Ncepncar ReanalysisEdit
The NCEP/NCAR Reanalysis is a long-running atmospheric data product that combines historical weather observations with a fixed numerical weather prediction model to produce a coherent, gridded record of atmospheric conditions. Developed through a collaboration between the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), the reanalysis project aims to create a consistent historical climate data set that can be used for research, model evaluation, and verification of climate hypotheses.
The reanalysis framework represents a particular approach to reconstructing past weather: it merges observations from a wide array of sources—surface stations, radiosondes, ships, aircraft, and, in later years, satellite data—with a weather-p prediction model and a data-assimilation system. The goal is to produce gridded fields of atmospheric variables (such as temperature, humidity, wind, and geopotential height) that are physically consistent over time, enabling researchers to study long-term patterns and variability in the climate system. For these reasons, the NCEP/NCAR Reanalysis has become a cornerstone resource in climate science, weather research, and the evaluation of climate models reanalysis and data assimilation techniques.
Background
The project emerged to address the need for a homogeneous, long-term record of atmospheric states. Before systematic reanalyses, researchers relied on disparate observations that were uneven in space, time, and instrumentation, making trend detection and variability studies difficult. By applying a single assimilation framework to a broad set of observations across decades, the NCEP/NCAR Reanalysis provides a unified baseline that supports investigations into teleconnections, storm tracks, precipitation regimes, and regional climate change signals. The effort reflects a broader push in climate science toward creating climate data records that are internally consistent and broadly usable by the research community climate data record.
Methodology
Assimilation framework: The reanalysis uses a fixed atmospheric model and a data-assimilation system to blend observations with the model forecast. This process yields gridded fields of meteorological variables that are dynamically consistent with the underlying physics encoded in the model. See data assimilation and NCEP for more on the modeling and assimilation structure.
Observational inputs: Early years rely heavily on in-situ observations from land-based stations, ships, and radiosondes, with gradual incorporation of satellite-derived data as such measurements became available and reliable. The evolution of observational coverage across the decades is a central feature of the reanalysis and a key source of its strengths and limitations. See radiosonde and satellite for background on data sources.
Spatial and temporal framing: Outputs are provided on a regular latitude–longitude grid and across multiple vertical levels, enabling users to analyze surface conditions as well as atmospheric structure aloft. The historical span begins in the mid-20th century and extends through the era of modern observing systems, making it a reference point for long-term climate studies. See global climate data and geopotential height for related concepts.
Variants and evolution: The NCEP/NCAR Reanalysis has undergone refinements and has inspired subsequent reanalysis projects that aim to improve bias handling, data coverage, and model physics. Comparative examples include other major reanalyses such as ERA-40, ERA-Interim, and JRA-55, which collectively illustrate the range of approaches to producing long-term climate records.
Data products and usage
Core variables: The reanalysis provides gridded fields for temperature, specific humidity, winds, geopotential height, and surface pressure, among others. These variables are available at multiple pressure levels and on various temporal aggregations (e.g., monthly means, yearly means), facilitating both climate-scale analyses and case-study investigations. See temperature and wind for related topics.
Historical coverage: The dataset covers multiple decades starting in the late 1940s, enabling researchers to examine long-term trends and variability in global circulation patterns, storm tracks, and regional climate anomalies. See NAO and ENSO for examples of large-scale variability analyzed with reanalysis data.
Applications: Researchers use the reanalysis to validate climate models, study historical weather events, examine teleconnections, and construct climate diagnostics. Its consistency across time makes it a common benchmark against which newer reanalysis products are compared. See model validation and teleconnections for context.
Strengths and limitations
Strengths: - Long, continuous time series built from a unified framework, which helps minimize artificial discontinuities that arise from stitching together disparate data sources. - Broad geographic and vertical coverage, allowing regional and global studies of atmospheric behavior. - Widely used in education and research, facilitating comparability across studies and domains.
Limitations: - Data-sparsity in early decades introduces uncertainties, particularly over ocean basins and remote regions where observations were scarce. - Dependence on a fixed model and assimilation system means that systematic biases in the underlying model can imprint on the reanalysis fields, especially in periods with limited observational input. - Changes in observation systems (e.g., the introduction of satellite data) affect the homogeneity of the time series, complicating trend analyses in some variables and regions.
Controversies and debates
Observational gaps and inhomogeneities: Because the quality and quantity of observations evolved over the decades, some researchers argue that certain long-term trends derived from the reanalysis should be interpreted cautiously. Critics emphasize that changes in instrumentation and data coverage can create artificial signals, particularly in the early decades and over data-sparse regions. Proponents respond that the assimilation framework is designed to mitigate many of these issues, and that reanalyses remain valuable for context and cross-comparison with other datasets. See radiosonde and satellite histories for related discussions.
Intercomparison with other reanalyses: Comparisons between the NCEP/NCAR Reanalysis and other products such as ERA-40, ERA-Interim, and JRA-55 have highlighted systematic differences in certain fields and regions. These discrepancies have spurred debates about the relative strengths and weaknesses of different assimilation systems and model constructions, prompting ongoing improvements in reanalysis methodology. See ECMWF and JRA-55 for background on alternative approaches.
Use in trend studies: While reanalyses are foundational for climate research, some scholars urge caution when using them to infer long-term climate trends, especially for variables with strong sensitivity to observation coverage or model biases. The nuanced view is that reanalysis data are best used in conjunction with other observational products and independent methods to triangulate climate signals. See climate trend and model validation for broader considerations.