The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua.

The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases-daytime only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical and microphysical algorithm changes include: (i) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space, and (vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively can result in significant changes relative to C5, though the magnitude depends on the dataset and the pixel's retrieval location in the cloud parameter space. Example Level-2 granule and Level-3 gridded dataset differences between the two collections are shown. While the emphasis is on the suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant.

Wildfire-Induced CO Plume Observations From NAST-I During the FIREX-AQ Field Campaign.

The fire influence on regional to global environments and air quality (FIREX-AQ) field campaign was conducted during August 2019 to investigate the impact of wildfire and biomass smoke on air quality and weather in the continental United States. One of the campaign's scientific objectives was to estimate the composition of emissions from wildfires. Ultraspectrally resolved infrared radiance measurements from aircraft and/or satellite observations contain information on tropospheric carbon monoxide (CO) as well as other trace species present in fire emissions. A methodology for retrieving tropospheric CO from such remotely sensed spectral data has been developed for the National Airborne Sounder Testbed-Interferometer (NAST-I) and is applied herein. Retrievals based on NAST-I measurements are used to demonstrate CO retrieval capability and characterize fire emissions. NAST-I remotely sensed CO from ER-2 flights are evaluated with concurrent in situ measurements from the differential absorption carbon monoxide measurements flown on the NASA DC-8 aircraft. Enhanced CO emissions along with plume evolution and transport from the fire ground site locations were captured by moderate vertical and high horizontal resolution observations obtained from the NAST-I IR spectrometer; these were intercompared and verified by the cloud physics lidar and the enhanced MODIS airborne simulator also hosted on the NASA ER-2 aircraft. This study will be beneficial to the science community for studying wildfire-related topics and understanding similar remotely sensed observations from satellites, along with helping to address the broader FIREX-AQ experiment objectives of investigating the impact of fires on air quality and climate.

MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP.

Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.