Techniques and resources for storm-scale numerical weather prediction

The topics discussed include the following: multiscale application of the 5th-generation PSU/NCAR mesoscale model, the coupling of nonhydrostatic atmospheric and hydrostatic ocean models for air-sea interaction studies; a numerical simulation of cloud formation over complex topography; adaptive grid simulations of convection; an unstructured grid, nonhydrostatic meso/cloud scale model; efficient mesoscale modeling for multiple scales using variable resolution; initialization of cloud-scale models with Doppler radar data; and making effective use of future computing architectures, networks, and visualization software

Coupled land surface/hydrologic/atmospheric models

The topics covered include the following: prototype land cover characteristics data base for the conterminous United States; surface evapotranspiration effects on cumulus convection and implications for mesoscale models; the use of complex treatment of surface hydrology and thermodynamics within a mesoscale model and some related issues; initialization of soil-water content for regional-scale atmospheric prediction models; impact of surface properties on dryline and MCS evolution; a numerical simulation of heavy precipitation over the complex topography of California; representing mesoscale fluxes induced by landscape discontinuities in global climate models; emphasizing the role of subgrid-scale heterogeneity in surface-air interaction; and problems with modeling and measuring biosphere-atmosphere exchanges of energy, water, and carbon on large scales

Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models.

A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evaluation of AQM performance based on MM performance is a major advance over traditional evaluation methods. First, meteorological cluster analysis is used to assign the days of a historical measurement period among a small number of weather patterns having distinct air quality characteristics. The clustering algorithm groups days sharing similar empirical orthogonal function (EOF) representations of their measurements. In this study, EOF analysis is used to extract space–time patterns in the surface wind field reflecting both synoptic and mesoscale influences. Second, simulated wind fields are classified among the determined weather patterns using the measurement-derived EOFs. For a given period, the level of agreement between the observation-based clustering labels and the simulation-based classification labels is used to assess the validity of the simulation results. Mismatches occurring between the two sets of labels for a given period imply inaccurately simulated conditions. Moreover, the specific nature of a mismatch can help to diagnose the downstream effects of improperly simulated meteorological fields on AQM performance. This pattern-based model evaluation technique was applied to extended simulations of fine particulate matter (PM2.5) covering two winter seasons for the San Francisco Bay Area of California. Copyright 2010 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or [email protected], Faculty ofEarth and Ocean Sciences, Department ofReviewedFacult

A study of cumulus arameterization in a global circulation model /

Objectives of this research are: (1) to critically evaluate the Kuo and Arakawa Schubert (A-S) cumulus parameterization schemes for numerical weather prediction; and (2) to improve these parameterization schemes to improve precipitation forecasts on the global scale. Major improvements to the Kuo scheme include prediction of cloud top height and incorporation of the effect of entrainment on cloud temperature and mixing ratio profiles. This enables the Kuo scheme to parameterize shallow to medium clouds as well as deep clouds. Tests of the Kuo scheme using a semi-prognostic approach and a cloud cluster model indicate that the improved version verifies better with observation during weaker convective periods. Experimental predictions were made and the results clearly demonstrated the ability of the AFGL model to predict large-scale stratiform precipitation. With incorporation of the modified Kuo scheme, the area of convective precipitation can also be well predicted. However, the predicted convective precipitation area is generally broader and the amount of rainfall smaller than observed which may be attributed to the resolution of the AFGL model in that it cannot resolve the narrow band of the cold front, ther major mesoscale rain-producing system. Implementation of the A-S scheme in the AFGL model also produced a reasonable distribution of convective precipitation but the precipitation area is more concentrated and sometimes is produced in an observed clear area. This result may be caused by the current implementation of the A-S scheme in the AFGL model, in which the cloud base is assumed to be 500 m above the ground surface.Research supported by the Air Force Geophysics Laboratory, United States Air Force, Hanscom AFB, Massachusetts.Performing organization: University of Illinois, Department of Atmospheric Sciences, Urbana, Illinois."Final Report: 8 April 1982-8 June 1985.""June 1985."Includes bibliograpic references (pages 111-113)Objectives of this research are: (1) to critically evaluate the Kuo and Arakawa Schubert (A-S) cumulus parameterization schemes for numerical weather prediction; and (2) to improve these parameterization schemes to improve precipitation forecasts on the global scale. Major improvements to the Kuo scheme include prediction of cloud top height and incorporation of the effect of entrainment on cloud temperature and mixing ratio profiles. This enables the Kuo scheme to parameterize shallow to medium clouds as well as deep clouds. Tests of the Kuo scheme using a semi-prognostic approach and a cloud cluster model indicate that the improved version verifies better with observation during weaker convective periods. Experimental predictions were made and the results clearly demonstrated the ability of the AFGL model to predict large-scale stratiform precipitation. With incorporation of the modified Kuo scheme, the area of convective precipitation can also be well predicted. However, the predicted convective precipitation area is generally broader and the amount of rainfall smaller than observed which may be attributed to the resolution of the AFGL model in that it cannot resolve the narrow band of the cold front, ther major mesoscale rain-producing system. Implementation of the A-S scheme in the AFGL model also produced a reasonable distribution of convective precipitation but the precipitation area is more concentrated and sometimes is produced in an observed clear area. This result may be caused by the current implementation of the A-S scheme in the AFGL model, in which the cloud base is assumed to be 500 m above the ground surface.Mode of access: Internet