STRONGLY COUPLED ENKF DATA ASSIMILATION

Transcription

1 STRONGLY COUPLED ENKF DATA ASSIMILATION WITH THE CFSV2 Travis Sluka Acknowledgements: Eugenia Kalnay, Steve Penny, Takemasa Miyoshi CDAW Toulouse Oct 19, 2016

2 Outline 1. Overview of strongly coupled DA with LETKF 2. OSSE with intermediate complexity model SPEEDYNEMO-LETKF 3. Initial real data experiments with CFSv2-LETKF OCEAN ATMOSPHERE Oct 19, 2016 Sluka - CDAW Toulouse 2

3 Weakly Coupled DA forecast Data Assimilation ATM obs analysis A single coupled model generates background for separate DA systems. Already operational or planned at many centers. forecast Coupled OCN/ATM model Data Assimilation analysis EnKF GFDL CDA (Zhang et al. 2007) 3D/4DVAR CDAS, Met Office, ECMWF (Saha, 2010 Laloyaux et al., 2015; Lea et al. 2015) OCN obs Oct 19, 2016 Sluka - CDAW Toulouse 3

4 Strongly Coupled DA OCN obs forecast Data Assimilation ATM obs analysis Studied w/ simple models: EnKF (Lu et al., 2015; Liu et al., 2013; Han et al., 2013; Luo & Hoteit, 2014; Tardif et al., 2013) 4DVAR (Smith et al., 2015) Coupled OCN/ATM model Studied w/ realistic models: EnKF (Lu et al., 2015) 4DVar (Sugiura et al., 2008) 3D En-var (Frolov, 2016) Oct 19, 2016 Sluka - CDAW Toulouse 4

5 Choice of Data Assimilation 4DVar vs EnKF? 4DVar works best with longer time windows (since flow-dependent error covariance is not saved, though propagated implicitly within window) EnKF works best at shorter windows Explicitly saves background error covariance EnKF has several benefits Don t need to develop coupled TL/adjoint Cross domain covariance generated automstically by the ensemble Assimilation can be performed at the shorter window of the atmosphere Most importantly should be easier, I want my PhD at some point! Oct 19, 2016 Sluka - CDAW Toulouse 5

6 Local Ensemble Transform Kalman Filter model grid point observation LETKF one flavor of the EnKF, operates by independently and in parallel creating analysis one grid point at a time, using a subset of observations around it. Parallelization scales very well LETKF (Hunt et al., 2006) Oct 19, 2016 Sluka - CDAW Toulouse 6

7 Local Ensemble Transform Kalman Filter Observation operator analysis mean analysis ensemble perturbations LETKF calculates analysis for each grid point in parallel using subset of obs around it. For weakly coupled DA, y contains only obs from its own domain. For strongly coupled DA, y contains obs from all domains. Oct 19, 2016 Sluka - CDAW Toulouse 7

8 Local Ensemble Transform Kalman Filter Observation operator analysis mean analysis ensemble perturbations Cross-domain observation impacts analysis mean W varies smoothly across domain interface, allows individual ensemble members to stay matched together, allowing for better balance of ensemble members Oct 19, 2016 Sluka - CDAW Toulouse 8

9 Coupled LETKF Separate LETKF for each domain helps keep implementation simpler. Ocean LETKF and Atmosphere LETKF can be developed independently. Sharing of observational departures allows system to act as single strongly coupled system. Data assimilation systems, normally separate weakly coupled DA Addition of cross-domain observational departures strongly coupled DA Oct 19, 2016 Sluka - CDAW Toulouse 9

10 II. SPEEDYNEMO- LETKF SPEEDYNEMO nature run Jet Stream Winds Strongly coupled DA with an intermediate complexity CGCM Sluka, T. C., Penny, S. G., Kalnay, E. & Miyoshi, T. Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation. Geophys. Res. Lett. 43, (2016). Oct 19, 2016 Sluka - CDAW Toulouse 10

11 SPEEDY-NEMO OSSE Using the fast SPEEDY- NEMO (one year run takes only 12 hours on 1 core) Perfect model OSSE conducted first using only atmospheric observations SPEEDY-NEMO T30 atmosphere 2 degree ocean Coupling every 6 hours 6 hr ATM observations Experiment parameters 40 ensemble members Localization: 1000km Hz Relaxation to prior spread: 90% for OCN, 60% for ATM Oct 19, 2016 Sluka - CDAW Toulouse 11

12 SPEEDY-NEMO Strongly Coupled DA STRONG-WEAK analysis RMSE Temperature [C] Salinity [PSU] Surface Deeper Ocean (500m-2km) -37% -52% Northern Hemisphere Improvement Improvement Southern Hemisphere Global Tropics Improvement Improvement Analysis RMSE improvement of ocean, from strongly coupled DA of simulated atmospheric observations Oct 19, 2016 Sluka - CDAW Toulouse 12

13 SPEEDY-NEMO Strongly Coupled DA STRONG-WEAK analysis RMSE OCN Temperature OCN Salinity Upper 500m Pacific Atlantic Oct 19, 2016 Sluka - CDAW Toulouse 13

14 SPEEDY-NEMO Strongly Coupled DA STRONG-WEAK analysis RMSE Ocean Observations Atmosphere Observations STRONG-WEAK, blue is good The opposite experiment (assimilating OCN obs into the atmosphere) shows improvement as well A possible latitudinal dependence on cross domain impact? atm U Oct 19, 2016 Sluka - CDAW Toulouse 14

15 SPEEDY-NEMO LETKF Assimilating ATM obs into the ocean reduces analysis RMSE for both domains The opposite experiment (assimilating OCN obs into the atmosphere) shows improvement as well Problems with SPEEDYNEMO prevent further work with 6 hour DA cycles. System might still be useful for research with climate length runs. Improvement strongest in NH extratropics during spring/winter months (over 50% RMSE reduction) 4/11/2016 Sluka - Strongly Coupled DA 15

16 CFSv2 - SST III. CFSv2-LETKF Strongly coupled data assimilation with the Climate Forecasting System v2, using real observations Oct 19, 2016 Sluka - CDAW Toulouse 16

17 CFSv2-LETKF Not at all using operational CDAS for CFS. Combined existing GFS- LETKF (Lien, 2013) and MOM-LETKF (Penny, 2013) ATM T62/L64 atm 0.5deg ocn (reduced resolution ATM) 50 member ensemble (initialized from CFSR, run freely for 6 months to develop sufficient spread) rawinsonde SATWND scatterometer aircraft OCN land surface marine surface observations from operational ATM PREPBUFR and OCN profiles used by GODAS T&S profiles (ARGO, XBT, moored buoys) Oct 19, 2016 Sluka - CDAW Toulouse 17

18 Weakly Coupled DA - JJA 5m OCN T bias ATM T bias SFCSHP obs Oct 19, 2016 Sluka - CDAW Toulouse 18

19 Weakly Coupled DA cross covariances Cross correlations given by the ensemble for a single date ATM and OCN temperature max correlation of 0.36, highest values in that hemisphere s summer, below 850mb and above top of thermocline June values likely artificially large do to insufficient spin up time for the ocean June December Mixed Layer depth (depth of T 10m ± 0.2 C) Oct 19, 2016 Sluka - CDAW Toulouse 19

20 Weakly Coupled DA cross covariances ATM_Q x OCN_T correlations weaker, though same pattern as ATM_T x OCN_T Increase in windspeed correlated with decrease in surface temperature These are the correlations LETKF will be using Humidity Windspeed Mixed Layer depth (depth of T 10m ± 0.2 C) Oct 19, 2016 Sluka - CDAW Toulouse 20

21 Strongly coupled DA 1 way strongly coupled DA Strongest cross correlations are between OCN_T and ATM_T/ATM_q, so OCN assimilates surface marine T and q as well, given by the SFCSHP section of the PREPBUFR OCN obs (JJA) ATM SFCHSP obs (JJA) ocn profiles (argo, XBT, ) ATM SFCSHP T&q Oct 19, 2016 Sluka - CDAW Toulouse 21

22 SFCSHP T bias Known diurnal bias in SFCSHP T, is a problem (sensors placed over warm deck of a ship, no NSST in model) also visible in MERRA2 reanalysis (Carton et al., personal comm) Still, there are areas of persistent bias of same sign, caused by SST bias in our weakly coupled run, which strongly coupled DA should improve upon B-O using SFCSHP T JJA average Oct 19, 2016 Sluka - CDAW Toulouse 22

23 Options for coupled DA windows Atm obs every hour, 6 hour window Ocn obs once a day Synchronous strongly coupled DA, atm updates Ocn at sane time, ev 6 hours Oct 19, 2016 Sluka - CDAW Toulouse 23

24 Options for coupled DA windows Atm obs every hour, 6 hour window Ocn obs once a day Synchronous strongly coupled DA, atm updates Ocn at sane time, ev 6 hours Asynchronous Atm obs deps saved and only assimilated ev 24 hours Oct 19, 2016 Sluka - CDAW Toulouse 24

25 CFSv2-LETKF single obs test After 1 month of weakly coupled DA, several locations, especially near coasts, exhibit large SST errors. Yellow Sea is chosen for strongly coupled single observation test (synthetic obs) Feb 1, Z SST background error Surface atmosphere T error Feb 1, Z obs location Oct 19, 2016 Sluka - CDAW Toulouse 26

26 CFSv2-LETKF single obs test 2 strongly coupled DA experiments (single ATM T ob [left], single OCN T ob [right]) Provide similar analysis increment pattern. But, in this case, OCN observation improves atmosphere MORE than ATM observation Oct 19, 2016 Sluka - CDAW Toulouse 27

27 Strongly Coupled CFS - results Errors in 6 hour background for ATM T are greatly reduced in the NH -13.1% -3.8% -2.1% Oct 19, 2016 Sluka - CDAW Toulouse 28

28 Strongly Coupled CFS - results Strong improvement in both ATM and OCN in NH Some problems immediately near coast of NA observations too close to land should probably be excluded Low resolution of ATM likely causing problems near gulfstream Oct 19, 2016 Sluka - CDAW Toulouse 29

29 Depth (m) Strongly Coupled CFS - results <- worse better -> NH TP 5m Caused by naïve fixed vertical localization of ATM observations into ocn (σ=50m), need to limit impact to mixed layer only Mixed layer depth* JJA average 15m 25m 35m % RMSD Improvment *MLD defined as level where θ = θ 10m ± 0.2 C Oct 19, 2016 Sluka - CDAW Toulouse 30

30 Future work Currently: Progress has been made on one-way strong coupling of ocean/atmosphere model with a subset of real observations with the CFSv2 Next- Fun with localization! Full ATM observation set (U/V may be more difficult given lower cross domain covariance), Full 2-way strongly coupled DA OCN observations (night-time level 2 SST) impacting the atmosphere Oct 19, 2016 Sluka - CDAW Toulouse 31

31 Ultimate Goal CFSv3 - NCEP transitioning to hybrid- GODAS, based on LETKF for the ocean. AEROSOL ATMOSPHERE LAND Increased potential at that point for an operational strongly coupled hybrid-letkf global DA system SEA ICE OCEAN WAVES Oct 19, 2016 Sluka - CDAW Toulouse 32

32 STRONGLY COUPLED ENKF DATA ASSIMILATION WITH THE CFSV2 Travis Sluka, and S. Penny, E. Kalnay, T. Miyoshi Travis Sluka University of Maryland