Transformative Urban Changes of Beijing in the Decade of the 2000s

The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.

The Gaussian Statistical Predictability of Wind Speeds

The statistical predictability of wind speed using Gaussian predictors, relative to the predictability of orthogonal vector wind components, is considered. With the assumption that the vector wind components are Gaussian, analytic expressions for the correlation-based wind speed prediction skill are obtained in terms of the prediction skills of the vector wind components and their statistical moments. It is shown thatat least one of the vector wind components is generally better predicted than the wind speed (often much more so);wind speed predictions constructed from the predictions of vector wind components are more skillful than direct wind speed predictions; andthe linear predictability of wind speed (relative to that of the vector wind components) decreases as the variability in the vector wind increases relative to the mean. These idealized model results are shown to be broadly consistent with linear predictive skills assessed using observed sea surface wind from the SeaWinds scatterometer. Biases in the model predictions are shown to be related to the degree to which vector wind variations are non-Gaussian.

Observations of Gulf of Tehuantepec Gap Wind Events from QuikSCAT: An Updated Event Climatology and Operational Model Evaluation

A climatology of gale- and storm-force gap wind events in the Gulf of Tehuantepec is constructed for the first time using 10 yr of ocean surface vector wind data from the SeaWinds scatterometer on board NASA’s Quick Scatterometer (QuikSCAT) satellite. These wind events are among the most severe that occur within the National Hurricane Center’s (NHC) area of marine forecasting responsibility outside of tropical cyclones. The 10-yr climatology indicates that on average 11.9 gale-force events and 6.4 storm-force events occur in the Gulf of Tehuantepec each cold season. About 84% of these events occur between November and March, with the largest number of gale-force events occurring in December. Storm-force events are most frequent in January. Operational numerical weather prediction model forecasts of these events from the NCEP Global Forecast System (GFS) and North American Mesoscale (NAM) models were evaluated during the 2006/07 cold season. Results show that neither model is able to consistently forecast storm-force Tehuantepec wind events; however, the models do have some ability to forecast gale-force events. The NAM model showed a significant increase in probability of detection over the GFS, possibly due to increased horizontal and vertical resolutions as well as differences in boundary layer mixing and surface flux schemes. Finally, the prospects of observing these gap wind events in the post-QuikSCAT era will be discussed.

The Effects of SST-Induced Surface Wind Speed and Direction Gradients on Midlatitude Surface Vorticity and Divergence

The effects of surface wind speed and direction gradients on midlatitude surface vorticity and divergence fields associated with mesoscale sea surface temperature (SST) variability having spatial scales of 100–1000 km are investigated using vector wind observations from the SeaWinds scatterometer on the Quick Scatterometer (QuikSCAT) satellite and SST from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Aqua satellite. The wind–SST coupling is analyzed over the period June 2002–August 2008, corresponding to the first 6+ years of the AMSR-E mission. Previous studies have shown that strong wind speed gradients develop in response to persistent mesoscale SST features associated with the Kuroshio Extension, Gulf Stream, South Atlantic, and Agulhas Return Current regions. Midlatitude SST fronts also significantly modify surface wind direction; the surface wind speed and direction responses to typical SST differences of about 2°–4°C are, on average, about 1–2 m s−1 and 4°–8°, respectively, over all four regions. Wind speed perturbations are positively correlated and very nearly collocated spatially with the SST perturbations. Wind direction perturbations, however, are displaced meridionally from the SST perturbations, with cyclonic flow poleward of warm SST and anticyclonic flow poleward of cool SST. Previous observational analyses have shown that small-scale perturbations in the surface vorticity and divergence fields are related linearly to the crosswind and downwind components of the SST gradient, respectively. When the vorticity and divergence fields are analyzed in curvilinear natural coordinates, the wind speed contributions to the SST-induced vorticity and divergence depend equally on the crosswind and downwind SST gradients, respectively. SST-induced wind direction gradients also significantly modify the vorticity and divergence fields, weakening the vorticity response to crosswind SST gradients while enhancing the divergence response to downwind SST gradients.