Showing posts with label clouds. Show all posts
Showing posts with label clouds. Show all posts

2013/02/09

What Affects DLWIR?

Using the same data source as before, the same parameter nulling gives this set of curves


This is the variation of DLWIR with day of the year (as before but low prob results retained)

This is absolute humidity effect - not linear

Interesting (night is disabled - no cloud information) but DLWIR is greater in mornings and evenings.  Why not midday?

Station Pressure - Possibly a problem with conversion between % hum and abs humidity causes this.

Linear effect with temperature as would be expected

Again a non linear relation with ULWIR
Wild errors are removed from the result by using the trimmean funcion disposing of 25% of highest and 25% lowest values.
Cloud values are measured using a visual light camera - hence no results will be returned for hours of darkness for this analysis.

===========UPDATE====================================================
Instrumentation
u/dlwir
PRECISION INFRARED RADIOMETER
Model PIR
The Precision Infrared Radiometer, Pyrgeometer, is intended for unidirectional operation in the measurement, separately, of incoming or outgoing terrestrial radiation as distinct from net long-wave flux. The PIR comprises a circular multi-junction wire-wound Eppley thermopile which has the ability to withstand severe mechanical vibration and shock. Its receiver is coated with Parson's black lacquer (non-wavelength selective absorption). Temperature compensation of detector response is incorporated. Radiation emitted by the detector in its corresponding orientation is automatically compensated, eliminating that portion of the signal. A battery voltage, precisely controlled by a thermistor which senses detector temperature continuously, is introduced into the principle electrical circuit.
Isolation of long-wave radiation from solar short-wave radiation in daytime is accomplished by using a silicone dome. The inner surface of this hemisphere has a vacuum-deposited interference filter with a transmission range of approximately 3.5 to 50 µm.
SPECIFICATIONS
Sensitivity: approx. 4 µV/Wm-2.
Impedance: approx. 700 Ohms.
Temperature Dependence: ±1% over ambient temperature range -20 to +40°C.
Linearity: ±1% from 0 to 700 Wm-2.
Response time: 2 seconds (1/e signal).
Cosine: better than 5%.
Mechanical Vibration: tested up to 20 g's without damage.
Calibration: blackbody reference.
Size: 5.75 inch diameter, 3.5 inches high.
Weight: 7 pounds.
Orientation: Performance is not affected by orientation or tilt.
-------------------------
This looks as if it is measuring the heating effect (thermopile) of radiation hitting the dome of the sensor (transmission 3.5 to 50um. The thermopile of course generates a voltage dependant on the temperature difference between one side and the other The non-dome side is not exposed to external radiation so no effect there. However, the nondome side temperature must be measured and compensated.
The instrument also compensates for its own generated IR.
No assumption of BB radiation is assumed. It is the ACTUAL heating effect of IR radiation of narrow or wide bandwith hitting the sensor that is the cause.

If the radiative "temperature" is less than the receiver temperature then the thermopile still measures - see series of posts about thermal imaging - the camera microbolometers sitting at 20+C shows temperatures down to -40C

======================================================================
Dry bulb temperature / wet bulb / relative humidity

HMP45C-L Specifications

  • Supply Voltage: 12 Vdc nominal (typically powered by datalogger)
  • Current Drain: ≤4 mA (active)
  • Sensor Diameter: 2.5 cm (1 in.)
  • Sensor Length: 25.4 cm (10 in.)
  • Cable Diameter: 0.8 cm (0.3 in.)
  • Weight: 0.27 kg (0.6 lb)

Relative Humidity

  • Sensor: Vaisala’s HUMICAP® H-chip
  • Measurement Range:
    0.8% to 100% RH, non-condensing
  • Output Signal Range:
    0.008 to 1 Vdc
  • Accuracy at 20°C (against factory reference): ±1% RH
  • Accuracy at 20°C (field-calibrated against references):
    ±2% (0% to 90% RH);
    ±3% (90% to 100% RH)
  • Temperature Dependence: ±0.05% RH/°C
  • Long-Term Stability: Typically, better than 1% RH per year
  • Response Time: 15 s with membrane filter (at 20°C, 90% response)
  • Settling Time: 500 ms

Temperature

  • Temperature Sensor: 1000 ohm Platinum Resistance Thermometer
  • Measurement Range: -39.2° to +60°C
  • Output Signal Range:
    0.008 to 1.0 V
  • Accuracy:
    ±0.5°C (-40°C),
    ±0.4°C (-20°C),
    ±0.3°C (0°C),
    ±0.2°C (20°C),
    ±0.3°C (40°C),
    ±0.4°C (60°C)
====================================================================
Cloud - total and opaque

TSI-880 AUTOMATIC TOTAL SKY IMAGER

General Description The Total Sky Imager Model TSI-880 is an automatic, full-color sky imager system that provides real-time processing and display of daytime sky conditions. At many sites, the accurate determination of sky conditions is a highly desirable yet rarely attainable goal. Traditionally, human observers reported sky conditions, resulting in considerable discrepancies from subjective observations. In practice, the use of human observers is not always feasible due to budgetary constraints. The TSI-880 now replaces the need for these human observers under all weather conditions.
An onboard processor computes both fractional cloud cover and sunshine duration, storing the results and presenting data to users via an easy-to-use web browser interface. The self-contained design makes it well suited for mission-critical applications such as aviation and military meteorology monitoring. It captures images into standard JPEG files that are analyzed into fractional cloud cover; if networked via TCP/IP (10/100BaseT) or PPP (modem) it becomes a sky image server to remote any user via the web.

TSI-880 AUTOMATIC TOTAL SKY IMAGER

General Description The Total Sky Imager Model TSI-880 is an automatic, full-color sky imager system that provides real-time processing and display of daytime sky conditions. At many sites, the accurate determination of sky conditions is a highly desirable yet rarely attainable goal. Traditionally, human observers reported sky conditions, resulting in considerable discrepancies from subjective observations. In practice, the use of human observers is not always feasible due to budgetary constraints. The TSI-880 now replaces the need for these human observers under all weather conditions.
An onboard processor computes both fractional cloud cover and sunshine duration, storing the results and presenting data to users via an easy-to-use web browser interface. The self-contained design makes it well suited for mission-critical applications such as aviation and military meteorology monitoring. It captures images into standard JPEG files that are analyzed into fractional cloud cover; if networked via TCP/IP (10/100BaseT) or PPP (modem) it becomes a sky image server to remote any user via the web.
Specifications

Image Resolution: 352 x 288 color, 24-bit JPEG format
Sampling rate: Variable, with max of 30 sec
Operating Temperature: -40 C to +44 C
Weight/Size: Approx.70 lbs.(32 kg); dims: 20.83"x18.78"; height is 34.19"; mounts on 16.75x12" 1/4-20 bolt square
Power Requirements: 115/230 Vac; mirror heater duty cycle varies with air temperature: 560W with heater on / 60W off
Software: None required for immediate real time display; uses Internet Explorer or Netscape Browsers on MS-Windows, Mac, UNIX (an optional DVE/YESDAQ package is available for data archiving, display, MPEG day movie creation and data reprocessing)
Data Telemetry: LAN Ethernet (TCP/IP), telephone modem (PPP) or Data Storage Module option (for off grid sites)
====================================================================================
Precipitation:

TE525-L Specifications

  • Sensor Type: Tipping bucket/magnetic reed switch
  • Material: Anodized aluminum
  • Temperature: 0° to +50°C
  • Resolution: 1 tip
  • Volume per Tip: 0.16 fl. oz/tip (4.73 ml/tip)
  • Rainfall per Tip: 0.01 in. (0.254 mm)
  • Accuracy
    Up to 1 in./hr: ±1%
    1 to 2 in./hr: +0, -3%
    2 to 3 in./hr: +0, -5%
  • Funnel Collector Diameter:
    15.4 cm (6.06 in.)
  • Height: 24.1 cm (9.5 in.)
  • Tipping Bucket Weight:
    0.9 kg (2.0 lb)
====================================================================================
Station Pressure

CS105/CS105MD Barometric Pressure
Sensor
1. General
The CS105 analog barometer uses Vaisala’s Barocap silicon capacitive
pressure sensor. The Barocap sensor has been designed for accurate and stable
measurement of barometric pressure. The CS105 outputs a linear 0 to 2.5
VDC signal that corresponds to 600 to 1060 mb. It can be operated in a
powerup or continuous mode. In the powerup mode the datalogger switches
12 VDC power to the barometer during the measurement. The datalogger then
powers down the barometer between measurements to conserve power.
2. Specifications
Operating Range
Pressure: 600 mb to 1060 mb
Temperature: -40 C to +60 C
Humidity: non-condensing
Accuracy
Total Accuracy*** 0.5 mb @ +20 C
2 mb @ 0 C to +40 C
4 mb @ -20 C to +45 C
6 mb @ -40 C to +60 C
Linearity*: 0.45 mb @ 20 C
Hysteresis*: 0.05 mb @ 20 C
Repeatability*: 0.05 mb @ 20 C
Calibration uncertainty**: 0.15 mb @ 20 C
Long-Term Stability: 0.1 mb per year
* Defined as 2 standard deviation limits of end-point non-linearity,
hysteresis error, or repeatability error
** Defined as 2 standard deviation limits of inaccuracy of the working
standard at 1000 mb in comparison to international standards (NIST)
*** Defined as the root sum of the squares (RSS) of end-point non-linearity,
hysteresis error, repeatability error and calibration uncertainty at room
temperature


 

2013/01/20

Yearly CO2 variation Shown as Change in DLWIR?

Not sure about this post.
The data used is short
The data is noisy
Subtracting noisy signals does not improve accuracy!!

{UPDATE This data has now changed - I have nulled out the day of year changes and the long term variation(whole record) which significantly changes the results - the results will be posted at a later date]

Basically if CO2 is low then "back radiation" (DLWIR) should be lower than when CO2 is high
There is an annual cycly where CO2 dips in late spring and rises in autumn - see other posts.

So if you remove all factors changing downward long wave infrared radiation other than CO2 then what should be left is the yearly change in CO2 plus the long term increase.

The nulled data is inspected and a simple curve fit is applied and limits chosen that provide the best null for that factor.

Returned data that meets the criteria are averaged using a TRIMMEAN function to remove spurious high/low values

If the data is treated as a reapeated annual set then the long term becomes averaged and only the annual effect remains.

In the plots below the Nulled measurements are shown and CO2 at La Jolla is plotted for comparison.

The hourly measurement data is used

The analysis has been run many times each time there is always a dip starting at ~190 ( some ~60 days after the CO2 starts reducing)
Accuracy is nonsensical if less than 3 valid data are returned This unfortunately eliminates dec jan feb!.

However here are the final plots:
The raw data  (all points returning under 3 samples ignored) compared to La Jolla CO2

The smoothed data  (all points returning under 3 samples ignored) compare to La Jolla CO2
To pick sensible values for a number of variables the following limits are used.

Precipitation limit is set to eliminate any reading during "precipitation"
Cloud can only be measured during daylight
Only opaque cloud is considered
Humidity % is not used but is converted to absolute water vapour 

The Nulling Process

Each of the variables is nulled by plotting dlwir against the variable. Fitting a polynomial (order 1 to 6) to the resultant and then providing limits that deviate from the polynomial.  The polynomial is then applied to the extracted data.
Each variable is treated this way and then the process repeated until little change occurs. This produces the follwing limits.

start month1
End month12
hour min11
hour max15
Temp min12.4
Temp max29.4
Humidity Min0
Humidity Max1000
opaque Cloud Cover % min2.8
opaque Cloud Cover % Max30.9
cloud cover min-999999
cloud cover max 1000
abs humid min2.12
abs humid max10.5
dlwir min0
dlwir max1000
ulwir min445
ulwir max595
dlwir as pc uplwir min0
dlwir as pc uplwir max100
start day1
end day19.2499
Pressure Min809
Pressure Max825
precipitation min-1
precipitation Max0.00001

These are the corrections applied:

Temperature opaque cldABS HUMIDITYULWIRhourStation pressure
x^6-2.925607E-060.00E+000000
x^54.16E-04-1.30E-050-1.73074E-090.011610750
x^4-2.35E-021.05E-03-0.019222434.37603E-06-0.7941290
x^36.78E-01-3.04E-020.5449762-0.00440410821.56523-0.001826181
x^2-1.05E+013.83E-01-5.6047922.20558-290.41164.45165
x8.40E+01-1.07E+0029.91783-549.63951938.646-3617.085
c3.22E+012.81E+02108.47335.40E+04-5133.338979613.7

The nulling plots (not prettied up!)



Red plots are the result of nulling
blue lines are before nulling

Excel sheet is available (large)
Data is from (hourly):
http://www.nrel.gov/midc/srrl_bms/

Currently ~ 80,000 lines are analysed








2012/12/25

Sky Temperature and Thermal Imaging


Sky, High and Low cloud temperatures  as measured on a thermal imaging camera.
These images show the cloud and sky temperatures as measured by a camera with a 2µ to 13µ pass band.
From a previous test done at night the clear sky temperature is less than -40°C (the camera lower limit).
These pictures show that this clear sky value is maintained as expected during daylight (about -43°C).
Cloud temperatures range from -20°C for high light cloud to +1.1°C for low heavy cloud.
The pictures were taken on 21st December 2012 at approx. 14:00pm  (sunset @ 16:00)
All area temperatures are maximum for that area.
 
 


 
 
These temperatures of course represent what the camera "sees" through its Germanium lense. And as can be seen from the previous thermal camera stuff the camera struggles to measure temperature of gasses - they just do not give black body radiation.
 
Previous posts:
 
 

 

2012/09/04

Some more analysis of u/d lwir and clouds

Total / opaque cloud vs Temperature
No slope on the opaque cloud but a definite dip when teperatures are between 16 and 22C
 
The following plots limit RH to 20 to 40%. Day refers to time that cloud can be measured Night to when cloud is not measured.

D/U LWIR vs Temperature (night values - 0-100% cloud)

Both upward and downward LWIR linear proportional to temperature

D/U LWIR vs Temperature (day values cloud 0-100%)
Very similar to night - slopes are a bit different.

D/U LWIR vs Temperature (day values but limiting cloud to 20 to 40%) (note change in humidity limits

D/U LWIR vs Humidity Temp 22-24C cloud 40-50%
By constraining the temperature to a 2C band The temperature effects on IR are minimised whilst still returning a reasonable number  of results. Note that the ULWIR falls with increasing cloud but the DLWIR rises by 100w/sq m 

D/U LWIR vs Opaque Cloud cover temp 22-24C RH 35-40%
Temperature RH are constrained to minimise these effects. The DLWIR increases by approx 80 w/sqm
 
I think these last two plots conclusively prove relative humidity and cloud cover have a positive effect on the downward long wave ir (ir increases if cloud and/or RH increase)
 
Now how do you do this for CO2?

Data available from:
http://www.nrel.gov/midc/srrl_bms/

 



2012/05/27

The Complex World of Humidity and Temperature

During sunlight hours (minimal cloud) the water vapour in the air will absorb long wave IR (Green House Effect) from the sun and from the ground.
This will be radiated back in all directions.
The solar radiation will therefore be modified such that the wavelengths absorbed by the water vapour will be reduced depending on the effectiveness of the green house effect.
 However the same wavelengths radiated upwards will be more effectively "reflected" back down.

At night the solar input stops and the only radiation hitting the earth is that from GHGs So more water vapour = better "reflector"

Is this visible in the data previously used below.
Firstly data is limited and to get sensible results each point needs significantly more than one result to be significant.

Night time readings cannot include cloud coverage as this is not measured when dark
The former plots used either average or min/max values. The min max tend to plot one off anomalies. In the plots below 1st and 3rd quartile results have been used to improve this.

First a whole year all possible times with up to 9% opaque cloud cover

http://www.nrel.gov/midc/srrl_bms/

not sure why humidity so high!
Now in sequence 2 months at a time a couple of hours and up to 9% opaque cloud cover
Now for some night responses 2 months at a time
Conclusion?

  • Well minimum temperature shows a increase with increasing water vapour (positive slope) over coldest period but turns to negative slope during warmer months
  • The max temp during the night shows little change with water vapour.



2012/04/28

Cloud effect on Temperature limited to 3 hour window

As below but now liminted to a 3 hour window around midday. Note expanded Cloud cover scale (uneven)




Here's one with same RH and Months as previous example (diff scale)

The effect seems to be modified by the RH. At a high humidity the clouds have a more negative effect whilst a low humidity - maximum is cooled while minimum is heated.

All data and excel sheets available on request!

Just how sensitive is temperature to cloud coverage

Data is from the same place as the other plots below:
http://www.nrel.gov/midc/srrl_bms/

NREL Solar Radiation Research Laboratory
Baseline Measurement System BMS
Latitude: 39.742o North Longitude: 105.18o West
Elevation: 1828.8 meters AMSL

Since the previous plots I have downloaded another 4 years of hourly date from 2004 onwards. This of course will give better results
The following plots show : Temperature (max/min) variation with opaque cloud coverage. The data is only counted if :
1. It falls within the month selected.
2. The humidity is within selected limits
3. The cloud coverage is within 5% selected boundary
4. It is sufficiently light that cloud coverage is measurable optically (daylight!)

Each "returned data " count refers to 1 hour slots within the time period selected (months) Some data plots shown are for very sparse data. Any plot point with one point is just about irrelevant and certainly shows no difference in max and min!

The returned data is the max and min for the resuts returned for that period and could therefore show a spurios figure.

Further limitations on time of day would remove the pick-up of minimum at dawn / max after midday.






















Dont Know where November went!

It seems that if it is cool then clouds warm even during the day
If it is hot then clouds cool.

Only one location, and very little data for each month but cloud effects on temperature seem not to be as negative (lower temps with more cloud) than others suggest.

Now if there was another 10 years of data from another location then a much better idea of the effect of clouds could be obtained.

2012/04/21

DLWIR Holding 2 parameters at the limit allowing sensible results

A couple of re-plots with less variation in the 2 other params. Also added a count of returned results for each measurement

Basically More cloud = less escaping radiation - not a lot of difference between total and opaque cloud coverage

But the biggest effect is from water vapour. With the small number of samples it looks as if the response is logarithmic with percentage relative humidity.


2012/04/16

Backradiation - fixing the effect of 2 variables plotting the third

Up to now I've plotted the effect of cloud coverage, humidity and temperature on the difference between DLWIR and ULWIR.

However these plots are not a simple xy since there may be a correlation between temperature/humidity and clouds.

To improve the plots it would be best to plot for example humidity vs dlwir/ulwir at a fixed temperature and cloud cover. The problem is there are too few corresponding points to get a meaningful result.

The following plots were made by inspecting plots and choosing a range of values for each parameter where the dlwir/ulwir change is minimal (about 10% or less)

As a trial cloud coverage was replotted at a much closer variation in the other 2 parameters - this shows a good correspondance with the wider variation but with increased variability.

It should be pointed out that the dlwir as a % of ulwir is a combination of at least all the 3 parameters considered. All that can be gleaned from these plots is the effect of variation of  one parameter whilst holding the others static.

It should be noted that cloud cover is only measured during daylight. All the plots below are therefore only relevant for daylight.








From the above it can be seen that the:

temperature effect is inconsistent and small
relative humidity is the largest effect - more humidity more DLWIR
Cloud cover is significant - more clouds more DLWIR