1. 2013 DOE/EU Retrieval Workshop, Köln “Common Algorithm Evaluation Approaches ” Session Shaocheng Xie, Kerstin Ebell, Dave Turner, and Ulrich Lohnert EU/DOE Ground-based Cloud and Precipitation Retrieval Workshop, 13-14 May 2013, Cologne, Germany Goals Identify common algorithm evaluation approaches for retrieval developments and uncertainty quantification Identify group activities to address those challenging issues that arise from previous intercomparison studies

2. 2013 DOE/EU Retrieval Workshop, Köln What Will Be Covered? Common algorithm evaluation approaches Observation System Simulation Experiments (OSSEs) In-situ comparisons Radiative closure Comparison with other retrieval datasets (satellite, other instruments) Intercomparison of retrievals Talks (~40 minutes) Dave Turner: Some examples on using these approaches to evaluate cloud retrievals Shaocheng Xie: Questions for discussion Discussion (~50 minutes)

3. 2013 DOE/EU Retrieval Workshop, Köln Earlier Efforts Made By EU/DOE Major cloud retrieval intercomparison studies: EG-CLIMET Lohnert, Donovan, Ebell, et al. (2013): Assessment of ground-based cloud liquid water profiling retrieval techniques - (3 algorithms, Continental Stratus – CABAUW; Maritime Stratocumulus – ASTEX, only liquid, both OSSEs and real cases) DOE: Comstock et al. (2007): High level ice clouds (16 algorithms, SGP- March 2000 IOP) Turner et al. (2007): Optically thin liquid clouds (18 algorithms, SGP-March 2000 IOP) Shupe et al. (2008): Mixed-phase clouds (8 algorithms, NSA-MPACE)

4. 2013 DOE/EU Retrieval Workshop, Köln  Limitations of instruments and uncertainties in the measurements and assumptions used in the algorithms account for a significant portion of the differences  The accuracy varies greatly with instrument, analysis method, and cloud type  No single retrieval method can work properly for all instruments and all cloud conditions

5. 2013 DOE/EU Retrieval Workshop, Köln Evaluating Retrieval Algorithm Results DOE / EU Ground-based Cloud and Precipitation Retrieval Workshop

6. 2013 DOE/EU Retrieval Workshop, Köln Evaluating Different Retrievals: Which is Better? (1) Observation System Simulation Experiments (OSSEs) –Start with known atmospheric conditions –Use forward model to compute radiance / backscatter and by adding realistic random noise create the ‘observations’ –Retrievals applied to these observations can be compared against a known truth –Do simulations cover entire range of possible conditions? –Assumes that the forward model is ‘perfect’ –Biases are generally not evaluated here –However, careful, well-constructed, simulations can be quite illuminating, especially when comparing sensitivities of different instruments and techniques

7. 2013 DOE/EU Retrieval Workshop, Köln Assessment of ground-based cloud liquid water profiling retrieval techniques Ulrich Löhnert (University of Cologne) Dave Donovan (KNMI) Kerstin Ebell (University of Cologne) Giovanni Martucci (Galway University) Simone Placidi (TU Delft) Christine Brandau (TU Delft) Ewan O’Connor (FMI/University of Reading) Herman Russchenberg (TU Delft) weatherreport.com

8. 2013 DOE/EU Retrieval Workshop, Köln MISSION To recommend an optimum European network of economical and unmanned ground-based profiling stations for observing winds, humidity, temperature and clouds (together with associated erorrs) for use in evaluating climate and NWP models on a global and high resolution (typically 1km) scale and ultimately for assimilating into NWP. European Ground-based Observations of Essential Variables for Climate and Operational Meteorology EG-CLIMET: ES-0702 www.eg-climet.org 2008-2012www.eg-climet.org 16 countries, 13 national weather services European COST action EG-CLIMET

9. 2013 DOE/EU Retrieval Workshop, Köln Objective of this study Evaluate current liquid cloud profiling techniques identify errors and discuss assumptions correct for errors  recommendations for an optimal retrieval method Simulation case “truth” known direct evaluation need to simulate measurements Real case application to real measurements evaluation with radiation closure ECSIM (EarthCareSimulator) radar, lidar, microwave radiometer SW, LW fluxes

10. 2013 DOE/EU Retrieval Workshop, Köln Overview of measurements and parameters... measurements which are used... Lidar/cloud radar: cloud base & top (Cloudnet TC) Z: cloud radar reflectivity factor (dBZ) MWR: brightness temperature TB (K) LWP: MWR liquid water path (gm -2 )... parameters to be retrieved... LWC: liquid water content (gm -3 ) Reff: cloud droplet effective radius (μm) N 0 : cloud droplet concentration (cm -3 )

11. 2013 DOE/EU Retrieval Workshop, Köln Retrievals BRANDAU (Brandau et al. / Frisch et al.)  retrieve LWC(z), Reff(z) and N 0 input: MWR LWP and radar Z, cloud boundaries uni-modal drop size distribution relation between moments (2nd and 3rd) of DSD (Brenguier et al. 2011) Cloudnet (O’Connor et al.)  retrieve LWC(z) input: MWR LWP, cloud boundaries & temperature linearly scaled adiabatic LWC, non-drizzle IPT (Löhnert et al. / Ebell et al.)  retrieve LWC(z), Reff(z) input: MWR TB, radar Z and a priori LWC, cloud boundaries, cloudnet TC minimize cost function to meet TB, LWC a priori profiles and radar Z-LWC relation, Reff accoring to Frisch et al. (2002) All retrievals use common “measurements” and cloud boundaries

12. 2013 DOE/EU Retrieval Workshop, Köln Simulated case #1: Continental Stratus One continental case simulated with LES CABAUW: only bulk microphysics (LWC), reasonable drop size distribution assumed (uni-modal), non-drizzling, only liquid ASTEX

13. 2013 DOE/EU Retrieval Workshop, Köln CABAUW case: Cloudnet large random error due to linear scaling rand. error: 50-60% sys. error: ~0% LWC / gm -3

14. 2013 DOE/EU Retrieval Workshop, Köln CABAUW case: Brandau LWC & Reff red: retrieval black: „truth“ LWC / gm -3 rand. error: <10% sys. error: <10% Reff / m -6 rand. error: < 5% sys. error: ~10%

15. 2013 DOE/EU Retrieval Workshop, Köln CABAUW case: Brandau with LWP error LWP error: +25 gm -2 ( 20 < LWP < 50 gm -2 ) LWP accuracy crucial! Reff / m -6 rand. error: ~10% sys. error: ~50% rand. error: < 5% sys. error: ~15% LWC / gm -3

16. 2013 DOE/EU Retrieval Workshop, Köln CABAUW case: IPT LWC & Reff rand. error: <15% sys. error: 20-35% rand. error: ~5% sys. error: up to 70% LWC / gm -3 Reff / m -6 red: retrieval black: „truth“

17. 2013 DOE/EU Retrieval Workshop, Köln Simulated case #2: Maritime Stratocumulus One maritime case simulated with LES ASTEX: spectrally resolved microphysics, low LWP (< 100 gm -2 ), partially drizzling, only liquid ASTEX

18. 2013 DOE/EU Retrieval Workshop, Köln ASTEX: Brandau rand. error: 20-50% sys. error: ~50% rand. error: >100% sys. error: ~50% drop size distribution no longer uni-modal  small number of drizzle droplets lead to Reff overestimation LWC / gm -3 Reff / m -6 red: retrieval black: „truth“

19. 2013 DOE/EU Retrieval Workshop, Köln ASTEX: IPT rand. error: 30-50% sys. error: <30% rand. error: ~50% sys. error: <60% fairly robust LWC profile in drizzle „contaminated“ region LWC / gm -3 Reff / m -6 red: retrieval black: „truth“

20. 2013 DOE/EU Retrieval Workshop, Köln Evaluating Different Retrievals: Which is Better? (2) Comparisons with other retrieved datasets –Which is truth? Different retrievals applied in single-layer warm liquid clouds

21. 2013 DOE/EU Retrieval Workshop, Köln Evaluating Different Retrievals: Which is Better? (3) Comparisons against in-situ observations –Sampling volumes can be quite different –Temporal and spatial sampling issues –Statistics are key –Aircraft are expensive –In-situ obs aren’t necessarily truth! Radar volume 45 m 30 m 50-100 m/s

22. 2013 DOE/EU Retrieval Workshop, Köln Evaluating Different Retrievals: Which is Better? (4) Closure exercises can provide a robust test, if the closure variable is independent of the retrieval Broadband fluxes are critical for ARM, so agreement in radiative flux is a nice metric to use Compute broadband fluxes to compare with observed fluxes at the surface –Use retrieved cloud properties as input (what we want to test) –Need ancillary observations also (T/q profiles, surface albedo, aerosol properties, etc) –Cloud fraction is important modulator of the observed flux, so need to select cases that are ‘homogeneously’ overcast –Generally evaluate improvement in RMS, not in bias This is a necessary, but not sufficient, closure exercise

23. 2013 DOE/EU Retrieval Workshop, Köln Radiative Closure Example Using ARM Mobile Facility data from Pt. Reyes, California in July-August 2005 –Overcast ~85% of the time –Clouds were low altitude (~500 m) and warm (i.e., liquid only) –Very few cases with overlapping higher-altitude clouds Evaluated two different passive retrieval methods –Combined MWR+AERI retrieval of LWP and Reff –MWR-only retrieved LWP (used AERI+MWR retrieval for R eff ) Comparison in SW radiative flux at surface and TOA –Should not use LW flux, since AERI measures LW radiance –Compare both BIAS and RMS of flux differences Turner JGR 2007

24. 2013 DOE/EU Retrieval Workshop, Köln 2-Day Example from Pt. Reyes Turner JGR 2007

25. 2013 DOE/EU Retrieval Workshop, Köln 2-Day Example from Pt. Reyes Combined AERI+MWR retrieval has smaller bias in SW surface flux Turner JGR 2007

26. 2013 DOE/EU Retrieval Workshop, Köln Surface SW Closure Exercise Similar exercise as LW closure No aerosols included in the calculations MIXCRA shows negative bias, but small amount of aerosol would improve results (and worsen MWRRET results) Variance in MIXCRA results is much lower than variance in MWRRET results for LWP below 120 g/m 2 Turner JGR 2007

27. 2013 DOE/EU Retrieval Workshop, Köln TOA SW Closure Exercise Similar exercise as SW surface closure No aerosols included in the calculations Both methods show negative bias, but small amount of aerosol would improve result slightly Unable to get agreement with both surface and TOA by changing LWP Variance in MIXCRA results is much lower than variance in MWRRET results for LWP below 100 g/m 2 Turner JGR 2007

28. 2013 DOE/EU Retrieval Workshop, Köln Questions for discussion What are the major issues in using these evaluation approaches for algorithm development and uncertainty quantification? What are the strategies for better using these algorithm evaluation approaches? What are the key areas that need the EU/DOE retrieval community to work together to improve algorithm evaluation approaches? What are our future plans?

29. 2013 DOE/EU Retrieval Workshop, Köln Some thoughts on these questions

30. 2013 DOE/EU Retrieval Workshop, Köln Q#1: What are the major issues in using these evaluation approaches for algorithm development and uncertainty quantification? No one is perfect –OSSEs: simulations may not cover entire range of possible conditions and the forward model is not perfect –In-Situ data: sampling issues, uncertainties, limited cases –Radiative closure: uncertainties in other input data and surface/TOA radiative fluxes; cannot evaluate vertical structures of cloud properties –Intercomparison of different retrievals: differences in retrieval basis, parameters, and underlying assumptions, as well as input and constraint parameters –Compare with other retrievals (e.g., MFRSR, satellite): no one is truth!

31. 2013 DOE/EU Retrieval Workshop, Köln Q#2: What are the strategies for better using these algorithm evaluation approaches? Need to identify what types of retrievals are of interest to the EU/DOE joint effort –We may only focus on those algorithms that are used to retrieve cloud properties from radar, lidar, and radiometer since they are available for both ARM and European sites and provide long-term continuous retrieval data Uncertainty is large in both retrieved products and in-situ observations –Statistics could reduce the uncertainty –Case studies vs. statistical evaluations Can new instruments help? What are critically needed for algorithm development and uncertainty quantification? What are critically needed by the modeling community? –Error bars; statistics for various types of clouds

32. 2013 DOE/EU Retrieval Workshop, Köln Q#2: What are the strategies for better using these algorithm evaluation approaches? Possible improvements –OSSEs: develop more OSSE cases that cover various types of clouds –In-Situ data: statistics is key – need long-term continuous observations to build up these statistics for different cloud types –Radiative closure: facilitate the evaluation by retrievals using BBHRP –Intercomparison of different retrievals: Common input dataset and consistent set of assumptions –Compare with other retrievals (e.g., MFRSR): is there a consensus in the community on which instruments or retrievals are more reliable for a particular cloud parameter? Compare with new instrument retrievals?

33. 2013 DOE/EU Retrieval Workshop, Köln Q#2: What are the strategies for better using these algorithm evaluation approaches? Develop a cloud retrieval testbed –Suitable for both case studies and statistical evaluation –Combine strengths of these common algorithm evaluation approaches –Build-up a cloud retrieval test case library that will include OSSE cases, as well as radar, lidar, radiometer measurements co-located with reference in-situ microphysical parameters –Build-up common input dataset and use consistent assumptions for key parameters in current retrieval algorithms –Make use of BBHRP –Quantify uncertainties in validation datasets –Develop statistics for each type of clouds based on long-term observations

34. 2013 DOE/EU Retrieval Workshop, Köln Q#3: What are the key areas that need the EU/DOE retrieval community to work together to improve algorithm evaluation approaches? Develop the cloud retrieval testbed –Data sharing –Algorithm sharing Intercomparison studies on both retrieval algorithms and forward models

35. 2013 DOE/EU Retrieval Workshop, Köln Q#4: What are our future plans? Collaborate with other existing science focus groups (e.g., ASR QUICR, IcePro; EG-CLIMET) Develop the cloud retrieval testbed Intercomparison studies –retrievals –forward models –Key assumptions Future workshops (coordinated with ASR and AGU meetings)

36. 2013 DOE/EU Retrieval Workshop, Köln Questions: What are the major issues in using these evaluation approaches for algorithm development and uncertainty quantification? What are the strategies for better using these algorithm evaluation approaches? What are the key areas that need the EU/DOE retrieval community to work together to improve algorithm evaluation approaches? What are our future plans? Discussion on Common Algorithm Evaluation Approaches

37. 2013 DOE/EU Retrieval Workshop, Köln