The application of MPAS in TTFRI

06.03.2015CWB, Taipei, Taiwan

Li-Huan Hsu, Li-Rung Hwang, Yi-Chiu Lin, Gong-Do Hwang,

Taiwan Typhoon and Flood Research Institute (TTFRI),National Applied Research Laboratories, Taiwan

• Introduction about TTFRI

• Mission of water resource management in TTFRI

• The Model for Prediction Across Scales (MPAS)

• Results of 5 days typhoon forecasts in 2013, 2014

• Results of spring rainfall simulations/outlooks

• Summary and future works

OUTLINE

Vision

Taiwan Typhoon and Flood Research Institute (TTFRI) is established to improve the key technologies for the observation, analysis, simulation and forecast of typhoon and flood, and to enhance the interdisciplinary hydro-meteorology researches. In addition, an advanced forecast system will be developed and systematic evaluated to help improve the prediction for the national meteorological and hydrological services and the national disaster reduction operation.

Bridge the gap between the academia and the government agencies

Introduction about TTFRI

Keep water in wet season !

Save water in dry season !

Annual

MAY – OCT

(Wet season)

NOV – APR

(Dry season)

Mission of water resource management

Taiwan rainfall distribution in spatial and temporal is uneven

(Climate 1989-2014)

Mission of water resource management

Historical drought event distribution in Taiwan (1973-2006)

(Figure provided by NCDR)

event numbers

FEB-APR

MAY-JUNE

JULY-SEP

OCT-NOV

DEC-FEB

ShihmenReservoir

watershed

ZengwenReservoir

watershed

from WRA webpage (0504)

Some meteorology issues associated with water resource in Taiwan (cause the drought problems in 2015)

Few typhoons affecting Taiwan in 2014. How many rainfall can we keep from the last typhoon ?

How many rains in spring ?

Water rationing area in May

• Extend the typhoon forecast to 5-10 days, and provide the rainfall information in Taiwan during typhoon warning periods.

• Do the seasonal predictions for FEB-APR , and provide seasonal rainfall outlook information for water resource management .

Increase the resolution in WNP area with

variable resolution mesh

For the purpose of water resource management we want to:

NCAR MPAS meshes

• MPAS is an open sourced model with user-friendly documentations. The current MPAS release is version 4.0. Please refer to http://mpas-dev.github.io/ .

• Non-hydrostatic model with C-Grid Spherical Centroidal VoronoiMeshes.

• Unstructured variable resolution mesh allows smooth scale transition; potential application when computational resources are limited

• A subset of the Advanced Research WRF (ARW) model physics

• Data assimilation for MPAS is available as an ensemble Kalmanfilter (EnKF) implemented through Data Assimilation Research Testbed (DART) in 2015.

The Model for Prediction Across Scales (MPAS)

• WNP Typhoon Experiments in TTFRI – MPAS V1.5

– 10 days forecast experiments

– Preliminary results of 5 days performance for typhoon cases in 2013 and 2014.

Experiment design for typhoon experiments

grid resolution : 15 – 60 kmcenter : 20N 140E

TC name dtg numbersRainfall analysis period

(include sea warning period)TRAMI (2013) 2 082003-082300UTC

KONG-REY (2013) 5 082700-083000UTCUSAGI (2013) 7 091900-092300UTC

MATMO (2014) 5 072100-072400UTC

Domain resolution

• Model settings :– NCEP GFS initialization

– WSM6 microphysic scheme

– Tiedtke convection scheme

– No DA

Tracks and rainfall in typhoon experiments

CWB OBS

CWB OBS

rainfall analysis period082000-082300 UTC

CWB OBS

CWB OBS

rainfall analysis period091900-092300 UTC

rainfall analysis period082700-083000 UTC

rainfall analysis period072100-072400 UTC

Anomaly correlation coefficient with FNL

850hPa Temp NH (20N-80N)

TRAMIKONGREYUSAGIMATMOMean

500hPa Height NH (20N-80N)

Average typhoon track errors (2013, 2014)Along-Track and Cross-Track errors

Typhoon track verification

• The averaged 24h, 48h, 72h and 120h track errors are about 100 km, 150 km, 240 km and 350 km.

• The numbers of left/right and slow/fast cases are similar, but the errors of slower cases are larger.

(km)

(km)

TS score for forecast day 1, day 2 and day3

TS score for 24h, 48h, and 72h accumulated rainfall

TS score for total rainfall period (~ 72h)

Typhoon rainfall verification

• The TS score of forecast day 1 exceed 0.39 at threshold 200mm/24h.

• The TS score for 72h accumulated rainfall is better, but is similar with others for higher threshold.

• The TS score of total rainfall period at threshold 200mm exceed 0.42 for 4 typhoon cases in 2013, 2014.

day1

day2

day3

24h

48h

72h

• Model settings– MPAS V1.5

– 120 km horizontal uniform resolution

– 120 days of Spring simulation

– Kain-fritsch convection scheme

– No data assimilation

• Ensemble experiments settings– 2005-2014 Hindcast experiments

• 10 ensemble members per year

• I.C.s : 01/01-01/10 NCEP GFS analysis

• B.C.s: NCEP NOAA RTG SST analysis

– 2014, 2015 Forecast experiments

• 20 ensemble members

• I.C.s : 01/01-01/10 NCEP GFS analysis (10)

• B.C.s: NOAA CFSV2 7248 hours forecast SST from different date(2).

Experiment design for spring rainfall experiments

Monthly mean anomaly correlation coefficient with FNL(2014, 2015 forecast)

2014

2015

FEB MAR

FEB-APR

Precipitation probability for 2014

below aboveModel climate : 2005-2014

normal

(%)

APR

Precipitation probability for 2015FEB MAR

APR

below abovenormal

(%)

FEB-APR

Model climate : 2005-2014

• Monthly rainfall of four regions (N, C, S, E) of Taiwan.

• Compare observation data from averaged CWB stations (rain gauge included) rainfall in N, C, S, E regions.

60.1 %

39%

0.9%

Estimated Rainfall of Northern

TaiwanERNT = R(1)*0.9% + R(2)*60.1%

+ R(3)*39%

Shihmen

Reservoir watershed

ZengwenReservoir

watershed

1

2 3N

C

S

E

Taiwan spring rainfall estimation

N

C

S

E

Monthly rainfall averaged over 2005-2014 Hindcast

• The model simulations have large variance and increase with time.

• Overestimates during the dry season in the C, S and E of Taiwan, but the monthly rainfall tendency still can be reference.

Ensemble mean

Median

Q3S : the third quartile

Q1S : the first quartile

High : Q3S+1.5(Q3S-Q1S)

Low : Q1S-1.5(Q3S-Q1S)

Outlier

February rainfall from 2005-2014

• Underestimate in Northern Taiwan and overestimate in others.

• The rainfall amount may be affected by a torrential rainfall event during the dry season.

• Result of 2014 forecast in Northern Taiwan is underestimated. The performance of 2015 forecast is better.

Daily rainfall (OBS, 02/09/14)

Forecast

N

C

S

E

14’ 15’

March rainfall from 2005-2014

• Overestimate in C, S and E of Taiwan.

• The model can catch the interannual variation of monthly rainfall in Northern Taiwan.

• Result of 2014 forecast in Northern Taiwan is underestimated. The rainfall in C, S of Taiwan is slightly overestimate in 2015.

N

C

S

E

Forecast

14’ 15’

April rainfall from

2005-2014

• The rainfall tendency of April can be represented by model simulation.

• Overestimate in C, S and E of Taiwan.• Results of 2014, 2015 forecasts in C,

S Taiwan are overestimated.

N

C

S

E

Forecast

14’ 15’

3 Month total rainfall from 2005-2014

• The rainfall tendency can be represented by model simulation.

• For hindcast experiments, the results show overestimate in C, S, E Taiwan and underestimate in N Taiwan.

• For forecast experiments, the results show overestimate in C, S Taiwan.

Forecast

14’ 15’

• Four typhoon cases are analyzed in 2013, 2014 with MPAS. TC track errorsare about 100 km, 150 km, 240 km and 350 km for 24h, 48h, 72h and120h track forecast. Some typhoon forecasts will behave slowertranslation speed than the observation.

• The TS score of forecast day 1 exceed 0.39 at threshold 200mm/24h. TheTS score of total rainfall period at threshold 200mm exceed 0.42.

• The spring rainfall simulations have large variance and increase with time,but the rainfall tendency of April can be represented by model simulation.

• The rainfall amount may be affected by a torrential rainfall event duringthe dry season. MPAS results show that the annual trend of spring rainfallcan be reproduced, but is overestimate in C, S Taiwan and underestimatein N Taiwan.

Summary

Future works We are currently updating the MPAS version, and evaluate more typhoon

cases The impact of Increasing horizontal grid resolution to seasonal forecast The application of rainfall information to hydrologic model

ENDThank you for your attention