Monthly Weather Review

Ullrich, Paul A.; Lauritzen, Peter H.; Jablonowski, Christiane

doi: 10.1175/2008MWR2817.1pmid: N/A

Land, ocean, and atmospheric models are often implemented on different spherical grids. As a conseqence coupling these model components requires state variables and fluxes to be regridded. For some variables, such as fluxes, it is paramount that the regridding algorithm is conservative (so that energy and water budget balances are maintained) and monotone (to prevent unphysical values). For global applications the cubed-sphere grids are gaining popularity in the atmospheric community whereas, for example, the land modeling groups are mostly using the regular latitude––longitude grid. Most existing regridding schemes fail to take advantage of geometrical symmetries between these grids and hence accuracy of the calculations can be lost. Hence, a new Geometrically Exact Conservative Remapping (GECoRe) scheme with a monotone option is proposed for remapping between regular latitude––longitude and gnomonic cubed-sphere grids. GECoRe is compared with existing remapping schemes published in the meteorological literature. It is concluded here that the geometrically exact scheme significantly improves the accuracy of the resulting remapping in idealized test cases.

Thatcher, Marcus; McGregor, John L.

doi: 10.1175/2008MWR2599.1pmid: N/A

This article examines dynamical downscaling with a scale-selective filter in the Conformal Cubic Atmospheric Model (CCAM). In this study, 1D and 2D scale-selective filters have been implemented using a convolution-based scheme, since a convolution can be readily evaluated in terms of CCAM’s native conformal cubic coordinates. The downscaling accuracy of 1D and 2D scale-selective filters is evaluated after downscaling NCEP Global Forecast System analyses for 2006 from 200-km resolution to 60-km resolution over Australia. The 1D scale-selective filter scheme was found to downscale the analyses with similar accuracy to a 2D filter but required significantly fewer computations. The 1D and 2D scale-selective filters were also found to downscale the analyses more accurately than a far-field nudging scheme (i.e., analogous to a boundary-value nudging approach). It is concluded that when the model is required to reproduce the host model behavior above a specified length scale then the use of an appropriately designed 1D scale-selective filter can be a computationally efficient approach to dynamical downscaling for models having a cube-based geometry.

Bryan, George H.; Rotunno, Richard

doi: 10.1175/2008MWR2709.1pmid: N/A

An axisymmetric numerical model is used to evaluate the maximum possible intensity of tropical cyclones. As compared with traditionally formulated nonhydrostatic models, this new model has improved mass and energy conservation in saturated conditions. In comparison with the axisymmetric model developed by Rotunno and Emanuel, the new model produces weaker cyclones (by ∼10%, in terms of maximum azimuthal velocity); the difference is attributable to several approximations in the Rotunno–Emanuel model. Then, using a single specification for initial conditions (with a sea surface temperature of 26°C), the authors conduct model sensitivity tests to determine the sensitivity of maximum azimuthal velocity ( υ max ) to uncertain aspects of the modeling system. For fixed mixing lengths in the turbulence parameterization, a converged value of υ max is achieved for radial grid spacing of order 1 km and vertical grid spacing of order 250 m. The fall velocity of condensate ( V t ) changes υ max by up to 60%, and the largest υ max occurs for pseudoadiabatic thermodynamics (i.e., for V t > 10 m s −1 ). The sensitivity of υ max to the ratio of surface exchange coefficients for entropy and momentum ( C E / C D ) matches the theoretical result, υ max ∼ ( C E / C D ) 1/2 , for nearly inviscid flow, but simulations with increasing turbulence intensity show less dependence on C E / C D ; this result suggests that the effect of C E / C D is less important than has been argued previously. The authors find that υ max is most sensitive to the intensity of turbulence in the radial direction. However, some settings, such as inviscid flow, yield clearly unnatural structures; for example, υ max exceeds 110 m s −1 , despite a maximum observed intensity of ∼70 m s −1 for this environment. The authors show that turbulence in the radial direction limits maximum axisymmetric intensity by weakening the radial gradients of angular momentum (which prevents environmental air from being drawn to small radius) and of entropy (which is consistent with weaker intensity by consideration of thermal wind balance). It is also argued that future studies should consider parameterized turbulence as an important factor in simulated tropical cyclone intensity.

Souma, Kazuyoshi; Wang, Yuqing

doi: 10.1175/2008MWR2800.1pmid: N/A

The effect of Eurasian spring snow amount on the summer monsoon rainfall over East Asia has been studied both observationally and numerically. The results indicate that the Eurasian spring snow amount could be important for seasonal prediction of East Asian summer monsoon (EASM) rainfall. Therefore, accurately initializing snow could be critical to improving seasonal prediction of EASM rainfall by numerical models. An attempt has been made in this study to initialize snow in a regional climate model using snow water equivalent (SWE) data derived from a microwave imager. Results from an ensemble seasonal prediction experiment for the 2005 EASM show that the satellite-derived SWE data can be effectively used to initialize a dynamical seasonal prediction model, which leads to improved seasonal prediction of EASM rainfall. Possible effects of snow anomalies over the Tibetan Plateau on EASM rainfall were also studied through a comparative ensemble simulation in which snow was initialized by spinning up the same model from the previous winter. It is found that the anomalous snow amount over the Tibetan Plateau could lead to cooling of the surface and lower troposphere not only over the Tibetan Plateau but also in the surrounding areas because of the reduced net surface shortwave radiation associated with the high snow albedo. This would result in positive anomalies in geopotential height and weaken the cyclonic monsoon circulation in the lower troposphere in East Asia, causing a rainfall increase in South China but a reduction in the Yangtze River Valley in early summer (May–June). The difference in rainfall in midsummer (July–August) was not significant when compared with that in early summer. The surface heat budget indicates that the reduced net surface shortwave radiation is largely balanced by the reduced surface sensible heat flux.

Aksoy, Altuğ; Dowell, David C.; Snyder, Chris

doi: 10.1175/2008MWR2691.1pmid: N/A

The effectiveness of the ensemble Kalman filter (EnKF) for assimilating radar observations at convective scales is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used for data assimilation, while the Weather Research and Forecasting (WRF) Model is employed as a simplified cloud model at 2-km horizontal grid spacing. In each case, reflectivity and radial velocity measurements are utilized from a single Weather Surveillance Radar-1988 Doppler (WSR-88D) within the U.S. operational network. Observations are assimilated every 2 min for a duration of 60 min and correction of folded radial velocities occurs within the EnKF. Initial ensemble uncertainty includes random perturbations to the horizontal wind components of the initial environmental sounding. The EnKF performs effectively and with robust results across all the cases. Over the first 18–30 min of assimilation, the rms and domain-averaged prior fits to observations in each case improve significantly from their initial levels, reaching comparable values of 3–6 m s −1 and 7–10 dB Z . Representation of mesoscale uncertainty, albeit in the simplest form of initial sounding perturbations, is a critical part of the assimilation system, as it increases ensemble spread and improves filter performance. In addition, assimilation of “no precipitation” observations (i.e., reflectivity observations with values small enough to indicate the absence of precipitation) serves to suppress spurious convection in ensemble members. At the same time, it is clear that the assimilation is far from optimal, as the ensemble spread is consistently smaller than what would be expected from the innovation statistics and the assumed observation-error variance.

Reisner, J. M.; Jeffery, C. A.

doi: 10.1175/2008MWR2576.1pmid: N/A

In this paper a large-eddy “smooth” cloud (SC) model will be presented with smooth implying that the entire model converges under a Newton-based solution procedure or that time scales within the SC model are being resolved. Besides ensuring that time scales within microphysical parameterizations are resolved, convergence of Newton’s method requires that advection schemes near cloud boundaries should not induce fast time scales. For example, flux-corrected transport (FCT) schemes that force cloud variables to stay oscillation free near boundaries are typically not differentiable in time and hence may prevent convergence of Newton’s method. To circumvent the use of a FCT scheme, an alternative approach, a cloud-edge (CE) diffusion-based approach, will be presented in this paper. Since the diffusion produced by the CE approach could conceivably lead to the fictitious evaporation of a real cloud, the first major point of this paper will be to document that the SC model when employing an evaporative limiter is able, like most traditional large-eddy cloud models, to reasonably reproduce nondrizzling stratus clouds observed during flight 1 of the Second Dynamics and Chemistry of Marine Stratocumulus field study (DYCOMS-II). However, the SC model obtains the accuracy offered by higher-order time-stepping approaches, unlike most traditional cloud models. In fact, temporal errors from the SC model are shown to be at least two orders of magnitude smaller than those of a traditional large-eddy cloud model. Hence, the second major point of this paper will be to demonstrate the consequence of these large temporal errors found in traditional large-eddy cloud models, that is, the inability to accurately track an identifiable cloud feature in time.

Wakamatsu, Tsuyoshi; Foreman, Michael G. G.; Cummins, Patrick F.; Cherniawsky, Josef Y.

doi: 10.1175/2008MWR2621.1pmid: N/A

The effects of the parameterized wind stress error covariance function on the a priori error covariance of an ocean general circulation model (OGCM) are examined. These effects are diagnosed by computing the projection of the a priori model state error covariance matrix to sea surface height (SSH). The sensitivities of the a priori error covariance to the wind stress curl error are inferred from the a priori SSH error covariance and are shown to differ between the subpolar and subtropical gyres because of different contributions from barotropic and baroclinic ocean dynamics. The spatial structure of the SSH error covariance due to the wind stress error indicates that the a priori model state error is determined indirectly by the wind stress curl error. The impact of this sensitivity on the solution of a four-dimensional inverse problem is inferred.

Heinrich, P.; Blanchard, X.

doi: 10.1175/2008MWR2648.1pmid: N/A

Atmospheric transport of the natural radionuclide 210 Pb is simulated by a general circulation model (GCM) and calculated surface concentrations are compared with those recorded at the Tahiti station on a daily scale. Numerical results for 2006 show the underestimation of concentrations for most recorded peaks. The purpose of this paper is to explain the observed discrepancies, to evaluate the GCM physical parameterizations, and to determine by numerical means the concentrations at Tahiti for a pollutant circulating across the South Pacific Ocean. Three meteorological situations in 2006 are further analyzed. Circulation over Tahiti for these periods is simulated by a mesoscale meteorological model using four nested grids with resolutions ranging from 27 to 1 km. The calculated wind fields are validated by those observed at two stations on the northwest coast of Tahiti, which is exposed both to topography-induced vortices and to thermally driven local breezes. Atmospheric dispersion of an offshore plume is then calculated by a particle Lagrangian transport model, driven by the mesoscale model at 1- and 81-km resolutions, representing local and global circulations, respectively. Simulations at 1-km resolution show the complex atmospheric circulation over Tahiti, which results in a large spatial and temporal variability of 210 Pb surface concentrations on an hourly scale. The impact of local circulation is, however, limited when daily averaged concentrations at the station are considered. Under the studied regimes, transport simulations at the two resolutions lead to similar daily averaged concentrations. The deficiencies of the GCM in simulating daily averaged 210 Pb concentrations could be attributable to the deep convection parameterization.

Gao, Shuanzhu; Meng, Zhiyong; Zhang, Fuqing; Bosart, Lance F.

doi: 10.1175/2008MWR2669.1pmid: N/A

This observational study attempts to determine factors responsible for the distribution of precipitation over large areas of southern China induced by Bilis, a western North Pacific Ocean severe tropical storm that made landfall on the southeastern coast of mainland China on 14 July 2006 with a remnant circulation that persisted over land until after 17 July 2006. The heavy rainfalls associated with Bilis during and after its landfall can be divided into three stages. The first stage of the rainfall, which occurred in Fujian and Zhejiang Provinces, could be directly induced by the inner-core storm circulation during its landfall. The third stage of rainfall, which occurred along the coastal areas of Guangdong and Fujian Provinces, likely resulted from the interaction between Bilis and the South China Sea monsoon enhanced by topographical lifting along the coast. The second stage of the rainfall, which appeared inland around the border regions between Jiangxi, Hunan, and Guangdong Provinces, caused the most catastrophic flooding and is the primary focus of the current study. It is found that during the second stage of the rainfall all three ingredients of deep moist convection (moisture, instability, and lifting) are in place. Several mechanisms, including vertical wind shear, warm-air advection, frontogenesis, and topography, may have contributed simultaneously to the lifting necessary for the generation of the heavy rainfall at this stage.

Ke, Zongjian; Zhang, Peiqun; Dong, Wenjie; Li, Laurent

doi: 10.1175/2008MWR2676.1pmid: N/A

Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble.