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Takuya KAWABATA ([email protected]), Tohru KURODA, Hiromu SEKO, Kazuo SAITO (Meteorological Research Institute/JMA) NHM-4DVAR is a cloud-resolving nonhydrostatic 4D-Var data assimilation system NHM-4DVAR v1 with dry dynamics and water vapor Nerima heavy rainfall event on 21 July 1999 NHM-4DVAR v2 with warm rain Heavy rainfall event around Tokyo on 4 5 Sep 2005 based on the JMA Nonhydrostatic Model (NHM), to investigate the mechanism of heavy rainfall events induced by mesoscale convective systems (MCSs). The horizontal resolution of the NHM-4DVAR is set to 2 km to resolve MCSs. . NHM-4DVAR v1: The forward model is the full nonlinear NHM, while the adjoint model only considers perturbations to the dry dynamics and the advection of water vapor. Doppler radar’s radial wind data (RW), GPS precipitable water vapor data (GPS- PWV) and surface temperature and wind data are assimilated. The system was applied to a heavy rainfall event in Tokyo in July 1999 called the Model specifications Category Forward model (nonlinear NHM) adjoint model Basic equations Fully compressible with a map factor O Prognostic variables Momentums, potential temperature, pressure, mixing ratios of water Momentums, potential temperature, pressure, mixing ratio of Nerima heavy rainfall event on 21 July 1999 Heavy rainfall event around Tokyo on 4-5 Sep. 2005 adjoint model O Momentums, potential temperature, pressure, mixing ratio of Tokyo A heavy rainfall event occurred around Tokyo on 4-5 Sep. 2005. Maximum amount of total precipitation reached 264 mm at Suginami in Tokyo. rainfall event in Tokyo in July 1999, called the Nerima heavy rainfall.. Time sequence of observed rainfall intensity is well reproduced in the assimilation window and the subsequent forecast. . NHM-4DVAR v2: The warm rain cloud microphysical process has been implemented to the adjoint model. New control variables relevant to the water substances are introduced, and the observation operator of the radar reflectivity is developed. This system was applied to a heavy rain fall event around Tokyo occurred on 4-5 Sep. 2005. With the assimilation of the radar reflectivity and mesoscale data, location, horizontal size, and rainfall intensity of the observed rain band is well reproduced p C R p = π + = z surf surf B dz z ϑ π π ) ( Control variables vapor, cloud water, cloud ice, rain, snow and graupel, and kinetic turbulent energy. water vapor Cloud microphysics 3 ice bulk microphysics Advection of water vapor Variables Formula water vapor, cloud water, rain water Warm rain process Variables Formula Assimilation area Assimilation period peak of heavy rainfall the observed rain band is well reproduced. . p = 0 π Wind u, v, w Non-Hydrostatic pressure π U = π - π B Potential temperature θ Surface pressure P s Pseudo relative humidity q u = qv / q s b Wind u, v, w Non-Hydrostatic pressure π U = π - π B Potential temperature θ Surface pressure P s Total water qv+qc Rain water qr / q s b Pseudo relative humidity (for LBCs) q u = qv / q s b Assimilation result Left : PWV circles : observations contour : analysis Right: surface wind red arrows : observations black arrows : analysis color contour : topography Assimilation area ( ) ( ) 75 . 1 10 log 10 qr dns cqr Z qr × × × = cqr : constant , dns : density, qr : mixing ratio of rain water Assumption: Marshall-Palmer drop size distribution Observational error:15dBZ 0 5 10 15 20 25 30 1510 1520 1530 1540 1550 1600 1610 1620 1630 1640 1650 mm Observation Forecast Forecast results Observation Ob i Radar reflectivity (dBZ) 1448JST 1507JST 1525JST 10-minute rainfall amount at Nerima 1545JST Hourly accumulated rainfall amount Observation operator for reflectivity (Z-Qr relation) HANEDA airport radar elevation angle = 0.7 zFormation and development of the convective cells, which caused the Nerima heavy rainfall, are well reproduced. z10-minute rainfall amount of the forecast are quantitatively in good agreement with the observation. Assimilation results Analysis reproduces the location, intensity and horizontal size of the main rain band, furthermore, convective cells located on the east of the main rain band. 1.0 10.0 20.0 30.0 50.0 70.0 (mm/h) 1999. 7.21.15-16JST 0 200km (Valid:21.1600JST) 2hour 0min Forecast Observation Forecast 1450JST 1510JST 1530JST 1550JST Nerima heavy rainfall A A A A A A A A B B B B C C Radar reflectivity (dBZ) Observation 2059JST 2039JST 2019JST HANEDA airport radar elevation angle = 0.7 1999.07.21 14JST 15JST 16JST 17JST Assimilation window (1 hour) 3-hour forecast Time evolution of Nerima heavy rainfall Formation Develop Mature Design of the assimilation experiment 0.1 1.0 10.0 20.0 30.0 50.0 70.0 max=103.90 0 50km min=-.00019 (Valid:21.1600JST) 5hour 0min Fist guess No heavy rainfall C C C Analysis 2059JST 2039JST 2019JST GPS-PWV 5 minute interval Radial Wind 1 minute interval Surface wind & temperature 10 minute interval 2005.09.04 Assimilation window (10 min) GPS-PWV 5 minute interval Radial Wind 1 minute interval Surface wind & temperature 10 minute interval Reflectivity 1 minute interval 0.1 1.0 10.0 20.0 30.0 50.0 70.0 max=14.643 m 0 50km Rainfall intensity(mm/h) Observation 2100JST Observation nothing If reflectivity is 9over 10 dBZ in the first guess field and 9under 10 dBZ in the observations, the grid is regarded as the false precipitation to be li i td d “0 dBZ” Assimilate “0 dBZ” nothing Assimilation of “0 dBZ” observation 2010 2020 2030 2040 2050 2100 20JST Design of the assimilation experiment Analysis Acknowledgment The GPS precipitable water vapor data are reanalyzed and provided by Mr. Shoji of MRI, the Doppler radar data are provided by Dr. Akaeda of JMA, and the UMIHOTARU surface observation data are provided by the Ministry of the Environment of Japan. The authors express our gratitude to them. Reference Kawabata et al, 2007: An Assimilation and Forecasting Experiment of the Nerima Heavy Rainfall with a Cloud-Resolving Nonhydrostatic 4-Dimensiojnal Variational Data Assimilation System, Journal of the Meteorological Society of Japan, Vol. 85, No. 3, 255-276 First guess failure eliminated, and “0 dBZ” information is used. Observational error: 9normal error x 4 “0 dBZ” observations are NOT real observations. Without “0 dBZ” failure 0.1 1.0 10.0 20.0 30.0 40.0 50.0 max=234.16 (Valid:04.2100JST) 0hour 10min 0 50km Initial : 2005.09.04.1150UTC 2100JST

Takuya KAWABATA ([email protected]) KAWABATA ([email protected]), TohruKURODA, HiromuSEKO, Kazuo SAITO (Meteorological Research Institute/JMA) NHM-4DVAR

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Page 1: Takuya KAWABATA (tkawabat@mri-jma.go.jp) KAWABATA (tkawabat@mri-jma.go.jp), TohruKURODA, HiromuSEKO, Kazuo SAITO (Meteorological Research Institute/JMA) NHM-4DVAR

Takuya KAWABATA ([email protected]), Tohru KURODA, Hiromu SEKO, Kazuo SAITO (Meteorological Research Institute/JMA)

NHM-4DVAR i s a cloud-resolvingnonhydrostatic 4D-Var data assimilation system

NHM-4DVAR v1 with dry dynamics and water vaporNerima heavy rainfall event on 21 July 1999

NHM-4DVAR v2 with warm rainHeavy rainfall event around Tokyo on 4 5 Sep 2005o yd ostat c a data ass at o syste

based on the JMA Nonhydrostatic Model (NHM), toinvestigate the mechanism of heavy rainfall eventsinduced by mesoscale convective systems (MCSs).The horizontal resolution of the NHM-4DVAR is set to2 km to resolve MCSs. .

NHM-4DVAR v1: The forward model is thefull nonlinear NHM, while the adjoint model onlyconsiders perturbations to the dry dynamics and theadvection of water vapor. Doppler radar’s radial winddata (RW), GPS precipitable water vapor data (GPS-PWV) and surface temperature and wind data areassimilated. The system was applied to a heavyrainfall event in Tokyo in July 1999 called the

Model specificationsCategory Forward model

(nonlinear NHM)adjoint model

Basic equations

Fully compressiblewith a map factor

O

Prognostic variables

Momentums, potential temperature, pressure, mixing ratios of water

Momentums, potential temperature, pressure, mixing ratio of

Nerima heavy rainfall event on 21 July 1999 Heavy rainfall event around Tokyo on 4-5 Sep. 2005

adjoint model

O

Momentums,potential temperature, pressure, mixing ratio of

Tokyo

A heavy rainfall event occurred around Tokyo on 4-5 Sep. 2005. Maximum amount of total precipitation reached 264 mm at Suginami in Tokyo.

rainfall event in Tokyo in July 1999, called theNerima heavy rainfall.. Time sequence of observedrainfall intensity is well reproduced in the assimilationwindow and the subsequent forecast. .

NHM-4DVAR v2: The warm rain cloudmicrophysical process has been implemented to theadjoint model. New control variables relevant to thewater substances are introduced, and the observationoperator of the radar reflectivity is developed. Thissystem was applied to a heavy rain fall event aroundTokyo occurred on 4-5 Sep. 2005 . With theassimilation of the radar reflectivity and mesoscaledata, location, horizontal size, and rainfall intensity ofthe observed rain band is well reproduced

pCR

p⎟⎟⎞

⎜⎜⎛

∫+=z

surfsurfBdzzϑ

ππ )( Control variables

gvapor, cloud water, cloud ice, rain, snow and graupel, andkinetic turbulent energy.

gwater vapor

Cloud microphysics

3 ice bulk microphysics

Advection of water vapor

Variables Formula

gwater vapor,cloud water,rain water

Warm rain process

Variables Formula Assimilation area

Assimilation period peak of heavy rainfall

the observed rain band is well reproduced. .p ⎟⎟⎠

⎜⎜⎝

=0

π Wind u, v, w

Non-Hydrostatic pressure πU = π - πB

Potential temperature θ

Surface pressure Ps

Pseudo relative humidity qu = qv / qsb

Wind u, v, w

Non-Hydrostatic pressure πU = π - πB

Potential temperature θ

Surface pressure Ps

Total water qv+qc

Rain water qr / qsb

Pseudo relative humidity(for LBCs)

qu = qv / qsb

Assimilation resultLeft : PWVcircles : observationscontour : analysis

Right: surface windred arrows : observationsblack arrows : analysiscolor contour : topography

Assimilation area

( )( )75.110log10 qrdnscqrZ qr ×××=

cqr : constant , dns : density, qr : mixing ratio of rain waterAssumption: Marshall-Palmer drop size distributionObservational error:15dBZ

051015202530

1510 1520 1530 1540 1550 1600 1610 1620 1630 1640 1650

mm

JST

Observation ForecastForecast results

Observation

Ob i

Radar reflectivity (dBZ)

1448JST 1507JST 1525JST

10-minute rainfall amount at Nerima

1545JST

Hourly accumulated rainfall amount

Observation operator for reflectivity (Z-Qr relation)

HANEDA airport radar elevation angle = 0.7

Formation and development of the convective cells, which caused the Nerima heavy rainfall, are well reproduced.

10-minute rainfall amount of the forecast are quantitatively in good agreement with the observation.

Assimilation resultsAnalysis reproduces the location, intensity and horizontal size of themain rain band, furthermore, convective cells located on the east of themain rain band.

0.1 1.0 10.0 20.0 30.0 50.0 70.0(mm/h)

1999. 7.21.15-16JST

0 200km

(Valid:21.1600JST) 2hour 0min

Forecast

Observation

Forecast

1450JST 1510JST 1530JST 1550JST Nerimaheavy rainfall

A

AA A A

A A A

BBB

B

CC

Radar reflectivity (dBZ)Observation

2059JST2039JST2019JSTHANEDA airport radar elevation angle = 0.7

1999.07.2114JST

15JST 16JST 17JST

Assimilation window (1 hour)

3-hour forecast

Time evolution of Nerima heavy rainfall Formation Develop Mature

Design of the assimilation experiment 0.1 1.0 10.0 20.0 30.0 50.0 70.0

max

=10

3.90

0 50km

min

=-.

0001

9

(Valid:21.1600JST) 5hour 0min

Fist guess

Noheavy rainfall

C C C

Analysis2059JST2039JST2019JST

GPS-PWV5 minute interval

Radial Wind1 minute interval

Surface wind & temperature

10 minute interval

2005.09.04Assimilation window (10 min)

GPS-PWV5 minute interval

Radial Wind1 minute interval

Surface wind & temperature

10 minute interval

Reflectivity1 minute interval

0.1 1.0 10.0 20.0 30.0 50.0 70.0

max

=14

.643

mi

0 50km Rainfall intensity(mm/h)Observation2100JST

Observation

nothing

If reflectivity isover 10 dBZ in the first

guess field andunder 10 dBZ in the

observations, the grid is regarded as the false precipitation to be

li i t d d “0 dBZ”

Assimilate “0 dBZ”

nothing

Assimilation of “0 dBZ” observation

2010 2020 2030 2040 2050 210020JST

Design of the assimilation experimentAnalysis

AcknowledgmentThe GPS precipitable water vapor data are reanalyzed and provided by Mr. Shoji of MRI, the Doppler radar data are provided by Dr. Akaeda of JMA, and the UMIHOTARU surface observation data are provided by the Ministry of the Environment of Japan. The authors express our gratitude to them.

ReferenceKawabata et al, 2007: An Assimilation and Forecasting Experiment of the Nerima Heavy Rainfall with a Cloud-Resolving Nonhydrostatic 4-Dimensiojnal Variational Data Assimilation System, Journal of the Meteorological Society of Japan, Vol. 85, No. 3, 255-276

First guess

failure

eliminated, and “0 dBZ” information is used.

Observational error:normal error x 4

“0 dBZ” observations are NOT real observations.

Without “0 dBZ”

failure

0.1 1.0 10.0 20.0 30.0 40.0 50.0

max

=23

4.16

Prec mm/h (z*= 20m) (Valid:04.2100JST) 0hour 10min

0 50km

Initial : 2005.09.04.1150UTC2100JST