Sihye Lee

Development of an Observation Processing Package for Data Assimilation in KIAPS

A new observation processing system, the Korea Institute of Atmospheric Prediction Systems (KIAPS) Package for Observation Processing (KPOP), has been developed to provide optimal observation datasets to the data assimilation (DA) system for the Korean Integrated Model, KIM. This paper presents the KPOP’s conceptual design, how the principal modules have been developed, and some of their preliminary results. Currently, the KPOP is capable of processing almost all observation types used by the Korea Meteorological Administration (KMA) and some new observation types that have a positive impact in other operational centers. We have developed an adaptive bias correction (BC) method that only uses the background of the analysis time and selects the best observations through the consecutive iteration of BC and quality control (QC); it has been verified that this method will be the best suited for the KIAPS DA system until the development of variational BC (VarBC) has been completed. The requirement of considering the radiosonde balloon drift in the DA according to the increase of spatial resolution of the NWP model was accounted for using a balloon drift estimation method that considers the pressure difference and wind speed; thus the distance error was less than 1% in the sample test. Some kind of widely used methods were tested for height adjustment of the SURFACE observation, and a new method for temperature adjustment was outlined that used the correlation between temperature and relative humidity. In addition, three types of map projection were compared: the cubed-sphere (CS), equidistance (ED), and equirectangular (ER) projection for thinning. Data denial experiments were conducted to investigate how the KPOP affected the quality of the analysis fields in the three-dimensional variational data assimilation system (3D-Var). Qualified observations produced by the KPOP had a positive impact by reducing the analysis error.

Development of an Operational Hybrid Data Assimilation System at KIAPS

This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.

Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.