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• I weather net radar verification#

While most radar data assimilation focused on assimilating the reflectivity and radial velocity according to the precipitation echoes, few studies attempted to assimilate non-precipitation echoes that are detected within the radar observation radius.

As a result, precipitation forecasting skills were improved and MCS was predicted more realistically. Radar data were assimilated to improve water vapor and hydrometeor mixing ratios (rainwater, snow, graupel, and hail). (2019) used a coupled hybrid ensemble square root filter and three-dimensional ensemble-variational (EnSRF-En3DVar) radar data assimilation system to simulate a mesoscale convective system (MCS) in southeastern China on 5 June 2009. The role of a relative humidity operator is to provide a humid environment where precipitation can persist, assuming that the area where the reflectivity is greater than a certain threshold becomes saturated. The linearization error was improved by using the retrieved rain mixing ratio and water vapor from reflectivity. (2013) showed that the rain mixing ratio did not increase properly during radar data assimilation because of the linearization error of the reflectivity-to-rain mixing ratio. By applying the reflectivity operator, the cold pool was simulated similarly to the real world, and the spin-up time was reduced to improve the analysis and prediction accuracy of short-lived storms. Gao and Stensrud (2012) developed a reflectivity operator that classifies reflectivity information into hydrometeors (rain, snow, and hail) according to the temperature field of a numerical model.

To effectively simulate deep convective clouds during the cold rain process, both liquid and solid water particles must exist. In addition, overestimated hydrometeors were reduced through null-echo data assimilation. An analysis of a contoured frequency by altitude diagram (CFAD) and time–height cross-sections showed that increased hydrometeors throughout the data assimilation period enhanced precipitation formation, and reflectivity under the melting layer was simulated similarly to the observations during the peak precipitation times.

I weather net radar verification#

The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. Numerical experiments with conventional radar DA over-predicted the precipitation. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. The model removes excessive humidity and four types of hydrometeors (wet and dry snow, graupel, and rain) based on the radar reflectivity by using a three-dimensional variational (3D-Var) data assimilation technique within the WRFDA system. A null-echo is defined as a region with non-precipitation echoes within the radar observation range. In addition to the conventional assimilation of radar data, which focuses on assimilating the radial velocity and reflectivity of precipitation echoes, this study assimilates null-echoes and analyzes the effect of null-echo data assimilation on short-term quantitative precipitation forecasting (QPF). Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) radial velocity and reflectivity data for three events of convective systems along the Changma front are conducted. This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area.