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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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Presentation Transcript
slide1

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
slide3

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

slide4

Accessing AHPS Information

“click on” the water tab for current river conditions

http://weather.gov/

4

slide5

Accessing AHPS Information

“click on” the forecast location to access local hydrograph

http://water.weather.gov/

5

slide6

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
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

slide12

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

slide15

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
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.
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

slide29

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
slide32

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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