In order to better test and constrain GCMs, it is imperative to have accurate, long-term global measurements of cloud structure and properties on the scale that only our long-term passive sensors on satellites, such as the Multiangle Imaging Spectroradiometer or MISR (Diner et al., 2002) and the Moderate Resolution Imaging Spectroradiometer or MODIS (King et al., 2003) can provide us. Both of these instruments on board NASA’s EOS Terra satellite that has been maintaining a hitherto unforeseen stable equator crossing time since early 2002 and hence represents our most stable long-term climate record (Zhao et al., 2016). However, even after nearly two decades worth of stable detection of clouds on a global scale, there still remains a lot that can be done to understand the global distribution of the simplest aspect of cloud vertical structure – the cloud top height (CTH), through the systematic inter-comparison of results from different sensors and retrieval approaches to better quantify the biases involved.
The goal of this work is to improve our capabilities in detecting cloud vertical structure, especially under conditions of cloud overlap, in recognizing and removing systematic biases in our satellite records of CTH by combining products from multiple platforms.
Unfortunately, our most well-known and stable satellite active sensors, namely CLOUDSAT and CALIPSO, orbiting in the A-Train constellation of satellites (Anderson et al., 2005) have equator-crossing times that are quite different from those of Terra, and as such, it is impossible to find co-located data points for CTH product validation all over the globe. Fortunately, The ISS-CATS or simply, CATS (Cloud-Aerosol Transport System) was an elastic backscatter lidar that operated from the Japanese Experiment Module-Exposed Facility of the International Space Station (ISS) in the period from February 2015 to late 2017. Although the CATS mission was too short-lived to be a climate record in itself, we realize its unique worth in offering us a means to use a satellite-based active sensor for validating the records of CTH from the Terra-based passive sensors.
We hypothesize that the inter-comparison of the performances of active and passive sensor instruments in detecting CTH will provide us with new insight into the inherent biases involved in the passive remote sensing of CTH, as well as provide us with a way forward in using MODIS and MISR CTH differences as a means to quantify cloud overlap, as well as using fused MISR-MODIS CTH values to understand vertical cloud distribution patterns and change over the entire Terra record. This will mark the first time such an inter-comparison has been attempted on a quasi-global scale (60N to 60S), with previous studies focusing on regional inter-comparison.
The workflow of this project is to use the 3-year (early 2015-late 2017) data record from ISS-based CATS lidar instrument to validate the MISR and MODIS CTH product by means of statistical analysis of coincidental data. Use CATS backscatter to quantify the effect of cloud layering on the biases of each instrument, especially how much the properties of the top layer (such as geometrical/ optical thickness, heterogeneity, phase, height) affects the performance of MISR and MODIS in detecting the top layer or the second layer of clouds (if present), globally.