Towards a 3D EnKF for surface data assimilation of raw satellite radiances

Transcription

1 Towards a 3D EnKF for surface data assimilation of raw satellite radiances Tomas Landelius, David Gustafsson, Magnus Lindskog, Patrick Samuelsson SMHI Swedish Meteorological and Hydrological Institute Joint International Surface Working Group and Satellite Applications Facility on Land Surface Analysis Workshop IPMA, Lisbon, June 2018

2 Roadmap for MetCoOp surface DA Now: 2D OI for surface parameters (T2m, RH2m, Snow) followed by 1D vertical OI_MAIN for Ts, T2, Wg, W2 in ISBA force restore. Soon: Replace 1D vertical OI_MAIN with 1D EKF and ISBA force restore with ISBA dif (14 lvs) and ES snow (12 lvs). Add ASCAT SM product. Later: Replace 1D vertical EKF with 1D EnKF. Future: Replace OI & 1D DA with 3D EnKF and products with radiances. Vision: Coupled DA for atmosphere and surface based on 4D EnKF.

3 SMHI land DA project overview ASCAT SURFEX Observations DA method Fwd model EU H2020 IMPREX ISBA force restore 2 patches ASCAT soil moisture GlobSnow SWE EKF (1D) - SNSB Land DA ISBA DIF & ES 2 patches AMSR2, SMOS, Sentinel-1 SAR River runoff EnKF (3D) CMEM, MEMLS3&a AMSR2 (GCOM-W1) MIRAS (SMOS) SAR (Sentinel-1)

4 Results LOCAL METEOROLOGICAL EVALUATION OF IMPROVED SYSTEM FOR TWO CASES Two cases and two parallel runs: REF: Reference IMP: (S)EKF, ASCAT, aniso B Positive impact of improved system seen in local and subjective verification of cases. 4 Funded under the Horizon 2020 Framework Programme of the European Union Grant Agreement No

5 Steps taken towards the 3D EnKF Spin up an initial ensemble using perturbed forcing Study resulting B matrix chose control variables and number of members Prepare observation operators Preliminary study O-F statistics

6 Forcing - ideally MetCoOp ensemble (MEPS) Only control member archived at SMHI

7 Creating an initial SURFEX ensemble (spin-up) NWP HARMONIE 00 & 12 + archived NMCNMC errorserror SURFEX... x i + z j i=1...k, j=1...l NWP HARMONIE 00 & 12 + archived NMCNMC errorserror... x i + z j i=1...k, j=1...l SURFEX... NWP HARMONIE 00 & 12 + archived NMCNMC errorserror... SURFEX For this presentation; only fc00 and fc12, no NMC perturbations added. Spin up run for (B from 304 members ).

8 Archived perturbations Archive available with fc00+48, fc00+24, fc12+48 and fc NMC perturbations: (fc00,12+48) (fc00,12+24) valid at same time. + Consistency between parameters + Realistic correlation patterns (if many enough) + Can time shift to get early/late seasonal perturbations - Not representing the B of the day (e.g. rainfall patterns)

9 Perturbations at analysis time Perturbations via forcing enough? dxs = SURFEX(dxf) Could also perturb SURFEX ctrl using B = E{ dxs dxst } Not perturb as violently as the Lisbon earthquake

10 SURFEX set up for spin up experiment SURFEX Version 8.1. ISBA-DIF soil diffusion, 14 layers. ISBA-ES Explicit snow, 12 snow layers. Nature tile; two patches, low and high vegetation.

11 ISBA prognostic variables (ctrl for nature tile) Vegetation Snow QCP1 veg canopy air specific humidity [kg/kg] ASN_VEGP1 Albedo TCP1 veg canopy air temperature WRVNP1 snw interc on can veg leaves [kg/m2] TLP1 litter temperature HSN_VEG1-12P1 Heat content [J/m3] TVP1 canopy vegetation temperature RSN_VEG1-12P1 Snow density [kg/m3] WRP1 water intercepted on leaves [kg/m2] SAG_VEG1-12P1 Age parameter WRLIP1 ice on litter [kg/m2] WSN_VEG1-12P1 Snow reservoir [kg/m2] WRLP1 wtr intercp on canopy veg leaves [kg/m2] RESAP1 aerodynamical resistance [s/m] Soil Radiation TSRAD_NAT patch averaged rad temp [K] TG1-14P1-2 soil temperatures WG1-14P1-2 soil liquid water contents [m3/m3] WGI1-14P1-2 soil ice water contents [m3/m3] Total of 1 + 2*10 + 2*14*3 + 2*12*4 = 201 var

12 DA on a slow manifold fewer DoF and members!? Heiter, P., Lebiedz, D. Towards differential geometric characterization of slow invariant manifolds SIAM J. Appl. Dyn. Syst. 17, 732 (2018) Lebiedz, D., Unger, J. On unifying concepts Math. Comp. Model. Dyn. 22, 87 (2016)

13 Vertical covariances We have satellite measured soil/snow properties close to the surface. To what extent do these tell us something about the deeper layers?

14 TG P1 P2 WG WGI

15 HSN RSN SAG WSN

16 Snow layers are not consistent over time Graph from Bolli Palmason (Icelandic Met Office) Layers may be redistributed at times; not inter-comparable any more.

17 Most surface models and DA schemes work per column. Does a measurement at one location say something about a point further away? About the other patch? Horizontal covariances

18 WG1,3,6 P1,P1 TG1,3,6 P1,P1

19 Satellite data and observation operators Sentinel-1/SAR-C: wet snow, snow extent, (dry snow?) - S1A_IW_GRDH_1SDV - Interferometric Wide swath mode VV+VH, ca 88 x 87 m - MEMLS3&a (now part of SMRT package) GCOM-W1/AMSR2 soilm (7 Ghz), deep (10, 19 GHz), moderate (37 GHz), shallow snow (89 GHz) - L1SGRTBR - Level 1R V,H, ca 40 x 60 km - Community Microwave Emission Modeling Platform (CMEM): 1 20 Ghz - Water: CMEM, FASTEM or NOAA CSEM? SMOS/MIRAS, L band 1.4 GHz: soil moisture - MIR_SCL[FD]1C - Level 1C Land Science Measurements product, dual (or full) polarization, ca 50 x 50 km, ISEA 4-9 hexagonal grid - Water: CMEM or FASTEM or NOAA CSEM?

20 Sentinel-1 SAR-C challenges How to aggregate and compare to SURFEX scale at 2.5 km? - ECOCLIMAP info about sub-grid location of P1 and P2 regions? - Only compare snow covered SURFEX areas? - Classify SAR pixels as snow, ice, water, vegetation and aggregate? Follow work at Météo France by Gaëlle Veyssière et. al.

21 AMSR2 6.9 GHz, V CMEM 6.9 GHz, V LP filtered CMEM FG - OB AMSR2 6.9 GHz, H CMEM 6.9 GHz, H LP filtered CMEM FG - OB

22 Nordic region has a heterogeneous surface Mixed foot print: Forest Lakes Rock Snow Ice Frozen soil...

23 Challenges EnKF EnKF for SURFEX which implementation (ETKF & time consistency)? How to make EnKF compatible with MEPS members? Will NMC archive provide enough added members/spread? Use DA on a slow manifold to reduce the number of members? Model bias better physics may lead to worse results for UA... Observations and operators Working with Nordic surfaces; frozen soil, forest, lakes, water, ice, snow... How to make Sentinel-1 data fit HARMONIE scale? How to make HARMONIE fit scale of SMOS/AMSR2 footprint/antenna func? Water emissivity - CMEM, FASTEM or CSEM? Sea/lake ice model?

24 Conclusions SURFEX errors have horizontal correlations motivates 3D/4D DA. EnKF feasible for 3D/4D surface DA and fits with NWP road map. Spinup with fc00 and fc12 show little variation in deep layers (9 months) Deep (below lvl 8?) layers not necessary to include in control vector? Fairly robust B based on ~ 300 forecast differences (not good for snow?) More to learn about obs-operators for soil and snow in Northern conditions.

25 LUNCH!

26 Next steps Modify CMEM to allow for variable grain size, soil depths, etc. Try CMEM with 100 % water Run CMEM for SMOS Pre-process Sentinel-1 SAR files with ESA snap tool errors... EnKF implementation for t2m and h2m (P1 info P2) How get SMRT (MEMLS&a) input from SURFEX information? How aggregate Sentinel-1 SAR to SURFEX grid (tile & patch)

27 Perturbations during spinup run Force with forecasts from 00 and 12: 2 members. Add archived forecast differences from any given date to these 2: fc00 + fc_arch_48(day_1) fc_arch_24(day_1) fc12 + fc_arch_48(day_1) fc_arch_24(day_1) fc00 + fc_arch_48(day_n) fc_arch_24(day_n) fc12 + fc_arch_48(day_n) fc_arch_24(day_n) Total of 2N members. Linear interpolation between fc diff valid at 00 and 12 for hourly values.

28 SMOS data example

29 Forcing - ideally MetCoOp ensemble (MEPS)

30 CMEM HUT model The Helsinki University of Technology (HUT) snow emission model (used for ESA GlobSnow). Semi-empirical RTM for interaction of the snow medium with microwaves; accounts for vegetation, the atmosphere, and emission from the ground surface. CMEM input (grain size and salinity set to constant values) - Soil temperature and moisture (for ground emissivity) - Snow temperature - Snow depth - Snow density Newer MATLAB version 1.4 adds multiple layers and fraction of liquid water.

31 SMRT The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter RTM with multiple microstructure and scattering formulations. SMRT includes Improved Born Approximation (IBA), Dense Media Radiative transfer (DMRT) and independent Rayleigh scattering theories to compute the scattering and absorption coefficients and the phase function. Written in Python with bindings to facilitate a direct comparison with widely-used models (DMRT-QMS, MEMLS and HUT). MEMLS input similar to HUT. Uses correlation length, not snow grain size. - How to get correlation length from SURFEX ES snow variables?