David F. Parrish

The National Meteorological Center's Spectral Statistical-Interpolation Analysis System

Abstract At the National Meteorological Center (NMC), a new analysis system is being extensively tested for possible use in the operational global data assimilation system. This analysis system is called the spectral statistical- interpolation (SSI) analysis system because the spectral coefficients used in the NMC spectral model are analyzed directly using the same basic equations as statistical (optimal) interpolation. Results from several months of parallel testing with the NMC spectral model have been very encouraging. Favorable features include smoother analysis increments, greatly reduced changes from initialization, and significant improvement of 1-5-day forecasts. Although the analysis is formulated as a variational problem, the objective function being minimized is formally the same one that forms the basis of all existing optimal interpolation schemes. This objective function is a combination of forecast and observation deviations from the desired analysis, weighted by the invent of the correspon...

Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances

Abstract In this second part of a two-part study of recursive filter techniques applied to the synthesis of covariances in a variational analysis, methods by which non-Gaussian shapes and spatial inhomogeneities and anisotropies for the covariances may be introduced in a well-controlled way are examined. These methods permit an analysis scheme to possess covariance structures with adaptive variations of amplitude, scale, profile shape, and degrees of local anisotropy, all as functions of geographical location and altitude. First, it is shown how a wider and more useful variety of covariance shapes than just the Gaussian may be obtained by the positive superposition of Gaussian components of different scales, or by further combinations of these operators with the application of Laplacian operators in order for the products to possess negative sidelobes in their radial profiles. Then it is shown how the techniques of recursive filters may be generalized to admit the construction of covariances whose charact...

Introduction of the GSI into the NCEP Global Data Assimilation System

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...

The New Global Operational Analysis System at the National Meteorological Center

Abstract At the National Meteorological Center (NMC), a new analysis system was implemented into the operational Global Data Assimilation System on 25 June 1991. This analysis system is referred to as Spectral Statistical Interpolation (SSI) because the spectral coefficients used in the NMC spectral model are analyzed directly using the same basic equations as statistical (optimum) interpolation. The major differences between the SSI analysis system and the conventional optimum interpolation (OI) analysis system previously used operationally at NMC are: –The analysis variables are closely related to the coefficients of the NMC spectral model. –Temperature observations are used, not heights as in the previous procedure. As a result, aircraft temperatures are being used for the first time at NMC. –Nonstandard observations, such as satellite estimates of total precipitable water and ocean-surface wind speeds, can be easily included. –No data selection is necessary. All observations are used simultaneously. –...

Improving Incremental Balance in the GSI 3DVAR Analysis System

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.

The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development

AbstractIn 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis u...

Four-Dimensional Variational Data Assimilation for the Blizzard of 2000

Abstract Four-dimensional variational (4DVAR) data assimilation experiments for the East Coast winter storm of 25 January 2000 (i.e., “blizzard of 2000”) were performed. This storm has received wide attention in the United States, because it was one of the major failures of the operational forecast system. All operational models of the U.S. National Weather Service (NWS) failed to produce heavy precipitation over the Carolina–New Jersey corridor, especially during the early stage of the storm development. The considered analysis cycle of this study is that of 0000 to 1200 UTC 24 January. This period was chosen because the forecast from 1200 UTC 24 January had the most damaging guidance for the forecasters at the National Weather Service offices and elsewhere. In the first set of experiments, the assimilation and forecast results between the 4DVAR and the operational three-dimensional variational (3DVAR) data assimilation method are compared. The most striking difference is in the accumulated precipitation...

A Case Study of the Sensitivity of the Eta Data Assimilation System

Abstract A case study is utilized to determine the sensitivity of the Eta Data Assimilation System (EDAS) to all operational observational data types used within it. The work described in this paper should be of interest to Eta Model users trying to identify the impact of each data type and could benefit other modelers trying to use EDAS analyses and forecasts as initial conditions for other models. The case study chosen is one characterized by strong Atlantic and Pacific maritime cyclogenesis, and is shortly after the EDAS began using three-dimensional variational analysis. The control run of the EDAS utilizes all 34 of the operational data types. One of these data types is then denied for each of the subsequent experimental runs. Differences between the experimental and control runs are analyzed to demonstrate the sensitivity of the EDAS system to each data type for the analysis and subsequent 48-h forecasts. Results show the necessity of various nonconventional observation types, such as aircraft data,...

Fine-Resolution 4DVAR Data Assimilation for the Great Plains Tornado Outbreak of 3 May 1999

Abstract The National Centers for Environmental Prediction fine-resolution four-dimensional variational (4DVAR) data assimilation system is used to study the Great Plains tornado outbreak of 3 May 1999. It was found that the 4DVAR method was able to capture very well the important precursors for the tornadic activity, such as upper- and low-level jet streaks, wind shear, humidity field, surface CAPE, and so on. It was also demonstrated that, in this particular synoptic case, characterized by fast-changing mesoscale systems, the model error adjustment played a substantial role. The experimental results suggest that the common practice of neglecting the model error in data assimilation systems may not be justified in synoptic situations similar to this one.

Retrieval of Moisture from Simulated GPS Slant-Path Water Vapor Observations Using 3DVAR with Anisotropic Recursive Filters

Abstract Anisotropic recursive filters are implemented within a three-dimensional variational data assimilation (3DVAR) framework to efficiently model the effect of flow-dependent background error covariance. The background error covariance is based on an estimated error field and on the idea of Riishojgaard. In the anisotropic case, the background error pattern can be stretched or flattened in directions oblique to the alignment of the grid coordinates and is constructed by applying, at each point, six recursive filters along six directions corresponding, in general, to a special configuration of oblique lines of the grid. The recursive filters are much more efficient than corresponding explicit filters used in an earlier study and are therefore more suitable for real-time numerical weather prediction. A set of analysis experiments are conducted at a mesoscale resolution to examine the effectiveness of the 3DVAR system in analyzing simulated global positioning system (GPS) slant-path water vapor observat...