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Introduction Processing Selected Results Summary, Future Plans, Conclusions

ALMA Water Vapour Radiometr y: Tests at the SMA
P.G.Anathasubramanian1,4, R.E.Hills1 , K.G.Isaak1,5 , B.Nikolic1 , M.Owen1 , J.S.Richer1 , H.Smith1 , A.J.Stirling1,6 , ¨ R.Williamson1,7 , V.Belitsky2 , R.Booth2 , M.Hagstrom2 , L.Helldner2, M.Pantaleev2 , L.E.Pettersson2 , T.R.Hunter3, S.Paine3 , A.Peck3 , M.A.Reid3 , A.Schinckel3 , K.Young3
1 Cavendish Lab, Cambridge University, UK, 2 Onsala Space Obser vator y Harvard-Smithsonian Center for Astrophysics, Submillimeter Array Project Raman Research Instituted 5 University of Cardiff 6 The Meteorological Office, UK, 7 Columbia University, NY, USA 3

4

Grenoble, June 2007
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Atmospheric Phase Fluctuations



Physical proper ties of atmosphere along line of sight of each telescope are different and vary with time Water most impor tant Also `dry' fluctuations (due to temperature)





B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Atmospheric Phase Fluctuations



Physical proper ties of atmosphere along line of sight of each telescope are different and vary with time Water most impor tant Also `dry' fluctuations (due to temperature)





B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Atmospheric Phase Fluctuations


Physical proper ties of atmosphere along line of sight of each telescope are different and vary with time Water most impor tant Also `dry' fluctuations (due to temperature)





B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Atmospheric Phase Fluctuations


Physical proper ties of atmosphere along line of sight of each telescope are different and vary with time Water most impor tant Also `dry' fluctuations (due to temperature)





B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Atmospheric Phase Fluctuations (2)


To first order, de-correlation is propor tional to square of the 2 root-mean-square of phase fluctuations, . (More precisely R ( ) e
2 - /2

).



Magnitude of fluctuations depends baseline length (as well as the weather):
2 = ((r) - (r + L)) 2

=

L L0



,

(1)

where most likely between 2/3 and 5/3


Dominant timescales of fluctuation depend on wind speed.

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Illustration of Phase Fluctuations
750

500



Mauna Kea, Hawaii 200 m baseline About 3.5 mm line-of-sight water = 2 0 7 µ m .

250


p (µm) 0

-250



-500

-750 16.8

17

17.2

17.4 t (hours UT)

17.6

17.8

18



B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

The 183 GHz Water Vapour Line
250

200

Tb (K)

150

100

50

0 175 180 185 (GHz)
B. Nikolic ALMA WVR

190

195


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

The 183 GHz Water Vapour Line (+ Ozone)
250

200

Tb (K)

150

100

50

0 175 180 185 (GHz)
B. Nikolic ALMA WVR

190

195


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

The Sub-Millimetre Array (SMA)

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

The Radiometers


Tests used the two ALMA prototype radiometers:


1 Hz sampling One baseline only Two different designs (correlation and Dicke) Production design to be based on the Dicke switching principle although fur ther simplifications Sideband separation, pseudo correlation design Double sideband, chop-wheel at about 20 Hz



Correlation radiometer




Dicke radiometer




Calibration: Both designs with integrated cold and ambient loads.
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

The Radiometers on the SMA


People at the SMA: M. Reid, A. Peck, S. Paine, T. Hunter The SMA was an evolving facility during these tests Optical interface to the SMA




Design by R. Williamson Polarising grid, so radiometer beam in the same direction as the astronomical beam Significant amount of additional optics Not based on ALMA software Some problems arose (more on this later)



Software interface to the SMA




Most data on a 200 m baseline Interferometer sampled at 2.6 s (slower than the radiometers, ALMA)
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Sample obser vation (Feb. 17, 200 m baseline)
Path as measured by the interferometer (red) and as predicted by radiometers (blue)
600


400

Observed = 2 0 7 µ m . Fluctuation around 5-min average: = 1 5 3 µ m . Residual after correction: = 6 2 µ m . 1 hour observation


200 p (µm)

0

-200


-400

-600 16.8

17

17.2

17.4 t (hours UT)

17.6

17.8

18



B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Sample obser vation (Feb. 17, 200 m baseline)
Path as measured by the interferometer (red) and as predicted by radiometers (blue)

600



Observed = 2 0 7 µ m . Fluctuation around 5-min average: = 1 5 3 µ m . Residual after correction: = 6 2 µ m . 25-minute section

400


200

p (µm)

0

-200


-400

-600 17.2

17.25

17.3

17.35

17.4 t (hours UT)

17.45

17.5

17.55

17.6



B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA

Sample obser vation (Feb. 17, 200 m baseline)
Path as measured by the interferometer (red) and as predicted by radiometers (blue)

600



400

Observed = 2 0 7 µ m . Fluctuation around 5-min average: = 1 5 3 µ m . Residual after correction: = 6 2 µ m . 5-minute section


200

p (µm)

0

-200


-400

-600 17.36 17.38 17.4 t (hours UT) 17.42 17.44



B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Radiometer Outputs
Correlation Radiometer
300



Eight outputs Blue line highest, red line lowest frequency Pseudocontinuum can be seen in data from correlation radiometer

­ Correlation radiometer

250

200


150

TB (K)

100 16.8

17

17.2

17.4 t (hours UT)

17.6

17.8

18

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Radiometer Outputs
Dicke Radiometer

300



Eight outputs Blue line highest, red line lowest frequency Pseudocontinuum can be seen in data from correlation radiometer

250 ­ Dicke radiometer

200

TB (K)


150

100 16.8

17

17.2

17.4 t (hours UT)

17.6

17.8

18

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Centre channel outputs
275

300

272.5
250

Tb (K)

200

270 TB (K)

150

100

50 175 180 185 (GHz) 190 195

267.5


265

Most sensitive in very dry conditions

262.5 17.3

17.325

17.35

17.375

17.4 t (hours UT)

17.425

17.45

17.475

17.5

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Centre channel outputs
255

252.5

250

300

250

TB (K)

Tb (K)

247.5

200

150

245

100

50 175 180 185 (GHz) 190 195

242.5


240 237.5 17.3

17.325

17.35

17.375

17.4 t (hours UT)

17.425

17.45

17.475

17.5

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Outside channel comparison
210

205
300

200
Tb (K)

250

200

TB (K)

195

150

100

50

190

175

180

185 (GHz)

190

195

185



180 17.3

17.325

17.35

17.375

17.4 t (hours UT)

17.425

17.45

17.475

17.5

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Outside channel comparison
140

300

135
Tb (K)

250

200

150

TB (K)

100

130
50 175 180 185 (GHz) 190 195


125

Most sensitive in the wettest conditions

120 17.3

17.325

17.35

17.375

17.4 t (hours UT)

17.425

17.45

17.475

17.5

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Interferometer Data


Phase measurements by tracking bright quasars

750

Single baseline data Significant phase wrapping in some conditions

500

250

p (µm)

0

-250

-500

-750 16.8

17

17.2

17.4 t (hours UT)

17.6

17.8

18

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Interferometer Data


Phase measurements by tracking bright quasars


Single baseline data Significant phase wrapping in some conditions



Normal dump time 5 s so some drop-outs seen, easily excised. Contribution of interferometer phase stability to observed phase fluctuations not well quantified. Some concern over synchronisation of data. Data taken at 1 s sampling suffered badly from timing drifts (most likely in radiometer computers)





B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Conversion factors: the simplest model
Simplest model where fluctuations occur in a single layer p




p c

TB /

TB c





p is fluctuation in path, TB is fluctuation in radiometer brightness temp c is water column TB c depends on water column, temperature, etc. Trickiest to determine. p c less uncer tain to estimate but (relatively weak) function of observing frequency
TB c



Assuming

,

p c

constant can linearise as p

i

ai T

B,i

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Conversion factors (2)



Slightly more sophisticated: consider fluctuations in optical depth: TB = T and use p
atm

1-e
i

-

=

TB , Tatm - TB

(2)

i

bi



This adjustment significant in one observation with large airmass change ­ otherwise very small improvement.

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

The Data from the Radiometers Interferometer Data Conversion factors

Conversion factors (3)
Determining ai for a par ticular set of atmospheric conditions (also radiometer pair?) a key problem:




Physical modelling; compute p , TB c c Machine learning, neural network



In these tests we measure phase We are interested in the best obtainable performance of radiometers:


Best obtainable performance means optimal ai set Look for optimal set directly by least-squares comparison of measured phase and path predicted by i ai TB,i

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

May 3: Good Conditions
About 1.4 mm line-of-sight water, shor t baseline
250 ­ Correlation radiometer TB (K)

200

150

100

50 16.2

16.3

16.4

16.5 t (hours UT)

16.6

16.7

16.8

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

May 3: Good Conditions
reduced from 46 to 29 µm
150

100

50

p (µm)

0

-50

-100

-150 16.3 16.4 16.5 t (hours UT) 16.6 16.7

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

May 3: Good Conditions
reduced from 46 to 29 µm
150

100

50

p (µm)

0

-50

-100

-150 16.5

16.525

16.55

16.575

16.6 t (hours UT)

16.625

16.65

16.675

16.7

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

July 18: Can track long time-scale fluctuations
Total fluctuations (no running mean removed): reduced from 271 to 75 µm
1000

500

p (µm)

0

-500

-1000 4 4.5 5 t (hours UT) 5.5 6 6.5

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

July 18: Can track long time-scale fluctuations
Fluctuations from five minute average: reduced from 164 to 56 µm
600

400

200

p (µm)

0

-200

-400

-600 4 4.5 5 t (hours UT) 5.5 6 6.5

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Februar y 24: Shor t time scale fluctuations
Total fluctuations ­ observed = 258 µm, residual = 93 µm
1500

1000

500

p (µm)

0

-500

-1000

-1500 20 20.25 20.5 20.75 t (hours UT) 21 21.25 21.5 21.75

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Februar y 24: Shor t time scale fluctuations
Fluctuations from five minute average ­ observed = 241 µm, residual = 72 µm
1500

1000

500

p (µm)

0

-500

-1000

-1500 20 20.25 20.5 20.75 t (hours UT) 21 21.25 21.5 21.75

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Summary of observations Final Remarks

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Summary of observations Final Remarks

Table of obser vations
Date 20060217 -- 20060224 -- 20060503 20060524 -- 20060601 -- 20060718 Time (UT) 16.9­17.9 19.8­20.2 18.4­19.4 20.0­21.75 15.3­16.8 5.1­5.7 5.9­8.8 5.3­6.7 7.3­8.3 4.3­6.5 Elev (deg) 16­30 38­44 25­40 47­72 44-65 50­55 55­64 57­64 55 ­63 40­62 Baseline (m) 212 212 212? 212? 64 212 212 212 212 212 Raw (µm) 207 238 81 258 54 103 90 62 154 271


5-min (µm) 153 239 79 241 37 87 73 40 72 163



Res. (µm) 62 73 47 72 28 34 31 31 56 56

c (mm) 3.6 2.0 2.5 2.4 1.4 2.6 1.8 2.4 2.4 2.3

Sp ec (µm) 68 47 51 52 35 53 41 48 50 50

Sampling (s) 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6

Comment

20060920

4.2-5.5

27­41

64

83

71

60

3.1

60

1.3

-- 20061030

6-6.7 19.3-20.3

46­61 67­72

64 415

72 332

62 282

50 139

2.3 7.0

48 119

1.3 1.3

11 s offset, timing issues. High intf. noise. Timing issues. -- Very wet conditions. Quality of fit limited by time drift.

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Summary of observations Final Remarks

Outline
Introduction Water Vapour Radiometry: Why and How? The Set-up at the SMA A Typical Result at the SMA Processing The Data from the Radiometers Interferometer Data Conversion factors Selected Results Summary, Future Plans, Conclusions Summary of observations Final Remarks
B. Nikolic ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Summary of observations Final Remarks

Status, future plans


Prototype radiometers now back in Europe


Currently used for low level software integration work at ESO, Garching



Contract for production radiometers signed First production radiometers to be completed by mid-summer 2008. In Cambridge:




Development of WVR algorithms Possibly involvement in atmospheric profiling (most likely using O2 sounding)

B. Nikolic

ALMA WVR


Introduction Processing Selected Results Summary, Future Plans, Conclusions

Summary of observations Final Remarks

Final Remarks



Results from SMA tests very encouraging ­ the radiometers clearly can meet the spec in the majority if not all of conditions. Few issues with radiometers identified. Majority of problems arose from interfacing to the SMA. Development of WVR algorithms most likely to proceed without fur ther observational data until end of 2008.





B. Nikolic

ALMA WVR