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G. A. Jones and S. K. Avery

Abstract

The effects of the zonal mean circulation and planetary-wave winds on the distribution of nitric oxide in the 55–120 km height region is investigated. A time-dependent numerical model is used to investigate the interaction between planetary waves and the zonal mean circulation, and the effect of the circulation on the nitric oxide distribution is determined. The initial nitric oxide (NO) distribution is obtained by using a simple source/sink chemistry, vertical eddy diffusion, and advective transport by the zonal mean circulation. Changes in the initial NO distribution which result from the addition of planetary-wave winds are described. Planetary waves are found to induce a wave-like structure in the nitric oxide distribution which resembles that derived from observational data. Planetary waves can affect the nitric oxide concentration in two ways: first,through the wave-induced changes in the mean meridional circulation, and second, through the nitric oxide perturbation induced by wave winds themselves. The changes in total nitric oxide are due primarily to the zonal asymmetries in nitric oxide induced by the planetary waves. Implications of this result for explaining the winter anomaly are discussed.

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Matthew S. Jones, Brian A. Colle, and Jeffrey S. Tongue

Abstract

A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

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Matthew S. Jones, Mark A. Saunders, and Trevor H. Guymer

Abstract

The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.

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T. Vukicevic, M. Sengupta, A. S. Jones, and T. Vonder Haar

Abstract

This study addresses the problem of four-dimensional (4D) estimation of a cloudy atmosphere on cloud-resolving scales using satellite remote sensing measurements. The motivation is to develop a methodology for accurate estimation of cloud properties and the associated atmospheric environment on small spatial scales but over large regions to aid in better understanding of the clouds and their role in the atmospheric system. The problem is initially approached by the study of the assimilation of the Geostationary Operational Environmental Satellite (GOES) imager observations into a cloud-resolving model with explicit bulk cloud microphysical parameterization. A new 4D variational data assimilation (4DVAR) research system with the cloud-resolving capability is applied to a case of a multilayered cloud evolution without convection. In the experiments the information content of the IR window channels is addressed as well as the sensitivity of estimation to lateral boundary condition errors, model first guess, decorrelation length in the background statistical error model, and the use of a generic linear model error. The assimilation results are compared with independent observations from the Atmospheric Radiation Measurement (ARM) central facility archive.

The modeled 3D spatial distribution and short-term evolution of the ice cloud mass is significantly improved by the assimilation of IR window channels when the model already contains conditions for the ice cloud formation. The assimilated ice cloud in this case is in good agreement with the independent cloud radar measurements. The simulation of liquid clouds below thick ice clouds is not influenced by the IR window observations. The assimilation results clearly demonstrate that increasing the observational constraint from individual to combined channel measurements and from less to more frequent observation times systematically improves the assimilation results. The experiments with the model error indicate that the current specification of this error in the form of a generic linear forcing, which was adopted from other data assimilation studies, is not suitable for the cloud-resolving data assimilation. Instead, a parameter estimation approach may need to be explored in the future. The experiments with varying decorrelation lengths suggest the need to use the model horizontal grid spacing that is several times smaller than the GOES imager native resolution to achieve equivalent spatial variability in the assimilation.

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Peter A. Stott, Gareth S. Jones, and John F. B. Mitchell

Abstract

Current attribution analyses that seek to determine the relative contributions of different forcing agents to observed near-surface temperature changes underestimate the importance of weak signals, such as that due to changes in solar irradiance. Here a new attribution method is applied that does not have a systematic bias against weak signals.

It is found that current climate models underestimate the observed climate response to solar forcing over the twentieth century as a whole, indicating that the climate system has a greater sensitivity to solar forcing than do models. The results from this research show that increases in solar irradiance are likely to have had a greater influence on global-mean temperatures in the first half of the twentieth century than the combined effects of changes in anthropogenic forcings. Nevertheless the results confirm previous analyses showing that greenhouse gas increases explain most of the global warming observed in the second half of the twentieth century.

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T. Vukicevic, T. Greenwald, M. Zupanski, D. Zupanski, T. Vonder Haar, and A. S. Jones

Abstract

This study focuses on cloudy atmosphere state estimation from high-resolution visible and infrared satellite remote sensing measurements and a mesoscale model with explicit cloud prediction. The cloud state is defined as 3D spatially distributed hydrometeors characterized with microphysical properties: mixing ratio, number concentration, and size distribution. The Geostationary Operational Environmental Satellite-9 (GOES-9) imager visible and infrared measurements were used in a new four-dimensional variational data assimilation (4DVAR) mesoscale algorithm for a warm continental stratus cloud system case to test the impact of these observations on the cloud simulation. The new data assimilation algorithm includes the Regional Atmospheric Modeling System (RAMS) with explicit cloud state prediction, the associated adjoint system, and an observational operator for forward and adjoint integrations of the GOES radiances. The results show positive impact of GOES imager measurements on the 3D cloud short-term simulation during and after the assimilation. The impact was achieved through sensitivity of the radiances to the cloud droplet mixing ratio at observation time and a 4D correlation between the cloud and atmospheric thermal and dynamical environment in the forecast model. The dynamical response to the radiance observations was through enhanced large mesoscale vertical mixing while horizontal advection was weak in the case of stable continental stratus evolution.

Although the current experiments show measurable positive impact of the cloudy radiance measurements on the stratus cloud simulation, they clearly suggest the need to further address the problem of negative cloud cover forecast errors. These errors were only weakly corrected in the current study because of the small sensitivity of the visible and infrared window radiances to the cloud-free atmosphere.

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C. A. Balfour, M. J. Howarth, D. S. Jones, and T. Doyle

Abstract

An evolving coastal observatory has been hosted by the National Oceanography Centre at Liverpool, United Kingdom, for more than nine years. Within this observatory an instrumented ferry system has been developed and operated to provide near-surface scientific measurements of the Irish Sea. Passenger vessels such as ferries have the potential to be used as cost-effective platforms for gathering high-resolution regular measurements of the properties of near-surface water along their routes. They are able to operate on an almost year-round basis, and they usually have a high tolerance to adverse weather conditions. Examples of the application of instrumented ferry systems include environmental monitoring, the generation of long-term measurement time series, the provision of information for predictive model validation, and data for model assimilation purposes.

This paper discusses the development of an engineering system installed on board an Irish Sea passenger ferry. Particular attention is paid to explaining the engineering development required to achieve a robust, automated measuring system that is suitable for long-term continuous operation. The ferry, operating daily between Birkenhead and Belfast or Dublin, United Kingdom, was instrumented between December 2003 and January 2011 when the route was closed. Measurements were recorded at a nominal interval of 100 m and real-time data were transmitted every 15 min. The quality of the data was assessed. The spatial and temporal variability of the temperature and salinity fields are investigated as the ferry crosses a variety of shelf sea and coastal water column types.

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R. A. Morrow, Ian S. F. Jones, R. L. Smith, and P. J. Stabeno

Abstract

No abstract available.

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T. Koyama, T. Vukicevic, M. Sengupta, T. Vonder Haar, and A. S. Jones

Abstract

Information content analysis of the Geostationary Operational Environmental Satellite (GOES) sounder observations in the infrared was conducted for use in satellite data assimilation. Information content is defined as a first-order response of the top-of-atmosphere brightness temperature to perturbations of simulated temperature and humidity profiles, obtained from a cloud-resolving model, both in the presence and absence of clouds. Sensitivity to the perturbations was numerically evaluated using an observational operator for visible and infrared radiative transfer developed within a research satellite data assimilation system. The vertical distribution of the sensitivities was analyzed as a function of cloud optical thickness covering the range from a cloud-free scene to an optically thick cloud. The clear-sky sensitivities to temperature and humidity perturbations for each channel are representative of the corresponding channel weighting functions for a clear-sky case. For optically thin–moderate ice clouds, the vertical distributions of the sensitivities resemble clear-sky results, indicating that the use of infrared sounding observations in data assimilation can potentially improve temperature and humidity profiles below those clouds. This result is significant, as GOES infrared sounder data have until now only been used in cloud-cleared scenes. It is expected that the use of sounder data in data assimilation, even in the presence of optically thin to moderate high clouds, will help reduce errors in temperature and water vapor mixing ratio profiles below the clouds.

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Simon T. K. Lang, Sarah C. Jones, Martin Leutbecher, Melinda S. Peng, and Carolyn A. Reynolds

Abstract

The sensitivity of singular vectors (SVs) associated with Hurricane Helene (2006) to resolution and diabatic processes is investigated. Furthermore, the dynamics of their growth are analyzed. The SVs are calculated using the tangent linear and adjoint model of the integrated forecasting system (IFS) of the European Centre for Medium-Range Weather Forecasts with a spatial resolution up to TL255 (~80 km) and 48-h optimization time. The TL255 moist (diabatic) SVs possess a three-dimensional spiral structure with significant horizontal and vertical upshear tilt within the tropical cyclone (TC). Also, their amplitude is larger than that of dry and lower-resolution SVs closer to the center of Helene. Both higher resolution and diabatic processes result in stronger growth being associated with the TC compared to other flow features. The growth of the SVs in the vicinity of Helene is associated with baroclinic and barotropic mechanisms. The combined effect of higher resolution and diabatic processes leads to significant differences of the SV structure and growth dynamics within the core and in the vicinity of the TC. If used to initialize ensemble forecasts with the IFS, the higher-resolution moist SVs cause larger spread of the wind speed, track, and intensity of Helene than their lower-resolution or dry counterparts. They affect the outflow of the TC more strongly, resulting in a larger downstream impact during recurvature. Increasing the resolution or including diabatic effects degrades the linearity of the SVs. While the impact of diabatic effects on the linearity is small at low resolution, it becomes large at high resolution.

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