Comprehensive Forecasting System for Variable Renewable Energy

Size: px
Start display at page:

Download "Comprehensive Forecasting System for Variable Renewable Energy"

Transcription

1 Branko Kosović Sue Ellen Haupt, Gerry Wiener, Luca Delle Monache, Yubao Liu, Marcia Politovich, Jenny Sun, John Williams*, Daniel Adriaansen, Stefano Alessandrini, Susan Dettling, and Seth Linden (NCAR, *WSI) ICEM 2015 Boulder, Colorado, June 22-26,

2 Xcel Energy Wind Prediction Project 3.4 million customers annual revenue $11B

3 Xcel Energy Wind Forecasting Project Forecast Requirements minute forecasts (emergency ramp adjustments) 1-3 hour forecasts (anticipate upcoming ramp adjustments) 24 hour forecasts (energy trading and planning) 3-5 day forecasts (long term trading & resource planning) Xcel Energy needed power forecasts for 55 connection nodes representing 94 wind farms with 3283 turbines totaling 4000 MW of power generation capability. But. The required forecasts need to be for POWER, not wind speed! 3

4 Variable Energy Forecasting System NCEP Data NAM, GFS, RUC GEM (Canada) WRF RTFDDA System Ensemble System Solar Energy Forecast Supplemental Wind Farm Data Met towers Wind profiler Surface Stations Windcube Lidar Wind Farm Data Nacelle wind speed Generator power Node power Met tower Availability Dynamic, Integrated Forecast System (DICast ) VDRAS (nowcasting) Expert System (nowcasting) CSV Data Statistical Verification Wind to Energy Conversion Subsystem Probabilistic and Analog Forecast Potential Power Forecasting Data Mining for Load Estimation Operator GUI Meteorologist GUI WRF Model Output Extreme Weather Events 4

5 DICast System Blends Output from Several Numerical Weather Prediction Models Public Service of Northwest Texas Area Total Power, 03/14 Ramp CAPACITY (%) TIME

6 Wind Power Forecasts Resulted in Savings for Ratepayers Forecasted MAE Percentage Savings * Improvement 16.83% 10.10% 40% $49,000,000 *Data through November, 2014 Also: saved > 267,343 tons CO2 (2014) Drake Bartlett, Xcel 6

7 Icing Forecasting System ExWx Provides Categorical Forecast of Icing Predicting wind turbine icing is critical for power trading on open market and short term load balancing. In order to successfully develop a robust wind turbine icing forecasting system, a truth dataset must be developed. Limited documentation of icing events and monitoring equipment make identifying icing after the fact difficult. Plus, there is a Big Data problem.

8 Power Data Datasets For Icing Forecast Sensor Data PRIMARY DICast Data SECONDARY NWS Data METAR Data

9 ExWx Uses WRF-RTFDDA and DICast Blended NWP Output to Compute Icing Potential A B A B B C A B C D W W CPOI W D B W B WRF icing potential Evaluates all WRF model levels < 1km Combines model level height, model predicted supercooled liquid water, and temperature at each level using fuzzy logic maps (configurable) Final potential at each WRF grid point is the maximum of the icing potential at each level < 1km DICast icing potential Conditional probability of icing (CPOI) deterministic forecast from DICast Combines five NWP model solutions Typically one site per farm, more in some cases UCAR Confidential and Proprietary. 2015, University Corporation for Atmospheric Research. All rights reserved.

10 Icing Forecasting System Provides Categorical Icing Forecast Note no missing data-wherever DICast was missing the WRF is used exclusively (and vice-versa) Threshold of 0.5 is configurable based on experience of operators Event well forecast by ExWx 12/25/14 ExWx icing potential forecasts for all ExWx runs affecting the event window (8 hours centered on 00Z) Icing potential < 0.5 inside window Icing potential > 0.5 inside window Icing potential > 0.5 outside window Icing potential < 0.5 outside window 12/26/14 UCAR Confidential and Proprietary. 2015, University Corporation for Atmospheric Research. All rights reserved.

11 Scaled Load Load Forecasting and Distributed Solar Energy Forecasting Power [MW] 70 Actual Load Forecast Date Increasing distributed generation behind the meter enhances need for load forecasting to mitigate rapid changes in weather-sensitive power production Hour

12 Develop and Evaluate Solar Energy Prediction Techniques Distributed Solar Forecasts Determined load cutout due to distributed generation solar (higher load on cloudy days) Used historical production data (gridded) and determined representativeness Season Higher load days p-value Sample size (clear/cloudy) Spring Cloudy /17 Summer Clear /8 Fall Cloudy /24 Winter Cloudy /21 Capacity of DPV installations Correlation of hourly gridded PV percent capacity to those from a grid box near the NREL-NWTC site. Also shown are BVSD PV (blue dots) and Sun Edison PV installations (green dots), and METAR sites (black dots).

13 Evaluation Develop and Evaluate Solar Energy Prediction Techniques Difficult to evaluate given lack of actual production data (behind the meter) Used data from BVSD Scatter plot shows majority of forecasts along Y=X line UCAR Confidential and Proprietary. 2014, University Corporation for Atmospheric Research. All rights reserved.

14 Develop and Evaluate Solar Energy Prediction Techniques Normalized RMSE Bias Normalized RMSE and Bias are shown for 6 BVSD Sites Errors are for 11z Initialization Forecast over 6 month period Most nrmse values under 3%

15 Data Mining For Load Estimation Select machine learning method Identify, compute predictors Optimize spatial averaging weights Optimize temporal averaging weights Select optimal set of predictor variables Select machine learning parameters Evaluate model via historical playback Overview of System Development Machine Learning methods evaluated Ridge Regression Decision Trees Random forest K-nearest neighbor Gradient boosted regression Cubist Cubist and Gradient Boosted Regression were the Performing learning methods. Cubist was chosen due to existing software infrastructure and extensive experience with the learning method

16 Data Mining For Load Estimation Overview of System Development Basic predictors identified DICast Forecast Variables: temperature, wind speed, dew point, probability of rain, snow, and ice, quantitative precipitation 1 hour forecast, probability of precipitation in 1 hour, cloud coverage, downward solar radiation flux at the surface, precipitation Solar Angles Date and Time Variables: hour of day, day of week, week of year, season indicators

17 Data Mining For Load Estimation Overview of System Development Predictors enhanced or combined to maximize skill Physical insight used to meaningfully combine variables improves performance Spatial weighted averages Temporal decay averages Feature extraction of functional relationships cos(2*pi*local_hour/24) Load vs. Temp

18 Data Mining For Load Estimation Overview of System Development Machine Learning parameters tuned Iterative search performed over parameter combinations Minimize complexity Maintain performance Parameter Search Space complexity ERROR Parameter 1 (committee members) Parameter 2 (rules) complexity

19 Data Mining For Load Estimation Real time load observations removed as input to forecasts Predictors enhanced and skill maintained Enhanced predictors tend to be favored by Cubist NEW MODEL vs OLD MODEL

20 Data Mining For Load Estimation Select machine learning method Load observations dataset begins 1 Sep 2012 Results from 1 Oct Sep 2014 Identify, compute predictors Optimize spatial averaging weights Optimize temporal averaging weights Select optimal set of predictor variables Select machine learning parameters Evaluate model via historical playback Average load MAPE Day-ahead MAPE = 2.40% Week-ahead MAPE = 2.88% Peak MAPE occurs during ramp to peak load

21 Summary We have develop a comprehensive variable power forecasting system that integrates recent advances in forecasting at a range of time scales including probabilistic forecasting and forecasting of extreme events. Day-ahead forecasting system resulted in significant savings for ratepayers. The effectiveness of a forecasting system for efficient integration of variable generation depends on the quality and quantity of data. More data (amount, frequency) is better, however, First data from existing sources should be: Standardized Quality controlled Delivered in timely manner, and Archived for future use (e.g., training for machine learning algorithms).

22 CO-Labs - Governor s Award 2014 for Sustainability 22

23 Branko Kosović Research Applications Laboratory National Center for Atmospheric Research 23

An Evaluation of Statistical Learning Methods for Gridded Solar Irradiance Forecasting

An Evaluation of Statistical Learning Methods for Gridded Solar Irradiance Forecasting An Evaluation of Statistical Learning Methods for Gridded Solar Irradiance Forecasting David John Gagne 1,2 Sue Ellen Haupt 1 Seth Linden 1 Gerry Weiner 1 Amy McGovern 3 John Williams 4 1. National Center

More information

A Wind Power Forecasting System to Optimize Power Integration

A Wind Power Forecasting System to Optimize Power Integration A Wind Power Forecasting System to Optimize Power Integration Dr. Sue Ellen Haupt and Gregory Thompson National Center for Atmospheric Research 3450 Mitchell Lane, Boulder, CO 80301 USA, haupt@ucar.edu

More information

>>> Best practice in wind power forecasting. Matthias Lange, Ulrich Focken, Anne Lenz. Southwest Power Pool Stakeholder Meeting

>>> Best practice in wind power forecasting. Matthias Lange, Ulrich Focken, Anne Lenz. Southwest Power Pool Stakeholder Meeting Best practice in wind power forecasting Matthias Lange, Ulrich Focken, Anne Lenz Southwest Power Pool Stakeholder Meeting Overview 2 Introduction to wind power prediction Reasons for forecasting errors

More information

Variational Doppler Radar Assimilation System (VDRAS) for Ramp Prediction

Variational Doppler Radar Assimilation System (VDRAS) for Ramp Prediction Variational Doppler Radar Assimilation System (VDRAS) for Ramp Prediction Juanzhen (Jenny) Sun MMM/RAL, NCAR May 12, 2010 Outline Challenge of ramp forecast VDRAS - background and technique Real-time low-level

More information

Partnership to Improve Solar Power Forecasting

Partnership to Improve Solar Power Forecasting Partnership to Improve Solar Power Forecasting Venue: EUPVSEC, Paris France Presenter: Dr. Manajit Sengupta Date: October 1 st 2013 NREL is a national laboratory of the U.S. Department of Energy, Office

More information

ERCOT LOAD FORECASTING. By Dr. Todd Crawford Chief Meteorologist The Weather Company, an IBM Business

ERCOT LOAD FORECASTING. By Dr. Todd Crawford Chief Meteorologist The Weather Company, an IBM Business ERCOT LOAD FORECASTING By Dr. Todd Crawford Chief Meteorologist The Weather Company, an IBM Business Table of Contents Methodology 3 Weather Forecasting 3 Load Forecasting 3 Results 4 Aggregate 4 Seasonal

More information

An Introduction to Wind and Solar Power Forecasting

An Introduction to Wind and Solar Power Forecasting GREENING THE GRID An Introduction to Wind and Solar Power Forecasting Jessica Katz National Renewable Energy Laboratory June 7, 2016 ENHANCING CAPACITY FOR LOW EMISSION DEVELOPMENT STRATEGIES (EC-LEDS)

More information

Predicting daily incoming solar energy from weather data

Predicting daily incoming solar energy from weather data Predicting daily incoming solar energy from weather data ROMAIN JUBAN, PATRICK QUACH Stanford University - CS229 Machine Learning December 12, 2013 Being able to accurately predict the solar power hitting

More information

Forecasting Wind. Barbara O Neill, Grid Integration Manager

Forecasting Wind. Barbara O Neill, Grid Integration Manager Forecasting Wind Barbara O Neill, Grid Integration Manager Presented to the Southeastern Wind Coalition UAG Forecasting and Integration Meeting Raleigh, North Carolina March 30, 2016 NREL/PR-5D00-66383

More information

Forecasting wind and radiation for energy production and balancing of the network

Forecasting wind and radiation for energy production and balancing of the network Forecasting wind and radiation for energy production and balancing of the network Claus Petersen DMI (Danish Meteorological Institute) Lyngbyvej 100 DK 2100 Copenhagen Denmark Email: cp@dmi.dk Outline

More information

SHORT TERM SOLAR RADIATION FORECASTS USING WEATHER REGIME-DEPENDENT

SHORT TERM SOLAR RADIATION FORECASTS USING WEATHER REGIME-DEPENDENT SHORT TERM SOLAR RADIATION FORECASTS USING WEATHER REGIME-DEPENDENT J3.5 ARTIFICIAL INTELLIGENCE TECHNIQUES Tyler C. McCandless 1,2*, Sue Ellen Haupt 1,2, and George S. Young 2 1 Research Applications

More information

Data Assimilation As A Verification Tool

Data Assimilation As A Verification Tool Data Assimilation As A Verification Tool Dr. Dale M. Barker, MMM Division, NCAR Data Assimilation Overview Assimilation system combines: Wide range of observations - y o Previous background forecast(s)

More information

03 December 2013 «WIRE» COST Action, benchmark results

03 December 2013 «WIRE» COST Action, benchmark results 03 December 2013 «WIRE» COST Action, benchmark results Stefano Alessandrini, Simone Sperati - RSE SpA George Kariniotakis MINES ParisTech Pierre Pinson - DTU Mod. PPT v. 00 Objectives & Motivation In the

More information

Application and verification of ECMWF products 2015

Application and verification of ECMWF products 2015 Application and verification of ECMWF products 2015 Karl-Ivar Ivarsson, Swedish meteorological and hydrological institute 1. Summary of major highlights There are no notable change in the use of ECMWF

More information

Seasonal Winter Weather Forecast for UK and Europe: December February 2001

Seasonal Winter Weather Forecast for UK and Europe: December February 2001 Seasonal Winter Weather Forecast for UK and Europe: December 2000 - February 2001 Public Release: 5th December, 2000 (First Issued: 25th November, 2000) by Mark Saunders, Tony Hamilton, and Steve George

More information

Is the WRF better than the GFS?

Is the WRF better than the GFS? Is the WRF better than the GFS? Barry Lynn, Asher Meir, Yaakov Consor, and Guy Kelman A Weather It Is, LTD Presentation Does Downscaling Work? If you start with GFS initial data does the WRF at higher

More information

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract This work was performed under the auspices of the U.S. Department of Energy by under contract DE-AC52-07NA27344 and was funded by Wind Uncertainty Quantification Laboratory Directed Research and Development

More information

MET 200 Lecture 22 Weather Forecasting. Motivation. Lecture 22 Weather Forecasting. Numerical Weather Prediction (NWP)

MET 200 Lecture 22 Weather Forecasting. Motivation. Lecture 22 Weather Forecasting. Numerical Weather Prediction (NWP) MET 200 Lecture 22 Weather Forecasting Lecture 22 Weather Forecasting Motivation and history Collect observations Run numerical weather prediction models Construct Forecasts Issue advisories, watches and

More information

Convective Weather PDT. Marilyn Wolfson MIT LL and Cindy Mueller NCAR

Convective Weather PDT. Marilyn Wolfson MIT LL and Cindy Mueller NCAR Convective Weather PDT Marilyn Wolfson MIT LL and Cindy Mueller NCAR Outline CWPDT Approach Improving 0-2 hr Forecasts 1-hr TCWF 2-hr RCWF AutoNowcaster Initial Work on 2-6 hr Forecasts 2-hr NCWF Combined

More information

The Wind Integration National Dataset (WIND) toolkit

The Wind Integration National Dataset (WIND) toolkit The Wind Integration National Dataset (WIND) toolkit EWEA Wind Power Forecasting Workshop, Rotterdam December 3, 2013 Caroline Draxl NREL/PR-5000-60977 NREL is a national laboratory of the U.S. Department

More information

Forecast applications driven by High-Performance Computing at The Weather Company. Todd Hutchinson 28 October 2016

Forecast applications driven by High-Performance Computing at The Weather Company. Todd Hutchinson 28 October 2016 Forecast applications driven by High-Performance Computing at The Weather Company Todd Hutchinson 28 October 2016 Overview Consumer Products and Forecast Production Business applications and examples Use

More information

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Jin Xu, Shinjae Yoo, Dantong Yu, Dong Huang, John Heiser, Paul Kalb Solar Energy Abundant, clean, and secure

More information

Cloud Model Verification at the Air Force Weather Agency

Cloud Model Verification at the Air Force Weather Agency 2d Weather Group Cloud Model Verification at the Air Force Weather Agency Matthew Sittel UCAR Visiting Scientist Air Force Weather Agency Offutt AFB, NE Template: 28 Feb 06 Overview Cloud Models Ground

More information

Forecaster comments to the ORTECH Report

Forecaster comments to the ORTECH Report Forecaster comments to the ORTECH Report The Alberta Forecasting Pilot Project was truly a pioneering and landmark effort in the assessment of wind power production forecast performance in North America.

More information

ENERGY, WATER AND PHENOLOGY CONTROLS ON THE ANNUAL CARBON AND WATER CYCLES

ENERGY, WATER AND PHENOLOGY CONTROLS ON THE ANNUAL CARBON AND WATER CYCLES ENERGY, WATER AND PHENOLOGY CONTROLS ON THE ANNUAL CARBON AND WATER CYCLES USING REMOTE SENSING TO UNDERSTAND CLIMATE VARIABILITY NATURE GEOSCIENCE Julia Green Columbia University, Pierre Gentine Columbia

More information

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning.

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. 31st Annual International Symposium on Forecasting Lourdes Ramírez Santigosa Martín

More information

GHI / GHI clear = f(ci) (1)

GHI / GHI clear = f(ci) (1) FORECASTING SOLAR RADIATION -- PRELIMINARY EVALUATION OF AN APPROACH BASED UPON THE NATIONAL FORECAST DATA BASE Richard Perez ASRC, The University at Albany 251 Fuller Rd, Albany, NY 1223 Perez@asrc.cestm.albany.edu

More information

J12.3 THE NEW AND IMPROVED LOCALIZED AVIATION MOS PROGRAM (LAMP) ANALYSIS AND PREDICTION SYSTEM

J12.3 THE NEW AND IMPROVED LOCALIZED AVIATION MOS PROGRAM (LAMP) ANALYSIS AND PREDICTION SYSTEM J12.3 THE NEW AND IMPROVED LOCALIZED AVIATION MOS PROGRAM (LAMP) ANALYSIS AND PREDICTION SYSTEM Bob Glahn* and Judy E. Ghirardelli Meteorological Development Laboratory Office of Science and Technology

More information

Weather Forecasting - Introduction

Weather Forecasting - Introduction Chapter 14 - Weather Forecasting Weather Forecasting - Introduction Weather affects nearly everyone nearly every day Weather forecasts are issued: to save lives reduce property damage reduce crop damage

More information

Electricity demand forecasting and the problem of embedded generation

Electricity demand forecasting and the problem of embedded generation Electricity demand forecasting and the problem of embedded generation Place your chosen image here. The four corners must just cover the arrow tips. For covers, the three pictures should be the same size

More information

Use of microwave radiances for weather forecasting

Use of microwave radiances for weather forecasting 11.30 local satellite image Use of microwave radiances for weather forecasting Roger Saunders SFCG-24 20 Sep 2004 Crown copyright 2004 Page 1 Cloud is common Band Instrument Cloud-free Cloud-free upper-trop

More information

Master Thesis projects on Wind Energy

Master Thesis projects on Wind Energy Master Thesis projects on Wind Energy Who we are Vattenfall is one of the largest wind power developers and operators in Europe with more than 1000 operating turbines in Sweden, Denmark, Germany, the Netherlands,

More information

An overview of road surface conditions forecasting in Météo-France

An overview of road surface conditions forecasting in Météo-France ID 0034 An overview of road surface conditions forecasting in Météo-France Ludovic BOUILLOUD Météo-France 16th International SIRWEC Road Weather Conference Helsinki, Finland, 23-25 May 2012 Outlook 1.

More information

*Corresponding author address: Jamie Wolff, NCAR/RAL, P.O. Box 3000, Boulder, CO 80307,

*Corresponding author address: Jamie Wolff, NCAR/RAL, P.O. Box 3000, Boulder, CO 80307, 8B.6 QUANTITATIVE PRECIPTIATION FORECAST (QPF) VERIFICATION COMPARISON BETWEEN THE GLOBAL FORECAST SYSTEM (GFS) AND NORTH AMERICAN MESOSCALE (NAM) OPERATIONAL MODELS Jamie Wolff*, Barbara Brown, John Halley

More information

IBM Big Green Innovations Environmental R&D and Services

IBM Big Green Innovations Environmental R&D and Services IBM Big Green Innovations Environmental R&D and Services Smart Weather Modelling Local Area Precision Forecasting for Weather-Sensitive Business Operations (e.g. Smart Grids) Lloyd A. Treinish Project

More information

Solar Energy prediction and verification using model forecasts and ground based solar measurements

Solar Energy prediction and verification using model forecasts and ground based solar measurements Solar Energy prediction and verification using model forecasts and ground based solar measurements Kazadzis S. 1*, Kosmopoulos P.G. 1, Lagouvardos K. 1, Kotroni V. 1 1 Institute for Environmental Research

More information

Virginia Department of Transportation Weather and Pavement Forecasting RFP (May 1998)

Virginia Department of Transportation Weather and Pavement Forecasting RFP (May 1998) Virginia Department of Transportation Weather and Pavement Forecasting RFP (May 1998) Document Outline: General Information Project Description Concept of Operation Mandatory Requirements Statement of

More information

PJM LOAD FORECASTING. By Dr. Todd Crawford Chief Meteorologist The Weather Company, an IBM Business

PJM LOAD FORECASTING. By Dr. Todd Crawford Chief Meteorologist The Weather Company, an IBM Business PJM LOAD FORECASTING By Dr. Todd Crawford Chief Meteorologist The Weather Company, an IBM Business Table of Contents Methodology 3 Weather Forecasting 3 Load Forecasting 3 Results 4 Aggregate 4 The Weather

More information

Wind Power Forecasting with Focus on Extremes. SafeWind Project. Highlight of a successful EU-India collaboration

Wind Power Forecasting with Focus on Extremes. SafeWind Project. Highlight of a successful EU-India collaboration www.safewind.eu Workshop Wind Power Forecasting with Focus on Extremes SafeWind Project Highlight of a successful EU-India collaboration George Kariniotakis MINES ParisTech/ARMINES georges.kariniotakis@mines-paristech.fr

More information

Quantitative Precipitation Estimation and Quantitative Precipitation Forecasting by the Japan Meteorological Agency

Quantitative Precipitation Estimation and Quantitative Precipitation Forecasting by the Japan Meteorological Agency Quantitative Precipitation Estimation and Quantitative Precipitation Forecasting by the Japan Meteorological Agency Kazuhiko NAGATA Forecast Division, Forecast Department Japan Meteorological Agency 1.

More information

NC STATE UNIVERSITY Exploratory Analysis of Massive Data for Distribution Fault Diagnosis in Smart Grids

NC STATE UNIVERSITY Exploratory Analysis of Massive Data for Distribution Fault Diagnosis in Smart Grids Exploratory Analysis of Massive Data for Distribution Fault Diagnosis in Smart Grids Yixin Cai, Mo-Yuen Chow Electrical and Computer Engineering, North Carolina State University July 2009 Outline Introduction

More information

Using High-resolution Weather Data to Improve Winter Weather Maintenance Operations

Using High-resolution Weather Data to Improve Winter Weather Maintenance Operations Using High-resolution Weather Data to Improve Winter Weather Maintenance Operations Mike Baldwin Purdue University Dept of Earth, Atmospheric, and Planetary Sciences Acknowledgements Kim Hoogewind Derrick

More information

Wayne O. Miller

Wayne O. Miller Wayne O. Miller miller99@llnl.gov Livermore, CA USA This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. ^

More information

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES

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

More information

Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models. Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD

Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models. Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD Solar Radiation Reaching the Surface Incoming solar radiation can be reflected,

More information

Improving Accuracy of Solar Forecasting February 14, 2013

Improving Accuracy of Solar Forecasting February 14, 2013 Improving Accuracy of Solar Forecasting February 14, 2013 Solar Resource Forecasting Objectives: Improve accuracy of solar resource forecasts Enable widespread use of solar forecasts in power system operations

More information

Model studies atmospheric icing Mapping of ice and snow loads

Model studies atmospheric icing Mapping of ice and snow loads Model studies atmospheric icing Mapping of ice and snow loads Bjørn Egil Kringlebotn Nygaard bjornen@met.no Forecast for today Atmospheric icing of power lines Atmospheric icing of power lines Atmospheric

More information

Questionnaire on customers requirements of solar energy resource information

Questionnaire on customers requirements of solar energy resource information International Energy Agency, Solar Heating and Cooling programme, Task 36 Solar Resource Knowledge Management Address, OA, web, email Questionnaire on customers requirements of solar energy resource information

More information

3.13 INTEGRATION OF SATELLITE-DERIVED CLOUD PHASE, CLOUD TOP HEIGHT, AND LIQUID WATER PATH INTO AN OPERATIONAL AIRCRAFT ICING NOWCASTING SYSTEM

3.13 INTEGRATION OF SATELLITE-DERIVED CLOUD PHASE, CLOUD TOP HEIGHT, AND LIQUID WATER PATH INTO AN OPERATIONAL AIRCRAFT ICING NOWCASTING SYSTEM 3.13 INTEGRATION OF SATELLITE-DERIVED CLOUD PHASE, CLOUD TOP HEIGHT, AND LIQUID WATER PATH INTO AN OPERATIONAL AIRCRAFT ICING NOWCASTING SYSTEM Julie Haggerty *, Frank McDonough, Jennifer Black, Scott

More information

Cloud/Hydrometeor Assimilation into 20-km Rapid Update Cycle. Dongsoo Kim* and Stanley G. Benjamin

Cloud/Hydrometeor Assimilation into 20-km Rapid Update Cycle. Dongsoo Kim* and Stanley G. Benjamin Cloud/Hydrometeor Assimilation into 20-km Rapid Update Cycle Dongsoo Kim* and Stanley G. Benjamin NOAA Research Forecast Systems Laboratory *Also affiliated with CIRES/University of Colorado Boulder, CO

More information

An Overview of the North American Monsoon and Seasonal Outlook for 2008

An Overview of the North American Monsoon and Seasonal Outlook for 2008 An Overview of the North American Monsoon and Seasonal Outlook for 2008 Christopher L. Castro Department of Atmospheric Sciences University of Arizona UA Climate Science Applications Program (CSAP) and

More information

COMPARISON OF PRECIPITATION RATE INTENSITIES AS DETERMINED BY VISIBILITY VERSUS LIQUID WATER EQUIVALENT MEASUREMENTS

COMPARISON OF PRECIPITATION RATE INTENSITIES AS DETERMINED BY VISIBILITY VERSUS LIQUID WATER EQUIVALENT MEASUREMENTS COMPARISON OF PRECIPITATION RATE INTENSITIES AS DETERMINED BY VISIBILITY VERSUS LIQUID WATER EQUIVALENT MEASUREMENTS Jennifer Black, Roy Rasmussen and Scott Landolt National Center for Atmospheric Research,

More information

Advantages of a precipitation analysis to verify the ECMWF forecast for Europe

Advantages of a precipitation analysis to verify the ECMWF forecast for Europe Advantages of a precipitation analysis to verify the forecast for Europe Anna Ghelli, Thank you to Tiziana Cherubini Verification Workshop Boulder July 2002 Slide 1 Up-scaling technique General Circulation

More information

Radar Data Assimilation

Radar Data Assimilation Radar Data Assimilation David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Radar Data Assimilation (for analysis and prediction of convective

More information

Probabilistic forecast information optimised to end-users applications: three diverse examples

Probabilistic forecast information optimised to end-users applications: three diverse examples Probabilistic forecast information optimised to end-users applications: three diverse examples ECMWF User Seminar 2015 Vanessa Stauch, Renate Hagedorn, Isabel Alberts, Reik Schaab Our group Land Transport

More information

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

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

More information

Application and verification of ECMWF products 2015

Application and verification of ECMWF products 2015 Application and verification of ECMWF products 2015 Hellenic National Meteorological Service (HNMS) Flora Gofa, Dimitra Boukouvala and Panagiotis Skrimizeas 1. Summary of major highlights In order to determine

More information

The Operational Value of Social Media Information. Social Media and Customer Interaction

The Operational Value of Social Media Information. Social Media and Customer Interaction The Operational Value of Social Media Information Dennis J. Zhang (Kellogg School of Management) Ruomeng Cui (Kelley School of Business) Santiago Gallino (Tuck School of Business) Antonio Moreno-Garcia

More information

Development of a. Solar Generation Forecast System

Development of a. Solar Generation Forecast System ALBANY BARCELONA BANGALORE 16 December 2011 Development of a Multiple Look ahead Time Scale Solar Generation Forecast System John Zack Glenn Van Knowe Marie Schnitzer Jeff Freedman AWS Truepower, LLC Albany,

More information

Weather Scenarios. Tailoring Weather Scenarios to Suit Your Needs

Weather Scenarios. Tailoring Weather Scenarios to Suit Your Needs Weather Scenarios Tailoring Weather Scenarios to Suit Your Needs Tailoring Weather Scenarios 1. Need to ensure we have the right weather for the analysis 2. Need to maintain a level of consistency Amongst

More information

Weather Conditions Associated with Rapid Variations in Lake Erie Ice Cover

Weather Conditions Associated with Rapid Variations in Lake Erie Ice Cover Weather Conditions Associated with Rapid Variations in Lake Erie Ice Cover Colin Zarzycki Department of Earth & Atmospheric Sciences Cornell University Gena Renninger Department of Meteorology Penn State

More information

JPSS Program Overview

JPSS Program Overview JPSS Science and Implementation Strategy JPSS Program Overview Mitch Goldberg, Program Scientist Joint Polar Satellite System National Environmental Satellite, Data, and Information Service National Oceanic

More information

Advanced Root Cause Analysis for Product Quality Improvement using Machine Learning in TIBCO Spotfire

Advanced Root Cause Analysis for Product Quality Improvement using Machine Learning in TIBCO Spotfire Advanced Root Cause Analysis for Product Quality Improvement using Machine Learning in TIBCO Spotfire GRADIENT BOOSTING MACHINE MODELING Gradient Boosting Machine (GBM) modeling is a powerful machine learning

More information

Application of DMI-HIRLAM for Road Weather Forecasting

Application of DMI-HIRLAM for Road Weather Forecasting HIRLAM Newsletter no 54, December 2008 Application of DMI-HIRLAM for Road Weather Forecasting Alexander Mahura, Claus Petersen, and Bent Sass Danish Meteorological Institute, DMI, Lyngbyvej 100, DK-2100,

More information

Explaining Wind Farm Output Using Regression Analysis. Kate Geschwind Mayo High School Rochester, MN

Explaining Wind Farm Output Using Regression Analysis. Kate Geschwind Mayo High School Rochester, MN Explaining Wind Farm Output Using Regression Analysis Kate Geschwind Mayo High School Rochester, MN Contents Abstract... 3 Introduction... 4 Materials and Methods... 4 Results... 6 Discussion... 9 Conclusion...

More information

Application and verification of ECMWF products 2016

Application and verification of ECMWF products 2016 Application and verification of ECMWF products 2016 Czech Hydrometeorological Institute (CHMI) 1 Summary of major highlights ECMWF products have been widely used by the Central and Regional Forecasting

More information

The Weather Intelligence for Renewable Energies Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

The Weather Intelligence for Renewable Energies Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation Energies 2015, 8, 9594-9619; doi:10.3390/en8099594 Article OPEN ACCESS energies ISSN 1996-1073 www.mdpi.com/journal/energies The Weather Intelligence for Renewable Energies Benchmarking Exercise on Short-Term

More information

4-DIMENSIONAL VARIATIONAL ASSIMILATION OF GROUND-BASED MICROWAVE OBSERVATIONS DURING A WINTER FOG EVENT

4-DIMENSIONAL VARIATIONAL ASSIMILATION OF GROUND-BASED MICROWAVE OBSERVATIONS DURING A WINTER FOG EVENT 4-DIMENSIONAL VARIATIONAL ASSIMILATION OF GROUND-BASED MICROWAVE OBSERVATIONS DURING A WINTER FOG EVENT Francois VANDENBERGHE (1) and Randolph WARE (2) (1) National Center for Atmospheric Research, Boulder

More information

Forecasting Solar Power with Adaptive Models A Pilot Study

Forecasting Solar Power with Adaptive Models A Pilot Study Forecasting Solar Power with Adaptive Models A Pilot Study Dr. James W. Hall 1. Introduction Expanding the use of renewable energy sources, primarily wind and solar, has become a US national priority.

More information

Data assimilation over Northern polar region using WRF and WRFDA

Data assimilation over Northern polar region using WRF and WRFDA Data assimilation over Northern polar region using WRF and WRFDA Zhiquan Liu, Hui-Chuan Lin, and Ying-Hwa Kuo National Center for Atmospheric Research, Boulder, CO, USA David Bromwich and Lesheng Bai Ohio

More information

Kristina Lundgren, Andrea Steiner, Tobias Heppelmann, Richard Keane, Gernot Vogt & Stefan Declair Deutscher Wetterdienst, Germany

Kristina Lundgren, Andrea Steiner, Tobias Heppelmann, Richard Keane, Gernot Vogt & Stefan Declair Deutscher Wetterdienst, Germany Improved weather forecasts for wind energy applications: Lessons learned and perspectives based on EWeLiNE Kristina Lundgren, Andrea Steiner, Tobias Heppelmann, Richard Keane, Gernot Vogt & Stefan Declair

More information

NEW COOPERATIVE OBSERVER NETWORKS AND INSTRUMENTATION DATA QUALITY

NEW COOPERATIVE OBSERVER NETWORKS AND INSTRUMENTATION DATA QUALITY 2B.10 NEW COOPERATIVE OBSERVER NETWORKS AND INSTRUMENTATION DATA QUALITY Jason A. Karvelot* Davis Instruments Corporation, Hayward, California 1. INTRODUCTION In the past few years, the advent of new cooperative

More information

Weather Definition, Instruments, and Data Collection

Weather Definition, Instruments, and Data Collection Title: Weather Definition, Instruments, and Data Collection ( Meteorology ) Grade Level(s): 6-8 Introduction: Meteorology is the science that deals with the atmosphere and related phenomena. Research areas

More information

SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY

SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY Wolfgang Traunmüller 1 * and Gerald Steinmaurer 2 1 BLUE SKY Wetteranalysen, 4800 Attnang-Puchheim,

More information

CS771 Project PREDICT DEMAND FOR RENTING BIKES

CS771 Project PREDICT DEMAND FOR RENTING BIKES CS771 Project PREDICT DEMAND FOR RENTING BIKES Guide: Prof. Harish Karnick Rishav Raj Agarwal, Ritvik Srivastava, Anusha Chowdhury, Avikalp Kumar Gupta Group: 35 Abstract. Bicycle sharing programs have

More information

Storm Risk Mitigation through Improved Prediction and Impact Modelling

Storm Risk Mitigation through Improved Prediction and Impact Modelling Storm Risk Mitigation through Improved Prediction and Impact Modelling 1. Introduction This Implementation Plan details the method of execution of the Storm Risk Mitigation through Improved Prediction

More information

Recent Developments on Latent Heat Nudging and the Use of Wind Profiler Data

Recent Developments on Latent Heat Nudging and the Use of Wind Profiler Data COSMO General Meeting, Milano, 22-24 Sept. 2004 Recent Developments on Latent Heat Nudging and the Use of Wind Profiler Data Christoph Schraff Deutscher Wetterdienst, D-63067 Offenbach, Germany christoph.schraff@dwd.de

More information

Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study

Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study Robert Tardif National Center for Atmospheric Research Research Applications Laboratory 1 Overview of project Objectives:

More information

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data Mentor: Dr. Malcolm LeCompte Elizabeth City State University

More information

Vaisala Visualization. November 2010 Petteri Leppänen

Vaisala Visualization. November 2010 Petteri Leppänen Vaisala Visualization November 2010 Petteri Leppänen Roads Web visualization offering sales price Road Weather Advisor Road Weather Observer Road Weather Navigator 2.0 RWDSS customer value Page 2 / date

More information

Overview of the International H20 Project - IHOP

Overview of the International H20 Project - IHOP Atmospheric Measurements and Observations II EAS 535 Overview of the International H20 Project - IHOP Principal Investigators: Dave Parsons and Tammy Weckwerth All information in this lecture is attributed

More information

4.3. David E. Rudack*, Meteorological Development Laboratory Office of Science and Technology National Weather Service, NOAA 1.

4.3. David E. Rudack*, Meteorological Development Laboratory Office of Science and Technology National Weather Service, NOAA 1. 43 RESULTS OF SENSITIVITY TESTING OF MOS WIND SPEED AND DIRECTION GUIDANCE USING VARIOUS SAMPLE SIZES FROM THE GLOBAL ENSEMBLE FORECAST SYSTEM (GEFS) RE- FORECASTS David E Rudack*, Meteorological Development

More information

DAILY MULTIVARIATE FORECASTING OF WATER DEMAND IN A TOURISTIC ISLAND WITH THE USE OF ANN AND ANFIS

DAILY MULTIVARIATE FORECASTING OF WATER DEMAND IN A TOURISTIC ISLAND WITH THE USE OF ANN AND ANFIS DAILY MULTIVARIATE FORECASTING OF WATER DEMAND IN A TOURISTIC ISLAND WITH THE USE OF ANN AND ANFIS D. Kofinas, E. Papageorgiou, C. Laspidou, N. Mellios, K. Kokkinos CONTEMPORARY APPROACHES ON WDS MANAGEMENT

More information

9A.1 NEW METHODS FOR EVALUATING RAINFALL FORECASTS FROM OPERATIONAL MODELS FOR LANDFALLING TROPICAL CYCLONES

9A.1 NEW METHODS FOR EVALUATING RAINFALL FORECASTS FROM OPERATIONAL MODELS FOR LANDFALLING TROPICAL CYCLONES 9A.1 NEW METHODS FOR EVALUATING RAINFALL FORECASTS FROM OPERATIONAL MODELS FOR LANDFALLING TROPICAL CYCLONES Timothy Marchok 1, Robert Rogers 2 and Robert Tuleya 3 1 NOAA / Geophysical Fluid Dynamics Laboratory,

More information

Very High Resolution Arctic System Reanalysis for 2000-2011

Very High Resolution Arctic System Reanalysis for 2000-2011 Very High Resolution Arctic System Reanalysis for 2000-2011 David H. Bromwich, Lesheng Bai,, Keith Hines, and Sheng-Hung Wang Polar Meteorology Group, Byrd Polar Research Center The Ohio State University

More information

A NEW APPROACH TO LOCAL MODELING. James M. Frederick*, J. Brad McGavock, Steven F. Piltz NOAA/National Weather Service Forecast Office Tulsa, Oklahoma

A NEW APPROACH TO LOCAL MODELING. James M. Frederick*, J. Brad McGavock, Steven F. Piltz NOAA/National Weather Service Forecast Office Tulsa, Oklahoma JP 1.11 A NEW APPROACH TO LOCAL MODELING James M. Frederick*, J. Brad McGavock, Steven F. Piltz NOAA/National Weather Service Forecast Office Tulsa, Oklahoma 1. INTRODUCTION Locally run atmospheric models

More information

Temporal variation in snow cover over sea ice in Antarctica using AMSR-E data product

Temporal variation in snow cover over sea ice in Antarctica using AMSR-E data product Temporal variation in snow cover over sea ice in Antarctica using AMSR-E data product Michael J. Lewis Ph.D. Student, Department of Earth and Environmental Science University of Texas at San Antonio ABSTRACT

More information

Guy Carpenter Asia-Pacific Climate Impact Centre, School of energy and Environment, City University of Hong Kong

Guy Carpenter Asia-Pacific Climate Impact Centre, School of energy and Environment, City University of Hong Kong Diurnal and Semi-diurnal Variations of Rainfall in Southeast China Judy Huang and Johnny Chan Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong

More information

Exponential Smoothing Models for Prediction of Solar Irradiance

Exponential Smoothing Models for Prediction of Solar Irradiance Exponential Smoothing Models for Prediction of Solar Irradiance Vijaya Margaret, Jeenu Jose Professor, Christ University, Bangalore, India Student, Christ University, Bangalore, India ABSTRACT: Solar energy

More information

Global Seasonal Phase Lag between Solar Heating and Surface Temperature

Global Seasonal Phase Lag between Solar Heating and Surface Temperature Global Seasonal Phase Lag between Solar Heating and Surface Temperature Summer REU Program Professor Tom Witten By Abstract There is a seasonal phase lag between solar heating from the sun and the surface

More information

Historical Weather Data for Fire Planning Analysis from the North American Regional Reanalysis Dataset

Historical Weather Data for Fire Planning Analysis from the North American Regional Reanalysis Dataset Historical Weather Data for Fire Planning Analysis from the North American Regional Reanalysis Dataset January 2008 IMPORTANT NOTICE: DATA GENERATED FROM THIS PROJECT WILL BE BENEFICIAL IN PERFORMING CLIMATOLOGICAL

More information

Prediction of Bike Rentals

Prediction of Bike Rentals Prediction of Bike Rentals Tanner Gilligan tanner12@stanford.edu Jean Kono jkono@stanford.edu Abstract We constructed a custom linear regression model to try and impute missing data from a time series

More information

Water Resources Forecasting and the Need for Skillful Climate Forecasts

Water Resources Forecasting and the Need for Skillful Climate Forecasts NOAA s National Weather Service California-Nevada River Forecast Center Water Resources Forecasting and the Need for Skillful Climate Forecasts Rob Hartman Hydrologist in Charge Mission of NWS Hydrologic

More information

Lecture 9: Evaporation

Lecture 9: Evaporation Lecture 9: Evaporation Key Questions 1. What is a evaporation? 2. Why does evaporation cool water? 3. What are the main energy inputs into a lake? 4. What is a vapor pressure deficit? 5. How does wind

More information

Cloud verification: a review of methodologies and recent developments

Cloud verification: a review of methodologies and recent developments Cloud verification: a review of methodologies and recent developments Anna Ghelli ECMWF Slide 1 Thanks to: Maike Ahlgrimm Martin Kohler, Richard Forbes Slide 1 Outline Cloud properties Data availability

More information

PRECIPITATION PREDICTION USING ARTIFICIAL NEURAL NETWORKS KEVIN L. CROWELL. (Under the direction of Gerrit Hoogenboom) ABSTRACT

PRECIPITATION PREDICTION USING ARTIFICIAL NEURAL NETWORKS KEVIN L. CROWELL. (Under the direction of Gerrit Hoogenboom) ABSTRACT PRECIPITATION PREDICTION USING ARTIFICIAL NEURAL NETWORKS by KEVIN L. CROWELL (Under the direction of Gerrit Hoogenboom) ABSTRACT Precipitation, in meteorology, is defined as any product, liquid or solid,

More information

Measured Solar Radiation Over Illinois

Measured Solar Radiation Over Illinois Miscellaneous Publication 198 Measured Solar Radiation Over Illinois Wayne M. Wendland and Robert W. Scott December 2011 Illinois State Water Survey Prairie Research Institute University of Illinois at

More information

GPS derived water vapor in almo: status and outlook

GPS derived water vapor in almo: status and outlook GPS derived water vapor in almo: status and outlook Jean-Marie Bettems, MeteoSwiss With contributions from: Guergana Guerova, IAP, EPFL (GPS ZTD) Martin Zingg, MeteoSwiss/ETHZ (GPS ZTD) Marc Troller, Geodesy

More information

Spring WRF Conference, Jun. 24, 2009 Boulder

Spring WRF Conference, Jun. 24, 2009 Boulder Spring WRF Conference, Jun. 24, 2009 Boulder Coupling WRF 3 & CLM 3.5 for Regional Climate Simulation & Understanding Land Cover Feedbacks to Climate Zachary M. Subin*, Jiming Jin, Lara M. Kueppers, William

More information

Case 11 - Cell Phone Service: Multiple Regression Two Predictors. Marlene Smith, University of Colorado Denver Business School

Case 11 - Cell Phone Service: Multiple Regression Two Predictors. Marlene Smith, University of Colorado Denver Business School Case 11 - Cell Phone Service: Multiple Regression Two Predictors Marlene Smith, University of Colorado Denver Business School Cell Phone Service 1 : Multiple Regression - Two Predictors Background What

More information