Strategies of using radar/conventional data for improving QPF at cloud-resolving scale

by the ensemble Kalman filter

Kao-Shen Chung1 , Weiguang Chang1, Seung-Jong Baek2 and Luc Fillion2

Collaborators: Isztar Zawadzki1, M.K Yau1

1. Dept of Atmospheric and Oceanic Sciences, McGill University2. Meteorological Research Division, Environment Canada

Kao-Shen Chung( 鍾高陞 )National Central University

May 6th, 2014

Outline

1. Introduction of the Canadian High Resolution Ensemble Kalman Filter (HREnKF) system

2. Examine the impact of position error with radar data assimilation

3. Strategies of improving QPF at convective scale a) regional assimilation system b) Adaptive Radar observation

4. Summary and future works

1. High Resolution Ensemble Kalman Filter System ( HREnKF )

Initial guess

Ensemble members

Add random perturbations

Data assimilation

Observations

Perturbed observations

GEM-LAM forecast for all the members

Add random perturbations(model error) Analysis step

Forecast step

GEnKF(2005 operational system)

LAM 1-km (300x300)

HREnKF

for radar data assimilation

Features of the system

Sequential processing of batches of observations

60 10~y

Houtekamer and Mitchell 2001

Sub-ensemble 1

Sub-ensemble 2

Sub-ensemble 3

Ensemble members (80)

Sub-ensemble 4

Gain matrix K1K1 Gain matrix K2K2 Gain matrix K3K3 Gain matrix K4K4

Partitioning the ensemble (to deal with the underestimation of the error structure) )HK( ffa xyxx

• Control variables: U, V, W, T, HU (specific humidity)

• Observations are perturbed according to its variance (no correlation).

• Simplified random perturbations to consider the model errors

• Localization: 10-km in horizontal; 2 * ln( Pressure levels ) in vertical

• 80 members

Some features of the current set up:

For the HREnKF

For the GEM_LAM model at 1-km resolution

• Cycling hydrometeor variables

• Microphysical scheme: double moment scheme (Milbrandt and Yau, 2005)

• Fixed lateral boundary conditions Ensemble lateral boundary conditions for all ensemble members

Summer cases Features

June 12, 2011 Scattered and localized convection. (up to 90-min)

June 23, 2011 Wide spread stratiform system (up to 90-min)

June 29, 2011 Squall line system

QPF Improvement only up to 1-h

Analysis 30-min

90-min60-min

Position error of precipitation

Poor background fields

Is it important? (impact of assimilating radar data)

Is there any way to improve it?

sin)(cos)cossin( Tr VWVUV

In the low elevation angle:

Directly update U and V

Indirect update W through flow-dependent background error cross-covariance

)(),(

22VrInnov

WVrCovW j

oVrj Increment:

22

22

22

22

1

),(),(

VrO

W

OVrW

WVrWVrCov

Error reduction:

2. Examine the impact of position error with radar data assimilation

Experiment designed:

Global EnKF system(GEnKF)

Regional EnKF system(REnKF)

High resolution EnKF(HREnKF)

傳統氣象資料衛星資料

Obtain ensemble members

傳統氣象資料衛星資料

氣象雷達資料

3. Strategies of improving QPF at convective scale

a) conventional observations + regional assimilation system

Impact of Regional EnKF with conventional observations

Vertical velocity W

Global EnKF (ensemble mean) Regional EnKF (ensemble mean)

REnKF_15km 1-day cycling

0000 1200 0000 0600

6-h short-term forecast

Control run (from DF) Precipitation (mean of REnKF)

Improvement of the background field

Control run: No radar assimilation, from ensemble mean of the REnKF

0000

HREnKF: cycling for 60-min and launch the short-term forecast

0000 1830 UTC

0230

Radar radial wind (assimilating every 5-min)

0100

short-term ensemble forecasts 1.5 hr

2.5 h model integration

Cycling assimilation procedure

Experiment of the HREnKF with radial wind assimilation

Impact of using the ensemble set from REnKF system From random perturbations

From REnKF

Converge toward observations

underestimate

Covered the rms of Pf

Verification

Analysis 30-min

90-min60-min

Adaptive radar observations How to optimize using radar observations?

4. Summary and future works

• At cloud-resolving scale, if there is any position error of precipitation, it is important to correct it before assimilate radar observations.

• Initial ensemble set from the REnKF is better than random perturbations 1. Capture mesoscale circulation better 2. Ensemble spread is able to cover forecast errors assimilate more radar observations

• The verification of the radial component shows that the improvement of short-term forecast is up to 1.5-hr. (Both bias and root-mean-square errors)

• How to use radar observation properly? Adaptive observation strategy is able to improve the effectiveness of assimilating radar data

Data assimilation(bridge)

observationsNumerical

model

About future work

The solid line represents the theoretical limit of predictability, the dashed line indicates NWP models, and the dotted line represents nowcasting methods (Austin et al., 1987).

Berenguer et al. 2012

Forecast skill ( nowcasting versus NWP )precipitation

0 - 6 hr QPF

• Resolution of NWP• Extra Observations

Final goal

Reflectivity

0000 UTC 0000 UTC

Simulated reflectivity

0000 UTC

• Construct regional and mesoscale analysis fields

Simulated reflectivity

observations Poor background field Good background field

Large scale forcing Data assimilation

How many cycling of regional EnKF & conventional data?Optimal assimilation window ( 6-h or 3-h ) ?

a. Assimilate both radial wind and reflectivity observations

• Extra Observations (other than conventional data)

(Feng et al. 2009)

(Humidity)(Precipitation)

b. Refractivity Apply to a EnKF system

C. Dual-Polarization observations:

( Putnam et al 2013 )

Microphysics versus Dual-Polarization parameters

( Putnam et al 2013 )

d. Cloud radars

• Use more complicated observation operator

i

i

beam

M

jj

N

iii

e

beam

M

jj

N

iii

er

binr

drrrWddGr

rZ

drrrWddGr

rZrV

V

11

22

11

22

'cos,'cos2exp,,

'cos,'cos2exp,,

,,

• Identify error structure in a) observations

sin)(cos)cossin( Tr VWVUV

Consider: proper geometry, accurate propagation Include: the sampling volume, signal and its processing

< simplified operator >

Fabry and Kilambi (2011)

• Identify error structure in B) numerical model

Error correlation of TT profile V.S. Vertical correlation of TT tendency ( Ensemble Forecasts) (stochastic perturbation of SCM)

Single column model (SCM) Represent the error structure

Microphysics

移動雷達車移動雷達車移動雷達車移動雷達車

JWDJWD雨滴譜儀雨滴譜儀JWDJWD雨滴譜儀雨滴譜儀

2DVD2DVD雨滴譜儀雨滴譜儀2DVD2DVD雨滴譜儀雨滴譜儀

剖風儀剖風儀剖風儀剖風儀

MRRMRR微波雨量微波雨量雷達雷達

MRRMRR微波雨量微波雨量雷達雷達

雨量計雨量計雨量計雨量計

• Verify QPF (quantitative precipitation forecast) over Taiwan region

• Identify error structure in B) numerical model

Global Precipitation Mission (GPM)

報告完畢

歡迎指教

謝謝！

3. Impact of assimilating radial wind componentIs it able to propagate information to other control variables?

Temperature

HumidityV-wind

Obs Z

McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation(MAPLE)

Variational Echo Tracking technique (Laroche and Zawadzki 1995 ):

Estimate the motion field of precipitation and a modified semi-Lagrangian backward scheme for advection.(capable of stretching and rotation)