The mass-weighted isentropic zonal mean (Z-MIM) equations derived by T. Iwasaki are powerful tools for diagnosing meridional circulation and wave-mean interaction, especially for the lower boundary and unstable waves. Recently, some studies have extended the equations to three dimensions by using the time mean instead of the zonal mean. However, the relation between wave activity flux and residual mean flow (not mass-weighed mean flow) is unclear.In the present study, we derive the three-dimensional (3D) wave activity flux and residual mean flow for Rossby waves on the mass-weighted isentropic time mean equations. Next, we discuss the relation between the obtained formulae and 3D transformed Eulerian-mean (TEM) equations.

Accurate estimation of tropical cyclone (TC) intensity is of great significance for serious natural disasters. A new method is presented to estimate intensity of TC using satellite infrared data. Firstly, TC region is calculated according to the location of TC center. Secondly, 2D-PCA algorithm is used to extract feature of bright temperature image, and historical data of TC intensity is matched with the k-nearest neighbor algorithm. Thirdly, the matching results are analyzed and the intensity information of TC is estimated. In addition, a TC intensity database, which contains historical data during 2006-2010, is developed for estimation of TC intensity. Experiments show that the proposed method is efficient for real-time estimation of TC intensity, average error of estimation is lower than 15 hPa.

We evaluated the impact of sea surface temperature (SST) improvement realized by increasing horizontal resolution of an ocean model on dynamical downscaling (DDS) over Japan, focusing on the effects of the Kuroshio on summer precipitation in Japan. Two sets of SSTs were simulated using a high-resolution North Pacific (NP) model and a low-resolution global (GLB) ocean model. Using these SSTs as the lower boundary conditions for the atmosphere, two DDS experiments were conducted (NP-run and GLB-run). In NP-run, summer precipitation increases over the Kuroshio and reduces over Pacific coastal areas of Japan compared with GLB-run. Due to weaker southerly winds north of the Kuroshio in NP-run, the water vapor flux transported to Japan is smaller than in GLB-run. Both the pressure adjustment and the vertical mixing mechanisms weaken the southerly winds, with the latter being slightly more effective. Increasing the horizontal resolution of the ocean model, so that the Kuroshio is more realistically reproduced, improves the accuracy of simulated precipitation over Japan.

We propose a super-resolution (SR) simulation system that consists of a physics-based meteorological simulation and an SR method based on a deep convolutional neural network (CNN). The CNN is trained using pairs of high-resolution (HR) and low-resolution (LR) images created from meteorological simulation results for different resolutions so that it can map LR simulation images to HR ones. The proposed SR simulation system, which performs LR simulations, can provide HR prediction results in much shorter operating cycles than those required for corresponding HR simulation prediction system. We apply the SR simulation system to urban micrometeorology, which is strongly affected by buildings and human activity. Urban micrometeorology simulations that need to resolve urban buildings are computationally costly and thus cannot be used for operational real-time predictions even when run on supercomputers. We performed HR micrometeorology simulations on a supercomputer to obtain datasets for training the CNN in the SR method. It is shown that the proposed SR method can be used with a spatial scaling factor of 4 and that it outperforms conventional interpolation methods by a large margin. It is also shown that the proposed SR simulation system has the potential to be used for operational urban micrometeorology predictions.

The relationships between the prediction of near-surface winds and the corresponding time of observations in eastern China were explored using the Advanced Weather Research and Forecasting (WRF) model and the three-dimensional variational (3D-Var) scheme in the gridpoint statistical interpolation (GSI) system. A series of one-month experiments was conducted in January 2018 with different time window configurations from 0.01 to 3.0h. The relationship between the wind observation time and the model forecast was non-linear. An observational time closer to the initial time in the model usually have greater impact on the prediction of near-surface wind speeds. Observations in the 0.4-0.8 h time window associated with abnormally high with large near-surface wind speeds provide a negative impact. The predictions improved at a much smaller rate when the time window was increased from 0.8 to 3.0 h. No significant difference was seen as the time window increased in wind direction predictions, even with large wind increments. The optimum configuration of the time window in the GSI 3D-Var system for predicting near-surface winds should therefore be 0.2 or 0.4 h. A better understanding of the relationships between the observations and the predictions will help select more effective observations when using the 3D-Var scheme.

In the present study, we conducted dual-sonde observations and a numerical simulation when the “Karakkaze”, a local wind in Japan, blew. The result showed that the basic features of the Karakkaze coincide closely with the characteristics of convexity wind defined as “strong winds in the leeward region of a convex-shaped mountain range”.

Firstly, we investigated the horizontal distribution of surface winds during the Karakkaze event on January 24, 2019. The results showed that the Karakkaze blows in the downwind plain of the convexity of the mountain range.

Secondly, we compared the vertical distribution of the winds inside and outside the Karakkaze region, using the results of dual-sonde observations and a numerical simulation. Our results showed that strong winds blew from near ground level to a height of 1.8 km above mean sea level (AMSL) in the Karakkaze region. In contrast, weaker winds were observed and simulated outside the Karakkaze region. The reason of the weaker winds is that a hydraulic jump occurs on the slope of the mountain range and that the area outside the Karakkaze region is located in a more leeward direction than the hydraulic jump. These features closely match the characteristics of convexity winds.

The Qian atmospheric forcing dataset is used to drive version 4.5 of the Community Land Model (CLM4.5) in off-line simulation tests. Based on the Global Land Evaporation Amsterdam Model (GLEAM) data, we attempt to ameliorate the canopy interception parameterization scheme in CLM4.5 by improving the empirical parameter and the physical structure. Considering that different plant functional types (PFTs) have different capacities to intercept rainfall is denoted as SEN1, and accounting for the influence of wind speed on canopy interception on the basis of SEN1 is denoted as SEN2. SEN1 shows obvious improvement in the simulated evaporation of intercepted water from vegetation canopy (Ec), not only greatly reduces the positive bias of the model to simulate Ec, especially in the equatorial region, but also significantly reduces the root mean square error (RMSE). SEN2 further improves the simulation of Ec by lowering the RMSE and increasing consistency with GLEAM data. In addition, the percentages of Ec over total evapotranspiration in both SEN1 and SEN2 are more reasonable and much closer to GLEAM data than that in CLM4.5.

The western North Pacific (WNP) exhibited markedly enhanced tropical cyclone (TC, typhoon) activity during the boreal summer (June–August) of 2018; 18 named typhoons were generated and 13 of these approached near Japan, causing serious damage and disruption in the country. During the summer of 2018, warm sea surface temperature persisted over the tropical Northeastern Pacific, which are typical oceanic conditions of a positive phase of the Pacific meridional mode (PMM), while no El Niño condition was observed. The Japan Meteorological Agency seasonal forecast system successfully predicted the enhanced TC activity in the WNP as well as associated seasonal characteristics such as a deep monsoon trough and active convection. Results of sensitivity experiments clearly indicate that the positive phase of the PMM played a major role in establishing the active TC conditions in the WNP during the summer of 2018 and reveal predictable seasonal processes of TC activity (genesis and tracks) during the summer of 2018, when there was no El Niño.