ATBD - GHG-CCI
ESA Climate Change Initiative (CCI) Algorithm Theoretical Basis Document Version 2 (ATBDv2) – UoL-FP for the Essential Climate Variable (ECV) Greenhouse Gases (GHG) Page 16 Version 2 – Draft 1 18 March 2013 3.3 State Vector 3.3.1 Description of Carbon Dioxide Retrievals The retrievals for Carbon Dioxide use a state structure that represents the atmosphere, surface and instrument. The state structure presently consists of 97 state vector elements as shown in Table 3-1. Table 3-1. State Vector for CO 2 retrievals. Description Parameters Number of Elements Aerosols 3 x 20 levels 60 CO 2 20 levels 20 Albedo 2 x 3 bands 6 Dispersion 2 x 3 bands 6 Zero-Level Offset 1 1 Surface Pressure Scalar 1 Temperature Scalar 1 Water Vapour Scalar 1 CH 4 Scalar 1 Total 97 The following sub-sections describe the a priori value and corresponding uncertainties of each state vector quantity. 220.127.116.11 Profile of Carbon Dioxide Concentration The CO 2 a priori profile is obtained from 3 hourly model 3.75 degree longitude by 2.5 degree latitude gridded LMDZ 2009 CO 2 fields. The model CO 2 has a model run initialisation offset applied of 333 ppm as well as an additive correction of 18.246352 ppm which was derived by a comparison of the XCO 2 computed from the model fields to the NOAA Mauna Loa XCO 2 observations. The CO 2 profile is interpolated with latitude, longitude and time to the specific location desired. Additionally, the CO 2 is incremented according to yearly global CO 2 increases by NOAA.
ESA Climate Change Initiative (CCI) Algorithm Theoretical Basis Document Version 2 (ATBDv2) – UoL-FP for the Essential Climate Variable (ECV) Greenhouse Gases (GHG) Page 17 Version 2 – Draft 1 18 March 2013 18.104.22.168 Methane Concentration The a priori profile of CH 4 is acquired from a TM3 model run (Sander Houweling, personnel communication) and is interpolated to the desired latitude, longitude and time. This is only retrieved as a scalar, assuming the profile shape to be true. 22.214.171.124 Surface Pressure Both under-constrained and over-constrained retrieval scenarios rely on the use of a priori information to constrain the solution to be physically reasonable. In order to perform retrievals the a priori need to be well constrained. In particular, it is most important that the surface pressure is well constrained since this can have the largest effect on the spectral lines retrieved. The European Centre for Medium-Range Weather Forecasts (ECMWF) is an assimilation model that uses observations from surface buoy and satellite measurements [ECMWF Technical Notes, 2008]. ECMWF provide atmospheric profiles of pressure, temperature and specific humidity on a 1.125 degree by 1.125 degree global grid with 91 levels. Given the latitude, longitude and altitude of a site of interest the surface pressure can be determined from these profiles. ECMWF provide potential data for the lowest level of the same grid, which can be used to find the geopotential height of each grid point level as: where gravity, g, is calculated as a function of latitude and approximate altitude. Taking the four surrounding grid points of the site of interest, the pressure, P, at the site altitude can be found for each grid point by using the hydrostatic equation: where P 0 is the pressure of the grid point level lower than the site altitude, Z is the difference in altitude between the grid point level and the site altitude and Z 0 is the scale height defined as: where R is the ideal gas constant, T is the average temperature across the differential, M is the Molar mass of wet air and g is the gravitational acceleration as a function of latitude and altitude. The Molar mass of wet air can be calculated by: where SH is the ECMWF specific humidity, ρ d is the dry air mass and ρ w is the mass of wet air. The site altitude can be obtained from a global digital elevation model with a horizontal grid spacing of 30 arc seconds named GTOPO30 provided by the U.S. Geological Survey. Note, that in the case that