RECENT JAPANESE RESEARCH ON OROGRAPHIC SNOW CLOUD MODIFICATION FOR WATER RESOURCES AUGMENTATION Masataka MURAKAMI*, Narihiro ORIKASA*, Mizuho HOSHIMOTO*, Ken-ichi KUSUNOKI*, Masumi SEKI**, and Akihiro IKEDA*** *Meteorological Research Institute，Japan Meteorological Agency **Tone River Dams Integrated Control Office ***Idea Co., LTD.
Water Shortage in Summer of 1994 Yagisawa Dam OccurrenceOccurrence frequencyfrequency ofof waterwater shortageshortage inin JapanJapan (1983(1983--2002)2002) OrographicOrographic SnowSnow CloudCloud ModificationModification ProjectProject (MRI, Tone River Dams Integrated Control Office)
Feasibility Study (1990-1994) Routinely available data, microwave radiometer
PHASE I (1994-1997) HYVIS, rawinsonde, X-band Doppler radar, ground-based microphysical obs. & microwave radiometer Microphysical structures and seedability, No cloud seeding
PHASE II (1997-2000) An instrumented aircraft (B200), HYVIS, X-band Doppler radar Ka-band, instrumented 4-wheel drive van, ground-based microphysical obs. & microwave radiometer Small scale cloud seeding
PHASE III (2000-2003) An instrumented aircraft (C404), a seeding aircraft, HYVIS, X-band Doppler radar, Ka-band, ground-based microphysical obs. & microwave radiometer Small scale, repeated cloud seeding (still research basis) HypothesesHypotheses ofof SnowSnow CloudCloud ModificationModification TopographyTopography andand ObservationObservation FacilitiesFacilities
Shiozawa site (SO) Microwave radiometer 2250 HYVIS 2000 x-band Doppler radar 1750 ka-band Doppler radar 1500 1250 Shimizu site (SM) 1000 Microwave radiometer 750 Microphysical measurement 500 250 ﾟN Yagisawa dam site (YD) 38 JAPAN SEA SO 0 Microwave radiometer YD x-band Doppler radar (2001) SM ka-band Doppler radar (2001) Catchment of dams Microphysical measurement
KANTO PLAIN 0ﾟE 14 Aircraft for in-situ measurement ﾟN 37 (operated from Niigata AP)
Aircraft for cloud seeding 9ﾟE 13 (operated from Nagoya AP)
ﾟN 8ﾟE 36 13 OrographicOrographic SnowSnow CloudCloud ModificationModification ProjectProject (MRI, Tone River Dams Integrated Control Office)
Dry-ice seeding GMS
Rawin Sonde Hydrometeor Videosonde In-cloud observation
Microwave The Sea The Uonuma Radiometer The Pacific of Japan Hills coast
Shimizu Microwave Radiometer
Naramata catchment area
X-band Doppler radar Anemometer
Ka-band radar Rain Gauge Senjouji (Radar site) Shiozawa ObservationObservation FacilitiesFacilities INNERINNER STRUCTURESSTRUCTURES OFOF OROGRAPHICOROGRAPHIC SNOWSNOW CLOUDSCLOUDS (HYVIS(HYVIS ObsObs.).) MonthlMonthlyy appearance Freq. of Clouds with High Seedability
48 DEFINITION 44 清水（三国川ダム）－風上側斜面 Ttop:－5～－25℃ 40 1994～1995年 1995～1996年 1996～1997年 1997～1998年 1998～1999年 1999～2000年 Htop > 2.5km 36 2000～2001年 2001～2002年 2002～2003年 ● 9 ヵ年平均 Cloud amount > 9/10 32 1hr ave. LWP > 0.2mm 28 24
METHOD 20 16 Ttop,Htop,CA < GMS IR data 12 種蒔き有効雲の出現率(%) LWP < Microwave radiometer 8 4 0 11月 12月 1月 2月 3月 4月 12月～3月 SM (WINDWARD SLOPE)
48 48 44 塩沢（十日町，六日町）－風上側平野部 44 矢木沢ダム（奈良俣ダム）－風下側斜面 40 1994～1995年 1995～1996年 1996～1997年 40 1994～1995年 1995～1996年 1996～1997年 1997～1998年 1998～1999年 1999～2000年 1997～1998年 1998～1999年 1999～2000年 36 2000～2001年 2001～2002年 2002～2003年 ● 9 ヵ年平均 36 2000～2001年 2001～2002年 2002～2003年 ● 9 ヵ年平均 32 32 28 28 24 24
20 20 16 16 種蒔き有効雲の出現率(%)
種蒔き有効雲の出現率(%) 12 12 8 8 4 4 0 0 11月 12月 1月 2月 3月 4月 12月～3月 11月 12月 1月 2月 3月 4月 12月～3月 SO (WINDWARD FOOT) YD (LEEWARD SLOPE) A/CA/C SEEDINGSEEDING EXPERIMENTEXPERIMENT ChChangesanges iinn MiMicrophysicalcrophysical SStructurestructures DueDue toto DryDry--IceIce SeedingSeeding Changes in 2DC Conc. and Reflectivity due to Dry-Ice Seeding
10000 concic(ave) concic(max) concic(min) concic(ave)_surroundings concic(ave) concic(ave)_surroundings Ｌ） / 1000 y = 143.11e4E-05x （
100
10 2DC CONCENTRATION
y = 21.319e-4E-05x WINDWARD SUMMIT LEEWARD 1 30000 20000 10000 0 -10000 -20000 DISTANCE FROM DEVIDE （ｍ） 40 dbz3c(ave) dbz3c(max) dbz3c(min) dbz3c(ave)_surroundings dbz3c(ave) dbz(ave)_surroundings 30 y = -0.0002x + 11.876
20
10
0 y = -7E-05x + 9.0167
-10
-20 RADAR REFLECTIVITY (dBZ) REFLECTIVITY RADAR
-30 30000 20000 10000 0 -10000 -20000 DISTANCE FROM DEVIDE （ｍ） RHIRHI ofof KaKa--bandband RadarRadar ReflectivityReflectivity andand ExpectedExpected PositionPosition ofof SeedingSeeding CurtainsCurtains
Position of seeding curtain ModelModel DescriptionDescription
EXPERIMENT 0h 10h 16h No Seeding
Continuous Seeding Intermittent Seeding
Seeding for 5 sec every 5 min. VerticalVertical CrossCross SectionSection ofof SnowSnow CloudsClouds NO SEEDING SEEDING SEEDING CURTAIN
Qc depletion
Qs production
Qs production SeedingSeeding EffectsEffects onon SurfaceSurface PrecipPrecip.. SensitivitySensitivity ExperimentsExperiments OPTIMALOPTIMAL SEEDINGSEEDING POSITIONPOSITION
90
80 Htop=5km
70 Htop=4km Htop=3km
VIDE(km) 60
ON 50 TI
40
NG POSI NG 30
SEEDI 20
10 NDWARD DISTANCE FROM DI FROM DISTANCE NDWARD
WI 0 0 5 10 15 20 25 30 MEAN WIND SPEED OVER WI NDWARD SLOPE (m /s) OPTIMALOPTIMAL SEEDINGSEEDING RATERATE
10000
/s) 1000 3
100
10 Ns=0.1 SEEDING RATE (#/m Ns=1.0 Ns=10.0 1 0 0.2 0.4 0.6 0.8 1 LWP OVER WINDWARD SLOPE (mm) 2D2D SeedingSeeding ExperimentsExperiments (Winter(Winter ofof 19961996--1997)1997)
ＧＡＮＡＬの風（風向>40および風向<220)の場合 年 1996年 1997年 1997年 1997年 月 12月 １月２月３月 時391521391521391521391521 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム 風上 ダム １日 -0.31.0 -1.20.0-0.41.30.10.9 -1.10.90.01.3 ２日 -1.80.5-1.92.9 -0.41.6 -0.73.70.00.0 -2.32.5 -2.14.90.10.3 ３日0.34.40.12.1 -1.04.7-0.92.1 -0.84.1 -0.54.4 -1.22.1 -0.82.6 ４日0.00.4 -0.54.6-0.55.1-1.30.7-1.73.0 -0.74.90.73.00.13.70.75.6 ５日0.00.00.10.0 0.31.71.04.7 ６日0.30.3-3.67.90.20.50.01.1 -0.92.3 ７日0.00.10.41.4-0.62.5 -0.72.70.01.80.31.60.52.80.32.2 ８日 -1.91.50.23.0 -0.20.80.00.6 -0.43.70.00.00.22.3 ９日0.01.90.32.30.31.40.21.3 -0.10.60.14.00.25.1 １０日 -0.12.7-0.21.0 -0.32.40.51.8 １１日0.31.60.33.60.74.80.62.20.51.90.13.10.31.0 １２日0.32.30.62.40.21.40.44.70.00.10.31.00.40.9-0.81.9-0.72.6 -1.21.8 -0.93.9 １３日0.43.4 -0.42.60.00.00.10.7 １４日0.30.90.36.60.93.40.14.8 -0.65.0 -0.22.0 １５日 -0.14.10.24.3 １６日 0.32.10.51.3 -0.55.40.85.4 １７日 0.04.10.31.7 １８日1.47.6-3.17.7 -1.86.1 -0.10.6-0.22.4 -0.54.9 -0.24.40.31.8 １９日0.30.31.00.6 -1.4-1.0 -6.72.30.05.7-1.4 -1.5-0.62.1 ２０日 -0.40.80.12.4 ２１日0.41.0-0.10.2-0.51.7 -0.61.7 -0.60.9 ２２日1.3 -0.5-1.51.7 -1.22.0 -0.65.2-0.60.2-1.12.2 -0.63.80.43.10.20.5 ２３日1.52.6-1.12.5 -1.36.80.26.30.72.70.84.90.93.30.62.20.71.70.40.90.30.90.00.0 -0.22.7 ２４日0.11.40.52.20.10.1 ２５日0.02.8 -0.15.1 -0.63.5-1.11.6 -1.25.0 -1.33.1 ２６日 -0.15.4-0.75.1 -0.83.90.54.1 -0.63.6-1.34.0 -1.63.5 -0.35.51.55.40.21.90.10.0 ２７日0.02.4-0.44.9 -0.13.7 ２８日-1.14.4 -0.74.60.38.0 ２９日 -0.71.9-1.51.7 ３０日0.51.0 -0.22.20.23.40.46.5 -1.16.05.12.3 ３１日0.61.7 0.41.20.42.7
凡例 Value＞４ ２
No Seeding 1400.0 All S e e d ing 1200.0 Selected Seeding
1000.0
800.0
600.0 PRECIP.(mm) 400.0
200.0
0.0 W INDW ARD CATCHM ENT LEEW ARD 3D3D SeedingSeeding ExperimentsExperiments ((QcQc ++ NiNi ++ QsQs ++ QgQg)) CONCLUSIONS HYVIS observations showed the existence of two types of snow clouds with high seedability. Snow clouds of type A : Seeding is likely to result in an enhancement of precipitation efficiency. Snow clouds of type B : Overseeding is likely to result in the shift of precipitation area downwind.
Ttop (GMS IR data) & 1-hr ave. LWP (microwave radiometers) showed that: Snow clouds of type A appeared in Nov., Dec. and Mar. Snow cloud of type B appeared in Dec., Jan. and Feb. The total appearance freq. (A+B) reached 15-20 % of the time
A/C seeding experiments demonstrated: Generation of numerous tiny crystals by dry-ice seeding Subsequent growth of ice crystals up to mm-size precipitation particles.
Numerical simulations: Seeding would result in a significant increase of surface precipitation over the catchment area. With an ideal seeding method, total precipitation during winter months could be augmented by 30 – 40 %.
PlanPlan ofof JCSEPAJCSEPA
z Investigate the causes of drought at different areas in Japan by analyzing past meteorological and hydrological data z Sophisticate WM technology for orographic snow clouds – Monitoring technique of clouds with high seedability – Physical & statistical evaluation techniques of seeding effects – Evaluate the possibility of ground-based seeding z Investigate the possibility of rain enhancement in warm season – Monitoring technique of clouds with high seedability – Appearance frequency of clouds with high seedability – Possibility of glaciogenic seeding – Possibility of hygroscopic seeding z Evaluate the effects of cloud seeing on drought mitigation and water resource management by using a combination of NHM and hydrological model. – Sophisticated 2-moment bulk microphysics parameterization with seeding materials as prognostic variable – New bin microphysics scheme with aerosol (CCN) as prognostic variable for hygroscopic seeding experiments ThankThank youyou forfor youryour attentionattention !! COMPARISON OF WINTER PRECIPITATION FROM NHM SIMULATION AND RADAR OBSERVATION VerticalVertical crosscross sectionsection ofof orographicorographic snowsnow cloudcloud
(a) (dBZ) Conventional Radar Portable Radar
(b) (ms-1)
Distance (km)
:Radar positions Mean wind direction : WindWind ProfilerProfiler (1.3(1.3 GHz)GHz) (Windward(Windward Foot)Foot) MicrowaveMicrowave radiometerradiometer && MRRMRR(24GHz)(24GHz) （（WindwardWindward slopeslope）） MRR,MRR, OpticalOptical DisdrometerDisdrometer && POSSPOSS (10.5GHz)(10.5GHz) (Catchment)(Catchment) PrecipPrecip.. GaugeGauge (Tipping(Tipping--bucket,bucket, ElectricElectric balance),balance), 2D2D GreyGrey probeprobe (Catchment)(Catchment) SnowSnow CloudsClouds duringduring ColdCold AirmassAirmass OutbreaksOutbreaks OrographicOrographic SnowSnow CloudCloud ModificationModification ProjectProject (PHASE(PHASE IV:IV: 20032003--2006)2006) (PHASE(PHASE V:V: 20062006--2009)2009) (MRI, Tone River Dams Integrated Control Office)
PROBLEMS TO BE SOLVED FOR OPERATIONAL CLOUD SEEDING Water shortage (drought) in Japan is transient, but not chronic.
Keep the snowfall amount just more than usual (the average). (People do not want to have too much snow by cloud seeding)
To do so, need accurate measurements of snowfall in deep mountain areas (catchment).
To accurately estimate snowfall in deep mountain areas; - improve radar observation techniques (detect the echoes near surface, Z-R relation) - validate NHM with observation data and improve NHM WaterWater rationingrationing periodsperiods inin KantoKanto AreaArea FieldField CampaignCampaign InIn Dec.Dec. 20012001
WINDWARD PLAINS LEEWARD CATCHMENT
LEEWARD CATCHMENT LEEWARD CATCHMENT RequisitesRequisites forfor cloudsclouds withwith highhigh seedabilitiesseedabilities
For precip. enhancement
For shift of precip. area