Search Results

You are looking at 1 - 10 of 26 items for

  • Author or Editor: W. Yu x
  • All content x
Clear All Modify Search
Shenn-Yu Chao and Timothy W. Kao

Abstract

The frontal instability of major baroclinic ocean currents such as the Gulf Stream is numerically studied here, using a three-dimensional, primitive-equation model. The model current is driven by a buoyant discharge near the surface from the light water side of the front. Subsequent geostrophic adjustment produces a baroclinic analysis in which a prescribed mean current is subject to eddy dissipation with no provision for its maintenance. At low latitudes small-amplitude unstable waves are generated. The eddy fluxes are shown to be largely upgradient and responsible for the maintenance of the front. At higher latitudes the model produces anticyclonic barotropic instability. The triggering mechanism for instability is related to the outward surge of the front during the initial stage of geostrophic adjustment, which in turn is related to the inertial oscillations of surface isopycnals. The outward surge is surface-trapped. To trigger instability, the surge must be shallow and intense in lower latitudes, and becomes deeper and weaker in higher latitudes. The Richardson number decreases below 0.25 shortly before and after onset of instability, and increases again after unstable waves have fully developed. The instability is initially of grid scale, and subsequently evolves into larger scales through nonlinear cascading processes, rendering themselves to baroclinic instability.

Full access
B. Yu, A. Shabbar, and F. W. Zwiers

Abstract

This study provides further evidence of the impacts of tropical Pacific interannual [El Niño–Southern Oscillation (ENSO)] and Northern Pacific decadal–interdecadal [North Pacific index (NPI)] variability on the Pacific–North American (PNA) sector. Both the tropospheric circulation and the North American temperature suggest an enhanced PNA-like climate response and impacts on North America when ENSO and NPI variability are out of phase. In association with this variability, large stationary wave activity fluxes appear in the mid- to high latitudes originating from the North Pacific and flowing downstream toward North America. Atmospheric heating anomalies associated with ENSO variability are confined to the Tropics, and generally have the same sign throughout the troposphere with maximum anomalies at 400 hPa. The heating anomalies that correspond to the NPI variability exhibit a center over the midlatitude North Pacific in which the heating changes sign with height, along with tropical anomalies of comparable magnitudes. Atmospheric heating anomalies of the same sign appear in both the tropical Pacific and the North Pacific with the out-of-phase combination of ENSO and NPI. Both sources of variability provide energy transports toward North America and tend to favor the occurrence of stationary wave anomalies.

Full access
T-W. Yu, M. Iredell, and D. Keyser

Abstract

A neural network algorithm used in this study to derive Special Sensor Microwave/Imager (SSM/I) wind speeds from the Defense Meteorological Satellite Program satellite-observed brightness temperatures is briefly reviewed. The SSM/I winds derived from the neural network algorithm are not only of better quality, but also cover a larger area when compared to those generated from the currently operational Goodberlet algorithm. The areas of increased coverage occur mainly over the regions of active weather developments where the operational Goodberlet algorithm fails to produce good quality wind data due to high moisture contents of the atmosphere. These two main characteristics associated with the SSM/I winds derived from the neural network algorithm are discussed.

SSM/I wind speed data derived from both the neural network algorithm and the operational Goodberlet algorithm are tested in parallel global data assimilation and forecast experiments for a period of about three weeks. The results show that the use of neural-network-derived SSM/I wind speed data leads to a greater improvement in the first-guess wind fields than use of wind data generated by the operational algorithm. Similarly, comparison of the forecast results shows that use of the neural-network-derived SSM/I wind speed data in the data assimilation and forecast experiment gives better forecasts when compared to those from the operational run that uses the SSM/I winds from the Goodberlet algorithm. These results of comparison between the two parallel analyses and forecasts from the global data assimilation experiments are discussed.

Full access
W. H. Gemmill, T. W. Yu, and D. M. Feit

Abstract

The performance of various techniques which determine ocean surface winds using information from large-scale analyses and forecast models is discussed. The techniques evaluated are the geostrophic relation, a simple empirical law, National Meteorological Center (NMC) 1000-mb winds, a two-region analytically matched boundary layer, a two-region boundary layer based on Rossby number similarity theory, and the Fleet Numerical Oceanography Center (FNOC) marine winds. Statistical comparisons of the model winds were made with observed buoy and ship winds for wind speed, wind direction, and the vector wind. This study is based on analyses and 24-h forecasts made once a day at 0000 UTC from 3 December 1985 through 6 January 1986 on a 2.5 × 2.5 degree latitude, longitude, grid.

The statistical results indicate that no one Model was clearly the best. The absolute wind speed difference between all the models and observations is, on the average, about 3 m s−1, and the RMS difference is about 4.O m s−1. However, the geostrophic relation was definitely the poorest, as would be expected. Model wind speeds and directions compared better with buoy data (lower RMS differences) than ship data. Furthermore, the study indicated that comparisons with buoys for wind speed were better over the northwest Atlantic than over the northwest Pacific, but the reverse was true for direction. For high wind speed reported by ships (> 22.5 m s−1) all model winds were comparatively lower.

Full access
MARVIN W. BURLEY, RAYMOND PFLEGER, and JEN YU WANG

Abstract

A climatological study of hailstorms is presented for the State of Wisconsin and for four first order weather stations: Green Bay, La Crosse, Madison, and Milwaukee. The analysis considers the geographical and time distribution of hail and the ratio of monthly average number of hailstorms to monthly average number of thunderstorms.

Full access
Yingjian Chen, Fuqing Zhang, Benjamin W. Green, and Xiping Yu

Abstract

Tropical cyclone (TC) intensity is strongly influenced by surface fluxes of momentum and moist enthalpy (typically parameterized in terms of “exchange coefficients” C d and C k, respectively). The behavior of C d and C k remains quite uncertain especially in high wind conditions over the ocean; moreover, moist enthalpy flux is extremely sensitive to sea surface temperature (SST). This study focuses on numerical simulations of Hurricane Katrina (2005) from an atmosphere–ocean coupled modeling system to examine the combined impacts of air–sea flux parameterizations and ocean cooling on TC evolution. Three momentum flux options—which make C d increase, level off, or decrease at hurricane-force wind speeds—with five different C k curves are tested. Maximum 10-m wind speed V max is highly sensitive to C d, with weaker sensitivities for minimum sea level pressure P min and track. Atmosphere-only runs that held SST fixed yielded TCs with P min substantially deeper than observations. Introducing ocean coupling weakens TC intensity with much more realistic P min. The coupled run with the flux parameterization that decreases C d at high wind speeds yields a simulated TC intensity most consistent with observations. This C d parameterization produces TCs with the highest V max. Increasing C k generally increases surface heat fluxes and thus TC intensity. For coupled runs using the default C k parameterization, the simulated SST fields are similar (regardless of C d parameterization) and agree well with satellite observations. The mesoscale oceanic eddies, which are well resolved in the ocean model, contribute to the magnitude of TC-induced SST cooling and greatly influence TC intensity.

Full access
Hans R. Schneider, Malcolm K. W. Ko, Nien Dak Sze, Guang-Yu Shi, and Wei-Chyung Wang

Abstract

The effect of eddy diffusion in an interactive two-dimensional model of the stratosphere is reexamined. The model consists of a primitive equation dynamics module, a simplified HOx ozone model and a full radiative transfer scheme. The diabatic/residual circulation in the model stratosphere is maintained by the following processes: 1) nonlocal forcing resulting from dissipation in the parameterized model troposphere and frictional drag at mesospheric levels, 2) mechanical damping within the stratosphere itself, and 3) potential vorticity flux due to large scale waves. The net effect of each process is discussed in terms of the efficiency of the induced circulation in transporting ozone from the equatorial lower stratosphere to high latitude regions. The same eddy diffusion coefficients are used to parameterize the flux of quasi-geostrophic potential vorticity and diffusion in the tracer transport equation. It is shown that the ozone distributions generated with the interactive two-dimensional model are very sensitive to the choice of values for the friction and the eddy diffusion coefficients. The strength of the circulation increases with the mechanical damping and Kyy. At the same time, larger diffusion in the tracer transport equation reduces the equator to pole transport (Holton 1986). Depending on the amount of friction assumed in the stratosphere, increasing eddy diffusion can lead to an increase as well as a decrease in the net transport. It is shown that reasonable latitudinal gradients of ozone can be obtained by using small values for the mechanical damping [≈1/(100 days)] and Kyy (order 104 m2 s−1) for the mid- and high-latitude stratosphere.

Full access
Yonggang G. Yu, Ning Wang, Jacques Middlecoff, Pedro S. Peixoto, and Mark W. Govett

Abstract

A single software framework is introduced to evaluate numerical accuracy of the A-grid (NICAM) versus C-grid (MPAS) shallow-water model solvers on icosahedral grids. The C-grid staggering scheme excels in numerical noise control and total energy conservation, which results in exceptional stability for long time integration. Its weakness lies in the lack of model error reduction with increasing resolution in specific test cases (especially the root-mean-square error). The A-grid method conserves well potential enstrophy and shows a linear reduction of error with increasing resolution. The gridpoint noise manifests itself clearly on A-grid, but much less on C-grid. We show that the Coriolis force term on C-grid has a larger error than on A-grid. To treat the Coriolis term and kinetic energy gradient on an equal footing on C-grid, we propose combining these two quantities into a single tendency term and computing its value by a linear combination operation. This modification alone reduces numerical errors but still fails to converge the maximum error with resolution. The method of Peixoto can solve the maximum-error nonconvergence problem on C-grid but degrades the numerical stability. For the steady-state thin-layer test (0.01 m in depth), the A-grid method is less susceptible than C-grid methods, which are presumably disrupted by the Hollingsworth instability. The effect of horizontal diffusion on model accuracy and energy conservation is shown in detail. Programming experience shows that software implementation and optimization can strongly influence computational performance for models, although memory requirement and computational load of the two schemes are comparable.

Restricted access
Tsing-Chang Chen, Ming-Cheng Yen, Shih-Yu Wang, and Raymond W. Arritt

On 11 January 1998, a cold front formed in southeast China as a result of a cold-air outbreak in northeast Asia. During this synoptic development, a series of roll clouds (along the SW–NE direction) was observed in East Asia; some of the clouds stretched for over a thousand kilometers. This roll cloud formation moved southeastward across Taiwan, the Ryukyu Islands, and Japan, and eventually into the open ocean. In order to explore the possible cause of these roll clouds the following preliminary analyses were made in this study:

These observations imply that the series of roll clouds formed in association with solitary wave disturbances generated on the density current (i.e., the outflow from the cold-air break) but behind its leading edge.

Full access
M. J. McPhaden, G. Meyers, K. Ando, Y. Masumoto, V. S. N. Murty, M. Ravichandran, F. Syamsudin, J. Vialard, L. Yu, and W. Yu

The Indian Ocean is unique among the three tropical ocean basins in that it is blocked at 25°N by the Asian landmass. Seasonal heating and cooling of the land sets the stage for dramatic monsoon wind reversals, strong ocean-atmosphere interactions, and intense seasonal rains over the Indian subcontinent, Southeast Asia, East Africa, and Australia. Recurrence of these monsoon rains is critical to agricultural production that supports a third of the world's population. The Indian Ocean also remotely influences the evolution of El Nino-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), North American weather, and hurricane activity. Despite its importance in the regional and global climate system though, the Indian Ocean is the most poorly observed and least well understood of the three tropical oceans.

This article describes the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), a new observational network designed to address outstanding scientific questions related to Indian Ocean variability and the monsoons. RAMA is a multinationally supported element of the Indian Ocean Observing System (IndOOS), a combination of complementary satellite and in situ measurement platforms for climate research and forecasting. The article discusses the scientific rationale, design criteria, and implementation of the array. Initial RAMA data are presented to illustrate how they contribute to improved documentation and understanding of phenomena in the region. Applications of the data for societal benefit are also described.

Full access