The authors investigate the mesoscale dynamics that produce the lower-stratospheric energy spectra in idealized moist baroclinic waves, using the moist nonhydrostatic formulation of spectral energy budget of kinetic energy and available potential energy by J. Peng et al. The inclusion of moist processes energizes the lower-stratospheric mesoscale, helping to close the gap between observed and simulated energy spectra. In dry baroclinic waves, the lower-stratospheric mesoscale is mainly forced by weak downscale cascades of both horizontal kinetic energy (HKE) and available potential energy (APE) and by a weak conversion of APE to HKE. At wavelengths less than 1000 km, the pressure vertical flux divergence also has a significant positive contribution to the HKE; however, this positive contribution is largely counteracted by the negative HKE vertical flux divergence. In moist baroclinic waves, the lower-stratospheric mesoscale HKE is mainly generated by the pressure and HKE vertical flux divergences. This additional HKE is partly converted to APE and partly removed by diffusion. Another negative contribution to the mesoscale HKE is from the forcing of a visible upscale HKE cascade. Besides the conversion of HKE, however, the three-dimensional divergence also has a significant positive contribution to the mesoscale APE. With these two direct APE sources, the lower-stratospheric mesoscale also undergoes a much stronger upscale APE cascade. These results suggest that both downscale and upscale cascades through the mesoscale are permitted in the real atmosphere and the direct forcing of the mesoscale is available to feed the upscale energy cascade.
Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net’s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.
In this second part of a two-part study, a newly developed moist nonhydrostatic formulation of the spectral energy budget of both kinetic energy (KE) and available potential energy (APE) is employed to investigate the dynamics underlying the mesoscale upper-tropospheric energy spectra in idealized moist baroclinic waves. By calculating the conservative nonlinear spectral fluxes, it is shown that the inclusion of moist processes significantly enhances downscale cascades of both horizontal KE and APE. Moist processes act not only as a source of latent heat but also as an “atmospheric dehumidifier.” The latent heating, mainly because of the depositional growth of cloud ice, has a significant positive contribution to mesoscale APE. However, the dehumidifying reduces the diabatic contribution of the latent heating by 15% at all scales. Including moist processes also changes the direction of the mesoscale conversion between APE and horizontal KE and adds a secondary conversion of APE to gravitational energy of moist species. With or without moisture, the vertically propagating inertia–gravity waves (IGWs) produced in the lower troposphere result in a significant positive contribution to the upper-tropospheric horizontal KE spectra at the large-scale end of the mesoscale. However, including moist processes generates additional sources of IGWs located in the upper troposphere; the upward propagation of the convectively generated IGWs removes much of the horizontal KE there. Because of the restriction of the anelastic approximation, the three-dimensional divergence has no significant contribution. In view of conflicting contributions of various direct forcings, finally, an explicit comparison between the net direct forcing and energy cascade is made.
This study investigates the effects of tropospheric vertical wind shear on gravity waves generated by tropical cyclones using idealized tropical cyclone simulations with different vertical profiles of environmental zonal wind. It is found that without shear, gravity wave momentum fluxes are distributed uniformly over a broad spectral range, with fluxes in opposite directions showing symmetry. Westerly shear in the troposphere leads to asymmetry between eastward and westward momentum fluxes, resulting in net westward fluxes and a resultant westward drag forcing on the background flow in the lower stratosphere. Under westerly shear, the active momentum fluxes extend to shorter horizontal wavelengths, and contract to longer periods and slower phase speeds. In addition, clear local peaks appear at large horizontal wavelengths, long periods, and moderate phase speeds, particularly for westward momentum fluxes.
Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the effectiveness of the proposed model, we compare it with operational near-real-time satellite-based precipitation products and deep learning models which proved to be effective in precipitation estimation. We use classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR), Critical success index (CSI), and regression metrics including Root Mean Square Error (RMSE) and Pearson correlation coefficient (CC) to evaluate the performance of precipitation identification and precipitation amounts estimation, respectively. Furthermore, we select an extreme precipitation event to validate the generalization ability of our proposed model. Statistics and visualizations of the experimental results show the proposed model has better performance than operational precipitation products and baseline deep learning models in both precipitation identification and precipitation amounts estimation. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. This study suggests that applying an appropriate deep learning algorithm may provide an opportunity to improve the quality of satellite-based precipitation products.
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