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.
NWS Hydrologic Forecasting
February 21, 2013
Hydrologic Services Division
NWS Office of Climate, Water and Weather Services
Advanced Hydrologic Prediction Service (AHPS)
“click on” the water tab for current river conditions
“click on” the forecast location to access local hydrograph
“click on” tabs for probabilistic forecasts
For over 2500 locations, NWS provide probabilistic river forecasts
Forecast Lead Time
Protection of Life & Property
Flood Mitigation & Navigation
River Gage Data
ForecastsHydrologic Forecasting Inputs/Outputs
Soil Moisture States
Deterministic / Probabilistic
+ “Hydrologic Uncertainty”
model initial conditions,
(regulation, diversions, etc.)
Meteorological Ensemble forecast processor
Verification system (EVS)
Forcing input ensembles
Input flow data
Product generation system
Hydrologic Ensemble Post-processor
Initial conditions and model parameters (e.g. DA)
No specific uncertainty modeling in HEFSv1
wx modelsEnsemble Forecasting Challenge
global circulation models
climate forecasts and indexes
Calibrated short- to long-range
(out to 8/9 months)
(out to one year)
Meteorological Ensemble Forecast Processor
Internal NWS customers (WFOs)
External partners and customers (Water Managers, USACE, BoR, EMs, local communities, public)
Basin A techniques.
RFC Inflow Ensembles
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.
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.
Record Flooding WY 2011 techniques.
March 6, 2011
$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
Accessing AHPS Information techniques.
Long range outlook
Probability of non-exceedance
Observed flowModel ExecutionQuality of forecast depends on inputs
Data availability and Future uncertainty