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This study explores the impact of assimilating radar radial velocity (RV) on the forecast of Super Typhoon Lekima (2019) using the Weather Research and Forecasting (WRF) model and three-dimensional variational (3DVAR) assimilation system with different background error length scales. The results of two single observation tests show that the smaller background error length scale is able to constrain the spread of radar observation information within a relatively reasonable range compared with the larger length scale. During the five data assimilation cycles, the position and structure of the near-land typhoon are found to be significantly affected by the setting of the background error length scale. With a reduced length scale, the WRF-3DVAR system could effectively assimilate the radar RV to produce more accurate analyses, resulting in an enhanced typhoon vortex with a dynamic and thermal balance. In the forecast fields, the experiment with a smaller length scale not only reduces the averaged track error for the 24-h forecasts to less than 20 km, but it also more accurately captures the evolutions of the typhoon vortex and rainband during typhoon landing. In addition, the spatial distribution and intensity of heavy precipitation are corrected. For the 24-h quantitative precipitation forecasts, the equitable threat scores of the experiment with a reduced length scale are greater than 0.4 for the threshold from 1 to 100 mm and not less than 0.2 until the threshold increases to 240 mm. The enhanced prediction performances are probably due to the improved TC analysis. [ABSTRACT FROM AUTHOR]

Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model. [ABSTRACT FROM AUTHOR]

This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems (MCSs) with a duration greater than seven hours that occurred in the Guangdong Province of China during June 2020 were included in the experiments. The results show that both the radar reflectivity data assimilation and lightning data assimilation improved the analyses and short-term forecasts of the precipitation and lightning of the MCSs. On average, for precipitation forecasts, the experiments with radar reflectivity data assimilation performed better than those with lightning data assimilation; however, for lightning forecasts, the experiments with lightning data assimilation performed better in the analysis period and 1 h forecast, and for some cases, the superiority lasted to three forecast hours. This highlights the potential of lightning data assimilation in short-term lightning forecasting. [ABSTRACT FROM AUTHOR]

This work describes the implementation of an updated radar reflectivity assimilation scheme with the three-dimensional variational (3D-Var) system of Weather Research and Forecast (WRF). The updated scheme, instead of the original scheme assuming the relative humidity to a fixed value where radar reflectivity is higher than a threshold, assimilates pseudo water vapor retrieved by the Bayesian method, which would be consistent with clouds/precipitations provided by the model in theory. To verify the effect of the updated scheme to the improvement of precipitation simulation, a convective case in Wenquan County and the continuous monthly simulation with contrasting experiments in Xinjiang were performed. The test of single reflectivity observation demonstrates that the water vapor retrieved by the Bayesian method is consistent with the meteorological situation around. In the convective case, both the updated and original scheme results show that the assimilation of pseudo water vapor can adjust to the environmental conditions of water vapor and temperature. This can improve the hourly precipitation forecast skill more than the contrasting experiment, which was designed to only assimilate conventional observations and radar radial velocity data. In the continuous monthly experiments, the updated scheme reveals that the analysis of water vapor is more reasonable, and obtains a better precipitation forecast skill for 6 h accumulated precipitation than the contrasting experiments. [ABSTRACT FROM AUTHOR]

Based on the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR) data assimilation system, this study investigates the effects of assimilation of radar reflectivity and radial velocity under different momentum control variables on the forecast of Southwest China Vortex precipitation. It is shown that the U−V control variable strengthens the wind speed and vorticity to be better matching the observation, while using ψ−χ as the control variable will produce too large increments which are unphysical. The root mean square errors (RMSE) of radar radial velocity are around 2.4 m/s in the experiment using ψ−χ control variables, while the RMSE are below 2 m/s in the experiment with U−V control variables. The composite reflectivity from the analysis of the U−V control variables matches better with the observation than that from the analysis of the ψ−χ control variables, i.e., the forecast rain band location under U−V control variables is more accurate. ψ−χ control variable enhances the cold high-pressure system in near surface, while the U−V control variable is not significant. The water vapor flux convergence in the lower layers of the ψ−χ control variable is overestimated leading excessive precipitation in the forecast. The Equitable Threat Score (ETS) of the U−V control variable is about 0.1 higher than ψ−χ control variable. In summary, the U−V control variable is superior to the ψ−χ control variable in terms of analysis and forecasting about Southwest China Vortex precipitation. [ABSTRACT FROM AUTHOR]

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. 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. 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). A null-echo is defined as a region with non-precipitation echoes within the radar observation range. 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. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. Numerical experiments with conventional radar DA over-predicted the precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. 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. In addition, overestimated hydrometeors were reduced through null-echo data assimilation. [ABSTRACT FROM AUTHOR]