Inverse modelling method named Maximum likelihood Ensemble Filter (MLEF) was used to estimate gridded surface CO fluxes using continuous, flask and Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) data for the years 2009-2011. Here, MLEF coupled with Parametric Chemistry Transport Model (PCTM) driven by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) weather data has been used. Flux estimation was done by solving separate multiplicative biases for photosynthesis, respiration, and air-sea gas exchange fluxes. Hourly land fluxes derived from Simple Biosphere-version 3 (SiB3) model, Takahashi ocean fluxes and Brenkert fossil fuel emissions were used as the prior fluxes. The inversion was carried out by assimilating hourly CO observations, According to this study, North America showed about 60-80% uncertainty reduction while the Asian and European regions showed moderate results with 50-60% uncertainty reduction. Most other land and oceanic regions showed less than 30% uncertainty reduction. The results were mainly compared with well-known CarbonTracker and some parallel inversion studies by considering long-term averages of the estimated fluxes for the TransCom regions. Boreal North America, Temperate North America and Australia showed similar annual averages in each case. Tropical Asia and Europe showed comparable results with all other studies except for the CarbonTracker. The biases were poorly constrained in the regions having few measurement sites like South America, Africa and Eurasian Temperate which showed completely different result with other studies.

Imagery from the GOES series has been a key element of U.S. operational weather forecasting for four decades. While GOES observations are used extensively by human forecasters for situational awareness, there has been limited usage of GOES imager data in numerical weather prediction (NWP), and operational data assimilation (DA) has ignored cloud and precipitating pixels. The motivation of this project is to bring the benefits of GOES-R Series enhanced capabilities to advance convective-scale DA for improving convective-scale forecasts. We have developed a convolutional neural network (CNN) prototype, dubbed “GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN)” that fuses GOES-R Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) information to produce maps of synthetic composite radar reflectivity. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. Making use of ABI spatial information potentially provides benefits over radiance assimilation approaches that are limited by saturation of radiances in pixels with precipitation. This presentation will briefly describe the GREMLIN model and characterize its performance across meteorological regimes. We are developing a dense neural network (DNN) to produce vertical profiles of radar reflectivity and latent heating based on the two-dimensional fields output from GREMLIN. The resulting three-dimensional fields of latent heating will be used to initialize NWP simulations of convective-scale phenomena. Another DNN will be developed to produce uncertainty estimates of latent heating for each pixel. Our approach for data assimilation will be described and is innovative in being the first-time machine learning (ML) will be used for the nonlinear latent heating observation operator in the NOAA hybrid Gridpoint Statistical Interpolation (GSI) and/or JEDI systems. The approach will provide all the elements of the Jacobian needed for GSI DA and has the advantage of automatically maintaining the tangent linear and adjoint models through finite difference mathematics.