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Integration of Ozone Products in ERA5 System: Impact Assessment and User Requirements

In this project, various O3-CCI products are integrated into the ERA5 system for quality assessment, experimentation, and impact evaluation. The work plan includes assessing individual dataset quality, conducting assimilation assessments, and analyzing the impact on the system. The project aims to compare datasets, characterize biases and errors, and evaluate the impact on the system. Various assimilation assessments are performed, considering user requirements such as vertical resolution and viewing geometry. The project explores the impact of different datasets on the system's consistency and performance.

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Integration of Ozone Products in ERA5 System: Impact Assessment and User Requirements

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  1. Atmosphere:Integration of several O3-CCI products in the ERA5 system (WP3.2) Rossana Dragani ECMWF rossana.dragani@ecmwf.int

  2. WP3.2: work plan NPO3 TCO3 LPO3 O3 CCI data store OMI TOMS (NASA) OMI DOAS (KNMI) MIPAS (ESA) SCIA (KNMI) GOME-2 (O3M SAF) Available runs for competing algorithms Individual dataset quality assessment, experimentation and impact assessment GOME-2 GOME-2 OMI GOME-2 SCIA MIPAS GOME-2 MIPAS Impact vertical resolut. Algorithm Round Robin Impact of viewing geom.

  3. Aims of WP3.2 Quality assessment • Six (+ one optional) datasets are considered: • TCO3: SCIAMACHY, GOME, GOME-2 (+ OMI); • NPO3: GOME and GOME-2; • LPO3: MIPAS. • Data assessment (for each chosen dataset): • Comparison with model equivalent (before the assimilation); • Bias characterization; • Error characterization [based on the Desroziers et al (2005) method]. • Impact on the system (for each chosen dataset) • RR assimilation assessments for selected datasets: • OMI, SCIA, GOME-2 TCO3; • MIPAS LPO3. • User Requirements (UR): • Vertical resolution: GOME-2 TCO3 vs. NPO3; • Viewing geometry: GOME-2 NPO3 vs. MIPAS LPO3. Impact assessment

  4. Aims of WP3.2 Quality assessment GOME-2 NPO3 • Six (+ one optional) datasets are considered: • TCO3: SCIAMACHY, GOME, GOME-2 (+ OMI); • NPO3: GOME and GOME-2; • LPO3: MIPAS. • Data assessment (for each chosen dataset): • Comparison with model equivalent (before the assimilation); • Bias characterization; • Error characterization [based on the Desroziers et al (2005) method]. • Impact on the system (for each chosen dataset) • RR assimilation assessments for selected datasets: • OMI, SCIA, GOME-2 TCO3; • MIPAS LPO3. • User Requirements (UR): • Vertical resolution: GOME-2TCO3 vs. NPO3; • Viewing geometry: GOME-2 NPO3 vs. MIPAS LPO3. Impact assessment

  5. Experiment set-up: • Four month assimilation experiments (Jul-Oct 2008) • Except for ERS-2 GOME (Jul-Oct 1997) • Impact assessment results shown for Aug-Oct only.

  6. Aims of WP3.2 Quality assessment • Six (+ one optional) datasets are considered: • TCO3: SCIAMACHY, GOME, GOME-2 (+ OMI); • NPO3: GOME and GOME-2; • LPO3: MIPAS. • Data assessment (for each chosen dataset): • Comparison with model equivalent (before the assimilation); • Bias characterization; • Error characterization [based on the Desroziers et al (2005) method]. • Impact on the system (for each chosen dataset) • RR assimilation assessments for selected datasets: • OMI, SCIA, GOME-2 TCO3; • MIPAS LPO3. • User Requirements (UR): • Vertical resolution: GOME-2 TCO3 vs. NPO3; • Viewing geometry: GOME-2 NPO3 vs. MIPAS LPO3. Impact assessment

  7. Comparison with the model <Observation> Jul-Oct 2008 <Background> <Analyses> Before assimilation After assimilation

  8. Bias characterization 1. 1. 2-3 hPa Jul-Oct 2008 20-30 hPa 0. -1. 1. 2. 30-50 hPa 3-5 hPa -1. -2. Global bias correction (DU) 2. 2. 5-10 hPa 50-100 hPa -2. -2. 4. 2. 100-170 hPa 10-20 hPa -4. -2. Time Time

  9. Uncertainty characterization 100 *(se– so) / so s Observation uncertainty (so) Pressure Latitude Estimated uncertainty (se) s Latitude Time

  10. Aims of WP3.2 Quality assessment • Six (+ one optional) datasets are considered: • TCO3: SCIAMACHY, GOME, GOME-2 (+ OMI); • NPO3: GOME and GOME-2; • LPO3: MIPAS. • Data assessment (for each chosen dataset): • Comparison with model equivalent (before the assimilation); • Bias characterization; • Error characterization [based on the Desroziers et al (2005) method]. • Impact on the system (for each chosen dataset) • RR assimilation assessments for selected datasets: • OMI, SCIA, GOME-2 TCO3; • MIPAS LPO3. • User Requirements (UR): • Vertical resolution: GOME-2 TCO3 vs. NPO3; • Viewing geometry: GOME-2 NPO3 vs. MIPAS LPO3. Impact assessment

  11. Round-Robin assimilation: GOME2 TCO3 (1) Control Control + G2T_CCI Control + G2T_SAF • The O3M SAF GOME2 TCO3 show almost no impact on the O3 analyses compared to Control. • The CCI GOME2 TCO3 improves the analysis fit to MLS compared to Control. Zonal Mean Temporal Mean of (MLS – Analyses)

  12. Round-Robin assimilation : GOME2 TCO3 (2) 60-90N 30S-30N 30-60N 232 209 64 60-90S 30-60S CTRL G2T_SAF G2T_CCI Number of WOUDC sondes Degradation in southern lower troposphere during winter/spring 83 37 RMS(Sonde-An)

  13. UR: impact of vertical resolutionGOME2 TCO3 vs GOME2 NPO3 CCI GOME2 TCO3 CCI GOME2 NPO3 Control better Mean Perturbation better Std Dev STAT(MLS – Analyses)Perturbation – STAT(MLS – Analyses)Control

  14. CCI GOME-2 NPO3 impact on the rest of the system: example of consistency GOME-2 NPO3 degrades RMS of Z fc error GOME-2 NPO3 improves RMS of Z fc error Better usage of AIRS IR/O3 when GOME2 NPO3 is used.

  15. UR: impact of viewing geometry: GOME2 NPO3 vs MIPAS LPO3 (1) CCI MIPAS LPO3 CCI GOME2 NPO3 -ve Mean +ve -ve Std Dev +ve STAT(MLS – Analyses)Perturbation – STAT(MLS – Analyses)Control

  16. UR: impact of viewing geometry:GOME2 NPO3 vs MIPAS LPO3 (2) 30-60N 30S-30N 60-90N 232 209 64 60-90S 30-60S CTRL G2 NPO3 MIPAS 232 83 37 RMS(Sonde-An)

  17. OMI RR: next time!  KNMI OMI TCO3 CCI OMI TCO3 -ve +ve NASA OMI TCO3 • All products have +ve impact in the extra-tropics compared with Control. • The CCI & NASA OMI TCO3 show stronger –ve impact on the analyses in the tropical stratosphere. Mean(MLS – Analyses)Perturbation – Mean(MLS – Analyses)Control

  18. Summary and recommendations + Not assessed based on previous runs that led to –ve impact. § Data not yet available. $ Not assessed based on data assessment results (Dragani, 2012). Important to consider continuation in “NRT” of data production.

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