The simgen software package: User guide and notes

Size: px
Start display at page:

Download "The simgen software package: User guide and notes"

Transcription

1 The simgen software package: User guide and notes Arthur M. Greene International Research Institute for Climate and Society The Earth Institute, Columbia University New York, NY June 28, 2012 Abstract The software simgen is provided in conjunction with the technical report, A framework for the simulation of regional decadal variability for agricultural and other applications, prepared at the request of the Climate Change, Agriculture and Food Security (CCAFS) program of the Consultative Group on International Agricultural Research (CGIAR). (See acknowledgments in that report for detailed attribution.) The report describes a generalized approach to statistical climate simulation, as applied to near-term climate change time horizons. Section 5 in the report describes a case study, in which the general approach is realized in a particularized climatological and applications-oriented setting, in the Western Cape region of South Africa. (This work was reported in a separate publication.) The simgen software comprises the various code elements used to produce the simulations described in that case study. Here, we describe both the code and its manner of deployment. As pointed out in the technical report, simulation in a particular regional setting will require a corresponding elaboration of the simulation framework, which effectively constitutes a template for this purpose. We discuss here how this plays out in the specific context of the case study, in the process identifying the degree of generality applying to specific elements of the code. One component of the detailed scheme was implemented using the statistical software R. This is also described, and the necessary references provided. simgen itself is written in the Python programming language, and makes use of additional external Python packages. No fees or other charges are required for use of any of the code or external packages employed in this project, all of which are available under various open source licenses. 1

2 1 Introduction The technical report A framework for the simulation of regional decadal variability for agricultural and other applications [Greene et al., 2011, referred to hereinafter as GHG] includes discussion of a case study. This study, described in detail in Greene et al. [2012], involved the statistical generation of an ensemble of climate simulations for the Berg and Breede Water Management Areas in the Western Cape province of South Africa. The simulations, comprising precipitation as well as minimum and maximum daily temperatures, were designed to drive the ACRU hydrology model [Schulze, 1995], developed at the University of KwaZulu-Natal, South Africa, and were part of an ambitious, effort, involving multiple institutional participants, to characterize future climate in the region of the Western Cape. The present document describes the code developed to produce those simulations and is provided, along with that code, as an adjunct to GHG. As discussed in GHG, simulation of regional decadal variability will be conditioned by a number of factors, including characteristics of the regional climate, available observational records and follow-on modeling requirements. The code, whose main routine is named simgen, has thus been designed with ACRU-based studies in mind. This means, inter alia, that input routines are designed to read, and output routines to write, ACRU-formatted files. (This format is described in Appendix A.) It is expected that programmers adapting simgen for other settings will modify these routines, and indeed, other aspects of the code as well, to suit the requirements of particular applications for which the simulations will be used. Because the various software components derive from different sources, licensing details vary among them. However, none of the components or programs utilized are commercial software, in the sense that they involve acquisition costs or licensing fees, in particular for noncommercial use. References to the various licenses and terms are provided in Sec (Disclaimer: The author is not an attorney; nothing in this document should be construed as legal advice.) The sections of this guide describe the various code components (Sec. 2), setup of the computing environment (Sec. 3), individual function calls (Sec. 4) and the sequence of operations in simgen (Sec. 5). Final remarks are provided in (Sec. 6). 2 Components The simulation process, as realized in the case study, utilizes several software components, most, but not all of which reside in the simgen code itself. The R programming language (http://www.r-project.org) was also employed for certain tasks. Some functions are executed only once, in setting up the simulation environment, while 2

3 others are repeated, typically in looping over individual locations in the modeled network. Tasks accomplished with R belong to the former group, and were logically performed offline, i.e., outside the scope of the simgen code. The simgen code itself is written in Python (http://www.python.org), an objectoriented programming language that has found wide application in many scientific and technical fields. There are a number of Python distributions, each typically including a set of modules, or packages, keyed to a defined range of tasks; we utilize one of these, described below, but also suggest alternatives, so as to facilitate the deployment of simgen. 2.1 Python The main simgen code, as stated, is written in Python. The code also invokes functions from a number of Python modules that are not part of the core Python language. Important among these are numpy, which provides key mathematical functions, as well as numerical arrays and random variables, and scipy, which supplies a linear regression function. A module necessary for the present version of simgen, but that may possibly be dispensed with in certain circumstances, is cdms (Climate Data Management System), which is part of the CDAT (Climate Data Analysis Tools) Python distribution. In fact, CDAT provides both numpy and scipy, so if the modeler chooses to install CDAT (available at all the necessary tools will be available from the start. Version 5.2 was the version utilized for the simulations described herein. CDAT is available for both the Linux operating system and for Mac OS X. The author has run it only under Linux, but the developers of CDAT are known to also use OS X, so it is likely that this is a viable option. It may also be possible to run CDAT using a virtual environment on Windows computers, but we lack the experience to offer guidance here. cdms is used only internally, in order to facilitate certain data manipulations. Input and output are both in form of ACSII files, and most of the computational work is performed using numpy arrays (rather than cdms transient variables ). Dispensing with cdms would require rewriting some of the code, however. We also note the existence of cdat-lite, a Python package that includes cdms and other core CDAT libraries. Available at it provides the necessary toolkit while avoiding the necessity of installing the (much larger) full CDAT distribution. We have also successfully run simgen using cdat-lite version 6.0, in a non-cdat environment. Note that unless modifications are made to the simgen code as it now stands, it will be necessary for the user to obtain and install CDAT (or cdat-lite) in order to run simgen; We do not distribute either CDAT or cdat-lite. 3

4 The simulation code is designed to be run from within an interactive Python session: The initial call (issued from a terminal) can be to cdat itself; we happen prefer the ipython shell (see but this is strictly a user preference. Once Python is started (and all of the ancillary files are in place), simulations are generated by first importing simgen, then issuing a call to the gen routine, using appropriate arguments. To facilitate comprehension and usability, simgen has been extensively commented, with an initial docstring (set off by triple quotes) at the head of the module, additional docstrings placed strategically throughout the code and with many individual comment lines (lines beginning with the hash symbol #). The use of docstrings permits access to the included information via use of the Python help function, as well as other docstring functions, from within the interactive shell, while the individual comment lines provide a more granular description of the various code sequences. 2.2 R A key feature of the simulations described in Greene et al. [2012] is the generation of stochastic sequences on the annual time step. The statistical structure used for this step in the case study is identified as a vector autoregressive (VAR) model of order unity. This model is fit to the regional observational data, in the case study a trivariate annualized series of length 50, using the Dynamic Systems Estimation (dse) time series package for the R programming language, i.e., external to the main simgen code. The call in R was to the routine estvarxls, which implements a least-squares estimation of VAR parameters. A call to simulate in the dse package was used to generate the long simulated sequence referred to in Greene et al. [2012]. Other routines were used for model checking and testing various aspects of the inferred model, but details of these procedures will depend on the specifics of any simulation setting, as well as the form of time series model employed. The modularity of the simulation code permits the fitting of an infinite variety of statistical models, in Ror in other software of the modeler s choice, and the generation of a long simulation sequence outside the main simgen code. When simgen is run it reads this long sequence and slices it to produce the detailed downscaled simulations. 2.3 Licenses We summarize below licensing information for the software elements included in, or utilized by simgen, to the best of our knowledge. Users are responsible for observing the terms of these licenses. 1. Python: Open source software, compatible with the GNU General Public Li- 4

5 cense (GPL). See 2. CDAT: Full license terms are included within the source code distribution. Commercialization of CDAT requires notification of either the United States Department of Energy or Lawrence Livermore National Laboratory. Certain third-party components are distributed subject to additional licensing terms. 3. R: The R language is licensed under the GNU General Public License version 2. Some files may be covered by the GNU General Public License version 3. See for details. 4. simgen: simgen is provided under the Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Creative Commons license. Under this form (see commercial use (i.e., the redistribution of simgen or a derivative for profit) is restricted, and requires a written license agreement. Noncommercial distribution must be on the same terms under which simgen and its ancillary files is originally provided, and must include proper attribution. This attribution is defined in the leading docstring of the simgen code. 3 Preliminary setup Some files must exist, or must be created, prior to executing the main simgen code. These include (a) observational data, both in the form of disaggregated station-level daily files and as a three-component regional mean series at annual time resolution, (b) a global-mean, multimodel-mean temperature record and (c) the long stochastic sequence from which particular simulation instances are drawn. As explained in Section 2.2, the R model that generates the stochastic low-frequency realizations on which the detailed station-level simulations are based is fit to the observational data outside the simgen code structure. For illustrative purposes, examples of those files which must exist a priori are provided along with the simgen code. 3.1 Observational data Observational records may be utilized in three ways. First, when taken to represent the regional climatology they are used for for the fitting of the statistical model with which annual-to-decadal simulations are generated. Here, catchment averages for the three variables, reduced to annual time resolution, are used for this purpose. Second, when a simulation instance is downscaled, both the spatial and temporal disaggregation steps are conditioned by the relationship between the regional signal and 5

6 the fine-scale observational data at individual stations. Third, a k-nearest-neighbor (k-nn) resampling scheme is utilized as part of the downscaling process. It is the daily observational record that is resampled, in one-year blocks, in this step. 3.2 The regional record For the case study the regional signal, representing the behavior of the study region as a whole, consists of an average over the 171 records representing quinary-level catchment values. This signal is multivariate, the three components being precipitation and minimum and maximum daily temperatures. This record is used by the R routine to generate the long simulation sequence, and directly by simgen, in conjunction with the individual catchment values, for broadcasting of simulated annual-to-decadal values to individual catchments. 3.3 Multimodel mean temperature signal For detrending, as well as for the projection of future trends, a global-mean, multimodel ensemble mean temperature record is utilized. The ensemble is composed of a set of global climate models (GCMs) from the Coupled Model Intercomparison Project (CMIP5) [Taylor et al., 2011]. Global temperature records from the individual GCMs are smoothed and a multimodel average is computed. For the purposes of consistent projection, the multimodel mean signal must extend from the start time of the observational record through the end of the simulation period, several decades into the future, requiring a choice of scenario for the future. The case study utilizes the 4.5 W m 2 Representative Concentration Pathway (RCP4.5) experiment [Taylor et al., 2011]. Like the regional mean record described above, the multimodel mean signal is generated offline, and stored prior to runtime. 3.4 Systematic and random components The regional mean series (Section 3.2) are detrended by regression on the multimodel mean signal (Section 3.3), the residual being a multivariate target that the simulations are designed to emulate. The target, having annual time resolution, represents annual-to-decadal variability, and must initially be screened for the identification of possible systematic elements, which are operationally defined as signal components that differ significantly from AR(1) red noise. As discussed in GHG, if such elements are identified they must be modeled independently, the specific form of model depending on data characteristics and simulation requirements. Such models might assume forms as diverse as the WARM models described by Kwon et al. [2007, 2009] or the nonhomogeneous hidden Markov models that were applied to paleorecords Lee s 6

7 Ferry streamflow on the Colorado River in the American Southwest by Prairie et al. [2008]. The range of models that might be deployed in this important step is limited only by the range of observed regional behavior. Deterministic elements were not identified in the regional series associated with the case study [see Sec and Fig. 6 in Greene et al., 2012]. Since the series were tested against an AR(1) null hypothesis, the annual-to-decadal component is thus modeled as a multivariate stochastic (red noise) process. For this purpose a firstorder vector autoregressive (VAR) model was fit, using the R dse package. After suitable verification, this model was used to generate a single, extended simulation sequence. This sequence is stored using the savetxt function in numpy, in the form of an ASCII file. 3.5 File locations and names A directory containing one possible arrangement of files is provided at amg/ccafs/simgen/. Included subdirectories and the files they contain are listed here. The programmer may of course organize files and directories as desired, modifying pathnames in the simgen code accordingly. dat: This directory holds the regional time series, in obsav.dat. input_sim: This holds the single long simulated sequence. The example file, sim 100kyr.dat, includes 100 kyr of simulated data. obs: The obs directory holds the individual (i.e., station-level) observational data, here a single example file, named for historical reasons obshis 2626.txt. In a full region-scale experiment there would be a possibly large set of such files. The simgen code is set up to read files from the obs directory by specifying just the four-digit identifiers before the.txt suffix (in the form of a Python list), but this default can easily be modified. output_sim: The directory into which the output simulations are written. Here it holds a single sample file, named sim 100k obshis txt. The first part of the filename identifies the regional file from which the simulation is derived. The 2626 label corresponds to the station whose variables are being simulated. Finally, the identifier refers to the index into the long simulation file at which the simulated sequence begins. That is, the long file consists of 100 kyr of data; the signifies that the simulated sequence uses values beginning with index 1000 into the file. 7

8 pickled: This directory contains tasav cmip5 comb sm p, the smoothed global multimodel mean temperature series. The year range is indicated in the file name. python: The python directory contains three Python scripts, simgen9s.py, the main simgen code, readqc.py, a routine for reading ACRU-formatted files such as obshis 2626.txt and detrend2.py, a simple linear detrender used by simgen. The simgen version, as of this writing, is 9s, the 9 signifying the version number and s that this is a special edit, where file and directory names have been modified to reflect the demonstration environment. 4 Functions There are number of function definitions within the simgen module and also within the scripts called by simgen; They are described here first the functions in simgen, in the order in which they appear, and then the two external scripts imported by simgen. Comments within the code (either lines beginning with the hash symbol # or text enclosed by triple quotes """) provide further details. 4.1 The simgen module gen(obsix, simix, trendq, fname= sim_100kyr.dat,\ write=1, simlen=66, locate=2041, xval=0, M=1): This is the main routine in simgen, calling other functions as needed to produce the simulations. Note that the \ character above represents a a line break and has no effect on execution. The arguments are as follows: obsix: A Python list of four-digit index numbers (integers or strings) corresponding to the observational files for the locations to which the regional simulation will be downscaled. If there is only a single station (as in the demonstration folder) its index must still be provided as a list, for example [2626], rather than simply The code that translates these indices into the filenames to be read, as well as the filenames themselves, will very likely differ in particular application settings. simix: An integer whose value must be less than the length of the long simulation file minus the length of the sequences being simulated. In the example provided above, the value given was This is the index into the long simulation at which the chosen segment begins. 8

9 trendq: Specifies the quantile of the multimodel distribution to be used for the future precipitation trend simulation. fname: The filename of the long simulation sequence. The enclosing directory name input_sim is not included but is prepended by simgen, an arrangement that can easily be modified to suit the user s computing environment. Note that the default pathname given in the function definition may be overridden by providing an alternate in the call to gen.. write=1: Whether or not to write output files. When running simgen for diagnostics the writing of output files may not be required. The default may be overridden as described above. This also applies to the defaults given below. simlen=66: The desired simulation length, in years. locate=2041: The year, in the simulated sequence, at which a specified decadal fluctuation is to begin. xval=0: A value of zero will cause the values of the simulation to replicate the observations; if the value is unity the values will be simulated. M=1: Whether or not to use the Mahalanobis distance metric in the k-nn routine, basing distance on the three-component (pr, Tmax, Tmin) vector. If zero, only precipitation is used for the distance computation. yrgen: This function takes a daily (univariate) time series and returns annualized values, as well as the indices into the daily series at which year breaks occur. Called by gen. This function requires cdms to properly interpret arrays returned by the readqc module (see below). acru: Called by gen, this function writes ACRU-formatted files. (Note that this is also the format of the obshis demonstration file.) The arguments, simdat, simlen, infile, fname and simix, refer to the simulation data to be written, the length in years of the 21 st -century component of the simulation, names of the input observational and simulation files, and the index into the latter for slicing the simulation sequence. A number of these parameters are used simply for naming the output file. getmoda: Provides a day-by-day list of month and day, each in two-digit numerical form, for either normal or leap years. Called by acru, for formatting the files to be written out. 9

10 leap: Takes a four-digit integer year and returns 1 if a leap year, 0 if not. This, as well as the function above, are necessitated by the hydrological model s requirement that leap and normal years be differentiated. In the observational files as well as the simulations, the former will include the extra day. If obsix is a list with more than one entry, i.e., if simulations are being generated for a network of stations, simgen does not return a value to the interactive window (it does write out the simulation files, assuming write=1). However, if a simulation is created for just a single station, in which case obsix holds a single value, then three arrays, identified in the code as datmat, fmat and scalemat are returned. These hold the complete simulation, at daily resolution, the trend component and the and non-trend component, respectively. fmat and scalemat have annual resolution. These files can be be useful for diagnosis as well as plotting the forced and unforced components of the simulation, separately or in combination. 4.2 The readqc module The Python program readqc contains a single function, r, whose argument is the name of a file (either observational or simulated) written in the ACRU format. It returns four Python objects, designated in the code as prvar, tmaxvar, tminvar, and datmat. The first three of these are one-dimensional cdms TransientVariable objects, holding, besides data values, embedded time axis and calendar information, and correspond to precipitation, maximum and minimum daily temperatures, respectively. The last of the variables, datmat, is a single numpy array object (i.e., without the time axis information) holding all three variables. In all of these arrays the time resolution is daily. prvar, tmaxvar and tminvar are provided as arguments to the yrgen function in simgen to recover annualized versions of the respective variables. 4.3 The detrend2 module The program detrend2 is a short script that provides linear detrending. It is used by simgen to remove small random trends from segments that have been extracted from the long simulated sequence. It contains a single function, dt, which calls the scipy routine linregress, which performs univariate linear regression. 5 Processing sequence We present here an overview of program flow. Additional details are provided in Greene et al. [2012] and in the simgen code itself. The line numbers provided are approximate, and may shift as comments are modified or updated. 10

11 1. Ingestion of preexisting fixed files (line 159 et seq.) Certain files, as described in Secs. 3 and 3.5 must exist prior to running simgen. These files, with the exception of the individual station observations, are read. A simulation instance (i.e., a sequence of length simlen yr, typically a few decades) is extracted from the long simulation file (line 166 et seq.). Although the latter has no overall trend, small random trends may appear in short extracted segments. These are removed with detrend2. The multimodel mean global temperature corresponding to the simulation period is extracted (line 173 et seq., also line 223). Regional mean observational records are read (line 180). 2. The 21 st -century regional precipitation trend is computed, based on the specified value of trendq and parameters of the ensemble distribution (line 208 et seq.) 3. Years in the observed sequence are resampled using a k-nearest-neighbor bootstrap, in order to generate subannual variations. For coherence across the watershed, the same sequence of years must be used at all stations. The sequence is created in this fairly involved routine (lines ). 4. The main loop over catchments (line 448, reading for obsno in obsix:) The station record is annualized (line 451 et seq.). This is performed (a) for detrending, via regression on the multimodel mean signal and (b) for computing the degree of dependence on the regional decadal signal, by regressing the residuals from step (a) on it. In the case of temperature, coefficients from step (a) will be used to project simulated trends forward in time; for precipitation the coefficients are used to scatter catchment trends around the imposed value computed in step 2. Coefficients from step (b) determine the degree to which the simulated regional signal is mixed with uncorrelated noise in the simulated station record. Using the regional data, trends and coefficients from the above steps, station-level simulations are generated (line 456 et seq.) These have an annual time step, and incorporate both the simulated decadal signal and the inferred future trends. The annualized station-level signal is downscaled to daily resolution, by rescaling the subannual variations from the resampled sequence of observational years (line 578 et seq.) 11

12 Two short routines (line 688 et seq.) check the simulated sequences for days on which the maximum temperature is less than or equal to the minimum daily temperature, and for negative values of precipitation, neither of which is acceptable to ACRU (although the temperature condition could conceivably occur in nature). Maximum and minimum temperatures may occasionally be rounded to equal values if the former exceeds the latter by a small amount. The correction consists in adding a small increment to the maximum temperature, so that it exceeds the minimum temperature by 0.25 C. Since precipitation is scaled multiplicatively, negative values are largely avoided. However, in the admixture of uncorrelated noise with the scaled regional signal, such values may (rarely) occur, typically if there exists a long drying trend, bringing scaled values close to zero. The correction consists of setting negative precipitation values to zero. The final, checked variables are assembled into arrays (line 719 et seq.) and are written out as text files with a call to acru (line 733). 5. If a single station was designated, datmat, fmat and scalemat are returned to the interactive session; otherwise simgen loops over the stations whose indices are provided in ixlist, generating and writing out simulation files for the catchments designated thereby. 6 Final remarks In this document we have tried to describe the workings of simgen. We have included details of the software, including required ancillary packages, and have described the setup of the working environment and the steps involved in preparing and running the code. Example files permit actual execution of simgen and should be adequate for testing the code, helping to understand what it does and how, and for preparing the programmer to apply simgen in other settings. As we have emphasized, the simgen code is not a universal solution to the problem of decadal simulation, but represents a particularized realization of the decadal simulation framework outlined in GHG. It is thus unlikely that simgen will be deployed by others in exactly its present form. Rather, it is hoped that it will prove a useful template on which to base simulation models in a diverse array of environments. 12

13 A The ACRU format The first two lines of obshis 2626.txt are reproduced below: P P In the first field, the first eight digits are an internal identifier, not utilized by simgen. After this are four digits representing the year, which for both of these lines is Then, utilizing two spaces each, are fields for the month (January) and day (first and second of January, for the two lines). After this are the three key fields, precipitation, maximum daily temperature and minimum daily temperature, which for the first record are 0.0 mm, 24.6 C and 8.4 C, respectively. The P following the precipitation values in both lines indicates that these particular data have been filled ( patched ), i.e., that the values were initially missing: The readqc module reports to the screen the number of such values in any observational record being ingested. The remaining fields are not utilized by simgen, nor is data written to them in the simulation files, which include only a single space after the minimum temperature value. References Greene, A. M., L. Goddard, and J. W. Hansen, A framework for the simulation of regional decadal variability for agricultural and other applications, Tech. rep., International Research Institute for Climate and Society, Palisades, NY, Greene, A. M., M. Hellmuth, and T. Lumsden, Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa, Water Resourc. Res., 48, Kwon, H.-H., U. Lall, and A. F. Khalil1, Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature, Water Resour. Res., 43, Kwon, H.-H., U. Lall, and J. Obeysekera, Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida, Stoch. Environ. Res. Risk. Assess., 23, , Prairie, J., K. Nowak, B. Rajagopalan, U. Lall, and T. Fulp, A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data, Water Resour. Res., 44,

14 Schulze, R. E., Hydrology and Agrohydrology: A Text to Accompany the ACRU 3.00 Agrohydrological Modelling System, WRC Report TT 69/95, Water Research Commission, Pretoria, RSA, Taylor, K. E., R. J. Stouffer, and G. A. Meehl, An overview of CMIP5 and the experiment design, Bull. Am. Met. Soc.,

A framework for the simulation of regional decadal variability for agricultural and other applications

A framework for the simulation of regional decadal variability for agricultural and other applications A framework for the simulation of regional decadal variability for agricultural and other applications Arthur M. Greene, Lisa Goddard and James W. Hansen International Research Institute for Climate and

More information

Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa

Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa WATER RESOURCES RESEARCH, VOL. 48, W06504, doi:10.1029/2011wr011152, 2012 Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa Arthur

More information

CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler

CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler 1) Operating systems a) Windows b) Unix and Linux c) Macintosh 2) Data manipulation tools a) Text Editors b) Spreadsheets

More information

Software Review: ITSM 2000 Professional Version 6.0.

Software Review: ITSM 2000 Professional Version 6.0. Lee, J. & Strazicich, M.C. (2002). Software Review: ITSM 2000 Professional Version 6.0. International Journal of Forecasting, 18(3): 455-459 (June 2002). Published by Elsevier (ISSN: 0169-2070). http://0-

More information

Addendum to the CMIP5 Experiment Design Document: A compendium of relevant emails sent to the modeling groups

Addendum to the CMIP5 Experiment Design Document: A compendium of relevant emails sent to the modeling groups Addendum to the CMIP5 Experiment Design Document: A compendium of relevant emails sent to the modeling groups CMIP5 Update 13 November 2010: Dear all, Here are some items that should be of interest to

More information

Assignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart

Assignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart Assignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart Overview Due Thursday, November 12th at 11:59pm Last updated

More information

CCI-HYDR Perturbation Tool. A climate change tool for generating perturbed time series for the Belgian climate MANUAL, JANUARY 2009

CCI-HYDR Perturbation Tool. A climate change tool for generating perturbed time series for the Belgian climate MANUAL, JANUARY 2009 CCI-HYDR project (contract SD/CP/03A) for: Programme SSD «Science for a Sustainable Development» MANUAL, JANUARY 2009 CCI-HYDR Perturbation Tool A climate change tool for generating perturbed time series

More information

PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU

PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard

More information

NASA Earth Exchange Global Daily Downscaled Projections (NEX- GDDP)

NASA Earth Exchange Global Daily Downscaled Projections (NEX- GDDP) NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) 1. Intent of This Document and POC 1a) This document provides a brief overview of the NASA Earth Exchange (NEX) Global Daily Downscaled

More information

Intro to scientific programming (with Python) Pietro Berkes, Brandeis University

Intro to scientific programming (with Python) Pietro Berkes, Brandeis University Intro to scientific programming (with Python) Pietro Berkes, Brandeis University Next 4 lessons: Outline Scientific programming: best practices Classical learning (Hoepfield network) Probabilistic learning

More information

Bridging the gap between climate science and development practice

Bridging the gap between climate science and development practice Bridging the gap between climate science and development practice FIC/IEH Methodology for analyzing climate change impacts on productive systems and value chains Climate model simulations are essential

More information

APPENDIX E THE ASSESSMENT PHASE OF THE DATA LIFE CYCLE

APPENDIX E THE ASSESSMENT PHASE OF THE DATA LIFE CYCLE APPENDIX E THE ASSESSMENT PHASE OF THE DATA LIFE CYCLE The assessment phase of the Data Life Cycle includes verification and validation of the survey data and assessment of quality of the data. Data verification

More information

Comment on "Observational and model evidence for positive low-level cloud feedback"

Comment on Observational and model evidence for positive low-level cloud feedback LLNL-JRNL-422752 Comment on "Observational and model evidence for positive low-level cloud feedback" A. J. Broccoli, S. A. Klein January 22, 2010 Science Disclaimer This document was prepared as an account

More information

STOCHASTIC STREAMFLOW SIMULATION AT INTERDECADAL TIME SCALES AND IMPLICATIONS TO WATER RESOURCES MANAGEMENT IN THE COLORADO RIVER BASIN

STOCHASTIC STREAMFLOW SIMULATION AT INTERDECADAL TIME SCALES AND IMPLICATIONS TO WATER RESOURCES MANAGEMENT IN THE COLORADO RIVER BASIN STOCHASTIC STREAMFLOW SIMULATION AT INTERDECADAL TIME SCALES AND IMPLICATIONS TO WATER RESOURCES MANAGEMENT IN THE COLORADO RIVER BASIN By KENNETH C NOWAK B.S., Rensselaer Polytechnic Institute, 2006 M.S.,

More information

Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com

Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING

More information

NOWCASTING OF PRECIPITATION Isztar Zawadzki* McGill University, Montreal, Canada

NOWCASTING OF PRECIPITATION Isztar Zawadzki* McGill University, Montreal, Canada NOWCASTING OF PRECIPITATION Isztar Zawadzki* McGill University, Montreal, Canada 1. INTRODUCTION Short-term methods of precipitation nowcasting range from the simple use of regional numerical forecasts

More information

River Dell Regional School District. Computer Programming with Python Curriculum

River Dell Regional School District. Computer Programming with Python Curriculum River Dell Regional School District Computer Programming with Python Curriculum 2015 Mr. Patrick Fletcher Superintendent River Dell Regional Schools Ms. Lorraine Brooks Principal River Dell High School

More information

Time Series Analysis of Aviation Data

Time Series Analysis of Aviation Data Time Series Analysis of Aviation Data Dr. Richard Xie February, 2012 What is a Time Series A time series is a sequence of observations in chorological order, such as Daily closing price of stock MSFT in

More information

CEQ Draft Guidance for GHG Emissions and the Effects of Climate Change Committee on Natural Resources 13 May 2015

CEQ Draft Guidance for GHG Emissions and the Effects of Climate Change Committee on Natural Resources 13 May 2015 CEQ Draft Guidance for GHG Emissions and the Effects of Climate Change Committee on Natural Resources 13 May 2015 Testimony of John R. Christy University of Alabama in Huntsville. I am John R. Christy,

More information

Modeling with Python

Modeling with Python H Modeling with Python In this appendix a brief description of the Python programming language will be given plus a brief introduction to the Antimony reaction network format and libroadrunner. Python

More information

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES Mitigating Energy Risk through On-Site Monitoring Marie Schnitzer, Vice President of Consulting Services Christopher Thuman, Senior Meteorologist Peter Johnson,

More information

Instructional Design Framework CSE: Unit 1 Lesson 1

Instructional Design Framework CSE: Unit 1 Lesson 1 Instructional Design Framework Stage 1 Stage 2 Stage 3 If the desired end result is for learners to then you need evidence of the learners ability to then the learning events need to. Stage 1 Desired Results

More information

Package empiricalfdr.deseq2

Package empiricalfdr.deseq2 Type Package Package empiricalfdr.deseq2 May 27, 2015 Title Simulation-Based False Discovery Rate in RNA-Seq Version 1.0.3 Date 2015-05-26 Author Mikhail V. Matz Maintainer Mikhail V. Matz

More information

Computational Mathematics with Python

Computational Mathematics with Python Boolean Arrays Classes Computational Mathematics with Python Basics Olivier Verdier and Claus Führer 2009-03-24 Olivier Verdier and Claus Führer Computational Mathematics with Python 2009-03-24 1 / 40

More information

CS 1133, LAB 2: FUNCTIONS AND TESTING http://www.cs.cornell.edu/courses/cs1133/2015fa/labs/lab02.pdf

CS 1133, LAB 2: FUNCTIONS AND TESTING http://www.cs.cornell.edu/courses/cs1133/2015fa/labs/lab02.pdf CS 1133, LAB 2: FUNCTIONS AND TESTING http://www.cs.cornell.edu/courses/cs1133/2015fa/labs/lab02.pdf First Name: Last Name: NetID: The purpose of this lab is to help you to better understand functions:

More information

7 Time series analysis

7 Time series analysis 7 Time series analysis In Chapters 16, 17, 33 36 in Zuur, Ieno and Smith (2007), various time series techniques are discussed. Applying these methods in Brodgar is straightforward, and most choices are

More information

Numerical Algorithms Group. Embedded Analytics. A cure for the common code. www.nag.com. Results Matter. Trust NAG.

Numerical Algorithms Group. Embedded Analytics. A cure for the common code. www.nag.com. Results Matter. Trust NAG. Embedded Analytics A cure for the common code www.nag.com Results Matter. Trust NAG. Executive Summary How much information is there in your data? How much is hidden from you, because you don t have access

More information

Guidance and Requirements for. NCCWSC/CSC Data Management Plans

Guidance and Requirements for. NCCWSC/CSC Data Management Plans Guidance and Requirements for NCCWSC/CSC Plans (Required for NCCWSC and CSC Proposals and Funded Projects) Prepared by the CSC/NCCWSC Working Group Emily Fort, Data and IT Manager for the National Climate

More information

SWMM-CAT User s Guide

SWMM-CAT User s Guide EPA/600/R-14/428 September 2014 www.epa.gov/research n t SWMM-CAT User s Guide photo photo Office of Research and Development Water Supply and Water Resources Division EPA 600-R-14-428 September 2014 SWMM-CAT

More information

Regionalizing global models:

Regionalizing global models: Regionalizing global models: value-adding for impacts and adaptation Jason Evans University of New South Wales Yann Arthus-Bertrand / Altitude Regionalizing Global models Why would we want to regionalize

More information

Climate modelling. Dr. Heike Huebener Hessian Agency for Environment and Geology Hessian Centre on Climate Change

Climate modelling. Dr. Heike Huebener Hessian Agency for Environment and Geology Hessian Centre on Climate Change Hessisches Landesamt für Umwelt und Geologie Climate modelling Dr. Heike Huebener Hessian Agency for Environment and Geology Hessian Centre on Climate Change Climate: Definition Weather: momentary state

More information

We will learn the Python programming language. Why? Because it is easy to learn and many people write programs in Python so we can share.

We will learn the Python programming language. Why? Because it is easy to learn and many people write programs in Python so we can share. LING115 Lecture Note Session #4 Python (1) 1. Introduction As we have seen in previous sessions, we can use Linux shell commands to do simple text processing. We now know, for example, how to count words.

More information

Analytic Modeling in Python

Analytic Modeling in Python Analytic Modeling in Python Why Choose Python for Analytic Modeling A White Paper by Visual Numerics August 2009 www.vni.com Analytic Modeling in Python Why Choose Python for Analytic Modeling by Visual

More information

Computational Mathematics with Python

Computational Mathematics with Python Numerical Analysis, Lund University, 2011 1 Computational Mathematics with Python Chapter 1: Basics Numerical Analysis, Lund University Claus Führer, Jan Erik Solem, Olivier Verdier, Tony Stillfjord Spring

More information

Computational Mathematics with Python

Computational Mathematics with Python Computational Mathematics with Python Basics Claus Führer, Jan Erik Solem, Olivier Verdier Spring 2010 Claus Führer, Jan Erik Solem, Olivier Verdier Computational Mathematics with Python Spring 2010 1

More information

ASSESSING CLIMATE FUTURES: A CASE STUDY

ASSESSING CLIMATE FUTURES: A CASE STUDY ASSESSING CLIMATE FUTURES: A CASE STUDY Andrew Wilkins 1, Leon van der Linden 1, 1. SA Water Corporation, Adelaide, SA, Australia ABSTRACT This paper examines two techniques for quantifying GCM derived

More information

Selecting members of the QUMP perturbed-physics ensemble for use with PRECIS

Selecting members of the QUMP perturbed-physics ensemble for use with PRECIS Selecting members of the QUMP perturbed-physics ensemble for use with PRECIS Isn t one model enough? Carol McSweeney and Richard Jones Met Office Hadley Centre, September 2010 Downscaling a single GCM

More information

2. Simple Linear Regression

2. Simple Linear Regression Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according

More information

Interactive comment on Total cloud cover from satellite observations and climate models by P. Probst et al.

Interactive comment on Total cloud cover from satellite observations and climate models by P. Probst et al. Interactive comment on Total cloud cover from satellite observations and climate models by P. Probst et al. Anonymous Referee #1 (Received and published: 20 October 2010) The paper compares CMIP3 model

More information

New Tools for Spatial Data Analysis in the Social Sciences

New Tools for Spatial Data Analysis in the Social Sciences New Tools for Spatial Data Analysis in the Social Sciences Luc Anselin University of Illinois, Urbana-Champaign anselin@uiuc.edu edu Outline! Background! Visualizing Spatial and Space-Time Association!

More information

Conjoint Survey Design Tool: Software Manual

Conjoint Survey Design Tool: Software Manual Conjoint Survey Design Tool: Software Manual Anton Strezhnev, Jens Hainmueller, Daniel J. Hopkins, Teppei Yamamoto Version 1.3 (BETA) May 16, 2014 Designed as a companion to Causal Inference in Conjoint

More information

Multivariate Analysis of Ecological Data

Multivariate Analysis of Ecological Data Multivariate Analysis of Ecological Data MICHAEL GREENACRE Professor of Statistics at the Pompeu Fabra University in Barcelona, Spain RAUL PRIMICERIO Associate Professor of Ecology, Evolutionary Biology

More information

The Standardized Precipitation Index

The Standardized Precipitation Index The Standardized Precipitation Index Theory The Standardized Precipitation Index (SPI) is a tool which was developed primarily for defining and monitoring drought. It allows an analyst to determine the

More information

Strong coherence between cloud cover and surface temperature

Strong coherence between cloud cover and surface temperature Strong coherence between cloud cover and surface temperature variance in the UK E. W. Mearns* and C. H. Best * School of Geosciences, University of Aberdeen, Kings College, Aberdeen AB24 3UE Independent

More information

DATA MINING IN FINANCE

DATA MINING IN FINANCE DATA MINING IN FINANCE Advances in Relational and Hybrid Methods by BORIS KOVALERCHUK Central Washington University, USA and EVGENII VITYAEV Institute of Mathematics Russian Academy of Sciences, Russia

More information

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value IJSTE - International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349-784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode

More information

Open Access Design of a Python-based Wireless Network Optimization and Testing System

Open Access Design of a Python-based Wireless Network Optimization and Testing System Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 2015, 7, 353-357 353 Open Access Design of a Python-based Wireless Network Optimization and Testing

More information

Univariate and Multivariate Methods PEARSON. Addison Wesley

Univariate and Multivariate Methods PEARSON. Addison Wesley Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston

More information

Impact of Warming on Outflows from Selected Upper Watersheds in California

Impact of Warming on Outflows from Selected Upper Watersheds in California Impact of Warming on Outflows from Selected Upper Watersheds in California Guobiao Huang (CA DWR), Tariq Kadir (CA DWR) and Francis Chung (CA DWR) California Water and Environmental Modeling Forum Pacific

More information

10 Database Utilities

10 Database Utilities Database Utilities 10 1 10 Database Utilities 10.1 Overview of the Exporter Software PRELIMINARY NOTE: The Exporter software, catalog Item 709, is optional software which you may order should you need

More information

Exercise 1: Python Language Basics

Exercise 1: Python Language Basics Exercise 1: Python Language Basics In this exercise we will cover the basic principles of the Python language. All languages have a standard set of functionality including the ability to comment code,

More information

A Programming Language for Mechanical Translation Victor H. Yngve, Massachusetts Institute of Technology, Cambridge, Massachusetts

A Programming Language for Mechanical Translation Victor H. Yngve, Massachusetts Institute of Technology, Cambridge, Massachusetts [Mechanical Translation, vol.5, no.1, July 1958; pp. 25-41] A Programming Language for Mechanical Translation Victor H. Yngve, Massachusetts Institute of Technology, Cambridge, Massachusetts A notational

More information

WESTMORELAND COUNTY PUBLIC SCHOOLS 2011 2012 Integrated Instructional Pacing Guide and Checklist Computer Math

WESTMORELAND COUNTY PUBLIC SCHOOLS 2011 2012 Integrated Instructional Pacing Guide and Checklist Computer Math Textbook Correlation WESTMORELAND COUNTY PUBLIC SCHOOLS 2011 2012 Integrated Instructional Pacing Guide and Checklist Computer Math Following Directions Unit FIRST QUARTER AND SECOND QUARTER Logic Unit

More information

SyncTool for InterSystems Caché and Ensemble.

SyncTool for InterSystems Caché and Ensemble. SyncTool for InterSystems Caché and Ensemble. Table of contents Introduction...4 Definitions...4 System requirements...4 Installation...5 How to use SyncTool...5 Configuration...5 Example for Group objects

More information

Search and Replace in SAS Data Sets thru GUI

Search and Replace in SAS Data Sets thru GUI Search and Replace in SAS Data Sets thru GUI Edmond Cheng, Bureau of Labor Statistics, Washington, DC ABSTRACT In managing data with SAS /BASE software, performing a search and replace is not a straight

More information

Experiment #1, Analyze Data using Excel, Calculator and Graphs.

Experiment #1, Analyze Data using Excel, Calculator and Graphs. Physics 182 - Fall 2014 - Experiment #1 1 Experiment #1, Analyze Data using Excel, Calculator and Graphs. 1 Purpose (5 Points, Including Title. Points apply to your lab report.) Before we start measuring

More information

ANSA and μeta as a CAE Software Development Platform

ANSA and μeta as a CAE Software Development Platform ANSA and μeta as a CAE Software Development Platform Michael Giannakidis, Yianni Kolokythas BETA CAE Systems SA, Thessaloniki, Greece Overview What have we have done so far Current state Future direction

More information

Scicos is a Scilab toolbox included in the Scilab package. The Scicos editor can be opened by the scicos command

Scicos is a Scilab toolbox included in the Scilab package. The Scicos editor can be opened by the scicos command 7 Getting Started 7.1 Construction of a Simple Diagram Scicos contains a graphical editor that can be used to construct block diagram models of dynamical systems. The blocks can come from various palettes

More information

Review questions for Chapter 9

Review questions for Chapter 9 Answer first, then check at the end. Review questions for Chapter 9 True/False 1. A compiler translates a high-level language program into the corresponding program in machine code. 2. An interpreter is

More information

Time Series Analysis: Basic Forecasting.

Time Series Analysis: Basic Forecasting. Time Series Analysis: Basic Forecasting. As published in Benchmarks RSS Matters, April 2015 http://web3.unt.edu/benchmarks/issues/2015/04/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD jonathan.starkweather@unt.edu

More information

Module 10. Coding and Testing. Version 2 CSE IIT, Kharagpur

Module 10. Coding and Testing. Version 2 CSE IIT, Kharagpur Module 10 Coding and Testing Lesson 26 Debugging, Integration and System Testing Specific Instructional Objectives At the end of this lesson the student would be able to: Explain why debugging is needed.

More information

Postprocessing with Python

Postprocessing with Python Postprocessing with Python Boris Dintrans (CNRS & University of Toulouse) dintrans@ast.obs-mip.fr Collaborator: Thomas Gastine (PhD) Outline Outline Introduction - what s Python and why using it? - Installation

More information

1-04-10 Configuration Management: An Object-Based Method Barbara Dumas

1-04-10 Configuration Management: An Object-Based Method Barbara Dumas 1-04-10 Configuration Management: An Object-Based Method Barbara Dumas Payoff Configuration management (CM) helps an organization maintain an inventory of its software assets. In traditional CM systems,

More information

Introduction to Regression and Data Analysis

Introduction to Regression and Data Analysis Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it

More information

C Compiler Targeting the Java Virtual Machine

C Compiler Targeting the Java Virtual Machine C Compiler Targeting the Java Virtual Machine Jack Pien Senior Honors Thesis (Advisor: Javed A. Aslam) Dartmouth College Computer Science Technical Report PCS-TR98-334 May 30, 1998 Abstract One of the

More information

Operating Systems 4 th Class

Operating Systems 4 th Class Operating Systems 4 th Class Lecture 1 Operating Systems Operating systems are essential part of any computer system. Therefore, a course in operating systems is an essential part of any computer science

More information

Load testing with WAPT: Quick Start Guide

Load testing with WAPT: Quick Start Guide Load testing with WAPT: Quick Start Guide This document describes step by step how to create a simple typical test for a web application, execute it and interpret the results. A brief insight is provided

More information

THE COLORADO RIVER BASIN WATER SUPPLY AND DEMAND STUDY: MODELING TO SUPPORT A ROBUST PLANNING FRAMEWORK

THE COLORADO RIVER BASIN WATER SUPPLY AND DEMAND STUDY: MODELING TO SUPPORT A ROBUST PLANNING FRAMEWORK THE COLORADO RIVER BASIN WATER SUPPLY AND DEMAND STUDY: MODELING TO SUPPORT A ROBUST PLANNING FRAMEWORK Alan Butler, Hydrologic Engineer, Bureau of Reclamation, Lower Colorado Region, rabutler@usbr.gov;

More information

Forecasting Hospital Bed Availability Using Simulation and Neural Networks

Forecasting Hospital Bed Availability Using Simulation and Neural Networks Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels Michael E. Kuhl Industrial & Systems Engineering Department Rochester Institute of Technology Rochester, NY

More information

Introduction to time series analysis

Introduction to time series analysis Introduction to time series analysis Margherita Gerolimetto November 3, 2010 1 What is a time series? A time series is a collection of observations ordered following a parameter that for us is time. Examples

More information

IBM SPSS Forecasting 22

IBM SPSS Forecasting 22 IBM SPSS Forecasting 22 Note Before using this information and the product it supports, read the information in Notices on page 33. Product Information This edition applies to version 22, release 0, modification

More information

Cover. SEB SIMOTION Easy Basics. Collection of standardized SIMOTION basic functions. FAQ April 2011. Service & Support. Answers for industry.

Cover. SEB SIMOTION Easy Basics. Collection of standardized SIMOTION basic functions. FAQ April 2011. Service & Support. Answers for industry. Cover SEB SIMOTION Easy Basics Collection of standardized SIMOTION basic functions FAQ April 2011 Service & Support Answers for industry. 1 Preface 1 Preface The SEB is a collection of simple, standardized

More information

Example #1: Controller for Frequency Modulated Spectroscopy

Example #1: Controller for Frequency Modulated Spectroscopy Progress Report Examples The following examples are drawn from past student reports, and illustrate how the general guidelines can be applied to a variety of design projects. The technical details have

More information

Rainfall generator for the Meuse basin

Rainfall generator for the Meuse basin KNMI publication; 196 - IV Rainall generator or the Meuse basin Description o 20 000-year simulations R. Leander and T.A. Buishand De Bilt, 2008 KNMI publication = KNMI publicatie; 196 - IV De Bilt, 2008

More information

Dimensionality Reduction: Principal Components Analysis

Dimensionality Reduction: Principal Components Analysis Dimensionality Reduction: Principal Components Analysis In data mining one often encounters situations where there are a large number of variables in the database. In such situations it is very likely

More information

Problem of the Month Through the Grapevine

Problem of the Month Through the Grapevine The Problems of the Month (POM) are used in a variety of ways to promote problem solving and to foster the first standard of mathematical practice from the Common Core State Standards: Make sense of problems

More information

National Weather Service River Forecast System (NWSRFS) Reservoir Tools Enhancement

National Weather Service River Forecast System (NWSRFS) Reservoir Tools Enhancement NATIONAL WEATHER SERVICE OFFICE of HYDROLOGIC DEVELOPMENT CONCEPT OF OPERATIONS And REQUIREMENTS DOCUMENT National Weather Service River Forecast System (NWSRFS) Reservoir Tools Enhancement Revision History

More information

Chapter 3 Application Monitors

Chapter 3 Application Monitors Chapter 3 Application Monitors AppMetrics utilizes application monitors to organize data collection and analysis per application server. An application monitor is defined on the AppMetrics manager computer

More information

RIVERWARE DECISION SUPPORT TOOLS FOR PLANNING SUSTAINABLE RIVER DEVELOPMENT WITH HYDROPOWER

RIVERWARE DECISION SUPPORT TOOLS FOR PLANNING SUSTAINABLE RIVER DEVELOPMENT WITH HYDROPOWER RIVERWARE DECISION SUPPORT TOOLS FOR PLANNING SUSTAINABLE RIVER DEVELOPMENT WITH HYDROPOWER Edith A. Zagona 1, Balaji Rajagopalan 2 and Steven Setzer 3 1University of Colorado Center for Advanced Decision

More information

Space project management

Space project management ECSS-M-ST-80C Space project management Risk management ECSS Secretariat ESA-ESTEC Requirements & Standards Division Noordwijk, The Netherlands Foreword This Standard is one of the series of ECSS Standards

More information

Structural Health Monitoring Tools (SHMTools)

Structural Health Monitoring Tools (SHMTools) Structural Health Monitoring Tools (SHMTools) Getting Started LANL/UCSD Engineering Institute LA-CC-14-046 c Copyright 2014, Los Alamos National Security, LLC All rights reserved. May 30, 2014 Contents

More information

Capario Secure File Transfer User Guide

Capario Secure File Transfer User Guide Capario Secure File Transfer User Guide Notices This user guide (the Guide ) is provided by Capario in order to facilitate your use of the Capario Secure File Transfer Software. This Guide is subject to

More information

http://www.jstor.org This content downloaded on Tue, 19 Feb 2013 17:28:43 PM All use subject to JSTOR Terms and Conditions

http://www.jstor.org This content downloaded on Tue, 19 Feb 2013 17:28:43 PM All use subject to JSTOR Terms and Conditions A Significance Test for Time Series Analysis Author(s): W. Allen Wallis and Geoffrey H. Moore Reviewed work(s): Source: Journal of the American Statistical Association, Vol. 36, No. 215 (Sep., 1941), pp.

More information

Tail-Dependence an Essential Factor for Correctly Measuring the Benefits of Diversification

Tail-Dependence an Essential Factor for Correctly Measuring the Benefits of Diversification Tail-Dependence an Essential Factor for Correctly Measuring the Benefits of Diversification Presented by Work done with Roland Bürgi and Roger Iles New Views on Extreme Events: Coupled Networks, Dragon

More information

Leveraging Ensemble Models in SAS Enterprise Miner

Leveraging Ensemble Models in SAS Enterprise Miner ABSTRACT Paper SAS133-2014 Leveraging Ensemble Models in SAS Enterprise Miner Miguel Maldonado, Jared Dean, Wendy Czika, and Susan Haller SAS Institute Inc. Ensemble models combine two or more models to

More information

Final Report - HydrometDB Belize s Climatic Database Management System. Executive Summary

Final Report - HydrometDB Belize s Climatic Database Management System. Executive Summary Executive Summary Belize s HydrometDB is a Climatic Database Management System (CDMS) that allows easy integration of multiple sources of automatic and manual stations, data quality control procedures,

More information

Siebel Application Deployment Manager Guide. Siebel Innovation Pack 2013 Version 8.1/8.2 September 2013

Siebel Application Deployment Manager Guide. Siebel Innovation Pack 2013 Version 8.1/8.2 September 2013 Siebel Application Deployment Manager Guide Siebel Innovation Pack 2013 Version 8.1/8.2 September 2013 Copyright 2005, 2013 Oracle and/or its affiliates. All rights reserved. This software and related

More information

Analysis Programs DPDAK and DAWN

Analysis Programs DPDAK and DAWN Analysis Programs DPDAK and DAWN An Overview Gero Flucke FS-EC PNI-HDRI Spring Meeting April 13-14, 2015 Outline Introduction Overview of Analysis Programs: DPDAK DAWN Summary Gero Flucke (DESY) Analysis

More information

ENGINEERING PROBLEM SOLVING WITH C++

ENGINEERING PROBLEM SOLVING WITH C++ ENGINEERING PROBLEM SOLVING WITH C++ Third Edition Delores M. Etter Electrical Engineering Department Southern Methodist University, Dallas, TX Jeanine A. Ingber Accurate Solutions in Applied Physics,

More information

SMIB A PILOT PROGRAM SYSTEM FOR STOCHASTIC SIMULATION IN INSURANCE BUSINESS DMITRII SILVESTROV AND ANATOLIY MALYARENKO

SMIB A PILOT PROGRAM SYSTEM FOR STOCHASTIC SIMULATION IN INSURANCE BUSINESS DMITRII SILVESTROV AND ANATOLIY MALYARENKO SMIB A PILOT PROGRAM SYSTEM FOR STOCHASTIC SIMULATION IN INSURANCE BUSINESS DMITRII SILVESTROV AND ANATOLIY MALYARENKO ABSTRACT. In this paper, we describe the program SMIB (Stochastic Modeling of Insurance

More information

THE CERN/SL XDATAVIEWER: AN INTERACTIVE GRAPHICAL TOOL FOR DATA VISUALIZATION AND EDITING

THE CERN/SL XDATAVIEWER: AN INTERACTIVE GRAPHICAL TOOL FOR DATA VISUALIZATION AND EDITING THE CERN/SL XDATAVIEWER: AN INTERACTIVE GRAPHICAL TOOL FOR DATA VISUALIZATION AND EDITING Abstract G. Morpurgo, CERN As a result of many years of successive refinements, the CERN/SL Xdataviewer tool has

More information

Spreadsheet software for linear regression analysis

Spreadsheet software for linear regression analysis Spreadsheet software for linear regression analysis Robert Nau Fuqua School of Business, Duke University Copies of these slides together with individual Excel files that demonstrate each program are available

More information

Time Series Analysis

Time Series Analysis JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),

More information

Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs

Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs 1.1 Introduction Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs For brevity, the Lavastorm Analytics Library (LAL) Predictive and Statistical Analytics Node Pack will be

More information

In: Proceedings of RECPAD 2002-12th Portuguese Conference on Pattern Recognition June 27th- 28th, 2002 Aveiro, Portugal

In: Proceedings of RECPAD 2002-12th Portuguese Conference on Pattern Recognition June 27th- 28th, 2002 Aveiro, Portugal Paper Title: Generic Framework for Video Analysis Authors: Luís Filipe Tavares INESC Porto lft@inescporto.pt Luís Teixeira INESC Porto, Universidade Católica Portuguesa lmt@inescporto.pt Luís Corte-Real

More information

Copyright. Network and Protocol Simulation. What is simulation? What is simulation? What is simulation? What is simulation?

Copyright. Network and Protocol Simulation. What is simulation? What is simulation? What is simulation? What is simulation? Copyright Network and Protocol Simulation Michela Meo Maurizio M. Munafò Michela.Meo@polito.it Maurizio.Munafo@polito.it Quest opera è protetta dalla licenza Creative Commons NoDerivs-NonCommercial. Per

More information

Module 6: Introduction to Time Series Forecasting

Module 6: Introduction to Time Series Forecasting Using Statistical Data to Make Decisions Module 6: Introduction to Time Series Forecasting Titus Awokuse and Tom Ilvento, University of Delaware, College of Agriculture and Natural Resources, Food and

More information

Spline Toolbox Release Notes

Spline Toolbox Release Notes Spline Toolbox Release Notes Note The Spline Toolbox 3.1 was released in Web-downloadable form after Release 12.1 was released, but before Release 13. The Spline Toolbox 3.1.1 that is part of Release 13

More information

A Fuel Cost Comparison of Electric and Gas-Powered Vehicles

A Fuel Cost Comparison of Electric and Gas-Powered Vehicles $ / gl $ / kwh A Fuel Cost Comparison of Electric and Gas-Powered Vehicles Lawrence V. Fulton, McCoy College of Business Administration, Texas State University, lf25@txstate.edu Nathaniel D. Bastian, University

More information

CET W/32 Application Builder Version 9

CET W/32 Application Builder Version 9 CET W/32 Application Builder Version 9 Overview of the Product, Technical Specifications, And Installation Guide cet software, incorporated 6595 odell place boulder, colorado, 80301 Table of Contents INSTALLATION

More information