Comparisons of lidar derived humidification factors to surface optical and chemical measurements during the Combined HSRL and Measurement Study (CHARMS) at the DOE ARM Southern Great Plains site in northern Oklahoma

K. W. Dawson (kyle.w.dawson@nasa.gov)1,2; R. A. Ferrare1; R. Moore1; T. Thorsen1; E. Chemyakin1,3; S. P. Burton1; M. Clayton1,3; C. A. Hostetler1; Detlef Müller1,3; and the LaRC HSRL Team

1. NASA Langley Research Center (LaRC); 2. Universities Space Research Association; 3. Science Systems and Applications, Hampton, VA1. Introduction The Department of Energy (DOE) Atmospheric Radiation

Measurement (ARM) user facility makes remote sensing measurements of atmospheric aerosols and clouds using a variety of instrumentation providing high-quality characterization of the ambient atmosphere. The CHARMS campaign took place in northern Oklahoma at the Southern Great Plains site in the summer of 2015. During CHARMS, the University of Wisconsin High Spectral Resolution Lidar (HSRL) was deployed next to the DOE ARM SGP Raman lidar in order to acquire multiwavelength profiles of aerosol backscatter and extinction. HSRL acquired profiles of aerosol backscatter at 532 and 1064 nm and aerosol extinction and depolarization at 532 nm. The SGP Raman lidar acquired profiles of aerosol backscatter and extinction at 355 nm as well as profiles of water vapor mixing ratio. Profiles of relative humidity were derived from Raman lidar water vapor profiles and temperature profiles derived from simultaneous measurements from the SGP Atmospheric Emitted Radiance Interferometer (AERI). The ARM facility at SGP also had surface measurements of chemical composition from the Aerosol Chemical Speciation Monitor (ACSM) and humidification factors from surface-located humidity scanning nephelometers. The combination of these instruments provided a basis to explore lidar-derived ambient humidification factor compared to measurements from ground-based nephelometers and derived from ground-based measurements of aerosol chemical composition.

2. Methods

3. Results

4. Summary and Conclusions

Certain layers of the lidar profiles were selected based on the criteria in Table 1a. These criteria were used to isolate regions and periods where

changes in aerosol properties were due to changes in RH and not aerosol concentration/type.

DaytimeAt or below aerosol Mixed Layer Height

∂θv/∂z ≈ 0; potential temperature gradient∂r/∂z ≈ 0; water vapor mixing ratio gradient

∂α/∂RH > 0; extinction monotonic + with RH

Num. Profiles (N>10): 1498MLH = 1.15 ± 0.7 km

∂θv/∂z = 2.7 ± 1.7 K/km∂r/∂z = -1 ± 2.3 g/kg·km

19.6% < RH < 93.1%

Figure 1. Meteorological summary of lidar profiles analyzed during the summer, 2015 CHARMS campaign. Profiles are shown at bin medians for altitudes normalized by the mixed layer height (MLH) determined for each profile and binned in steps of 0.1. The MLH is at z = zi = 1. Bold lines are medians of (a) virtual potential temperature; (b) water vapor mixing ratio; and (c) relative humidity. Thin black lines show the interquartile range.

Humidification ModelsGamma

Kappa-Scattering

CHARMS Campaign Summary Plots (N =1739 profiles)

Steps for Model Comparisons to f(RH)

Case Study: August 2-3, 2015

Figure 3. Lidar retrieved quantities for 2015 August 02-03; (top-to-bottom) HSRL measured aerosol backscatter. Mixed Layer Height (MLH) delineated by black crosses and regions analyzed for finding humidification factor delineated by black contour; Raman measured water vapor mixing ratio; Raman derived relative humidity; Lidar-derived humidification factor

Figure 2. (a) Raman Lidar 355 aerosol extinction as a function of Raman lidar derived Relative Humidity; (b) HSRL Lidar 532 aerosol extinction; (c) 3β + 2α optimal estimation derived fine mode volume; (d) HSRL measured aerosol depolarization ratio and (e) HSRL measured lidar ratio (i.e. extinction over backscatter). Bullets are medians and bars are the interquartile range.

(a. 355 nm extinction) (b. 532 nm extinction) (c. Fine Mode Volume) (d. Depolarization Ratio) (e. Lidar Ratio)

• Aerosol extinction and relative humidity profiles were derived from lidar measurements acquired at the DOE ARM SGP site in northern Oklahoma. • Lidar measurements of the aerosol humidification factor were shown to lie within expected values based on surface measurements of humidification factors and estimated humidification factors assuming surface chemical composition and AERONET retrieved size distributions.• Changes in the aerosol size distribution contributed to increased hygroscopicity in the estimated humidification factor and probably in lidar retrievals compared to surface nephelometer measurements. • A time delay of the lidar-retrieved extinction hygroscopicity parameter (kappa) relating to cloud effects was observed. Lidar contributions to understanding aerosol-cloud interactions will be a future focus.

5. Acknowledgements

6. References 1) Kotchenruther, R. A., Hobbs, P. V., & Hegg, D. A. (1999). Humidification factors for atmospheric aerosols off the mid-Atlantic coast of the United States. Journal of Geophysical Research: Atmospheres, 104(D2), 2239–2251. https://doi.org/10.1029/98JD01751

2) Carrico, C. M. (2003). Mixtures of pollution, dust, sea salt, and volcanic aerosol during ACE-Asia: Radiative properties as a function of relative humidity. Journal of Geophysical Research, 108(D23), 8650. https://doi.org/10.1029/2003JD003405

3) Jefferson, A., Hageman, D., Morrow, H., Mei, F., & Watson, T. (2017). Seven years of aerosol scattering hygroscopic growth measurements from SGP: Factors influencing water uptake. Journal of Geophysical Research: Atmospheres, 122(17), 9451–9466. https://doi.org/10.1002/2017JD026804

4) Hegg, D., Larson, T., & Yuen, P.-F. (1993). A theoretical study of the effect of relative humidity on light scattering by tropospheric aerosols. Journal of Geophysical Research: Atmospheres, 98(D10), 18435–18439. https://doi.org/10.1029/93JD01928

This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science, Office of Biological and Environmental Research program, under Grant No. DE-SC0016274. We thank Rick Wagener, Laurie Gregory and Lynn Ma for their efforts in establishing and maintaining the SGP AERONET site. We also thank John Goldsmith, Robert Holz, Ed Eloranta, Willem Marais, and Rob Newsom for their efforts in collecting the CHARMS data.

Table 1a. Analysis region selection criteria Table 1b. Statistical summary

00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

Hours after Midnight (UTC)

40

50

60

70

80

90

98

Rel

ativ

e H

umid

ity (

%)

1

2

3

4

5

6

Hum

idifi

catio

n Fa

ctor

(u

nitle

ss)

0.1

1

2

3

4

Alti

tude

(km

)

0.1

0.3

0.6

1

3

6

Par

ticul

ate

Bac

ksca

tter

(Mm

-1 s

r-1)

0.1

1

2

3

4

Alti

tude

(km

)

6

8

10

12

14

16

18

Wat

er V

apor

Mix

ing

Rat

io (

g/kg

)

0.1

1

2

3

4

Alti

tude

(km

)

40

50

60

70

80

90

100

Rel

ativ

e H

umid

ity (

%)

2015-Aug-02 2015-Aug-03

MLH Height

analysis region

Particulate 532 nm Backscatter

Water Vapor Mixing Ratio

Relative Humidity

HumidificationFactor

40 45 50 55 60 65 70 75 80 85 90 95

Relative Humidity (%)

50

100

150

200

250

300

350

400

Aer

osol

Ext

inct

ion,

355

nm

(M

m-1)

45 50 55 60 65 70 75 80 85 90 95

Relative Humidity (%)

50

100

150

200

250

Aer

osol

Ext

inct

ion,

532

nm

(M

m-1)

40 50 60 70 80 90

4

6

8

10

12

14

16

18

Fine

Mod

e V

olum

e (µ

m3 /

cm3 )

40 50 60 70 80 902

4

6

8

10

12

14

Aer

osol

Dep

olar

izat

ion

Rat

io (

%)

40 50 60 70 80 9040

45

50

55

60

65

70

Lida

r R

atio

(sr

)

Relative Humidity (%) Relative Humidity (%) Relative Humidity (%)

�chem=0.15�ext=0.15� = 0.48

�ext=0.17� = 0.51

300 310 320 330Potential Temperature (K)

0

0.2

0.4

0.6

0.8

1

Nor

mal

ized

Alti

tude

5WVMR (g/kg)

0

0.2

0.4

0.6

0.8

1

30 50 70 90RH (%)

0

0.2

0.4

0.6

0.8

1

10 15 20

(a) (b) (c)

0.01 0.1 1 10 100 0

0.01

0.02

0.03

0.04

0.05

40 50 60 70 80 90 100Relative Humidity

0.2

0.25

0.3

Hygroscopicity Parameter

1

1.5

2

2.5

3

Gro

wth

Fac

tor

1.35

1.45

1.55 lidar sensitive region (530 nm)Real Refractive Index

Radius (µm)

Col

umn

dV/d

lnR

(µm

3 /µm

2 ) 1

0.8

0.6

0.4

0.2

0 Con

trib

utio

n to

lida

r ex

tinct

ion

RH = 0%RH = 85%

Organics

Sulfate

AmmoniumNitrate

40 50 60 70 80 90Relative Humidity (%)

1

2

3

4

5

6

7

8

Hum

idifi

catio

n Fa

ctor

(-)

Lidar

NephelometerModel

ACSM Chemical Composition

E-AIM Model RH dependent parameters

AERONET PSD+

Mie Modelf(RH)

α = α0100 − RH100 − RH 0

− γ

α = α0 1 + κscaRH

100 − RH

Each humidification model is simultaneously fit to the lidar retrievals of extinction. The models are fit as a system of equations so that they share a common estimate of the dry extinction coefficient. Profiles are fit as 30 minute moving averages so that the profiles to the left and right influence the fit result.

Figure 4. Required model inputs for calculating f(RH) are shown from left to right

Impacts of Cloudy Regions on Retrievals

14:24 16:48 18 19:12

0.1

0.2

0.3

0.4

Kap

pa

2 Aug 2015

09:36 10:48 12:00 13:12 14:24

0.2

0.4

0.6

0.8

1

Kap

pa

3 Aug 2015

Local Time (HH:MM)

cloud cloud

Figure 5. Kappa time series after cloud