DATA ASSIMILATION AND NWP IMPROVEMENTS

CARPE DIEM AREA 1

Magnus Lindskogon behalf of

Nils Gustafsson (AREA 1 Scientific Rapporteur) and

AREA 1 colleagues

Project organisationProject organisation

WP 2Extraction of information from

Doppler winds

• De-aliasing

• Radar radial wind super-observations

• Dual Doppler retrieval

• Clear air retrievals

De-aliasing of raw radial wind signal

De-aliasing algorithm

Linear wind model: coscoscossin vu

Map the measurements onto the surface of a torus

Case study (illustration de-aliasing)

Vantaa (Finland): 4 December 1999, 12:00 UTC

observed velocity de-aliased velocity

Generating radial wind superobservationsHorizontal filtering:

Time filtering of superobservations:unfiltered and filtered observations filtered observations and model

Terrain analysis

DUAL DOPPLER RETRIEVALPo Valley-radars and terrain analysis

Data Gridding• Radar data

– Polar data – 4 elevation

angles(deg) 0.5, 1.4, 2.3, 3.2

– ray resolutions 0.25km x 440 bins, beam width: 0.9 deg

● Gridded data● Cartesian data● 4 layer altitudes (km) ~

0.5, 1.4, 2.3, 3.2● horizontal resolutions

cell spacing: 0.5 km, no. of cells: 60 x 60

e.g. Doppler Vel.

Calculating wind field

• Three Fundamental Equations:– Radial Velocities (from each radar)

– Mass Continuity2,1,sin

sincoscoscossinˆ

iVWv

wvu

iitr

iiiii

i

vri

0

vvvtt

t

i

r

W

V

vi

wvu

i

velocity,terminal

velocity,radialnet

,radar at t measuremenlocity Doppler ve

,, vector,(velocity) wind v

Numerical procedures

• Iterative method:– horizontal components

– vertical component

• Boundary conditions: – zero vertical velocity on ground

– zero horizontal velocity gradient on ground (optional: simplify computation w/o loss of accuracy)

iterationth -,, 11 nwDCvwBAu nnnn

ln where,0dz

dw

z

w

y

v

x

u

Courtesy: K. Y. GohRadar:GattaticoDate: 17 Dec 2002Elevation: 1.4°Field: VThreshold: 0 dBZ

away

towards

Wind travelling to the East-North-Eastat the centre

Vel. (m/s)

Velocity-Azimuth Display (VAD)

Gattatico: minimum ~ 60 degwind travel to East-North-East

gat windowspc window

WP 3Data assimilation

• Observation operator for radial winds in variational data assimilation.

• Impact studies of radial winds on limited area NWP and assessment of suitability of radial wind measurements for use in operational NWP.

• Compare assimilation of SO,VAD and Dual Doppler

• Implementation of 4-dimensional continous assimilation based on IAU using radar satellite as well as surface and radiosonde observations.

Cost function:

)()(2

1)()(

2

1 11 yHxRyHxxxBxxJJJ TbTbob

where

TspqTvu ln

Background error covariancesB

R

HObservation error covariances

Observation operator

bx Background state

x

The HIRLAM 3D-Var

The Doppler radar radial wind observation operator

• Interpolation of the model wind vector to the observation location.

• Projection of the wind vector on the slanted direction of the radar beam.

• Broadening of the radar beam: Gaussian averaging kernel.

• Bending of the radar beam: Snell's law.

One-month (January 2002) comparison of Swedish radial winds and HIRLAM

model equivalents ● Figure: rms difference as

a function of measurement range.

● Rms difference for thinned raw data is significantly higher than for SOs.

● Method used to deter-mine optimal averaging length scales 10 km for 22 km model)

10 day Radar wind assimilation experiment

Integration area and radar sites

Three parallel runsCRL:conv. obs.RWD:conv. obs+rwdVAD:conv. obs.+VAD

10 day experiment(1-10 Dec., 1999)

RM

S o

f +

24 h

win

d fo

reca

sts

at 8

50 h

Pa

Radar wind assimilation experiment(case study)

Integration area radar sites

Four parallel runs:CRL:conv. obs.RWD:conv. obs+rwdVAD:conv. obs.+VADDUAL: conv obs.+ DUAL

17 Dec. 2002, 18 UTC- 18 Dec. 2004, 00 UTC

Radars, Dual Doppler area and model grid-points

17 Dec. 2002 18 UTC radar wind assimilation

VAD

Radars, Dual Doppler area and model grid-points

RWD

DUAL

925 hPawind

analyses-increments

WP 3: Data assimilation

University of Barcelona contribution to WP 3:

•Continuous data assimilation in the mesoscale MASS model using incremental analysis updates (IAU). IR satellite and radar data are used to enhance the 3D relative humidity field.

IAU assimilation cycle.

FIRST GUESS+

CONVENTIONALDATA

ANALYSIS+

RADAR & SATELLITE DATA

OI

RH ENHANCEMENT

MODIFIED ANALYSIS

SUBTRACTINGFIRST GUESS

ANALYSISINCREMENTS

Determination of the analysis increments.

WP 3: Data assimilation

Results for a test case: 021210.

Forecasted precipitation field at 13 UTC.

No IAU IAU

WP 3: Data assimilation

• Comparison experiment between nudging and IAU using the MASS model:

•Testing 2 assimilating frequencies: 6-h and 3-h.

•Different combinations of assimilated data used.

•Applied to 10 different cases.

00 06 12 18 24

“Perfect Observations”

IAU/Nudging

Control

Time (UTC)

First guess

First guess

First guess + OBSMethodology.

WP 3: Data assimilation

• Results: 3-hourly assimilation frequency minimizes the RMSE.

Sfc-500 mean relative humidity RMSE for 2 cases (all the variables assimilated).

WP 3: Data assimilation

• Results: IAU overestimates the total amount of precipitation while nudging gives a bias closer to zero.

24-h accumulated precipitation mean error (all the variables assimilated).

Case CNTL IAU6 NUD6 IAU3 NUD3

021210 -5.6 0.6 -3.7 2.1 -2.7

030106 1.7 3.9 -0.3 4.1 0.2

030213 -0.2 0.8 0.1 1.1 0.3

030220 3.2 2.9 0.3 3.5 0.3

030227 -0.8 4.7 1.1 5.6 1.5

030328 -2.3 4.5 -0.8 5.3 0.0

030409 1.6 1.0 0.5 0.8 0.5

030506 1.6 7.0 2.1 7.5 2.6

030817 3.3 1.2 -0.6 1.6 -1.1

030831 1.8 1.7 0.7 0.8 0.4

WP 3: Data assimilation

• Results: assimilating the combination of at least wind and humidity produces the best impact on the precipitation field.

Sfc-500 hPa mean relative humidity RMSE for different combinations of assimilated variables. Case 030213. a) 3-h IAU, b) 3-h nudging.

WP 4Assessment of NWP model

uncertainity including model errors

• Software modules for ML/SKF approach

• Software modules for KF/IIP

• Report on benefits from improved data assimilation

On-line estimation of error covariances

Covariances of innovation vectors (v=y-Hxb): RHPHvvE TT ,

Ttbtb xxxxEP )(),( Ttt HxyHxyER )(),( where

Innovation covariance model:)()( k

Tkkk RHPHS

Tuning of error covariance matrix:

)(

)),((

kT

kkk

Tkk

RHPHnorm

vvEnorm

Estimated from set of innovations by applying Kriging and Maximum Likelihood techniques.

Pre-scribed. (In HIRLAM case error stat const. in time)

Krieging and Maximum Likelihood

Based on Kriging, in observation space we estimate the covariance of the innovations with a covariogram, V:

vdwpVvdwpVawpvp T

n),,(

2

1exp),,(det

)2(

1,,| 12/1

2/

Parameters p, w, d are estimated with ML tecnique to find the maximum of the following pdf:

jidhwV

jidhwpV

jiji

jiji

,)/exp(

,)/exp(

,,

,,

dwpVRHHPvvE TfT ,,,

{

(n-number of innovations)

Application with HIRLAM 3D-Var

Tkkk HPH

kRbHxyv

Input from HIRLAM

(innovations from each assimilation cycle during a 14 day period)

(pre-scribed, assumed static HIRLAM observation error and background error model)

On-line estimation software module utilising Kriging/ML

Alpha parameter for each assimilation cycle, guiding when the pre-scribed -background errors should be increased/decreased. From that a time dependent scaling factor was calculated.

Assimilation and forecast experiment

Integration area

Two parallel runsCRL:static background errorINV: time dependent background error

14 day experiment(1-14 Jan., 2002)

RMS of 48 hour MSLP forecasts

(unit: hPa) as function of

assimilation cycle during the 14 day

period.

Extension to include spatial variations

Sub-division of model-domain

Example of alpha parameters for

each sub-domain

WP 5Assessment of improvements in

NWP

• Analysis of severe weather situations

• Set up of VSRF procedure

• Verification of forecasted field coming from VSRF procedure

• Model inter-comparison experiment

System ArchitectureGTS data

High resolution surface

networksLAM output

Radar data

Fast varying satellite products

Slow varying satellite products

Weather analysis module

Weather features identification module

Advection moduleVSRF module

OU

TP

UT

Assimilation Cycle Based on Nudging

Radar Data MSGData

LAPSAnalysis

CONVENTIONAL DATA: from GTS network

SURFACE DATA: from local (high density)networks and OTHER data

AOF (Analysis Observations File):

1-DVARTemperature and Humidity

Profiles retrieval

Background fromModel run

LAPS-Pseudo-observations

Retrieved Profiles

Boundary Conditions from GCM or from Coarser LM

CONTINUOS ASSIMILATION CYCLE

0 +1 +2 +3

+12 +24 time

LA

PS

an

alis

ys 0

LM

BC 0 BC 1

0 +24LAMI Boundary Condition

BC 2 BC 12 BC 24

LM

LM

LMLM very short range forecast +12

Assimilation Cycle (nudging)

LA

PS

an

alis

ys 1

LA

PS

an

alis

ys 2

LA

PS

an

alis

ys 1

1

LA

PS

an

alis

ys 1

2

LAPS background

Start LM runStop LM run

Ingestion of satellite data into the Local Analysis and Prediction System (LAPS)In Bologna is done using METEOSAT data via

•cloud cover analysis from VIS-IR channel

•water vapor content from the WV channel

Severe weather CASE study

METEOSAT WV

LAPS RAOB LAPS SATELLITE

LAPS background

RH (%)

400 hPa

18

June 97

1200 UTC

LAPS BACKGROUND

LAPS RAOB LAPS SATELLITE

Total Precipitable Water

(mm)

With METEOSAT (IR/VIS) data Without satellite data

Synoptic Analysis: IOP15 (5-9 nov)Day 5 november is very clear a blocking situation that will caracterize whole observing period. Mediterranean cut-off low start to move SE as an intense Atlantic short wave approaches Italy. Day 6 we observe a very intense developement, with thunderstorms activity in Adriatic sea and East Alps. The following days the centre of surface cyclone interest central and meridional Italy and from day 9 a new cut-off low carring Nord Sea cold air reach NE Alps.

CARPE DIEM Meeting 15-16 december 2003

Concluding Remarks

• A lot of knowledge exchange and co-operation between different institutes and expertises within AREA1.• Almost all deliverables within AREA 1 have proceeded according to plans and a few extra rather extensive deliverables have been added.• Comments from TSC report I and II valuable and are taken into account.