Verification of modeling of convective events based on radar reflectivity

<p>The use of numerical modeling for atmospheric research is complicated by the problem of verification by a limited set of measurement data. Comparison with radar measurements is widely used for assessing the quality of the simulation. The probabilistic nature of the development of convective phenomena determines the complexity of the verification process: the reproduction of the pattern of the convective event is prior to the quantitative agreement of the values at a particular point at a particular moment.</p><p>We propose a method for verifying the simulation results based on comparing areas with the same reflectivity. The method is applied for verification of WRF-modeling of convective events in the Aragats highland massif in Armenia. It is shown that numerical simulation demonstrates approximately the same form of distribution of areas of equal reflectivity as for radar-measured reflectivity. In this case, the model tends to overestimate on average reflectivity, while enabling us to obtain the qualitatively correct description of the convective phenomenon.</p><p>The proposed technique can be used to verify the simulation results using data on reflectivity obtained by a satellite or a meteoradar. The technique allows one to avoid subjectivity in the interpretation of simulation results and estimate the quality of reproducing the “general pattern” of the convective event.</p>

Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)

Ground surface characteristics (i.e., topography and landscape patterns) are important factors in geographic dynamics. Thus, the complexity of ground surface is a valuable indicator for designing multiscale modeling concerning the balance between computational costs and the accuracy of simulations regarding the resolution of modeling. This study proposes the concept of comprehensive surface complexity (CSC) to quantity the degree of complexity of ground by integrating the topographic complexity indices and landscape indices representing the land use and land cover (LULC) complexity. Focusing on the meteorological process modeling, this paper computes the CSC by constructing a multiple regression model between the accuracy of meteorological simulation and the surface complexity of topography and LULC. Regarding the five widely studied areas of China, this paper shows the distribution of CSC and analyzes the window size effect. The comparison among the study areas shows that the CSC is highest for the Chuanyu region and lowest for the Wuhan region. To investigate the application of CSC in meteorological modeling, taking the Jingjinji region for instance, we conducted Weather Research and Forecasting Model (WRF) modeling and analyzed the relationship between CSC and the mean absolute error (MAE) of the temperature at 2 meters. The results showed that the MAE is higher over the northern and southern areas and lower over the central part of the study area, which is generally positively related to the value of CSC. Thus, it is feasible to conclude that CSC is helpful to indicate meteorological modeling capacity and identify those areas where finer scale modeling is preferable.

Using a Balloon-Launched Unmanned Glider to Validate Real-Time WRF Modeling

The use of small unmanned aerial systems (sUAS) for meteorological measurements has expanded significantly in recent years. SUAS are efficient platforms for collecting data with high resolution in both space and time, providing opportunities for enhanced atmospheric sampling. Furthermore, advances in mesoscale weather research and forecasting (WRF) modeling and graphical processing unit (GPU) computing have enabled high resolution weather modeling. In this manuscript, a balloon-launched unmanned glider, complete with a suite of sensors to measure atmospheric temperature, pressure, and relative humidity, is deployed for validation of real-time weather models. This work demonstrates the usefulness of sUAS for validating and improving mesoscale, real-time weather models for advancements toward reliable weather forecasts to enable safe and predictable sUAS missions beyond visual line of sight (BVLOS).