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

• Platform for studying boundary layer processes in complex terrain • Two pillars:

1. Measurements 2. Modelling

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

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

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

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

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

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)

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)

Training on subsets of data

Thermal PBL structure

→ weak inversion

→ strong inversion

→ multiple inversion

→ strong inversion

→ low level inversion

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

Thank you for your attention!

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

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

Right algorithm

How to choose the correct algorithm? • sounding • Previous profile • Model • Probabilistic approach: most likely profile