal and geographic variations. The work here shows that the accuracy of aerosol retrieval over land can be significantly increased based on ancillary information on the regional and seasonal distribution of the regression coefficients. This information, which is called surface climatology, can be derived globally around Aerosol Robotic Network (AERONET) sites, using AERONET aerosol and water vapor information for accurate atmospheric correction. This paper describes the developed method in application to Landsat data and its initial validation with AERONET measurements for a set of ETM+ images of the Washington–Baltimore area, and studies biases of 1D retrievals.

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J. W. Maddison, S. L. Gray, O. Martínez-Alvarado, and K. D. Williams

Abstract

Atmospheric blocking has been shown to be a phenomenon that models struggle to predict accurately, particularly the onset of a blocked state following a more zonal flow. This struggle is, in part, due to the lack of a complete dynamical theory for block onset and maintenance. Here, we evaluate the impact cyclone representation had on the forecast of block onset in two case studies from the North Atlantic Waveguide and Downstream Impact Experiment field campaign and the 20 most unpredictable block onsets over the Euro-Atlantic region in medium-range forecasts from the ECMWF. The 6-day forecast of block onset in the case studies is sensitive to changes in the forecast location and intensity of upstream cyclones (one cyclone for one case and two for the other case) in the days preceding the onset. Ensemble sensitivity analysis reveals that this is often the case in unpredictable block onset cases: a one standard deviation change in 1000-hPa geopotential height near an upstream cyclone, or 320-K potential vorticity near the tropopause, two or three days prior to block onset is associated with more than a 10% change in block area on the analyzed onset day in 17 of the 20 onset cases. These results imply that improvement in the forecasts of upstream cyclone location and intensity may help improve block onset forecasts.

Open access
Simon D. P. Williams, Paul S. Bell, David L. McCann, Richard Cooke, and Christine Sams

Abstract

A low-cost [$30 (U.S. dollars)] consumer grade GPS receiver with a sideways-mounted antenna has been applied to measure tidal water levels at a mesotidal coastal site using an interferometric reflectometry approach. The proof-of-concept system was installed approximately 16 m above mean sea level in close proximity to a conventional bubbler tide gauge that provided validation data. The received signal-to-noise ratios (SNR) for the satellites in view were recorded for several months during two successive years and the observed frequencies of the interferometric oscillations used to calculate the difference in elevation between the receiver and the water surface. Comparisons with concurrent and historic in situ tide gauge data at the site initially helped to identify a calibration issue with the in situ gauge. The GPS-based measurements were shown to be in excellent agreement with the corrected in situ gauge, exhibiting a root-mean-square difference of 5.7 cm over a tidal range exceeding 3 m at spring tides and a daily averaged RMS of 1.7 cm. The SNR data from the low-cost GPS receivers are shown to provide significantly higher-quality data for this purpose compared with high-end geodetic grade receivers at similar sites. This low-cost, widely available technology has the potential to be applied globally for monitoring water levels in a wide variety of circumstances and applications that would otherwise be cost or situation prohibitive. It could also be applied as an independent cross check and quality control measure for conventional water-level gauges.

Open access
Alan E. Stewart, Castle A. Williams, Minh D. Phan, Alexandra L. Horst, Evan D. Knox, and John A. Knox

Abstract

Prior surveys of the public indicated that a variety of meanings and interpretations exist about the probability of precipitation (PoP). Does the same variety of meanings for the PoP exist among members of the professional atmospheric science community? What do members of the professional community think that the public should know to understand the PoP more fully? These questions were examined in a survey of 188 meteorologists and broadcasters. Meteorologists were observed to express a variety of different definitions of the PoP and also indicated a high degree of confidence in the accuracy of their definitions. Differences in the definitions stemmed from the way the PoP was derived from model output statistics, parsing of a 12-h PoP over shorter time frames, and generalizing from a point PoP to a wider coverage warning area. In this regard 43% of the online survey respondents believed that there was no or very little consistency in the definition of PoP; only 8% believed that the PoP definition has been used in a consistent manner. The respondents believed that the PoP was limited in its value to the general public because, on average, those surveyed believed that only about 22% of the population had an accurate conception of the PoP. These results imply that the atmospheric science community should work to achieve a wider consensus about the meaning of the PoP. Further, until meteorologists develop a consistent conception of the PoP and disseminate it, the public’s understanding of PoP-based forecasts may remain fuzzy.

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Xiaoyan Jiang, Sara A. Rauscher, Todd D. Ringler, David M. Lawrence, A. Park Williams, Craig D. Allen, Allison L. Steiner, D. Michael Cai, and Nate G. McDowell

Abstract

Rapid and broad-scale forest mortality associated with recent droughts, rising temperature, and insect outbreaks has been observed over western North America (NA). Climate models project additional future warming and increasing drought and water stress for this region. To assess future potential changes in vegetation distributions in western NA, the Community Earth System Model (CESM) coupled with its Dynamic Global Vegetation Model (DGVM) was used under the future A2 emissions scenario. To better span uncertainties in future climate, eight sea surface temperature (SST) projections provided by phase 3 of the Coupled Model Intercomparison Project (CMIP3) were employed as boundary conditions. There is a broad consensus among the simulations, despite differences in the simulated climate trajectories across the ensemble, that about half of the needleleaf evergreen tree coverage (from 24% to 11%) will disappear, coincident with a 14% (from 11% to 25%) increase in shrubs and grasses by the end of the twenty-first century in western NA, with most of the change occurring over the latter half of the twenty-first century. The net impact is a ~6 GtC or about 50% decrease in projected ecosystem carbon storage in this region. The findings suggest a potential for a widespread shift from tree-dominated landscapes to shrub and grass-dominated landscapes in western NA because of future warming and consequent increases in water deficits. These results highlight the need for improved process-based understanding of vegetation dynamics, particularly including mortality and the subsequent incorporation of these mechanisms into earth system models to better quantify the vulnerability of western NA forests under climate change.

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K. D. Williams, A. Bodas-Salcedo, M. Déqué, S. Fermepin, B. Medeiros, M. Watanabe, C. Jakob, S. A. Klein, C. A. Senior, and D. L. Williamson

Abstract

The Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in “weather forecast mode.” The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses.

The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models share a climatological bias for too little reflected shortwave radiation from cloud across the region. This is found to mainly occur behind cold fronts and/or on the leading side of transient ridges and to be associated with more stable lower-tropospheric profiles. Investigation of a case study that is typical of the bias and associated meteorological conditions reveals the models to typically simulate cloud that is too optically and physically thin with an inversion that is too low. The evolution of the models within the first few hours suggests that these conditions are particularly sensitive and a positive feedback can develop between the thinning of the cloud layer and boundary layer structure.

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Peter L. Read, Yasuhro H. Yamazaki, Stephen R. Lewis, Paul D. Williams, Robin Wordsworth, Kuniko Miki-Yamazaki, Joel Sommeria, Henri Didelle, and Adam M. Fincham
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Amy McGovern, Kimberly L. Elmore, David John Gagne II, Sue Ellen Haupt, Christopher D. Karstens, Ryan Lagerquist, Travis Smith, and John K. Williams

Abstract

High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.

Open access