Cloud Remote Sensing Research Articles

EPIC/DSCOVR as a Pathfinder in Cloud Remote Sensing Using Differential Oxygen Absorption Spectroscopy

We argue that the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate ObserVatoRy (DSCOVR) platform has blazed new pathways in observational technology, starting with its ∼ 1.5 × 106 km stand-off distance, but also in remote sensing science. We focus here on EPIC’s two oxygen absorption channels that 1) are unique in their spectral sampling and 2) have stimulated deep innovation in cloud remote sensing using Differential Oxygen Absorption Spectroscopy (DO2AS). Although first formulated 6 decades ago, DO2AS-based cloud probing from overhead assets is still an emerging observational technique. It is indeed somewhat paradoxical that one should use absorption by a gas to assay scattering by particles. After surveying the history of space-based DO2AS, and looking into its future, we see that EPIC/DSCOVR marks an inflection point in this important development. EPIC’s unique DO2AS capability motivated a notable sequence of papers revisited here. This research indeed spawned a rare occurrence of information content analysis coming from radically different—yet complementary—perspectives. First, we adopted the increasingly popular machinery of optimal estimation (OE) that is grounded in Bayesian statistics and uses a somehow linearized radiative transfer (RT) model. Nonetheless, OE feels like a black-box algorithm that outputs a number of “degrees of freedom” (a.k.a. independent pieces of information about clouds under observation). However, the very same conclusions are reached using fully transparent physics-based modeling for the RT, with a few approximations that enable closed-form analytical formulation. Lastly, we preview a novel DO2AS technique for regaining shortwave sensitivity to cloud optical thickness past the threshold where cloud reflectivity flattens off.

G-Band Radar for Humidity and Cloud Remote Sensing

VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated continuous-wave (FMCW) radar mode, offering a transmit power of 200-300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167-174.8 GHz results in a nominal range resolution of 15 m. The radar's measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as -26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR's absolute sensitivity was validated using a spherical metal target in the radar antenna's far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver.

三维云层遥感辐射传输中的水平方向邻域多次散射近似模型

三维云层遥感辐射传输中的水平方向邻域多次散射近似模型

Retrieval of cloud properties from spectral zenith radiances observed by sky radiometers

Abstract. An optimal estimation algorithm to retrieve the cloud optical depth (COD) and cloud particle effective radius (CER) from spectral zenith radiances observed by narrow field-of-view (FOV) ground-based sky radiometers was developed. To further address the filter degradation problem while analyzing long-term observation data, an on-site calibration procedure is proposed, which has good accuracy compared with the standard calibration transfer method. An error evaluation study conducted by assuming errors in observed transmittances and ancillary data for water vapor concentration and surface albedo suggests that the errors in input data affect retrieved CER more than COD. Except for some narrow domains that fall within a COD of < 15, the retrieval errors are small for both COD and CER. The retrieved cloud properties reproduce the broadband radiances observed by a narrow FOV radiometer more precisely than broadband irradiances observed by a wide-FOV pyranometer, justifying the quality of the retrieved product (at least of COD) and indicating the important effect of the instrument FOV in cloud remote sensing. Furthermore, CODs (CERs) from sky radiometer and satellite observations show good (poor) agreement.

Rice Yield Estimation Using Below Cloud Remote Sensing Images Acquired by Unmanned Airborne Vehicle System

A method using unmanned airborne vehicle system (UAVS) and image processing technique to enable estimation of rice yield was developed. A digital Tetracam camera was mounted on a CropCam unmanned airborne vehicle (UAV) to acquire red (R), green (G) and near infrared (NIR) images of rice crops at the height of 300 m above ground. NIR and R values were used to calculate normalised difference vegetation index (NDVI) value. Relationships between yield versus R, G, NIR and NDVI values were analysed. Results showed that the highest relationship was found in NDVI followed by R, G and NIR with coefficient of determination (r 2 ) values of 0.748, 0.727, 0.395 and 0.014 respectively. Therefore, a yield estimation model using NDVI value was developed from the linear regression analysis. The results showed that the model was capable of estimating rice yield with an average accuracy value of 80.3%.

Cloud Remote Sensing Using Midwave IR CO2 and N2O Slicing Channels near 4.5 μm

Narrow channels located in the longwave IR CO2 absorption region between approximately 13.2 and 14.5 μm, the well known CO2 slicing channels, have been proven to be quite effective for the estimates of cloud heights and effective cloud amounts as well as atmospheric temperature profiles. The designs of some of the near-future multi-channel earth observing satellite sensors cannot accommodate these longwave IR channels. Based on the analysis of the multi-channel imaging data collected with the NASA Moderate Resolution Imaging SpectroRadiometer (MODIS) instrument and on theoretical cloud radiative transfer modeling, we have found that narrow channels located at the midwave IR region between approximately 4.2 and 4.55 μm, where the combined CO2 and N2O absorption effects decrease rapidly with increasing wavelength, have similar properties as the longwave IR CO2 slicing channels. The scattering of solar radiation by clouds on the long wavelength side of the 4.3 μm CO2 absorption makes only a small contribution to the upwelling radiances. In order to retain the crucial cloud and temperature sensing capabilities, future satellite sensors should consider including midwave IR CO2 and N2O slicing channels if the longwave IR channels cannot be implemented on the sensors. The hyperspectral data covering the 3.7-15.5 mm wavelength range and measured with the Infrared Atmospheric Sounding Interferometer (IASI) can be used to further assess the utility of midwave IR channels for satellite remote sensing.

The Concept of “Normalized” Distribution to Describe Raindrop Spectra: A Tool for Cloud Physics and Cloud Remote Sensing

Abstract The shape of the drop size distribution (DSD) reflects the physics of rain. The DSD is the result of the microphysical processes that transform the condensed water into rain. The question of the DSD is also central in radar meteorology, because it rules the relationships between the radar reflectivity and the rainfall rate R. Normalizing raindrop spectra is the only way to identify the shape of the distribution. The concept of normalization of DSD developed in this paper is founded upon two reference variables, the liquid water content LWC and the mean volume diameter Dm. It is shown mathematically that it is appropriate to normalize by N0* ∝ LWC/Dm4 with respect to particle concentration and by Dm with respect to drop diameter. Also, N0* may be defined as the intercept parameter that would have an exponential DSD with the same LWC and Dm as the real one. The major point of the authors' approach is that it is totally free of any assumption about the shape of the DSD. This new normalization has be...

The Landsat Scale Break in Stratocumulus as a Three-Dimensional Radiative Transfer Effect: Implications for Cloud Remote Sensing

Several studies have uncovered a break in the scaling properties of Landsat cloud scenes at nonabsorbing wavelengths. For scales greater than 200–400 m, the wavenumber spectrum is approximately power law in k−5/3, but from there down to the smallest observable scales (50–100 m) follows another k−β law with β > 3. This implies very smooth radiance fields. The authors reexamine the empirical evidence for this scale break and explain it using fractal cloud models, Monte Carlo simulations, and a Green function approach to multiple scattering theory. In particular, the authors define the “radiative smoothing scale” and relate it to the characteristic scale of horizontal photon transport. The scale break was originally thought to occur at a scale commensurate with either the geometrical thickness Δz of the cloud, or with the “transport” mean free path lt = [(1 − g)σ]−1, which incorporates the effect of forward scattering (σ is extinction and g the asymmetry factor of the phase function). The smoothing scale is found to be approximatelyltΔz at cloud top; this is the prediction of diffusion theory which applies when (1 − g)τ = Δz /lt ≳ 1 (τ is optical thickness). Since the scale break is a tangible effect of net horizontal radiative fluxes excited by the fluctuations of τ, the smoothing scale sets an absolute lower bound on the range where one can neglect these fluxes and use plane-parallel theory locally, even for stratiform clouds. In particular, this constrains the retrieval of cloud properties from remotely sensed data. Finally, the characterization of horizontal photon transport suggests a new lidar technique for joint measurements of optical and geometrical thicknesses at about 0.5-km resolution.