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Job Summary
The Cooperative Institute of Great Lakes Research (CIGLR) is seeking a postdoctoral scholar to lead research related to the development, testing, and deployment of hydrological models across the Great Lakes basin. Representative objectives of projects include calibration and verification of Weather Research and Forecasting (WRF)-Hydro and its meteorological forcings to support development of NOAA’s National Water Model, evaluation of potential empirical relationships between the risk of nutrient loading and land surface model parameters, and customization to improve local flood forecasting capabilities. The fellow will be expected to lead one or more of these projects, and will be given the intellectual freedom to pursue additional ideas of their own that contribute to the broader goals of hydrological modeling in the Great Lakes.
The postdoctoral scholar will work with a team of hydrological modelers at CIGLR, and in collaboration with modeling teams at the NOAA Great Lakes Environmental Research Laboratory (GLERL), University of Michigan, and the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. The ideal candidate will have excellent communication skills, be prepared to guide technical support staff, report findings to internal and external audiences in reports and presentations, and lead publications in scientific journals.
The successful applicant’s appointment will be with CIGLR, which is part of the University of Michigan’s School for Environment and Sustainability located in Ann Arbor, Michigan. CIGLR is a collaboration between the University of Michigan and NOAA that brings together experts from academia and government research labs to work on pressing problems facing the Great Lakes region. The fellow will spend the majority of their time at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor and work in close collaboration with colleagues at the University of Michigan and the National Center for Atmospheric Research (NCAR).
The University of Michigan is consistently ranked among the top American public research universities, and Ann Arbor is routinely ranked as one of the best places to live in the U.S. due to its affordability, natural beauty, preservation of wooded areas, vibrant arts program, and lively downtown.
This position offers a highly competitive salary plus benefits. The initial appointment is for one year, with opportunity for extension based on performance, need, and availability of funds.
SEAS Diversity, Equity, and Inclusion Mission
At SEAS we are committed to creating and maintaining an inclusive and equitable environment that respects diverse experiences, promotes generous listening and communications, and discourages and restoratively responds to acts of discrimination, harassment, or injustice. Our commitment to diversity, equity and inclusion is deeply rooted in our values for a sustainable and just society
This position requires a Ph.D. in the natural sciences or engineering, with a background in hydrological science and modeling, and a solid record of scholarship.
To apply visit:
Applicants should prepare the following materials in a single PDF:•  Cover letter describing your qualifications related to the position and research accomplishments •  Curriculum vitae•  Contact information for three professional references•  Two representative publications
Applications are due by December 15, 2018.
U-M EEO/AA Statement
The University of Michigan is an equal opportunity/affirmative action employer.

Extreme weather and climate events, although rare at any particular location, can lead to different amount of loss to exposed human and natural systems, even to disasters. In this paper, the most authoritative definitions of “extreme events” given by the World Meteorological Organization and Intergovernmental Panel on Climate Change have been considered, as well as the underlying basic concepts, i.e., selected intensity levels, selected percentiles, multiples of the standard deviation, return period, distribution tails, imprint left, cause-effect relationships, natural disasters. Definitions and criteria have been tested with real world case studies using long instrumental records (300 years of daily temperature and 200 years of daily precipitation in Bologna, Italy) and proxy series (1000 years of Venice lagoon frozen over and 300 years of Po River outflow). The analysis reveals that each definition leads to particular consequences, e.g., in the peak over threshold theory, if the threshold is expressed in absolute terms, the number of extreme events may change with the climate period as tested with the Venice case study; as opposed, the relative definition based on percentiles or standard deviation will keep unchanged this frequency. Again, considering extreme events those external to the 10th or 90th percentile of the distribution may lead to a return period too short, e.g., 10 days for daily records, while a 10 year return period would require 1st or 99th percentiles, as tested with the daily temperature and precipitation. In addition, the distribution of a series may substantially change shape passing from daily to monthly and yearly averages as tested with the series taken as examples. The specific case of proxies is also considered analysing their uncertainties and categorization. The lagoon frozen over as a consequence of exceptionally severe winters constitutes an example of extremes based on an absolute threshold, as well as cause-effect relationship, and the return period was highly affected by the change of climate periods. The example of the overflow of the Po River suggests that the occurrence of extremes, and their intensity, may be altered by other factors that concur to the final result.

Hi, anyone tried this VIC ASSIST?  I tried to run it but it shows an interface and that doesn't take any command. I have edited paths of vic source code, cygwin and python according to my system. Do I have to edit the code manually?
I have posted screenshots of matlab editor and GUI which it shows after running the code. Can anybody help me with that?
Models / Re: Anybody working with VIC model
« Last post by Diwan on October 15, 2018, 11:08:16 AM »
Kindly provide sufficient information while posting your query. So that people can try to address it.
Models / Re: Anybody working with VIC model
« Last post by hsalehi on October 13, 2018, 11:16:37 PM »
hi to all
I am running the vic model using Python Codes.
I have a sample and I'm training with that model
I'm have problems with the rout and the codes are not responding.
Does anyone with Python run the model ?
Models / Re: Anybody working with VIC model
« Last post by hsalehi on October 12, 2018, 04:30:20 AM »
hi to all
I am running the vic model using Python Codes.
I have a sample and I'm training with that model
I'm having problems with the rout and the codes are not responding.
Does anyone with Python run the model ?
HydroSight is a highly flexible statistical toolbox for quantitative hydrogeological insights. It comprises of a powerful groundwater hydrograph time-series modelling and simulation framework plus a data quality analysis module. Multiple models can be built for one bore, allowing statistical identification of the dominant processes, or 100’s of bores can be modelled to quantify aquifer heterogeneity. This flexibility allows many novel applications such as:Separations of the impacts from pumping and drought over time.
  • Probabilistic estimation of aquifer hydraulic properties.
  • Estimation of the impacts of re-vegetation on groundwater level.
  • Exploration of groundwater management scenarios.
  • Interpolation and extrapolation irregularly observed hydrograph at a daily time-step.
The toolbox can be used from a highly flexible and stand-alone graphical user interface ( or programmatically from within Matlab 2014b (or later).
Interesting information / Targeting 1.5 °C
« Last post by Pankaj Dey on October 10, 2018, 07:24:26 PM »
In December 2015, representatives from 195 nations met in Paris to negotiate an international agreement to combat climate change. The resulting ‘Paris Agreement’ codified an aspiration to limit the level of global temperature rise to 1.5 °C above pre-industrial levels —  lower than the previously generally agreed target of 2 °C. From a research standpoint, a more ambitious temperature target poses many questions that could draw scientific and intellectual attention and resources. Furthermore, the timescales in which researchers must decide how to engage with this new policy context is very short.
The Intergovernmental Panel on Climate Change has agreed to publish a special report on the costs and implications of the 1.5 °C target in 2018. In order to inform that process, researchers must decide which efforts to prioritise and begin work almost immediately. But deciding what can and should be delivered is far from trivial. This evolving collection draws together content from Nature Climate Change, Nature Geoscience, Nature Communications and Nature to provide comment on how research might best inform decisions about limiting climate warming as well as presenting pertinent new research that adressess this very question.

In regression, we assume noise is independent of all measured predictors. What happens if it isn’t?

A number of key assumptions underlie the linear regression model – among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model.

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