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Zuohao Cao and Jianmin Ma

Abstract

A variational method is employed to compute surface sensible heat fluxes over a deciduous forest using observed temperature, temperature variance, and wind. Because the variational approach is able to take into account comprehensive observational meteorological conditions over a heterogeneous surface, it is applicable to the computations of sensible heat flux over a forest canopy in which the conventional flux-variance method is difficult to use. Verifications using the direct eddy-correlation measurements over a deciduous forest during the fully leafed summer of 1988 and the leafless winter of 1990 show that the variational method yields very good agreements between the computed and the measured sensible heat fluxes. It is also shown that the variational method is much more accurate than the flux-variance method in computations of sensible heat flux over a forest canopy.

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Zuohao Cao and Jianmin Ma

Abstract

During the last two decades (1979–2002), there has been an ever-increasing frequency of summer severe-rainfall events over Ontario, Canada. This observed upward trend is robust as demonstrated through the Mann–Kendall test with consideration of removing a lag-1 autoregressive process. It is shown through composite analyses using the NCEP reanalysis data that in the presence of warming conditions the summer severe-rainfall events occur more frequently over Ontario, especially under atmospheric conditions with stronger low-level cyclonic circulations and more precipitable water. Further analyses indicate that over north and central Ontario the summer severe-rainfall frequency is linked with a positive trend of precipitable water whereas over central and south Ontario there is a strong interannual response of summer severe-rainfall frequency to the changes in precipitable water through the variations of air temperature.

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Zuohao Cao and Jianmin Ma

Abstract

In this study, a variational approach was employed to compute surface sensible heat flux over the Arctic sea ice. Because the variational approach is able to take into account information from the Monin–Obukhov similarity theory (MOST) as well as the observed meteorological information, it is expected to improve the pure MOST-based approach in computation of sensible heat flux. Verifications using the direct eddy-correlation measurements over the Arctic sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment period of 1997/98 show that the variational method yields good agreement between the computed and the measured sensible heat fluxes. The variational method is also shown to be more accurate than the traditional MOST method in the computation of sensible heat flux over the Arctic sea ice.

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Jianmin Ma and R. E. Robson

Abstract

An adaptive pseudospectral method is applied to the solution of advection-diffusion problems arising from the turbulent dispersion of pollutants in the atmosphere. For a localized source term specified by a function with steep gradients or discontinuities, the authors show that the associated rapidly varying functions can be smoothed out and gradually varied by using polynomial approximations in a transformed coordinate system. The solutions obtained from the advection-diffusion equation still preserve spectral accuracy, and the usual spectral oscillation is avoided. The authors solve both one- and two-dimensional time-dependent advection-diffusion equations associated with both small and relatively large diffusion coefficients. The numerical solutions are compared with exact solutions. The results show that the adaptive pseudospectral solution for the advection-diffusion problems is very effective and accurate for an imposed shock function. No numerical diffusion is introduced. This method does not need any special treatment of nonperiodic boundary conditions, which is otherwise generally needed in spectral methods. The stability of the algorithm is discussed. Examples with Chebyshev nodes and uniformly spaced collocation points are given.

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Jianmin Ma and S. M. Daggupaty

Abstract

A variational method is developed to estimate the aerodynamic roughness length and roughness scaling length for temperature based on wind and temperature measurements conducted routinely at an observational network. The problem is formulated to find optimal estimates of roughness lengths for momentum and heat transfer through a minimization of a cost function with respect to these two roughness lengths that measures the errors between observed and predicted wind and temperature profiles. The method has been applied to data collected in two experimental campaigns. Some results are compared with other methods used to compute the aerodynamic roughness length. The variational computations show that the aerodynamic roughness lengths agree well with the estimated z 0m in the experimental campaigns. The roughness scaling lengths for temperature z 0t are in most cases one order of magnitude smaller than z 0m. It was found that the variations of z 0m and z 0t during the course of a day are not likely to follow a simple functional relationship, especially during the daytime, during which both z 0m and z 0t are highly oscillatory. The error test shows that z 0m and z 0t generated from the variational method are not very sensitive to measurement errors.

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Jianmin Ma and S. M. Daggupaty

Abstract

Dry deposition velocities of gases and particles are highly dependent on surface type. In a numerical model, each grid cell may contain multiple surface types, each with a different deposition velocity. Therefore, some kind of averaging technique generally is used to compute the average of the subgrid-scale deposition velocities within a grid cell. In this paper, effective surface parameters are suggested to relate the mean properties of concentration and wind speed to the mean surface fluxes. An effective deposition velocity is computed subject to these effective surface parameters and a weighted-average technique. This effective deposition velocity is compared with an alternate weighted-average deposition velocity that has been used widely in numerical air quality models. For particles, the effective deposition velocity can be significantly different from the weighted-average deposition velocity. For some gases, for which biological factors often control the deposition process, the difference between these two average deposition velocities can still be distinguished for typical gases and surface properties.

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Zuohao Cao, Jianmin Ma, and Wayne R. Rouse

Abstract

In this study, the authors have performed the variational computations for surface sensible heat fluxes over a large northern lake using observed wind, temperature gradient, and moisture gradient. In contrast with the conventional (Monin–Obukhov similarity theory) MOST-based flux-gradient method, the variational approach sufficiently utilizes observational meteorological conditions over the lake, where the conventional flux-gradient method performs poorly. Verifications using direct eddy-correlation measurements over Great Slave Lake, the fifth largest lake in North America in terms of surface area, during the open water period of 1999 demonstrate that the variational method yields good agreements between the computed and the measured sensible heat fluxes. It is also demonstrated that the variational method is more accurate than the flux-gradient method in computations of sensible heat flux across the air–water interface.

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Da-Lin Zhang, Zuohao Cao, Jianmin Ma, and Aiming Wu

Abstract

The summer nonconvective severe surface wind (NCSSW) frequency over Ontario, Canada, in relation to regional climate conditions and tropical Pacific Ocean sea surface temperatures (SSTs) during the period of 1979–2006 is examined using surface wind reports and large-scale analysis data. A statistically robust positive trend in Ontario summer NCSSW frequency is identified using three independent statistical approaches, which include the conventional linear regression that has little disturbance to the original time series, the Mann–Kendall test without a lag-1 autoregressive process, and the Monte Carlo simulation. A composite analysis of the large-scale monthly mean data reveals that the high- (low-) NCSSW occurrence years are linked to stronger (weaker) large-scale horizontal pressure gradients and more (less) intensive vector wind anomalies in the upper troposphere. Unlike the low-event years, anomalous anticyclonic circulations are found at 500 and 250 hPa in the high-event years, which are conducive to downward momentum transport and favorable for severe surface wind development. It is also found that the summer NCSSW occurs more frequently under the conditions of warmer surface air temperature over Ontario. Further analyses indicate that an increase in the summer NCSSW frequency is well correlated with an increase in the previous winter SSTs over the eastern equatorial Pacific, namely, in the Niño-1+2 and Niño-3 areas, through a decrease in sea level pressure over northern Ontario and an increase in surface air temperature over central and southern Ontario.

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Baixin Li, Huan Tang, Dongfang Ma, and Jianmin Lin

Abstract

Mesoscale eddies are a mechanism for ocean energy transfer, and identifying them on a global scale provides a means of exploring ocean mass and energy exchange between ocean basins. There are many widely used model-driven methods for detecting mesoscale eddies; however, these methods are not fully robust or generalizable. This study applies a data-driven method and proposes a mesoscale detection network based on the extraction of eddy-related spatiotemporal information from multisource remote sensing data. Focusing on the northwest Pacific, the study first analyzes mesoscale eddy characteristics using a combination of gridded data for the absolute dynamic topography (ADT), sea surface temperature (SST), and absolute geostrophic velocity (UVG). Then, a deep learning network with a dual-attention mechanism and a convolutional long short-term memory module is proposed, which can deeply exploit spatiotemporal feature relevance while encoding and decoding information in the gridded data. Based on the analysis of mesoscale eddy characteristics, ADT and UVG gridded data are selected to be the inputs for the detection network. The experiments show that the accuracy of the proposed network reaches 93.38%, and the weighted mean dice coefficient reaches 0.8918, which is a better score than those achieved by some of the detection networks proposed in previous studies, including U-Net, SymmetricNet, and ResU-Net. Moreover, compared with the model-driven approach used to generate the ground-truth dataset, the network method proposed here demonstrates better performance in detecting mesoscale eddies at smaller scales, partially addressing the problem of ghost eddies.

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