Weather Modifying Weapons

OSTM/Jason-2's predecessor TOPEX/Poseidon caug...
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This is part of a continuing series of theoretical weapons. The weapons are theoretical as the United Nations has already banned the weapons (but not banned the building of research of defense from these weapons).

Possible applications of weather modifying weapons.

1) Use surface modifiers on oceans including but not limited to submerged nuclear heaters, airborne solar powered  lasers, surface spreaders like oil slicks. This will help modify the temperature of the ocean in certain critical areas  at critical times, influencing weather esp winds that bring rains.

Example- Modifying or Enhancing El Nino to influence rain to specific countries.

2) Use of air borne or aircraft borne lasers to start forest fires

3) Use of lasers to enhance the rate of melting of strategic glaciers.

4) Modify and interfere with the timing of an active volcano to prevent big rupture, rather to go for controlled releases.

5) Use of harmonics to influence seismic wave activity in geological reasons.

Weather Weapons

water table high.
Image by glassblower via Flickr

possible weapons to modify weather and /or influence psychological reactions in people/mass events.

1) use of lasers to create hot spots on ocean ,sea surfaces for clouds and pressure winds

2) controlled demolitions to alter river trajectory

3) controlled sub terranean demolitions to influence water table levels

4) introduction of aerial oxidants and chemical leaching agents to alter soil productivity, water retention.

5) controlled sub nuclear explosions on glaciers as well as ice deposits.

6) MODIFICATION of ambient light /rain/sleet to influence or encourage dissent in populations usually in combination with some or all of the above.

the name of the game is the art of fighting a war without fighting a war. subtlety does it.

countries doing it are us, supported by pan democracies, china, and russia. may we live in interesting times

http://www.asitis.com/2/41.html

TRANSLATION

Those who are on this path are resolute in purpose, and their aim is one. O beloved child of the Kurus, the intelligence of those who are irresolute is many-branched.

Lovely forecasting blog

Eight different random walks.
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I really loved this simple, smart and yet elegant explanation of forecasting. even a high school quarterback could understand it, and maybe get a internship job building and running and re running code for Mars shot.

Despite my plea that you remain svelte in real life, I implore you to be naïve in business forecasting – and use a naïve forecasting model early and often. A naïve forecasting model is the most important model you will ever use in business forecasting.

and now the killer line

Purists may argue that the only true naïve forecast is the “no-change” forecast, meaning either a random walk (forecast = last known actual) or a seasonal random walk (e.g. forecast = actual from corresponding period last year). These are referred to as NF1 and NF2 in the Makridakis text (where NF = Naïve Forecast). In our 2006 SAS webseries Finding Flaws in Forecasting, an attendee asked “What about using a simple time series forecast with no intervention as the naïve forecast?” Is that allowed?

i did write a blog article on forecasting some time back, but back then I was a little blogger, with the website name being http://iwannacrib.com

great work in helping make forecasting easier to understand for people who have flower shops and dont have a bee, to help them with the forecasts, nor an geeky email list, not 4000$.

make it easier for the little guy to forecast his sales, so he cuts down on his supply chain inventory, lowering his carbon footprint.

Blog.sas.com take a bow, on labour day, helping workers with easy to understand models.

http://blogs.sas.com/forecasting/index.php?/archives/68-Which-Naive-Model-to-Use.html

Summer School on Uncertainty Quantification

Scheme for sensitivity analysis
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SAMSI/Sandia Summer School on Uncertainty Quantification – June 20-24, 2011

http://www.samsi.info/workshop/samsisandia-summer-school-uncertainty-quantification

The utilization of computer models for complex real-world processes requires addressing Uncertainty Quantification (UQ). Corresponding issues range from inaccuracies in the models to uncertainty in the parameters or intrinsic stochastic features.

This Summer school will expose students in the mathematical and statistical sciences to common challenges in developing, evaluating and using complex computer models of processes. It is essential that the next generation of researchers be trained on these fundamental issues too often absent of traditional curricula.

Participants will receive not only an overview of the fast developing field of UQ but also specific skills related to data assimilation, sensitivity analysis and the statistical analysis of rare events.

Theoretical concepts and methods will be illustrated on concrete examples and applications from both nuclear engineering and climate modeling.

The main lecturers are:
Dan Cacuci (N.C. State University): data assimilation and applications to nuclear engineering

Dan Cooley (Colorado State University): statistical analysis of rare events
This short course will introduce the current statistical practice for analyzing extreme events. Statistical practice relies on fitting distributions suggested by asymptotic theory to a subset of data considered to be extreme. Both block maximum and threshold exceedance approaches will be presented for both the univariate and multivariate cases.

Doug Nychka (NCAR): data assimilation and applications in climate modeling
Climate prediction and modeling do not incorporate geophysical data in the sequential manner as weather forecasting and comparison to data is typically based on accumulated statistics, such as averages. This arises because a climate model matches the state of the Earth’s atmosphere and ocean “on the average” and so one would not expect the detailed weather fluctuations to be similar between a model and the real system. An emerging area for climate model validation and improvement is the use of data assimilation to scrutinize the physical processes in a model using observations on shorter time scales. The idea is to find a match between the state of the climate model and observed data that is particular to the observed weather. In this way one can check whether short time physical processes such as cloud formation or dynamics of the atmosphere are consistent with what is observed.

Dongbin Xiu (Purdue University): sensitivity analysis and polynomial chaos for differential equations
This lecture will focus on numerical algorithms for stochastic simulations, with an emphasis on the methods based on generalized polynomial chaos methodology. Both the mathematical framework and the technical details will be examined, along with performance comparisons and implementation issues for practical complex systems.

The main lectures will be supplemented by discussion sessions and by presentations from UQ practitioners from both the Sandia and Los Alamos National Laboratories.

http://www.samsi.info/workshop/samsisandia-summer-school-uncertainty-quantification