Sensing the thermal PBL evolution in complex terrain using ... · • HATPRO (RPG) • Passive...
Transcript of Sensing the thermal PBL evolution in complex terrain using ... · • HATPRO (RPG) • Passive...
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Sensing the thermal PBL evolution in complex terrain using a passive
microwave profiler
Ivana Stiperski, Giovanni Massaro, Mathias W. Rotach
Institute for Meteorology and Geophysics
University of Innsbruck
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• Platform for studying boundary layer processes in complex terrain • Two pillars:
1. Measurements 2. Modelling
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Measurements: • In situ turbulence towers • Information on PBL evolution and mean structure (stability) • Information throughout the PBL • Testing model performance → remote sensing → Passive Microwave Temperature/Humidity Profiler
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Passive Microwave Temperature Humidity Profiler
• HATPRO (RPG)
• Passive Microwave
• Deriving temperature and humidity profile from brightness temperatures
• 14 channels, two channel bands
• Accuracy according to manufacturer:
0.25K 0-500m
0.5K 500-1200m
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Passive Microwave Temperature Humidity Profiler
• Modes: Zenith vs. scanning mode
Scanning mode (5-90°)
→ PBL structure
→ Optically thick channels
• Temporal resolution: 5 min
• Vertical resolution: 10 m ground
200 m 2000 m
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HATPRO in complex terrain
• i-Box: first time used continuously over several years
• Advantages:
→ passive
→ good temporal resolution (vs. soundings)
→ operates in all conditions (thick clouds)
→ vertical resolution comparable to NWP
• Challenges:
→ specific calibration for complex terrain needed
→ near surface inhomogeneity
→ complex vertical structure with multiple inversions
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Retrieval algorithm
→ Temperature is the most likely at this level of the profiler
Training on historic sounding
Brightness Temperatures
Calibration coefficients
Regression
Radiative Transfer model
Brightness temperatures from HATPRO
Temperature profile from HATPRO
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Improving the retrieval algorithm
• Original factory (RPG)
• Low resolution soundings
• Linear regression
• New (IMGI)
• High resolution soundings
• Surface pressure as extra regressor
• Quadratic regression
76 days (October 2012 - February 2013)
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0 – 500 m 500 - 1200 m 1200 – 4000 m 4000 – 10000 m
RPG 0.5 0.9 1.6 4.2
IMGI 0.4 0.7 1.3 2.3
Testing the performance
Crewell&Lohnert (2007)
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Training on subsets of data
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Thermal PBL structure
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→ weak inversion
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→ strong inversion
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→ multiple inversion
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→ strong inversion
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→ low level inversion
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Conclusions
• HATPRO is valuable tool for studying thermal PBL evolution
• Significant improvement in retrieval algorithm over the factory calibration
• Still lifted/multiple inversions a major challenge
• Good potential for further improvements using specialized retrievals → calibration coefficients for characteristic profiles
Future:
• Remaining question: which specialized retrievals to use
• Probabilistic approach - Ensemble retrieval
• Field campaign with intense radio-soundings and airplane measurements for further validation
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Thank you for your attention!
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Passive Microwave Temperature Humidity Profiler
• HATPRO (RPG)
• Passive Microwave
• Deriving temperature and humidity profile from brightness temperatures
• 14 channels, two channel bands:
K Band (H20): 22-31 GHz (7)
V Band (O2) : 51-58 GHz (7)
• Declared accuracy:
0.25K 0-500m
0.5K 500-1200m
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0 – 500 m 500 - 1200 m 1200 – 4000 m 4000 – 10000 m
0.5 0.9 1.6 4.2
0.4 0.7 1.3 2.3
RMS
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Right algorithm
How to choose the correct algorithm? • sounding • Previous profile • Model • Probabilistic approach: most likely profile