WP-M.1 (Task 1: SWOT) D-M.1.1 - Part 2.

Strengths

• State-of-the-art meteorological transport model ICON-ART [RR] DWD-KIT

• Continuous and dynamic development of ICON-ART through large development community [RR]

• Experience in simulating trace substance transport and chemistry [RR]

• Experience with data assimilation from numerical weather prediction (NWP) [RR] DWD-KIT

Weaknesses

• Some metrics for model validation still need to be developed [RR]

• Likewise, the workflow for eliminating model bugs or for implementing improvements in the used model version [RR] DWD-KIT

• No estimation of model uncertainty regarding chemistry and transport available [RR]

Opportunities

• The open source release of ICON increases the developer community so that ICON-ART can benefit from improvements outside of ITMS [RR]

Threats

• High computing time requirement for high resolution simulations with additional tracers [RR]

• Estimation of model uncertainty regarding chemistry and transport [RR]

Strengths

• Quality of meteorological transport model DWD-KIT

• Experience in data assimilation from numerical weather prediction (NWP) DWD-KIT

  • Ensemble-approaches, e.g. we can quantify meteorological transport uncertainty

  • DACE-framework for various DA methods (such as LETKF, 3D-VAR, etc.)

• First working version of a synthesis inversion

• First 3D-Var-Version for CH4-DA in DACE2

Weaknesses

• Resolution of urban areas

• Bugs in the present code base when extending ART forward modelling to long-lived greenhouse gases DWD-KIT

  • Quality and readability of the code and code documentation needs to be improved

• Standards for code and software management are partly still under development and need to be applied more consequently

  • Other bugs in the DWD ICON-ART experiments that remain to be resolved

    • e.g. differences between the mixing ratios definition in observations and models (e.g., with respect dry air, moist air or, even including mass of hydrometeors as specified in ICON)

• The uncertainty matrices for the data assimilation and inversion need development

  • More comprehensive and selective treatment of observations (especially their uncertainties and correlations) is needed

  • Currently, no data-based covariance matrix of a priori emissions is available and considered (not needed in current inversion scheme, but will be relevant in the future)

Opportunities

• Integration of data assimilation and inversion

• Possibility to run the system as an operational capability building onto the mature NWP system

• Flexible integration of flux estimates as well as time-varying emissions, as provided by Q&S

• Improvements in the quality and documentation of code and software will prevent some future weaknesses, increase user-friendliness and help new employees to start their work more easily

• Strong collaboration with partners with special expertise:

  • EMPA (e.g., ensemble approaches, estimation of uncertainties like B-Matrix, OEM)

  • FEHP (e.g., ICOS measurement uncertainties)

  • EZMW (e.g., 4D-VAR with Ensembles and simultaneous parameter estimation)

  • UBA (e.g., emission uncertainty)

• Change from CAMS-EGG4 initial and boundary conditions to improved CAMS-Inversion-Optimized product for DWD model experiments

• Opportunity to use more highly resolved emission data (less aggregated on sector, spatial and temporal levels than currently)

• Opportunity to use future observation products (e.g. for satellite data) in our DA system

Threats

• Threats with respects to observation data used in model experiments:

  • Too few observations

  • Unclear representativeness of observations

  • Partially unknown timeliness, quality, and usability of new observation products (e.g., from satellites)

• Accuracy of externally provided boundary conditions for concentration fields

• Long-term development vs. short-term schedule based on 3 to 4-year phases

  • The developed DA and the forward-modelling is highly complex and requires training and experiences of employees

Strengths

• Large signals for urban areas

• Independent model--> no need for icon model development, observations & recent Q&S

Weaknesses

• Many possible configurations of monitoring strategies prevent analyzing all of them

• Synthetic study has no direct application for emissions, but only for observation strategies in general

• Different model used than in the other ITMS-M modules 

Opportunities

• Link to ITMS-B as well as to ICOS Paul

• Importance of cities addressed in this WP → role for ITMS phase 2?

• Potential linkage to the WP “Multi-Tracer”

Threats

• Difference of German cities may prevent clear suggestions across cities

• No necessary links to other ITMS-M WPs, however synergies

Strengths

• History of inversions from global to regional scale

• STILT as regional transport model to generate transport adjoint (footprints) using NWP products from ECMWF

• Long time scales with single “assimilation window” over 15 years

• Maturity of CarboScope-Regional is developing, soon allowing contribution to the inventory report appendix.

  • Maturity requires consistency in flux estimates from consecutive inversions for overlapping periods.

• Evaluation of GHG transport using profile information from aircraft (IAGOS, HALO missions)

Weaknesses

• Not easily adoptable for remote sensing due to computational effort required to generate footprints

• Transport resolution not very high

• Link to global inversion through two-step scheme, requiring careful alignment of observational datasets (list of stations) used for both steps (global and regional)

• Rn as tracer for transport - uncertainties for exhalation rates are larger than expected

  • Needs joint inversion using realistic uncertainties for fluxes of Rn and tracer of interest (CO2, CH4, …)

Opportunities

• Backbone system for comparisons with DWD ICON GHG data assimilation

• Utilization of DWD ICON meteorological output

• Augmentation to methane

• 14C based fossil CO2 inversion to estimate emissions - external task by ICOS-D

• Detailed comparison of STILT and FLEXPART to improve regional atmospheric transport models

Threats

• Observation density too low when only using tall tower data and ground based stations