Overview on UV-Vis satellite work

Transcription

1 Overview on UV-Vis satellite work Where we are and what possible directions are? M. Van Roozendael Belgian Institute for Space Aeronomy ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

2 UV-Vis nadir missions in Europe GOME 320x40 km 2 SCIAMACHY 60x30 km 2 OMI 15x25 km 2 GOME-2 GOME-2 GOME-2 40x80 km 2 S5P 7x7 km 2 Tandem 40x40 km 2 Sentinel 4 Sentinel 5 GEO ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

3 UV-Vis target molecules as in 1995 ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

4 Actual UV-Vis tropospheric products Species Range Level of maturity NO 2 Global High O 3 Global Medium SO 2 Hot spot Medium HCHO Global High Glyoxal Global Low BrO Polar regions Medium IO Polar regions Demonstration Water vapor Global High ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

5 Status of UV-Vis data products validation Species Validation means Status NO 2 In-situ, MAXDOAS, aircraft, lidar Good but lack of remotesensing measurements O 3 O 3 sondes, lidar Comprehensive SO 2 HCHO In-situ, Brewer, MAXDOAS In-situ, FTIR, MAXDOAS Poor - measurements sparse and mostly inadequate Embryonary Glyoxal MAXDOAS Embryonary BrO DOAS, in-situ Partial IO DOAS Poor Water vapor ISCCP, SSM/I Good but only over sea ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

6 Examples of important science outcomes (1) First global mapping of a number of important tropospheric species Assessment of global trends in tropospheric NO 2 Characterisation of weekly cycles («religion seen from space») Richter Global tropospheric NO 2 trend van der A Beirle ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

7 Examples of important science outcomes (2) Identification of pollutant sources: Ship tracks (NO2 & HCHO) NOx production due to lightning Volcanic gas emission monitoring SO2 trend in China Polar bromine emissions and their impact on Hg and ozone First space detection of volcanic BrO emissions Beirle Ship tracks Theys Richter ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

8 Examples of important science outcomes (3) Global water vapor measurement and trend determination from GOME- SCIAMACHY Formaldehyde and glyoxal retrieval and their use to improve gloval emission inventories of VOCs HCHO Glyoxal Global water vapor from GOME-2 De Smedt & Lerot Mieruch & Noel T. Wagner ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

9 Contribution to service activities GAS = GMES Atmospheric Service, the European contribution to GEOSS UV-Vis data products have been extensively used in a number of GMES-related demonstration or precursor projects, e.g.: DUE TEMIS FP6 GEMS GSE PROMOTE DUE SAVAA FP7 MACC and PASODOBLE ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

10 UV-Vis trace gas retrieval Principle DOAS: VCD = SCD AMF Clouds shield surface Clouds enhance sensitivity to absorbers located above or at cloud altitude Tropospheric gas AMF = (1-f).AMF clear + f.amf cloud Surface (Independent pixel approximation) ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

11 UV-Vis retrievals Where do we stand after 15 years? Detection level: are there still «missing» tropospheric trace gases not yet identified in the UV-Vis range? Recently detected: IO and glyoxal What about HONO?? Detection levels must be increased, possibly through: Technology better detector, higher throughput Improved sampling (spectral more pixels) Longer integration times ( GEO) Multiply instruments (e.g. 3x GOME-2 ) Improved algorithms Accuracy of spectral fit: always an issue for minor trace gases improvements = consolidation of existing products ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

12 Examples of recent improvements of UV-Vis DOAS retrievals (1) Improvement of S/N ratio of NO 2 retrievals through expansion of fitting interval Richter & Hilboll Improved filtering of SAA artefacts Richter & Hilboll, 2009 ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

13 Examples of recent improvements of UV-Vis DOAS retrievals (2) Improvement of S/N and reduction of bias in HCHO columns, through inclusion of split-window BrO fit λ-dependent O 3 AMF correction De Smedt, 2009 Improvements in SO 2 retrievals through inclusion of λ- dependent O 3 and SO 2 AMFs (modified DOAS approach) Richter, 2009 ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

14 Examples of recent improvements of UV-Vis DOAS retrievals (3) Elimination of oceanic bias in glyoxal columns through improved correction for liquid water absorption Lerot, 2009 ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

15 Other aspects of UV-Vis retrievals (1) Improved approaches to stratospheric corrections in residual algorithms: Use synergy with other sounders ( limb-nadir NO 2 retrievals with SCIAMACHY, or OMI-MLS tropospheric O 3 product) Use model input to better represent the zonal variability (e.g. assimilation scheme of TEMIS NO 2 data product) Newdynamical BrO climatology, using O 3 and NO 2 columns as input parameters: Theys et al, 2009 ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

16 Other aspects of UV-Vis retrievals (2) Improved AMF calculations: use improved a-priori atmospheric and surface data bases Improved coupling of retrievals with 3D-CTM and AQ models, of adequate resolution Improved surface data base (terrain height) Improved surface albedo (e.g. consider BRDF effect) Zhou and Brunner, 2009 Improved aerosol correction Improved cloud correction BRDF map for one OMI orbit ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

17 Other aspects of UV-Vis retrievals (3) Cloud algorithms: how important are clouds for UV-Vis trace gas retrievals? What do we need as cloud product for trace gas retrievals? Clouds are the main source of uncertainty for tropospheric trace gas retrievals Current cloud algorithms use Lambertian cloud models (cloud = opaque lambertian reflector) relevant cloud fractions and cloud properties are effective values. Major issue: trace gas algorithms must use cloud models consistent with cloud algorithms any improvement to cloud algorithm must be done in synergy with trace gas algorithms. ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

18 Other aspects of UV-Vis retrievals (4) Cloud algorithms (cont.): clouds as reflecting boundaries (CRB) againsts «real» clouds CRB Real cloud surface Questions: Can we improve cloud models, and is this going to improve the accuracy of our trace gas retrievals? Can we find a way to detect clouds above snow/ice? How to consistently treat clouds and aerosols in a trace gas algorithm? ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

19 Correct for clouds but also use clouds to enhance trace retrievals E.g.: Quantify NO 2 production due to lightning Study long-range transport Quantify vertical extent of sources throughstudy of cloud shield indices Tropospheric O 3 from cloudslicing Mijling and R. van der A, 2007 Tropospheric NO 2 column Cloud shield index Beirle, 2007 ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

20 Assess consistency of multi-sensors data sets Key issue for: Trend analysis Multi-annual assimilation Long-term inventories of emissions HCHO column above Guatemala GOME/ERS-2 SCIAMACHY/ENVISAT GOME-2/METOP-A ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

21 Possible future directions for UV-Vis work (1) Improve and expand data products from current satellites: Refine scientific algorithms Minimise noise and bias (spectral fits) Search for missing gases? Improve atmospheric and surface data bases Improve cloud and aerosol corrections Improve data distribution and its quality control (role of operational data centers!) Further develop synergistic retrievals where possible (e.g. GOME-2/IASI tropospheric O 3 ) Improve data integration in models (cf. GMES-MACC) ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

22 Possible future directions for UV-Vis work (2) Prepare and exploit future satellite missions Improved spatial resolution (e.g. 7x7 km 2 for S5P/TROPOMI in 2015) Improved time resolution (Sentinel 4, GEO) Improved sampling/resolution (hopefully overlapping GOME-2 on successive METOP missions) New missions are key for assimilation in AQ forecast models Inversion of emissions future models will enable ingestion of multiple species to improve selectivity. Error estimates and bias removals are a key issue ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

23 Requirements for validation High-quality long-term measurements from in-situ and ground-based remote-sensing instruments (FTIR + MAXDOAS), supported by aircraft campaigns Adequate spatial sampling (strategy of instrument deployment to be adjusted according to species e.g. need for HCHO, NO 2, glyoxal, SO 2 correlative measurements in regions of emissions) Need to design strategies to improve the time and spatial co-location of satellite and correlative measurements (to minimise «atmospheric noise effects»). Particularly important for the validation of short lived gases like NO 2, SO 2 or HCHO. Support and expand existing ground-based newtorks (e.g. NDACC) ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June

24 Summary the way forward? Improve existing data products and their error analysis Improve and systematise (long-term) validation Prepare for new (high resolution) sensors Improve data distribution and quality control (need to evolves towards more operational products of adequate quality) Develop synergy with other techniques and data products (retrievals and exploitation) ACCENT AT-2 Follow up meeting, MPI Mainz, Germany, June