http://www.mesonet.org/index.php/weather/meteogram/
The Norman time series of weather data, specifically wind, was rather strange today. Just after 2:20am the wind began to slow down dropping to 3-4 mph. Funny thing is I remember it being windy at the site around 9am. This is the effect of freezing rain on an anemometer. The propeller didn't freeze altogether as I have seen in Iowa data but the extra mass made it harder to spin. The entire assembly didn't freeze either as the wind direction did change albeit slightly.
The airport in Norman first reported unknown precipitation at 2:11 am at a temperature of 30 and it dropped from there, while the winds stayed in the 15-20mph range throughout the night.
As far as good data goes, I think there is a sonic anemometer but it is made of metal and I wonder how good the wind data would be with a coat of ice on it. Therefore it would be difficult to correct. Human quality control could handle this but I doubt with station to station comparison this time series could be flagged as being questionable. Realize that the rain gauge at the site is not a typical heated tipping bucket so there was no precipitation recorded at the station.
This is not a criticism of the data in any way; rather it highlights the difficulty of making all-weather observations year round even on well funded networks. This even happens in hurricanes when the power goes out or when water tops the instruments or penetrates the wiring.
Its important to maintain the observations that are good for future work either in weather or climate and hopefully this provides a gut-check. Some observations will sneak in that are bad and therefore it is important to realize that no observation system is perfect or error-free. Sometimes this is why "tricks" or manipulation must be performed because real data has issues and they must be sorted out, flagged, approved or removed.
Thursday, January 20, 2011
Monday, January 3, 2011
Science and Adults
Science had an interesting essay published:
http://www.sciencemag.org/content/early/2010/12/01/science.1186994
"Without a basic understanding of how science works, the public is vulnerable to antiscience propaganda, which engenders distrust of science when it comes to social issues, consumer choices, and policy decisions."
A profound statement. Sadly this is a core problem since we must consider that the scientific method is taught, at a minimum, from grades 3 to at least 10. Thus having any education in science should yield at least cursory understanding of how science works.
However education advances very slowly whereas the speed of science is accelerating. I think most people in a classroom in high school would acknowledge that the smart kids stand way out because they are continually challenged, whereas the rest prefer to not be challenged. Sometimes it is the education system itself which leads to a demoralization ... especially as teachers expect less from underachieving students. We are leaving behind students and advancing others ... this produces a gap.
Science itself produces gaps as increasingly complex problems become addressed by a self selected few (lets call them really intelligent scientists). This type of accelerated learning is happening in all fields due to the growth of science itself. It used to be in discrete leaps (think Einstein and his peers followed by the government development of nuclear weapons). It takes real hard work to follow in these folks footsteps. Consider that a particle physicist may spend 10 years getting a PhD! Contrast that with a meteorologist (a relatively young field of study) who may spend 3-6 years.
Now consider that, as is the current state of affairs, such gaps begin to effect science policy and fiscal expenditures. Some may say we lack the financial fortitude to increase our science spending, and they would be correct in their analysis of available funds. And in doing so they forget that research precedes development. In fact, industry does research specific to its problems. Interestingly enough, some of the largest practical science developments have occurred in the process of researching very different problems. So in order for big technological advances, the government funds research precisely so that development can ensue. Consider a quote taken from:
http://www.latimes.com/news/opinion/commentary/la-oe-mooney-science-20101226,0,4203638.story
"And we need to recognize that the cost of basic science, and the time it
takes, require a sustained government commitment because industry can't
be relied on to fund incremental and high-risk science for its own sake
without any guarantee of a payoff."
Industry seeks profit. This should not be a surprise nor a negative. Industry must be profitable to sustain itself. And even more profitable to fund its own research: think Bell labs! The terms high-risk high reward should be common knowledge. Are they? Most people should also know how expensive incremental science is, since that is how we fund graduate students in most fields. Sadly they do not.
So, we have some work to do on both educating our students so as to produce science-literate adults, and also scientific communication such that as the gaps continue to grow we have intelligent people keeping those with whom science is foreign or too difficult, in touch.
http://www.sciencemag.org/content/early/2010/12/01/science.1186994
"Without a basic understanding of how science works, the public is vulnerable to antiscience propaganda, which engenders distrust of science when it comes to social issues, consumer choices, and policy decisions."
A profound statement. Sadly this is a core problem since we must consider that the scientific method is taught, at a minimum, from grades 3 to at least 10. Thus having any education in science should yield at least cursory understanding of how science works.
However education advances very slowly whereas the speed of science is accelerating. I think most people in a classroom in high school would acknowledge that the smart kids stand way out because they are continually challenged, whereas the rest prefer to not be challenged. Sometimes it is the education system itself which leads to a demoralization ... especially as teachers expect less from underachieving students. We are leaving behind students and advancing others ... this produces a gap.
Science itself produces gaps as increasingly complex problems become addressed by a self selected few (lets call them really intelligent scientists). This type of accelerated learning is happening in all fields due to the growth of science itself. It used to be in discrete leaps (think Einstein and his peers followed by the government development of nuclear weapons). It takes real hard work to follow in these folks footsteps. Consider that a particle physicist may spend 10 years getting a PhD! Contrast that with a meteorologist (a relatively young field of study) who may spend 3-6 years.
Now consider that, as is the current state of affairs, such gaps begin to effect science policy and fiscal expenditures. Some may say we lack the financial fortitude to increase our science spending, and they would be correct in their analysis of available funds. And in doing so they forget that research precedes development. In fact, industry does research specific to its problems. Interestingly enough, some of the largest practical science developments have occurred in the process of researching very different problems. So in order for big technological advances, the government funds research precisely so that development can ensue. Consider a quote taken from:
http://www.latimes.com/news/opinion/commentary/la-oe-mooney-science-20101226,0,4203638.story
"And we need to recognize that the cost of basic science, and the time it
takes, require a sustained government commitment because industry can't
be relied on to fund incremental and high-risk science for its own sake
without any guarantee of a payoff."
Industry seeks profit. This should not be a surprise nor a negative. Industry must be profitable to sustain itself. And even more profitable to fund its own research: think Bell labs! The terms high-risk high reward should be common knowledge. Are they? Most people should also know how expensive incremental science is, since that is how we fund graduate students in most fields. Sadly they do not.
So, we have some work to do on both educating our students so as to produce science-literate adults, and also scientific communication such that as the gaps continue to grow we have intelligent people keeping those with whom science is foreign or too difficult, in touch.
Tuesday, December 28, 2010
Kids and Science
I heard through the grapevine that a bunch of 8-10 year old kids in the U.K. published in a prestigious journal, led by their teacher and a research scientist. The topic was bees and my interest lies not in the publishing part but in the science and kids part, especially treating science like a game or puzzle. The kids formulated the questions and hypothesis and constructed the game and analyzed the results ... even writing up the manuscript. As the essay stated, which accompanied the article, the kids were proud of what they had learned and wanted to share that knowledge. Now that is some good science education.
http://rsbl.royalsocietypublishing.org/content/early/2010/12/18/rsbl.2010.1056.full?sid=c43f1415-9fbc-4f1b-94b3-51866e5edbd2
http://rsbl.royalsocietypublishing.org/content/early/2010/12/18/rsbl.2010.1056.full?sid=c43f1415-9fbc-4f1b-94b3-51866e5edbd2
Saturday, December 25, 2010
Scales of uncertainty
The latest fuss in meteorological circles has focused rather intently on the Storm of Christmas, then the day after, and now the day after the day after Christmas storm. The storm has been "delayed" by 2 days over the course of the last week, and has shifted position numerous times in the forecast (gonna miss far to the east, bounce back to the west for a hit). We call this behavior uncertainty.
So what is uncertainty? "not confirmed", "still undecided", a "lack in confidence"
In modeling terms, the forecast solution has not yet converged. Our current capabilities, however, are quite robust at short range, meaning we expect the solution (over many initialization cycles) to converge. This time period is usually between 48 and 72 hours depending on the modeling system, the scale of the features that need to be resolved well, and the overall scale of the dynamical system which we seek to predict.
Over the years, despite many advances in satellite data, data assimilation systems, and numerical models and their increasingly sophisticated methods ... we still need to wait for the disturbances to enter the well instrumented radiosonde network*. Alas this too can be deceiving as was the case for the Surprise snowstorm of January 2000. You see a classic nor'easter can have origins from the Pacific Northwest which travels in amplified flow towards the gulf coast. The system itself may never leave the well instrumented but the warm gulf, cool temperatures aloft, and strong wind shear may foster convection ... thunderstorms ... or organized noise. This noise can then feedback from the large scale to the small scale ... outside the network and remain outside the network as the coastal low forms and deepens as it travels up the coast.
So, you can see how a "converged" solution of where the storm will be, can help us have confidence on predicting the other aspects of the storms. But uncertainty remains in where the precipitation will be, what precipitation type will fall, and for how long.
This uncertainty about snowfall placement and amount is very similar to the summer forecasting of thunderstorms. We may have a converged solution of the larger scale details but the smaller scale details can have a large effect on where, when, which storms will form and how severe they might be.
This where the art of forecasting kicks in. Where the analog experience of forecasters contributes. Knowing how they were fooled last time, or how they picked up on certain observed details which caused them to do better than the models. The forecaster has the ability to understand these scales of uncertainty. Only in the last few years have methods been developed, like the Ensemble Kalman Filter, which can show us where the uncertainty is for a specified region at 3,5,7 day lead time. In fact the Winter Weather Reconaissance program is designed to use these methods, then collect data in the uncertain regions to see how that can change the forecast and its uncertainty!
So what is uncertainty? "not confirmed", "still undecided", a "lack in confidence"
In modeling terms, the forecast solution has not yet converged. Our current capabilities, however, are quite robust at short range, meaning we expect the solution (over many initialization cycles) to converge. This time period is usually between 48 and 72 hours depending on the modeling system, the scale of the features that need to be resolved well, and the overall scale of the dynamical system which we seek to predict.
Over the years, despite many advances in satellite data, data assimilation systems, and numerical models and their increasingly sophisticated methods ... we still need to wait for the disturbances to enter the well instrumented radiosonde network*. Alas this too can be deceiving as was the case for the Surprise snowstorm of January 2000. You see a classic nor'easter can have origins from the Pacific Northwest which travels in amplified flow towards the gulf coast. The system itself may never leave the well instrumented but the warm gulf, cool temperatures aloft, and strong wind shear may foster convection ... thunderstorms ... or organized noise. This noise can then feedback from the large scale to the small scale ... outside the network and remain outside the network as the coastal low forms and deepens as it travels up the coast.
So, you can see how a "converged" solution of where the storm will be, can help us have confidence on predicting the other aspects of the storms. But uncertainty remains in where the precipitation will be, what precipitation type will fall, and for how long.
This uncertainty about snowfall placement and amount is very similar to the summer forecasting of thunderstorms. We may have a converged solution of the larger scale details but the smaller scale details can have a large effect on where, when, which storms will form and how severe they might be.
This where the art of forecasting kicks in. Where the analog experience of forecasters contributes. Knowing how they were fooled last time, or how they picked up on certain observed details which caused them to do better than the models. The forecaster has the ability to understand these scales of uncertainty. Only in the last few years have methods been developed, like the Ensemble Kalman Filter, which can show us where the uncertainty is for a specified region at 3,5,7 day lead time. In fact the Winter Weather Reconaissance program is designed to use these methods, then collect data in the uncertain regions to see how that can change the forecast and its uncertainty!
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