STAR Joint Polar Satellite System Algorithms & Data Products website National Oceanographic & Atmospheric Administration website NOAA Center for Satellite Applications and Research website
Global temperature and moisture retrieved near 700 hPa retrieved by NUCAPS (left) and ECMWF (right)<br>from CrIS/ATMS observations performed on 11/24/2014 - click to enlarge

Global temperature and moisture retrieved near 700 hPa retrieved by NUCAPS (left) and ECMWF (right)
from CrIS/ATMS observations performed on 11/24/2014 - click to enlarge

NUCAPS Products

Team Lead: Lihang Zhou, Antonia Gambacorta


Launched on-board the Joint Polar Satellite System (JPSS) Suomi National Polar-orbiting Partnership (NPP) platform on October 28, 2011, the Cross-track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS) represent the next generation of U.S. polar-orbiting operational sounding systems.

The NOAA Unique Combined Atmospheric Processing System (NUCAPS) is a heritage algorithm based upon the Atmospheric Infrared Sounder (AIRS) Science Team algorithm (Susskind, Barnet, Blaisdell, 2003), implemented operationally at NOAA since 2002. The NUCAPS algorithm holds a modular architecture that was specifically designed at NOAA/STAR to be compatible with "AIRS-like" sounding systems. The same retrieval algorithm and the same underlying spectroscopy are currently used to process the AIRS/AMSU suite, the IASI/AMSU/MHS suite (operational since 2008) and now the CrIS/ATMS suite operational at NOAA Comprehensive Large Array-data Stewardship System (CLASS) since April 8, 2014.

Algorithm Description

The NUCAPS algorithm is a heritage algorithm based on the AIRS Science Team is designed for deriving environmental data records (EDRs) from the JPSS satellites, starting with Suomi NPP. The code consists of six modules as described below:

  • A preliminary input quality control, look up tables and ancillary product acquisition.
  • A microwave (MW) retrieval module which derives cloud liquid water flags and microwave surface emissivity uncertainty (Rosenkranz, 2000).
  • A fast eigenvector regression retrieval for temperature and moisture that is trained against ECMWF analysis and CrIS all sky radiances (Goldberg et al., 2003).
  • A cloud clearing module that combines a set of microwave and infrared (IR) channels (along with, in the future, visible observations provided by the onboard VIIRS instrument) to produce cloud-cleared infrared (IR) radiances (Chahine, 1974).
  • A second fast eigenvector regression retrieval for temperature and moisture that is trained against ECMWF analysis and CrIS cloud cleared radiances (Goldberg et al., 2003).
  • The final infrared physical retrieval, which employs the previous regression retrieval as a first guess (Susskind, Barnet, Blaisdell, 2003).

NUCAPS Phase IV Algorithm Readiness Review (ARR) completed on July 6, 2017. The NUCAPS Phase IV upgrade adds ATMS block 2 and CrIS Full Spectral resolution input to the retrievals and updates the look up table coefficients. NUCAPS Phase IV was declared ready for operations on April 18, 2018 and is expected to be transitioned to operations (i.e., disseminated to the users) on April 30, 2018. See ReadMe for Data Users for details.

Details on the NUCAPS algorithm can be found in the Algorithm Theoretical Basis Document (ATBD) linked below.

Products and Data Access

Products and Documentation:

EDR Long-Term Monitoring


The NUCAPS suite of EDR products includes two different files in NetCDF format: the Standard Product and the Cloud-Cleared Radiance (CCR) Product. The Standard Product consists of retrieved estimates of hydrological variables such as atmospheric vertical temperature and moisture (water vapor) profiles (AVTP and AVMP, respectively), cloud fraction and cloud top pressure, trace gas retrievals including ozone (O3), methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), SO2, N2O, and HNO3, and a flag indicating the presence of dust and volcano emission. The vertical sampling of each retrieved atmospheric profile variable consists of 100 points total between 1100 hPa and 0.016 hPa. Intermediate solutions from the microwave retrieval (used primarily under cloudy conditions when the infrared retrieval fails to converge and is otherwise not accepted) and the regression first guess are also a part of the delivered standard output. Full spectrum CCR spectra are also produced along with the Standard Product, as the CCR radiances are the radiances used to retrieve the Standard Product. Both the Standard Product and CCR file are generated at all locations where the atmospheric soundings are taken. Each product file encompasses one granule of CrIS/ATMS data, where granules are formally defined as the smallest cluster of data that is independently managed (i.e., described, inventoried, retrievable). Each NUCAPS granule contains 32 seconds of data, corresponding to 4 scan lines of CrIS/ATMS data. Each scan line contains 30 Fields-of-Regard (FOR) for the ATMS instrument viewed on the Earth's surface with a scan width of +/- 48 degrees. Each FOR contains a simultaneously measured 3x3 set of Fields-of-View (FOV) from the CrIS instrument. The CrIS FOV are circular and have a diameter of 14 km at nadir. The UTC start time of the Nth granule is [146+360(N-1)]/3600 hours.

Granule products are operationally accessible to the science community in near real time (i.e., a 3-hour delay from the raw data acquisition) through the CLASS environment.


NUCAPS has the potential to support basic and applied geophysical science research/investigation, direct broadcast, nowcasting and Scientific Data Record (SDR) monitoring. Near real-time retrievals of NUCAPS temperature and moisture profiles can aid in warnings of severe weather, for example atmospheric stability conditions for tropical storms as well as for tornado warnings. For the latter retrieval products of higher spatial resolution (~10km) are needed.

Present users are represented by Advanced Weather Interactive Processing System (AWIPS)-II and the Fleet Numerical Meteorology and Oceanography Center (FNMOC), along with NOAA data centers such as CLASS and the National Climatic Data Center (NCDC), as well as basic and applied research. NUCAPS trace gas products can be useful to air quality forecasting and climate studies.


The figure at the top shows example layers of the global AVTP and AVMP products retrieved by NUCAPS (left plots, top and bottom respectively). For comparison purposes, corresponding results derived by the European Centre for Medium-Range Weather Forecasts (ECMWF) model are included (right plots).


The validation process provides insight about the quality of the products retrieved, helping us to identify the conditions where improvements are needed in order to ensure that product specifications are met. The validation process also provides reliable and consistent monitoring of operational and research products supporting the scientific development of NUCAPS. The validation methodology is documented in the paper of Nalli et al. (2013).

In the case of temperature and moisture, spatial and temporal collocations against Numerical Weather Prediction (NWP) models and radiosonde observations (RAOBs) are performed. The NOAA Products Validation System (NPROVS) (Reale et al. 2012) provides validation support since it routinely compiles collocation datasets of temperature and moisture products. NPROVS is primarily designed for automatic collocation of satellite sounding products against conventional RAOBs, but has been recently extended to also acquire matchups for reference RAOBs (e.g., GRUAN) and include dedicated RAOBs when available. More information about NPROVS can be found here.

Leveraging this base RAOB matchup system, NOAA/STAR is also assembling an EDR validation archive (VALAR) whereby SDR, Temperature Data Record (TDR) and EDR granules in the vicinity of RAOB "anchor points" are acquired for offline EDR retrieval reprocessing (or "re-retrieval" for short), thus allowing validation flexibility for future algorithm and validation methodology development.

Ongoing Improvements and Future Plans

The improvements and identification of conditions where the performance of retrievals is degraded represent a very active part in the development of the NUCAPS system. Areas of improvement have been identified in the microwave (cloudy) retrieval, update of the infrared and microwave surface emissivity tables and further refinement of the trace gas retrieval algorithm. Major future plans correspond to the standardization of the retrieval code and the investigation of the impact by using radiance and noise-equivalent change in radiance (NEDN) directly.


Chahine, M. T. (1974). Remote Sounding of Cloudy Atmospheres .1. Single Cloud Layer. Journal of the Atmospheric Sciences, 31(1), 233-243. [10.1175/1520-0469(1974)031<0233:rsocai>;2]

Gambacorta, A., Barnet, C., Wolf, W., King, T., Maddy, E., Strow, L., Xiong, X., Nalli, N., & Goldberg, M. (2014). An Experiment Using High Spectral Resolution CrIS Measurements for Atmospheric Trace Gases: Carbon Monoxide Retrieval Impact Study. IEEE Geoscience and Remote Sensing Letters, 11(9), 1639-1643. [10.1109/lgrs.2014.2303641]

Gambacorta, A., & Barnet, C. D. (2013). Methodology and Information Content of the NOAA NESDIS Operational Channel Selection for the Cross-Track Infrared Sounder (CrIS). IEEE Transactions on Geoscience and Remote Sensing, 51(6), 3207-3216. [10.1109/tgrs.2012.2220369]

Goldberg, M. D., Qu, Y. N., McMillin, L. M., Wolf, W., Zhou, L. H., & Divakarla, M. (2003). AIRS near-Real-Time Products and Algorithms in Support of Operational Numerical Weather Prediction. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 379-389. [10.1109/tgrs.2002.808307]

Nalli, N. R., Barnet, C. D., Reale, A., Tobin, D., Gambacorta, A., Maddy, E. S., Joseph, E., Sun, B., Borg, L., Mollner, A. K., Morris, V. R., Liu, X., Divakarla, M., Minnett, P. J., Knuteson, R. O., King, T. S., & Wolf, W. W. (2013). Validation of Satellite Sounder Environmental Data Records: Application to the Cross-Track Infrared Microwave Sounder Suite. Journal of Geophysical Research-Atmospheres, 118(24), 13628-13643. [10.1002/2013jd020436]

Rosenkranz, P. W. (2001). Retrieval of Temperature and Moisture Profiles from AMSU-a and AMSU-B Measurements. IEEE Transactions on Geoscience and Remote Sensing, 39(11), 2429-2435. [10.1109/36.964979]