• Anthes, R. A., 1974: Data assimilation and initialization of hurricane prediction models. J. Atmos. Sci.,31, 702–719.

  • ——, and T. T. Warner, 1978: Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev.,106, 270–286.

  • ——, E.-Y. Hsie, and Y. H. Kuo, 1987: Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4). NCAR Tech. Note NCAR/TN-282+STR, 66 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80307.].

  • Bengtsson, L., 1975: Four-dimensional data assimilation of meteorological observations. GARP Publ. Series 15, WMO/ICSU, Geneva, Switzerland, 76 pp. [Available from World Meteorological Organization, Case Postale 5, CH-11 Geneva, Switzerland.].

  • ——, and J. Shukla, 1988: Integration of space and in situ observations to study global climate change. Bull. Amer. Meteor. Soc.,69, 1130–1143.

  • Blackadar, A. K., 1976: Modeling the nocturnal boundary layer. Preprints, Third Symp. on Atmospheric Turbulence and Air Quality, Raleigh, NC, Amer. Meteor. Soc., 46–49.

  • Bleck, R., and S. G. Benjamin, 1993: Regional weather prediction with a model combining a terrain-following and isentropic coordinates. Part I: Model description. Mon. Wea. Rev.,121, 1770–1775.

  • Bougeault, P., 1983: A non-reflective upper boundary condition for limited height hydrostatic models. Mon. Wea. Rev.,111, 428–445.

  • Bourke, W., R. Seaman, and K. Puri, 1985: Data assimilation. Advances in Geophysics, Vol. 28B, Academic Press, 124–149.

  • Browning, K. A., 1994: Survey of perceived priority issues in the parameterization of cloud-related processes in GCMs. Quart. J. Roy. Meteor. Soc.,120, 483–487.

  • Charney, J., M. Halem, and R. Jastrow, 1969: Use of incomplete historical data to infer the present state of the atmosphere. J. Atmos. Sci.,26, 1160–1163.

  • Chen, C., and W. R. Cotton, 1983: A one-dimensional simulation of the stratocumulus capped mixed layer. Bound.-Layer Meteor.,25, 289–321.

  • Dabberdt, W. F., and R. M. Hardesty, 1990: Summary of the Symposium on Lower Tropospheric Wind Profiling: Needs and Technology—31 May–3 June 1988, Boulder, Colorado. Bull. Amer. Meteor. Soc.,71, 665–671.

  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci.,46, 3077–3107.

  • ——, 1993: A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev.,121, 1493–1513.

  • Ghil, M., and P. Malanotte-Rizzoli, 1991: Data assimilation in meteorology and oceanography. Advances in Geophysics, Vol. 33, Academic Press, 141–266.

  • Grabowski, W. W., X. Wu, and M. W. Moncrieff, 1996: Cloud-resolving modeling of tropical cloud systems during Phase III of GATE. Part I: Two-dimensional experiments. J. Atmos. Sci.,53, 3689–3709.

  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev.,121, 764–787.

  • ——, J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model. NCAR Tech. Note NCAR/TN-398+IA, 107 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80307.].

  • Hack, J. J., 1994: Parameterization of moist convection in the National Center for Atmospheric Research community climate model (CCM2). J. Geophys. Res.,99, 5551–5568.

  • Harm, D. E., S. Raman, and R. V. Madala, 1992: An examination of four-dimensional data-assimilation techniques for numerical weather prediction. Bull. Amer. Meteor. Soc.,73, 425–440.

  • Hoke, J. E., and R. A. Anthes, 1976: The initialization of numerical models by a dynamic initialization technique. Mon. Wea. Rev.,104, 1551–1556.

  • Hollingsworth, A., 1986: Objective analysis for numerical weather prediction. Short and medium-range weather prediction. Proc. WMO/IUGG NWP Symp., Tokyo, Japan, World Meteorological Organization, 11–59.

  • Horst, T. W., and J. C. Weil, 1992: Footprint estimation for scalar flux measurements in the atmospheric surface layer. Bound.-Layer Meteor.,59, 279–296.

  • Hsie, E.-Y., 1984: Simulations of frontogenesis in a moist atmosphere using alternative parameterizations of condensation and precipitation. J. Atmos. Sci.,41, 2701–2716.

  • Kalnay, E., and R. Jenne, 1991: Summary of the NMC/NCAR reanalysis workshop of April 1991. Bull. Amer. Meteor. Soc.,72, 1897–1904.

  • Klemp, J. B., and D. R. Durran, 1983: An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon. Wea. Rev.,111, 430–444.

  • Kuo, Y.-H., and Y.-R. Guo, 1989: Dynamic initialization using observations from a hypothetical network of profilers: Impact on short range numerical weather prediction. Mon. Wea. Rev.,117, 1975–1998.

  • ——, and ——, 1992: Meso-beta-scale data assimilation of the Winter Icing and Storms Program/Atmospheric Radiation Measurement Program 91 Intensive Observing Period Case on 6 March 1991. Second Atmospheric Radiation Measurement (ARM) Science Team Meeting, Denver, CO, Department of Energy, 70–72.

  • ——, ——, and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale numerical model. Mon. Wea. Rev.,121, 1215–1238.

  • Lin, X., and R. H. Johnson, 1996: Kinematic and thermodynamic characteristics of the flow over the western Pacific warm pool during TOGA COARE. J. Atmos. Sci.,53, 695–715.

  • Lorenc, A., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc.,112, 1177–1194.

  • Louis, J. F., 1993: Single-column data assimilation for the Atmospheric Radiation Measurement (ARM) Program. Proc. 3rd Atmospheric Radiation Measurement (ARM) Science Team Meeting, Norman, OK, Department of Energy, Environmental Sciences Division, 127–133.

  • Mace, G. G., and T. P. Ackerman, 1996: Assessment of error in synoptic-scale diagnostics derived from wind profiler and radiosonde network data. Mon. Wea. Rev.,124, 1521–1534.

  • McPherson, R. D., 1975: Progress, problems, and prospects in meteorological data assimilation. Bull. Amer. Meteor. Soc.,56, 1154–1166.

  • Nuss, W. A., and D. W. Titley, 1994: Use of multiquadric interpolation for meteorological objective analysis. Mon. Wea. Rev.,122, 1611–1631.

  • Ooyama, K. V., 1987: Scale-controlled objective analysis. Mon. Wea. Rev.,115, 2479–2506.

  • Parsons, D. B., and Coauthors, 1994: The Integrated Sounding System: Description and preliminary observations from TOGA COARE. Bull. Amer. Meteor. Soc.,75, 553–567.

  • Paukkunen, A., 1995: Sensor heating to enhance reliability of radiosonde humidity measurement. Preprints, Ninth Symp. on Meteorological Observations and Instrumentation, Charlotte, NC, Amer. Meteor. Soc., 65–69.

  • Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartman, 1989: Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science,246, 57–63.

  • Randall, D. A., K.-M. Zu, R. C. J. Somerville, and S. Iacobellis, 1996: Single-column models and cloud ensemble models as links between observations and climate models. J. Climate,9, 1683–1697.

  • Rasmussen, R. M., and Coauthors, 1992: Winter Icing and Storms Project. Bull. Amer. Meteor. Soc.,73, 951–974.

  • ——, B. C. Bernstein, M. Murakami, G. Stossmeister, J. Reisner, and B. Stankov, 1995: The 1990 Valentine’s Day Arctic weak front: Mesoscale and microscale structure and evolution of a Colorado Front Range shallow upslope cloud. J. Appl. Meteor.,34, 1481–1511.

  • Rutledge, S., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones, VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci.,40, 1185–1206.

  • Schubert, S. D., R. B. Rood, and J. Pfaendtner, 1993: An assimilated dataset for earth science applications. Bull. Amer. Meteor. Soc.,74, 2331–2342.

  • Slingo, J. M., 1987: The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc.,113, 899–927.

  • Smith, T. L., and S. G. Benjamin, 1993: Impact of network wind profiler measurements on a 3-h data assimilation system. Bull. Amer. Meteor. Soc.,74, 801–807.

  • Stauffer, D. R., and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limited area mesoscale model. Part I: Experiments with synoptic data. Mon. Wea. Rev.,118, 1250–1277.

  • ——, and ——, 1994: Multiscale four-dimensional data assimilation. J. Appl. Meteor.,33, 416–434.

  • Stokes, G. M., and S. E. Schwartz, 1994: The Atmospheric Radiation Measurement (ARM) Project: Programmatic background and design of the cloud and atmospheric radiation testbed. Bull. Amer. Meteor. Soc.,75, 1201–1221.

  • Sundqvist, H., 1988: Parameterization of condensation and associated clouds in models for weather prediction and general circulation simulation. Physically-based Modelling and Simulation of Climate and Climate Change, M. E. Schlesinger, Ed., Kluwer Academic, 433–461.

  • Tiedtke, M., 1988: Parameterization of cumulus convection in large-scale models. Physically-based Modelling and Simulation of Climate and Climate Change, M. E. Schlesinger, Ed., Kluwer Academic, 375–431.

  • Uccellini, L. W., P. J. Kocin, and J. M. Sienkiewicz, 1994: Advances in forecasting extratropical cyclogenesis at the National Meteorological Center. The Life Cycles of Extratropical Cyclones, An International Symposium, Vol. I, Bergen, Norway, University of Bergen, 259–274.

  • U.S. Department of Commerce, 1994: Wind profiler assessment report and recommendations for future use. National Weather Service and the Office of Oceanic and Atmospheric Research, Silver Spring, MD, 141 pp.

  • Warner, T. T., Y.-H. Kuo, J. D. Doyle, J. Dudhia, D. R. Stauffer, and N. L. Seaman, 1992: Nonhydrostatic mesobeta-scale, real-data simulations with the Penn State University/National Center for Atmospheric Research Mesoscale Model, 1992. Meteor. Atmos. Phys.,49, 209–227.

  • Zhang, D.-L., and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor.,21, 1594–1609.

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Observing System Simulation Experiments and Objective Analysis Tests in Support of the Goals of the Atmospheric Radiation Measurement Program

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  • 1 National Center for Atmospheric Research,* Boulder, Colorado
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Abstract

Time continuous data assimilation or four-dimensional data assimilation (FDDA) is a collection of techniques where observations are ingested into a numerical model during the simulation in order to produce a physically balanced estimate of the true state of the atmosphere. Application of FDDA to the mesoalpha and subalpha scales is relatively new. One of many strategies for undertaking FDDA on the mesoscale is to employ Newtonian relaxation on increasingly finer horizontal grids. Encouraging results were found using this technique by Kuo et al. on a 40-km grid and by Stauffer and Seaman in a nested model with a 10-km inner grid. In these studies, the model is nudged toward the observations through adding an extra term(s) based on the difference between observations and the model predictions to the model’s prognostic equation(s). Since the model must retain a balance, this adjustment is spread over relatively large spatial and long temporal scales, and the nudging term is also multiplied by a coefficient that keeps the adjustment relatively small. Despite the positive findings of past studies, a number of questions arise in the application of this technique to fine grids. One area yet to be tested is how nudging will behave on fine grids under conditions with sharp horizontal and temporal gradients. Little improvement or even degradation of the model by the nudging might be expected as the timescale of nudging is relatively slow compared to the rapid evolution of the atmosphere, and spreading the observations out in time and space may not be representative of the actual atmospheric conditions. Other questions include 1) how the behavior of nudging at these scales and in active convection depends on boundary conditions, network density, and areal extent; 2) how the results depend on variations in the nudging coefficients; and 3) how nudging compares to simple objective analysis of the observations. In this study, Newtonian relaxation is used in a moist, full physics, nonhydrostatic mesoscale model to conduct simulations with horizontal resolutions as fine as 5 km in environments with deep convection and in mountainous terrain. Observing system simulation experiments were designed to address the previously mentioned questions. The authors show that nudging on these scales and in these conditions tends not to produce any large degradations but instead leads to improvements in the simulations even with a small number of observing sites. In applying nudging to a limited mesoscale area, the authors found that the results were more favorable if the nudging was undertaken over larger regions, which supports the nested approach used by Stauffer and Seaman. Some negative aspects of nudging were also uncovered with locally high rms errors due to data representativity problems and predictability issues. The accuracy of objective analysis was also explored and discussed in the context of the Atmospheric Radiation Measurement (ARM) Program. In agreement with Mace and Ackerman, the errors associated with objective analysis can be too large for the goals of ARM. However, the authors also found that a method proposed by Mace and Ackerman to detect time periods where significant errors exist in the objective analysis was not valid for this case. Based on this work, the authors propose that for a modest network of observing sites FDDA has a number of advantages over objective analysis.

Corresponding author address: Dr. David Parsons, Centre National de Rescherches Meteorologiques, Groupe Meteorologie de Moyenne Echelle, 42 Avenue Gustave Coriolis, 31057 Toulouse Cedex, France.

Email: dave.parsons@meteo.fr

Abstract

Time continuous data assimilation or four-dimensional data assimilation (FDDA) is a collection of techniques where observations are ingested into a numerical model during the simulation in order to produce a physically balanced estimate of the true state of the atmosphere. Application of FDDA to the mesoalpha and subalpha scales is relatively new. One of many strategies for undertaking FDDA on the mesoscale is to employ Newtonian relaxation on increasingly finer horizontal grids. Encouraging results were found using this technique by Kuo et al. on a 40-km grid and by Stauffer and Seaman in a nested model with a 10-km inner grid. In these studies, the model is nudged toward the observations through adding an extra term(s) based on the difference between observations and the model predictions to the model’s prognostic equation(s). Since the model must retain a balance, this adjustment is spread over relatively large spatial and long temporal scales, and the nudging term is also multiplied by a coefficient that keeps the adjustment relatively small. Despite the positive findings of past studies, a number of questions arise in the application of this technique to fine grids. One area yet to be tested is how nudging will behave on fine grids under conditions with sharp horizontal and temporal gradients. Little improvement or even degradation of the model by the nudging might be expected as the timescale of nudging is relatively slow compared to the rapid evolution of the atmosphere, and spreading the observations out in time and space may not be representative of the actual atmospheric conditions. Other questions include 1) how the behavior of nudging at these scales and in active convection depends on boundary conditions, network density, and areal extent; 2) how the results depend on variations in the nudging coefficients; and 3) how nudging compares to simple objective analysis of the observations. In this study, Newtonian relaxation is used in a moist, full physics, nonhydrostatic mesoscale model to conduct simulations with horizontal resolutions as fine as 5 km in environments with deep convection and in mountainous terrain. Observing system simulation experiments were designed to address the previously mentioned questions. The authors show that nudging on these scales and in these conditions tends not to produce any large degradations but instead leads to improvements in the simulations even with a small number of observing sites. In applying nudging to a limited mesoscale area, the authors found that the results were more favorable if the nudging was undertaken over larger regions, which supports the nested approach used by Stauffer and Seaman. Some negative aspects of nudging were also uncovered with locally high rms errors due to data representativity problems and predictability issues. The accuracy of objective analysis was also explored and discussed in the context of the Atmospheric Radiation Measurement (ARM) Program. In agreement with Mace and Ackerman, the errors associated with objective analysis can be too large for the goals of ARM. However, the authors also found that a method proposed by Mace and Ackerman to detect time periods where significant errors exist in the objective analysis was not valid for this case. Based on this work, the authors propose that for a modest network of observing sites FDDA has a number of advantages over objective analysis.

Corresponding author address: Dr. David Parsons, Centre National de Rescherches Meteorologiques, Groupe Meteorologie de Moyenne Echelle, 42 Avenue Gustave Coriolis, 31057 Toulouse Cedex, France.

Email: dave.parsons@meteo.fr

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