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I. Polichtchouk, T. G. Shepherd, R. J. Hogan, and P. Bechtold

Abstract

The role of parameterized nonorographic gravity wave drag (NOGWD) and its seasonal interaction with the resolved wave drag in the stratosphere has been extensively studied in low-resolution (coarser than 1.9° × 2.5°) climate models but is comparatively unexplored in higher-resolution models. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 0.7° × 0.7° resolution, the wave drivers of the Brewer–Dobson circulation are diagnosed and the circulation sensitivity to the NOGW launch flux is explored. NOGWs are found to account for nearly 20% of the lower-stratospheric Southern Hemisphere (SH) polar cap downwelling and for less than 10% of the lower-stratospheric tropical upwelling and Northern Hemisphere (NH) polar cap downwelling. Despite these relatively small numbers, there are complex interactions between NOGWD and resolved wave drag, in both polar regions. Seasonal cycle analysis reveals a temporal offset in the resolved and parameterized wave interaction: the NOGWD response to altered source fluxes is largest in midwinter, while the resolved wave response is largest in the late winter and spring. This temporal offset is especially prominent in the SH. The impact of NOGWD on sudden stratospheric warming (SSW) life cycles and the final warming date in the SH is also investigated. An increase in NOGWD leads to an increase in SSW frequency, reduction in amplitude and persistence, and an earlier recovery of the stratopause following an SSW event. The SH final warming date is also brought forward when NOGWD is increased. Thus, NOGWD is still found to be a very important parameterization for stratospheric dynamics even in a high-resolution atmospheric model.

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Jainn J. Shi, Simon W. Chang, Teddy R. Holt, Timothy F. Hogan, and Douglas L. Westphal

Abstract

In support of the Department of Defense's Gulf War Illness study, the Naval Research Laboratory (NRL) has performed global and mesoscale meteorological reanalyses to provide a quantitative atmospheric characterization of the Persian Gulf region during the period between 15 January and 15 March 1991. This paper presents a description of the mid- to late-winter synoptic conditions, mean statistical scores, and near-surface mean conditions of the Gulf War theater drawn from the 2-month reanalysis.

The reanalysis is conducted with the U.S. Navy's operational global and mesoscale analysis and prediction systems: the Navy Operational Global Atmospheric Prediction System (NOGAPS) and the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). The synoptic conditions for the 2-month period can be characterized as fairly typical for the northeast monsoon season, with only one significant precipitation event affecting the Persian Gulf region.

A comparison of error statistics to those from other mesoscale models with similar resolution covering complex terrains (though in different geographic locations) is performed. Results indicate similar if not smaller error statistics for the current study even though this 2-month reanalysis is conducted in an extremely data-sparse area, lending credence to the reanalysis dataset.

The mean near-surface conditions indicate that variability in the wind and temperature fields arises mainly because of the differential diurnal processes in the region characterized by complex surface characteristics and terrain height. The surface wind over lower elevation, interior, land regions is mostly light and variable, especially in the nocturnal surface layer. The strong signature of diurnal variation of sea–land as well as lake–land circulation is apparent, with convergence over the water during the night and divergence during the day. Likewise, the boundary layer is thus strongly modulated by the diurnal cycle near the surface. The low mean PBL height and light mean winds combine to yield very low ventilation efficiency over the Saudi and Iraqi plains.

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Robin J. Hogan, Lin Tian, Philip R. A. Brown, Christopher D. Westbrook, Andrew J. Heymsfield, and Jon D. Eastment

Abstract

The assumed relationship between ice particle mass and size is profoundly important in radar retrievals of ice clouds, but, for millimeter-wave radars, shape and preferred orientation are important as well. In this paper the authors first examine the consequences of the fact that the widely used “Brown and Francis” mass–size relationship has often been applied to maximum particle dimension observed by aircraft D max rather than to the mean of the particle dimensions in two orthogonal directions D mean, which was originally used by Brown and Francis. Analysis of particle images reveals that D max ≃ 1.25D mean, and therefore, for clouds for which this mass–size relationship holds, the consequences are overestimates of ice water content by around 53% and of Rayleigh-scattering radar reflectivity factor by 3.7 dB. Simultaneous radar and aircraft measurements demonstrate that much better agreement in reflectivity factor is provided by using this mass–size relationship with D mean. The authors then examine the importance of particle shape and fall orientation for millimeter-wave radars. Simultaneous radar measurements and aircraft calculations of differential reflectivity and dual-wavelength ratio are presented to demonstrate that ice particles may usually be treated as horizontally aligned oblate spheroids with an axial ratio of 0.6, consistent with them being aggregates. An accurate formula is presented for the backscatter cross section apparent to a vertically pointing millimeter-wave radar on the basis of a modified version of Rayleigh–Gans theory. It is then shown that the consequence of treating ice particles as Mie-scattering spheres is to substantially underestimate millimeter-wave reflectivity factor when millimeter-sized particles are present, which can lead to retrieved ice water content being overestimated by a factor of 4.

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D. L. Westphal, T. R. Holt, S. W. Chang, N. L. Baker, T. F. Hogan, L. R. Brody, R. A. Godfrey, J. S. Goerss, J. A. Cummings, D. J. Laws, and C. W. Hines

Abstract

The Marine Meteorology Division of the Naval Research Laboratory (NRL), assisted by the Fleet Numerical Meteorology and Oceanography Center, has performed global and mesoscale reanalyses to support the study of Gulf War illness. Realistic and quantitatively accurate atmospheric conditions are needed to drive dispersion models that can predict the transport and dispersion of chemical agents that may have affected U.S. and other coalition troops in the hours and days following the demolition of chemical weapons at Khamisiyah, Iraq, at approximately 1315 UTC 10 March 1991. The reanalysis was conducted with the navy’s global and mesoscale analysis and prediction systems: the Navy Operational Global Atmospheric Prediction System and the NRL Coupled Ocean–Atmosphere Mesoscale Prediction System. A comprehensive set of observations has been collected and used in the reanalysis, including unclassified and declassified surface reports, ship and buoy reports, observations from pibal and rawinsonde, and retrievals from civilian and military satellites. The atmospheric conditions for the entire globe have been reconstructed using the global system at the effective spatial resolution of 0.75°. The atmospheric conditions over southern Iraq, Kuwait, and northern Saudi Arabia have been reconstructed using the mesoscale system at the spatial resolutions of 45, 15, and 5 km. In addition to a baseline reanalysis, perturbation analyses were also performed to estimate the atmospheric sensitivity to observational error and analysis error. The results suggest that the reanalysis has bounded the variability and that the actual atmospheric conditions were unlikely to differ significantly from the reanalysis.

The synoptic conditions at and after the time of the detonation were typical of the transitional period after a Shamal and controlled by eastward-propagating small-amplitude troughs and ridges. On the mesoscale, the conditions over the Tigris–Euphrates Valley were further modulated by the diurnal variation in the local circulations between land, the Persian Gulf, and the Zagros Mountains. The boundary layer winds at Khamisiyah were from NNW at the time of the detonation and shifted to WNW in the nocturnal boundary layer. On the second day, a strong high passed north of Khamisiyah and the winds strengthened and turned to the ESE. During the third day, the region was dominated by the approach and passage of a low pressure system and the associated front with the SE winds veering to NW.

A transport model for passive scalars was used to illustrate the sensitivity to the reanalyzed fields of potential areas of contamination. Transport calculations based on various release scenario and reanalyzed meteorological conditions suggest that the mean path of the released chemical agents was southward from Khamisiyah initially, turning westward, and eventually northwestward during the 72-h period after the demolition. Precipitation amounts in the study area were negligible and unlikely to have an effect on the nerve agent.

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R.C.J. Somerville, P.H. Stone, M. Halem, J.E. Hansen, J.S. Hogan, L.M. Druyan, G. Russell, A.A. Lacis, W.J. Quirk, and J. Tenenbaum

Abstract

A model description and numerical results are presented for a global atmospheric circulation model developed at the Goddard Institute for Space Studies (GISS). The model version described is a 9-level primitive-equation model in sigma coordinates. It includes a realistic distribution of continents, oceans and topography. Detailed calculations of energy transfer by solar and terrestrial radiation make use of cloud and water vapor fields calculated by the model. The model hydrologic cycle includes two precipitation mechanisms: large-scale supersaturation and a parameterization of subgrid-scale cumulus convection.

Results are presented both from a comparison of the 13th to the 43rd days (January) of one integration with climatological statistics, and from five short-range forecasting experiments. In the extended integration, the near-equilibrium January-mean model atmosphere exhibits an energy cycle in good agreement with observational estimates, together with generally realistic zonal mean fields of winds, temperature, humidity, transports, diabatic heating, evaporation, precipitation, and cloud cover. In the five forecasting experiments, after 48 hr, the average rms error in temperature is 3.9K, and the average rms error in 500-mb height is 62 m. The model is successful in simulating the 2-day evolution of the major features of the observed sea level pressure and 500-mb height fields in a region surrounding North America.

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Dominique Bouniol, Alain Protat, Julien Delanoë, Jacques Pelon, Jean-Marcel Piriou, François Bouyssel, Adrian M. Tompkins, Damian R. Wilson, Yohann Morille, Martial Haeffelin, Ewan J. O’Connor, Robin J. Hogan, Anthony J. Illingworth, David P. Donovan, and Henk-Klein Baltink

Abstract

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

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Cloudnet

Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations

A. J. Illingworth, R. J. Hogan, E.J. O'Connor, D. Bouniol, M. E. Brooks, J. Delanoé, D. P. Donovan, J. D. Eastment, N. Gaussiat, J. W. F. Goddard, M. Haeffelin, H. Klein Baltink, O. A. Krasnov, J. Pelon, J.-M. Piriou, A. Protat, H. W. J. Russchenberg, A. Seifert, A. M. Tompkins, G.-J. van Zadelhoff, F. Vinit, U. Willén, D. R. Wilson, and C. L. Wrench

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

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A. J. Illingworth, H. W. Barker, A. Beljaars, M. Ceccaldi, H. Chepfer, N. Clerbaux, J. Cole, J. Delanoë, C. Domenech, D. P. Donovan, S. Fukuda, M. Hirakata, R. J. Hogan, A. Huenerbein, P. Kollias, T. Kubota, T. Nakajima, T. Y. Nakajima, T. Nishizawa, Y. Ohno, H. Okamoto, R. Oki, K. Sato, M. Satoh, M. W. Shephard, A. Velázquez-Blázquez, U. Wandinger, T. Wehr, and G.-J. van Zadelhoff

Abstract

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.

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