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Mitrescu C.,U.S. Navy | L'Ecuyer T.,Colorado State University | Haynes J.,Monash University | Miller S.,Cooperative Institute for Research in the Atmosphere | Turk J.,Jet Propulsion Laboratory
Journal of Applied Meteorology and Climatology | Year: 2010

Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat's millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle. © 2010 American Meteorological Society. Source


Grasso L.D.,Cooperative Institute for Research in the Atmosphere | Lindsey D.T.,National Oceanic and Atmospheric Administration
International Journal of Remote Sensing | Year: 2011

In preparation for the launch of the next generation of geostationary satellites, considerable effort has been placed on developing new products and algorithms for operational purposes. In addition to satellite-based products and algorithms, satellite imagery can be used to evaluate numerical weather prediction models. Important first steps have already been undertaken to produce synthetic satellite imagery from numerical model output. By comparing synthetic imagery with observed imagery, model performance can be evaluated with a relatively new metric. In this paper, synthetic Geostationary Operational Environmental Satellite (GOES)-12 imagery was used to improve the two-moment prediction of pristine ice in the RAMS (Regional Atmospheric Modeling System) mesoscale model. A thunderstorm event that occurred on 27 June 2005 over the central plains of the USA was chosen for study. Synthetic GOES-12 3.9 μm imagery of RAMS output was compared with observed GOES-12 3.9 μm imagery. A discrepancy between brightness temperatures of two anvils of thunderstorms led to an improvement in the prediction of pristine ice number concentrations. After the model was re-run, subsequent synthetic GOES-12 3.9 μm imagery of one anvil exhibited an improvement compared with observed imagery. Brightness temperatures of the second anvil became too warm, an issue that may be related to model-specified cloud condensation nuclei (CCN) concentrations. This example highlights the potential importance of using synthetic imagery to evaluate numerical weather prediction models. © 2011 Taylor & Francis. Source


Benjamin S.G.,National Oceanic and Atmospheric Administration | Jamison B.D.,National Oceanic and Atmospheric Administration | Jamison B.D.,Cooperative Institute for Research in the Atmosphere | Moninger W.R.,National Oceanic and Atmospheric Administration | And 4 more authors.
Monthly Weather Review | Year: 2010

An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived precipitable water, aviation routine weather report (METAR; surface), surface mesonet, and satellite-based atmospheric motion vectors. A series of observation sensitivity experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3-12h) wind, temperature, and relative humidity forecasts at different vertical levels and near the surface. These experiments were conducted for two 10-day periods, one in November-December 2006 and one in August 2007. These experiments show positive short-range forecast impacts from most of the contributors to the heterogeneous observing system over the RUC domain. In particular, aircraft observations had the largest overall impact for forecasts initialized 3-6h before 0000 or 1200 UTC, considered over the full depth (1000-100 hPa), followed by radiosonde observations, even though the latter are available only every 12 h. Profiler data (including at a hypothetical 8-km depth), GPS-precipitable water estimates, and surface observations also led to significant improvements in short-range forecast skill. © 2010 American Meteorological Society. Source


Noh Y.-J.,Colorado State University | Seaman .C.J.,Colorado State University | Vonder Haar .T.H.,Cooperative Institute for Research in the Atmosphere | Vonder Haar .T.H.,Colorado State University | Liu G.,Florida State University
Journal of Applied Meteorology and Climatology | Year: 2013

The vertical distribution of liquid and ice water content and their partitioning is studied using 34 cases of in situ measured microphysical properties in midlatitude mixed-phase clouds, with liquid water path ranging from near zero to ~248 g m-2, total water path ranging from near zero to ~562 g m-2, and cloud-top temperature ranging from -2° to -38°C. The 34 profiles were further divided into three cloud types depending on their vertical extents and altitudes. It is found that both the vertical distribution of liquid water within a cloud and the liquid water fraction (of total condensed water) as a function of temperature or relative position in a cloud layer are cloud-type dependent. In particular, it isfound that the partitioning between liquid and ice water for midlevel shallow clouds is relatively independent on the vertical position within the cloud while it clearly depends on cloud mean temperature. For synoptic snow clouds, however, liquid water fraction increases with the decrease of altitude within the cloud. While the liquid water fraction in synoptic clouds also decreases with lowering temperature, its magnitude is only about 50% near 0°C. 1. © 2013 American Meteorological Society. Source


Val Martin M.,Colorado State University | Val Martin M.,University of Sheffield | Heald C.L.,Massachusetts Institute of Technology | Lamarque J.-F.,U.S. National Center for Atmospheric Research | And 4 more authors.
Atmospheric Chemistry and Physics | Year: 2015

We use a global coupled chemistry-climate-land model (CESM) to assess the integrated effect of climate, emissions and land use changes on annual surface O3 and PM2.5 in the United States with a focus on national parks (NPs) and wilderness areas, using the RCP4.5 and RCP8.5 projections. We show that, when stringent domestic emission controls are applied, air quality is predicted to improve across the US, except surface O3 over the western and central US under RCP8.5 conditions, where rising background ozone counteracts domestic emission reductions. Under the RCP4.5 scenario, surface O3 is substantially reduced (about 5 ppb), with daily maximum 8 h averages below the primary US Environmental Protection Agency (EPA) National Ambient Air Quality Standards (NAAQS) of 75 ppb (and even 65 ppb) in all the NPs. PM2.5 is significantly reduced in both scenarios (4 μg m-3; ~50%), with levels below the annual US EPA NAAQS of 12 μg m-3 across all the NPs; visibility is also improved (10-15 dv; >75 km in visibility range), although some western US parks with Class I status (40-74 % of total sites in the US) are still above the 2050 planned target level to reach the goal of natural visibility conditions by 2064. We estimate that climate-driven increases in fire activity may dominate summertime PM2.5 over the western US, potentially offsetting the large PM2.5 reductions from domestic emission controls, and keeping visibility at present-day levels in many parks. Our study indicates that anthropogenic emission patterns will be important for air quality in 2050. However, climate and land use changes alone may lead to a substantial increase in surface O3 (2-3 ppb) with important consequences for O3 air quality and ecosystem degradation at the US NPs. Our study illustrates the need to consider the effects of changes in climate, vegetation, and fires in future air quality management and planning and emission policy making. © Author(s) 2015. Source

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