General Conclusions

In terms of making cloud in-situ measurements, there have been many advances in recent years. However, it is difficult to provide suitable measurements for use in climate models and some of the reasons are summarized below:

• Cloud microphysical measurements are collected today without commonly accepted standards for calibration and data analysis. Users are often unaware of the limitations of the calibrations, if any were done, and the problems associated with the data analysis.

Table 4.5 Parameters given here in the original volume units from the measurement level and volume units at standard temperature and pressure (STP) (0°C and 1013 hPa). Data were obtained from 30 s or 3 km averages representing 8596 values for LWC; 15,202 values for TWC; 6297 values for Nd; and 8298 values for Ni.

Liquid Water Content (LWC) vs. Temperature (T) T (°C) LWC (g m3) LWC (g m 3) at STP

Table 4.5 Parameters given here in the original volume units from the measurement level and volume units at standard temperature and pressure (STP) (0°C and 1013 hPa). Data were obtained from 30 s or 3 km averages representing 8596 values for LWC; 15,202 values for TWC; 6297 values for Nd; and 8298 values for Ni.

Liquid Water Content (LWC) vs. Temperature (T) T (°C) LWC (g m3) LWC (g m 3) at STP

°C

25%

50%

75%

95%

Mean

25%

50%

75%

95%

Mean

-2

0.04

0.11

0.20

0.35

0.13

0.05

0.13

0.23

0.41

0.16

-6

0.04

0.10

0.18

0.34

0.12

0.05

0.11

0.21

0.40

0.14

-10

0.03

0.10

0.20

0.35

0.13

0.04

0.11

0.25

0.46

0.16

-14

0.02

0.06

0.14

0.41

0.11

0.03

0.08

0.18

0.52

0.15

-18

0.01

0.04

0.07

0.15

0.05

0.02

0.05

0.11

0.24

0.08

-22

0.02

0.04

0.07

0.14

0.05

0.03

0.05

0.11

0.29

0.09

-26

0.01

0.01

0.05

0.15

0.04

0.01

0.02

0.09

0.26

0.08

Total Water Content (TWC)

vs. Temperature (T)

T (°C )

TWC (g m

3)

TWC (g m 3) at STP

25%

50%

75%

95%

Mean

25%

50%

75%

95%

Mean

-2

0.05

0.12

0.20

0.34

0.14

0.06

0.14

0.25

0.41

0.17

-6

0.04

0.09

0.17

0.32

0.12

0.04

0.11

0.20

0.38

0.14

-10

0.02

0.07

0.15

0.31

0.10

0.03

0.09

0.19

0.41

0.13

-14

0.02

0.04

0.09

0.27

0.08

0.02

0.07

0.14

0.40

0.11

-18

0.01

0.02

0.06

0.15

0.04

0.02

0.04

0.10

0.26

0.08

-22

0.01

0.02

0.04

0.12

0.04

0.02

0.03

0.07

0.23

0.07

-26

0.01

0.01

0.03

0.11

0.03

0.01

0.02

0.05

0.22

0.05

• There is no established data archive for cloud microphysical measurements that should be used for climate simulations.

• The current accuracy of most measurement techniques is at best 15% for cloud LWC and worse for most other variables. Slingo (1990) and Rotstayn (1999) suggest that accuracies better than 5% are required.

• The current methods of measuring IN and ice particle concentrations (at small sizes) are not adequate. For ice particle concentration, the errors are at least a factor of two at the moment. For IN, considering all the potential nucleation mechanisms, all measurements today must be considered as estimates only with unknown errors. These problems demand urgent attention.

• There are no agreed upon formats for providing data to the modeling community in terms of scale of the measurements, probability

Cloud Properties from In-situ and Remote-sensing Measurements 101 Table 4.5 (continued)

Droplet Number Concentration (N.) vs. Temperature (T)

25%

50%

75%

95%

Mean

25%

50%

75%

95%

Mean

-2

22

72

151

329

106

25

87

184

371

123

-6

59

121

240

446

165

70

146

286

492

191

-10

43

120

240

568

173

51

143

303

714

214

-14

18

68

142

437

114

27

88

181

520

139

-18

12

28

52

106

45

20

44

70

147

61

-22

14

38

71

87

43

27

63

77

130

59

-26

0

9

38

98

27

0

16

54

142

42

Ice Particle Concentration (Ni) vs. Temperature (T)

T (°C )

Nj (L >)

Ni (L ') at STP

25%

50%

75%

95%

Mean

25%

50%

75%

95%

Mean

-2

3

8

14

27

10

3

9

18

37

13

-6

3

7

13

26

10

3

9

16

34

12

-10

2

4

9

17

6

2

5

12

26

8

-14

2

5

11

23

8

3

8

18

40

13

-18

2

5

10

20

7

3

9

18

38

13

-22

2

6

9

20

7

3

10

17

36

13

-26

2

5

12

23

8

3

8

23

46

15

density function requirements, or even the unit of measurements (mass versus volume).

From the remote-sensing perspective, current technology has allowed the miniaturization of instruments which could previously not be deployed in space. Advances have been made in energy efficiency, laser technology, cooling, detectors, and onboard calibration. New satellite instrumentation has boosted the understanding of the hydrological cycle, precipitation, and vertical distribution of aerosols and clouds. In addition, data preprocessing, data transfer, retrieval algorithms, and user interfaces (data visualization and download) have improved in the "A-Train era." Challenges in spaceborne applications are limited onboard energy supply, data downlink volume, and the stability of calibration and orbit, as well as aliasing due to limited spatial coverage, which may introduce spurious trends. For the detection of climate signals, long-term observations with high accuracy and stability are required. New projects are underway to tackle these difficulties. At the same time, the continuity of existing capabilities are at risk, such as instruments of the A-Train or solar irradiance measurements in space.

Ground-based remote-sensing observations are particularly useful when organized in networks, such as AERONET, EARLINET, MPL-Net, ARM, or radar networks. They provide detailed measurements where satellite observations have only just begun or are very inaccurate (aerosol optical thickness, single-scattering albedo, vertical distribution and composition, cloud LWC and precipitation). A problem here is the regional nature of such networks. AERONET, for example, is not well represented in Africa and Asia; radar networks are only established in Europe, North America, and parts of Asia. Currently, efforts are underway to extend such networks to obtain better global coverage, and use models and satellites to obtain high-accuracy global datasets of climate-relevant cloud and aerosol parameters. Ground-based networks provide directly the surface radiation budget terms that would otherwise have to be inverted from satellite measurements or estimated from models. Some general recommendations can be made as follows:

• It would be advantageous if common calibration and data analysis techniques could be established. That way measurements made in different parts of the world by various investigators could easily be compared. Unfortunately, such standards do not exist.

• There is a need to integrate the remote-sensing and in-situ measurement communities better so that global or regional datasets can be obtained for use by the modeling community.

• The continuity of existing satellites is currently at risk. It is very important to ensure data continuity in the near future.

• Ground-based remote-sensing networks should be supported and extended because they represent an indispensable tool for measurement of parameters that are not accessible from space. Global datasets require instrument inter-calibration and a common metric for data quality estimates

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