Assimilation of MSG visible and near-infrared reflectivity in KENDA/COSMO

Transcription

1 Assimilation of MSG visible and near-infrared reflectivity in KENDA/COSMO Leonhard Scheck1,2, Tobias Necker1,2, Pascal Frerebeau2, Bernhard Mayer2, Martin Weissmann1,2 1) Hans-Ertl-Center for Weather Research, Data Assimilation Branch 2) Ludwig-Maximilians-Universität, Munich SEVIRI COSMO 1

2 MOTIVATION Satellites dominate number of assimilated observations and have contributed strongly to improvements in NWP #obs./day assimilated at ECMWF Visible and near-infrared obs.: information on cloud properties Not used operationally, mainly because no fast forward operator exists (scattering complicates radiative transfer) Development of fast VIS/NIR forward operator for convective scale ensemble data assimilation Instrument: SEVIRI on Meteosat second generation resolution 2-5km in Europe, new image every 15min visible/near-ir channels at 600nm, 800nm, 1600nm 2

3 DESIGN OF THE OPERATOR Parallax correction: (first order 3D effect) Kostka et al. Observation Operator for Visible and Near-Infrared Satellite Reflectances, JTAC, submitted 3

4 RADIATIVE TRANSFER SOLVERS available at MIM: Time required to compute SEVIRI scene on 1 core Accuracy (RMS reflectivity error) Mayer, 2009; Buras and Mayer, 2011: Monte Carlo code for the physically correct tracing of photons in cloudy atmospheres (MYSTIC) 3D code, reference Philipp Kostka et al. (JTECH, revised, 2014): First steps towards a fast operator for assimilation of visible and NIR cloudy radiances including a 1st order correction of 3D radiative transfer effects (HErZ data assimilation branch) Based on discrete ordinate method (DISORT) Pascal Frerebeau, PhD thesis (11/2013): Fast, look-up table based 1D radiative transfer solver (PASTAT) Work in progress: Improved PASTAT in forward operator meet operational requirements for speed exact CPU- days CPU- ~5% hours ~10% < 1 min <10% CPU- CPU- < 1 min 4

5 15UTC SEVIRI OBS. 12UTC 18UTC OPERATOR RESULTS 3D (MYSTIC) Observation vs. Model: Realistic structures. Significant differences, mainly due to discrepancy between forecast and reality 1D (DISORT) 1D vs. 3D: Agreement quite good for UTC (RMS Error < 5% with parallax correction), worse for larger sun zenith angles (> 70, cloud shadows) 22 June

6 PASTAT: Look-up table based radiative transfer solver for 1D radiances PhD thesis Pascal Frerebeau (finished 2013/11) Method: fit function for radiance, coefficients from look-up tables sat angles parameters { { sun angles 20 coefficients, 6 parameters p (wavelength, albedo, water & ice optical depths, max. effective scattering radius, solar zenith angle) 20 six-dimensional tables (computed by least-squares fit to DISORT results) Developing adjoint should be relatively easy... Results: Only small errors, compared to DISORT, directional dependency of radiance well reproduced DISORT PASTAT 6

7 DISORT vs. PASTAT Example: 10 June UTC, VIS006 channel DISORT PASTAT 10 CPU hours <1 CPU minute _ Difference < 5% RMS diff. 2.5% DISORT-PASTAT = Same day, 18 UTC (larger sun zenith angle): Difference < 12% RMS diff. 4.8% 7

8 Quantification of systematic differences between SEVIRI observations and synthetic images generated from COSMO-DE forecasts (Master thesis Tobias Necker) identification of model and operator deficiencies COSMO SEVIRI Contingency table model observation cloudy cloud-free cloudy hit 76.9% false alarm 9.1% cloud-free miss 4.7% correct negative 9.2% Period: June 10 28, 2012, 12UTC prob. of detection frequency bias false alarm ratio 94% +5.3% 10.6% agreement quite good, but too many clouds in the model 8

9 REFLECTANCE HISTOGRAMS Frequency bias, in particular too much clouds at high reflectances COSMO Next steps: Further characterize systematic error sources (e.g. determine properties of false alarm clouds ) Variation of model and operator parameters separation of error contributions from model and operator SEVIRI EUMETSAT SAF NWC cloud type Improve operator e.g. include more 3D effects (cloud shadows) benefit from HD(CP)2-O3 9

10 ASSIMILATION OF SEVIRI REFLECTANCES IN COSMO/KENDA COSMO model (in COSMO-DE configuration): non-hydrostatic grid-spacing 2.8km, 50 layers deep convection explicit, shallow convection parametrization KENDA (Km-Scale ENsemble-Based Data Assimilation) Based on LETKF (Local Ensemble Transform Kalman Filter, Hunt et al. 2006) Setup for SEVIRI assimilation experiments: 3-hourly data assimilation analysis ensemble with 20 ensemble members 20 member ECMWF EPS boundary conditions (Δx =16 km) Spin-up phase: several cycles with conventional observations only First experiment: Assimilation of 600nm SEVIRI observations at 12 UTC, observation error assumed to be 10% 10

11 Example: Assimilation of SEVIRI 600nm observations in COSMO/KENDA OBSERVATION FIRST GUESS 2012/06/18 12 UTC ANALYSIS Assimilation interval 3h 20 ensemble members Images: Operator[ensemble mean state] First cycle ( 09UTC 12UTC ): RMS error in reflectances: First guess: 14.2% Analysis: 12.1% 11

12 NEXT STEPS in data assimilation Verification with other observations Assess forecast impact of SEVIRI observations Single Observation studies Sensitivity experiments (assigned error, localization, obs. frequency...) Investigate impact of ensemble size and assimilation interval Improve linearity (double penalty problem): Smoothing? Warping? Assimilate several wavelengths (reduce ambiguity of reflectance obs.) and complementary observations (radar, GPS) Detect and exclude problematic cases from assimilation 12

13 SUMMARY & OUTLOOK Operator: The operator works and we found a promising way to even meet operational speed requirements Operator will be developed further (3D effects, benefits from HD(CP)2 project) Operator is used to quantify systematic differences between model and observations evaluate sensitivity to model physics/perturbations and operator parameters Data assimilation: Assimilation of SEVIRI data in KENDA works technically First results are promising: Cloud water content is modified, RMS error in reflectance is reduced Next steps: Quantifiy impact of SEVIRI observations, optimize assimilation 13

14 CLOUD TYPE CLASSIFICATION Example: 16 June 2012, 12UTC fractional transparent high medium low cloud-free Eumetsat SAF NWC Cloud Products: cloud type (based mainly on SEVIRI) Cloud type classification based on COSMO forecast + VISOP output (preliminary!) Percentage correct (cloudy vs. cloud-free) 86% Percentage almost correctly classified 66% (confusing low-medium, medium-high, high-transparent allowed) Percentage correctly classified 47% 14

15 Example: Assimilation of SEVIRI 600nm observations in COSMO/KENDA OBSERVATION FIRST GUESS 2012/06/18 12 UTC ANALYSIS Assimilation interval 3h 20 ensemble members Images: Operator[ensemble mean state] Second cycle ( 12UTC 15UTC ): RMS error in reflectances: First guess: 17.0% Analysis: 14.1% 15