ତ౵ഛ‫ܨ‬ထ༭

Numerical Weather Predictionനඤȁ၌হಅඤȁ݅ྶ‫ܨ‬ય಩Toshiharu Tauchi
Yoshiaki Takeuchi
Japan Meteorological Agency

7.1 ତ౵ഛ‫ܨ‬ထ༭͈‫ٽ‬ැGeneral Concept of Numerical Weather Prediction
ఊုSun‫͈׌‬໦ື
Cloud distribution༶ৣͥ͢ͅ‫ح‬෎͂႖‫ݕ‬෎ȁHeat༶ৣ႖‫ݕ‬
Radiative coolingୱຕCryosphereఊု࢕ͥ͢ͅ‫ح‬෎
Sunlight heating౷ࠁȆ૒୆
౷ນ࿂ً೾

ࣛକً೾

ఱ‫͈ܨ‬ၠͦ
Ȫ‫׋‬൲༷೾৆̈́̓ȫ

Heating and Coolingthrough radiation

Atmospheric transportation(Dynamic equation)෎Ȇ‫׋‬൲ၾ
Heat and momentumକ૊‫ܨ‬
Water vapor

Precipitation
process෎Ȇକ૊‫ܨ‬ࣛକȁPrecipitation

Heat andwater vaporఱ‫ٮޏܨ‬௄
Atmospheric boundary layer

Terrain, vegetation,
land surface process٬ຕȁSea iceၘ౷ȁLand٬ဢȁOcean଎ġġġ7.1.1 ତ౵ഛ‫ܨ‬ထ༭ͅဥ̞ͣͦͥ‫ڒ‬ঊ࿌͈‫ٽ‬ැ଎
Fig. 7.1.1 Schematic diagram of grid points used in numerical weather predictionତ౵ഛ‫ܨ‬ထ༭ĩତ౵ထ༭Ī͉Ȃఱ‫͈ܨ‬ႁ‫͞ڠ‬෎ႁ‫͈̓̈́ڠ‬໤ၑ༹͈௱ͅ‫̞̀̿ܖ‬໓͞‫أܨ‬͈̈́̓শ‫ۼ‬་‫ͬا‬ତ౵ࠗॳ̱̀Ȃ੿ြ͈ఱ‫ે͈ܨ‬ఠͬထ௶̳͈̳ͥ́͜ȃତ౵ഛ‫ܨ‬ထ༭࣐̠ͬ਀ਜ਼͉Ȃܰ௱ୃ̱̩໼̺ͭ‫ڒ‬ঊ́ఱ‫ͬܨ‬ळ̥̩ໞ̞Ȃ̷͈͈͌͂̾͌͂̾‫ڒ‬ঊത͈໓͞‫͈̓̈́أܨ‬౵͈ଔ֊ͬΑȜΩȜ΋ϋάνȜΉထ௶ࠗॳ̱̳͘ȃ̭͈ࠗॳ͉́Ȃ५‫͈̓̈́ڡ‬౷ࠁ͈‫ޣג‬Ȃఊု̥͈ͣ༶ৣȂ౷ນ࿂͈ླྀड़Ȃఱ‫͂ܨ‬౷ນ࿂͈෎͞କ૊‫۟࢐͈ܨ‬Ȃକ૊‫ࣛࠫ͞ާ͈ܨ‬କ͈̯̰̈́̓̈́࢘͘͘‫ࣉ̦ض‬ၪ̯̞̳ͦ̀͘ȃ଎ġġġ7.1.2 ତ౵ഛ‫ܨ‬ထ༭κΟσ͈‫ٽ‬ැ଎
Fig. 7.1.2 Schematic diagram of numerical weather prediction model

Numerical weather prediction predicts the future state of the atmosphere by calculating the timechange of atmospheric variables such as temperature, wind, humidity and surface pressure based ondynamics and thermodynamics in the atmosphere.
In numerical weather prediction, the global atmosphere is digitized at regular grid points in threedimensional space, and the atmospheric variables at each grid point are calculated for each time stepby using a supercomputer. The calculation includes many processes related to orographic effects,
solar radiation, thermal radiation, surface drag, water vapor condensation, precipitation, and heat andmoisture exchange between the atmosphere and the surface of the earth.

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7.2 ତ౵ഛ‫ܨ‬ထ༭͈ၠͦ

General Flow of Numerical Weather Prediction

GroundRadarRainGauge

Ship andBuoy౷ષτȜΘַၾࠗ஑ต͂ήͼ

SatelliteAirplane‫ିמ‬࣎ߗ‫ܥ‬

SondeΖϋΟ
Ȫ‫ݩܨ‬۷௶ȫObservation

Validation۷௶࠿બ

Observation
SystemData
Assimilation

ForecastModel۷௶ΏΑΞθΟȜΗ൳‫ا‬ထ༭κΟσForecastထ༭

Objective Analysis‫ݖ‬۷ٜଢ଼

Quality
ControlFirstGuess຦ৗ‫ۯ‬ၑల֚ଔ೰౵଎ġġġ7.2.1 ତ౵ഛ‫ܨ‬ထ༭͈ၠͦ
Fig. 7.2.1 Flowchart of numerical weather predictionྚြ͈ထ௶ࠗॳ̳̹͉ͬͥ͛ͅȂ̴࡛͘ह͈ેఠͬ౶ͥຈါ̦̜̳ͤ͘ȃତ౵ഛ‫ܨ‬ထ༭͉́Ȃଲ‫ٮ‬ಎ́۷௶̯̹ͦ‫ܨ‬યΟȜΗͬਬ͛̀ੜၑ࣐̞ͬȂ࡛ह͈ેఠȪ੝‫ܢ‬౵̞̠͂ȫͬ‫̳͛͘ݥ‬ȃ̷͈ࢃȂै଼̯̹ͦ੝‫ܢ‬౵ͬ‫ྚͅܖ‬ြ͈ထ௶ࠗॳ࣐̞̳ͬ͘ȃ̭͈ထ௶͈ୃ‫͉̯ږ‬੝‫ܢ‬౵͈ୃ‫̯ͅږ‬ջం̱̳͘ȃ̭͈̹͛Ȃୈഽ͈ၻ̞۷௶̧̺̫ͬ́ͥఉ̩Ȃ̷̧̱̺̫̀́ͥߗ‫ۼ‬എͅ޳ৗͅං̭̦ͥ͂ਹါ̳͂̈́ͤ͘ȃTo calculate a future atmospheric state accurately, the current state should be given at an initialtime. In numerical weather prediction, various kinds of meteorological observations all over the world
are gathered and processed to make a realistic current status, i.e., the initial value. This is followedby the prediction calculation based on the initial value. Accuracy of the prediction relies on theaccuracy of the initial value as well as the prediction model, so it is essential to obtain homogeneousand sufficient observations with high quality.

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7.3 ତ౵ഛ‫ܨ‬ထ༭ͅ၌ဥ̯ͦͥ۷௶

Observations for Numerical Weather Prediction

౷ષ۷௶Surface Observations

0306090120150

ࣞ௄۷௶Upper Observations

180 210 240 270
SYNOP METAR

300 330 360 0
SHIP BUOY‫ିמ‬٬ષ໓۷௶
Microwave Scatterometer

30

60

90

Aug. 01, 2004 00:00UTC

120 150 180 210 240 270 300 330 360
TEMP PILOT WIND PROFILER AVIATIONତ౵ഛ‫ܨ‬ထ༭͉́Ȃအș̈́ਅ႒͈۷௶ΟȜΗͬ၌ဥ̱̞̳̀͘ȃಿා̹ͩ̽̀ͅȂ౷ષ͈́۷௶̤͍͢ρΐ΂ΖϋΟͥ͢ͅఱ‫ܨ‬ષ௄͈۷௶̦࣐̤ͩͦ̀ͤȂତ౵ഛ‫ܨ‬ထ༭ͅ၌ဥ̯̞̳ͦ̀͘ȃ̹͘Ȃ۷௶ܿ੅͈อోͤ͢ͅȂτȜΘȜ͞;ͻϋΡίυέ͹ͼρ̞̹͂̽ςκȜΠΓϋΏϋΈ۷௶̤͍͢ྦྷ‫ͅܥߗ࣎ۼ‬୭౾̯̹ͦ۷௶ΟȜΗ͈၌ဥ͜ૺ̞̳ͭ́͘ȃ̱̥̱Ȃ̭͈ͦͣΟȜΗ͉Ȃगน͈̈́̓ুட۪‫̞̱ࡕ͈ޏ‬ાਫ਼͞Ȃ࣎ߗႹ̞́̈́٬ષ͉͕́͂ͭ̓ං̵̧̭̦ͥ͂́ͭ͘ȃ̭͈̹͛߃ා͉́Ȃֳಐ̥ͣ౷‫ݩ‬ષͬ޳ৗͅ۷௶̧́ͥ‫ିמ‬ΟȜΗ͈၌ဥܿ੅‫ٳ‬อ̦ૺ̞̳͛ͣͦ̀͘ȃ‫ࣞିמ‬௄໓۷௶
Satellite Wind Vector
Numerical weather prediction uses various types of observations such as
ground-based and atmospheric-sounding observations with radiosonde formany years. With the recent development of observation techniques such as
radar, wind profilers, and aeronautical instruments, utilization ofobservational data for numerical weather prediction is expanding. However,even the recent techniques above will not provide enough data points overoceans and deserts. Therefore, utilization of homogeneous data by spacebased observation has recently become important.

0306090120150180210240270300330 360 0SCAT

ζͼ·υ෨༶ৣࠗ۷௶
Microwave Imager

0306090120150306090120150180210

240270300 330 360
IR VIS WV

240270 300
AMSU-A‫؀‬ೄౝऔࠗ۷௶
Microwave / Infrared Sounder

180

210

240 270 300
SSM/I TMI

330 360 0AMSR-E

306090120150180210

330 360
AMSU-B଎ġġġ7.3.1 ତ౵ഛ‫ܨ‬ထ༭́၌ဥ̯ͦͥ۷௶ΟȜΗ̷͈͂໦ື͈႕
Fig. 7.3.1 Examples of observation data and their distribution used by numerical weather prediction

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7.4 ତ౵ഛ‫ܨ‬ထ༭̤̫ͥͅ‫ିמ‬ΟȜΗ͈࿨‫ڬ‬

Role of Satellite Data in Numerical Weather Prediction

Temperature, Humidity[ IR, MW Sounder ]Wind Vector[ VIS, IR, WV Imager ]Precipitable Water[ MW Imager ]Rain Rate[ MW Imager ]Sea Surface Wind
[ MW Scatterometer ]Soil Moisture[ MW Imager ]Snow Depth[ MW Imager ]Sea IceConcentration[ MW Imager ]Sea Surface Temperature[ IR, MW Imager ]Land Surface

Snow

Sea IceSea Surface଎ġġġ7.4.1 ତ౵ഛ‫ܨ‬ထ༭́ဥ̞ͣͦͥအș̈́‫ିמ‬۷௶ΟȜΗ
Fig. 7.4.1 Various satellite measurements used in numerical weather prediction̩̻͌͂ͅȶ‫ିמ‬ȷ̞͂̽̀͜Ȃအș̈́Γϋ΍̦ంह̱Ȃ̷̸ͦͦ۷௶̧́ͥ໤ၑၾ͞౷
ത̦։̳̈́ͤ͘ȃ଎͉ତ౵ഛ‫ܨ‬ထ༭ͅঀͩͦͥ‫ିמ‬Γϋ΍͂໤ၑၾ͈చ؊ͬນ̱̞̳̀͘ȃ̭͈̠͢ͅȂ‫ିמ‬ΟȜΗͬໝତ၌ဥ̳̭ͥ͂́Ȃఉ̩͈ႀ֖́۷௶ΟȜΗ̦ං̠ͣͦͥ͢
̞̳̈́̽̀͘ͅȃζͼ·υ෨༶ৣࠗAMSR-E̥͉ͣȂఱ‫ئܨ‬௄͈କ૊‫̳ͥ۾ͅܨ‬۷௶͞Ȃ౷ນ࿂ͅ‫̳ͥ۾‬۷௶ૂ༭ͬං̧̭̦̳ͥ͂́͘ȃ

Satellite observation uses a wide variety and number of instruments with different performances,observation capabilities, and observation regions. Fig. 7.4.1 illustrates the satellite data distribution
used in numerical weather prediction. Synergetic use of many satellite instruments satisfies
requirements for data coverage in numerical weather prediction. A microwave radiometer, such as
AMSR-E, enables us to observe hydrologic variables such as water vapor, cloud liquid water, andrain rates in the lower atmosphere as well as surface variables such as sea surface temperature, sea
surface wind, sea ice concentration, snow depth, and soil moisture.

46

7.5 AMSR-E ‫ࣛخ‬କၾȆࣛକၾΟȜΗ͈൳‫ا‬৘ࡑ႕Experiment of AMSR-E Precipitable Water and Rain Rate Assimilation

Analyzed TCPW field (July 17, 2004 18:00UTC)
60

8650

42040-2-430

w/o AMSR-E
AMSR-E data coverage

w/ AMSR-E

Fukui
20[mm]

Analyzed TCPW

TCPW difference(w/ AMSR-E - w/o AMSR-E)

Improvement of the forecast (3hours rainfall)
w/o AMSR-E

w/ AMSR-E

RA Obs.

FT = 3-6-8[mm]ఱַ̦ࣛͥೄஜ͈AMSR-E‫ࣛخ‬କၾΟȜΗͬତ౵ഛ‫ܨ‬ထ༭ͅ၌ဥ̳̭ͥ͂́ȂAMSR-Eͬ၌ဥ̧̱̥̹̈́̽͂ͅ๤͓̀۷௶ͅ߃̞ࣛକၾ͈ထ༭̳̭̦ͬͥ͂‫خ‬ෝ̳̈́ͤ͘ͅȃ
଎7.5.1͉2004ා7࠮͈໛ַ͈֔ࣧম႕̳́ȃ඾ུ٬̥ͣ൐Ώ΢٬̥̫̀ͅAMSR-Eͥ͢ͅ‫ࣛخ‬କၾΟȜΗ̦၌ဥ̯̭ͦͥ͂́Ȃ໛֔ັ߃Ȫ࿦֣ȫַ͈̦‫̩ޑ‬ထே̯ͦͥ͂‫ͅވ‬Ȃ
AMSR-EΟȜῌ̧̦̞̈́͂ࡉ̹ͣͦ५࢛ࡇັ߃ַ͈͈֖ܺȪఔ‫׫‬ȫ̦̩̈́̈́ͤȂထே̦٨஝̯̱̹ͦ͘ȃ̭͈̠͢ͅ૧̹̈́۷௶ΟȜΗͬ೏‫̱̀ح‬ͤ͢ୃ‫̈́ږ‬੝‫ܢ‬౵ͬං̭ͥ͂́Ȃͤ͢ୈഽ͈̞ࣞထ༭ͬං̧̭̦̳ͥ͂́͘ȃIn numerical weather prediction, forecasts ofheavy rain improve when we assimilate the AMSRE precipitable water data just before we expect theheavy rain to occur.

FT = 6Fig. 7.5.1 presents the Fukui heavy rain event
on July 17, 2004. With the AMSR-E data obtained
around the Japan Sea and the East China Sea,the forecast over the Fukui area (denoted byarrow) predicted heavier rain, and the forecast
over the west part of the Japan Sea (denoted byFT = 9ellipse) predicted less rain. Both agreed with theobservations.
-0.5

0.5

1.0

5.0

10.0

20.0

30.0 100.0 [mm/3hour]଎ġġġ7.5.1 ໹଼16ා7࠮໛ַ͈֔ࣧম႕ͅచ̳ͥAMSR-EΟȜΗ͈൳‫ا‬৘ࡑ႕Fig. 7.5.1 Example of AMSR-E data assimilation experiment for Fukui heavy rain event in July 2004

The AMSR-E observation data gives a moreaccurate initial state and thus a more accurate
prediction.

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