Showing posts with label nrel. Show all posts
Showing posts with label nrel. Show all posts

2013/08/18

Factors affecting DLWIR - NREL data (re-analysis)

Data from NREL re-analysed - spread sheet corrected, ULWIR nulling removed as this is basically the same as temperature, latest data added.

Data is dependent on cloud cover. Unfortunately cloud data is only available during daylight. Hours of darkness therefore are not included in this analysis



 The first plot shows the variation with date  (2004 to 2013) Unfortunately the calibration of the pyrgeometer (including device swapping on every calibration) shows up as a signal greater than any trend. The calibration dates are shown as dotted blue lines.

2004 - 2006 31192F3 large error
2006 - 2008 31194F3 10 w/sqm drop
2008 - 2009 31192F3 random 8w/sqm p-p
2009 - 2011 31194F3 10 w/sqm drop
2011 - 2012 31192F3 random 13w/sqm p-p
2012 - 2013 31194F3 random 10w/sqm p-p
It appears that 31194F3 has a drift with time (now possibly corrected)
Also it seems that +-5w/sqm is the expected accuracy for this type of pyrgeometer



Luckily this is the only data extraction that is synchronous with date. Other extraction will tend to remove the drift by averaging. It is significant that the calibration adjustments show up indicating that this spreadsheet successfully sees valid changes of <5w class="goog-spellcheck-word" span="" style="background: none repeat scroll 0% 0% yellow;">sqm

This next plot shows the expected variation of dlwir with temperature.

It should be noted that the drop in value at the high temperature end is most likely due to the small number of results returned and is therefore not valid.

The net plot shows the effect of increasing absolute humidity

The following pair show the change with day of the year. Note that temperature effects should have been nulled so the peak should not be due to summer temperature. The second plot compares La Jolla CO2 with the dwlir. The dwlir seems to show the inverse of what would be expected!




Station pressure below, has very little effect on dwlir


Opaque cloud cover increases the dwlir!



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










2013/07/08

Temperature dependence - more analysis of NREL data

This analysis shows the interdependence of temperature and other data.
Temperature may be the cause or the effect!
The second curve on each plot shows the number of results returned. Obviously the more results the more likely the data returned is valid.
All data is averaged with the top and bottom 30% discarded to remove outliers



Temperature is affected negatively by the absolute humidity (gms h2o/cu m). To heat air and water vapour takes more energy than air alone hence the negative slope.


Using the nulling technique produces a plot with little day of year dependence (no annual peak or dip is obvious).
The slope of the line is =0.0001602 per day. This equates to 0.585°C per decade and this is over a period that people say the warming has stopped!


As expected with opaque cloud cover the temperature is negatively correlated.


Temperature with day of year is as expected with a peak at day 200 (19th July) and a minimum at day 40 (9th February). These dates are of course offset from longest/shortest day.

Plots of the nulled variables:


Note that the nulled portion is sometimes limited to less than whole range. In this case a limit is used to only accept data for that nulled range on that variable

Also the nulling process is only used to produce a line of zero slope for each variable - the offset from zero is not relevant as only anomalies are plotted.

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







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


 

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/

 



2012/09/03

More stuff from NREL Solar Radiation BMS

Using hourly data from http://www.nrel.gov/midc/srrl_bms/ gives the possibility of checking the effect of cloud cover on upward and downward long wave infrared radiation (ULWIR and DLWIR), time of observation TOBs on temperature readings.

Cloud cover effect on D/ULWIR

Data from 2004 to present (hourly)

Using data at around dawn when solar heating is minimal (would be better to have data pre dawn but cloud coverage is not measured in the dark) it should be possible to see the effect of clouds on DLWIR.

This is the plot:


The effect of clouds is most noticeable up to 25% coverage but does continue increasing up to about 80%. In this plot temperature has been constrained to 16 to 22C and RH 20 to 45% for time from 0:00 to 8:00am.
Constraints cannot easily be made tighter else total data returned fall to zero.

Time of Observation (TOBS).

Assumed:
One measurement per day max and min calculated for 23 hours prior to last measurement. Constraints on cloud cover is tricky since no information is available over night so some expected perturbation may be seen at sunrise/sunset
Within the constraints noted in the chart header, the data for each hour over the record is split into 2 - 1st quartile and 3rd quartile (this lowers the effect of false max and min values). the hourly data returned is then averaged


Data is for all Augusts on record.
The day time cloud cover effect is noticeable (0 to 10% cover - night is o to 100%).

Removing the cloud constraint gives this plot:



It looks as if TOBs could change temperature measurements by +-1.5C

A couple of cloud coverage per hour plots January and July




The early and late drop offs in coverage may be an effect of the measurement method seems a bit consistent with both plots.

However it seems that cloud coverage in jan is costant with time of day wheras in july cloud coverage increases with time.

Effect of relative humidity on DLWIR.