The National Meteorological Center (NMC) is developing a new analysis system called the Spectral Statistical-Interpolation (SSI) analysis system, which directly analyzes spectral coefficients using the same basic equations as statistical (optimal) interpolation. The SSI system has shown promising results in initial testing, with smoother analysis increments, reduced initialization changes, and improved 1–5-day forecasts. The SSI system differs from traditional optimal interpolation schemes in two main ways: it uses spectral coefficients as analysis variables and solves a single global problem using all observations simultaneously. This approach allows for the straightforward inclusion of unconventional data, such as radiances, and provides advantages in handling temperature observations and achieving smoother analysis increments. The paper provides a detailed description of the SSI system, including the objective function, forecast and observation error covariances, and initial results from long-term data assimilation runs and forecasts. The SSI system is compared to the current operational NMC analysis system, showing smaller and smoother increments and better forecast skill. Future improvements include refining the forecast-error covariance matrix, enhancing quality control, and incorporating more nonconventional observations.The National Meteorological Center (NMC) is developing a new analysis system called the Spectral Statistical-Interpolation (SSI) analysis system, which directly analyzes spectral coefficients using the same basic equations as statistical (optimal) interpolation. The SSI system has shown promising results in initial testing, with smoother analysis increments, reduced initialization changes, and improved 1–5-day forecasts. The SSI system differs from traditional optimal interpolation schemes in two main ways: it uses spectral coefficients as analysis variables and solves a single global problem using all observations simultaneously. This approach allows for the straightforward inclusion of unconventional data, such as radiances, and provides advantages in handling temperature observations and achieving smoother analysis increments. The paper provides a detailed description of the SSI system, including the objective function, forecast and observation error covariances, and initial results from long-term data assimilation runs and forecasts. The SSI system is compared to the current operational NMC analysis system, showing smaller and smoother increments and better forecast skill. Future improvements include refining the forecast-error covariance matrix, enhancing quality control, and incorporating more nonconventional observations.