# Presentation: 328291

## Elements of a Science Infusion Strategy for Hydrologic Ensemble Prediction

- John Schaake, Pedro Restrepo and D.J. Seo
- NCEP Seminar
- March 15, 2005

## NOAA’s Strategic Goals(2005 – 2010 Strategic Plan)

- Understand climate variability and change to enhance society’s ability to plan and respond
- Serve society’s needs for weather and water information

## NOAA’sClimate Mission Outcomes

- A predictive understanding of the global climate system on the scales of weeks to decades with quantified uncertainties sufficient for making informed and reasoned decisions
- Climate-sensitive sectors and the climate-literate public effectively incorporating NOAA’s climate products into their plans and decisions

## NOAA’s Weather and Water Mission Outcomes

- Reduced loss of life, injury and damage to the economy
- Better, quicker and more valuable weather and water information to support improved decisions
- Increased customer satisfaction with weather and water information and services

## Predictions and Projections Objectives

- (NOAA Climate Program Plan FY 07 - 11: C. Koblinsky)

***Develop a  predictive understanding of the global climate system on timescales of weeks to decades with quantified uncertainties sufficient for making informed decisions***
- 	- Improve intraseasonal and interannual **climate predictions** to enable regional
- 		and national managers to better plan for the impacts of climate variability 	and change
- 	- Provide improved regional, national, and international **climate assessments ****	****and projections** to support policy decisions with objective information.
**Desired End-State: ****A seamless suite of forecasts (e.g. outlooks and projections) on intraseasonal, seasonal, interannual, and multi-decadal timescales and applications using ensembles of multiple climate models **in support of the mission outcome “*a** predictive understanding of the global climate system*”:
**FY07:** Provide regional resolution forecasts to decision makers through increased computer and model capacity.
**FY11:** Provide a broader suite of climate forecast products and services through development of Earth System Model.

**Notes:**
Science for ozone forecasting ready for transition to operations.
While computational/staffing are limiting factors in the current forecasting program, research results are needed for particulates forecasting:  
observational strategy, ingest methods (especially primary particulates sources)
Improved understanding and simulation of particulates formation and growth
effective treatment for real-time computation of much more complex chemical system reaction/transport

In the past earmarks have provided significant funding for the regional air quality assessments.

## What is Needed to Meet NOAA’s Strategic Goals

**Weather and Climate Forecasts**

**Products and**
**Services**

**Hydrologic Ensemble Prediction System**

**Observations**

## Elements of a Hydrologic Ensemble Prediction System

**Products and Services**

- Single-value and ensemble forecasts

- Reliable hydrologic products

**Weather and Climate Forecasts**

- Emerging Capabilities

- Product Properties

## Elements of a Hydrologic Ensemble Prediction System

**Atmospheric Ensemble**
**Pre-Processor**

**Products and Services**

- Single-value and ensemble forecasts

- Reliable hydrologic inputs

- Reliable hydrologic products

- Hydrologic Requirements

- AHPS PreProcessor

- Science Strategy

**Weather and Climate Forecasts**

## Elements of a Hydrologic Ensemble Prediction System

**Hydrologic Ensemble**
**Processor**

**Land Data **
**Assimilator**

**Hydrological**
**Models**
**(& Regulation)**

**Products and Services**

- Single-value and ensemble forecasts

- Reliable hydrologic inputs

- Reliable hydrologic products

- Ensemble initial conditions

- Ensemble forecasts

**Weather and Climate Forecasts**

***Automatic Data Assimilation is Essential Requirement of this System***

**Obs**

## Elements of a Hydrologic Ensemble Prediction System

**Product**
**Generator**

**Products and Services**

- Single-value and ensemble forecasts

- Reliable hydrologic products

- Ensemble forecasts

**Weather and Climate Forecasts**

## Elements of a Hydrologic Ensemble Prediction System

**Atmospheric Ensemble**
**Pre-Processor**

**Hydrologic Ensemble**
**Processor**

**Land Data **
**Assimilator**

**Hydrological**
**Models**

**Product**
**Generator**

**Products and Services**

- Single-value and ensemble forecasts

- Reliable hydrologic inputs

- Reliable hydrologic products

- Ensemble initial conditions

- Ensemble forecasts

**Weather and Climate Forecasts**

**Obs**

- Integration

## Verification

- Must be able to measure performance of every element in the system
- Need probabilistic measures
- Must be useful to forecasters and model developers

## HEPEX Goal

- HEPEX aims to bring the international hydrological and meteorological communities together to demonstrate how to produce reliable hydrological ensemble forecasts that can be used with confidence by the emergency management and water resources sectors to make decisions that have important consequences for the economy, for public health and safety.

## Some HEPEX science questions...

- 	What are the requirements of ensemble weather forecast for support of hydrologic prediction?
- 	Do meteorological forecast account for important meteorological and climate uncertainties?
- 	What is the role of operational forecasters?
- 	What are the sources of uncertainty in hydrologic predictions?

- Kristie Franz

## THORPEX/HEPEX collaboration

- THORPEX ensembles

- Feedback from the hydrologic applications community

- Focus of Proposed THORPEX/HEPEX Hydrologic Application Project

- Kristie Franz

## Thank You

## Properties of Existing Products

- Heterogeniety
- Assessment
- Model Bias
- Skill and reliability of probabilities
- Scale dependency

- Examples

- Examples

- Examples

- Implications

- Return

- Problems

## A lot happens inside a grid box

- Approximate
- size of one
- grid box in
- NCEP
- ensemble
- system

- Source: accessmaps.com

- Denver

- Rocky Mountains

- Next

- Tom Hammil, CDC

## Questions

- Can we accurately forecast the evolution of the pdf of the grid-box average weather? (*focus mostly on this*)
- How do we downscale from a grid-box average to a particular river basin or sub-area?

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- Tom Hammil, CDC

## Typical problems with current generation ensemble forecasts

- Would like to maximize pdf sharpness subject to calibration.  But:
- Ensemble forecasts are biased
- Ensemble mean different (systematic model error; improve the model or post-process to correct errors)
- Ensemble spread less than it ought to be (better initial conditions, higher-res forecasts, incorporating stochastic effects).

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- Tom Hammil, CDC

## Ensemble forecasts: where are we today?

- Generating initial conditions: Each center has adopted their own approximate way of sampling from initial condition pdf.
- Breeding (NCEP)
- Singular vector (ECMWF)
- Perturbed observation (Canada)
- Stochastic-dynamic ensemble work just beginning (e.g., Buizza et al. 1999)
- Many attempts to post-process ensemble forecasts to provide reliable probability forecasts.

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- Tom Hammil, CDC

## Ensemble Temperature Forecast

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## Ensemble Precipitation Forecast

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## Ensemble Streamflow Forecast

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## NWSRFSForecast Input Requirements

- Ensemble inputs for ESP
- For each RFC sub-basin and time step
- For all lead times 1hr to 1yr
- Ensemble inputs include:
- Precipitation
- Temperature
- Potential evaporation
- Freezing level
- Verification

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## Emerging Capabilities - Weather and Climate Ensemble Forecast Applications

- Medium range global ensembles (~2wks) (NCEP GFS)
- Short range regional ensembles (~2days) (NCEP SREF)
- Long range coupled ocean – land –atmosphere ensembles (~6mos) (CFS)
- Regional climate models
- Multi-model ensembles (e.g. TIGGE & IRI)
- Future MOS applications

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## Immediate AHPS Ensemble Precipitation Goals

- Create short & medium term precipitation ensembles for input to hydrologic models at basin scale.
- Use existing HPC deterministic forecasts (after modification by RFC HAS forecasters) (i.e. Maintain role of human forecaster for short-term forecasts).  Add confidence Factor?
- Use GFS fixed ensemble mean forecasts up to 14 days (to extend lead time and improve preprocessor parameters)

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## AHPS PreProcessorPerformance Objectives

- Preserve skill of the single-value forecasts (at all space and time scales)
- Remove forecast biases
- Produce reliable probabilities
- Account for space/time scale dependency
- Simple, efficient and robust

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## Precipitation Forecasts and ObservationsCalifornia – January Day 1

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## AHPS PreProcessorScience Strategy

- Develop basic capability using existing “single-value” forecasts and observations
- Apply to specific RFC Sub-basin areas using limited RFC and HPC archives of QPF
- Expand to gridded regions – include mult-scale properties
- Develop general Bayesian approach to using GFS ensemble forecasts
- Other approaches (e.g. analogs)?

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## Bayesian Merging of Information

- Bayes Theorem

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- Eric Wood, Princeton

## NCEP Global Ensemble Forecasts

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## Cumulative Distributions of Adjusted Ensemble Members

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## Uncertainty Analysis of Global Ensemble Precipitation Forecasts

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## Effect of Spatial Scale on 24hr Forecast Skill (July – 5 locations)

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## Ensemble Challenges

- Maintain spatial and temporal relationships across very large areas

- rainy
- + cold

- cloudy
- + hot

- clear
- + warm

- snowing

- Irrational outcomes

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## Short-term Ensemble Prototype

- Smith River

- Salmon River

- Mad River

- Navarro River

- American River
- (11 basins)

- Van Duzen River

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## Ensemble Challenges

- Include forecaster skill in short-term inputs (QPF, temperature, etc.)

- Forecasters add value to short-term QPF.
- HPC adds value to models
- RFC adds value to HPC

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## Ensemble Challenges

- Include forecaster guidance of hydrologic model operation

- Hydrologic models require on-going tuning
- Forecasters commonly adjust or influence raw model output

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