Hyo-Jong Song

The central Pacific as the export region of the El Niño‐Southern Oscillation sea surface temperature anomaly to Antarctic sea ice

[1] In the mature season of El Nino, Rossby waves do not easily propagate into the polar region, and the seasonal climatology of sea ice is minimal. Austral summer is a barrier to the persistent Antarctic dipole pattern (ADP) in sea ice. The sea surface temperature (SST) anomaly of central Pacific type El Nino (CP‐El Nino) in the central Pacific contributes to a strong Rossby wave response and weakening of the polar‐front jet that yields strong ADP in austral spring just before the ADP barrier. The strong ADP produces intensive sea‐ice‐air feedback, which allows the ADP anomaly to breach the barrier. In the conventional El Nino (EP‐El Nino) events, the upper‐level structure cannot contribute to the strong anomalous high pressure. In EP‐El Nino events, the anomalous high in the Bellingshausen Sea is replaced by an anomalous low after the austral autumn following the mature season, whereas the anomalous high pressure persists up to the austral winter in the CP‐El Nino. In the CP‐El Nino, the ADP persists until austral winter after the mature season of El Nino, whereas, in the EP‐El Nino, it does not persist after austral summer. The central Pacific cold SST anomaly of La Nina together with the seasonal SST climatology prolongs the opposite phase of the ADP anomaly up to the austral winter. Consequently, the tropical climate anomaly is exported to extratropics at the central Pacific in the Southern Hemisphere.

The impact of the diurnal cycle on the MJO over the Maritime Continent: a modeling study assimilating TRMM rain rate into global analysis

In the present study, we use modeling experiments to investigate the impact of the diurnal cycle on the Madden-Julian Oscillation (MJO) during the Australian summer. Physical initialization and a nudging technique enable us to assimilate the observed Tropical Rainfall Measuring Mission (TRMM) rain rate and atmospheric variables from the National Centers for Environmental Prediction—National Center for Atmospheric Research Reanalysis 2 (R2) into the Florida State University Global Spectral Model (FSUGSM), resulting in a realistic simulation of the MJO. Model precipitation is also significantly improved by TRMM rain rate observation via the physical initialization. We assess the influence of the diurnal cycle on the MJO by modifying the diurnal component during the model integration. Model variables are nudged toward the daily averaged values from R2. Globally suppressing the diurnal cycle (NO_DIURNAL) exerts a strong impact on the Maritime Continent. The mean state of precipitation increases and intraseasonal variability becomes stronger over the region. It is well known that MJO weakens as it passes over the Maritime Continent. However, the MJO maintains its strength in the NO_DIURNAL experiment, and the diminution of diurnal signals during the integration does not change the propagating speed of the MJO. We suggest that diminishing the diurnal cycle in NO_DIURNAL consumes less moist static energy (MSE), which is required to trigger both diurnal and intraseasonal convection. Thus, the remaining MSE may play a major role along with larger convective instability and stronger lower level moisture convergence in intensifying the MJO over the Maritime Continent in the model simulation.

Characteristics of a Spectral Inverse of the Laplacian Using Spherical Harmonic Functions on a Cubed-Sphere Grid for Background Error Covariance Modeling

AbstractIn this study, a spectral inverse method using spherical harmonic functions (SHFs) represented on a cubed-sphere grid (SHF inverse) is proposed. The purpose of the spectral inverse method studied is to help with data assimilation. The grid studied is the one that results from a spectral finite element decomposition of the six faces of the cubed sphere on Gauss–Legendre–Lobatto (GLL) points with equiangular gnomonic projection. For a given discretization of the cube in this form, as the total wavenumber of the test functions increases, there comes a point at which the cube’s eigenstructure fails to be able to replicate the spherical harmonic functions. The authors call this point a limit wavenumber in using the SHF inverse. In common with the authors’ previous research, the allowable total wavenumber of the SHF inverse increases more effectively with an enhanced polynomial order. The use of the eigenvectors and eigenvalues of the Laplacian, discretized on the grid spacing used in this study, to the...

The Recent Increase in the Occurrence of a Boreal Summer Teleconnection and Its Relationship with Temperature Extremes

AbstractThis study has investigated the relationship between temperature extremes and a subseasonal hemispheric teleconnection pattern over the Northern Hemisphere during boreal summer. By applying self-organizing map (SOM) analysis to 200-hPa geopotential fields from the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) for the period 1979–2012, a teleconnection pattern is identified that increased dramatically in its occurrence after the late 1990s. This pattern is characterized by a zonal wavenumber-5 pattern with anomalous high pressure cores over eastern Europe, northeastern Asia, the eastern North Pacific, the eastern United States, and Greenland. These high pressure centers coincide with regions of increasingly frequent temperature extremes in recent decades. To investigate the temporal evolution of the identified SOM pattern, time-lagged composites were performed relative to the days in which the 200-hPa geopotential field most closely resembled the SOM pattern. F...

Multi-resolution Hybrid Data Assimilation Core on a Cubed-sphere Grid (HybDA)

This study illustrates the characteristics of the data assimilation system at the Korea Institute of Atmospheric Prediction Systems (KIAPS), based on the cubed-sphere grid system. The most interesting feature is the use of spherical harmonic functions defined on cubed-sphere grid points, which makes it possible to control the allowable physical wavenumber for the analysis increments. The relevant computational costs and parallel scalability are represented. The multiple-resolution approach is a distinguishable aspect of this data assimilation system. The wavenumber, up to which the analysis is conducted, increases as the outer iteration progresses. This multiresolution strategy is based on an investigation into the change of spectral components of analysis increments. The multi-resolution outer-loop provides cost-effective analysis-improvement, by explicitly controlling the analysis increments entered into the observation operator. To utilize the high-resolution deterministic forecast as a background state, it is subtracted from the forecast ensemble, to produce ensemble forecast perturbation that is hybridized with static background error covariance. Based on the cycled analysis experiments, the higher-resolution deterministic forecast is shown to preserve the high-frequency feature of the analysis increment relative to the ensemble mean forecast.

Development of the Nonstationary Incremental Analysis Update Algorithm for Sequential Data Assimilation System

This study introduces a modified version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to improve the assimilation accuracy of the IAU while keeping the continuity of the analysis. Similar to the IAU, the NIAU is designed to add analysis increments at every model time step to improve the continuity in the intermittent data assimilation. However, unlike the IAU, the NIAU procedure uses time-evolved forcing using the forward operator as corrections to the model. The solution of the NIAU is superior to that of the forward IAU, of which analysis is performed at the beginning of the time window for adding the IAU forcing, in terms of the accuracy of the analysis field. It is because, in the linear systems, the NIAU solution equals that in an intermittent data assimilation method at the end of the assimilation interval. To have the filtering property in the NIAU, a forward operator to propagate the increment is reconstructed with only dominant singular vectors. An illustration of those advantages of the NIAU is given using the simple 40-variable Lorenz model.

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.

The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions: Advantages of Hybrid 4DEnVar

Hybrid four-dimensional ensemble-variational data assimilation (hybrid-4DEnVar) is a prospective successor to three-dimensional variational data assimilation (3DVar) in operational weather prediction centers currently developing a new weather prediction model and those that do not operate adjoint models. In experiments using real observations, hybrid-4DEnVar improved Northern Hemisphere (NH; 20°N-90°N) 500-hPa geopotential height forecasts up to 5 days in a NH summer month compared to 3DVar, with statistical significance. This result is verified against ERA-Interim through a Monte-Carlo test. By a regression analysis, the sensitivity of 5-d forecast is associated with the quality of the initial condition. The increased analysis skill for mid-tropospheric midlatitude temperature and sub-tropical moisture has the most apparent effect on forecast skill in the NH including a typhoon prediction case. Through attributing the analysis improvements by hybrid-4DEnVar separately to the ensemble background error covariance (BEC), its four-dimensional (4D) extension, and climatological BEC, it is revealed that the ensemble BEC contributes to the sub-tropical moisture analysis whereas the 4D extension does to the mid-tropospheric midlatitude temperature. This result implies that hourly wind-mass correlation in 6-h analysis window is required to extract the potential of hybrid-4DEnVar for the midlatitude temperature analysis to the maximum. However, the temporal ensemble correlation, in hourly time-scale, between moisture and another variable is invalid so that it could not work for improving the hybrid-4DEnVar analysis.

A comparison between a Monte Carlo implementation of retrospective optimal interpolation and an ensemble Kalman filter in nonlinear dynamics

To more correctly estimate the error covariance of an evolved state of a nonlinear dynamical system, the second and higher-order moments of the prior error need to be known. Retrospective optimal interpolation (ROI) may require relatively less information on the higher-order moments of the prior errors than an ensemble Kalman filter (EnKF) because it uses the initial conditions as the background states instead of forecasts. Analogous to the extension of a Kalman filter into an EnKF, an ensemble retrospective optimal interpolation (EnROI) technique was derived using the Monte Carlo method from ROI. In contrast to the deterministic version of ROI, the background error covariance is represented by a background ensemble in EnROI. By sequentially applying EnROI to a moving limited analysis window and exploiting the forecast from the average of the background ensemble of EnROI as a guess field, the computation costs for EnROI can be reduced. In the numerical experiment using a Lorenz-96 model and a Model-III of Lorenz with a perfect-model assumption, the cost-effectiveness of the suboptimal version of EnROI is demonstrated to be superior to that of EnKF using perturbed observations.