GOES-R Algorithms: A Common Science and Engineering Design and Development Approach for Delivering Next Generation Environmental Data Products

GOES-R, the next generation of the National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellite (GOES) System, represents a new technological era in operational geostationary environmental satellite systems. GOES-R will provide advanced products that describe the state of the atmosphere, land, oceans, and solar/ space environments over the western hemisphere. The Harris GOES-R Ground Segment team will provide the software, based on government-supplied algorithms, and engineering infrastructures designed to produce and distribute these next-generation data products. The Harris GOES-R Team has adopted an integrated applied science and engineering approach that combines rigorous system engineering methods, with modern software design elements to facilitate the transition of algorithms for Level 1 and 2+ products to operational software. The Harris Team GOES-R GS algorithm framework, which includes a common data model interface, provides general design principles and standardized methods for developing general algorithm services, interfacing to external data, generating intermediate and L1b and L2 products and implementing common algorithm features such as metadata generation and error handling. This work presents the suite of GOES-R products, their properties and the process by which the related requirements are maintained during the complete design/development life-cycle. It also describes the algorithm architecture/engineering approach that will be used to deploy these algorithms, and provides a preliminary implementation road map for the development of the GOES-R GS software infrastructure, and a view into the integration of the framework and data model into the final design

A Regional CO2 Observing System Simulation Experiment Using ASCENDS Observations and WRF-STILT Footprints

Knowledge of the spatiotemporal variations in emissions and uptake of CO2 is hampered by sparse measurements. The recent advent of satellite measurements of CO2 concentrations is increasing the density of measurements, and the future mission ASCENDS (Active Sensing of CO2 Emissions over Nights, Days and Seasons) will provide even greater coverage and precision. Lagrangian atmospheric transport models run backward in time can quantify surface influences ("footprints") of diverse measurement platforms and are particularly well suited for inverse estimation of regional surface CO2 fluxes at high resolution based on satellite observations. We utilize the STILT Lagrangian particle dispersion model, driven by WRF meteorological fields at 40-km resolution, in a Bayesian synthesis inversion approach to quantify the ability of ASCENDS column CO2 observations to constrain fluxes at high resolution. This study focuses on land-based biospheric fluxes, whose uncertainties are especially large, in a domain encompassing North America. We present results based on realistic input fields for 2007. Pseudo-observation random errors are estimated from backscatter and optical depth measured by the CALIPSO satellite. We estimate a priori flux uncertainties based on output from the CASA-GFED (v.3) biosphere model and make simple assumptions about spatial and temporal error correlations. WRF-STILT footprints are convolved with candidate vertical weighting functions for ASCENDS. We find that at a horizontal flux resolution of 1 degree x 1 degree, ASCENDS observations are potentially able to reduce average weekly flux uncertainties by 0-8% in July, and 0-0.5% in January (assuming an error of 0.5 ppm at the Railroad Valley reference site). Aggregated to coarser resolutions, e.g. 5 degrees x 5 degrees, the uncertainty reductions are larger and more similar to those estimated in previous satellite data observing system simulation experiments

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept

A New Laser Based Approach for Measuring Atmospheric Greenhouse Gases

In 2012, we developed a proof-of-concept system for a new open-path laser absorption spectrometer concept for measuring atmospheric CO2. The measurement approach utilizes high-reliability all-fiber-based, continuous-wave laser technology, along with a unique all-digital lock-in amplifier method that, together, enables simultaneous transmission and reception of multiple fixed wavelengths of light. This new technique, which utilizes very little transmitted energy relative to conventional lidar systems, provides high signal-to-noise (SNR) measurements, even in the presence of a large background signal. This proof-of-concept system, tested in both a laboratory environment and a limited number of field experiments over path lengths of 680 m and 1,600 m, demonstrated SNR values >1,000 for received signals of ~18 picoWatts averaged over 60 s. A SNR of 1,000 is equivalent to a measurement precision of ±0.001 or ~0.4 ppmv. The measurement method is expected to provide new capability for automated monitoring of greenhouse gas at fixed sites, such as carbon sequestration facilities, volcanoes, the short- and long-term assessment of urban plumes, and other similar applications. In addition, this concept enables active measurements of column amounts from a geosynchronous orbit for a network of ground-based receivers/stations that would complement other current and planned space-based measurement capabilities

Greenlite™: a year of carbon dioxide monitoring over paris, france, and recent progress in monitoring methane

GreenLITE™ is a ground-based laser absorption spectroscopy system capable of measuring and mapping CO2 concentrations over areas up to 25 km2. The system was deployed for COP21 as a demonstration and has now completed a year of CO2 measurements over the city of Paris, France. We will discuss lessons learned and relevant data from the year-long deployment. Recently, the system has demonstrated the same measurement capability for CH4, and results from preliminary testing are presented

Greenlite™: a year of carbon dioxide monitoring over paris, france, and recent progress in monitoring methane

GreenLITE™ is a ground-based laser absorption spectroscopy system capable of measuring and mapping CO2 concentrations over areas up to 25 km2. The system was deployed for COP21 as a demonstration and has now completed a year of CO2 measurements over the city of Paris, France. We will discuss lessons learned and relevant data from the year-long deployment. Recently, the system has demonstrated the same measurement capability for CH4, and results from preliminary testing are presented

Bias correction of long-path CO<sub>2</sub> observations in a complex urban environment for carbon cycle model inter-comparison and data assimilation

Abstract. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) trace gas measurement system, jointly designed and developed by Atmospheric and Environmental Research, Inc. and Spectral Sensor Solutions LLC, provides high-precision, long-path measurements of atmospheric trace gases including CO2 and CH4 over extended (0.04–25 km2) areas of interest. In 2015, a prototype unit was deployed in Paris, France, to demonstrate its ability to provide continuous observations of CO2 concentrations along horizontal air segments and two-dimensional (2-D) maps of time-varying CO2 concentrations over a complex urban environment. Subsequently, these data have been adapted to create a physically consistent set of horizontal segment mean concentrations for (1) comparisons to highly accurate in situ point measurements obtained for coincident times within the Greater Paris area, (2) inter-comparisons with results from high spatial and temporal regional carbon cycle model data, and (3) potential assimilation of these data to constrain and inform regional carbon cycle modeling frameworks. To achieve these ends, the GreenLITE™ data are calibrated against precise in situ point measurements to reconcile constant systematic as well as slowly varying temporal differences that exist between in situ and GreenLITE™ measurements to provide unbiased comparisons, and the potential for long-term co-assimilation of both measurements into urban-scale emission models. While both the constant systematic biases and the slowly varying differences may have different impacts on the measurement accuracy and/or precisions, they are in part due to a number of potential common terms that include limitation in the instrument design, uncertainties in spectroscopy and imprecise knowledge of the atmospheric state. This work provides a brief overview of the system design and the current gas concentration retrieval and 2-D reconstruction approaches, a description of the bias-correction approach, the results as applied to data collected in Paris, France, and an analysis of the inter-comparison between collocated in situ measurements and GreenLITE™ observations

Greenhouse Gas Laser Imaging Tomography Experiment (GreenLITE)

Exelis has recently developed a novel laser-based instrument to aid in the autonomous real-time monitoring and mapping of CO2 concentration over a two-dimensional area. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE) instrument uses two transceivers and a series of retroreflectors to continuously measure the differential transmission over a number of overlapping lines of sight or “chords”, forming a plane. By inverting the differential transmission measurements along with locally measured temperature (T), pressure (P) and relative humidity (RH) the average concentration of CO2 along each chord can be determined and, based on the overlap between chords, a 2D map of CO2 concentration over the measurement plane can be estimated. The GreenLITE system was deployed to the Zero Emissions Research and Technology (ZERT) center in Bozeman, Montana, in Aug-Sept 2014, where more than 200 hours of data were collected over a wide range of environmental conditions, while utilizing a controlled release of CO2 into a segmented underground pipe [1]. The system demonstrated the ability to identify persistent CO2 sources at the test facility and showed strong correlation with an independent measurement using a LI-COR based system. Here we describe the measurement approach, instrument design, and results from the deployment to the ZERT site

Greenhouse Gas Laser Imaging Tomography Experiment (GreenLITE)

Exelis has recently developed a novel laser-based instrument to aid in the autonomous real-time monitoring and mapping of CO2 concentration over a two-dimensional area. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE) instrument uses two transceivers and a series of retroreflectors to continuously measure the differential transmission over a number of overlapping lines of sight or “chords”, forming a plane. By inverting the differential transmission measurements along with locally measured temperature (T), pressure (P) and relative humidity (RH) the average concentration of CO2 along each chord can be determined and, based on the overlap between chords, a 2D map of CO2 concentration over the measurement plane can be estimated. The GreenLITE system was deployed to the Zero Emissions Research and Technology (ZERT) center in Bozeman, Montana, in Aug-Sept 2014, where more than 200 hours of data were collected over a wide range of environmental conditions, while utilizing a controlled release of CO2 into a segmented underground pipe [1]. The system demonstrated the ability to identify persistent CO2 sources at the test facility and showed strong correlation with an independent measurement using a LI-COR based system. Here we describe the measurement approach, instrument design, and results from the deployment to the ZERT site

Evaluation of the WRF-UCM mesoscale model and ECMWF global operational forecasts over the Paris region in the prospect of tracer atmospheric transport modeling

International audienceThe quantification of CO2 emissions from cities using atmospheric measurements requires accurate knowledge of the atmospheric transport. Complex urban terrains significantly modify surface roughness, augment surface energy budgets, and create heat islands, all of which lead to lower horizontal winds and enhanced convection over urban areas. The question remains whether these processes should be included in atmospheric transport models that are used for city scale CO2 inversion, and whether they need to be tailored on a city basis. In this study, we use the WRF model over Paris to address the following research question: does WRF runs at a 3 km resolution, including urban effects and the assimilation of local weather data, perform better than ECMWF forecasts that give fields at 16 km resolution? The analysis of model performances focuses on three variables: air temperature, wind and the planetary boundary layer (PBL) height. The results show that the use of objective analysis and nudging tools are required to obtain good agreements between WRF simulated fields with observations. Surface temperature is well reproduced by both WRF and ECMWF forecasts, with correlation coefficients with hourly observations larger than 0.92 and MBEs within 1°C over one month. Wind speed correlations with hourly observations are similar for WRF (range 0.76~0.85 across stations) and ECMWF (0.79~0.84), but the associated RMSEs and MBEs are better for ECMWF. Conversely, WRF outperforms ECMWF forecasts for its description of wind direction, horizontal and vertical gradients. Sensitivity tests with different WRF physics schemes show that the wind speed and the PBL height are strongly influenced by PBL schemes. The marginal advantage of WRF over ECMWF for the desired application is sufficient to motive additional testing with prescribed CO2 flux maps for comparing modeled CO2 concentrations with available observations in an urban environment