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The hybrid Ensemble Kalman Filter -Variational (EnKF-Var) data assimilation (DA) system based on Grid-point Statistical Interpolation (GSI) is extended for the Hurricane-WRF model (HWRF). Background ensemble forecasts initialized by the EnKF are used to provide the flow-dependent error covariance to be ingested by GSI using the extended control variable method. The hybrid system is then applied to assimilate airborne radar data.In this article, the newly developed HWRF hybrid system capable of assimilating airborne radar observations is introduced. The impact of using variously estimated background error covariances on tropical cyclone (TC) core analyses and subsequent forecasts is explored by a detailed study of Hurricane Sandy (2012) and by systematic comparison of various sensitivity experiments for multiple cases during the 2012-2013 seasons. The hybrid system using the HWRF EnKF ensemble covariance (Hybrid-HENS) is able to correct both the wind and mass fields in a dynamically and thermodynamically coherent fashion. In contrast, the wind and pressure adjustments by GSI three-dimensional variation (GSI3DVar) using the static covariance are inconsistent. The wind and pressure relation in the covariances derived from the GFS ensemble (Hybrid-GENS) improves upon the static covariance, but is still inconsistent compared to that of HWRF. Verifications against independent flight-level and Stepped Frequency Microwave Radiometer (SFMR) wind data, and Hurricane Research Division (HRD) radar wind composite reveal that the Hybrid-HENS system improves the analysed TC structure upon both GSI3DVar and Hybrid-GENS. Hybrid-HENS and Hybrid-GENS improve the track, minimum sea-level pressure (MSLP) and Vmax forecast relative to GSI3DVar. Hybrid-HENS further improves track forecasts compared to Hybrid-GENS. Hybrid-HENS provides the largest positive impact of the airborne radar data. In comparison, GSI3DVar shows consistently negative impact of the data when analysing the structure and verifying track forecasts. Blending the static background error covariance in the hybrid system improves the maximum wind forecast while little benefit is found in the analysed structures and the MSLP and track forecasts.

A blending method for generating initial condition (IC) perturbations in a regional ensemble prediction system is proposed. The blending is to combine the large-scale IC perturbations from a global ensemble prediction system (EPS) with the small-scale IC perturbations from a regional EPS by using a digital filter and the spectral analysis technique. The IC perturbations generated by blending can well represent both largescale and small-scale uncertainties in the analysis, and are more consistent with the lateral boundary condition (LBC) perturbations provided by global EPS. The blending method is implemented in the regional ensemble system Aire Limit ee Adaptation Dynamique D eveloppement International-Limited Area Ensemble Forecasting (ALADIN-LAEF), in which the large-scale IC perturbations are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS), and the small-scale IC perturbations are generated by breeding in ALADIN-LAEF. Blending is compared with dynamical downscaling and breeding over a 2-month period in summer 2007. The comparison clearly shows impact on the growth of forecast spread if the regional IC perturbations are not consistent with the perturbations coming through LBC provided by the global EPS. Blending can cure the problem largely, and it performs better than dynamical downscaling and breeding.

Although the quality of numerical ensemble prediction systems (EPS) has greatly improved during the last few years, these systems still show systematic deficiencies. Specifically, they are underdispersive and lack both reliability and sharpness. A variety of statistical postprocessing methods allows for improving direct model output. Since 2007, Aire Limité e Adaptation Dynamique Dé veloppement International Limited Area Ensemble Forecasting (ALADIN-LAEF) has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG), and its 2-m temperature model output subject to calibration. This work follows the approach of nonhomogeneous Gaussian regression (NGR) that addresses a statistical correction of the first and second moment (mean bias and dispersion) for Gaussian-distributed continuous variables. It is based on the multiple linear regression technique and provides a predictive probability density function (PDF) in terms of a normal distribution. Fitting the regression coefficients, a minimum continuous ranked probability score (CRPS) estimation has been chosen instead of the more traditional maximum likelihood technique. The use of high-resolution analysis data on a 1 km 3 1 km grid as training data improves the forecast skill in terms of CRPS by about 35%, especially on the local scale. The percentage of outliers decreases significantly without loss of sharpness. Sensitivity studies confirm that about half of the total improvement can be attributed to the effect of a bias correction. The training length plays a minor role, at least for the chosen verification period. A rescaling of the predictive PDF is important in order to obtain sharp forecasts, especially in the short range. Applying the same method to the global ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF) gives improvements of similar magnitude. However, the calibrated 2-m temperature of ALADIN-LAEF still remains slightly better than the 2-m temperature from calibrated ECMWF-EPS, which leads to the conclusion that statistical downscaling of EPS cannot replace dynamical downscaling. Finally, an advanced version of NGR, the so-called NGR-TD, which uses timeweighted averaging within minimum CRPS estimation, is able to yield a further improvement of about 5% in terms of the CRPS.

The Tibetan Plateau is regarded as the Earth's Third Pole, which is the source region of several major rivers that impact more 20% the world population. This high‐altitude region is reported to have been undergoing much greater rate of weather changes under global warming, but the existing reanalysis products are inadequate for depicting the state of the atmosphere, particularly with regard to the amount of precipitation and its diurnal cycle. An ensemble Kalman filter (EnKF) data assimilation system based on the limited‐area Weather Research and Forecasting (WRF) model was evaluated for use in developing a regional reanalysis over the Tibetan Plateau and the surrounding regions. A 3‐month prototype reanalysis over the summer months (June−August) of 2015 using WRF‐EnKF at a 30‐km grid spacing to assimilate nonradiance observations from the Global Telecommunications System was developed and evaluated against independent sounding and satellite observations in comparison to the ERA‐Interim and fifth European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) global reanalysis. Results showed that both the posterior analysis and the subsequent 6‐ to 12‐hr WRF forecasts of the prototype regional reanalysis compared favorably with independent sounding observations, satellite‐based precipitation versus those from ERA‐Interim and ERA5 during the same period. In particular, the prototype regional reanalysis had clear advantages over the global reanalyses of ERA‐Interim and ERA5 in the analysis accuracy of atmospheric humidity, as well as in the subsequent downscale‐simulated precipitation intensity, spatial distribution, diurnal evolution, and extreme occurrence.