University of Florida Department of Geography GEO 3280 Assignment 3

Size: px
Start display at page:

Download "University of Florida Department of Geography GEO 3280 Assignment 3"

Transcription

1 G E O A s s i g n m e n t # 3 Page 1 University of Florida Department of Geography GEO 328 Assignment 3 Modeling Precipitation and Elevation Solar Radiation Precipitation Evapo- Transpiration Vegetation Surface Sub- Surface Interception Effective Precipitation Evapo-transpiration Excess Precipitation Storage Infiltration Drainage Basin Direct Runoff Sub-surface Runoff Runoff INTRODUCTION: The changing seasonal role of topography in controlling precipitation within the Tiribí drainage basin is investigated and represented. Different precipitation generating processes operate with varying intensities during the course of the year (seasonality). The purpose of this assignment is to illustrate the role of geographic variables, such as elevation and aspect, in determining the nature of this changing relationship, and to find a way to estimate the basic precipitation input to our basin. A simple mathematical model is introduced and applied annually and seasonally. The changing parameters (coefficients) are interpreted physically. The important concepts of calibrating and validating a mathematical representation are introduced, as well as a very simple test of the reliability of the representation. The parameters of a mathematical model may also vary from year to year. This property is illustrated by investigating the relationship between elevation and mean annual precipitation in La Niña and El Niño years.

2 G E O A s s i g n m e n t # 3 Page 2 What you should take away from Assignment #3. Some introductory ideas about the nature of mathematical models and their application. In particular, the notion that the same model can be used during various seasons, but that the coefficients within the model will change with each season and that these changes can, and should be interpreted physically. We are specifically interested in the seasonally changing role of elevation as it influences the quantity and spatial pattern of precipitation input into the basin. The importance of testing the results from your model not just against the data that you used to formulate the model (calibration data set), but against a set of data which have not formerly been used (validation data set). The concept of a quantifiable measure of your confidence that the mathematical representation employed actually provide a reasonable approximation to the observed data and the degree to which it can be used as a reasonable forecast tool. Further evidence of how our small regional basin may be influenced by variability in atmosphere-ocean circulation patterns at a much larger scale, and how we can graphically portray those changes over space.

3 G E O A s s i g n m e n t # 3 Page 3 1. Does a Relationship Exist Between Topography and Precipitation? Using the information provided in the file ANSTATS: A) Produce a graph (scattergram, XY dots only) of station elevation (x-axis) versus the mean annual precipitation (y-axis) observed at all fifteen stations. (3 Marks) B) On the graph, identify those stations lying in the Caribbean watershed and those in the Pacific watershed, as defined in Table 2.1 of the previous asignment and Figure 3.2. Comment on any differences you observe in the relationship between annual precipitation and elevation, between these two sets (Pacific vs. Caribbean) of stations. Provide a physical explanation for your observation. (3 Marks) Building on what you know. Refer to the west-east cross section created in Assignment 1 and the discussion of dominant wind directions over Costa Rica at different times of the year from Assignment 2. C) Using only the 11 meteorological stations within the Pacific drainage, estimate the straight line relationship between station elevation and mean annual precipitation by means of the REGRESSION option in your software package, of the following form: PRECIP (mm) = b + m.elev (meters) Retain and report values of the coefficients for the INTERCEPT (a) (to one decimal place), X-VARIABLE (slope or b) (to 3 decimal places), and R-SQUARED (to 3 decimal places). (1 Mark) See EXCEL How-To #25 Hints: Representations of the graphical meaning of the regression coefficients of intercept (b), slope (m) and r-squared are shown in Figures 3.1. R-squared can be viewed as a measure of the variability in the observed data (the dots) which is explained by the fitted straight line. A perfect fit (1% of variability explained) yields an r-squared of 1., while a line which explains none of the observed variability (%) returns a value of..

4 G E O A s s i g n m e n t # 3 Page 4 1 Annual Precipitation, P (mm) Observed data Fitted Regression Line P(mm) = m.e(m) + b r 2 = 1. Intercept = 2mm Slope =.2 mm.m Station Elevation, E (m) Annual Precipitation, P(mm) m = mm b =.25 mm.m -1 r 2 = m = mm b = -.26 mm.m -1 r 2 = Elevation, E (m) m = 698. mm b = -.1 mm.m -1 r 2 = m = 1. mm b = -.2 mm.m -1 r 2 = Figure 3.1. Examples of changing values of the regression coefficients of intercept (m) and slope (b), and the measure of goodness of fit of the straight line to the data, r-squared.

5 G E O A s s i g n m e n t # 3 Page 5 Figure 3.2. Disposition of meteorological stations listed in ANSTATS with respect to the divide between Pacific and Caribbean drainage. D) On the basis of the coefficients you derive and the definitions provided in Figure 3.1, answer the following questions: i) What is your estimate of mean annual precipitation at sea level on the Pacific coast? ii) What is the expected increase, or decrease, of mean annual precipitation (in mm) with each additional meter of elevation within the Pacific drainage? iii) What percentage of the variation in the mean annual rainfalls observed at these stations can be explained by the differences in their elevations? (1.5 Marks) 2. How can we Generate Estimates of Annual Precipitation from the Digital Elevation Model? Fields, or maps, of annual and monthly precipitation for the basin are to be generated, based solely upon the elevation information contained in the DEM and the mathematical representation, or model, that you have just derived. From the previous discussion, we know that elevation is not the only control on precipitation, but it is an important one.

6 G E O A s s i g n m e n t # 3 Page 6 A. Using the coefficients derived the previous question (using the number of decimal places specified in question 1C), estimate mean annual precipitation at each cell within the D.E.M. (TIRIBELEV). Express answers to one decimal place. Produce a 3-D surface of mean annual precipitation across the basin - not outside of it. Use BASINLIMITS to achieve this. (5 Marks) Settings: Use a perspective of 315, a minimum vertical axis value of 19 mm and a maximum vertical axis value of 35 mm.! Warning: As annual precipitation is calculated as a simple linear function of elevation, this figure should look very similar in slope and orientation to the 3-D graph of elevation in Assignment Can Differences or Changes in Hydrologic Fields be Mapped? The phase of ENSO (El Niño/La Niña) has considerable effect upon rainfall in the region. Annual rainfall totals from the period from the 11 stations in the Pacific drainage have been extracted. These records are then sub-divided according to the COAPS classification scheme, and mean annual precipitation estimated at each station under El Niño (Warm Phase of ENSO) and La Niña (Cold Phase of ENSO). Table 3.1 contains a listing of the coefficients estimated for the best fit regression line relating station elevation and mean annual precipitation observed under each ENSO phases. ENSO Condition Intercept Slope r-squared La Niña (Cold phase) El Niño (Warm phase) Table 3.1 Coefficients of estimated regression relationships between station elevation and mean annual precipitation under La Niña and El Niño conditions.

7 G E O A s s i g n m e n t # 3 Page 7 A. Answer the following short questions, based on the information provided in Table 3.1: i) What is the expected difference in mean annual precipitation between Cold and Warm Phases of ENSO at a station located at sea level in the Pacific drainage? ii) Under which ENSO condition does a unit change in elevation (a meter) have a greater influence on the anticipated change in mean precipitation? How many more millimeters of precipitation would you expect to arise from the same increase in station elevation of 1m in a La Niña year than an El Niño year? (Note that this requires that you compare two stations separated by 1m) iii) Is there any evidence that the linear regression model is more appropriate (fits better) under one of the two ENSO conditions? Support your answer. (3 Marks) B. Use the D.E.M. data (TIRIBIELEV), BASINLIMITS and the coefficients listed in Table 3.1 to generate an anticipated mean annual precipitation field in the basin under a) El Niño conditions, and b) La Niña conditions. i) Create a new spreadsheet which represents the field of differences (in millimeters) between mean annual precipitation under La Niña and El Niño conditions (La Niña value minus El Niño value) within the basin. Produce and submit a 3-D view of this field. (3 Marks) Settings: A rotation of 315, a minimum vertical axis value of 2mm and a maximum vertical axis value of 7mm. ii) Using these two surfaces: Estimate and submit the mean annual precipitation input into the basin under these two ENSO phases. Express and submit the increase in mean annual precipitation between El Niño and La Niña as a percentage of the El Niño mean. (2 Marks) Building on what you know. You have already calculated mean basin precipitation via several different methods, and have been given observations at stations. Your computed values here will be different, but should be in the same ball park!

8 G E O A s s i g n m e n t # 3 Page 8 iii) Create a new spreadsheet which represents the field of percentage differences between mean annual precipitation under La Niña and El Niño conditions ({La Niña value minus El Niño value}/el Niño value*1) within the basin. Produce and submit a 3-D view of this field. (3 Marks) Settings: A rotation of 315, setting the lower limit of the vertical axis to 15%. 4. How are the Parameters of the Precipitation/Elevation Model Estimated (Calibrated) for Monthly Data? We use mathematical models or representations to provide physical insights and to allow us to make forecasts. In this series of questions, we are investigating whether the information provided by our simple mathematical model makes sense in terms of what we know to about rainfall generating processes within the basin at different seasons. The best fit linear regressions between station elevation and mean monthly precipitation have already been estimated for all months and are recorded in the file MONTHLY REGRESSION. Bearing in mind the seasonal definitions used early (pre-veranillos, dry, Veranillos etc.), answer the following: A) Create a total of three line and symbol graphs, each of which shows the twelve monthly values of the coefficients (listing provided in file MONTHLY REGRESSION), with month along the x-axis and the value of the coefficients - r- squared, intercept, and slope - on the y-axis. (3 Marks) B) Write brief answers (only a line or two) to the following questions. Reference to Figure 3.1 should help you: i) In which months/seasons does the elevation of a station provide the greatest explanation of the observed variations in monthly precipitation between stations? On what statistical basis do you make this claim? ii) In which months/seasons does the elevation of a station provide the least explanation of the observed variations in monthly precipitation between stations? On what statistical basis do you make this claim? iii) In which month/season does the elevation of a location have the greatest effect upon the seasonal precipitation total at that station? What provides the statistical basis for this claim? iv) In which month/season does elevation have the least effect upon the seasonal precipitation total at a station? On what statistical basis do you make this claim?

9 G E O A s s i g n m e n t # 3 Page 9 (2 Marks) Building on what you know D) Provide a physical interpretation (no more than one page) of the seasonal changes in the monthly numerical relationships between elevation and precipitation, bearing in mind the nature of the seasonally changing climatological setting of the region (refer to the previous assignments, such as Assignment 2 question 1, the article dealing with the seasonality of precipitation and dominant wind directions, and the topographic cross-section from Assignment 1). The seasonal divisions used in Assignment 2 are used throughout the manual and provide a structure for your discussion. Completion of Table 3.2 may help guide your thoughts. (5 Marks) JFMA Wind Direction Basin Windward or Leeward Intercept (mm) Slope (mm. m -1 ) r-squared MJ JA SO ND Table 3.2. Possible framework for developing a physical interpretation of the observed monthly/seasonal patterns of regression terms. 5. How Can the Reliability of the Model be Tested? One way in which the reliability of this representation (model) of the relationship between elevation and monthly precipitation can be tested is to estimate the mean monthly precipitation at the 6 recording stations, or calibration stations, that fall within the basin, from each of our twelve monthly surfaces. This has already been completed and graphical comparisons of the estimated and observed means, and ranges of historic data are shown in Figures 3.4, 3.5 and 3.6.

10 G E O A s s i g n m e n t # 3 Page 1 Validation: Comparisons of estimated values to those of the calibration stations are not the fairest test of the model, as these stations were themselves used in establishing the regression coefficients which form the numerical basis of the model. A more widely accepted test would be to apply the method to stations that had not been used previously in the model calibration procedure. Thus far, only precipitation stations with at least 2 complete years of record have been used in the study. There are several other stations in and near the basin, possessing between 1 and 2 years of record, which were not used in the calibration process. The historic data from these stations provide a useful, independent means of validating the model, comparing the forecast regimes to the observed regimes, because the data at these stations were not used in formulating our numerical model. The locations of 4 validation stations are shown in Figure 3.7. Unfortunately, only one, San Juan, actually falls within the limits of the Tiribí basin, but the other three are nearby and lie within the Pacific drainage. A. Using the monthly regression relationships (expressed to 3 decimal places) and the elevations of each validation station, estimate the mean monthly precipitation during each of the five missing months, at each station. Complete Table 3.3. Express all estimates of mean monthly precipitation to one decimal place only. (5 Marks) Validation Cell Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Elev. Station (mm) (mm) (mm) (mm) (mm) (mm) (mm) (mm) (mm) (mm) (mm) (mm) (m) San Joaquin G San Antonio I San Juan N IPIS V int slope Table 3.3. Validation station cell addresses and estimated mean monthly precipitation.

11 G E O A s s i g n m e n t # 3 Page 11 CALIBRATION STATIONS 7 6 Historic Estimated Curridabat Precipitation Total (mm) Historic Estimate Month Desamparados Precipitation Total (mm) Percentiles 95th 9th 75th Mean 5th Month 25th 1th 5th Figure 3.5. Comparison of observed monthly rainfall totals and mean monthly total estimated from the regression analysis for the calibration stations of Curridabat and Desamparados.

12 G E O A s s i g n m e n t # 3 Page 12 CALIBRATION STATIONS 7 6 Historic Estimate Rancho Redondo Precipitation Total (mm) Month 7 6 Historic Estimate San José Precipitation Total (mm) Month Figure 3.6. Comparison of observed monthly rainfall totals and mean monthly total estimated from the regression analysis for the calibration stations of Rancho Redondo and San José.

13 CALIBRATION STATIONS G E O A s s i g n m e n t # 3 Page Historic Estimated El Alto de Ochomogo Precipitation Total (mm) Historic Estimated Month Hacienda Concepción Precipitation Total (mm) Month Figure 3.7. Comparison of observed monthly rainfall totals and mean monthly total estimated from the regression analysis for the calibration stations of El Alto de Ochomogo and Hacienda Concepción.

14 G E O A s s i g n m e n t # 3 Page 14 B. The available historic observed monthly precipitation data for these validation stations and a possible framework for the analysis are found in the following files: SAN JUAN MONTHLY P SAN JOAQUIN MONTHLY P SAN ANTONIO MONTHLY P IPIS GUADALUPE MONTHLY P i. Open each file in turn. At the base of each column (month) estimate, and save, the following PERCENTILES from each of column (month) - 1 th, 25 th, 5 th, 75 th and 9 th, and the observed mean. There is no need to print these data. See EXCEL How-To #26 ii. Using a Line chart, construct a graph for each validation station showing the month along the x-axis and the values of the monthly percentiles (5 values) and observed mean on the y-axis (monthly precipitation). Each graph will therefore consist of 6 separate line plots (LINE ONLY, no symbol). iii. On the same graph, plot the forecast monthly means, both those provided in Table 3.3 and those you calculated above, as symbols only on the same graphs. Enlarge the default symbol shape, color and size so that it stands out clearly. (1 Marks) See EXCEL How-To #27 Check out time saving hint on next page. Settings: Ensure that the y axes run from to 6mm in each chart. C. Provide a short (half page) discussion of how well the estimated means at the validation stations match the historic data. (4 marks)

15 G E O A s s i g n m e n t # 3 Page 15 Figure 3.7. Mean annual precipitation total and the locations of the four validation stations. Codes beneath station names indicate the cell addresses corresponding to these stations.

16 G E O A s s i g n m e n t # 3 Page How can the Model be Applied to Estimate Mean Monthly Precipitation Input to the Basin? Maps such as Figure 3.7 are only approximations of the true distributions of precipitation, or Precipitation Fields, based on simple numerical interpolation between the existing meteorological stations. In areas of rapidly changing topography these mathematical interpolations may be very misleading and erroneous. Monthly precipitation fields for the basin can also be generated, based solely upon the elevation information contained in the DEM. From the previous discussion it is clear that elevation is not the only control on precipitation, and that the degree of control varies seasonally. However such an approach may give a better approximation to the actual precipitation falling into the basin, particularly during the rainy season. A. Using the information provided in MONTHLY REGRESSION, estimate mean monthly precipitation fields for all twelve months across the entire area. Use coefficients to 3 decimal places, and express answers to one decimal place. Save the computed precipitation fields as separate files. For each computed monthly precipitation field, calculate the average (mean) of precipitation entering the basin in that month. Insert these average monthly values in the appropriate cells in Table 3.4. Repeat the process for each month. Submit a completed version of Table 3.4, or the estimated monthly mean basin precipitation, in each of the five months, which are your responsibility. (6 Marks)! Warning: The annual value is merely the sum of the twelve monthly answers. J F M A M J J A S O N D Ann Prec Table 3.4. Mean monthly precipitation within the basin (mm).

17 G E O A s s i g n m e n t # 3 Page 17 Hint: In order to set up the above process more efficiently it is recommended that TIRIBIELEV and BASINLIMITS be opened. 1. On a new worksheet, named for example Precip, use the requisite monthly coefficients, e.g. those for January, to calculate estimates of mean January precipitation in each grid square from using elevation information stored in TIRIBIELEV. 2. On a second new worksheet, named perhaps Basin Precip, enter the product of the Precip and BASINLIMITS worksheets. 3. Below this, calculate the sum of all values in the Basin Precip worksheet and divide by the number of falling cells in the basin. 4. Using the SAVE AS command, save this as a file called January. 5. Return to the Precip worksheet, change the regression coefficients to those of the next month (e.g. February), and copy the result to all cells. All subsequent steps should change automatically. 6. Using the SAVE AS command, save this as a file called February. 7. Repeat steps 5 and 6 for all months changing the file name appropriately. Check: The calculations of mean monthly basin precipitation in the basin (step 3) in the 7 months, for which I have already furnished answers, should match those provided in Table 3.4.

18 Assignment #3 - Submission checklist: G E O A s s i g n m e n t # 3 Page 18 1A. 1B. 1C. Graph of Elevation vs. Annual Precipitation at 15 stations. Observations of differences in relationship between Pacific and Caribbean stations. Coefficients of regression line fitted to only those stations in the Pacific watershed. 1D. Physical interpretation of coefficients D surface of annual precipitation across the basin. 3A. Short answers interpreting changes in coefficients between El Niño and La Niña years. 3Bi. 3-D surface of differences in annual precipitation across the basin between La Niña and El Niño years. 3Bii. Mean annual basin input under El Niño and La Niña conditions, and the difference between the two as a percentage of input during El Niño years. 3Biii. 3-D surface of differences in annual precipitation across the basin between La Niña and El Niño years expressed as percentage of cell s precipitation during El Niño years. 3Biv. Observations on the spatial changes in absolute and percentage differences between conditions.

19 G E O A s s i g n m e n t # 3 Page 19 4A. Graph of monthly intercepts, slopes and r-squared values for all 12 months. 4B. Short answers showing the general physical interpretation of the regression coefficients. 4C. Interpretation of the seasonally changing values of the regression coefficients as related to the regional precipitation generating processes. 5A. Estimates of mean monthly precipitation at the four (4) validation stations for the 5 missing months. (Table 3.3) 5B. Four (4) graphs showing the historic percentiles and mean vales of monthly precipitation at the four (4) validation stations. Graphs should also display the mean monthly precipitation forecast by the regression of monthly precipitation on elevation. 5C. Discussion of goodness of fit of the fitted precipitation field to the observed regime of mean monthly precipitation at the four (4) validation stations. 6. Completed Table 3.4.

A Report on a Statistical Model to Forecast Seasonal Inflows to Cowichan Lake

A Report on a Statistical Model to Forecast Seasonal Inflows to Cowichan Lake A Report on a Statistical Model to Forecast Seasonal Inflows to Cowichan Lake Prepared by: Allan Chapman, MSc, PGeo Hydrologist, Chapman Geoscience Ltd., and Former Head, BC River Forecast Centre Victoria

More information

APPLICATIONS OF DOWNSCALING: HYDROLOGY AND WATER RESOURCES EXAMPLES

APPLICATIONS OF DOWNSCALING: HYDROLOGY AND WATER RESOURCES EXAMPLES APPLICATIONS OF DOWNSCALING: HYDROLOGY AND WATER RESOURCES EXAMPLES Dennis P. Lettenmaier Department of Civil and Environmental Engineering For presentation at Workshop on Regional Climate Research NCAR

More information

Atmospheric circulation analysis for seasonal forecasting

Atmospheric 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 information

Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC

Chiang 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 information

HYDROLOGIC AND WATER RESOURCES EVALUATIONS FOR SG. LUI WATERSHED

HYDROLOGIC AND WATER RESOURCES EVALUATIONS FOR SG. LUI WATERSHED HYDROLOGIC AND WATER RESOURCES EVALUATIONS FOR SG. LUI WATERSHED 1.0 Introduction The Sg. Lui watershed is the upper part of Langat River Basin, in the state of Selangor which located approximately 20

More information

Presentation Overview. Southwestern Climate: Past, present and future. Global Energy Balance. What is climate?

Presentation Overview. Southwestern Climate: Past, present and future. Global Energy Balance. What is climate? Southwestern Climate: Past, present and future Mike Crimmins Climate Science Extension Specialist Dept. of Soil, Water, & Env. Science & Arizona Cooperative Extension The University of Arizona Presentation

More information

Supplementary appendix

Supplementary 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 information

WHEN 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 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 information

Climate Variability. Eric Salathé. Climate Impacts Group & Department of Atmospheric Sciences University of Washington. Thanks to Nathan Mantua

Climate Variability. Eric Salathé. Climate Impacts Group & Department of Atmospheric Sciences University of Washington. Thanks to Nathan Mantua Climate Variability Eric Salathé Climate Impacts Group & Department of Atmospheric Sciences University of Washington Thanks to Nathan Mantua Northwest Climate: the mean Factors that influence local/regional

More information

Colorado s 2003 Moisture Outlook

Colorado s 2003 Moisture Outlook Colorado s 2003 Moisture Outlook Nolan Doesken and Roger Pielke, Sr. Colorado Climate Center Prepared by Tara Green and Odie Bliss http://climate.atmos.colostate.edu How we got into this drought! Fort

More information

Drought in Southeast Colorado

Drought in Southeast Colorado Drought in Southeast Colorado Nolan Doesken and Roger Pielke, Sr. Colorado Climate Center Prepared by Tara Green and Odie Bliss http://climate.atmos.colostate.edu 1 Historical Perspective on Drought Tourism

More information

How to Use the Guidance Tool (Producing Guidance and Verification)

How to Use the Guidance Tool (Producing Guidance and Verification) How to Use the Guidance Tool (Producing Guidance and Verification) Hiroshi Ohno Tokyo Climate Center (TCC)/ Climate Prediction Division of Japan Meteorological Agency (JMA) Workflow of the Excel Guidance

More information

Assessment of the Impact of El Niño-Southern Oscillation (ENSO) Events on Rainfall Amount in South-Western Nigeria

Assessment of the Impact of El Niño-Southern Oscillation (ENSO) Events on Rainfall Amount in South-Western Nigeria 2016 Pearl Research Journals Journal of Physical Science and Environmental Studies Vol. 2 (2), pp. 23-29, August, 2016 ISSN 2467-8775 Full Length Research Paper http://pearlresearchjournals.org/journals/jpses/index.html

More information

2003 Moisture Outlook

2003 Moisture Outlook 2003 Moisture Outlook Nolan Doesken and Roger Pielke, Sr. Colorado Climate Center Prepared by Tara Green and Odie Bliss http://climate.atmos.colostate.edu Through 1999 Through 1999 Fort Collins Total Water

More information

Earth Science Lesson Plan Quarter 2, Week 6, Day 1

Earth Science Lesson Plan Quarter 2, Week 6, Day 1 Earth Science Lesson Plan Quarter 2, Week 6, Day 1 1 Outcomes for Today Standard Focus: Earth Sciences 5.f students know the interaction of wind patterns, ocean currents, and mountain ranges results in

More information

OVERVIEW OF IMPROVED USE OF RS INDICATORS AT INAM. Domingos Mosquito Patricio

OVERVIEW OF IMPROVED USE OF RS INDICATORS AT INAM. Domingos Mosquito Patricio OVERVIEW OF IMPROVED USE OF RS INDICATORS AT INAM Domingos Mosquito Patricio domingos.mosquito@gmail.com Introduction to Mozambique /INAM Introduction to AGRICAB/SPIRITS Objectives Material & Methods Results

More information

Will a warmer world change Queensland s rainfall?

Will a warmer world change Queensland s rainfall? Will a warmer world change Queensland s rainfall? Nicholas P. Klingaman National Centre for Atmospheric Science-Climate Walker Institute for Climate System Research University of Reading The Walker-QCCCE

More information

What is happening to the Jamaican climate?

What is happening to the Jamaican climate? What is happening to the Jamaican climate? Climate Change and Jamaica: Why worry? Climate Studies Group, Mona (CSGM) Department of Physics University of the West Indies, Mona Part 1 RAIN A FALL, BUT DUTTY

More information

Rainfall Patterns across Puerto Rico: The Rate of Change

Rainfall Patterns across Puerto Rico: The Rate of Change Rainfall Patterns across Puerto Rico: The 1980-2013 Rate of Change Odalys Martínez-Sánchez Lead Forecaster and Climate Team Leader WFO San Juan UPRRP Environmental Sciences PhD Student Introduction Ways

More information

"STUDY ON THE VARIABILITY OF SOUTHWEST MONSOON RAINFALL AND TROPICAL CYCLONES FOR "

STUDY ON THE VARIABILITY OF SOUTHWEST MONSOON RAINFALL AND TROPICAL CYCLONES FOR "STUDY ON THE VARIABILITY OF SOUTHWEST MONSOON RAINFALL AND TROPICAL CYCLONES FOR 2001 2010" ESPERANZA O. CAYANAN, Ph.D. Chief, Climatology & Agrometeorology R & D Section Philippine Atmospheric Geophysical

More information

Global Climates. Name Date

Global Climates. Name Date Global Climates Name Date No investigation of the atmosphere is complete without examining the global distribution of the major atmospheric elements and the impact that humans have on weather and climate.

More information

Pre-Calc Chapter 1 Sample Test. D) slope: 3 4

Pre-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 information

ISOLINE MAPS and RAINFALL

ISOLINE MAPS and RAINFALL ISOLINE MAPS and RAINFALL Geography 101 Lab Name Purpose: Introduce students to one of the most common and useful types of maps used in studying the natural environment. When completed, the student should

More information

Using Reanalysis SST Data for Establishing Extreme Drought and Rainfall Predicting Schemes in the Southern Central Vietnam

Using Reanalysis SST Data for Establishing Extreme Drought and Rainfall Predicting Schemes in the Southern Central Vietnam Using Reanalysis SST Data for Establishing Extreme Drought and Rainfall Predicting Schemes in the Southern Central Vietnam Dr. Nguyen Duc Hau 1, Dr. Nguyen Thi Minh Phuong 2 National Center For Hydrometeorological

More information

Climate also has a large influence on how local ecosystems have evolved and how we interact with them.

Climate also has a large influence on how local ecosystems have evolved and how we interact with them. The Mississippi River in a Changing Climate By Paul Lehman, P.Eng., General Manager Mississippi Valley Conservation (This article originally appeared in the Mississippi Lakes Association s 212 Mississippi

More information

Winter Steve Todd Meteorologist In Charge National Weather Service Portland, OR

Winter Steve Todd Meteorologist In Charge National Weather Service Portland, OR Winter 07-08 Steve Todd Meteorologist In Charge National Weather Service Portland, OR Overview Winter Weather Outlook How to stay informed Winter Outlook LaNina conditions are present across the tropical

More information

CLIMATE OF THE ZUMWALT PRAIRIE OF NORTHEASTERN OREGON FROM 1930 TO PRESENT

CLIMATE OF THE ZUMWALT PRAIRIE OF NORTHEASTERN OREGON FROM 1930 TO PRESENT CLIMATE OF THE ZUMWALT PRAIRIE OF NORTHEASTERN OREGON FROM 19 TO PRESENT 24 MAY Prepared by J. D. Hansen 1, R.V. Taylor 2, and H. Schmalz 1 Ecologist, Turtle Mt. Environmental Consulting, 652 US Hwy 97,

More information

UWM Field Station meteorological data

UWM Field Station meteorological data University of Wisconsin Milwaukee UWM Digital Commons Field Station Bulletins UWM Field Station Spring 992 UWM Field Station meteorological data James W. Popp University of Wisconsin - Milwaukee Follow

More information

2016 Meteorology Summary

2016 Meteorology Summary 2016 Meteorology Summary New Jersey Department of Environmental Protection AIR POLLUTION AND METEOROLOGY Meteorology plays an important role in the distribution of pollution throughout the troposphere,

More information

HyMet Company. Streamflow and Energy Generation Forecasting Model Columbia River Basin

HyMet Company. Streamflow and Energy Generation Forecasting Model Columbia River Basin HyMet Company Streamflow and Energy Generation Forecasting Model Columbia River Basin HyMet Inc. Courthouse Square 19001 Vashon Hwy SW Suite 201 Vashon Island, WA 98070 Phone: 206-463-1610 Columbia River

More information

Chapter-1 Introduction

Chapter-1 Introduction Modeling of rainfall variability and drought assessment in Sabarmati basin, Gujarat, India Chapter-1 Introduction 1.1 General Many researchers had studied variability of rainfall at spatial as well as

More information

2003 Water Year Wrap-Up and Look Ahead

2003 Water Year Wrap-Up and Look Ahead 2003 Water Year Wrap-Up and Look Ahead Nolan Doesken Colorado Climate Center Prepared by Odie Bliss http://ccc.atmos.colostate.edu Colorado Average Annual Precipitation Map South Platte Average Precipitation

More information

Climate Variability in South Asia

Climate Variability in South Asia Climate Variability in South Asia V. Niranjan, M. Dinesh Kumar, and Nitin Bassi Institute for Resource Analysis and Policy Contents Introduction Rainfall variability in South Asia Temporal variability

More information

ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency

ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency ENSO Outlook by JMA Hiroyuki Sugimoto El Niño Monitoring and Prediction Group Climate Prediction Division Outline 1. ENSO impacts on the climate 2. Current Conditions 3. Prediction by JMA/MRI-CGCM 4. Summary

More information

REDWOOD VALLEY SUBAREA

REDWOOD VALLEY SUBAREA Independent Science Review Panel Conceptual Model of Watershed Hydrology, Surface Water and Groundwater Interactions and Stream Ecology for the Russian River Watershed Appendices A-1 APPENDIX A A-2 REDWOOD

More information

Webinar and Weekly Summary February 15th, 2011

Webinar and Weekly Summary February 15th, 2011 Webinar and Weekly Summary February 15th, 2011 -Assessment of current water conditions - Precipitation Forecast - Recommendations for Drought Monitor Upper Colorado Normal Precipitation Upper Colorado

More information

Changing Hydrology under a Changing Climate for a Coastal Plain Watershed

Changing 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 information

What is the difference between Weather and Climate?

What is the difference between Weather and Climate? What is the difference between Weather and Climate? Objective Many people are confused about the difference between weather and climate. This makes understanding the difference between weather forecasts

More information

Objectives. Materials

Objectives. Materials . Objectives Activity 13 To graphically represent and analyze climate data To use linear regressions to understand the relationship between temperatures as measured in the Fahrenheit and Celsius scale

More information

Appendix D. Model Setup, Calibration, and Validation

Appendix D. Model Setup, Calibration, and Validation . Model Setup, Calibration, and Validation Lower Grand River Watershed TMDL January 1 1. Model Selection and Setup The Loading Simulation Program in C++ (LSPC) was selected to address the modeling needs

More information

Sierra Weather and Climate Update

Sierra Weather and Climate Update Sierra Weather and Climate Update 2014-15 Kelly Redmond Western Regional Climate Center Desert Research Institute Reno Nevada Yosemite Hydroclimate Workshop Yosemite Valley, 2015 October 8-9 Percent of

More information

Climatography of the United States No

Climatography 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 information

Promoting Rainwater Harvesting in Caribbean Small Island Developing States Water Availability Mapping for Grenada Preliminary findings

Promoting Rainwater Harvesting in Caribbean Small Island Developing States Water Availability Mapping for Grenada Preliminary findings Promoting Rainwater Harvesting in Caribbean Small Island Developing States Water Availability Mapping for Grenada Preliminary findings National Workshop Pilot Project funded by The United Nations Environment

More information

An introduction to homogenisation

An introduction to homogenisation An introduction to homogenisation WMO-ETSCI Workshop, Pune 3-7 October 2016 Acacia Pepler Australian Bureau of Meteorology What do we want? A perfect station A well-situated and maintained station with

More information

HYDROLOGICAL MODELING OF HIGHLY GLACIERIZED RIVER BASINS. Nina Omani, Raghavan Srinivasan, Patricia Smith, Raghupathy Karthikeyan, Gerald North

HYDROLOGICAL MODELING OF HIGHLY GLACIERIZED RIVER BASINS. Nina Omani, Raghavan Srinivasan, Patricia Smith, Raghupathy Karthikeyan, Gerald North HYDROLOGICAL MODELING OF HIGHLY GLACIERIZED RIVER BASINS Nina Omani, Raghavan Srinivasan, Patricia Smith, Raghupathy Karthikeyan, Gerald North Problem statement Glaciers help to keep the earth cool High

More information

Investigating Factors that Influence Climate

Investigating Factors that Influence Climate Investigating Factors that Influence Climate Description In this lesson* students investigate the climate of a particular latitude and longitude in North America by collecting real data from My NASA Data

More information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

PRELIMINARY DRAFT FOR DISCUSSION PURPOSES

PRELIMINARY DRAFT FOR DISCUSSION PURPOSES Memorandum To: David Thompson From: John Haapala CC: Dan McDonald Bob Montgomery Date: February 24, 2003 File #: 1003551 Re: Lake Wenatchee Historic Water Levels, Operation Model, and Flood Operation This

More information

UPPLEMENT A COMPARISON OF THE EARLY TWENTY-FIRST CENTURY DROUGHT IN THE UNITED STATES TO THE 1930S AND 1950S DROUGHT EPISODES

UPPLEMENT A COMPARISON OF THE EARLY TWENTY-FIRST CENTURY DROUGHT IN THE UNITED STATES TO THE 1930S AND 1950S DROUGHT EPISODES UPPLEMENT A COMPARISON OF THE EARLY TWENTY-FIRST CENTURY DROUGHT IN THE UNITED STATES TO THE 1930S AND 1950S DROUGHT EPISODES Richard R. Heim Jr. This document is a supplement to A Comparison of the Early

More information

SPECIMEN. Date Morning/Afternoon. A Level Geography H481/01 Physical systems Sample Question Paper. Time allowed: 1 hour 30 minutes PMT

SPECIMEN. 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 information

Hail and the Climate System: Large Scale Environment Relationships for the Continental United States

Hail and the Climate System: Large Scale Environment Relationships for the Continental United States Hail and the Climate System: Large Scale Environment Relationships for the Continental United States 1979-2012 John T. Allen jallen@iri.columbia.edu Co-author: Michael K. Tippett WWOSC 2014, Thursday August

More information

Climatography of the United States No

Climatography 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 information

particular regional weather extremes

particular 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 information

Climate Forecasts and Forecast Uncertainty

Climate Forecasts and Forecast Uncertainty Climate Forecasts and Forecast Uncertainty Holly Hartmann Department of Hydrology and Water Resources University of Arizona, Tucson 520-626-8523 hollyh@hwr.arizona.edu CLIMAS-SAHRA press briefing August

More information

Climatography of the United States No

Climatography of the United States No Climate Division: CA 6 NWS Call Sign: LAX Elevation: 1 Feet Lat: 33 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 information

ALASKA REGION CLIMATE FORECAST BRIEFING. January 23, 2015 Rick Thoman ESSD Climate Services

ALASKA REGION CLIMATE FORECAST BRIEFING. January 23, 2015 Rick Thoman ESSD Climate Services ALASKA REGION CLIMATE FORECAST BRIEFING January 23, 2015 Rick Thoman ESSD Climate Services Today Climate Forecast Basics Review of recent climate forecasts and conditions CPC Forecasts and observations

More information

Climatography of the United States No

Climatography of the United States No Climate Division: CA 6 NWS Call Sign: TOA Elevation: 11 Feet Lat: 33 2W Temperature ( F) Month (1) Min (2) Month(1) Extremes Lowest (2) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number

More information

A Synoptic Climatology of Heavy Precipitation Events in California

A Synoptic Climatology of Heavy Precipitation Events in California A Synoptic Climatology of Heavy Precipitation Events in California Alan Haynes Hydrometeorological Analysis and Support (HAS) Forecaster National Weather Service California-Nevada River Forecast Center

More information

ARUBA CLIMATOLOGICAL SUMMARY 2017 PRECIPITATION

ARUBA CLIMATOLOGICAL SUMMARY 2017 PRECIPITATION ARUBA CLIMATOLOGICAL SUMMARY 2017 PRECIPITATION The total amount of rainfall recorded at Reina Beatrix International Airport for the year 2017 was 391.0 mm. This is 17.1 % below normal ( Figure 1 ). During

More information

Jackson County 2014 Weather Data

Jackson 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 information

Fiji Islands Weather Summary December 2005 Rainfall Outlook till March 2006

Fiji Islands Weather Summary December 2005 Rainfall Outlook till March 2006 Volume 5: Issue: 12 Inside this issue: In Brief and Weather Patterns 1 Table in three Months Forecast Verification and Graphs (Nadi, Labasa and Suva) Other Climatic variables and New Records Table 4 2

More information

Display and analysis of weather data from NCDC using ArcGIS

Display and analysis of weather data from NCDC using ArcGIS Display and analysis of weather data from NCDC using ArcGIS Helen M. Cox Associate Professor Geography Department California State University, Northridge and Stephen Krug Graduate Student Geography Department

More information

Modeling of peak inflow dates for a snowmelt dominated basin Evan Heisman. CVEN 6833: Advanced Data Analysis Fall 2012 Prof. Balaji Rajagopalan

Modeling of peak inflow dates for a snowmelt dominated basin Evan Heisman. CVEN 6833: Advanced Data Analysis Fall 2012 Prof. Balaji Rajagopalan Modeling of peak inflow dates for a snowmelt dominated basin Evan Heisman CVEN 6833: Advanced Data Analysis Fall 2012 Prof. Balaji Rajagopalan The Dworshak reservoir, a project operated by the Army Corps

More information

THE ROLE OF OCEAN STATE INDICES IN SEASONAL AND INTER-ANNUAL CLIMATE VARIABILITY OF THAILAND

THE ROLE OF OCEAN STATE INDICES IN SEASONAL AND INTER-ANNUAL CLIMATE VARIABILITY OF THAILAND THE ROLE OF OCEAN STATE INDICES IN SEASONAL AND INTER-ANNUAL CLIMATE VARIABILITY OF THAILAND Manfred Koch and Werapol Bejranonda Department of Geohydraulics and Engineering Hydrology, University of Kassel,

More information

Average temperature ( F) World Climate Zones. very cold all year with permanent ice and snow. very cold winters, cold summers, and little rain or snow

Average temperature ( F) World Climate Zones. very cold all year with permanent ice and snow. very cold winters, cold summers, and little rain or snow P r e v i e w Look carefully at the climagraph of Mumbai, India. What is the wettest month (or months) in Mumbai? What is the driest month (or months) in Mumbai? What effects might this city s climate

More information

Mozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1

Mozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1 UNDP Climate Change Country Profiles Mozambique C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2.Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

STATISTICAL FORECASTING and SEASONALITY (M. E. Ippolito; )

STATISTICAL FORECASTING and SEASONALITY (M. E. Ippolito; ) STATISTICAL FORECASTING and SEASONALITY (M. E. Ippolito; 10-6-13) PART I OVERVIEW The following discussion expands upon exponential smoothing and seasonality as presented in Chapter 11, Forecasting, in

More information

Appendix C. AMEC Evaluation of Zuni PPIW. Appendix C. Page C-1 of 34

Appendix C. AMEC Evaluation of Zuni PPIW. Appendix C. Page C-1 of 34 AMEC s Independent Estimate of PPIW Crop Water Use Using the ASCE Standardized Reference Evapotranspiration via Gridded Meteorological Data, and Estimation of Crop Coefficients, and Net Annual Diversions

More information

Science Standard 1: Students analyze monthly precipitation and temperature records, displayed in bar charts, collected in metric units (mm).

Science Standard 1: Students analyze monthly precipitation and temperature records, displayed in bar charts, collected in metric units (mm). Title: Precipitation Patterns across the Globe NSF GK-12 Fellow: Terry Legg Type of Lesson: STEM Grade Level(s): 4 th - 7 th grade This activity can be tailored to older, more advanced students by having

More information

What Does It Take to Get Out of Drought?

What Does It Take to Get Out of Drought? What Does It Take to Get Out of Drought? Nolan J. Doesken Colorado Climate Center Colorado State University http://ccc.atmos.colostate.edu Presented at the Insects, Diseases and Drought Workshop, May 19,

More information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography 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 information

National Integrated Drought Information System. Southeast US Pilot for Apalachicola- Flint-Chattahoochee River Basin 20-March-2012

National Integrated Drought Information System. Southeast US Pilot for Apalachicola- Flint-Chattahoochee River Basin 20-March-2012 National Integrated Drought Information System Southeast US Pilot for Apalachicola- Flint-Chattahoochee River Basin 20-March-2012 Current drought status from Drought Monitor http://www.drought.unl.edu/dm/monitor.html

More information

Tokyo Climate Center Website (TCC website) and its products -For monitoring the world climate and ocean-

Tokyo Climate Center Website (TCC website) and its products -For monitoring the world climate and ocean- Tokyo, 14 November 2016, TCC Training Seminar Tokyo Climate Center Website (TCC website) and its products -For monitoring the world climate and ocean- Yasushi MOCHIZUKI Tokyo Climate Center Japan Meteorological

More information

Workshop: Build a Basic HEC-HMS Model from Scratch

Workshop: Build a Basic HEC-HMS Model from Scratch Workshop: Build a Basic HEC-HMS Model from Scratch This workshop is designed to help new users of HEC-HMS learn how to apply the software. Not all the capabilities in HEC-HMS are demonstrated in the workshop

More information

How to use the guidance tool (Producing Guidance and Verification)

How to use the guidance tool (Producing Guidance and Verification) Ex.1 How to use the guidance tool (Producing Guidance and Verification) Masayuki Hirai Tokyo Climate Center (TCC)/ Climate Prediction Division of Japan Meteorological Agency (JMA) Schedule of exercise

More information

Climatography of the United States No

Climatography 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 information

Climatography of the United States No

Climatography of the United States No No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 46175 Climate Division: CA 6 NWS Call Sign: 3L3 Elevation: 1 Feet Lat: 33 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1)

More information

Climatography of the United States No

Climatography of the United States No No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 42713 Climate Division: CA 7 NWS Call Sign: Elevation: -3 Feet Lat: 32 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1)

More information

Climatography of the United States No

Climatography 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 information

Summary report for Ruamāhanga Whaitua Committee The climate of the Ruamāhanga catchment

Summary report for Ruamāhanga Whaitua Committee The climate of the Ruamāhanga catchment Summary report for Ruamāhanga Whaitua Committee The climate of the Ruamāhanga catchment The Tararua and Rimutaka ranges have a large influence on the climate of the Ruamāhanga catchment. The ranges shelter

More information

Climatography of the United States No

Climatography 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 information

JOINT BRIEFING TO THE MEMBERS. El Niño 2018/19 Likelihood and potential impact

JOINT BRIEFING TO THE MEMBERS. El Niño 2018/19 Likelihood and potential impact JOINT BRIEFING TO THE MEMBERS El Niño 2018/19 Likelihood and potential impact CURRENT EL NIÑO OUTLOOK (SEPTEMBER 2018) CPC/IRI ENSO Forecast from September. Red bars denote probability of an El Nino developing

More information

US Drought Status. Droughts 1/17/2013. Percent land area affected by Drought across US ( ) Dev Niyogi Associate Professor Dept of Agronomy

US Drought Status. Droughts 1/17/2013. Percent land area affected by Drought across US ( ) Dev Niyogi Associate Professor Dept of Agronomy Droughts US Drought Status Dev Niyogi Associate Professor Dept of Agronomy Deptof Earth Atmospheric Planetary Sciences Indiana State Climatologist Purdue University LANDSURFACE.ORG iclimate.org climate@purdue.edu

More information

4. THE HBV MODEL APPLICATION TO THE KASARI CATCHMENT

4. THE HBV MODEL APPLICATION TO THE KASARI CATCHMENT Application of HBV model to the Kasari River, 1994 Page 1 of 6 Application of the HBV model to the Kasari river for flow modulation of catchments characterised by specific underlying features by R. Vedom,

More information

Climatography of the United States No

Climatography 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 information

In this activity, students will compare weather data from to determine if there is a warming trend in their community.

In this activity, students will compare weather data from to determine if there is a warming trend in their community. Overview: In this activity, students will compare weather data from 1910-2000 to determine if there is a warming trend in their community. Objectives: The student will: use the Internet to locate scientific

More information

statistical methods for tailoring seasonal climate forecasts Andrew W. Robertson, IRI

statistical methods for tailoring seasonal climate forecasts Andrew W. Robertson, IRI statistical methods for tailoring seasonal climate forecasts Andrew W. Robertson, IRI tailored seasonal forecasts why do we make probabilistic forecasts? to reduce our uncertainty about the (unknown) future

More information

Time series and Forecasting

Time series and Forecasting Chapter 2 Time series and Forecasting 2.1 Introduction Data are frequently recorded at regular time intervals, for instance, daily stock market indices, the monthly rate of inflation or annual profit figures.

More information

Climate Change and Arizona s Rangelands: Management Challenges and Opportunities

Climate Change and Arizona s Rangelands: Management Challenges and Opportunities Climate Change and Arizona s Rangelands: Management Challenges and Opportunities Mike Crimmins Climate Science Extension Specialist Dept. of Soil, Water, & Env. Science & Arizona Cooperative Extension

More information