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Initialization and the spin‐up effect are critical to model performance in the four‐dimensional variational data assimilation (4DVar) system. The incremental analysis update (IAU) technique is able to combat these problems. This study introduces the IAU technique into the 4DVar framework (IAU‐4DVar) of the operational China Meteorological Administration Global Forecast System (CMA‐GFS) and evaluates the performance of the IAU‐4DVar. In this IAU‐4DVar framework, the initial increments optimally estimated are split up into equally weighted forcing terms and integrated into the tangent model and its adjoint model. Then, the optimal estimation of the analysis increment is gradually introduced into the model integration during the assimilation window. A set of two‐month assimilation and forecast experiments were established and executed in this study, and the preliminary results show that the IAU technique can reduce the spin‐up effect of the initial analysis increment from the operational 4DVar system with digital filter initialization (DFI‐4DVar) by retaining the balance in dynamics and thermodynamics. In the optimal minimization process, the IAU‐4DVar converges faster than the DFI‐4DVar with a reduction of the computational cost of up to ca 40%. In addition, the introduction of IAU‐4DVar into the CMA‐GFS model could have a positive impact on the analysis and forecast skills relative to the operational DFI‐4DVar. [ABSTRACT FROM AUTHOR]

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localization in implementation, but more difficult to directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations of radiance observations are undetermined and the transmission of observational information is thereby obstructed. To determine the vertical coordinates of radiance observations, a weighted average hypsometry is proposed. Using this hypsometry, AMSU-A radiance observations are directly assimilated with an ensemble four-dimensional variational (En4DVar) DA system. It consists of a four-dimensional ensemble-variational (4DEnVar) system providing ensemble covariance and a 4DVar system. Observing system simulation experiments show that the hypsometry alleviates the degradations in the late period of medium-range forecast in the Northern Extratropics that occur in the traditional peak-based hypsometry. It obviously improves the analysis qualities and forecast skills of the En4DVar system and its two components, especially in the Southern Extratropics, when incorporating AMSU-A radiance observations. The improvement in the En4DVar-initialized forecast is comparable to that in the 4DVar-initialized forecast in the Southern Extratropics and Tropics. It indicates that a proper hypsometry enables efficient extraction of useful information from AMSU-A radiance observations by 4DEnVar with observation space localization. Therefore, the 4DEnVar provides high-quality ensemble covariances for En4DVar. [ABSTRACT FROM AUTHOR]

When assimilating surface pressure data in synoptic-scale models, we find the utilization rate of surface pressure data in zones with complex terrains is not high. Therefore, it is particularly important and urgent to carry out quality control of surface pressure data. Numerical weather prediction model analysis and forecasting provide essential data that can be compared with surface observations. The main adverse effects on surface pressure quality control include elevation differences between the model terrain and the observation stations and continuous outliers with the same characteristics in the initiation stage of quality control. Therefore, we propose a progressive empirical orthogonal function (EOF) with simulated observation (EOFs) combining barometric height correction (BHC) and biweight average correction (BAC) methods for the quality control of surface pressure data in this study. From the quality control results of the surface pressure data in regions with complex topography in China during June–August 2013, it was found that the BHC method could effectively reduce the deviations caused by elevation differences between the model terrain and the observation stations, and the BAC method could obviously reduce systematic deviations due to physical processes and the parameterization schemes of the models. The BHC-BAC method integrated the advantages of both methods and had the best correction effect. When continuous outliers with the same characteristics occurred in the initiation stage of quality control, the progressive EOF method might unreasonably eliminate observations. However, the progressive EOFs method could effectively solve this problem and had better performance in data quality control. The progressive EOFs quality control method with the combined BHC-BAC method could obviously reject outliers. The observation increment (deviations between observations and background field) after quality control by the progressive EOFs method was the closest to normal distribution, satisfying the Gaussian distribution assumption of data assimilation. [ABSTRACT FROM AUTHOR]

The spin-up refers to the dynamic and thermal adjustments at the initial stage of numerical integration to reach a statistical equilibrium state. The analyses on the characteristics and effects of spin-ups are of great significance for optimizing the initial field of the model and improving its forecast skills. In this paper, three different initial fields are used in the experiments: the analysis field of four-dimensional variational (4D-VAR) assimilation, the 3-hour prediction field in the operational forecasting system, and the Final (FNL) Operational Global Analysis data provided by National Centers for Environmental Prediction (NCEP). Then, the characteristics of spin-ups in the GRAPES (Global/Regional Assimilation and Prediction System) global forecast system (GRAPES_GFS) under different initial fields are compared and analyzed. In addition, the influence of the lost cloud-field information on the spin-up and forecast results of the GRAPES model in the current operation is discussed as well. The results are as follows. With any initial field, the spin-up of GRAPES_GFS has to go through two stages - the dramatic adjustment in the first half hour of integration and the slow dynamic and thermal adjustments afterwards. The spin-up in GRAPES_GFS lasts for at least 6 hours, and the adjustment is gradually completed from lower to upper layers in the model. Therefore, in the evaluation of the GRAPES_GFS, the forecast results in the first 6 hours should be avoided. And the GRAPES_GFS with its own analysis field performs better than the one using FNL reanalysis data for the cold start in the spin-up, because the variation amplitudes of the temperature and humidity tendency are smaller and the spin-up time is slightly shorter. Based on the 4D-VAR assimilation analysis field, the forecast in the operational model is artificially interrupted and restarted after 3 hours of integration. In this process, as the cloud-field information is not retained, the spin-up should repeat in the model. The characteristics of spin-up are mostly consistent with those using the 4D-VAR assimilation analysis field as the initial field. However, as the cloud-field information is not retained in the current operation, the hydrometeor content in the atmosphere at the early stage of the forecast is underestimated, affecting the calculation accuracy of the radiation and causing a systematic positive bias of temperature and geopotential height fields at 500 hPa. Besides, the precipitation is also underestimated at the early stage of the simulation, affecting the forecast of typhoon tracks. [ABSTRACT FROM AUTHOR]