Water vapour intercomparison effort in the frame Convective and Orographically-induced Precipitatio 6th COPS Workshop 27 – 29 February 2008 University of Hohenheim, Stuttgart Rohini Bhawar, Paolo Di Girolamo, Cyrille Flamant, Dietrich Althausen, Andreas Behrendt, Alan Blyth, Olivier Bock, Pierre Bosser, Barbara J. Brooks, Marco Cacciani, Suzanne Crewell, Cedric Champollion , Fay Davies, Tatiana Di Iorio, Gerhard Ehret, Ronny Engelmann, Alan Gadian, Christoph Kiemle, Ina Mattis, Stephen Mobbs, Detlef Mueller, Sandip Pal, Marcus Radlach, Andrea Riede, Patric Seifert, Max Shiler, Victoria Smith, Donato Summa, Martin Wirth, Volker Wulfmeyer
Transcript
Water vapour intercomparison effort in the frame
of the Convective and Orographically-induced Precipitation Study
6th COPS Workshop
27 – 29 February 2008University of Hohenheim, Stuttgart
Rohini Bhawar, Paolo Di Girolamo, Cyrille Flamant, Dietrich Althausen, Andreas Behrendt,Alan Blyth, Olivier Bock, Pierre Bosser, Barbara J. Brooks, Marco Cacciani, Suzanne Crewell, Cedric Champollion , Fay Davies, Tatiana Di Iorio, Gerhard Ehret, Ronny
Engelmann, Alan Gadian, Christoph Kiemle, Ina Mattis, Stephen Mobbs, Detlef Mueller, Sandip Pal, Marcus Radlach, Andrea Riede,
Patric Seifert, Max Shiler, Victoria Smith, Donato Summa, Martin Wirth, Volker Wulfmeyer
• The main objective of this work is to provide accurate error estimates for the different water vapour profiling sensors based on an intensive inter-comparison effort.
• The inter-comparison is plan to involve airborne and ground-based water vapour lidar systems, radiosondes with different humidity sensors and MW radiometers.
• Simultaneous and co-located data from different sensors are used to compute relative bias and root-mean square (RMS) deviations as a function of altitude.
• First step is the definition of a complete and comprehensive inter-comparison table including all water vapour profiling sensors.
Sample from the intercomparison table for IOP-9c on 20 July 2007
Possible lidar-to-lidar intercomparisons for H2O
BASIL Raman Lidar vs SAFIRE-F20 DIAL
BASIL Raman Lidar vs DLR DIAL
UHOH DIAL vs SAFIRE-F20 DIAL
UHOH DIAL vs DLR DIAL
Bertha IFT vs SAFIRE-F20 DIAL
Bertha IFT vs DLR DIAL
IGN Raman Lidar vs SAFIRE-F20 DIAL
IGN Raman Lidar vs DLR DIAL
25 comparisons
10 comparisons
16 comparisons
11 comparisons
6 comparisons
9 comparisons
7 comparisons
1 comparisons
SAFIRE-FA20 DIAL vs BASIL Raman Lidar – EUFAR Experiment
• SAFIRE-FA20 flights in the frame of the EUFAR Project H2OLidar were performed on 16 July, 25 July and 31 July.
• Each flight had a duration of 3 hours for a total of 9 hours.
• In order to reduce statistical fluctuations, we considered for the SAFIRE-FA20 DIAL an integration time of 80 sec, corresponding to an horizontal integration length of 12-15 km. The integration time for BASIL was taken to be 1 min.
•The vertical step of the measurements is 25 m for the SAFIRE-FA20 DIAL, while it is 30 m for BASIL. Vertical resolution is 250 m and 150 m, respectively.
• Previous studies (Behrendt, 2007a,b) revealed that comparison of airborne and ground-based lidars are possible if distance between the aircraft footprint and the ground-based system is not exceeding 10 km. Thus, in our analysis we considered only DIAL profiles within 10 km from BASIL.
• The number of considered comparisons between SAFIRE-FA20 DIAL and BASIL is 18, 6 on each day.
21:00 04:0000:30
BASIL – Rhine Valley Supersite (Lat: 48.64 ° N, Long: 8.06 E, Elev.: 140 m)
25-26 July 2007 – Water vapour mixing ratiog/kg
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TIME (UTC)PRELIMINARY DATA T = 5 min, z = 150 m
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Water Vapour mixing ratio (g/kg)
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SAFIRE-FA20 DIAL BASIL Raman Lidar Sonde 06:36
BASIL vs SAFIRE FA 20 - 16 July 07: mean profiles
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SAFIRE F-20 DIAL BASIL Raman Lidar Sonde 20:00
BASIL vs. SAFIRE F 20 - 25 July 07: mean profiles
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SAFIRE FA-20 DIAL BASIL Raman Lidar Sonde 19:31
BASIL vs. SAFIRE FA 20 - 31 July 07: mean profiles
Bias (g/kg)
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Comparison between BASIL and SAFIRE-FA20 DIAL on 16, 25 and 31 July 07expressed in terms of mean daily profiles
Mean relative bias: 3.9 % (0.08 g/kg) in the altitude region 0–3.5 km a.g.l.
Mean RMS: 13.7 % (0.97 g/kg)
Larger deviations between the two instruments are found at the top of the boundary layer, where the effect of inhomogeneities may be larger.
Bias intercomparison BASIL Raman Lidar vs. SAFIRE-FA20 DIALincluding all possible flights (EUFAR+COPS)
Mean relative bias: 2.9 % (0.02 g/kg)in the altitude region 0–3.5 km a.g.l.
160707 250707 310707 140707 150707 190707 300707010807 mean
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DLR BASIL
DLR-Basil 18 july 07Time 16:05 (UTC)
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DLR data BASIL data
DLR-Basil 30 july 07Time 10:53 (UTC)
Bias (g/kg)
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DLR BASIL
DLR-Basil 30 july 07Time 11:53 (UTC)
Bias (g/kg)
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Mean relative bias: 2.5 % (0.05 g/kg) in the altitude region 0–3.5 km a.g.l.
Mean RMS: 13 % (0.45 g/kg)
BASIL Raman Lidar vs DLR DIAL
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Safire F20
15 July 2007
Water vapor mixing ratio (g/Kg)
IGN RL
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Safire F20
26 July 2007
IGN RL
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Safire F20 IGN RL
IGN Raman Lidar vs SAFIRE F20 DIAL
Between IGN Raman Lidar and SAFIRE F20 DIAL based on the available dataset 6 comparisons were possible with three during daytime (10 min avg) while 3 during night (5 min avg).
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1916 hrs
Water vapor mixing ratio (g/Kg)
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IGN
2032 hrs
2053 hrs
NIGHT TIME COMPARISON:Mean relative bias: 4.7 % in the altitude region 0–3.5 km a.g.l.
Radiosonde inter-comparison on July 13th
Vaisala RS92, RS80-A and RS80-H were launched on July 13th for the Radiosonde inter-comparison effort.
The known different types of systematic errors for the RS80-A and H
1) Chemical Contamination error Wang et al., 2002, 2) Temperature dependence error Miloshevich et al., 2004,3) Basic calibration model error Vomel et al., 2007,4) Sensor-arm-heating error5) Ground-check errors6) Radiation error
The RS92 is also known to be affected by the solar radiation which induces a dry bias in the relative humidity measured by the sensor.
226 radiosondes launched in Supersite R during COPSSondes with different humidity sensors: Vaisala RS92, RS80-A and RS80-H 95 sondes RS92 – 13 July through 2 August, 21-30 August RS 80 launched in all other periods (88 RS80-A and 43 RS80-H).
BASIL Raman Lidar vs RS80H (with advanced humicap sensor)
26 July 2007
Example of temperature dependent error leading to a radisonde dry bias in the upper troposphere
dry bias
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RS92 vs RS80 13 july 07Time 09:05 (UTC)
Bias (g/kg)
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RS80_corr RS92_origi RS80_origi
Bias origi
Bias corr
For this specific case study, mean bias of RS80 H vs. RS92 in the altitude region 0–4.75 km a.g.l. is 7.91 % and 1.19 %, respectively, before and after the application of correction algorithms
RS92_original RS80_corrected RS80_original Mean bias between RS80
(A&H) and RS92 for all five inter-comparison launches on 13 July 07 - after correction - is found to be approx. 2 %
Future work
We are particularly eager to compare all ground-based lidar systems with airborne lidars (SAFIRE-FA20 DIAL and DLR DIAL).
So far we got data only for SAFIRE-FA20 DIAL, DLR DIAL, IGN Raman Lidar and BASIL Raman Lidar.
Extend the inter-comparison to all possible couples of water vapour profiling sensors operated during COPS in order to get an accurate error estimates for the different water vapour profiling sensors.
The right portion of figure 5 shows the deviations between the two sensor types, before and after the application of the correction algorithms. For this specific case study, mean bias of RS80 H vs. RS92 in the altitude region 0–4.75 km a.g.l. is 7.91 % and 1.19 %, respectively before and after the application of correction algorithms. Mean bias between RS80 (A&H) and RS92 for the five inter-comparison launches on 13 July 07 is found to be approx. 2 %.
