VAMP. Vertical Aeolus Measurement Positioning. Gert-Jan Marseille, Ad Stoffelen, Karim Houchi, Jos de Kloe (KNMI) Heiner Körnich (MISU)

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1 VAMP Vertical Aeolus Measurement Positioning Gert-Jan Marseille, Ad Stoffelen, Karim Houchi, Jos de Kloe (KNMI) Heiner Körnich (MISU) (to optimize Harald its vertical Schyberg sampling) (MetNo)

2 ADM vertical sampling Limitation of 24 vertical samples for both Mie & Rayleigh channels How to distribute these effectively? Mie Rayleigh 26 km 16 km Vertical sampling scenario can be changed 8 times per orbit possibility of targeting 0 km 2

3 Objective Consider the atmospheric dynamical and optical characteristics and their interaction with the ADM-Aeolus measurement system in order to optimize the user benefit of the Aeolus system The study will conclude with a recommendation for the operation of the instrument spatial and temporal sampling, to provide maximum mission benefit 3

4 Method Define a set of vertical sampling scenarios taking into account instrument hardware, operation, commanding and calibration constraints Quantification of the information content of these sampling scenarios; as a function of season/climate zone Aeolus wind quantity and quality in heterogeneous atmospheric scenes Impact on NWP Theoretical tool based on the analysis equations ECMWF Ensemble Data Assimilation experiments 4

5 Optional vertical sampling scenarios and many more. courtesy Jos de Kloe (KNMI) Maximum Mie/Rayleigh overlap Mie focus on PBL/troposphere Mie oversampling in Tropics (cirrus) 5

6 Heterogeneous atmosphere What is the frequency of occurrence of heterogeneous atmospheric scenes as a function of height/climate zone/season first indication of where to position Mie bins 6

7 Atmospheric heterogeneity Combined optical and dynamical variability 1/1/2007 CALIPSO attenuated backscatter ECMWF model fields interpolated to CALIPSO orbit Typical backscatter variability inside clouds ~ 1 order of magnitude In combination with large wind-shear of (ms -1 )/km wind error ~ 5-10 ms -1 ; detrimental for NWP! ECMWF HLOS wind along orbit CALIPSO 532 nm attenuated backscatter HLOS wind-shear along orbit 7

8 Atmospheric database CALIPSO optics+ecmwf dynamics Lidar inversion algorithm β 532 => β 355, α 355 Attenuated backscatter along CALIPSO orbit 3.5 km / 125 m Database of collocated atmospheric and dynamics CALIPSO orbit Atmospheric dynamics obtained from closest ECMWF model analysis NWP model CALIPSO orbit ECMWF (u,v,w,p,t,q,..) 8 along CALIPSO orbit

9 Statistics of atmosphere dynamical variability Occurence of zonal wind-shear > 10 ms -1 /km range 7-15 km 9

10 statistics of atmosphere optical variability Occurence of optical variability exceeding one order of magnitude range 7-15 km

11 Combined optical and dynamical variability Generally small correlation between large optical and dynamical variability!! 11

12 LIPAS HLOS wind simulation CALIPSO raw data, 1 orbit HLOS wind (from ECMWF)) Mie channel HLOS wind Rayleigh channel HLOS wind 12

13 Aeolus - burst mode ECMWF Aeolus Rayleigh channel No burst mode Aeolus Rayleigh channel Burst mode 13

14 ADM-Aeolus HLOS wind quality L2B Mie L2B Rayleigh L2B Rayleigh; classification L2B Rayleigh; X-talk correction bias mission requirement standard deviation m/s m/s bias < 0.5 m/s; standard deviation ~ 1.5 m/s (Rayleigh), ~1 m/s (Mie) 14

15 Scenario WVM1 tropical cirrus CALIPSO retrieved scattering ratio HLOS wind from ECMWF s -1 Mie channel wind Mie channel wind error 2 km Mie bin 1 km height assignm. error 20 ms -1 wind error 15

16 Increasing the Mie channel resolution improves Mie wind data quality Maximum overlap scenario WVM1 Tropical scenario WVM_tr_zwc2 16

17 NWP impact Theoretical tool (courtesy Harald Schyberg, MetNo) 17

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23 NWP impact Stratospheric flow (courtesy Heiner Körnich, MISU) 23

24 Optimizing stratospheric flow (Remember: wind quality reduces with altitude) 1. Explicitly: maximize data (of lower quality) coverage in the stratosphere 2. Implicitly: maximize data (of higher quality) in the troposphere This improves the tropospheric flow upward propagation improves the stratospheric flow 24

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27 Ensemble Data Assimilation experiments (with perturbed observations) ECMWF model, cy35r2 (T399,L91) Period 1/1/ /1/

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31 Aeolus_UTLS - Control 31

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33 Backup slides 33

34 Limited correlation between occurence of large optical and dynamical variability over the oceans < 5% 34

35 statistics of combined optical and dynamical variability ECMWF dynamics adaptation tropical cirrus 1% 10% before wind adaptation after wind adaptation Adaptation of ECMWF dynamics Add uncorrelated small-scale wind structures to make model wind statistics compatible with highresolution radiosondes wind (variability) statistics tropical cirrus convective clouds Occurence of large optical and dynamical variability < 20% 35

36 Data coverage Mie channel tropical cirrus clouds LIPAS run for 2 full months (January, August 2007) 900 CALIPSO orbits ~ 1800 ADM orbits or 4 months (no 150 km gaps) January 2007 wvm1(1) / wvm2 (2) / wvm_et_zwc2 (3) / wvm_tr_zwc2 (4) Mie + Rayleigh Rayleigh channel PBL low.trop UTLS Strat. NH.ml NH.po NH.st SH.ml SH.po SH.st Trop Similar results for august 2007 period Rayleigh winds dominant wvm2 has best coverage but many closely spaced Mie winds in PBL. Informative for NWP? 36

37 High-Resolution radiosondes ( ~30 m. resolution) courtesy Karim Houchi (KNMI) ECMWF underestimates the wind variability ECMWF effective vertical resolution ~ km 37

38 Model wind adaptation Use (o-b) statistics of high-resolution radiosonde dataset to add small-scale variability to the model winds Statistics of adapted model winds in agreement with (o-b) statistics Model wind adaptation ignores correlation with optical structures Further study needed, e.g. using combined (T,u,v,q) from Hi-Res radiosondes model wind adapted model wind zonal wind component radiosonde wind 38