Search Results

You are looking at 1 - 10 of 11 items for

  • Author or Editor: Daryl T. Kleist x
  • All content x
Clear All Modify Search
Daryl T. Kleist

Abstract

The assimilation of official advisory minimum sea level pressure observations has been developed and tested in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) to address forecaster concerns regarding some tropical systems being far too weak in operational Global Forecast System (GFS) analyses. The assimilation of these observations has been operational in the GFS since December 2009. Using the T574 version of the NCEP GFS model, it is demonstrated that the assimilation of these observations results in a substantial reduction in the initial intensity bias for tropical systems, resulting in improved track and intensity guidance for lead times out to 5 days.

Full access
Daryl T. Kleist and Kayo Ide

Abstract

This work describes the formulation of a hybrid four-dimensional ensemble--variational (4DEnVar) algorithm and initialization options utilized within the National Centers for Environmental Prediction global data assimilation system. Initialization schemes that are proposed for use are the tangent-linear normal mode constraint, weak constraint digital filter, and a combination thereof.

An observing system simulation experiment is carried out to evaluate the impact of utilizing hybrid 4DEnVar with various initialization techniques. The experiments utilize a dual-resolution configuration, where the ensemble is run at roughly half the resolution of the deterministic component. It is found that by going from 3D to 4D, analysis error is reduced for most variables and levels. The inclusion of a time-invariant static covariance when used without a normal mode–based strong constraint is found to have a small, positive impact on the analysis. The experiments show that the weak constraint digital filter degrades the quality of analysis, due to the use of hourly states to prescribe high-frequency noise. It is found that going from 3D to 4D ensemble covariances has a relatively larger impact in the extratropics, whereas the original inclusion of ensemble-based covariances was found to have the largest impact in the tropics. The improvements found in going from 3D to 4D covariances in the hybrid EnVar formulation are not as large as was found in Part I from the original introduction of the hybrid algorithm. The analyses generated by the 4D hybrid scheme are found to yield slightly improved extratropical height and wind forecasts, with smaller impacts on other variables and in general in the tropics.

Full access
Daryl T. Kleist and Kayo Ide

Abstract

An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments.

It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reduction for wind and moisture, most notably in the tropics. Forecast impact experiments show that the hybrid-initialized forecasts improve upon the 3DVar-based forecasts for most metrics, lead times, variables, and levels. An additional experiment that utilizes 3DEnVar (100% ensemble) demonstrates that the use of a 25% static error covariance contribution does not alter the quality of hybrid analysis when utilizing the tangent-linear normal mode constraint on the total hybrid increment.

Full access
Daryl T. Kleist and Michael C. Morgan

Abstract

A 36-h adjoint-based forecast sensitivity study of three response functions defined in the lower troposphere—average temperature in an isolated region of the upper Midwest (R 1), meridional temperature difference (R 2), and average zonal component of the wind (R 3)—is conducted with the goal of providing a synoptic and dynamic interpretation of the sensitivity gradient structure and evolution. In addition to calculating and interpreting the sensitivity gradients with respect to basic model variables along the model forecast trajectory, a technique is outlined that allows for the calculation of the sensitivity gradients with respect to variables derivable from the model state vector (including geopotential, relative vorticity, and divergence), and a method for visualizing the sensitivities with respect to the horizontal components of the wind is proposed and demonstrated.

The sensitivity of R 1 to all model and derived variables revealed that R 1 was controlled by nearly adiabatic processes associated with the addition or generation of temperature perturbations upstream of the region in which R 1 was defined. For R 2, the sensitivity gradients revealed the well-known influence of confluent horizontal flow and vertical tilting of isentropes to increase the north–south temperature gradient over the region within which R 2 was defined. The sensitivity of R 3 to the components of the horizontal wind reveals that simply adding or generating an upstream zonal wind perturbation is insufficient to change the zonal wind at 36 h as these wind perturbations upstream of the domain within which R 3 is defined are torqued by the Coriolis force as they are advected toward the domain. These results suggest adjoint-derived sensitivities of quasi-conserved response functions may be more easily interpretable than sensitivities calculated for nonconserved response functions.

Full access
Daryl T. Kleist and Michael C. Morgan

Abstract

The 24–25 January 2000 eastern United States snowstorm was noteworthy as operational numerical weather prediction (NWP) guidance was poor for lead times as short as 36 h. Despite improvements in the forecast of the surface cyclone position and intensity at 1200 UTC 25 January 2000 with decreasing lead time, NWP guidance placed the westward extent of the midtropospheric, frontogenetically forced precipitation shield too far to the east.

To assess the influence of initial condition uncertainties on the forecast of this event, an adjoint model is used to evaluate forecast sensitivities for 36- and 48-h forecasts valid at 1200 UTC 25 January 2000 using as response functions the energy-weighted forecast error, lower-tropospheric circulation about a box surrounding the surface cyclone, 750-hPa frontogenesis, and vertical motion. The sensitivities with respect to the initial conditions for these response functions are in general very similar: geographically isolated, maximized in the middle and lower troposphere, and possessing an upshear vertical tilt. The sensitivities are maximized in a region of enhanced low-level baroclinicity in the vicinity of the surface cyclone’s precursor upper trough. However, differences in the phase and structure of the gradients for the four response functions are evident, which suggests that perturbations could be constructed to alter one response function but not necessarily the others.

Gradients of the forecast error response function with respect to the initial conditions are used in an iterative procedure to construct initial condition perturbations that reduce the forecast error. These initial condition perturbations were small in terms of both spatial scale and magnitude. Those initial condition perturbations that were confined primarily to the midtroposphere grew rapidly into much larger amplitude upper-and-lower tropospheric perturbations. The perturbed forecasts were not only characterized by reduced final time forecast error, but also had a synoptic evolution that more closely followed analyses and observations.

Full access
Donald E. Lippi, Jacob R. Carley, and Daryl T. Kleist

Abstract

This work describes developments to improve the Doppler radial wind data assimilation scheme used in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) data assimilation system with a focus on convection-permitting, 0–18-h forecasts of a heavy precipitation single case study. This work focuses on two aspects: 1) the extension of the radial wind observation operator to include vertical velocity and 2) a refinement of the radial wind super-observation processing. The refinement includes reducing the magnitude of observation smoothing and allowing observations from higher scan angles into the analysis with the intent to improve the assimilation of the radar data for operational, convection-permitting models. The results of this study demonstrate that there is sensitivity to the refinement in super-observation settings. The inclusion of vertical velocity in the observation operator is shown to have a neutral to slightly positive impact on the forecast. Results from this study are suggested to be used as a foundation to prioritize future research into the effective assimilation of radial winds in an operational setting.

Free access
Bo Huang, Xuguang Wang, Daryl T. Kleist, and Ting Lei

Abstract

A scale-dependent localization (SDL) method was formulated and implemented in the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (4DEnVar) system for NCEP FV3-based Global Forecast System (GFS). SDL applies different localization to different scales of ensemble covariances, while performing a single-step simultaneous assimilation of all available observations. Two SDL variants with (SDL-Cross) and without (SDL-NoCross) considering cross-wave-band covariances were examined. The performance of two- and three-wave-band SDL experiments (W2 and W3, respectively) was evaluated through 1-month cycled data assimilation experiments. SDL improves global forecasts to 5 days over scale-invariant localization including the operationally tuned level-dependent scale-invariant localization (W1-Ope). The W3 SDL-Cross experiment shows more accurate tropical storm–track forecasts at shorter lead times than W1-Ope. Compared to the W2 SDL experiments, the W3 SDL counterparts applying tighter horizontal localization at medium-scale wave band generally show improved global forecasts below 100 hPa, but degraded global forecasts above 50 hPa. While the outperformance of the W3 SDL-NoCross experiment versus the W2 SDL-NoCross experiment below 100 hPa lasts for 5 days, that of the W3 SDL-Cross experiment versus the W2 SDL-Cross experiment lasts for 3 days. Due to local spatial averaging of ensemble covariances that may alleviate sampling error, the SDL-NoCross experiments show slightly better forecasts than the SDL-Cross experiments at shorter lead times. However, the SDL-Cross experiments outperform the SDL-NoCross experiments at longer lead times, likely from retention of more heterogeneity of ensemble covariances and resultant analyses with improved balance. Relative performance of tropical storm–track forecasts in the W2 and W3 SDL experiments are generally consistent with that of global forecasts.

Restricted access
Daryl T. Kleist, David F. Parrish, John C. Derber, Russ Treadon, Ronald M. Errico, and Runhua Yang

Abstract

The gridpoint statistical interpolation (GSI) analysis system is a unified global/regional three-dimensional variational data assimilation (3DVAR) analysis code that has been under development for several years at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center. It has recently been implemented into operations at NCEP in both the global and North American data assimilation systems (GDAS and NDAS, respectively). An important aspect of this development has been improving the balance of the analysis produced by GSI. The improved balance between variables has been achieved through the inclusion of a tangent-linear normal-mode constraint (TLNMC). The TLNMC method has proven to be very robust and effective. The TLNMC as part of the global GSI system has resulted in substantial improvement in data assimilation at NCEP.

Full access
Thomas M. Hamill, Jeffrey S. Whitaker, Daryl T. Kleist, Michael Fiorino, and Stanley G. Benjamin

Abstract

Experimental ensemble predictions of tropical cyclone (TC) tracks from the ensemble Kalman filter (EnKF) using the Global Forecast System (GFS) model were recently validated for the 2009 Northern Hemisphere hurricane season by Hamill et al. A similar suite of tests is described here for the 2010 season. Two major changes were made this season: 1) a reduction in the resolution of the GFS model, from 2009’s T384L64 (~31 km at 25°N) to 2010’s T254L64 (~47 km at 25°N), and some changes in model physics; and 2) the addition of a limited test of deterministic forecasts initialized from a hybrid three-dimensional variational data assimilation (3D-Var)/EnKF method.

The GFS/EnKF ensembles continued to produce reduced track errors relative to operational ensemble forecasts created by the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC). The GFS/EnKF was not uniformly as skillful as the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. GFS/EnKF track forecasts had slightly higher error than ECMWF at longer leads, especially in the western North Pacific, and exhibited poorer calibration between spread and error than in 2009, perhaps in part because of lower model resolution. Deterministic forecasts from the hybrid were competitive with deterministic EnKF ensemble-mean forecasts and superior in track error to those initialized from the operational variational algorithm, the Gridpoint Statistical Interpolation (GSI). Pending further successful testing, the National Oceanic and Atmospheric Administration (NOAA) intends to implement the global hybrid system operationally for data assimilation.

Full access
Daryl T. Kleist, David F. Parrish, John C. Derber, Russ Treadon, Wan-Shu Wu, and Stephen Lord

Abstract

At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains.

Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

Full access