NASA Evolutionary Programming Analytic Center (NEPAC) for Climate Data Records, Science Products and Models

We propose to develop an Analytic Center Framework (ACF), called the NASA Evolutionary Programming Analytic Center (NEPAC), that will enable scientists and engineers to rapidly formulate algorithms for satellite data products using both satellite and in situ observations. ACFs are one of two primary thrusts of the NASA Advanced Information Systems Technology (AIST) Program. NEPAC's primary scientific goal is to discover and apply new and novel algorithms for ocean chlorophyll a (Chl-a), the key biological state variable for ocean and inland water ecosystem assessment, historically the first-order proxy for phytoplankton biomass estimates (Cullen, 1982) and necessary inputs for contemporary carbon-based productivity estimates (Behrenfeld et al., 2005), making Chl-a observations a critical element for global phytoplankton climate assessment (Behrenfeld, 2014). The ACF will initially focus on generating Chl-a algorithms to target improvement of the uncertainties and for annealing these estimates across multiple Ocean Color (OC) satellite data sets, a needed step for establishing a coherent Chl-a climate data record. Ocean phytoplankton are primary components of the global biogeochemical cycle, generating nearly half of the global atmospheric oxygen supply, and are drivers of the global ocean carbon sequestration processes (Dutkiewicz et al., 2019). The core element for this ACF is a Genetic Programming (GP) application that generates either prognostic equations, such as satellite algorithms, or coupled systems of equations, such as those used in ocean ecosystem models. The proposed design of the ACF's work environment centers on providing ocean scientists, the ACF target community, easy-access through a web-enabled, user-friendly, Graphical User Interface (GUI) to an evolutionary search algorithm toolbox which will connect data and applications to high end computer resources. This GUI-based environment will be the portal through which users will: i) select the independent and dependent variables from relevant satellite and in situ data sets; ii) select the performance metrics from a range of traditional and improved techniques; iii) set the GP application's free parameters and options, and; iv) select the computational infrastructure to which the work will be submitted to. The GUI will operate outside of the high end computational and data storage infrastructure. A set of Application Programming Interfaces (APIs) will be developed, or a pre-existing and compatible one modified, to link the GUI application to the computational infrastructure that will carry out the GP computations using the chosen data sets. The overall technology goal is to enable the ocean science community to use computers to objectively, efficiently and rapidly generate algorithm-based satellite products that will have improved outcomes (lower errors, bias and uncertainty) necessary for answering key science questions regarding the sensitivity of ocean ecology to relevant issues, such as global climate variability. The initial target satellite product for this proposal is Chl-a. However, we, and others, have demonstrated that satellite observations are capable of retrieving pigments (Moisan et al., 2011), phytoplankton functional types (Hirata et al., 2011; Moisan et al., 2017), and primary production estimates (Behrenfeld, 2005), all important components for ecosystem and carbon cycle studies. Algorithms for these and other potential satellite products (new production, carbon flux) can also be generated using this ACF. In addition, this ACF will be extensible so that it can support other algorithm development needs within and outside of the NASA community. NEPAC will lead to considerable time and cost savings, and yield improved algorithms that will enable improved science results.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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