Satellite Observation Information Service for Data Assimilation ApplicationsM. Lee1, R. Weidner1, C. Lynnes2, I. Gerasimov2

1 : Jet Propulsion Laboratory /California Institute of Technology, 2: Goddard Space Flight Center

Target Atmospheric Science Missions

National Aeronautics and Space Administration

This research has been enabled by the contributions from GEOS-Chem-Adjoint working group (D. Henze and K. Bowman), GES DISC team (E. Seiler, J. Johnson, R. Strub, A. Albayrak, and G. Leptoukh),and with the funding from the NASA-ACCESS program.

Acknowledgement

AbstractMulti-mission Observation Operator (M2O2) team at NASA is developing a streamlined interface mechanism that simplifies the assimilation process of satellite-observations by providing “assimilation-ready” observation information “on-demand”. The “assimilation-ready” observation information is referred to as L2# (L2-sharp) data in contrast to level-2 (L2) data.

The “on-demand” indicates a web-service protocol for L2# data request handling. A L2# data service is developed for each atmospheric component of a mission to apply component-specific quality screening and post processing and deliver mission-generic observation information required for assimilation. The observation information is organized for sampling (time and location), sounding (pressure profile and averaging kernel), and retrieval results (a priori state, estimated state, and error).

The M2O2 extensions to GEOS-Chem (version 9.0.1) and GEOS-Chem-Adjoint (version 34) have been employed to assimilate MLS-O3 (2004-2012), TES-O3 (2005-2009), TES-CH4 (2009) and ACOS-XCO2 (2009-2011). The L2# data services for MLS-O3 (2004-current), ACOS-XCO2 (2009-current), and AIRS-CO (2002-current) have been installed at GES DISC for assimilation-community-wide access.

We will present the “lessons learned” in three areas: 1) diversity of level-2 data product organization, 2) observation information formulation and validation, and 3) generalized model-coupling process. The M2O2 research is supported by NASA’s Advancing Collaborative Connections for Earth System Science (ACCESS) program.

Microwave Limb Sounder (MLS)Airborne Infrared Sounder (AIRS)Atmospheric Carbon Observaton (ACOS)

Tropospheric Emission Spectrometer (TES)AIRS observes the global water and energy cycles, climate variation and trends, and the response of the climate system to increased greenhouse gases. AIRS uses infrared technology to create three-dimensional maps of air and surface temperature, water vapor, and cloud properties. AIRS can also measure trace greenhouse gases such as ozone, carbon monoxide, carbon dioxide, and methane.

MLS observes the details of ozone chemistry by measuring many radicals, reservoirs, and source gases in chemical cycles which destroy ozone. This set of measurements will provide stringent tests on the understanding of global stratospheric chemistry, will help explain observed trends in ozone, and can provide early warnings of any changes in the chemistry of this region.

TES is capable of observing chemicals from the ground up to the middle of the stratosphere (~32 Km). TES measurements reveal that the distribution of tropospheric ozone, carbon monoxide, water vapor, and methane.

Japanese spacecraft ‘IBUKI’

ACOS project processes level-1 data products from GOSAT and generates level-2 data products for Carbon dioxide and methane. The Greenhouse gases Observing SATellite (GOSAT) Project is a joint effort promoted by the Japan Aerospace Exploration Agency (JAXA), the National Institute for Environmental Studies (NIES) and the Ministry of the Environment (MOE).

1. Diversity of Level-2 data product organization

2. Observation information formulation and validation

3. Generalized model coupling process

a) Information may be missing or archived separately.

b) Temporal coverage alignment may either on orbit boundaries or on elapsed time of the mission.

c) Retrieval quality and science analysis quality may be significantly different.

d) Auxiliary information may be required for proper utilization of the averaging kernel.

e) Error covariance matrix (observation uncertainty) may not be invertible.

a) Data transformer performs information filter, quality control filter, and HDF to NetCDF file conversion, to generate L2# data from L2 data.

b) Customized quality control filter is developed to extract science analysis quality samples by removing the samples acquired during poor observation condition.

c) L2# data is organized daily with time, location, pressure levels, averaging kernel, a priori state, retrieved state, and uncertainty.

d) Inversion is applied to error covariance matrix to ensure computational robustness and efficiency.

a) Model transformer computes observation force of each sample by transforming the model state into pseudo observed state.

b) The pseudo sample profile is extracted from the nearest model grid to the sample location and mapped to the observation pressure levels.

c) The pseudo observation is derived by applying the observation a priori and averaging kernel.

d) The observation force is computed as a squared difference between the pseudo observation and the retrieved state divided by the uncertainty.

L2# service overage: 2004/08-currentL2# service coverage: 2002/01-currentL2# service coverage: 2009/07-2011/12L2# service coverage: 2006/01-2009/12

O3 CO, CO2 xCO2 O3

Note: M2O2 will be extended to other mission products and model systems (e.g., WRF-Chem).