Rajesh Poudyal

Radiative Transfer Simulations of the Two-Dimensional Ocean Glint Reflectance and Determination of the Sea Surface Roughness

An optimized discrete-ordinate radiative transfer model (DISORT3) with a pseudo-two-dimensional bidirectional reflectance distribution function (BRDF) is used to simulate and validate ocean glint reflectances at an infrared wavelength (1036 nm) by matching model results with a complete set of BRDF measurements obtained from the NASA cloud absorption radiometer (CAR) deployed on an aircraft. The surface roughness is then obtained through a retrieval algorithm and is used to extend the simulation into the visible spectral range where diffuse reflectance becomes important. In general, the simulated reflectances and surface roughness information are in good agreement with the measurements, and the diffuse reflectance in the visible, ignored in current glint algorithms, is shown to be important. The successful implementation of this new treatment of ocean glint reflectance and surface roughness in DISORT3 will help improve glint correction algorithms in current and future ocean color remote sensing applications.

Radiative Characteristics of Clouds Embedded in Smoke Derived from Airborne Multiangular Measurements

Clouds in the presence of absorbing aerosols results in their apparent darkening, observed at the Top of Atmosphere (TOA), which is associated with the radiative effects of aerosol absorption. Owing to the large warming radiative effect and potential impacts on regional climate, above-cloud aerosols have recently been characterized in multiple satellite-based studies. While satellite data are particularly useful in demonstrating the radiative impact of above-cloud aerosols at the TOA, there remains uncertainties in the in-depth understanding of aerosol-cloud radiative interactions and climate effects. Furthermore, recent literature indicates large uncertainties in satellite retrievals of above-cloud Aerosol Optical Depth (AOD) and Single Scattering Albedo (SSA), which are among the most important parameters in the assessment of associated radiative effects. In this study, we analyze radiative characteristics of clouds in the presence of wildfire smoke using airborne data primarily from NASAs Cloud Absorption Radiometer, collected during the ARCTAS campaign in Canada during the 2008 summer season. We found a strong positive reflectance (R) gradient in the UV-VIS-NIR spectrum for clouds embedded in dense smoke, as opposed to an (expected) negative gradient for cloud-free smoke and a flat spectrum for smoke-free cloud cover. Several cases of clouds embedded in thick smoke were found, when the aircraft made circular/spiral measurements, which not only allowed the complete characterization of angular distribution of smoke scattering, but also provided the vertical distribution of smoke and clouds (within 0.5 – 5 km). Specifically, the largest darkening by smoke was found in the UV/VIS, with R0.34μm reducing to 0.2 (or 20%), in contrast to 0.8 observed at NIR wavelengths (e.g. 1.27 µm). The observed darkening was found to be associated with large AODs (0.5 – 3.0) and moderately low SSA (0.85 – 0.93 at 0.53 µm), resulting in a significantly large instantaneous aerosol forcing efficiency of 254 ± 47 Wm-2τ-1. Our observations of smoke-cloud radiative interactions were found to be physically consistent with theoretical plane-parallel 1D and Monte Carlo 3D radiative transfer calculations, capturing the observed gradient across UV-VIS-NIR. Results from this study offer insights into aerosol-cloud radiative interactions, and may help in better constraining satellite-retrieval algorithms.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

Abstract The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python ( PolarBRDF ) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASAs Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Impact of forests on snow albedo: Lessons from the SnowEx campaign

There is a clear and urgent need to quantify seasonally varying snow water equivalent and albedo. In this study, the snow bidirectional distribution function (BRDF) from NASA Cloud Absorption Radiometer (CAR) will be used to analyze the impact of forests on snow albedo based on measurements from SnowEx field campaign in western Colorado over Grand Mesa and Senator Beck. Seasonal snow cover is the largest single component of the cryosphere in areal extent (covering an average of 46M km2 of Earths surface (31 % of land areas) each year. Current space-based techniques underestimate storage of snow water equivalent (SWE) by as much as 50% [1, 2]. That number is likely to be greater in the boreal forest and other densely-forested areas around the globe, with the boreal forest being the largest biome on Earth (20M km2).