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NWS Hydrologic Forecasting AHPS Program February 21, 2013 PowerPoint PPT Presentation


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National Weather Service. NWS Hydrologic Forecasting AHPS Program February 21, 2013. Ernie Wells Hydrologic Services Division NWS Office of Climate, Water and Weather Services. 1. Outline. AHPS Program Focus on Forecast Uncertainty Hydrologic Ensemble Forecasting Challenges.

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Nws hydrologic forecasting ahps program february 21 2013

National Weather Service

NWS Hydrologic Forecasting

AHPS Program

February 21, 2013

Ernie Wells

Hydrologic Services Division

NWS Office of Climate, Water and Weather Services

1


Outline

Outline

  • AHPS Program

  • Focus on Forecast Uncertainty

  • Hydrologic Ensemble Forecasting

  • Challenges


Nws hydrologic forecasting ahps program february 21 2013

Advanced Hydrologic Prediction Service (AHPS)

  • Provide enhanced water availability and flood warning information by leveraging NOAA’s infrastructure and expertise

  • Modernize services through infusion of new science and technology

  • - Flash-flood to seasonal freshwater forecasts

  • - Quantification of forecast certainty

  • - More accurate and timely forecasts and warnings

  • - Partnered flood-forecast area mapping

  • - Visually-oriented products

  • Provide consistent access to standardized graphics via web interface

3


Nws hydrologic forecasting ahps program february 21 2013

Accessing AHPS Information

“click on” the water tab for current river conditions

http://weather.gov/

4


Nws hydrologic forecasting ahps program february 21 2013

Accessing AHPS Information

“click on” the forecast location to access local hydrograph

http://water.weather.gov/

5


Nws hydrologic forecasting ahps program february 21 2013

Accessing AHPS Information

“click on” tabs for probabilistic forecasts

For over 2500 locations, NWS provide probabilistic river forecasts

6


Uncertainty estimates needed across all time scales

Uncertainty estimates needed across all time scales

Years

Seasons

Months

Weeks

Days

Hours

Minutes

Forecast Uncertainty

Forecast Lead Time

Benefits

Protection of Life & Property

Recreation

State/Local Planning

Hydropower

Ecosystem

Environment

Flood Mitigation & Navigation

Reservoir Control

Agriculture

Health

Commerce


Need for uncertainty estimates confirmed

Need for Uncertainty Estimates Confirmed

  • Consistent feedback from customers and research community indicated the need for this uncertainty information

    • 2006 NRC report

    • 2008 CFI survey

    • Aptima study

  • Multiple Internal NWS Service Assessments re-affirmed this need:

    • Red River Floods in 1997 and 2009

    • Central U.S. Floods in 2008

    • Nashville Flooding in 2010

    • Missouri-Souris Flooding in 2011

  • Recent Request for long-range low flow forecasts for Middle Mississippi


Current ensemble capabilities long range

Current Ensemble Capabilities (Long Range)

  • RFCs use the Ensemble Streamflow Prediction (ESP) component to produce long-term probabilistic forecasts for water supply applications and long range outlooks.

  • Limitations of existing operational approach

    • Addresses only the uncertainty in future atmospheric conditions using historical observations of temperature and precipitation

    • Produces primarily longer-term probabilistic forecasts


Current seasonal ensemble streamflow prediction vs hefs

Current (Seasonal) Ensemble Streamflow Prediction vs. HEFS


Hydrologic forecasting inputs outputs

Hydrologic

Modeling

Satellite Data

Precipitation

Estimates

River Gage Data

Reservoir

Releases Diversion

Radar Data

Precipitation

Forecasts

Hydrologic Forecasting Inputs/Outputs

Snow

Soil Moisture States

Deterministic / Probabilistic

River Forecasts

Temperature

Forecasts


Nws hydrologic forecasting ahps program february 21 2013

Uncertainty in hydrologic forecast

=

+ “Hydrologic Uncertainty”

“Input Uncertainty”

model initial conditions,

model parameters,

model structure,

anthropogenic impacts

(regulation, diversions, etc.)

precipitation,

temperature,

potential evaporation,

etc.


Hefs system architecture

HEFS System Architecture

Verification products

Meteorological Ensemble forecast processor

Verification system (EVS)

Weather/climate forecasts

Forcing input ensembles

Post- processed

“Raw”

Input flow data

Hydrologic

Ensemble Processor

(models)

Product generation system

flow ensembles

Streamflow

Hydrologic Ensemble Post-processor

Initial conditions and model parameters (e.g. DA)

No specific uncertainty modeling in HEFSv1

Ensemble products


Ensemble forecasting challenge

Mesh ensemble forcings from short, medium, and long range techniques.

medium range

wx models

mesoscale

wx models

Ensemble Forecasting Challenge

long range

global circulation models

downscaling

downscaling

downscaling

time

variable

downscaling

forecaster skill

climate forecasts and indexes


Nws hydrologic forecasting ahps program february 21 2013

HPC/RFC

forecasts

Ensembles

(days 1-5)

Short-Range

Calibrated short- to long-range

forcing ensembles

GFS/GEFS forecasts

Ensembles

(Day 1-14)

Medium-Range

Merging

Ensembles

(out to 8/9 months)

CFSv1/CFSv2

forecasts

Long-Range

Ensembles

(out to one year)

Climatology

Meteorological Ensemble Forecast Processor


Ensemble forecast challenge

Ensemble Forecast Challenge

  • Accurately incorporate the impacts of reservoirs and diversions

    • Ensure meaningful uncertainty information is retained beyond water control structure

    • Need operating rules for reservoirs

    • Reservoir models only approximate the actual operator decisions


Ensemble forecast challenge of a different kind

Ensemble Forecast Challenge of a different kind

  • Provide uncertainty information in a form and context that is useful to our customers

    • Education and training

    • Context, validation and verification

    • Compatibility with decision support tools

  • Realizing the full utility of this information

    Internal NWS customers (WFOs)

    External partners and customers (Water Managers, USACE, BoR, EMs, local communities, public)


What is needed for partners

What is needed for partners?

  • Proving the skill/value in these forecasts

    • Verification Information

    • Event specific

  • Communicating effectively (understandable,formats,etc)

  • Commitment to overcoming hurdles (policy,legislative mandates, bureaucracy, process, education, etc)

  • Silver Jackets, MB forecasters group, and others part of solution?

    • Local knowledge

    • Closer to specific issues/hurdles


Back up slides

Back-up Slides


Hefs service level objectives

HEFS Service Level Objectives

  • Produce ensemble streamflow forecasts:

    • Seamlessly span lead times from one hour to one year

    • Calibrated (unbiased, accurate spread)

    • Spatially and temporally consistent (linkable)

    • Effectively capture the information from current NWS weather to climate forecast systems

    • Consistent with retrospective forecasts

    • Verified

  • Deliver a wide range of products


Ensemble forecasting challenge1

Basin A

Basin B

  • Ensure forecast ensembles maintain spatial and temporal relationships across many scales

Ensemble Forecasting Challenge

rainy

+ cold

clear

+ warm

snowing

cloudy

+ hot

Irrational outcomes

  • Similarly, ensure consistency between precipitation and temperature is preserved in the forecast ensembles.


Ensemble forecasting challenge2

Ensemble Forecasting Challenge

  • Maintain coherence between deterministic and ensemble forecasts


New york dept of environmental protection nydep project

New York Dept. of Environmental Protection (NYDEP) Project

  • Water Management for part of NYC water supply system

  • Will optimize a decision support system based on retrospective simulation using past forecasts

  • Avoidance of building expensive water filtration system

  • Better management to limit turbidity violations

  • Requirements include:

  • High Priority

    • Daily time-step ensemble streamflow forecasts with two week lead time

    • Forecast updates daily

    • Hindcasts for retrospective period of several decades

    • Strong (but not perfect) consistency in methods between real-time forecasts and retrospective hindcasts

  • Lower Priority

    • Forecast lead times out to one year

    • More frequent forecasts (3-hourly) during flooding

    • Additional forecast variables associated with streamflow ensembles to use for water quality prediction, etc.


Nycdep ost for operations support

NYCDEP OST for Operations Support

RFC Inflow Ensembles


Mefp methodology

MEFP Methodology

Goal: Produce reliable ensemble forcings that capture the skill and quantify the uncertainty in the source forecasts.

Key Idea: Condition the joint distribution of single-valued forecasts and the corresponding observations using the forecast.

Use forecasts from multiple modelsto cover short- to long-range.

Model the joint probability distribution between the single-valued forecast and the corresponding observation from historical records.

Sample the conditional probability distribution of the joint distribution given the single-valued forecast.

Rank ensembles based on the magnitude of the correlation coefficients between forecast and observation for the time scales and associated forecast sources.

Generate blended ensembles (using Schaake Shuffle) iteratively for all time scales from low correlation to high correlation.

26


Development and implementation plan

Development and Implementation Plan

  • Testing and Implementation at 5 test RFCs

  • Development Release 1 – Completed

    • Workshop: April 2012

    • Beta Test: May - Aug 2012

  • Development Release 2

    • Workshop: Sep 2012

    • Beta Test: mid-late Sep 2012 – early Jan. 2013

  • Development Release 3

    • Workshop: late April 2013

    • Beta Test: late April 2013 – late July 2013

  • Final HEFSv1

    • Beta Test: early Oct. ’13 – early Nov. ‘13

    • Operational Readiness Review – early Nov. ’13

  • Operational Implementation at 2-5 RFCs by Dec 31 2013

  • Operational Implementation at remaining RFCs –2014


  • Nws mission and goals

    NWS Mission and Goals

    NOAA NWS Mission

    “NOAA’s NWS provides weather, hydrologic, and climate forecasts and warnings for the United States, its territories, adjacent waters and ocean areas, for the protection of life and property and the enhancement of the national economy.”

    NOAA Weather Ready Nation Objectives

    Reduced loss of life, property, and disruption from high-impact events.

    Improved freshwater resource management

    Improved transportation efficiency and safety

    Healthy people and communities due to improved air and water quality services

    A more productive and efficient economy through environmental information relevant to key sectors of the U.S. economy services.

    28


    Nws hydrologic forecasting ahps program february 21 2013

    Record Flooding WY 2011

    March 6, 2011

    http://water.weather.gov

    29


    Advanced hydrologic prediction service ahps

    Advanced Hydrologic Prediction Service (AHPS)

    $60 million/10 year program (completion year of 2015)

    Over 3,500 forecast locations with new Web-based services

    AHPS 60% complete

    $766 million estimated annual recurring benefit (National Hydrologic Warning Council study)

    Expanding AHPS Coverage

    30


    Short range ensemble forecasting underway at most rfcs

    • Met-model ensemble forecast system (MMEFS)

    • OHRFC, SERFC, NERFC, MARFC

    • Ensemble Preprocessor (EPP3) based approach (led by OHD)

    • CNRFC, CBRFC, NWRFC

    Short range Ensemble Forecasting underway at most RFCs

    • “HPC QPF” Approach

    • NCRFC, LMRFC, MBRFC, ABRFC


    Nws hydrologic forecasting ahps program february 21 2013

    Accessing AHPS Information

    Long range outlook

    Probability of non-exceedance

    32


    Push for enhanced ensemble forecasting

    Push for Enhanced Ensemble Forecasting

    • Well defined need for providing uncertainty estimates

    • Limitations of current NWS ensemble forecasting

    • HEFS science development maturing

    • Prototype ensemble forecasting underway at RFCs

    • CHPS implementation completed in 2011

    • NYCDEP requirement for hydrologic ensembles


    Model execution quality of forecast depends on inputs

    Reservoir

    Releases Diversion

    Observed

    precipitation

    Forecast

    Temperature

    Forecast

    Precipitation

    CHPS

    Model

    Observed flow

    Model ExecutionQuality of forecast depends on inputs

    Model states

    Data availability and Future uncertainty


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