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PolyU Micro-LARGE Observing Water Vapor (WV) Variation During Heavy Precipitation Events in Hong Kong by GPS Tomography Biyan Chen 1 , Zhizhao Liu 1 , Wai-kin Wong 2 , and Wang-chun Woo 2 1 Department of Land Surveying and Geo-Informatics The

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PolyU Micro-LARGE

Observing Water Vapor (WV) Variation During Heavy Precipitation Events in Hong Kong by GPS Tomography

Biyan Chen1, Zhizhao Liu1, Wai-kin Wong2, and Wang-chun Woo2

1Department of Land Surveying and Geo-Informatics

The Hong Kong Polytechnic University 181 Chatham Road South, Hung Hom, Kowloon, Hong Kong E-mail: by.chen@connect.polyu.hk/lszzliu@polyu.edu.hk

2Hong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong

WMO WWRP 4th International Symposium on Nowcasting and Very-short-range Forecast 2016 (WSN16), 25-29 July 2016, Hong Kong

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Outline

  • Research background
  • Principle of WV tomography
  • Observing WV variation during heavy rain

 Case study 1: 22 July 2010  Case study 2: 21 May 2013  Case study 3: 30 March 2014

  • Conclusion
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Significance of WV in meteorology

  • Weather phenomena

 cloud, drizzle, rain, snow, sleet, and hail

  • Latent heat release

 source of atmospheric energy transportation  fuel for storms

  • Water vapor distribution and

variation  evolution of atmospheric storms  the vertical stability of atmosphere Water vapor variation during heavy rain

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Hong Kong

  • Highly populated and extremely

humid coastal city

  • Average humidity: 78%
  • Annual rainfall: 2400 mm
  • More extreme weather events
  • ccur in recent years

 Landslip and urban waterlogging

Climate in Hong Kong

  • To enhance the monitoring of

atmospheric WV

  • 3D WV fields from the

tomography

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WV observation techniques

Atmospheric Water Vapor Observation Numerical weather prediction (NWP) model Aerosol Robotic NETwork (AERONET) sunphotometer Microwave Water Vapor Radiometer (WVR) Radiosonde Global Navigation Satellite System (GNSS)

low operational cost high accuracy all-weather operability high accuracy low temporal resolution high spatial resolution Relative low accuracy good accuracy

  • nly works with direct sunlight

high temporal resolution unavailable rain

  • r heavy fog

Satellite remote sensing

large spatial coverage relative low accuracy

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Outline

  • Research background
  • Principle of WV tomography
  • Observing WV variation during heavy rain

 Case study 1: 22 July 2010  Case study 2: 21 May 2013  Case study 3: 30 March 2014

  • Conclusion
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Principle of WV tomography

  • Tomography technique

enables us to precisely probe the atmosphere:

 3D WV distribution  Under all weather conditions  With high temporal and spatial resolution

  • Inversion process:

 Slant wet delay (SWD)  Wet refractivity Voxel GNSS satellites GNSS receivers troposphere

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GNSS signal delayed by troposphere

Zenith Tropospheric Delay

Zenith tropospheric delay (ZTD): ~90% Zenith hydrostatic delay (ZHD) + ~10% Zenith wet delay (ZWD) Saastamoinen ZHD model: Pressure Latitude Height

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  • GPS data processing

 GPS data processed by Bernese 5.0 software  Zenith tropospheric delay (ZTD), wet delay gradients and the residuals  Parameters are estimated once an hour

Estimation of WV from GPS obs

  • The slant wet delay (SWD) can be derived from:

R G G z f z f ZHD ZTD SWD

W E W N

+ ⋅ + ⋅ ⋅ ∂ ∂ + ⋅ − = )) sin( ) cos( ( ) ( ) (

. .

φ φ

zenith hydrostatic delay satellite zenith distance azimuth angle Wet delay gradient in the northern and eastern directions post-fit residuals

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GPS stations in Hong Kong

Geographical distribution of the 12 GPS stations used in this study

113.8 113.9 114.0 114.1 114.2 114.3 114.4 22.6 22.5 22.4 22.3 22.2 22.1

Longitude (°E) Latitude (°N)

Shenzhen Hong Kong

HKNP HKMW HKOH Radiosonde HKSC HKPC HKSL HKLT HKKT HKFN HKST HKSS HKWS

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Principle of WV tomography

Length of ray in voxel 2 Wet refractivity in voxel 2

x

G G

⋅ = A y

receiver satellite

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Tomography equation

Solved by using Least-Squares method Vertical constraint Horizontal constraint Sun photometer Microwave radiometer Synoptic observations

x H A A A A A A y y y y y y ⋅                       =                      

R s W A N G R s W A N G

NWP GPS

More information about the tomographic modeling, please refer to: Chen, B. and Liu, Z.: Assessing the Performance of Troposphere Tomographic Modeling Using Multi-Source Water Vapor Data During Hong Kong’s Rainy Season from May to October 2013,

  • Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-158, in review, 2016.

Chen, B. and Liu, Z.: Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model, J. Geod., 88(7), 691–703, doi:10.1007/s00190-014- 0715-y, 2014.

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Evolution of water vapor field over Hong Kong solved by tomography with an interval of 30 min

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Outline

  • Research background
  • Principle of WV tomography
  • Observing WV variation during heavy rain

 Case study 1: 22 July 2010  Case study 2: 21 May 2013  Case study 3: 30 March 2014

  • Conclusion
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Case study 1: 22 July 2010

19 July 2010 22 July 2010

Track of the Typhoon Chanthu （颱風燦都）

  • 2900 houses destroyed in

Guangdong province

  • economic losses ~ US$ 350

million in Guangdong province

  • Heavy rain in HK

 UT 07:00~10:30, 22 July 2010 (LT=UT+8)  rainfall of 169 mm  150 mm (UT 08:30 ~10:30)

23 July 2010 21 July 2010 20 July 2010

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Meteorological situation

  • Black contours

represent the geopotential heights.

  • Coloring contours are

relative humidity, which are marked in red

  • Data are from ECMWF

Longitude (°E) Latitude (°N) (a) 22/07/2010 UTC 06:00 500 hPa

5890 5880 5 8 8 5870 5870 5870 5860 5850 5 8 4 5 8 3 5820 5810 5850 5840 5 8 3 5 8 2 90 80 80 8 80 70 8 90 9 90 9 9 90 90 8 8 80 80 60 70 80 70 50 60 7 80 7 8 60 70 80 8 60 60 8 8 7 6 50 4 40 60 70 80 80 7 6 8 80 7 8 80 80 90 90 9 9 90 90 104 108 112 116 120 124 15 20 25 30

Longitude (°E) Latitude (°N) (b) 22/07/2010 UTC 06:00 850 hPa

1450 1460 1470 1480 1490 1 5 1510 1520 1530 1510 1510 1 5 1490 1480 1470 1460 1450 1440 1 4 3 1470 1460 1 4 5 1450 8 5 65 7 75 80 8 8 5 85 8 5 80 8 5 90 85 80 8 5 90 85 85 85 80 85 85 85 85 85 8 5 8 5 8 5 85 8 9 9 9 85 90 80 8 5 9 90 85 80 70 7 5 7 5 8 8 5 90 70 7 5 7 75 80 80 80 8 85 8 80 7 5 70 6 5 60 8 6 6 5 70 75 80 75 80 90 9 8 5 9 90 85 95 95 95 95 104 108 112 116 120 124 15 20 25 30

Hong Kong Hong Kong SOUTH CHINA SEA SOUTH CHINA SEA CHINA CHINA Chanthu Chanthu Longitude (°E) Latitude (°N) (d) 22/07/2010 UTC 12:00 850 hPa

1 4 4 1 4 5 1530 1520 1510 1500 1500 1490 1 4 9 1 4 8 1480 1470 1470 1470 1 4 6 1460 1450 1440 1440 1 4 4 1 4 5 1450 1430 80 75 80 85 8 8 80 85 9 8 5 85 85 80 9 8 75 8 5 6 5 70 75 85 9 80 85 90 70 70 6 5 7 5 80 85 90 95 95 90 90 8 5 80 75 65 70 75 65 70 7 5 7 75 80 85 9 80 85 85 80 7 60 7 5 80 85 80 75 7 7 75 70 80 85 90 7 5 80 9 85 85 85 90 85 90 9 5 9 5 9 5 100 1 100 1 4 4 1450 1 5 3 1520 1510 1510 1500 1500 1490 1480 1470 1 4 6 1450 1 4 4 1440 1450 1460 1470 1 4 8 1490 1470 1430 1440 1450 1 4 6 8 8 5 85 8 90 90 90 8 5 6 5 8 85 85 90 85 8 5 7 5 80 8 8 8 8 70 6 5 70 75 8 90 9 85 80 80 70 70 7 5 85 90 9 9 8 75 75 85 85 85 65 75 65 60 6 5 8 85 7 80 85 85 90 75 7 75 80 8 5 90 95 9 5 9 5 95 95 95 95 95 1 104 108 112 116 120 124 15 20 25 30

Longitude (°E) Latitude (°N) (c) 22/07/2010 UTC 12:00 500 hPa

5 8 9 5880 5 8 8 5870 5870 5860 5860 5850 5850 5 8 4 5840 5840 5830 5 8 2 5 8 3 70 6 5 60 6 7 8 9 7 60 70 60 70 8 90 8 80 9 9 80 8 80 70 80 70 6 5 40 4 50 50 60 70 60 70 60 8 80 70 5 60 70 80 8 70 80 9 8 8 70 5880 90 90 5880 5890 5880 5 8 8 5870 5870 5860 5860 5850 5840 5 8 3 5 8 2 5810 5 8 5 5840 5 8 3 5820 8 9 80 60 70 80 80 70 70 70 60 80 7 60 8 7 6 50 4 40 50 6 80 80 7 7 6 5 5 60 70 80 80 7 7 70 60 80 90 80 90 9 7 70 50 90 80 70 70 90 90 90 104 108 112 116 120 124 15 20 25 30

Hong Kong CHINA CHINA SOUTH CHINA SEA SOUTH CHINA SEA Chanthu Chanthu Hong Kong

UT 06:00, 22 July 2010 UT 12:00, 22 July 2010 Rain: UT 07:00~10:30, 22 July 2010

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380 400 420 440 460 480 ZWD (mm) 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 50 60 70 80 90 100 110 120 130 UTC (21~23 July 2010) ZWD (mm) 10 20 30 40 50 Rainfall (mm) 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 1 2 3 4 5 6 7 8 Height (km)

below 1000 m 1000~2000 m 2000~3000 m 3000~5000 m above 5000 m

20 40 60 80 100 120 140 160 180 200 (b) Wet Refractivity (mm/km)

(c)

(a)

Evolution of atmospheric water vapor resolved by tomography ZWD variations

  • f solid lines are

derived from tomography Dashed lines are from ECMWF

Onset of heavy rain End of heavy rain

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420 440 460 480 ZWD (mm) 6 8 10 12 50 60 70 80 90 100 110 120 130 UTC (22 July 2010) ZWD (mm) 10 20 30 40 50 Rainfall (mm) 6 8 10 12 1 2 3 4 5 6 7 8 Height (km)

below 1000 m 1000~2000 m 2000~3000 m 3000~5000 m above 5000 m

20 40 60 80 100 120 140 160 180 200 (b) Wet Refractivity (mm/km)

(c)

(a)

Onset of heavy rain End of heavy rain

460 mm 434 mm 479 mm 417 mm 89 mm 118 mm

ZWD above 5 km increased significantly, due to massive amount of water vapor flowing into the upper- air

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Wind profiler observation

strong southeasterly winds

  • ccurred in altitudes above 5 km

Local Time Rain: LT 15:00~18:30, 22 July 2010

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Case study 2: 21 May 2013

  • A trough of low pressure

lingering over the coast of Guangdong province

  • Heavy rain in HK

 UT 17:30, 21 May to UT 02:00, 22 May 2013  rainfall of 190 mm  102 mm (UT 19:30 ~20:30), 21 May 2013

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Radar reflectivity images for the height of 3 km Local Time Rain: LT 01:30~10:00, 22 May 2013

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300 330 360 390 420 ZWD (mm) 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 20 30 40 50 60 70 80 90 100 110 120 UTC (20~22 May 2013) ZWD (mm) 10 20 30 40 50 60 Rainfall (mm) 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 1 2 3 4 5 6 7 8 Height (km) 20 40 60 80 100 120 140

below 1000 m 1000~2000 m 2000~3000 m 3000~5000 m above 5000 m

(c) (b) (a) Wet Refractivity (mm/km)

Evolution of atmospheric water vapor resolved by tomography

Onset of heavy rain End of heavy rain

ZWD variations

  • f solid lines are

derived from tomography Dashed lines are from ECMWF

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PolyU Micro-LARGE Onset of heavy rain End of heavy rain

370 380 390 400 410 420 ZWD (mm) 14 16 18 20 22 2 4 6 40 50 60 70 80 90 100 110 120 UTC (20~22 May 2013) ZWD (mm) 10 20 30 40 50 60 Rainfall (mm) 14 16 18 20 22 2 4 6 1 2 3 4 5 6 7 8 Height (km) 20 40 60 80 100 120 140

below 1000 m 1000~2000 m 2000~3000 m 3000~5000 m above 5000 m

(c) (b) (a) Wet Refractivity (mm/km)

420 mm 382 mm 416 mm 94 mm

ZWD in the layer 3~5 km increased, validated by wind profiler

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Wind profiler observation

Active southwesterly winds persisted with gales force or above (i.e. exceeding 34 knots and wind barbs in green) Local Time Rain: LT 01:30~10:00, 22 May 2013

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Case study 3: 30 March 2014

  • Active trough of low

pressure dominated the southern China

  • Heavy rain in HK

 UT 11:00~15:00, 30 March 2013  rainfall more than 100 mm  56 mm (UT 13:00 ~14:00), 30 March 2014, highest hourly record of March rainfall since 1884

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Radar reflectivity images for the height of 3 km Local Time Rain: LT 19:00~23:00, 30 March 2013

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240 270 300 330 360 390 420 ZWD (mm) 6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22 10 20 30 40 50 60 70 80 90 100 110 120 UTC (29~31 March 2014) ZWD (mm) 10 20 30 40 50 Rainfall (mm)

below 1000 m 1000~2000 m 2000~3000 m 3000~5000 m above 5000 m

6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22 1 2 3 4 5 6 7 8

Height (km)

20 40 60 80 100 120 Wet Refractivity (mm/km) (c) (b) (a)

Evolution of atmospheric water vapor resolved by tomography

Onset of heavy rain End of heavy rain

ZWD variations

  • f solid lines are

derived from tomography Dashed lines are from ECMWF

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300 330 360 390 420 ZWD (mm) 8 10 12 14 16 18 30 40 50 60 70 80 90 100 110 120 UTC (30 March 2014) ZWD (mm) 10 20 30 40 50 Rainfall (mm)

below 1000 m 1000~2000 m 2000~3000 m 3000~5000 m above 5000 m

8 10 12 14 16 18 1 2 3 4 5 6 7 8

Height (km)

20 40 60 80 100 120 Wet Refractivity (mm/km) (c) (b) (a)

323 mm 415 mm 337 mm 118 mm 44 mm

Onset of heavy rain End of heavy rain

ZWD above 5 km increased remarkably from 44 mm to 118 mm

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Wind profiler observation

passage of upper-level southwesterly jet Local Time Rain: LT 19:00~23:00, 30 March 2014

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Outline

  • Research background
  • Principle of WV tomography
  • Observing WV variation during heavy rain

 Case study 1: 22 July 2010  Case study 2: 21 May 2013  Case study 3: 30 March 2014

  • Conclusion
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Conclusions drawn from this study

  • Total ZWD generally increases prior to the onset of a heavy

precipitation and decreases after the heavy rain pouring

  • Maximum total ZWD in all the three cases exceeded 400 mm prior

to the onset of heavy rain

  • The fluctuations in the total ZWD largely attribute to WV variations

in the layers above 3 km

  • WV fluctuations in the altitude layers, especially above 3 km:

precursors to heavy precipitations.

  • If the total ZWD continues to increase during a rainfall, heavier

precipitation is likely to occur

  • WV variations detected by the tomography are well validated by

ERA-Interim reanalysis data, weather radar and wind profiler

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