BAYESIAN PROCESSOR OF ENSEMBLE (BPE): PRIOR DISTRIBUTION FUNCTION
|
|
- Mavis Fleming
- 6 years ago
- Views:
Transcription
1 BAYESIAN PROCESSOR OF ENSEMBLE (BPE): PRIOR DISTRIBUTION FUNCTION Parametric Models and Estimation Procedures Tested on Temperature Data By Roman Krzysztofowicz and Nah Youn Lee University of Virginia 4 December 2008 Acknowledgments: Work supported by NSF, Grant No. ATM Data provided by Environmental Modeling Center, NCEP/NWS/NOAA.
2 DATA Predictand: 2m temperature at 12 UTC Forecast time: 00 UTC Location: Savannah, GA Actual Location: 32º 8'N / 81º 13'W Climatic Data: 32.5º N / 80º W Data Climatic Data: 1 January December 1998 (40 years) 29 February (leap year) excluded 5 March 1997 filled in
3 PRIOR DISTRIBUTION FUNCTION k index of the day (k = 1,, 365) l lead time in days (l = 1, 2,, 16) W k predictand (variate) w k realization of W k Prior Marginal D. F. G k (w k ) = P(W k w k ) Prior Markov D. F. H kl (w k w k l ) = P(W k w k W k l = w k l ) Prior D. F. Climatic Probabilistic Forecast Challenges Reference for calibration of ensemble Limit to which calibrated ensemble converges as LT Time series W 1,, W 365 is nonstationary Distributions G k and H kl are non-gaussian
4 STANDARDIZATION Purpose: obtain time series that has stationary mean, variance, marginal D.F. (possibly) Climatic sample for each day k (k = 1,, 365) 15-day sampling window centered on day k w k n : n 1,...,M M = 15 days x 40 years = 600 Sample estimates m k prior (climatic) mean s k prior (climatic) standard deviation Standardized climatic sample w k n w k n m k s k n 1,...,M w k n : n 1,...,M
5 Sample deciles in original space (15-day window), SAV 300 Temperature ( K) Day Maximum Minimum
6 Sample mean (15-day), Fourier series (2 nd order), SAV Mean (degree K) Day
7 5.5 Sample std. dev. (15-day), Fourier series (2 nd order), SAV 5 Standard Deviation (degree K) Day
8 Sample deciles in standard space (15-day window), SAV Standardized Temperature Day Maximum Minimum
9 PRIOR MARGINAL D.F. in Standard Space Two alternative hypotheses (tested empirically) 1. Nonstationary Prior (estimate 12 parametric D.F.) 48 = 4 x 12 parameters + interpolation (=> 365 D.F.) MAD across 12 samples (1 Jan,, 1 Dec): 12 (min), 24 (average), 39 (max) 2. Stationary Prior (estimate 1 parametric D.F.) 4 parameters (=> 365 D.F.) MAD across 12 samples (1 Jan,, 1 Dec): 38 (min), 53 (average), 65 (max) MAD = max Empirical D. F. Parametric D. F.
10 Prior Marginal D. F.: Analyses 0. Given: standardized climatic sample for day k w k n : n 1,...,M M = days k (1 Jan,..., 1Dec) 1. Hypothesize several parametric models for 2. Estimate parameters of each model 3. Choose model that minimizes average (across k): MAD = max Empirical D. F. Parametric D. F. Model: G k G w 1 exp 1 ln ln U L U w 1 Parameters:,, L, U
11 Empirical and Parametric Distribution Functions on 1 Jan (k = 1) (M = 600), SAV MAD: 277 α = 971 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
12 Empirical and Parametric Distribution Functions on 1 Feb (k = 32) (M = 600), SAV MAD: 201 α = 467 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
13 Empirical and Parametric Distribution Functions on 1 Mar (k = 60) (M = 600), SAV MAD: 236 α = 139 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
14 Empirical and Parametric Distribution Functions on 1 Apr (k = 91) (M = 600), SAV MAD: 234 α = 974 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
15 Empirical and Parametric Distribution Functions on 1 May (k = 121) (M = 600), SAV MAD: 387 α = 703 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
16 Empirical and Parametric Distribution Functions on 1 Jun (k = 152) (M = 600), SAV MAD: 199 α = 259 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
17 Empirical and Parametric Distribution Functions on 1 Jul (k = 182) (M = 600), SAV MAD: 289 α = 362 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
18 Empirical and Parametric Distribution Functions on 1 Aug (k = 213) (M = 600), SAV MAD: 185 α = 738 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
19 Empirical and Parametric Distribution Functions on 1 Sep (k = 244) (M = 600), SAV MAD: 115 α = 015 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
20 Empirical and Parametric Distribution Functions on 1 Oct (k = 274) (M = 600), SAV MAD: 249 α = 507 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
21 Empirical and Parametric Distribution Functions on 1 Nov (k = 305) (M = 600), SAV MAD: 242 α = 238 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
22 Empirical and Parametric Distribution Functions on 1 Dec (k = 335) (M = 600), SAV MAD: 262 α = 528 β = η L = η U = P(W ' k w ' ) Standardized Temperature w'
23 Prior Marginal D. F. : Conclusions Small diversity of empirical D. F. Large similarity of parametric D. F. Near-stationarity of parameters,, L, U
24 Empirical Distribution Functions: the two most diverse on 1 Jun (k = 152) and 1 Dec (k = 335) (M = 600), SAV 1 Jun 1 Dec P(W ' k w ' ) Standardized Temperature w'
25 Parametric Distribution Functions on First Day of Each Month (M = 600), SAV Distribution P(W ' k w ' ) Standardized Temperature w'
26 Time-series distribution parameters: First of Each Month (M = 600), SAV Distribution 6 5 Alpha Beta EtaL EtaU 4 3 Parameter Value Day
27 Stationary Prior Marginal D.F. in Standard Space 1. Form a pooled standardized climatic sample from several days 2. Determine bounds from the grand sample (365 days, 40 years) L U min w k n k,n max w k n k,n 3. Estimate stationary D.F. from the pooled sample G w, L w U stationary parameters:,, L, U
28 Stationary Prior Marginal Distribution Functions: Empirical and Parametric, SAV Pooled Sample from 4 Days: 1 Jan, 1 Apr, 1 Jul, 1 Oct (M = 2400) MAD: 139 α = 659 β = η L = η U = P(W ' w' ) Standardized Temperature w'
29 Prior Marginal D.F. for day k in Original Space 0. Given: prior mean and standard deviation m k, s k stationary parameters 1. Construct,, L, U G k w G w m k s k Lk w Uk Lk L s k m k Uk U s k m k
30 Empirical Distribution Function on 1 Jan (k = 1) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 455 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
31 Empirical Distribution Function on 1 Feb (k = 32) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 464 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
32 Empirical Distribution Function on 1 Mar (k = 60) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 588 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
33 Empirical Distribution Function on 1 Apr (k = 91) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 421 α = 659 β = η Lk = η Uk = 301 P(W k w ) Temperature w ( K)
34 Empirical Distribution Function on 1 May (k = 121) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 604 α = 659 β = η Lk = η Uk = 304 P(W k w ) Temperature w ( K)
35 Empirical Distribution Function on 1 Jun (k = 152) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 592 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
36 Empirical Distribution Function on 1 Jul (k = 182) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 652 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
37 Empirical Distribution Function on 1 Aug (k = 213) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 583 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
38 Empirical Distribution Function on 1 Sep (k = 244) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 382 α = 659 β = η Lk = η Uk = P(W k w ) Temperature w ( K)
39 Empirical Distribution Function on 1 Oct (k = 274) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 580 α = 659 β = η Lk = 285 η Uk = P(W k w ) Temperature w ( K)
40 Empirical Distribution Function on 1 Nov (k = 305) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 432 α = 659 β = η Lk = 275 η Uk = P(W k w ) Temperature w ( K)
41 Empirical Distribution Function on 1 Dec (k = 335) (M = 600), SAV Parametric Distribution Function from Pooled Sample from 4 Days MAD: 620 α = 659 β = η Lk = 267 η Uk = P(W k w ) Temperature w ( K)
42 Stationary Prior Marginal D.F.: Conclusions Sampling Window: 5 15 days ( realizations) 15-day => empirical D. F. smoother, tails better delineated Pooled Sample Size M = 2400, 4 days, MAD = 14 M = 3600, 6 days, MAD = 14 M = 7200, 12 days, MAD = 11 Families of Good Parametric D.F. Bounded: (Weibull, Log-Logistic) Bounded Above: (Weibull, Log-Logistic)
43 CLIMATIC AUTOCORRELATION W k ' standardized predictand G' prior marginal D. F. (stationary) V k normalized predictand Q standard normal D. F. Normal Quantile Transform (NQT) V k = Q 1 (G'(W k ')) Autocorrelation coefficient (Pearson s product-moment) c k = Cor(V k 1, V k ) k = 1,, 365 *Note: Standardization does not make c k stationary
44 Autocorrelation Autocorrelation in normal in normal space space (15-day), Fourier series series (8th order), (8 th order), SAV SAV 5 5 Correlation Day
45 Prior Markov D. F. for day k in Original Space k day for which forecast is made (k = 1,, 365) l lead time in days (l = 1, 2,, 14) k l day of the last observation (forecast day) 0. Given: prior marginal D. F. G k, G k l autocorrelation coefficient c k 1. Construct H kl w k w k l Q Q 1 G k w k c k l Q 1 G k l w k l 1 c k 2l 1/2
GAMINGRE 8/1/ of 7
FYE 09/30/92 JULY 92 0.00 254,550.00 0.00 0 0 0 0 0 0 0 0 0 254,550.00 0.00 0.00 0.00 0.00 254,550.00 AUG 10,616,710.31 5,299.95 845,656.83 84,565.68 61,084.86 23,480.82 339,734.73 135,893.89 67,946.95
More informationTime Series Analysis
Time Series Analysis A time series is a sequence of observations made: 1) over a continuous time interval, 2) of successive measurements across that interval, 3) using equal spacing between consecutive
More informationWHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and Rainfall For Selected Arizona Cities
WHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and 2001-2002 Rainfall For Selected Arizona Cities Phoenix Tucson Flagstaff Avg. 2001-2002 Avg. 2001-2002 Avg. 2001-2002 October 0.7 0.0
More informationDAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR
DAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR LEAP AND NON-LEAP YEAR *A non-leap year has 365 days whereas a leap year has 366 days. (as February has 29 days). *Every year which is divisible by 4
More informationSuan Sunandha Rajabhat University
Forecasting Exchange Rate between Thai Baht and the US Dollar Using Time Series Analysis Kunya Bowornchockchai Suan Sunandha Rajabhat University INTRODUCTION The objective of this research is to forecast
More informationBAYESIAN PROCESSOR OF OUTPUT FOR PROBABILISTIC FORECASTING OF PRECIPITATION OCCURRENCE. Coire J. Maranzano. Department of Systems Engineering
BAYESIAN PROCESSOR OF OUTPUT FOR PROBABILISTIC FORECASTING OF PRECIPITATION OCCURRENCE By Coire J. Maranzano Department of Systems Engineering University of Virginia P.O. Box 400747 Charlottesville, VA
More informationINDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -12 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.
INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Lecture -12 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Summary of the previous lecture Data Extension & Forecasting Moving
More informationTIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA
CHAPTER 6 TIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA 6.1. Introduction A time series is a sequence of observations ordered in time. A basic assumption in the time series analysis
More informationLife Cycle of Convective Systems over Western Colombia
Life Cycle of Convective Systems over Western Colombia Meiry Sakamoto Uiversidade de São Paulo, São Paulo, Brazil Colombia Life Cycle of Convective Systems over Western Colombia Convective System (CS)
More informationCalculations Equation of Time. EQUATION OF TIME = apparent solar time - mean solar time
Calculations Equation of Time APPARENT SOLAR TIME is the time that is shown on sundials. A MEAN SOLAR DAY is a constant 24 hours every day of the year. Apparent solar days are measured from noon one day
More informationJayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein. Time variance and defect prediction in software projects: additional figures
Jayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein TECHNICAL REPORT No. IFI-2.4 Time variance and defect prediction in software projects: additional figures 2 University of Zurich Department
More informationJackson County 2018 Weather Data 67 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center
Jackson County 2018 Weather Data 67 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationHYDROLOGIC FORECAST PRODUCTS from BAYESIAN FORECASTING SYSTEM
HYDROLOGIC FORECAST PRODUCTS from BAYESIAN FORECASTING SYSTEM Roman Krzysztofowicz University of Virginia USA Presented at the CHR-WMO Workshop-Expert Consultation on Ensemble Prediction and Uncertainty
More informationLong-term Water Quality Monitoring in Estero Bay
Long-term Water Quality Monitoring in Estero Bay Keith Kibbey Laboratory Director Lee County Environmental Laboratory Division of Natural Resource Management Estero Bay Monitoring Programs Three significant
More informationHazard assessment based on radar-based rainfall nowcasts at European scale The HAREN project
Hazard assessment based on radar-based rainfall nowcasts at European scale The HAREN project Marc Berenguer, Daniel Sempere-Torres 3 OPERA radar mosaic OPERA radar mosaic: 213919 133 Precipitation observations
More informationSYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
More informationJackson County 2013 Weather Data
Jackson County 2013 Weather Data 61 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationENGINE SERIAL NUMBERS
ENGINE SERIAL NUMBERS The engine number was also the serial number of the car. Engines were numbered when they were completed, and for the most part went into a chassis within a day or so. However, some
More informationClimate Change Impact Analysis
Climate Change Impact Analysis Patrick Breach M.E.Sc Candidate pbreach@uwo.ca Outline July 2, 2014 Global Climate Models (GCMs) Selecting GCMs Downscaling GCM Data KNN-CAD Weather Generator KNN-CADV4 Example
More informationApproximating Fixed-Horizon Forecasts Using Fixed-Event Forecasts
Approximating Fixed-Horizon Forecasts Using Fixed-Event Forecasts Malte Knüppel and Andreea L. Vladu Deutsche Bundesbank 9th ECB Workshop on Forecasting Techniques 4 June 216 This work represents the authors
More informationChanges in spatial distribution of chub mackerel under climate change: the case study using Japanese purse seine fisheries data in the East China Sea
Changes in spatial distribution of chub mackerel under climate change: the case study using Japanese purse seine fisheries data in the East China Sea Tohya Yasuda, Ryuji Yukami, Seiji Ohshimo Seikai National
More informationDetermine the trend for time series data
Extra Online Questions Determine the trend for time series data Covers AS 90641 (Statistics and Modelling 3.1) Scholarship Statistics and Modelling Chapter 1 Essent ial exam notes Time series 1. The value
More informationStandardized Anomaly Model Output Statistics Over Complex Terrain.
Standardized Anomaly Model Output Statistics Over Complex Terrain Reto.Stauffer@uibk.ac.at Outline statistical ensemble postprocessing introduction to SAMOS new snow amount forecasts in Tyrol sub-seasonal
More informationAnnual Average NYMEX Strip Comparison 7/03/2017
Annual Average NYMEX Strip Comparison 7/03/2017 To Year to Year Oil Price Deck ($/bbl) change Year change 7/3/2017 6/1/2017 5/1/2017 4/3/2017 3/1/2017 2/1/2017-2.7% 2017 Average -10.4% 47.52 48.84 49.58
More informationTechnical note on seasonal adjustment for M0
Technical note on seasonal adjustment for M0 July 1, 2013 Contents 1 M0 2 2 Steps in the seasonal adjustment procedure 3 2.1 Pre-adjustment analysis............................... 3 2.2 Seasonal adjustment.................................
More informationPre-Calc Chapter 1 Sample Test. D) slope: 3 4
Pre-Calc Chapter 1 Sample Test 1. Use the graphs of f and g to evaluate the function. f( x) gx ( ) (f o g)(-0.5) 1 1 0 4. Plot the points and find the slope of the line passing through the pair of points.
More informationHow to find Sun's GHA using TABLE How to find Sun's Declination using TABLE 4...4
1 of 8 How to use- TABLE 4. - GHA and Declination of the Sun for the Years 2001 to 2036- Argument Orbit Time How to find Sun's GHA using TABLE 4... 2 How to find Sun's Declination using TABLE 4...4 Before
More informationYACT (Yet Another Climate Tool)? The SPI Explorer
YACT (Yet Another Climate Tool)? The SPI Explorer Mike Crimmins Assoc. Professor/Extension Specialist Dept. of Soil, Water, & Environmental Science The University of Arizona Yes, another climate tool for
More informationMultivariate Regression Model Results
Updated: August, 0 Page of Multivariate Regression Model Results 4 5 6 7 8 This exhibit provides the results of the load model forecast discussed in Schedule. Included is the forecast of short term system
More informationAlterations to the Flat Weight For Age Scale BHA Data Published 22 September 2016
Alterations to the Flat Weight For Age Scale BHA Data Published 22 September 2016 Introduction What is weight for age? It is an allowance given to younger horses, usually three-year-olds, to enable them
More informationREPORT ON LABOUR FORECASTING FOR CONSTRUCTION
REPORT ON LABOUR FORECASTING FOR CONSTRUCTION For: Project: XYZ Local Authority New Sample Project Contact us: Construction Skills & Whole Life Consultants Limited Dundee University Incubator James Lindsay
More informationScarborough Tide Gauge
Tide Gauge Location OS: 504898E 488622N WGS84: Latitude: 54 16' 56.990"N Longitude: 00 23' 25.0279"W Instrument Valeport 740 (Druck Pressure Transducer) Benchmarks Benchmark Description TGBM = 4.18m above
More informationR&D Research Project: Scaling analysis of hydrometeorological time series data
R&D Research Project: Scaling analysis of hydrometeorological time series data Extreme Value Analysis considering Trends: Methodology and Application to Runoff Data of the River Danube Catchment M. Kallache,
More informationGTR # VLTs GTR/VLT/Day %Δ:
MARYLAND CASINOS: MONTHLY REVENUES TOTAL REVENUE, GROSS TERMINAL REVENUE, WIN/UNIT/DAY, TABLE DATA, AND MARKET SHARE CENTER FOR GAMING RESEARCH, DECEMBER 2017 Executive Summary Since its 2010 casino debut,
More informationJackson County 2014 Weather Data
Jackson County 2014 Weather Data 62 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationparticular regional weather extremes
SUPPLEMENTARY INFORMATION DOI: 1.138/NCLIMATE2271 Amplified mid-latitude planetary waves favour particular regional weather extremes particular regional weather extremes James A Screen and Ian Simmonds
More informationAverage 175, , , , , , ,046 YTD Total 1,098,649 1,509,593 1,868,795 1,418, ,169 1,977,225 2,065,321
AGRICULTURE 01-Agriculture JUL 2,944-4,465 1,783-146 102 AUG 2,753 6,497 5,321 1,233 1,678 744 1,469 SEP - 4,274 4,183 1,596 - - 238 OCT 2,694 - - 1,032 340-276 NOV 1,979-5,822 637 3,221 1,923 1,532 DEC
More informationStatistical Models for Rainfall with Applications to Index Insura
Statistical Models for Rainfall with Applications to April 21, 2008 Overview The idea: Insure farmers against the risk of crop failure, like drought, instead of crop failure itself. It reduces moral hazard
More informationAverage 175, , , , , , ,940 YTD Total 944,460 1,284,944 1,635,177 1,183, ,954 1,744,134 1,565,640
AGRICULTURE 01-Agriculture JUL 2,944-4,465 1,783-146 102 AUG 2,753 6,497 5,321 1,233 1,678 744 1,469 SEP - 4,274 4,183 1,596 - - 238 OCT 2,694 - - 1,032 340-276 NOV 1,979-5,822 637 3,221 1,923 1,532 DEC
More informationWeak Constraints 4D-Var
Weak Constraints 4D-Var Yannick Trémolet ECMWF Training Course - Data Assimilation May 1, 2012 Yannick Trémolet Weak Constraints 4D-Var May 1, 2012 1 / 30 Outline 1 Introduction 2 The Maximum Likelihood
More informationTemporal Trends in Forest Fire Season Length
Temporal Trends in Forest Fire Season Length Alisha Albert-Green aalbertg@sfu.ca Department of Statistics and Actuarial Science Simon Fraser University Stochastic Modelling of Forest Dynamics Webinar March
More informationSupplementary appendix
Supplementary appendix This appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Lowe R, Stewart-Ibarra AM, Petrova D, et al.
More informationRecord date Payment date PID element Non-PID element. 08 Sep Oct p p. 01 Dec Jan p 9.85p
2017/18 Record date Payment date PID element Non-PID element 08 Sep 17 06 Oct 17 9.85p - 9.85p 01 Dec 17 05 Jan 18-9.85p 9.85p 09 Mar 18 06 Apr 18 9.85p - 9.85p Final 22 Jun 18 27 Jul 18 14.65p - 14.65p
More informationClimate Change and Water Supply Research. Drought Response Workshop October 8, 2013
Climate Change and Water Supply Research Drought Response Workshop October 8, 2013 DWR Photo Oroville Reservoir, 2009 Talk Overview Expectations History Atmospheric Rivers and Water Supply Current Research
More informationSYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
More informationSPECIMEN. Date Morning/Afternoon. A Level Geography H481/01 Physical systems Sample Question Paper. Time allowed: 1 hour 30 minutes PMT
Oxford Cambridge and RSA A Level Geography H481/01 Physical systems Sample Question Paper Date Morning/Afternoon Time allowed: 1 hour 30 minutes You must have: the Resource Booklet the OCR 12-page Answer
More informationDummy Variables. Susan Thomas IGIDR, Bombay. 24 November, 2008
IGIDR, Bombay 24 November, 2008 The problem of structural change Model: Y i = β 0 + β 1 X 1i + ɛ i Structural change, type 1: change in parameters in time. Y i = α 1 + β 1 X i + e 1i for period 1 Y i =
More informationComputing & Telecommunications Services Monthly Report January CaTS Help Desk. Wright State University (937)
January 215 Monthly Report Computing & Telecommunications Services Monthly Report January 215 CaTS Help Desk (937) 775-4827 1-888-775-4827 25 Library Annex helpdesk@wright.edu www.wright.edu/cats/ Last
More informationTime Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia
International Journal of Applied Science and Technology Vol. 5, No. 5; October 2015 Time Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia Olayan
More informationTechnical note on seasonal adjustment for Capital goods imports
Technical note on seasonal adjustment for Capital goods imports July 1, 2013 Contents 1 Capital goods imports 2 1.1 Additive versus multiplicative seasonality..................... 2 2 Steps in the seasonal
More informationAN OVERVIEW OF ENSEMBLE STREAMFLOW PREDICTION STUDIES IN KOREA
AN OVERVIEW OF ENSEMBLE STREAMFLOW PREDICTION STUDIES IN KOREA DAE-IL JEONG, YOUNG-OH KIM School of Civil, Urban & Geosystems Engineering, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul,
More informationSupplementary Figure 1 Annual number of F0-F5 (grey) and F2-F5 (black) tornado observations over 30 years ( ) for Canada and United States.
SUPPLEMENTARY FIGURES Supplementary Figure 1 Annual number of F0-F5 (grey) and F2-F5 (black) tornado observations over 30 years (1980-2009) for Canada and United States. Supplementary Figure 2 Differences
More informationLocation. Datum. Survey. information. Etrometa. Step Gauge. Description. relative to Herne Bay is -2.72m. The site new level.
Tide Gauge Location OS: 616895E 169377N WGS84: Latitude: 51 o 22.919196 N Longitude: 01 o 6.9335907 E Instrument Type Etrometa Step Gauge Benchmarks Benchmark TGBM = 5.524m above Ordnance Datum Newlyn
More informationIntroduction to TIGGE and GIFS. Richard Swinbank, with thanks to members of GIFS-TIGGE WG & THORPEX IPO
Introduction to TIGGE and GIFS Richard Swinbank, with thanks to members of GIFS-TIGGE WG & THORPEX IPO GIFS-TIGGE/NCAR/NOAA Workshop on EPS developments, June 2012 TIGGE THORPEX Interactive Grand Global
More information2019 Settlement Calendar for ASX Cash Market Products. ASX Settlement
2019 Settlement Calendar for ASX Cash Market Products ASX Settlement Settlement Calendar for ASX Cash Market Products 1 ASX Settlement Pty Limited (ASX Settlement) operates a trade date plus two Business
More informationHow are adding integers and subtracting integers related? Work with a partner. Use integer counters to find 4 2. Remove 2 positive counters.
. How are adding integers and subtracting integers related? ACTIVITY: Work with a partner. Use integer counters to find 4. Start with 4 positive counters. Remove positive counters. What is the total number
More informationLocation. Datum. Survey. information. Etrometa. Step Gauge. Description. relative to Herne Bay is -2.72m. The site new level.
Tide Gauge Location OS: 616895E 169377N WGS84: Latitude: 51 o 22.919196 N Longitude: 01 o 6.9335907 E Instrument Type Etrometa Step Gauge Benchmarks Benchmark TGBM = 5.524m above Ordnance Datum Newlyn
More informationFlorida Courts E-Filing Authority Board. Service Desk Report March 2019
Florida Courts E-Filing Authority Board Service Desk Report March 219 Customer Service Incidents March 219 Status January 219 February 219 March 219 Incidents Received 3,261 3,51 3,118 Incidents Worked
More informationClimatography of the United States No
Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 63.9 39.3 51.6 86 1976 16 56.6 1986 20 1976 2 47.5 1973
More informationClimatography of the United States No
Temperature ( F) Month (1) Min (2) Month(1) Extremes Lowest (2) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 32.8 21.7 27.3 62 1918 1 35.8 1983-24 1950 29 10.5 1979
More informationConstructing a typical meteorological year -TMY for Voinesti fruit trees region and the effects of global warming on the orchard ecosystem
Constructing a typical meteorological year -TMY for Voinesti fruit trees region and the effects of global warming on the orchard ecosystem ARMEANU ILEANA*, STĂNICĂ FLORIN**, PETREHUS VIOREL*** *University
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 55.6 39.3 47.5 77
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 56.6 36.5 46.6 81
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 57.9 38.9 48.4 85
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 44.8 25.4 35.1 72
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 49.4 37.5 43.5 73
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 69.4 46.6 58.0 92
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 58.5 38.8 48.7 79 1962
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 57.8 39.5 48.7 85 1962
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 67.5 42. 54.8 92 1971
More informationSalem Economic Outlook
Salem Economic Outlook November 2012 Tim Duy, PHD Prepared for the Salem City Council November 7, 2012 Roadmap US Economic Update Slow and steady Positives: Housing/monetary policy Negatives: Rest of world/fiscal
More informationChanging Hydrology under a Changing Climate for a Coastal Plain Watershed
Changing Hydrology under a Changing Climate for a Coastal Plain Watershed David Bosch USDA-ARS, Tifton, GA Jeff Arnold ARS Temple, TX and Peter Allen Baylor University, TX SEWRU Objectives 1. Project changes
More informationEvapo-transpiration Losses Produced by Irrigation in the Snake River Basin, Idaho
Nov 7, 2007 DRAFT Evapo-transpiration Losses Produced by Irrigation in the Snake River Basin, Idaho Wendell Tangborn and Birbal Rana HyMet Inc. Vashon Island, WA Abstract An estimated 8 MAF (million acre-feet)
More informationClimatography of the United States No
Climate Division: AK 5 NWS Call Sign: ANC Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 90 Number of s (3) Jan 22.2 9.3 15.8
More informationClimatography of the United States No
Climate Division: ND 8 NWS Call Sign: BIS Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 21.1 -.6 10.2
More informationClimatography of the United States No
Climate Division: TN 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 47.6 24.9 36.3 81
More informationJuly 2017 LOGISTICAL HARMONY
Li s&staging(wpedestriansidewalkprot.) (4/18/through7/18/2017) July2017 101BelvidereEastFaçadeWorkPipeStaging (4/18/through7/18/2017) 101BelvidereEastFaçadeWork (through8/31/2017) Legend RampaccesstoTMCX
More information2.1 Inductive Reasoning Ojectives: I CAN use patterns to make conjectures. I CAN disprove geometric conjectures using counterexamples.
2.1 Inductive Reasoning Ojectives: I CAN use patterns to make conjectures. I CAN disprove geometric conjectures using counterexamples. 1 Inductive Reasoning Most learning occurs through inductive reasoning,
More informationP7.7 A CLIMATOLOGICAL STUDY OF CLOUD TO GROUND LIGHTNING STRIKES IN THE VICINITY OF KENNEDY SPACE CENTER, FLORIDA
P7.7 A CLIMATOLOGICAL STUDY OF CLOUD TO GROUND LIGHTNING STRIKES IN THE VICINITY OF KENNEDY SPACE CENTER, FLORIDA K. Lee Burns* Raytheon, Huntsville, Alabama Ryan K. Decker NASA, Marshall Space Flight
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: FAT Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 53.6 38.4 46. 78
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: 1L2 N Lon: 118 3W Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 63.7
More information2015 Fall Conditions Report
2015 Fall Conditions Report Prepared by: Hydrologic Forecast Centre Date: December 21 st, 2015 Table of Contents Table of Figures... ii EXECUTIVE SUMMARY... 1 BACKGROUND... 2 SUMMER AND FALL PRECIPITATION...
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: BFL Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 56.3 39.3 47.8
More informationPractice Test Chapter 8 Sinusoidal Functions
FOM 12 Practice Test Chapter 8 Sinusoidal Functions Name: Multiple Choice Identify the choice that best completes the statement or answers the question. Block: _ 1. Convert 120 into radians. A. 2" 3 B.
More informationChiang Rai Province CC Threat overview AAS1109 Mekong ARCC
Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC This threat overview relies on projections of future climate change in the Mekong Basin for the period 2045-2069 compared to a baseline of 1980-2005.
More informationMr. XYZ. Stock Market Trading and Investment Astrology Report. Report Duration: 12 months. Type: Both Stocks and Option. Date: Apr 12, 2011
Mr. XYZ Stock Market Trading and Investment Astrology Report Report Duration: 12 months Type: Both Stocks and Option Date: Apr 12, 2011 KT Astrologer Website: http://www.softwareandfinance.com/magazine/astrology/kt_astrologer.php
More informationClimatography of the United States No
Climate Division: TN 3 NWS Call Sign: BNA Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 45.6 27.9 36.8
More informationPredictability of Sudden Stratospheric Warmings in sub-seasonal forecast models
Predictability of Sudden Stratospheric Warmings in sub-seasonal forecast models Alexey Karpechko Finnish Meteorological Institute with contributions from A. Charlton-Perez, N. Tyrrell, M. Balmaseda, F.
More informationEXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY
EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA, 011 MODULE 3 : Stochastic processes and time series Time allowed: Three Hours Candidates should answer FIVE questions. All questions carry
More informationDates and Prices ICAEW - Manchester In Centre Programme Prices
Dates and Prices ICAEW - Manchester - 2019 In Centre Programme Prices Certificate Level GBP ( ) Intensive Accounting 690 Assurance 615 Law 615 Business, Technology and Finance 615 Mangement Information
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Elevation: 6 Feet Lat: 37 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3)
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Elevation: 2 Feet Lat: 37 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3)
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Elevation: 13 Feet Lat: 36 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Elevation: 1,14 Feet Lat: 36 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of
More informationAtmospheric circulation analysis for seasonal forecasting
Training Seminar on Application of Seasonal Forecast GPV Data to Seasonal Forecast Products 18 21 January 2011 Tokyo, Japan Atmospheric circulation analysis for seasonal forecasting Shotaro Tanaka Climate
More informationFEB DASHBOARD FEB JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Positive Response Compliance 215 Compliant 215 Non-Compliant 216 Compliant 216 Non-Compliant 1% 87% 96% 86% 96% 88% 89% 89% 88% 86% 92% 93% 94% 96% 94% 8% 6% 4% 2% 13% 4% 14% 4% 12% 11% 11% 12% JAN MAR
More informationBUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7
BUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7 1. The definitions follow: (a) Time series: Time series data, also known as a data series, consists of observations on a
More informationLecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University
Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,
More informationJackson County 2019 Weather Data 68 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center
Jackson County 2019 Weather Data 68 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationFolsom Dam Water Control Manual Update Joint Federal Project, Folsom Dam
Folsom Dam Water Control Manual Update Joint Federal Project, Folsom Dam Public Workshop May 28, 2015 Library Galleria 828 I Street, Sacramento, CA US Army Corps of Engineers BUILDING STRONG WELCOME &
More information