Resolution dependence of deep convections in a global simulation from over 10-kilometer to sub-kilometer grid spacing

The success of sub-kilometer global atmospheric simulation opens the door for resolving deep convections, which are fundamental elements of cloudy disturbances that drive global circulation. A previous study found that the essential change in the simulated convection properties occurred at a grid spacing of about 2 km as a global mean. In grid-refinement experiments, we conducted further comprehensive analysis of the global-mean state and the characteristics of deep convection, to clarify the difference of the essential change by location and environment. We found that the essential change in convection properties was different in the location and environment for each cloudy disturbance. The convections over the tropics show larger resolution dependence than convections over mid-latitudes, whereas no significant difference was found in convections over land or ocean. Furthermore, convections over cloudy disturbances [(i.e., Madden-Julian oscillation (MJO), tropical cyclones (TCs)] show essential change of convection properties at about 1 km grid spacing, suggesting resolution dependence. As a result, convections not categorized as cloudy disturbances make a large contribution to the global-mean convection properties. This implies that convections in disturbances are largely affected organization processes and hence have more horizontal resolution dependence. In contrast, other categorized convections that are not involved in major cloudy disturbances show the essential change at about 2 km grid spacing. This affects the latitude difference of the resolution dependence of convection properties and hence the zonal-mean outgoing longwave radiation (OLR). Despite the diversity of convection properties, most convections are resolved at less than 1 km grid spacing. In the future, longer integration of global atmosphere, to 0.87 km grid spacing, will stimulate significant discussion about the interaction between the convections and cloudy disturbances.

Data assimilation experiments of phased array weather radar with 30-second-update ensemble Kalman filter with 100-m resolution For preventing and mitigating natural disasters caused by local severe rainstorms, precise numerical weather prediction with higher spatial and temporal resolution would be essential. In this study, we developed a 30-second-update data assimilation system based on an ensemble Kalman filter using JMA-NHM with 100-m resolution. In the present setting of the experiments, we assimilated radar reflectivity and radial velocity derived from the every 30-second volume scan of the phased array weather radar (PAWR) at the Osaka University.
The data assimilation experiments were performed to reproduce the local heavy rainfall that had occurred in Kyoto on 13 July 2013. During the data assimilation cycles, the reflectivity patterns in the model became closer to the observations, indicating that the PAWR data were appropriately assimilated. However, the extended forecast showed a rapid error growth in about 10 minutes. This very short limit of predictability would be related to the time scales of convective activities represented by 100-m resolution, and may also be caused by an imbalance in the initial conditions due to 30-second update cycles, or could be related to potential inconsistency with the lateral boundaries. This presentation will include an introduction to the experimental system and the results of the data assimilation experiments.
data assimiilation, ensemble Kalman filter, phased array weather radar AAS02-03 It is generally difficult to assimilate precipitation data into numerical models mainly because of non-Gaussianity of precipitation variables and nonlinear precipitation processes. Lien et al. (2013Lien et al. ( , 2015 proposed to use an ensemble Kalman filter approach to avoid explicit linearization of models, and a Gaussian transformation (GT) method to deal with the non-Gaussianity of precipitation variables. Lien et al. pioneering results show that using an EnKF and GT helps improve the forecasts by assimilating global precipitation data, in both a simulated study using the SPEEDY model, and in a real-world study using the NCEP GFS and TRMM Multi-satellite Precipitation Analysis (TMPA) data. This presentation discusses the benefits of using a hybrid ensemble Kalman filter and four-dimensional variational (4D-Var) data assimilation (DA) system rather than a 4D-Var system employing the National Meteorological Center (NMC)-method to predict severe weather events. This hybrid system is an adjoint-based 4D-Var system that uses a background error covariance matrix B leading to minimize a cost function. This cost function depends on the second moment of the prior distribution which cannot be produced by VAR itself. Therefore the climatological background covariance is used instead of the true "errors of the day". This second moment is available into EnKF by sampling the prior distribution with a number of ensemble members. However, with a limited number of ensemble members, only a subspace of the error space can be represented. This means partial "errors of the day" are considered in EnKF. To introduce "errors of the day" into assimilation as in EnKF but can still explore the full error space as in VAR, some hybrid methods have been proposed.
In a hybrid method a specific operator is taken as a linear combination from the corresponding operators in the variational part and the ensemble part. If this operator is the background covariance we have the hybrid covariance method (hybrid B). In case the Kalman gain is chosen we have the hybrid gain method (hybrid K). In some variants of the hybrid covariance method, the operators in the factor form of the background covariance can be used. That means we can take a linear combination between background variances (hybrid V) or background correlations.
In the Strategic Programs for Innovative Research (SPIRE) Field 3, besides the traditional methods like 4DVAR and EnKF, hybrid methods have been implemented in the K Computer under a unified hybrid assimilation system for the Japan Meteorological Agency (JMA) limited-area operational model NHM.
The system consisted of two components: the variational one 4DVAR and the ensemble one 4D-LETKF.
The variational part was adopted from the JNoVA system developed at JMA. The ensemble part was based on the NHM-LETKF system developed at JMA. There is a two-way interaction between two sub-systems.
Real observation experiments were carried out for the August in 2014. This month was characterized by abnormal rainfall over the western Japan with two tropical cyclones and several heavy rainfall we found that the long range covariance structures up to several thousand km helped to extract information from distant observations. By contrast, the improvement in the tropical regions was relatively small. In this study, we hypothesize that this little improvements be related to the non-Gaussianity of the error statistics due to highly-nonlinear processes of convections. Actually, we found that strong non-Gaussianity such as bimodal distributions frequently appears in the tropical regions, and the spatial patterns of the occurrences of the non-Gaussian error statistics correspond well to that of the analysis error. We test some ideas to partly account for non-Gaussianity in the EnKF framework. We will present the results up to the time of the workshop.
data assimilation, numerical weather prediction, non-Gaussianity Hydro-debris 3D has been developed in order to simulate flow-particle interaction in the debris flow using Euler-lagrangean coupling numerical simulation. By precisely routing particle segregation, the mechanism of "Inverse grading" in debris flow observed in steep slope channel experiment in large eddies of debris flow is being reproduced by the model. Impacts of dense and frequent surface observations on a sudden severe rainstorm forecast: A case of an isolated convective system CTRL showed strong echoes and surface rainfalls, although the rainfall intensity is smaller than the JMA analyzed precipitation based on the radar and gauge networks. NOBC showed a significant decrease in surface relative humidity because of the dry biases of the surface station data, and consequently, showed decreased surface rainfalls. By contrast, BC showed stronger rainfall intensity, better matching with the JMA analyzed precipitation. The results suggest that the dense and frequent surface DA have a potential to improve the forecast accuracy for sudden severe rainstorms.
Data assimilation, Sudden severe rainstorm forecast, An isolated convective system

3.Meteorological Satellite Center
The Typhoon Haiyan in 2013 was among the strongest ever observed for tropical cyclones globally. The typhoon is characterized by fast translation, rapid intensification and extremely intense intensity at such a low latitude. To understand the behavior of the typhoon and to improve the intensity prediction, numerical simulations were performed by a regional coupled atmosphere-wave-ocean model with a horizontal resolution of 2 km. The effect of sea spray was included in the regional coupled model. Even using the model with a horizontal resolution of 2 km, it was difficult to reproduce rapid intensification of the typhoon and the maximum intensity without the effect of sea spray. An issue on the impact of horizontal resolution of numerical models on the simulation will be addressed. The effect of sea spray was confined to the near-surface boundary layer and led the typhoon to intensify more rapidly. It is crucial to develop a numerical weather prediction system including data assimilation in order to predict the extreme weather such as heavy rainfalls and typhoons in the post-K era. We have been developing the NICAM-LETKF system to assimilate the conventional observations, satellite microwave radiances from AMSU-A (Advanced Microwave Sounding Unit-A), and satellite-based global precipitation data GSMaP (Global Satellite Mapping of Precipitation). The NICAM-LETKF may be run at very high resolution, or may provide boundary conditions for even higher resolution systems.
Improving the NICAM-LETKF performance is at the center of enhancing mesoscale predictability for better preparedness for severe weather events well in advance.
Data assimilation experiments have been conducted with NICAM-LETKF at 112-and 28-km horizontal resolution with 100 ensemble members. Higher resolution experiment can reproduce the precipitation field well by assimilating precipitation observations. We need to keep improving the physical and computational performances of NICAM-LETKF to increase the resolution and the ensemble size, and to assimilate "Big Data" from the next-generation observations.