Search Results (5)

Based on the “21.7” Henan extreme rainstorm case, this study investigates the influence of updating model state variables in the GSI-EnKF (Gridpoint Statistical Interpolation–ensemble Kalman filter) system with the Thompson microphysics scheme. Six sensitivity experiments are conducted to assess the impact of updating different model state variables on the EnKF analysis and subsequent forecast. The experiments include the Z_ALL experiment (updating all variables), the Z_NoEnv experiment (excluding dynamical and thermodynamical variables), the Z_NoNr experiment (excluding rainwater number concentration), and three additional experiments that examine the removal of updating horizontal wind (U, V), vertical wind (W), and perturbation potential temperature (T), which are marked as Z_NoUV, Z_NoW, and Z_NoT. The results indicate that updating different model state variables leads to various effects on dynamical, thermodynamical, and hydrometeor fields. Specifically, excluding the update of vertical wind or perturbation potential temperature has little effect on the rainwater mixing ratio, whereas excluding the update of the rainwater number concentration causes a significant increase in the rainwater mixing ratio, particularly in the northern region of Zhengzhou. Not updating horizontal wind or environmental variables shifts the rainwater mixing ratio northward, deviating from the observed rainfall center. The analysis of near-surface divergence and vertical wind also reveals that not updating certain variables could result in weaker or less detailed wind structures. Although radar reflectivity, which is mainly influenced by the mixing ratios of hydrometeors, shows consistent spatial distribution across experiments, their intensity varies, with the Z_ALL experiment showing the most accurate prediction. The 4 h deterministic forecasts based on the ensemble mean analysis demonstrate that updating all variables provides the best improvement in predicting the “21.7” Henan extreme rainstorm. These results emphasize the importance of updating all relevant model variables for improving predictions of extreme rainstorms. Full article

Assimilation of cloud properties in the convective scale ensemble data assimilation system is one of the prime topics of research in recent years. Satellites can retrieve cloud properties that are important sources of information of the cloud and atmospheric state. The Advance Baseline Imager (ABI) aboard the GOES-16 geostationary satellite brings an opportunity for retrieving high spatiotemporal resolution cloud properties, including cloud water path over continental United States. This study investigates the potential impacts of assimilating adaptively thinned GOES-16 cloud water path (CWP) observations that are assimilated by the ensemble-based Warn-on-Forecast System and the impact on subsequent weather forecasts. In this study, for CWP assimilation, multiple algorithms have been developed and tested using the adaptive-based thinning method. Three severe weather events are considered that occurred on 19 July 2019, 7 May and 21 June 2020. The superobbing procedure used for CWP data smoothed from 5 to 15 km or more depending on thinning algorithm. The overall performance of adaptively thinned CWP assimilation in the Warn-on-Forecast system is assessed using an object-based verification method. On average, more than 60% of the data was reduced and therefore not used in the assimilation system. Results suggest that assimilating less than 40% of CWP superobbing data into the Warn-on-Forecast system is of similar forecast quality to those obtained from assimilating all available CWP observations. The results of this study can be used on the benefits of cloud assimilation to improve numerical simulation. Full article

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a landfall typhoon Lekima (2019). Observations from a single operational coastal Doppler are quality-controlled and assimilated. Compared with the baseline experiment initialized by GFS analysis, the reflectivity data assimilation experiment (Z-DA) resulted in an obvious improvement in both structural analysis and typhoon forecast skills in terms of intensity, precipitation, and track. Sensitivity experiments were conducted to evaluate the ability of EnKF to update certain state variables considering that the degree of freedom of analytical variables increased with a DM MP scheme. When either the total number concentration or other large-scale state variables that are not directly linked to reflectivity observations via the observation operator are not updated, the tendency of RMSIs and PS to be imbalanced is significantly increased during DA cycles compared to those of Z-DA with updating a full set of state variables, resulting in increased intensity and track forecast errors. These results indicate that the reliable ensemble covariance could handle the underconstraint issue associated with the DM scheme, and helps in obtaining more physically balanced analytical fields. Full article

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data. Full article

The data assimilation method to improve the sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied to this weather phenomenon. In this paper, two sea fog cases over the Yellow sea, one spread widely and the other spread narrowly along the coastal area, are studied in detail by a series of numerical experiments with 3DVAR and EnKF based on the Grid-point Statistical Interpolation (GSI) system and the Weather Research and Forecasting (WRF) model. The results show that the assimilation effect of EnKF outperforms that of 3DVAR: for the widespread-fog case, the probability of detection and equitable threat scores of the forecasted sea fog area are improved respectively by ~57.9% and ~55.5%; the sea fog formation of the other case completely mis-forecasted by 3DVAR was produced successfully by EnKF. These improvements of EnKF relative to 3DVAR benefit from its flow-dependent background error covariances, resulting in more realistic depiction of sea surface wind for the widespread-fog case and better moisture distribution for the other case in the initial conditions. More importantly, the correlation between temperature and humidity in the background error covariances of EnKF plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement in the initial moisture conditions for sea fog forecast. Extra sensitivity experiments of EnKF indicate that the forecast result is sensitive to ensemble inflation and localization factors, in particular, highly sensitive to the latter. Full article