# Presentation: 075179

## 
Science and Technology Infusion Plan
for
Numerical Prediction

**Science and Technology Infusion Plan**
**for**
**Numerical Prediction**

***Jeff McQueen***

**NWS S&T Committee**
**September 17, 2002**

## Outline

**Team Composition**
**Expected Results / Outcomes**
**Key Gaps **
**Key Solutions**
**DA/NP Roadmap**
**Outstanding R & D Needs**
**Outstanding Enabling challenges**
**Potential FY 05 Initiative Themes**
**Summary**

## Numerical PredictionTeam Composition

**Jeff McQueen – NWS/OST**
**Paul Dallavalle  – NWS/OST**
**Steve Koch – OAR/FSL**
**Ralph Petersen – NWS/NCEP**
**Michael Smith – NWS/OHD**

**Dave Stensrud – OAR/NSSL**
**Dan Tarpley – NESDIS**
**Stan Benjamin – OAR/FSL**
**Geoff Dimego – NWS/NCEP**

## Winter Weather ServicesVision / Benefits

_**Vision**_
**Winter Storm Warning Lead Time > 24 Hrs**
**Probabilistic Guidance Out to 7 Days***** ***

**Better Information = Better Decisions by Public During Inclement Wx**
**$ Millions in Savings for Public Works (Snow Removal, etc.)**
**$ Millions in Savings for Transportation Sector**

## Numerical Prediction VisionDeterministic &Probabilistic fully coupled ocean-atmosphere-hydrosphere modeling system

## Numerical Prediction VisionWRF Framework 

**WRF ****Infrastructure**

**Numerical Prediction Vision****WRF Framework **

**NAVY**

**Regional**
**WRF Core**

- GLOBAL

**(ESMF)**

**Hurricane**
**WRF Core**

**Air Quality**
**WRF Core**

**Regional**
**HRW**
**RRW**

**Assimilation**

**SREF**

**Ensemble**
** ****Perturbations**

**Initial**
**Conditions**

**Convection   PBL    Radiation     Land/Ocean coupling **

**AFWA**

## Numerical Prediction PlanComponents

***Data Assimilation, Physics, Modeling, Postprocessing***** **
***Climate***
***Global***
***Regional ******(National & Rapid Refresh)***
***High Resolution Nests ******(HRW)***
***Oceanic & Hurricane***
***Hydrologic ***
***Hazards ******(NBC, Fire wx, air quality)***
***Ensembles***

## Numerical Prediction Key S&T Solution Timeline

**Numerical Prediction ***Key S&T Solution Timeline*

**Adv. FDDA, NPOESS**

**88D radial velocities**

**8km WRF**

**AQ/aerosols**

**GFS 10 mem.**

**GFS T254**

- Deployment

- OTE

- DTE

- R&D

**Data Assimilation**

**Global**

**Regional**

**Probabilistic**

**Physics**

**GFS 40 km coupled atm/ocean/land **

**Eta-12 km RUC-20 km**

**Limited cloud microphysics **

**Cloud anal.**

**Multi-core 18 km WRF**

**4 km cpld AQ WRF**

**Multi-model 50 mem.**

## Numerical Prediction AdvancesResolution vs. Ensemble Members

**2003**

**2007**

**2012**

**Physics**

**Dta Assim**

**Regional**

**Global**

**Numerical Prediction Advances****Resolution vs. Ensemble Members**

## Numerical Prediction Key Gaps and S&T Solutions

| GAP | Solution | Impact | Resources (ROM) |
| --- | --- | --- | --- |
| Higher Resolution & Accuracy of Needed Quantities

Probabilistic; Range of Uncertainties

Improved assimilation | 8km WRF
5 km HRW
40 km  Cpld GFS

Advanced Ensembles (SREF & Global)

WRF 3DVAR
Adv. FDDA
Satellite radiances
Radar reflectivities | Rain/Snow Line Resolved w/in 30km
Impr. low-level wind
Forecasts by 20% 
Impr. Convection forecasts by 15%

Improved confidence

Improved representation of mesoscale, cloud-scale features
 | Base

Base

Base +JCSDA, EMP iniitiatives |

## Numerical Modeling Roadmap

**Green=deployed  **** ****Blue=acquisition  ****Yellow=****demonstration**
**Red=R&D **** ****Gray=not programmed**

** ****2002  ****	****          2003****	****2004              2005            2006**

## North American ModelingMetadata Example

## Data Assimilation Roadmap

**Green=deployed  **** ****Blue=acquisition  ****Yellow=****demonstration**
**Red=R&D **** ****Gray=not programmed**

## Postprocessing Roadmap

|  | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 |
| --- | --- | --- | --- | --- | --- | --- |
| Statistical
 | NGM/AVN MOS |  | Principle Component
Analysis |  |  | Neural networks |
| Ensemble | Means/SD/ Spagetti charts |  | Ensemble Clustering | Probability
Density 
Functions |  | Ensemble Kalman Filters |

## Numerical Weather Prediction Future Emphasis         Outstanding Observational Needs

**Observations:**
***Vertical moisture distribution ***
**incl. Water vapor, cloud ice & liquid **
***Improved troposphere global wind coverage***
***Land/ocean/lake surface characteristics ***
**soil, veg, water**** **
***Snowfall for MOS/LDAS***
**8x/day, zone coverage**

**Notes:**
Cloud scale model can predict inten

## Numerical Weather Prediction Future Emphasis         Outstanding S&T Needs

**Assimilation of increasing volume of remote sensed data**
***10,000 increase by 2010***
**Mesoscale assimilation techniques**
***For un-balanced flow & pre-convective environments***
**Improved representation of non-hydrostatic scale physics ****	**
*** ******Convective parameterizations***
*** ******Cloud microphysics***
***  ******Radiation***
**  *****Turbulence mixing***
**Mesoscale verification techniques**

**Notes:**
Cloud scale model can predict inten

## Numerical Prediction         Enabling Challenges

**Dissemination of  high resolution & ensemble outputs**
**Probabilistic output postprocessing **
**    ****& training**
**Increased data handling and data reduction capabilities**
**Demonstration projects to assess relative  impacts of model**
**    ****changes & new techniques**

## Numerical Prediction AdvancesIncreases in data to be assimilated

**Numerical Prediction Advances****Increases in data to be assimilated**

**Increasing volume of data (10000x)**
**Extract maximum information  given expected computer  increases (40x)**

## Numerical Prediction         Team Challenges

**Mix of Deterministic vs probabilistic NP solutions**
**Process for quantifying future NP benefits**
***Demonstration projects, begin V&V of key svc params***
**Data requirements for model improvements**
**Many NP products produced for customer but not directly related to GPRA/Strategic goals**

## Expected Key Benefits  FY07 regional Deployment

_**Low-level wind**_** : ****20% Improvement**
_**Turbulence:**_** ****5 km location & 15 minute timing improve**
_**Icing**_**:  ****10 km location, 500 ft vertical improvement**
_**Ceiling**_** (IFR AIRMETs): ****200 ft location & 30 min timing  impr.**
_**Visibility**_** (IFR AIRMETs):**** 10% POD & 30 min timing improve**
_**Convection**_**:**** 15% CCFP improvement**
_**Jet streams**_**:**** 10 km location & 15 minute timing improve**

## BACKUPS

## Next-Generation Operational Models

- Cloud-scale Modeling
- (dx = dy = 2 km, dz = 500 m, dt = 12 s, 160 x 160 x 20 km domain )
- Surface temperature, surface winds and cloud field at 2 hours

## Cloud-scale model benefits

**Notes:**
Storm-scale can help predict intensity changes/development of svr wx

## Numerical Prediction AdvancesDissemination Requirements2007

**Numerical Prediction Advances****Dissemination Requirements****2007**

## Current Operational Forecast Models

***Operational forecast models predict:***
**Mesoscale and synoptic flow patterns**
**Precipitation via parameterizations that are unable to resolve individual storms**
***Important storm-scale weather cannot be resolved******:***

##  Climate ModelingMetadata

## Global ModelingMetadata

## Regional ModelingMetadata

## HR ModelingMetadata