The transformed Eulerian mean (TEM) description has been widely used as a standard basic analysis tool for describing wave-mean flow interactions in geophysical fluid dynamics. However, the TEM implicitly assumes that the eddy diffusion tensor is antisymmetric, although the assumption does not hold in general data analyses. To remedy the defect, a generalized transformed Eulerian mean (GTEM) set of equations is derived based on a nonneutral (unstable/dissipative) wave under dissipation and/or diabatic heating conditions in a Boussinesq stratified fluid. All the nine components of the three-dimensional eddy diffusion tensor are derived based on the wave-form. However, the explicit form of the wave frequency and wavenumber of eddies is not referred to so as to apply the GTEM to actual atmospheric and oceanic data analyses. The symmetric part of the eddy diffusion tensor is proportional to the growth rate for the weakly nonneutral wave. It is shown that the Stokes drift velocity defined in the generalized Lagrangian mean (GLM) description agrees to the leading order with the minus sign of the divergence of the transposed eddy diffusion tensor, so that the direction of the transport velocity induced by the symmetric (antisymmetric) part is opposite (identical) to the Stokes drift velocity. An application to the Eady unstable wave is made to illustrate the differences between TEM, GLM, and GTEM.
Mineral dust suspended in the atmosphere affects the Earth’s radiation budget. To accurately predict the effect of dust on the climate system, information regarding its extinction profiles is needed. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite has enabled the global observation of the vertical distributions of aerosols and clouds since June 2006. To correctly retrieve extinction coefficients from CALIOP signals, the lidar-observed layers must be classified into aerosols or clouds. The cloud masking algorithms of CALIOP should be improved since the cloud mask products occasionally misclassify dense dust as clouds. This study attempts to discriminate misclassified clouds from the CALIOP cloud mask with a discriminant analysis. The training data are collected by tests with the CloudSat cloud mask, the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask, and relative humidity. Discrimination of dust from clouds is successful in cases over land and water surfaces during the daytime and nighttime. In contrast, the discrimination model of previous studies was inadequate during the nighttime since training data were not collected during the nighttime. The accuracy rate of the linear discriminant function classification is 91.7 % for misclassified clouds. The cloud mask is most frequently misclassified in the Taklimakan Desert. The proportion of misclassified clouds to the observed dust is ∼34.6 % (below 2 km) in the desert. Comparison of our results with CALIOP level 3 products indicates that the extinction profile using the improved cloud mask is at most twice larger than that of CALIOP level 3 products. This study suggests that the smaller extinction coefficients of CALIOP level 3 products are mainly caused by misclassification of dust as clouds in the vertical feature mask.
A stationary rainband brought heavy rainfall across Gifu and Aichi prefectures, Japan, on 15 July 2010. The orientation of the rainband was initially southwest-northeast, and then changed from west-southwest to east-northeast, before reverting to its original orientation, although the rainband remained stationary over the same area. This study analyzes the organization of the rainband during these three orientation periods using polarimetric Doppler radar. The rainband was maintained by south-southwesterly inflows of high equivalent potential temperature (≥340 K) below 2 km, while southwesterly winds prevailed at middle level during the rainfall. It is suggested that these rainband orientations were determined by the travel directions of the convective cells and positions of cell generation relative to the rainband, which in turn were governed by intensities of low-level inflow and cell-origin outflow. During the first orientation period, convective cells formed over a wide area within the rainband and traveled northeastward. Low-level outflows from deep convective cells in the northern section of the rainband shifted the cell-generation area southward, and enhanced south-southwesterly inflows caused the southwestern portion of the rainband to drift slightly to the north; hence, the rainband was oriented from west-southwest to east-northeast. The convective cells were deeper during the second period and low-level outflows were stronger than those in the first stage. The strong outflows formed a cell generation area on the southern lateral side of the rainband, while enhanced low-level inflows contributed to the north-northeastward motion of the generated cells crossing the rainband at an angle of 45°. The outflows and south-southwesterly inflows then weakened, and convective cells formed successively on the southwestern edge of the rainband and moved to the northeast. As a result, the rainband reverted to its original southwest-northeast orientation.
A severe rainfall event occurred in the downstream Yangtze River Valley (YRV) during 28-30 June 2009. This study focused on the role of the “key sensitive area” (KSA) in the southeastern edge of the Tibetan Plateau (TP) in transporting water vapor downstream. The characteristics of water-vapor transport from KSA and the relation with the summer rainfall event were first investigated using the National Centers for Environmental Prediction Final Operational Global Analysis data and conventional observations. The observations included temperature, specific humidity (q), and surface pressure that are all from the automatic weather stations (AWS) over TP; precipitable water vapor (PWV) from the Global Positioning System (GPS) stations over TP; and rainfall from rain gauge measurements in YRV. The results showed high correlations between variables (i.e., q and PWV) observed over KSA and the summer rainfall in YRV, with a lagged time of 48-72 h, suggesting that the former is a good early-warning signal for the latter. To confirm the importance of KSA and its impact on the rainfall in the downstream YRV, the observations from the AWS and GPS of the New Integrated Observational System over TP were assimilated into the Advanced Research Weather Research and Forecast model with 30-km mesh using three-dimensional variational method. A set of sensitivity experiments were also conducted during a different summer, namely, June 2008, and Threat Score is used to evaluate the rainfall forecast skill. The results showed that the assimilation of observations from AWS and GPS in KSA helped adjust the structures of moisture, temperature, and wind fields, which improved the rainfall forecast in YRV, especially the heavy rainfall event. Both data analysis and numerical experiments demonstrated that the observations in KSA improved the forecast of high-impact weather in YRV.