Muenkel_2004_aerosol_concentration_measurements_with_lidar_ceilometer.pdf

Aerosol concentration measurements with a lidar ceilometer: results ofa one year measuring campaign

Christoph Münkel*a, Stefan Emeis**b, Wolfgang J. Müller***c, Klaus Schäfer**b

aVaisala; bForschungszentrum Karlsruhe; cNiedersächsisches Landesamt für Ökologie

ABSTRACT

The Vaisala ceilometer CT25K is an eye-safe commercial lidar mainly used to report cloud base heights and verticalvisibility for aviation safety purposes. Compared to ceilometers with biaxial optics, its single lens design provides ahigher signal-to-noise ratio for lidar return signals from distances below about 600 m, thus increasing its abilities to ex-amine the mixing layer. A CT25K ceilometer took part in the environmental research project VALIUM at the LowerSaxony State Agency for Ecology (NLÖ) in Hannover, Germany, investigating the air pollution in an urban surroundingwith various sensors. Lidar return signals are reported every 15 s with a height resolution of 15 m. This paper covers twoaspects of the interpretation of these signals. The aerosol backscatter of the atmosphere up to 30 m is compared to thePM10 concentration reported by an in situ sensor every 30 minutes, and the results are interpreted in respect of mete-orological parameters such as humidity, temperature, wind, and global radiation. With relative humidity values below62 % and no rain present the correlation between ceilometer backscatter and PM10 values is good enough to qualifystandard ceilometers as instruments for a quantitative analysis of the atmospheric aerosol contents. Backscatter values upto 1000 m height are presented that allow an estimation of the convective boundary layer top in dry weather situations.The atmospheric boundary layer structures derived from ceilometer data are compared to those reported by a SODARand a RASS that also took part in the VALIUM research project. Finally the backscatter data quality of a double lensceilometer is compared to that of the single lens CT25K ceilometer to investigate to what extent these lidar systems arealso able to report aerosol concentration.

Keywords: lidar, ceilometer, aerosol, PM10, mixing layer height, boundary layer

1. INTRODUCTION

The characterization of atmospheric aerosol near ground is performed as well by in situ as by optical sensors. Point sam-pling techniques determine PM in different size ranges as PM10 or PM2.5 using different inlet probes for differentiationof PM. These techniques are widely used in ground-based monitoring network and are described in national and interna-tional regulations. Satellite sensors are detecting aerosols by optical sensors1 which are used for ground-based studiesalso. LIDAR techniques offer the additional possibility to determine altitude profiles of aerosol parameters2,3. The com-parability of measurement results of these different measurement techniques is not well determined. Extending the basislaid by a preceding presentation4 to one year of data sets this paper discusses lidar ceilometer and point sampling PM10measurement results.

Additionally backscatter profiles up to an elevation of 1000 m will be investigated in respect of the ability of a lidar cei-lometer to determine atmospheric boundary layer structures, in particular the mixing layer height. For this purpose somecomparisons with SODAR and RASS data will be presented.

2. VALIUM RESEARCH PROJECT

The German research project VALIUM “Development and validation of instruments for the implementation of Europeanair quality policy in Germany” with the subproject “Evaluation and extension of field data on dispersion of pollutantsacquired in a city district” is sponsored by the German Federal Ministry of Education and Research within the frame of

* [email protected]; phone 49 40 83903-132; fax 49 40 83903-110; http://www.vaisala.com; Vaisala GmbH, Schnack-enburgallee 41d, 22525 Hamburg, Germany; ** [email protected], [email protected]; http://www.imk-ifu.fzk.de;Institut für Meteorologie und Klimaforschung, Bereich Atmosphärische Umweltforschung, Forschungszentrum Karlsruhe GmbH,Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany; *** [email protected]; http://www.nloe.de;Niedersächsisches Landesamt für Ökologie, Göttinger Straße 14, 30449 Hannover, Germany

Remote Sensing of Clouds and the Atmosphere VIII, edited by Klaus P. Schäfer,Adolfo Comerón, Michel R. Carleer, Richard H. Picard, Proc. of SPIE Vol. 5235

(SPIE, Bellingham, WA, 2004) · 0277-786X/04/$15 · doi: 10.1117/12.511104

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the Atmospheric Research Program ‘AFO2000’. The purpose of VALIUM is the development of tools for urban airquality assessments according to the European Air Quality Guideline (96/62/EG) and its daughter directives and involvesseveral measuring campaigns at the site of the Lower Saxony State Agency for Ecology (NLÖ, Niedersächsisches Lan-desamt für Ökologie) in Hannover, Germany.

3. INSTRUMENTATION

3.1 CeilometerThe main technical properties of the CT25K single-lens ceilometer are measurement range 0 – 7.5 km measurement resolution 15 m laser source InGaAs MOCVD laser diode wavelength 905 nm pulse duration 100 ns pulse energy 1.6 µJ pulse repetition frequency 5.57 kHz beam divergence 1.4 mrad detector type silicon avalanche photodiode detector responsivity 65 A/W at 905 nm detector field of view divergence 1.4 mrad measurement cycle 15 s optics focal length 377 mm effective lens diameter 145 mm

Its eye-safety class according to IEC/EN 60825-1 is 1M, so it is “safe under reasonably foreseeable conditions of opera-tion, but may be hazardous if the user employs optics within the beam”.

Figure 1: Vaisala ceilometer CT25K, PM10 and PM2.5 inlet probes, Thies Clima precipitation monitor.

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3.2 In situ PM10 and PM2.5 sensorsOn the roof of the NLÖ building situated 20 m above the ceilometer two point sampling sensors are monitoring thePM10 and PM2.5 concentration. A new value is reported every 30 minutes. Figure 1 shows the inlet probes of the twosensors.

3.3 Precipitation monitorA precipitation monitor as shown in Figure 1 with a sensor area of 25 cm2 is configured in a way that it reports every30 minutes the number of seconds during the past 30 minutes when precipitation was detected.

3.4 Synoptic weather stationA synoptic weather station situated close to the PM point sampling sensors provides 30 minute average values of tem-perature, relative humidity, wind speed and direction, and global radiation values.

3.5 SODARA Metek DSD3x7 mono-static Doppler SODAR operated by the Institute for Meteorology and Climate Research of theForschungszentrum Karlsruhe was situated at a distance of 550 m from the NLÖ building on the grounds of a large fac-tory away from housing areas. It reports 30 minute average values of acoustic backscatter intensities and horizontal windvectors with a vertical resolution of 25 m.

Figure 2: Metek DSD3x7 mono-static Doppler SODAR and RASS WTR designed and built at Max Planck Institute for Meteorology.

3.6 RASSDuring 14 days in spring 2002 and 12 days in autumn 2002 a Wind-Temperature-Radar (WTR) working in RASS andclear-air mode was taking part in the VALIUM intensive measuring campaigns at the NLÖ. It is a prototype instrumentdesigned and built by G. Peters from the Max Planck Institute for Meteorology and the Meteorological Institute of theUniversity of Hamburg, Germany that was operated also by the Institute for Meteorology and Climate Research of theForschungszentrum Karlsruhe. Situated at a distance of 150 m from the NLÖ building it reports 30 minute averages oftemperature values and horizontal wind vectors with a vertical resolution of 60 m.

4. AEROSOL CONCENTRATION MEASUREMENTS IN THE LOWEST PART OF THEATMOSPHERE

4.1 One lens ceilometer vs. in situ sensorsThe investigations presented last year4 have been extended to the period March 1, 2002 to April 23, 2003. The ceilometerbackscatter from the two lowest range gates covering the elevation interval from 0 to 30 m have been averaged over aperiod of 30 minutes and compared to the PM10 and PM2.5 values reported by the in situ sensors.

Wet haze, fog, and precipitation situations should be excluded from the data sets because in these cases scattering at wa-ter droplets much larger than 10 µm is dominating the backscatter intensity received by the ceilometer. Two differentapproaches have been used for this exclusion.

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a) Using only those 30 minute intervals with relative humidity values below 62 % and no precipitation monitored.

b) Using all 30 minute intervals with an average ceilometer backscatter intensity below 7*10-4 m-1 sr-1.

The first approach excludes 75.5 % of all 30 minute average data sets from the comparison. Figures 3 and 4 give detailedresults for the remaining 5736 “dry” data sets, the correlation of PM10 and PM2.5 values recorded by the in situ sensorsis only slightly higher than that of the ceilometer backscatter values compared to PM10.

Figure 5 illustrates that the second approach was less successful, the correlation coefficient barely exceeds 50 %. A reli-able estimation of the aerosol concentration in the lowest parts of the atmosphere with a ceilometer depends on theknowledge of at least the relative humidity.

Assuming a linear relationship between ceilometer backscatter and PM10 aerosol concentration, and using the line ofregression from Figure 3 as this relationship it is possible to estimate the PM10 concentration with ceilometer backscat-ter data.

PM10ceilometer = (-1.6304 + 2.023⋅105 m sr * backscatterceilometer) µg m-3 (1)

Figures 6 to 8 give the results of applying (1) to fairly long dry weather periods.

Up to now only 30 minute averaging has been considered. Since the reporting frequency of the ceilometer is muchhigher than that, it is worthwhile examining situations with a sharp increase of aerosol concentration values to seewhether the ceilometer is in fact reacting faster in these situations. The New Year’s Eve fireworks should produce such asharp increase at midnight. Figure 9 shows this event using one minute averaging for ceilometer data. While an increaseof the ceilometer PM10 concentration is clearly visible right after midnight, the in situ sensor reacts much slower. Itsconcentration values are still rising when the ceilometer data peak is long past. This may be due to the different measur-ing volume of the two sensors, but it also raises the question whether the measuring principle of the in situ sensor impliesa delay in addition to the long averaging period.

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Line of regression: f(x) = −1.6304 + 2.023⋅105 * xCorrelation coefficient: 0.8351

Figure 3: Ceilometer backscatter vs. PM10 concentration, 5736 dry weather data sets, NLÖ Hannover, 01. 03. 2002 - 23. 04. 2003.


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Line of regression: f(x) = 0.6846 + 0.8608 * xCorrelation coefficient: 0.8461

Figure 4: PM10 concentration vs. PM2.5 concentration, 5736 dry weather data sets, NLÖ Hannover, 01. 03. 2002 - 23. 04. 2003.

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Line of regression: f(x) = 11.73 + 0.7295⋅105 * xCorrelation coefficient: 0.5214

Figure 5: Ceilometer backscatter vs. PM10 concentration, all data sets with backscatter < 7*10-4 m-1 sr-1.


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Figure 6: Measured in situ PM10 concentration and PM10 concentration derived from ceilometer backscatter.

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Figure 9: PM10 concentration values on New Year’s Eve 2002/2003, ceilometer: one minute averaging.


4.2 Two lens ceilometer vs. one lens ceilometerAfter the measuring campaign in Hannover, the one lens Vaisala ceilometer CT25K was transferred to Hamburg forcomparison measurements with a double lens ceilometer Vaisala LD-40 that is operated in the company test field.

Since the overlap of the transmitter’s light cone and the receiver’s field-of-view starts at a height of 60 m above thebistatic double lens ceilometer LD-40, signal received from lower heights is coming from double scattering and near-field single scattering effects caused by light scattering at the reference diode in the transmitter optics. This has beendiscussed in detail in a previous presentation5.

In spite of this obvious disadvantage compared to the CT25K single lens system, the attempt of using also the doublelens ceilometer as a PM10 concentration detector gives reasonable results. Applying the method described in section 4.1to data collected during two days resulted in a correlation coefficient of 0.95 for the two minute average values of thebackscatter data from the lowest 30 m of the atmosphere. Figure 10 shows the estimated PM10 concentration values forboth ceilometers, the volatility of the LD-40 data is higher but it follows the course of the CT25K. The effect of the op-tics layout on backscatter intensities up to 60 m is clearly visible in Figures 11 and 12.

5. ATMOSPHERIC BOUNDARY LAYER STRUCTURES

Figures 11 and 12 show vertical structures within the backscatter intensity varying with time. To find out whether theposition of these structures are also detectable by instruments using different measuring principles to scan the atmos-pheric boundary layer, ceilometer data collected at the NLÖ site in Hannover have been compared to SODAR and WTRdata. Figure 13 gives an example illustrating that both convective boundary layer (CBL) structures derived from SODARdata and structures in WTR clear-air mode echoes are visible as minima of ceilometer backscatter intensity gradients.

A detailed discussion of this comparison has been accepted for publication in the Journal Atmospheric Environment6.

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Figure 10: PM10 concentration values derived from monostatic and bistatic ceilometers, Hamburg 10/052003 – 11/05/2003.


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Figure 11: Ceilometer backscatter intensity up to 1000 m, Hamburg 10/052003.

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Figure 12: Ceilometer backscatter intensity up to 1000 m, Hamburg 11/052003.


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Figure 13: Ceilometer backscatter intensity up to 1000 m with convective boundary layer (CBL) structures derived from SODAR andWTR.

6. CONCLUSIONS

The ability of ceilometers to monitor aerosol concentration already mentioned in last year’s presentation4 has been con-firmed by the investigations presented in this paper. The comparison with bistatic systems shows that even these couldbe used for this purpose.

The shape and scattering phase function of hygroscopic aerosols is depending on the relative humidity. Future work willtherefore include investigations of humidity dependant correction functions of the ceilometer backscatter intensity.

Comparisons with SODAR and WTR data confirm that the structures visible in the ceilometer backscatter intensity pro-files are related to physical parameters of the atmospheric boundary layer. In dry weather situations with distinct struc-tures within these profiles a good estimation of the convective boundary layer top is possible.

ACKNOWLEDGMENTS

The author listed first would like to express his gratitude for the support he experienced at the Lower Saxony StateAgency for Ecology (NLÖ) in Hannover, and for the valuable and effective co-operation with the Garmisch-Partenkirchen and Karlsruhe VALIUM team.

REFERENCES

1. B. A. Holmén, T. A. James, L. L. Ashbaugh, and R. G. Flocchini, “LIDAR-assisted measurement of PM10 emis-sions from agricultural tilling in California's San Joaquin Valley - Part I: LIDAR”, Atmos. Environm. 35, pp. 3251-3264, 2001.


2. B. A. Holmén, T. A. James, L. L. Ashbaugh, and R. G. Flocchini, “LIDAR-assisted measurement of PM10 emis-sions from agricultural tilling in California's San Joaquin Valley - Part II: emission factors”, Atmos. Environm. 35,pp. 3265-3277, 2001.

3. M. D. King, Y. J. Kaufman, D. Tanré, and T. Nakajima, “Remote sensing of tropospheric aerosols from space: Past,present and future”, Bulletin of the American Meteorological Society 80 (11), pp. 2229-2259, 1999.

4. C. Münkel, S. Emeis, W. J. Müller, and K. Schäfer, “Observation of aerosol in the mixing layer by a ground-basedlidar ceilometer”, Proceedings of SPIE Vol. 4882, pp. 344-352, 2003.

5. C. Münkel, J. Ojanpera, and P. Ravila, “Investigation of falling precipitation with a lidar ceilometer”, Proceedingsof SPIE Vol. 4539, pp. 205-213, 2002.

6. S. Emeis, C. Münkel, S. Vogt, W. J. Müller, and K. Schäfer, “Atmospheric boundary-layer structure from simulta-neous SODAR, RASS, and ceilometer measurements”, accepted for publication in Atmos. Environm.


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Muenkel_2004_aerosol_concentration_measurements_with_lidar_ceilometer.pdf