The National Meteorological Center (NMC) is testing a new analysis system called the spectral statistical-interpolation (SSI) analysis system for use in the operational global data assimilation system. This system uses the same basic equations as statistical (optimal) interpolation to analyze spectral coefficients from the NMC spectral model. Results from parallel testing with the NMC spectral model have been encouraging, showing smoother analysis increments, reduced changes from initialization, and improved 1–5-day forecasts. The SSI system is formulated as a variational problem but uses the same objective function as existing optimal interpolation schemes. The objective function is a combination of forecast and observation deviations from the desired analysis, weighted by the inverses of the corresponding forecast- and observation-error covariance matrices. The SSI system differs from the current OI system in that it uses spectral coefficients instead of gridpoint values and uses all observations at once to solve a global problem. This allows the inclusion of unconventional data such as radiances. The SSI system is closely related to a three-dimensional variational analysis system being developed at the European Centre for Medium-Range Weather Forecasts. The SSI system has several advantages over the current OI system, including the ability to use temperature observations instead of height observations and the ability to include a wide variety of observations. The system is currently being tested and is expected to improve forecast accuracy and reduce computational costs. The SSI system is based on a general objective function that can be used for both existing OI procedures and variational analysis schemes. The system is being developed based on this objective function and is named spectral statistical interpolation (SSI) to emphasize its differences from traditional OI. The SSI system is being tested for use in the operational global data assimilation system and has shown promising results in terms of forecast accuracy and computational efficiency. The system is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The SSI system is being developed based on the general objective function and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The system is currently being tested and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The SSI system is being developed based on the general objective function and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The system is currently being tested and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments.The National Meteorological Center (NMC) is testing a new analysis system called the spectral statistical-interpolation (SSI) analysis system for use in the operational global data assimilation system. This system uses the same basic equations as statistical (optimal) interpolation to analyze spectral coefficients from the NMC spectral model. Results from parallel testing with the NMC spectral model have been encouraging, showing smoother analysis increments, reduced changes from initialization, and improved 1–5-day forecasts. The SSI system is formulated as a variational problem but uses the same objective function as existing optimal interpolation schemes. The objective function is a combination of forecast and observation deviations from the desired analysis, weighted by the inverses of the corresponding forecast- and observation-error covariance matrices. The SSI system differs from the current OI system in that it uses spectral coefficients instead of gridpoint values and uses all observations at once to solve a global problem. This allows the inclusion of unconventional data such as radiances. The SSI system is closely related to a three-dimensional variational analysis system being developed at the European Centre for Medium-Range Weather Forecasts. The SSI system has several advantages over the current OI system, including the ability to use temperature observations instead of height observations and the ability to include a wide variety of observations. The system is currently being tested and is expected to improve forecast accuracy and reduce computational costs. The SSI system is based on a general objective function that can be used for both existing OI procedures and variational analysis schemes. The system is being developed based on this objective function and is named spectral statistical interpolation (SSI) to emphasize its differences from traditional OI. The SSI system is being tested for use in the operational global data assimilation system and has shown promising results in terms of forecast accuracy and computational efficiency. The system is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The SSI system is being developed based on the general objective function and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The system is currently being tested and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The SSI system is being developed based on the general objective function and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments. The system is currently being tested and is expected to improve the accuracy of weather forecasts by providing more accurate and balanced analysis increments.