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Sixth Symposium on Data Assimilation. Washington, DC. Oct. 7-11, 2013 Improved Initialization and Prediction of Clouds in Numerical Weather Prediction Tom Aulign National Center for Atmospheric Research Acknowledgments: Gael Descombes,

SLIDE 1

Sixth Symposium on Data Assimilation. Washington, DC. Oct. 7-11, 2013

Improved Initialization and Prediction of Clouds

in Numerical Weather Prediction

Tom Auligné National Center for Atmospheric Research

Acknowledgments: Gael Descombes, Francois Vandenberghe, Dongmei Xu, Thomas Nehrkorn, Brian Woods, Yann Michel, Greg Thompson.

SLIDE 2

Initialization of clouds in NWP models

Model Observation Non-linear model & radiative transfer Complex balance Underdetermined problem Significant model errors …

Challenging NWP initialization

SLIDE 3

Our approach to initializing clouds

WRF (Weather Research and Forecasting)

regional, non-hydrostatic model

All-sky satellite radiances
Expansion of analysis control variable
Total water + linearized physics
Microphysical parameters
Hybrid data assimilation (variational/ensemble)

SLIDE 4

NEW ALGORITHM: update ensemble perturbations within variational analysis

3D/4D-Hybrid: ensemble covariance included via state augmentation

(Lorenc 2003, Wang et al. 2008, Fairbairn et al., 2012)

Ensemble/Variational Integrated Localized (EVIL)

dx = bcdxc + bedxe

dxe = (Pf Ca)1/2va =

dxc = B1/2v

with

Climatology Ensemble Localization

X a = X f I + zk qk

2 -1

æ è ç ö ø ÷ zk

T k=1 K

å

æ è ç ö ø ÷

(Gratton et al., 2011)

J(v,va) = Jo + 1 2 vTv + 1 2 va

Tva[

where zk,qk

( ) are Ritz pairs from Lanczos algorithm

SLIDE 5

Control Variable Transform

Multivariate covariances for qc, qr, qi, qsn
Binning using dynamical cloud mask
Vertical and Horizontal autocorrelations (Recursive Filters)
3D Variance

Poster Descombes (A-p06)

SLIDE 6

Displacement Pre-Processing

Forecast Calibration & Alignment (Grassotti et al. 1999) OSSE: Hurricane Katrina Synthetic observations

(Total Column Precipitable Water)

Balanced displacement

(Nehrkorn et al. 2013)

Poster Nehrkorn (H-p22)

SLIDE 7

IR and MW radiance: AIRS, IASI, CrIS, MODIS, GOES,

AMSU-A/B, MHS, SSMI/S

VarBC: Variational Bias Correction
Revisited QC and thinning: to conserve cloudy information
Huber Norm: robust definition of observation error
Land Surface: T

skin, εs introduced as sink variable

Field of View: advanced interpolation scheme
CRTM Jacobians: rescaled base state

(floor and ceiling values for cloud parameters)

Middle Loop: Multiple re-linearizations of obs. operator

Processing All-Sky Satellite data

Normalized departures

SLIDE 8

First Guess Second Guess Third Guess Observation

Update of qcloud, qice in WRF

Observations

AIRS Window Channel #787

Guess 1 Guess 2 Guess 3

SLIDE 9

CONUS 15km, 20012/06/03 (12UTC) WRF-ARW model, Thompson microphysics First Guess = Mean of 50-member ensemble from EnKF experiment (courtesy Romine) No displacement pre-processing

CTRL = no DA

3DVAR

Multivariate B matrix (5 middle-loops)

EVIL

3D-Hybrid-EnVar (5 middle-loops)

Experimental Demonstration

SLIDE 10

qcloud

(level 10)

3DVAR EVIL qice

(level 20)

SLIDE 11

3DVAR EVIL qcloud