Some Applications of WRF/DART

Transcription

1 Some Applications of WRF/DART Chris Snyder, National Center for Atmospheric Research Mesoscale and Microscale Meteorology Division (MMM), and Institue for Mathematics Applied to Geoscience (IMAGe)

2 WRF/DART Consists of: Interfaces between WRF and DART (e.g. translate state vector, compute distances, ) Observation operators Scripts to generate IC ensemble, generate LBC ensemble, advance WRF Easy to add fields to state vector (e.g. tracers, chem species) Plan to add namelist control of fields in state vector A few external users (5-10) so far

3 Nested Grids in WRF/DART Perform analysis across multiple nests simultaneously Innovations calculated w.r.t. finest availble grid All grid points within localization radius updated D1. obs D3. obs D2

4 Some Applications Radar assimilation for convective scales Example courtesy Altug Aksoy (NOAA/HRD) Assimilation of surface observations Examples courtesy David Dowell (NCAR) Also have single-column version of WRF/DART from Josh Hacker (NCAR) Tropical cyclones Typhoon Sinlaku (2008) example, courtesy Hui Liu (NCAR)

5 Radar Assimilation for Convective Scales Assimilate radial velocity and radar reflectivity. Convective scale: O(1 km) horizontal resolution in WRF Initialize with single sounding and use open lateral boundaries (thereby ignoring any mesoscale structure in environment) 2-min assimilation cycle Crucial to account for uncertainty in sounding. Clear biases in reflectivity owing to errors in microphysics? Analysis quality comparable to standard dual-doppler retrievals.

6 Radar Assimilation for Convective Scales Aksoy et al. (2008) consider four cases Diverse storm types Chosen in part based on availability of nearby sounding Other WRF/DART users are considering additional cases Storm type Date Location (radar) Supercell 8 May 2003 Oklahoma (KTLX) Supercell 11 April 2005 Oklahoma (KTLX) Line, bow echo 15 June 2002 Kansas (KGLD) Multicell 8 May 2005 Oklahoma (KTLX)

7 15 June 2002 Squall Line

8 15 June 2002 Squall Line Innovation statistics

9 15 June 2002 Squall Line Reflectivity analysis 18:56 UTC (after 60 min assimilation) Differences from observations

10 15 June 2002 Squall Line (Forecast) Reflectivity (dbz) at 0.5 scan angle Forecast Observed 6 minutes

11 15 June 2002 Squall Line (Forecast) Reflectivity (dbz) at 0.5 scan angle Forecast Observed 12 minutes

12 15 June 2002 Squall Line (Forecast) Reflectivity (dbz) at 0.5 scan angle Forecast Observed 16 minutes

13 15 June 2002 Squall Line (Forecast) Reflectivity (dbz) at 0.5 scan angle Forecast Observed 22 minutes

14 15 June 2002 Squall Line (Forecast) Reflectivity (dbz) at 0.5 scan angle Forecast Observed 26 minutes

15 Assimilation of Surface Observations 30-km resolution, CONUS domain Hourly assimilation of 2-m T, T d and 10-m u,v Assimilate for 9 h, beginning from 00z NAM analysis as ensemble mean Multi-physics ensemble Each member uses distinct configuration of WRF Choose from 3 PBL, 3 cumulus, 2 shortwave radiation Hope to capture, at least partially, uncertainty of forecast model Perform ensemble forecasts on subdomain with 3-km resolution Again, see significant problems from deficiencies in model

16 28-29 March 2007: With and Without Surface-Data Assimilation! Supercells in 3-km Ensemble! 0000, 0100, and 0200 UTC 29 March 2007! SPC Storm Reports! no assim hr fcst! 9 hr sfc DA hr fcst!

17 28-29 March 2007: With and Without Surface-Data Assimilation! Supercells in 3-km Ensemble! 0000, 0100, and 0200 UTC 29 March 2007! SPC Storm Reports! no assim hr fcst! 9 hr sfc DA hr fcst!

18 28-29 March 2007: With and Without Surface-Data Assimilation! Supercells in 3-km Ensemble! 0000, 0100, and 0200 UTC 29 March 2007! SPC Storm Reports! no assim hr fcst! 9 hr sfc DA hr fcst!

19 Results of Surface-Data Assimilation on 30-km grid:! Water Vapor at 30 m AGL, 2100 UTC 28 March 2007! Ensemble-Mean Analysis! with sfc data assimilation! Analysis Difference:! (ens mean with sfc data assim)! - (ens mean without sfc data assim)!

20 6 hr sfc DA + 3 hr fcst ensemble-mean T and Td profiles at 1800 UTC 12 June 2002 too early 700 mb with sfc DA 850 mb without sfc DA 1000 mb 2100 UTC 12 June 2002 Observations FWD 0000 UTC Observations OUN 0000 UTC WRF ensemble, no assimilation WRF ensemble, no assimilation

21 Surface DA in Presence of Model Bias actual profile forecast analysis surface dewpoint ob EnKF uses ensemble-estimated vertical covariances to determine how surface observation influences analysis of PBL Analysis is biased if forecast profiles all have the wrong shape in ~ same way

22 Outstanding Issues Model imperfections, including errors in sub-grid processes Essential to account for these in mesoscale assimilation Multi-physics, adaptive inflation, additive noise Wish to estimate and predict across range of scales Require better techniques for covariance localization, or alternate approach Nonlinearity and non-gaussianity So far, dynamical nonlinearities not alarming Bigger problems with positive-definite quantities?

23 References Bengtsson T., C. Snyder, and D. Nychka, 2003: Toward a nonlinear ensemble filter for highdimensional systems. J. Geophys. Res., 62(D24), Dowell, D., F. Zhang, L. Wicker, C. Snyder and N. A. Crook, 2004: Wind and thermodynamic retrievals in the 17 May 1981 Arcadia, Oklahoma supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, Snyder, C. and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, Hacker, J. P., and C. Snyder, 2005: Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL. Mon. Wea. Rev., 133, Caya, A., J. Sun and C. Snyder, 2005: A comparison between the 4D-Var and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133, Chen, Y., and C. Snyder, 2007: Assimilating vortex position with an ensemble Kalman filter. Mon. Wea. Rev., 135, Anderson, J. L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus A, 59, Snyder, C. T. Bengtsson, P. Bickel and J. L. Anderson, 2008: Obstacles to high-dimensional particle filtering. Mon. Wea. Rev., accepted.

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25 EnKF Applied to Convective Storms Model and EnKF details Open lateral BCs, no terrain or PBL, Lin et al. microphysics Horizontal resolution 2 km, vertical resolution 500 m ~ 2 min cycling assimilate scan at each elevation angle separately 50 members Observations Radial velocity and reflecitivity on each elevation angle Removal of clutter, other simple QC from Dowell/NSSL package Obs on each elevation angle pre-processed to ~ model grid in horizontal Distinguish reflectivity > 5 dbz (precip) from < 5 dbz (clear air) Automated velocity unfolding within EnKF

26 15 June 2002 Squall Line (Forecast) Reflectivity (dbz) at 2.4 scan angle (24-min forecast)

27 15 June 2002 Squall Line (Sounding Perturbations) U Component V Component

28 15 June 2002 Squall Line (Sounding Perturbations) Innovation statistics impact of perturbing the sounding

29 15 June 2002 Squall Line (Sounding Perturbations) Reflectivity (dbz) at 4.3 scan angle (60-min analysis, 18:56 UTC) Without Sounding Pert. With Sounding Pert.

30 Ensemble Initialization ICs include random temperature perturbations Restricted to neighborhood of first echoes to be assimlated Uncertainty in sounding/environment (u,v) sounding for each member includes noise in three gravest vertical modes, with variance 2 m/s in each mode At present, not perturbing θ or moisture

31 Comparison of EnKF and 4DVar Simulated observations of radial velocity and reflectivity for supercell storm (perfect model), available every 5 min 4DVar: full fields (not incremental), mesoscale background, simple covariance model, 10-min window EnKF: 100 members, initialized with noise in T where first scan shows reflectivity Caya, A., J. Sun and C. Snyder, 2005: A comparison between the 4D-Var and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133,

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33 Comparison of EnKF and 4DVar Kalman filter/smoother and 4DVar are mathematically equivalent for linear, Gaussian systems Result also assumes both use same P, R, etc. Overall, EnKF and 4DVar perform comparably in this case After multiple cycles (30-40 min), EnKF beats 4DVar EnKF propagates information from previous obs through cycling of P f In principle, updating of P could be included in 4DVar too Given only obs over limited period (10-20 min), 4DVar beats EnKF Estimation errors large with limited obs, so nonlinear effect more important and 4DVar has advantage?

34 Mesoscale and Storm-Scale Ensemble Forecasts! mesoscale domain! "(Δx=30 km)! regional (storm-scale)! "domain (Δx=3 km)! surface ob site! 30-km ensemble provides initial and boundary conditions for 3-km ensemble!

35 Appeal of Ensemble Filters for Mesoscale DA General covariance model Independent of assumptions about nature of flow (e.g. approximate geostrophic balance), applicable across variety of dynamical regimes Basis for probabilistic forecasts For convective storms, 1 hour is a long-range forecast Ease of implementation and maintenance Doesnʼt require adjoints for sub-grid schemes, which are crucial in these flows but often discontinuous or highly nonlinear or adjoints of complex observation operators (e.g. radar) See Straightforward application to domains with multiple nests

36 Nonlinearity and non-gaussianity EnKF uses only second moments; can find non-gaussian examples where EnKF is not effective. At same time, EnKF is not strictly a Gaussian method Can find examples where resampling from same mean and covariance as EnKF posterior does much worse than EnKF (Bengtsson et al. 2003) EnKF thus preserves some useful information about higher moments Particle filters are a fully general, non-parametric approach. But they fail in high dimensions unless sample size is v. large (Snyder et al. 2008) Is there a way to perform spatially local updates in nonlinear ensemble filters, as in the EnKF?