Surface upwelling longwave radiation (LWUP) is an important component in the surface radiation budget. Existing satellite-derived LWUP data sets are too coarse to support high-resolution numerical models, and their accuracy needs to be improved. In this paper, we evaluate three methods for estimating clear-sky land LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1-km spatial resolution. The three methods are as follows: (1) the temperature-emissivity method; (2) the linear model method; and (3) the artificial neural network (ANN) model method. Methods 2 and 3 are new methods based on extensive radiative transfer simulations and statistical analysis. We explicitly considered surface emissivity effects by incorporating the University of California Santa Barbara emissivity library in the radiative transfer simulation. The three methods were evaluated using ground-measured LWUP from six SURFRAD sites. Although methods 2 and 3 were developed using MODIS Terra atmospheric profiles, they were applied to both Terra and Aqua data because the designs of the two sensors are similar. The root mean squared errors (rmses) of the ANN model method are smaller than that of the other two methods at all sites. The averaged rmses of the ANN model method are 15.89 W/m² (Terra) and 14.57 W/m>² (Aqua); the averaged biases are -8.67 W/m² (Terra) and -7.21 W/m² (Aqua). The biases and rmses for Aqua are ~1.3 W/m² smaller than that of Terra. The biases and rmses of the ANN model method are ~5 W/m² smaller than that of the temperature-emissivity method and ~2.5 W/m² smaller than that of the linear model method.

The Visible and Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)/Suomi National Polar-Orbiting Partnership (SNPP) satellite provide sensor data records for the retrievals of many environment data records. It is critical to monitor the VIIRS long-term calibration stability to ensure quality EDR retrieval. This study investigates the radiometric calibration stability of the NOAA operational SNPP VIIRS Reflective Solar Bands (RSB) and Day-Night-Band (DNB) using Deep Convective Clouds (DCC). Monthly and semi-monthly DCC time series for 10 moderate resolution bands (M-bands, M1–M5 and M7–M11, March 2013–September 2015), DNB (March 2013–September 2015, low gain stage), and three imagery resolution bands (I-bands, I1–I3, January 2014–September 2015) were developed and analyzed for long-term radiometric calibration stability monitoring. Monthly DCC time series show that M5 and M7 are generally stable, with a stability of 0.4%. DNB has also been stable since May 2013, after its relative response function update, with a stability of 0.5%. The stabilities of M1–M4 are 0.6%–0.8%. Large fluctuations in M1–M4 DCC reflectance were observed since early 2014, correlated with F-factor (calibration coefficients) trend changes during the same period. The stabilities of M8-M11 are from 1.0% to 3.1%, comparable to the natural DCC variability at the shortwave infrared spectrum. DCC mean band ratio time series show that the calibration stabilities of I1–I3 follow closely with M5, M7, and M10. Relative calibration changes were observed in M1/M4 and M5/M7 DCC mean band ratio time series. The DCC time series are generally consistent with results from the VIIRS validation sites and VIIRS/MODIS (the Moderate-resolution Imaging Spectroradiometer) simultaneous nadir overpass time series. Semi-monthly DCC time series for RSB M-bands and DNB were compared with monthly DCC time series. The results indicate that semi-monthly DCC time series are useful for stability monitoring at higher temporal resolution.