Abstract

The method of statistical analysis of wind innovation (observation minus forecast) vectors is refined upon the work of Hollingsworth and Lönnberg (HL). The new refinements include (i) improved spectral representations of wind forecast error covariance functions, and (ii) simplified and yet more rigorously constrained formulations for multilevel analysis. The method is applied to wind innovation data over North America from the Navy Operational Global Atmospheric Prediction System (NOGAPS). The major products of the analysis include (i) wind observation error variance and vertical correlation, (ii) wind forecast error covariances as functions of height and horizontal distance, (iii) their spectra as functions of height and horizontal wavenumber, and (iv) partitioned vector wind error variances and correlation structures for the large-scale and synoptic-scale components and for the rotational and divergent components of synoptic scale. The results are compared with HL, showing a 20% overall reduction in wind forecast errors and a slight reduction in wind observation errors for the NOGAPS data in comparison with the European Centre for Medium-Range Weather Forecasts (ECMWF) global model data 16 years ago. The spatial structures of the estimated observation and forecast error correlation functions are found to be roughly comparable to those in HL.

Abstract

The method of statistical analysis of wind innovation (observation minus forecast) vectors is extended and applied to the innovation data collected over North America for a 3-month period from the Navy Operational Global Atmospheric Prediction System to estimate the height–wind forecast error correlation and to evaluate the related geostrophy. Both single-level and multilevel analyses are performed. The single-level analysis shows that the geostrophy is well satisfied in the middle troposphere but is not well satisfied in the boundary layer and around the tropopause. The multilevel analysis indicates that the cross correlation between height and tangential wind forecast errors at different vertical levels is not small and thus should not be neglected.

Abstract

While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications.

Abstract

The statistical analysis of innovation (observation minus forecast) vectors is one of the most commonly used techniques for estimating observation and forecast error covariances in large-scale data assimilation. Building on the work of Hollingsworth and Lönnberg, the height innovation data over North America from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are analyzed. The major products of the analysis include (i) observation error variances and vertical correlation functions, (ii) forecast error autocovariances as functions of height and horizontal distance, (iii) their spectra as functions of height and horizontal wavenumber. Applying a multilevel least squares fitting method, which is simpler and more rigorously constrained than that of Hollingsworth and Lönnberg, a full-space covariance function was determined. It was found that removal of the large-scale horizontal component, which has only small variation in the vertical, reduces the nonseparability. The results were compared with those of Hollingsworth and Lönnberg, and show a 20% overall reduction in forecast errors and a 10% overall reduction in observation errors for the NOGAPS data in comparison with the ECMWF global model data 16 yr ago.

Abstract

To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model’s state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met.

Abstract

Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF.

Abstract

A prognostic closure is introduced to, and evaluated in, NOAA’s Unified Forecast System. The closure addresses aspects that are not commonly represented in traditional cumulus convection parameterizations, and it departs from the previous assumptions of a negligible subgrid area coverage and statistical quasi-equilibrium at steady state, the latter of which becomes invalid at higher resolution. The new parameterization introduces a prognostic evolution of the convective updraft area fraction based on a moisture budget, and, together with the buoyancy-driven updraft vertical velocity, it completes the cloud-base mass flux. In addition, the new closure addresses stochasticity and includes a representation of subgrid convective organization using cellular automata as well as scale-adaptive considerations. The new cumulus convection closure shows potential for improved Madden–Julian oscillation (MJO) prediction. In our simulations we observe better propagation, amplitude, and phase of the MJO in a case study relative to the control simulation. This improvement can be partly attributed to a closer coupling between low-level moisture flux convergence and precipitation as revealed by a space–time coherence spectrum. In addition, we find that enhanced organization feedback representation and stochastic effects, represented using cellular automata, further enhance the amplitude and propagation of the MJO, and they provide realistic uncertainty estimates of convectively coupled equatorial waves at seasonal time scales. The scale-adaptive behavior of the scheme is also studied by running the global model with 25-, 13-, 9-, and 3-km grid spacing. It is found that the convective area fraction and the convective updraft velocity are both scale adaptive, leading to a reduction of subgrid convective precipitation in the higher-resolution simulations.

Abstract

The geographical and temporal characteristics of upper-tropospheric cold low (UTCL) and their relationship to tropical cyclone (TC) track and intensity change over the western North Pacific (WNP) during 2000–12 are examined using the TC best track and global meteorological reanalysis data. An analysis of the two datasets shows that 73% of 346 TCs coexist with 345 UTCLs, and 21% of the latter coexist with TCs within an initial cutoff distance of 15°. By selecting those coexisted systems within this distance, the possible influences of UTCL on TC track and intensity change are found, depending on their relative distance and on the sectors of UTCLs where TCs are located. Results show that the impact of UTCLs on TC directional changes are statistically insignificant when averaged within the 15° radius. However, left-turning TCs within 5° distance from the UTCL center exhibit large deviated directional changes from the WNP climatology, due to the presence of highly frequent abrupt left turnings in the eastern semicircle of UTCL. The abrupt turnings of TCs are often accompanied by their slow-down movements. Results also show that TCs seem more (less) prone to intensify at early (late) development stages when interacting with UTCLs compared to the WNP climatology. Intensifying (weakening) TCs are more distributed in the southern (northern) sectors of UTCLs, with less hostile conditions for weakening within 9°–13° radial range. In addition, rapid intensifying TCs take place in the south-southwest and east-southeast sectors of UTCLs, whereas rapid weakening cases appear in the western semicircle of UTCLs due to their frequent proximity to mainland coastal regions.

Abstract

Capitalizing on recently released reanalysis datasets and diabatic heating estimates based on Tropical Rainfall Measuring Mission (TRMM), the authors have conducted a composite analysis of vertical anomalous heating structures associated with the Madden–Julian oscillation (MJO). Because diabatic heating lies at the heart of prevailing MJO theories, the intention of this effort is to provide new insights into the fundamental physics of the MJO. However, some discrepancies in the composite vertical MJO heating profiles are noted among the datasets, particularly between three reanalyses and three TRMM estimates. A westward tilting with altitude in the vertical heating structure of the MJO is clearly evident during its eastward propagation based on three reanalysis datasets, which is particularly pronounced when the MJO migrates from the equatorial eastern Indian Ocean (EEIO) to the western Pacific (WP). In contrast, this vertical tilt in heating structure is not readily seen in the three TRMM products. Moreover, a transition from a shallow to deep heating structure associated with the MJO is clearly evident in a pressure–time plot over both the EEIO and WP in three reanalysis datasets. Although this vertical heating structure transition is detectable over the WP in two TRMM products, it is weakly defined in another dataset over the WP and in all three TRMM datasets over the EEIO.

The vertical structures of radiative heating Q_R associated with the MJO are also analyzed based on TRMM and two reanalysis datasets. A westward vertical tilt in Q_R is apparent in all these datasets: that is, the low-level Q_R is largely in phase of convection, whereas Q_R in the upper troposphere lags the maximum convection. The results also suggest a potentially important role of radiative heating for the MJO, particularly over the Indian Ocean. Caveats in heating estimates based on both the reanalysis datasets and TRMM are briefly discussed.

Abstract

For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.