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Rolf Lueck and Daniel Huang

Abstract

A moored and autonomous instrument that measures velocity and temperature fluctuations in the inertial subrange using shear probes and FP07 thermistors has been deployed in a swift [O(1 m s−1)] tidal channel for eight days. The measured velocity signals are free from body vibrations for frequencies below 16 Hz in flows faster than 0.5 m s−1 and below 8 Hz for slower flows. At lower frequencies, fluctuations of torque on the instrument, due mainly to fluctuations of the ambient current, produce large pitching and rolling motions (≈4° peak) that can easily be reduced by minor mechanical changes. Vibrations at higher frequencies do not scale with flow speed and stem mainly from mechanical structures. The velocity spectrum is free from contamination by body motions between 0.2 and 20 cpm (the inertial subrange), and consistent estimates of the rate of dissipation of kinetic energy are obtained from the spectral levels of vertical and lateral velocity fluctuations within this range.

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Xianglei Huang, Xiuhong Chen, Daniel K. Zhou, and Xu Liu

Abstract

While current atmospheric general circulation models (GCMs) still treat the surface as a blackbody in their longwave radiation scheme, recent studies suggest the need for taking realistic surface spectral emissivity into account. There have been few measurements available for the surface emissivity in the far IR (<650 cm−1). Based on first-principle calculation, the authors compute the spectral emissivity over the entire longwave spectrum for a variety of surface types. MODIS-retrieved mid-IR surface emissivity at 0.05° × 0.05° spatial resolution is then regressed against the calculated spectral emissivity to determine the surface type for each grid. The derived spectral emissivity data are then spatially averaged onto 0.5° × 0.5° grids and spectrally integrated onto the bandwidths used by the RRTMG_LW—a longwave radiation scheme widely used in current climate and numerical weather models. The band-by-band surface emissivity dataset is then compared with retrieved surface spectral emissivities from Infrared Atmospheric Sounding Interferometer (IASI) measurements. The comparison shows favorable agreement between two datasets in all the bands covered by the IASI measurements. The authors further use the dataset in conjunction with ERA-Interim to evaluate its impact on the top-of-atmosphere radiation budget. Depending on the blackbody surface assumptions used in the original calculation, the globally averaged difference caused by the inclusion of realistic surface emissivity ranges from −1.2 to −1.5 W m−2 for clear-sky OLR and from −0.67 to −0.94 W m−2 for all-sky OLR. Moreover, the difference is not spatially uniform and has a distinct spatial pattern.

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Xianglei Huang, Xiuhong Chen, Mark Flanner, Ping Yang, Daniel Feldman, and Chaincy Kuo

Abstract

Surface longwave emissivity can be less than unity and vary significantly with frequency. However, most climate models still assume a blackbody surface in the longwave (LW) radiation scheme of their atmosphere models. This study incorporates realistic surface spectral emissivity into the atmospheric component of the Community Earth System Model (CESM), version 1.1.1, and evaluates its impact on simulated climate. By ensuring consistency of the broadband surface longwave flux across different components of the CESM, the top-of-the-atmosphere (TOA) energy balance in the modified model can be attained without retuning the model. Inclusion of surface spectral emissivity, however, leads to a decrease of net upward longwave flux at the surface and a comparable increase of latent heat flux. Global-mean surface temperature difference between the modified and standard CESM simulation is 0.20 K for the fully coupled run and 0.45 K for the slab-ocean run. Noticeable surface temperature differences between the modified and standard CESM simulations are seen over the Sahara Desert and polar regions. Accordingly, the climatological mean sea ice fraction in the modified CESM simulation can be less than that in the standard CESM simulation by as much as 0.1 in some regions. When spectral emissivities of sea ice and open ocean surfaces are considered, the broadband LW sea ice emissivity feedback is estimated to be −0.003 W m−2 K−1, assuming flat ice emissivity as sea ice emissivity, and 0.002 W m−2 K−1, assuming coarse snow emissivity as sea ice emissivity, which are two orders of magnitude smaller than the surface albedo feedback.

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Daniel K. Zhou, William L. Smith Sr., Xu Liu, Allen M. Larar, Stephen A. Mango, and Hung-Lung Huang

Abstract

A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error <1 km). National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the The Observing-System Research and Predictability Experiment (THORPEX) Atlantic Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing cloud physics lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project, and the follow-on NPOESS series of satellites.

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Yi Huang, Steven T. Siems, Michael J. Manton, Daniel Rosenfeld, Roger Marchand, Greg M. McFarquhar, and Alain Protat

Abstract

This study employs four years of spatiotemporally collocated A-Train satellite observations to investigate cloud and precipitation characteristics in relation to the underlying properties of the Southern Ocean (SO). Results show that liquid-phase cloud properties strongly correlate with the sea surface temperature (SST). In summer, ubiquitous supercooled liquid water (SLW) is observed over SSTs less than about 4°C. Cloud-top temperature (CTT) and effective radius of liquid-phase clouds generally decrease for colder SSTs, whereas the opposite trend is observed for cloud-top height, cloud optical thickness, and liquid water path. The deduced cloud depth is larger over the colder oceans. Notable differences are observed between “precipitating” and “nonprecipitating” clouds and between different ocean sectors. Using a novel joint SST–CTT histogram, two distinct liquid-phase cloud types are identified, where the retrieved particle size appears to increase with decreasing CTT over warmer water (SSTs >~7°C), while the opposite is true over colder water. A comparison with the Northern Hemisphere (NH) storm-track regions suggests that the ubiquitous SLW with markedly smaller droplet size is a unique feature for the cold SO (occurring where SSTs <~4°C), while the presence of this cloud type is much less frequent over the NH counterparts, where the SSTs are rarely colder than about 4°C at any time of the year. This study also suggests that precipitation, which has a profound influence on cloud properties, remains poorly observed over the SO with the current spaceborne sensors. Large uncertainties in precipitation properties are associated with the ubiquitous boundary layer clouds within the lowest kilometer of the atmosphere.

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Pius Lee, Jeffery McQueen, Ivanka Stajner, Jianping Huang, Li Pan, Daniel Tong, Hyuncheol Kim, Youhua Tang, Shobha Kondragunta, Mark Ruminski, Sarah Lu, Eric Rogers, Rick Saylor, Perry Shafran, Ho-Chun Huang, Jerry Gorline, Sikchya Upadhayay, and Richard Artz

Abstract

The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 μm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers.

There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow- and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseous-phase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U.S. Environmental Protection Agency’s (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005.

Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA’s AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 μg m−3 against a monthly average of 9.4 μg m−3 for the north-central United States.

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Jianping Huang, Jeffery McQueen, James Wilczak, Irina Djalalova, Ivanka Stajner, Perry Shafran, Dave Allured, Pius Lee, Li Pan, Daniel Tong, Ho-Chun Huang, Geoffrey DiMego, Sikchya Upadhayay, and Luca Delle Monache

Abstract

Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.

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Timothy R. Whitcomb, Daniel J. Arevalo, Jonathan R. Moskaitis, William A. Komaromi, James D. Doyle, Andrew Huang, Jay Wodka, Frederick Noon, Margaret M. May, Fred Williamson, and Patrick A. Reinecke

Abstract

Despite improvements in predicting the track and intensity of tropical cyclones (TCs), these storms with major societal and economic impacts continue to pose challenges for statically provisioned computational resources. The number of active storms varies from day to day, leading to regular bursts of irregular computational loads atop an already busy production schedule for weather prediction centers. The emergence of high-resolution ensemble TC prediction to quantify the uncertainty in track and intensity exacerbates this problem by requiring multiple forecasts run for each storm, each representing a possible outcome. With more than a decade of progress in the literature describing research and real-time numerical weather prediction in the cloud, we set out to evaluate if the commercial cloud environment could cope with the unique demands of TC ensemble forecasts. We describe a demonstration using a high-performance computing environment within the Microsoft Azure cloud to test dynamic resource provisioning to address time-varying resource challenges. We deployed existing operational models, implemented a combination of vendor-provided and open-source tools to orchestrate the cycling production workflows, and developed techniques for automatic error handling to keep production on schedule with minimal operator intervention. Despite challenges, our production pipeline from data ingest, forecast integration, graphics generation, and dissemination via social media was able to produce real-time forecasts of storm track and intensity with product latencies commensurate with existing operational forecasting systems.

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Nilton O. Rennó, Earle Williams, Daniel Rosenfeld, David G. Fischer, Jürgen Fischer, Tibor Kremic, Arun Agrawal, Meinrat O. Andreae, Rosina Bierbaum, Richard Blakeslee, Anko Boerner, Neil Bowles, Hugh Christian, Ann Cox, Jason Dunion, Akos Horvath, Xianglei Huang, Alexander Khain, Stefan Kinne, Maria C. Lemos, Joyce E. Penner, Ulrich Pöschl, Johannes Quaas, Elena Seran, Bjorn Stevens, Thomas Walati, and Thomas Wagner

The formation of cloud droplets on aerosol particles, technically known as the activation of cloud condensation nuclei (CCN), is the fundamental process driving the interactions of aerosols with clouds and precipitation. The Intergovernmental Panel on Climate Change (IPCC) and the Decadal Survey indicate that the uncertainty in how clouds adjust to aerosol perturbations dominates the uncertainty in the overall quantification of the radiative forcing attributable to human activities.

Measurements by current satellites allow the determination of crude profiles of cloud particle size, but not of the activated CCN that seed them. The Clouds, Hazards, and Aerosols Survey for Earth Researchers (CHASER) mission concept responds to the IPCC and Decadal Survey concerns, utilizing a new technique and high-heritage instruments to measure all the quantities necessary to produce the first global survey maps of activated CCN and the properties of the clouds associated with them. CHASER also determines the activated CCN concentration and cloud thermodynamic forcing simultaneously, allowing the effects of each to be distinguished.

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Ben P. Kirtman, Dughong Min, Johnna M. Infanti, James L. Kinter III, Daniel A. Paolino, Qin Zhang, Huug van den Dool, Suranjana Saha, Malaquias Pena Mendez, Emily Becker, Peitao Peng, Patrick Tripp, Jin Huang, David G. DeWitt, Michael K. Tippett, Anthony G. Barnston, Shuhua Li, Anthony Rosati, Siegfried D. Schubert, Michele Rienecker, Max Suarez, Zhao E. Li, Jelena Marshak, Young-Kwon Lim, Joseph Tribbia, Kathleen Pegion, William J. Merryfield, Bertrand Denis, and Eric F. Wood

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.

Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

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