As part of a National Aeronautics and Space Administration (NASA) MEaSUREs (Making Earth System Data Records for Use in Research Environments) Land Surface Temperature and Emissivity project, the Space Science and Engineering Center (UW-Madison) and the NASA Jet Propulsion Laboratory (JPL) developed a global monthly mean emissivity Earth System Data Record (ESDR). This new Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UW BF) and the JPL ASTER Global Emissivity Dataset Version 4 (GEDv4). The dataset includes monthly global records of emissivity and related uncertainties at 13 hinge points between 3.6–14.3 µm, as well as principal component analysis (PCA) coefficients at 5-km resolution for the years 2000 through 2016. A high spectral resolution (HSR) algorithm is provided for HSR applications. This paper describes the 13 hinge-points combination methodology and the high spectral resolutions algorithm, as well as reports the current status of the dataset.

Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types.

The Cross‐track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments aboard the Suomi National Polar‐orbiting Partnership satellite provide high‐quality hyperspectral infrared and microwave observations to retrieve atmospheric vertical temperature and moisture profiles (AVTP and AVMP) and many other environmental data records (EDRs). The official CrIS and ATMS EDR algorithm, together called the Cross‐track Infrared and Microwave Sounding Suite (CrIMSS), produces EDR products on an operational basis through the interface data processing segment. The CrIMSS algorithm group is to assess and ensure that operational EDRs meet beta and provisional maturity requirements and are ready for stages 1–3 validations. This paper presents a summary of algorithm optimization efforts, as well as characterization and validation of the AVTP and AVMP products using the European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis, the Atmospheric Infrared Sounder (AIRS) retrievals, and conventional and dedicated radiosonde observations. The global root‐mean‐square (RMS) differences between the CrIMSS products and the ECMWF show that the AVTP is meeting the requirements for layers 30–300 hPa (1.53 K versus 1.5 K) and 300–700 hPa (1.28 K versus 1.5 K). Slightly higher RMS difference for the 700 hPa‐surface layer (1.78 K versus 1.6 K) is attributable to land and polar profiles. The AVMP product is within the requirements for 300–600 hPa (26.8% versus 35%) and is close in meeting the requirements for 600 hPa‐surface (25.3% versus 20%). After just one year of maturity, the CrIMSS EDR products are quite comparable to the AIRS heritage algorithm products and show readiness for stages 1–3 validations.

Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth’s radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it is common practice in land surface and climate models to set BBE to a single constant over the globe. This can lead to systematic biases in the estimated net and longwave radiation for any particular location and time of year. Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, a new global, high spectral resolution land surface emissivity dataset has recently been made available at monthly at 0.05 degree resolution since 2000. Called the Combined ASTER MODIS Emissivity over Land (CAMEL), this dataset is created by the merging of the MODIS baseline-fit emissivity database developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset (GED) produced at the Jet Propulsion Laboratory. CAMEL has 13 hinge points between 3.6–14.3 µm which are expanded to cover 417 infrared spectral channels within the same wavelength region using a principal component regression approach. This work presents the method for calculating BBE using the new CAMEL dataset. BBE is computed via numerical integration over the CAMEL High Spectral Resolution product for two different wavelength ranges—3.6–14.3 µm which takes advantage of the full, available CAMEL spectra and 8.0–13.5 µm which has been determined to be an optimal range for computing the most representative all wavelength, longwave net radiation. CAMEL BBE uncertainty estimates are computed, and comparisons are made to BBE computed from lab validation data for selected case sites. Variations of BBE over time and land cover classification schemes are investigated and converted into flux to demonstrate the equivalent error in longwave radiation which would be made by the use of a single, constant BBE value. Misrepresentations in BBE by 0.05 at 310 K corresponds to potential errors in longwave radiation of over 25 W/m².

The U.S. National Oceanic and Atmospheric Administration (NOAA) recently began operational processing to derive vertical temperature profiles from two new sensors, Cross‐Track Infrared Sounder and Advanced Technology Microwave Sounder, which were developed for the next generation of U.S. weather satellites. The NOAA‐Unique Combined Atmospheric Processing System (NUCAPS) has been developed by NOAA to routinely process data from future Joint Polar Satellite System operational satellites and the preparatory Suomi‐NPP satellite. This paper assesses the NUCAPS vertical temperature profile product from the upper troposphere into the middle stratosphere using radiosonde and GPS radio occultation (RO) data. Radiosonde data from the Department of Energy Atmospheric Radiation Measurement (ARM) program are=] compared to both the NUCAPS and GPS RO temperature products to evaluate bias and RMS errors. At all three fixed ARM sites for time periods investigated the NUCAPS temperature in the 100–40 hPa range is found to have an average bias to the radiosondes of less than 0.45 K and an RMS error of less than 1 K when temperature averaging kernels are applied. At a 95% confidence level, the radiosondes and RO were found to agree within 0.4 K at the North Slope of Alaska site and within 0.83 K at Southern Great Plains and Tropical Western Pacific. The GPS RO‐derived dry temperatures, obtained from the University Corporation for Atmospheric Research Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, are used as a common reference for the intercomparison of NUCAPS temperature products to similar products produced by NASA from Atmospheric Infrared Sounder (AIRS) and by European Organisation for the Exploitation of Meteorological Satellites from MetOp‐B Infrared Atmospheric Sounding Interferometer (IASI). For seasonal and zonal scales, the NUCAPS agreement with AIRS and IASI is less than 0.5 K after application of averaging kernels.

The Cross-track Infrared Sounder (CrIS) is an infrared Fourier-transform spectrometer that measures the Earth’s infrared radiance at high spectral resolution and high accuracy. The potential for polarization errors contributing significantly to the radiometric uncertainty of infrared remote sounders has been well recognized and documented, particularly due to polarization-dependent scene select mirrors operated in conjunction with grating-based instruments. The issue is equally applicable to FTS-based sensors. While the CrIS sensor utilizes an unprotected gold scene select mirror which has extremely low polarization in the infrared and the angle of incidence at the mirror is maintained for all calibration and Earth scene views, the radiometric bias due to polarization effects was determined to be non-negligible for cold scenes. A model for the polarization-induced calibration bias and the associated correction is presented for the CrIS instrument, along with details of the model parameter determination, and the impact of the correction on the calibrated radiances for a range of scene temperatures and types.