An investigation of the implications of lunar illumination spectral changes for Day/Night Band‐based cloud property retrieval due to lunar phase transition

Abstract

The Moon reflects sunlight like a huge mirror hanging in the sky at night, which presents the obviously periodical changes in its luminance or irradiance due to Sun‐Earth‐Moon geometry variation. The potential effect of the periodical changes in lunar phase angle on nighttime Day/Night Band (DNB) radiative transfer simulation in the presence of cloud has seldom been reported thus far. In this study, a radiative transfer model is developed by coupling the lunar light source with various Sun‐Earth‐Moon geometries. To elucidate the stability of DNB‐averaged cloud bulk scattering properties, we simulate nighttime reflectance and radiances under four typical lunar phase angles (0°, 45°, 90°, and 135°) from 7 April 2016 to 8 May 2016 (e.g., two lunar cycles). Explicit simulation analyses indicated that DNB‐averaged cloud bulk scattering properties exhibit weak sensitivity to lunar phase angles. The maximum DNB reflectance differences between any and 90° lunar phase angles are less than 0.05% (0.01%) in the presence of water (ice) clouds, indicating a negligible effect of periodically changes on lunar spectral irradiances. Our findings suggest that the differences of reflectance at lunar phase angle = 90° are less than approximately 0.05% (water cloud)/0.01% (ice cloud), much smaller than 11% radiometric calibration uncertainties of DNB. This means that these differences could be ignored in both nighttime cloud property retrieval and DNB radiative transfer modeling.

Fast Calculation on Atmospheric Rayleigh Scattering Extinction Optical Depth for Day/Night Band Radiative Transfer Model With Periodic Lunar Illumination

“…Since RayOD is a function of temperature and pressure (Tomasi & Petkov, 2015), the related predictors in the RTTOV V13 version are only chosen here. In addition, this study also takes into account the effects of lunar illumination for DNB, so the convolutional band optical depth τ cntrl should convolve normalized lunar spectral irradiance (Min et al, 2017). We record the original optical depth that convolutes DNB SRF and normalized lunar spectral irradiance as ⌢ * , which can be given by the following equation:…”

“…Figure 2 shows the distribution maps of relative error between the calculated DNB-averaged reflectance (R' DNB ) based on the ODPS algorithm and the true reflectance R DNB at four lunar phase angles with the mid-latitude summer atmospheric profile and the surface albedo of 0.05. R' DNB and R DNB are calculated as follow (Min et al, 2017):…”

“…Since RayOD is a function of temperature and pressure (Tomasi & Petkov, 2015), the related predictors in the RTTOV V13 version are only chosen here. In addition, this study also takes into account the effects of lunar illumination for DNB, so the convolutional band optical depth τ cntrl should convolve normalized lunar spectral irradiance (Min et al., 2017). We record the original optical depth that convolutes DNB SRF and normalized lunar spectral irradiance as $${\stackrel{{\frown}}{\tau }}_{cntrl}^{\ast }$$, which can be given by the following equation: $$${\stackrel{{\frown}}{\tau }}_{cntrl}^{\ast }=\int \nolimits_{{\lambda }_{1}}^{{\lambda }_{2}}W(\lambda ){\tau }_{Ray}(\lambda ),$$$ where τ Ray ( λ ) is the RayOD of at the wavelength λ. W ( λ ) is the weight coefficient related to the wavelength λ , which can be calculated as follow: $$$W(\lambda )=\frac{{\mathit{SRF}}_{\mathit{DNB}}(\lambda ){E}_{l}(\lambda )}{\int \nolimits_{{\lambda }_{1}}^{{\lambda }_{2}}{\mathit{SRF}}_{\mathit{DNB}}(\lambda ){E}_{l}(\lambda )},$$$ where E l (λ) is the normalized lunar spectral irradiance at the wavelength λ .…”

“…As a consequence, developing a fast and accurate radiative transfer model is important and indispensable for nocturnal DNB quantitative applications (Li et al, 2020;Liu et al, 2015). Compared with the approximately invariant solar irradiance spectrum (for daytime remote sensing), some prior studies [Kieffer & Stone, 2005;Miller et al, 2009;Min et al, 2017;Min et al, 2021; have already pointed out that the lunar irradiance spectrum with periodic changes will cause more difficulties and interactable challenges in DNB remote sensing application, fast radiative transfer calculation, and radiometric calibration. Recently, Min et al (2020b) developed a novel low-light radiative transfer model with diversified city light and lunar illuminations, which is capable of accurately simulating clearsky radiances observed by SNPP/VIIRS-DNB.…”

The Radiance Differences between Wavelength and Wavenumber Spaces in Convolving Hyperspectral Infrared Sounder Spectrum to Broadband for Intercomparison

“…Spectral radiance convolution calculation is broadly used in various applications of satellite remote sensing including radiance simulation [1][2][3][4][5], validation [6,7], and space-based intercomparison or intercalibration [8][9][10][11][12][13][14][15][16][17][18]. Generally, the monochromatic radiance spectrum of high spectral resolution calculated from an accurate line-by-line model is regarded as one source of references for calibration and validation of radiometric accuracies of broadband imagers.…”