TEMPO Simulation and Retrieval Tools and Algorithm Testing at SAO Xiong Liu 3 rd TEMPO Science Team Meeting Huntsville, Al, May 27, 2015

11/12/132 TEMPO Retrieval Sensitivity Studies Performed RTM simulations of TEMPO radiance spectra and retrieval sensitivity studies for determining instrument requirements and verifying instrument performance.  VLIDORT: adapted from previous GEO-CAPE tools, with interfaces for viewing geometry, built-in cross sections, pre- and after- convolution, HITRAN line by line calculations, aerosol profiles and plumes, scattering clouds, IPA, Koelemeijer GOME surface albedo database or input surface reflectance spectra  Hourly fields of GEOS-Chem model fields over TEMPO field of regard for 12 days (1 day/month) up to SZA 80°  ~90000 simulations with 10 gases (O 3, NO 2, H 2 CO, SO 2, C 2 H 2 O 2, H 2 O), BrO, OClO, O 2, O 4, 6 types of aerosols, water/ice clouds  TEMPO SNR model: account for optical transmission and grating efficiency, including shot, dark current, RTN, readout, quantization, smear, CTE noise terms  Climatological a priori: climatological for O 3, unconstrained for other trace gas VCDs, consistent with current algorithms Performed RTM simulations of TEMPO radiance spectra and retrieval sensitivity studies for determining instrument requirements and verifying instrument performance.  VLIDORT: adapted from previous GEO-CAPE tools, with interfaces for viewing geometry, built-in cross sections, pre- and after- convolution, HITRAN line by line calculations, aerosol profiles and plumes, scattering clouds, IPA, Koelemeijer GOME surface albedo database or input surface reflectance spectra  Hourly fields of GEOS-Chem model fields over TEMPO field of regard for 12 days (1 day/month) up to SZA 80°  ~90000 simulations with 10 gases (O 3, NO 2, H 2 CO, SO 2, C 2 H 2 O 2, H 2 O), BrO, OClO, O 2, O 4, 6 types of aerosols, water/ice clouds  TEMPO SNR model: account for optical transmission and grating efficiency, including shot, dark current, RTN, readout, quantization, smear, CTE noise terms  Climatological a priori: climatological for O 3, unconstrained for other trace gas VCDs, consistent with current algorithms

11/12/133 TEMPO Retrieval Sensitivity Studies Continue:  Optimal estimation based retrieval sensitivity tool (IDL-based)  Calculate retrieval averaging kernels, estimate retrieval errors for both O 3 profile and other trace gas VCDs and perform error analysis (e.g., radiance errors, polarization sensitivity, forward model parameters) for determining retrieval error budgets and calibration requirements  To be added: BRDF, EOFs of surface reflectance spectra with high-resolution MODIS BRDF climatology  To be added: Rotational-Raman scattering (look-up table) Continue:  Optimal estimation based retrieval sensitivity tool (IDL-based)  Calculate retrieval averaging kernels, estimate retrieval errors for both O 3 profile and other trace gas VCDs and perform error analysis (e.g., radiance errors, polarization sensitivity, forward model parameters) for determining retrieval error budgets and calibration requirements  To be added: BRDF, EOFs of surface reflectance spectra with high-resolution MODIS BRDF climatology  To be added: Rotational-Raman scattering (look-up table) Same as NLLS for VCDs (unconstrained)

11/12/134 Algorithm testing Fully test L0-L2 algorithms using synthetic data:  Hourly high spatial resolution data (e.g., GEOS-5 nature run at 7×7 km 2 from A. Silva): 1 day/month  RTM simulations  Radiance spectrum  L1 data  L0 data  Test L0  L1 and L1  L2 algorithms Fully test L0-L2 algorithms using synthetic data:  Hourly high spatial resolution data (e.g., GEOS-5 nature run at 7×7 km 2 from A. Silva): 1 day/month  RTM simulations  Radiance spectrum  L1 data  L0 data  Test L0  L1 and L1  L2 algorithms