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  • 1
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    Online Resource
    Intercomparison and evaluation of cumulus parametrizations under summertime midlatitude continental conditions
    Wiley ; 2002
    In:  Quarterly Journal of the Royal Meteorological Society Vol. 128, No. 582 ( 2002-04), p. 1095-1135
    Details
    In: Quarterly Journal of the Royal Meteorological Society, Wiley, Vol. 128, No. 582 ( 2002-04), p. 1095-1135
    Abstract: This study reports the Single‐Column Model (SCM) part of the Atmospheric Radiation Measurement (ARM)/the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) joint SCM and Cloud‐Resolving Model (CRM) Case 3 intercomparison study, with a focus on evaluation of cumulus parametrizations used in SCMs. Fifteen SCMs are evaluated under summertime midlatitude continental conditions using data collected at the ARM Southern Great Plains site during the summer 1997 Intensive Observing Period. Results from ten CRMs are also used to diagnose problems in the SCMs. It is shown that most SCMs can generally capture well the convective events that were well‐developed within the SCM domain, while most of them have difficulties in simulating the occurrence of those convective events that only occurred within a small part of the domain. All models significantly underestimate the surface stratiform precipitation. A third of them produce large errors in surface precipitation and thermodynamic structures. Deficiencies in convective triggering mechanisms are thought to be one of the major reasons. Using a triggering mechanism that is based on the vertical integral of parcel buoyant energy without additional appropriate constraints results in overactive convection, which in turn leads to large systematic warm/dry biases in the troposphere. It is also shown that a non‐penetrative convection scheme can underestimate the depth of instability for midlatitude convection, which leads to large systematic cold/moist biases in the troposphere. SCMs agree well quantitatively with CRMs in the updraught mass fluxes, while most models significantly underestimate the downdraught mass fluxes. Neglect of mesoscale updraught and downdraught mass fluxes in the SCMs contributes considerably to the discrepancies between the SCMs and the CRMs. In addition, uncertainties in the diagnosed mass fluxes in the CRMs and deficiencies with cumulus parametrizations are not negligible. Similar results are obtained in the sensitivity tests when different forcing approaches are used. Finally, sensitivity tests from an SCM indicate that its simulations can be greatly improved when its triggering mechanism and closure assumption are improved. © Royal Meteorological Society, 2002. J. C. Petch's contribution is Crown copyright.
    Type of Medium: Online Resource
    ISSN: 0035-9009 , 1477-870X
    URL: Article
    DOI: 10.1256/003590002320373229
    RVK:
    UA 1000
    RVK:
    UA 7650
    Language: English
    Publisher: Wiley
    Publication Date: 2002
    detail.hit.zdb_id: 3142-2
    detail.hit.zdb_id: 2089168-4
    SSG: 14
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  • 2
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    Evaluating Parameterizations in General Circulation Models: Climate Simulation Meets Weather Prediction
    American Meteorological Society ; 2004
    In:  Bulletin of the American Meteorological Society Vol. 85, No. 12 ( 2004-12-01), p. 1903-1916
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 85, No. 12 ( 2004-12-01), p. 1903-1916
    Abstract: To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework. To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-85-12-1903
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2004
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 3
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    Modeling springtime shallow frontal clouds with cloud‐resolving and single‐column models
    American Geophysical Union (AGU) ; 2005
    In:  Journal of Geophysical Research: Atmospheres Vol. 110, No. D15 ( 2005-08-16)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 110, No. D15 ( 2005-08-16)
    Abstract: This modeling study compares the performance of eight single‐column models (SCMs) and four cloud‐resolving models (CRMs) in simulating shallow frontal cloud systems observed during a short period of the March 2000 Atmospheric Radiation Measurement (ARM) intensive operational period. Except for the passage of a cold front at the beginning of this period, frontal cloud systems are under the influence of an upper tropospheric ridge and are driven by a persistent frontogenesis over the Southern Great Plains and moisture transport from the northwestern part of the Gulf of Mexico. This study emphasizes quantitative comparisons among the model simulations and with the ARM data, focusing on a 27‐hour period when only shallow frontal clouds were observed. All CRMs and SCMs simulate clouds in the observed shallow cloud layer. Most SCMs also produce clouds in the middle and upper troposphere, while none of the CRMs produce any clouds there. One possible cause for this is the decoupling between cloud condensate and cloud fraction in nearly all SCM parameterizations. Another possible cause is the weak upper tropospheric subsidence that has been averaged over both descending and ascending regions. Significantly different cloud amounts and cloud microphysical properties are found in the model simulations. All CRMs and most SCMs underestimate shallow clouds in the lowest 125 hPa near the surface, but most SCMs overestimate the cloud amount above this layer. These results are related to the detailed formulations of cloud microphysical processes and fractional cloud parameterizations in the SCMs, and possibly to the dynamical framework and two‐dimensional configuration of the CRMs. Although two of the CRMs with anelastic dynamical frameworks simulate the shallow frontal clouds much better than the SCMs, the CRMs do not necessarily perform much better than the SCMs for the entire period when deep and shallow frontal clouds are present.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2004JD005153
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2005
    detail.hit.zdb_id: 161666-3
    SSG: 16,13
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  • 4
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    Simulations of midlatitude frontal clouds by single‐column and cloud‐resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period
    American Geophysical Union (AGU) ; 2005
    In:  Journal of Geophysical Research: Atmospheres Vol. 110, No. D15 ( 2005-08-16)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 110, No. D15 ( 2005-08-16)
    Abstract: This study quantitatively evaluates the overall performance of nine single‐column models (SCMs) and four cloud‐resolving models (CRMs) in simulating a strong midlatitude frontal cloud system taken from the spring 2000 Cloud Intensive Observational Period at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The evaluation data are an analysis product of constrained variational analysis of the ARM observations and the cloud data collected from the ARM ground active remote sensors (i.e., cloud radar, lidar, and laser ceilometers) and satellite retrievals. Both the selected SCMs and CRMs can typically capture the bulk characteristics of the frontal system and the frontal precipitation. However, there are significant differences in detailed structures of the frontal clouds. Both CRMs and SCMs overestimate high thin cirrus clouds before the main frontal passage. During the passage of a front with strong upward motion, CRMs underestimate middle and low clouds while SCMs overestimate clouds at the levels above 765 hPa. All CRMs and some SCMs also underestimated the middle clouds after the frontal passage. There are also large differences in the model simulations of cloud condensates owing to differences in parameterizations; however, the differences among intercompared models are smaller in the CRMs than the SCMs. In general, the CRM‐simulated cloud water and ice are comparable with observations, while most SCMs underestimated cloud water. SCMs show huge biases varying from large overestimates to equally large underestimates of cloud ice. Many of these model biases could be traced to the lack of subgrid‐scale dynamical structure in the applied forcing fields and the lack of organized mesoscale hydrometeor advections. Other potential reasons for these model errors are also discussed in the paper.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2004JD005119
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2005
    detail.hit.zdb_id: 161666-3
    SSG: 16,13
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  • 5
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    An intercomparison of cloud‐resolving models with the atmospheric radiation measurement summer 1997 intensive observation period data
    Wiley ; 2002
    In:  Quarterly Journal of the Royal Meteorological Society Vol. 128, No. 580 ( 2002-01), p. 593-624
    Details
    In: Quarterly Journal of the Royal Meteorological Society, Wiley, Vol. 128, No. 580 ( 2002-01), p. 593-624
    Abstract: This paper reports an intercomparison study of midlatitude continental cumulus convection simulated by eight two‐dimensional and two three‐dimensional cloud‐resolving models (CRMs), driven by observed large‐scale advective temperature and moisture tendencies, surface turbulent fluxes, and radiative‐heating profiles during three sub‐periods of the summer 1997 Intensive Observation Period of the US Department of Energy's Atmospheric Radiation Measurement (ARM) program. Each sub‐period includes two or three precipitation events of various intensities over a span of 4 or 5 days. The results can be summarized as follows. CRMs can reasonably simulate midlatitude continental summer convection observed at the ARM Cloud and Radiation Testbed site in terms of the intensity of convective activity, and the temperature and specific‐humidity evolution. Delayed occurrences of the initial precipitation events are a common feature for all three sub‐cases among the models. Cloud mass fluxes, condensate mixing ratios and hydrometeor fractions produced by all CRMs are similar. Some of the simulated cloud properties such as cloud liquid‐water path and hydrometeor fraction are rather similar to available observations. All CRMs produce large downdraught mass fluxes with magnitudes similar to those of updraughts, in contrast to CRM results for tropical convection. Some inter‐model differences in cloud properties are likely to be related to those in the parametrizations of microphysical processes. There is generally a good agreement between the CRMs and observations with CRMs being significantly better than single‐column models (SCMs), suggesting that current results are suitable for use in improving parametrizations in SCMs. However, improvements can still be made in the CRM simulations; these include the proper initialization of the CRMs and a more proper method of diagnosing cloud boundaries in model outputs for comparison with satellite and radar cloud observations. Copyright © 2002 Royal Meteorological Society
    Type of Medium: Online Resource
    ISSN: 0035-9009 , 1477-870X
    URL: Article
    DOI: 10.1256/003590002321042117
    RVK:
    UA 1000
    RVK:
    UA 7650
    Language: English
    Publisher: Wiley
    Publication Date: 2002
    detail.hit.zdb_id: 3142-2
    detail.hit.zdb_id: 2089168-4
    SSG: 14
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  • 6
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    Comparison of SCM and CSRM forcing data derived from the ECMWF model and from objective analysis at the ARM SGP site
    American Geophysical Union (AGU) ; 2003
    In:  Journal of Geophysical Research: Atmospheres Vol. 108, No. D16 ( 2003-08-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 108, No. D16 ( 2003-08-27)
    Abstract: The large‐scale forcing data diagnosed from the European Center for Medium Range Weather Forecast (ECMWF) model for driving Single‐Column Models (SCMs) and Cloud System Resolving Models (CSRMs) are compared with forcing data derived using the objective variational analysis constrained by observations collected at the Atmospheric Radiation Measurement program (ARM) Southern Great Plains (SGP) site. The comparison covers the following three different synoptic conditions: a strong precipitation period dominated by subgrid scale processes during the ARM summer 1997 Intensive Operational Period (IOP), a moderate precipitation period dominated by synoptic scale processes during the spring 2000 IOP, and a nonprecipitation period during the fall 2000 IOP. In the study we demonstrate that the differences between the two forcing data sets are considerably large during the strong convective precipitation period, while they are much less during the moderate and nonprecipitation periods. By analyzing the column‐integrated heat and moisture budgets we show that errors in the ECMWF‐model‐derived forcing are closely associated with errors in the model‐predicted surface precipitation, which largely reflect deficiencies of model parameterizations. In SCM tests we show that SCM simulations are sensitive to the prescribed large‐scale forcing data. The simulation errors are not well correlated between the SCM runs with the two different forcing data sets for all the three cases. Some important SCM simulated fields, such as surface precipitation, tend to follow the ECMWF model simulations rather than the observations when it is forced with the ECMWF forcing, especially for the summer case.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2003JD003541
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2003
    detail.hit.zdb_id: 161666-3
    SSG: 16,13
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  • 7
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    7th Drug hypersensitivity meeting: part two
    Wiley ; 2016
    In:  Clinical and Translational Allergy Vol. 6, No. S3 ( 2016-8)
    Details
    In: Clinical and Translational Allergy, Wiley, Vol. 6, No. S3 ( 2016-8)
    Type of Medium: Online Resource
    ISSN: 2045-7022
    URL: Article
    DOI: 10.1186/s13601-016-0122-y
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 2630865-4
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  • 8
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    A Microphysical Retrieval Scheme for Continental Low-Level Stratiform Clouds: Impacts of the Subadiabatic Character on Microphysical Properties and Radiation Budgets
    American Meteorological Society ; 2000
    In:  Monthly Weather Review Vol. 128, No. 7 ( 2000-07), p. 2511-2527
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 128, No. 7 ( 2000-07), p. 2511-2527
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/1520-0493(2000)128〈2511:AMRSFC〉2.0.CO;2
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2000
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 9
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    Developing large‐scale forcing data for single‐column and cloud‐resolving models from the Mixed‐Phase Arctic Cloud Experiment
    American Geophysical Union (AGU) ; 2006
    In:  Journal of Geophysical Research: Atmospheres Vol. 111, No. D19 ( 2006-10-16)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 111, No. D19 ( 2006-10-16)
    Abstract: This study represents an effort to develop Single‐Column Model (SCM) and Cloud‐Resolving Model large‐scale forcing data from a sounding array in the high latitudes. An objective variational analysis approach is used to process data collected from the Atmospheric Radiation Measurement Program (ARM) Mixed‐Phase Arctic Cloud Experiment (M‐PACE), which was conducted over the North Slope of Alaska in October 2004. In this method the observed surface and top of atmosphere measurements are used as constraints to adjust the sounding data from M‐PACE in order to conserve column‐integrated mass, heat, moisture, and momentum. Several important technical and scientific issues related to the data analysis are discussed. It is shown that the analyzed data reasonably describe the dynamic and thermodynamic features of the Arctic cloud systems observed during M‐PACE. Uncertainties in the analyzed forcing fields are roughly estimated by examining the sensitivity of those fields to uncertainties in the upper‐air data and surface constraints that are used in the analysis. Impacts of the uncertainties in the analyzed forcing data on SCM simulations are discussed. Results from the SCM tests indicate that the bulk features of the observed Arctic cloud systems can be captured qualitatively well using the forcing data derived in this study, and major model errors can be detected despite the uncertainties that exist in the forcing data as illustrated by the sensitivity tests. Finally, the possibility of using the European Center for Medium‐Range Weather Forecasts analysis data to derive the large‐scale forcing over the Arctic region is explored.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2005JD006950
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2006
    detail.hit.zdb_id: 161666-3
    SSG: 16,13
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  • 10
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    An assessment of ECMWF analyses and model forecasts over the North Slope of Alaska using observations from the ARM Mixed‐Phase Arctic Cloud Experiment
    American Geophysical Union (AGU) ; 2006
    In:  Journal of Geophysical Research: Atmospheres Vol. 111, No. D5 ( 2006-03-16)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 111, No. D5 ( 2006-03-16)
    Abstract: European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis and model forecast data are evaluated using observations collected during the Atmospheric Radiation Measurement (ARM) October 2004 Mixed‐Phase Arctic Cloud Experiment (M‐PACE) at its North Slope of Alaska (NSA) site. It is shown that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of the large‐scale systems that affected the NSA during M‐PACE. The model‐analyzed near‐surface horizontal winds, temperature, and relative humidity also agree well with the M‐PACE surface measurements. Given the well‐represented large‐scale fields, the model shows overall good skill in predicting various cloud types observed during M‐PACE; however, the physical properties of single‐layer boundary layer clouds are in substantial error. At these times, the model substantially underestimates the liquid water path in these clouds, with the concomitant result that the model largely underpredicts the downwelling longwave radiation at the surface and overpredicts the outgoing longwave radiation at the top of the atmosphere. The model also overestimates the net surface shortwave radiation, mainly because of the underestimation of the surface albedo. The problem in the surface albedo is primarily associated with errors in the surface snow prediction. Principally because of the underestimation of the surface downwelling longwave radiation at the times of single‐layer boundary layer clouds, the model shows a much larger energy loss (−20.9 W m −2 ) than the observation (−9.6 W m −2 ) at the surface during the M‐PACE period.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2005JD006509
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2006
    detail.hit.zdb_id: 161666-3
    SSG: 16,13
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