Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity > −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms.
Sea breezes are one of the most important weather processes affecting the environmental and climatic features over coastal areas, and the sea breeze from the Pearl River Estuary (PRE) has significant effects on the Pearl River Delta (PRD) region. We simulated a typical sea breeze process that occurred on 27 December 2020 in the PRD region using the Weather Research and Forecasting (WRF) model to quantify the effects of topography and city clusters on the development of the sea breeze circulation. The results show that: (1) the topography on the west coast of the PRD tends to block the intrusion of the sea breeze and detour it along the eastern part of the terrain in the southeast of Jiangmen. The depth of sea breeze along the position of the detour is increased by 120 m, the penetration distance is increased by 40 km, the maximum intensity of sea breeze decreases by ~0.4 m/s, and the time of maximum speed delays for 4 h. However, on the east coast, the topography promotes the sea breeze, resulting in an occurrence about 4 h earlier due to the heating effects. The depth and the speed of the sea breeze are increased by 466 m and 1.2 m/s, respectively. (2) Under the influence of Urban Heat Island Circulation (UHIC), the sea breezes reach cities near the coast an hour earlier and are later inhibited from propagating further inland. Moreover, a wind convergence zone with a speed of 3–5 m/s and a width of about 25 km is formed along the boundary of suburbs and cities in the PRD region. As a result, two important convergence areas: Foshan–Guangzhou, and Dongguan–Shenzhen are formed. (3) Overall, the topography has a more remarkable impact on the mesoscale wind field especially in the mountain and bay areas, resulting in an average speed disturbance of 2.8 m/s. The urban heat island effect is relatively small and on average it causes only ± 0.9–1.8 m/s wind speed perturbations in the periphery of two convergence areas and over PRE.
To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts.
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