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Regional O3 OSSEs Brad Pierce (NOAA)

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Presentation on theme: "Regional O3 OSSEs Brad Pierce (NOAA)"— Presentation transcript:

1 Regional O3 OSSEs Brad Pierce (NOAA)
GEOCAPE Regional and Urban OSSE WG members Co-Leads: Brad Pierce (NOAA) and Vijay Natraj (JPL) Regional Nature run: Brad Pierce and Allen Lenzen (CIMSS) Forward modeling: Vijay Natraj, Susan Kulawit, (JPL) Urban Nature run: Ken Pickering and Chris Loughner (NASA/GSFC) Averaging Kernel regression development: Helen Worden (NCAR) Additional contributions from Jerome Vidot (Meteo/France) and Eva Borbas (UW-Madison/SSEC) who have provided the WG with the High Spectral Resolution (HSR) IR emissivity and VIS reflectivity data bases for the OSSE studies. 4th TEMPO Science Team Meeting, June 1-2, 2016 Washington, DC

2 July 2011 DISCOVER-AQ Period
GEOCAPE O3 OSSE Goals July 2011 DISCOVER-AQ Period Utilize independent modeling systems for generation of Nature atmosphere and conducting assimilation impact experiments Account for realistic atmospheric variability, which requires evaluation of the nature runs with respect to observations Include realistic variability in the synthetic radiances, which requires using realistic albedos and emissivities Include realistic sensitivities, which requires generation of averaging kernels (AK) for each retrieval for use in assimilation studies

3 OSSE Flow Chart

4 Nature/Retrieval Correlations
Regional Multiple Regression AK Retrievals (UV, UV-VIS, UV-VIS-TIR) All retrievals show high correlations (>0.9) in upper troposphere/lower stratosphere UV-VIS-TIR shows the highest correlations (>0.8) in the mid and lower troposphere UV-VIS shows improvement over the UV only retrievals below 800mb 3km 6km 12km 1km 18km

5 Nature/Retrieval Biases
Regional Multiple Regression AK Retrievals (UV, UV-VIS, UV-VIS-TIR) Generally low biases (<20%) except: between mb at sunrise and sunset (UV and UV-VIS only) Below 900mb (underestimate daytime and overestimate nighttime O3) 3km 6km 12km 1km 18km

6 July 2011 WRF-CHEM/GSI OSSE Experiments
Synthetic OMI (using actual retrieval efficiency factors and apriori) OMI+ Multiple Regression GEOCAPE UV-VIS-TIR synthetic retrievals OMI+ Multiple Regression GEOCAPE UV-VIS synthetic retrievals OMI+ Multiple Regression GEOCAPE UV synthetic retrievals Multiple Regression GEOCAPE UV-VIS-TIR synthetic retrievals Multiple Regression GEOCAPE UV-VIS synthetic retrievals Multiple Regression GEOCAPE UV synthetic retrievals All OSSE experiments include: 1 hour cycling Inflation of background error covariances near surface Application of linear observation operator (AK) in GSI enter loop needed for stability during initial large UTLS adjustments Results compared to nature run integrated over atmospheric layers (12-18km, 6-12km, 0-6km, 0-3km, 0-1km)

7 sfc-6km Nature vs OMI DA 21Z 07/30/2011

8 sfc-6km Nature vs OMI+UVVIS DA 21Z 07/30/2011

9 Diurnal Averaged Bias OMI+MR OSSE results
Significant reductions in high biases introduced by OMI for all MR retrievals except the surface-1km and surface-3km layers OMI+MR UV and OMI+MR UVVIS show similar results OMI+UVVISTIR shows lower biases between 12-18km but larger biases then UV or UVVIS below 12km 12-18km 6-12km sfc-6km sfc-3km sfc-1km

10 Diurnal Averaged Bias MR OSSE results
Significant reductions in high biases introduced by OMI for all MR retrievals except the surface-1km and surface-3km layers OMI+MR UV and OMI+MR UVVIS show similar results OMI+UVVISTIR shows lower biases between 12-18km but larger biases then UV or UVVIS below 12km 12-18km 6-12km sfc-6km sfc-3km sfc-1km

11 Diurnal Averaged RMS Error
MR OSSE results Diurnal Averaged RMS Error Slight improvements in RMS error over OMI for all MR retrievals except the surface-1km layer OMI+MR UV and OMI+MR UVVIS show similar results OMI+UVVISTIR shows largest improvements 12-18km 6-12km sfc-6km sfc-3km sfc-1km

12 Diurnal Averaged RMS Error
OMI+MR OSSE results Diurnal Averaged RMS Error Slight improvements in RMS error over OMI only for all MR retrievals OMI+MR UV and OMI+MR UVVIS show similar results OMI+UVVISTIR shows largest improvements 12-18km 6-12km sfc-6km sfc-3km sfc-1km

13 Diurnal Averaged Correlation
OMI+MR OSSE results Diurnal Averaged Correlation Slight improvements in correlation over OMI only for all MR retrievals OMI+MR UV and OMI+MR UVVIS show similar results OMI+UVVISTIR shows largest improvements 12-18km 6-12km sfc-6km sfc-3km sfc-1km

14 Diurnal Averaged Correlation
MR OSSE results Diurnal Averaged Correlation Slight improvements in correlation over OMI for all MR retrievals MR UV and UVVIS retrievals show similar results OMI+UVVISTIR shows largest improvements 12-18km 6-12km sfc-6km sfc-3km sfc-1km

15 Summary Hourly geostationary UV, UVVIS, and UVVISTIR ozone retrievals show high correlations with nature run (>0.8) except for sunrise/sunset UV and UVVIS retrievals and below 700mb (3km) Hourly geostationary UV, UVVIS, and UVVISTIR ozone retrievals show fairly low biases relative to nature run (<20%) except for sunrise/sunset UV and UVVIS retrievals and below 900mb (1km) Assimilation of hourly geostationary UV, UVVIS and UVVISTIR ozone retrievals improves correlation, rms error and bias relative to control and OMI polar orbit retrievals in the upper troposphere lower stratosphere (12-18km), middle troposphere (6-12km) and lower troposphere (0-6km) Surface-1km and surface-3km layers show neutral results for correlation and rms errors and slight increases in diurnally averaged biases when hourly geostationary UV, UVVIS, and UVVISTIR ozone retrievals are assimilated. These WRF-CHEM/GSI results suggest that it will be difficult to obtain improvements in boundary layer ozone analyses through 3D variational assimilation of geostationary ozone retrievals alone due to the strong dependence on ozone precursor emissions.

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