Specifically, Watts did not apply a time
of observation bias correction according to Howard Universitychemistry professor Josh Halpern, who blogs under the pseudonym Eli Rabett.
It is a bit interesting to me that many are willing to let site issues slide, but the time
of observation bias not slide.
I've seen a credible explanation for why, beginning in 1950, time
of observation bias (TOBS) and station homogeneity (SHAP) became so skewed.
Therefore one must correct for the time
of observation bias before one tries to determine the effect of the urban heat island»
«There is practically no time
of observation bias in urban - based stations which have taken their measurements punctually always at the same time, while in the rural stations the times of observation have changed.
Do the raw data figures in the paper include time
of observation bias adjustment and / or any other similar «instrument» - like adjustments?
-LSB-...] to correct for the time
of observation bias (TOB).
Vose, R.S., C.N. Williams Jr., T.C. Peterson, T.R. Karl, and D.R. Easterling, 2003: An evaluation of the time
of observation bias adjustment in the U.S. Historical Climatology Network, Geophysical Research Letters, 30, 2046, doi: 10.1029 / 2003GL018111.
Am I correct, that in this case «raw» excludes corrections for time
of observation bias, which the NOAA data includes?
Karl, T.R., C.N. Williams, Jr., P.J. Young, and W.M. Wendland, 1986: A model to estimate the time
of observation bias associated with monthly mean maximum, minimum, and mean temperature for the United States, Journal of Climate and Applied Meteorology, 25, 145 - 160.
jim2, now that we have accurate hourly / daily records, we can predict what the time
of observation bias (TOB) would be, if we were still recording temps the same way today as 50 years ago.
This US increase is due to the time
of observation bias and the transition to the MMTS.
Not exact matches
Finally, some
of the
observations above re the deflationary
bias suggest that interest rates may well be too low when we hit the next recession to give the Fed much
of a perch to climb down from.
Can you learn anything worthwhile with your heart set against a just and non
biased observation of a particular subject
of supposed interest?
is that the impression the Jays have a long term trend
of under performing bad teams is simply
observation bias rather than actually having merit.
I'm so tired
of people making these damning comments that aren't even based on
observation; it's almost funny how
biased some people are AGAINST their own players.
I get quite a lot
of stick for being critical
of referees in Arsenal games, and I'll be the first to admit that my
observations are built on
bias, so
of course there is a good chance that people may disagree with my viewpoints, particularly when their
biases lay elsewhere.
Of course, the same applies to messaging via Facebook or MySpace, but here's where my personal
bias connects with Cerf's
observation: I'll submit that the thing that made Facebook messsaging useful (to me, at least) was when the «you have a message» notification emails began including the actual text someone was sending to you.
«Whilst the possibility
of this is extremely low, possibly even zero, as scientists it's important that we avoid complacency and examine
observations and evidence without
bias.»
However, scientists from the Canadian - French - Hawaiian project OSSOS detected
biases in their own
observations of the orbits
of the TNOs, which had been systematically directed towards the same regions
of the sky, and considered that other groups, including the Caltech group, may be experiencing the same issues.
Biologists have to undertake large studies that can guarantee the statistical significance
of observations, and they need self - critical analysis to avoid inadvertent
biases.
Based on these
observations, the researchers developed a mathematical model to identify how this
biased partitioning
of the drug pump affects the bacterial population.
That made it less likely that the clumping might be due to an
observation bias such as pointing a telescope at a particular part
of the sky.
If Turner's team is correct, then all the
observations that yield higher values
of the Hubble constant must be
biased by what he calls «a common systematic error».
FMI has been involved in research project, which evaluated the simulations
of long - range transport
of BB aerosol by the Goddard Earth Observing System (GEOS - 5) and four other global aerosol models over the complete South African - Atlantic region using Cloud - Aerosol Lidar with Orthogonal Polarization (CALIOP)
observations to find any distinguishing or common model
biases.
Since the temperature changes since 1979 are on the order
of 0.6 C or so, it is relatively easy for
bias, due to changing
observation times, to swamp the underlying climate signal.
Some
of the discontinuities (which can be
of either sign) in weather records can be detected using jump point analyses (for instance in the new version
of the NOAA product), others can be adjusted using known information (such as
biases introduced because changes in the time
of observations or moving a station).
Supporting this is our
observation that approximately one third
of both OR and VR genes with interrupted ORFs are not expressed in olfactory tissues, a
bias that had been noted previously [41].
We demonstrated that a regression - based statistical correction for the proportion
of the students in each teacher's class that are English - language learners, have education disabilities, are from low - income families, and so forth, wrings most
of the
bias out
of classroom
observations.
The
bias in classroom
observation systems that derives from some teachers being assigned much more able students than other teachers is very important to the overall performance
of the teacher evaluation system.
But in the districts we examined, only teachers at the very tail end
of the distribution are dismissed because
of their evaluation scores, and it turns out that teachers who get the very worst evaluation scores remain at the tail end
of the distribution regardless
of whether their classroom
observation ratings are
biased.
In our report, we introduced a method for adjusting for the
bias in classroom
observation scores by taking into account the demographic make - up
of teachers» classrooms.
The advantages come at the price
of concerns about the limited number
of country
observations, the mostly cross-sectional character
of available achievement data, and possible
bias from unobserved country factors such as culture.
Measures that allow for more subjectivity and local control, such as classroom
observations and SLOs, are subject to their own types
of bias.
An untrained observer may introduce
bias into
observations; the observer's expectations
of a teacher may influence the
observation to a greater degree than the actual teacher behaviors displayed (Mujis, 2006).
Following - up on two prior posts about potential
bias in teachers»
observations (see prior posts here and here), another research study was recently released evidencing, again, that the evaluation ratings derived via
observations of teachers in practice are indeed related to (and potentially
biased by) teachers» demographic characteristics.
This is much different than just jumping in right away on our first
observation of a price action signal or market
bias.
This is not a criticism
of the strategy, which is tractable and implementable, but an
observation on how pernicious our cognitive
biases are.
In this case, there has been an identification
of a host
of small issues (and, in truth, there are always small issues in any complex field) that have involved the fidelity
of the
observations (the spatial coverage, the corrections for known
biases), the fidelity
of the models (issues with the forcings, examinations
of the variability in ocean vertical transports etc.), and the coherence
of the model - data comparisons.
And
of course the new paper by Hausfather et al, that made quite a bit
of news recently, documents how meticulously scientists work to eliminate
bias in sea surface temperature data, in this case arising from a changing proportion
of ship versus buoy
observations.
It's not about someone saying there is urban heat
bias, it's about the method
of modeling used to model the
observations which reduces the error extent.
Some
of the discontinuities (which can be
of either sign) in weather records can be detected using jump point analyses (for instance in the new version
of the NOAA product), others can be adjusted using known information (such as
biases introduced because changes in the time
of observations or moving a station).
Indeed, globally averaged systematic
observation biases, sampling array issues and steric changes below 1500m depth together are smaller than the error
of SLRES.
However, my statistics experience would hesitate to call that a representative sample since most
of the
observations in my sample are Wesleyan students and would therefore have an element
of bias.
A lot
of the
observation based estimates are likely
biased low, as outlined in the Ringberg report just due to assumptions
of linearity in the evolution
of surface temperature in response to some given radiative nudge on the system.
Some
biases are corrected for (time
of observation, re-siting to place), but it is fair to say not all
biases are accounted for.
Progress in the longer term depends on identifying and correcting model
biases, accumulating as complete a set
of historic
observations as possible, and developing improved methods
of detection and correction
of observational
biases.»
Whether the issue is tracking Arctic methane or American stream flows, there's a vital need for sustained, consistent
observations, but — unfortunately — there's a two - edged
bias against such investments, given the appeal
of focusing on science's frontiers and the tendency to target monitoring programs — which are akin to bridge maintenance — when looking to cut budgets.
That is the whole point
of theory laden
observation and confirmation
bias.
As they point out, «In reality, however, observational coverage varies over time,
observations are themselves prone to
bias, either instrumental or through not being representative
of their wider surroundings, and these observational
biases can change over time.