Evaluating the impact of the GPS-Radio occultation data on regional data assimilation

Shu-Chih Yang,

Chih-Chien Chang, Zih-Mao Huang and Ching-Yung Huang

Dept. of Atmospheric sciences, National Central University

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Impact of RO with a regional assimilation system

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TPW w/o RO TPW w/ RO

Rainfall w/o RO Rainfall w/ RO 24-h ACC Rainfall

A regional assimilation based on WRF-Local Ensemble Transform Kalman Filter has been used to investigate RO’s impact.

1. Impact of F7/C2 RO data on heavy precipitation prediction

2. Impact of GPS-RO with a regional hybrid assimilation system

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Impact of F7/C2 RO on heavy precipitation prediction

Motivation

• FORMOSAT-3/COSMIC-1 has a great impact on severe weather prediction

– TC prediction (genesis, track, intensity, Kuo et al. 2015, Liu et al. 2012, Huang et al. 2010, Chen et al. 2009).

– The Mei-Yu related heavy precipitation prediction (moisture transport, Yang et al. 2014, Tu et al. 2014).

• Investigating the impact of FORMOSAT-7/COSMIC-2 RO on severe weather prediction in Taiwan

– F7/C2 has 6 satellites in low-inclination orbits and 6 satellites in high-inclination orbits

– Additional impact from tropical constellation?

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Experiment setup

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Phase I (491, low inclination)

Phase II (435, high inclination) Exps Observations

CTRL N/A

GTS GTS only

PH1 GTS+Phase I

PH2 GTS+Phase II

F7 GTS+Phase I+Phase II

• OSSE • Natural run is generated by MM5 • RO locations are generated by the ray tracing model

(Chen et al. 2006) • Regional assimilation system: NCU WRF-LETKF • DA cycles: 0608 0000UTC- 0610 0000UTC

0800Z 0712Z 0900Z 1000Z

6-h LETKF analysis cycling 3-day fcst

0700Z

WRF Spin-up

Total precipitable water analysis

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Nature

F7 GTS

CTRL

Rainfall accumulation 2012061006~2012061106

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Nature CTRL

F7 PHASE I GTS PHASE II

Rainfall accumulation 2012061106~2012061206

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F7 PHASE I GTS PHASE II

CTRL Nature

RO data helps to maintain the rainfall prediction skill at longer forecast range

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1st day

2nd day

Both PHASE I and PHASE II show great benefit at longer forecast range

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rainfall(mm)

ETS

CTR GTS PHI PHII ALL

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rainfall(mm)

Bias

CTR GTS PHI PHII ALL

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rainfall(mm)

ETS

CTR GTS PHI PHII ALL

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score

rainfall(mm)

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CTR GTS PHI PHII ALL

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5 10 15 25 35 50 100 150 200 300

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rainfall(mm)

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CTR GTS PHI PHII ALLETS

ETS

BIAS

BIAS

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1106Z

1112Z

1118Z

1200Z

Nature GTSPI GTSPII GTSF7 CTRL

Short summary

• Assimilation of F7/C2 RO data provides a better upstream moisture condition, improving the rainfall prediction skill at longer forecast range.

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Impact of GPS-RO with a regional hybrid assimilation system

Background and motivations

• The hybrid data assimilation combines the advantages of VAR and EnKF

• In comparison with the covariance-hybrid scheme (Lorenc, 2003, Wang et al. 2007), the Gain hybrid DA (Penny, 2014) has the potential to better use the advantage of EnKF.

– Bonivata et al. (2015) shows that HG-DA in ECMWF is comparable to its operational model without tuning.

• This study aims to establish a regional HG-DA system with WRF 3DVAR and WRF-LETKF systems and investigates whether the HG-DA is able to provide more accurate analysis.

• Can RO observations provide more positive impacts on QV and temperature fields with the HG-DA and improve severe whether prediction?

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Regional Gain-Hybrid assimilation system

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LETKF

VAR Analysis of Hybrid DA

𝛼 ×

(1 − 𝛼) ×

¢XaLETKF

xaLETKF

XbLETKF

xaLETKF

xa3DVAR

xa3DVAR

xaLETKF

xaHYB

dynamical error mode corrections

Statistical-average error mode correction

Nature error mode

Dynamical error mode

3DVar Error mode

Experimental setting

• Observation simulation system experiment (OSSE)

• Observation: GTS (Sounding, Synop, Ship, Airep) and GPS RO refractivity

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AIREP ┼ SOUND ◇ SYNOP △ SHIP ✽ GPS RO Refractivity

00z 14

00z 15

FNL 12z 13

00z 16

12z 16

Spin-up Forecast

6-h DA cycling

12z 15

Hurricane Helene (Sep. 2006)

Correction from single RO profile

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U850

T200

Qv900

VAR LETKF HYB

• LETKF provides flow-dependent corrections.

• When the BLETKF is less reliable, BVAR plays an important role to mitigate the negative impact.

• Cross-variable correlation allows LETKF/HYB better corrects hurricane-associated winds.

Results from cycling run

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RMSE U RMSE Temp RMSE Qv

• LETKF generally provides more accurate analysis that the VAR analysis. • HYB shows large improvement in the dynamical field, mid-level temperature

and low-mid level moisture.

Analysis at 1200 UTC June 15

Impact of GPS RO on Qv at 850hPa

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RO impact with HYB RO impact with LETKF

RO impact with VAR RO impact= RMSEW/ RO – RMSEW/O RO Negative: RO has larger positive impact

Impact of RO

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Improvement with GPS-RO data (negative: improve)

The hybrid system helps better use the RO data, especially in the mid-low atmosphere.

Short summary

• An advanced data assimilation system that combines the advantages of VAR and EnKF can improve the effectiveness of RO assimilation.

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