# Presentation: 667540

## The Future of NWP Stephen J. LordNCEP Environmental Modeling CenterEMC Senior StaffFred Toepfer	         John DerberHua-Lu Pan              Ken MitchellGeoff DiMego          Naomi SurgiD. B. Rao 

## Overview

**Why have we been so successful?**
**What can we do for an encore?**
**Shortfalls (or what do we need to do better)?**
**Caveat**
- Mostly a personal perspective, colored by experience at EMC, NCEP...NOAA

## Why have we been so successful?

- Improved technology (computing, data assimilation & modeling techniques, obs)
- Societally-relevant products with a demand for
- Improved product performance
- Increased product areas
- Focused goals with quantitative scores
- Systems evaluated every day
- Vs obs by weather & climate experts
- By diverse users with a lot at stake

**Save Lives & Property**
**Weather-Sensitive Commerce ($2+ T)**

## Correlations

- Of HPC with:

- Eta: 0.99
- GFS: 0.74
- NGM: 0.85

- 1 day of QPF
- skill gained
- every 25 years

- Impact of NCEP Models on HPC Precipitation Forecasts

## Why have we been so successful? (cont)

**Competition**
- With ourselves
- Across international, operational and research, weather & climate forecast centers
**Diverse approaches**
- Highly accurate NWP analysis & forecast systems with different approaches
- No single solution (normalized for available resources)
- Very few “breakthroughs” (although 4D-VAR, “physics”, “vertical coordinate” are some individual reasons for success)

## What can we do for an encore?

**Continue to exploit the systems we have built***
**Increase the rate of development for possible operational implementation***
***To be discussed further**

## What can we do for an encore?

**Continue to exploit the systems we have built.  Increase the**
**Range of** _**Skillful**_** forecasts***
**Number of **_**Useful**_** products***
- Increase the
- Available _information used_ (data assimilation)
- Useful _information produced_
- Probabilistic information (e.g. ensembles)
- _Information used_ from all products
**Product accessibility***
- User education & training

## Seamless Suite of Forecasts

**Initial Conditions**

**Boundary Conditions**

**NOW**

**Increase the Range of **_**Skillful**_** Forecasts**

## Seamless Suite of Forecasts

**Initial Conditions**

**Boundary Conditions**

- Range of skillful forecasts

**FUTURE**

**Increase the Range of **_**Skillful**_** Forecasts**

## Increase the Range of Skillful Forecasts

**S/I Climate**
**The new NCEP ****C****oupled atmosphere-ocean **
**F****orecast ****S****ystem ****(CFS)**** **
**Components**
- 	a) T62/64-layer version of the current NCEP atmospheric GFS (Global Forecast System) model and
- 	b) 40-level GFDL Modular Ocean Model (MOM, version 3)
- 	c) Global Ocean Data Assimilation (GODAS)
**Notes:**
- CFS has direct coupling with no flux correction
- GODAS
- Implemented September 2003, runs daily
- Salinity analysis, improved use of altimeter data
- Real time global ocean data base in WMO standard format
- Ready for GODAE

## Slide 11

## Peitao Peng CPC

- AC=.86

- AC=.80

- AC=.43

**Peitao Peng CPC**

**Tropical Precipitation Performance**

## Increase the Number of Useful Products

**Real-time Ocean**
**Air Quality**
**Fire Weather**
**Homeland Security**
**Seasonal**
**Monthly**
**Systems sensitive to **
**    ****environmental parameters**
**Ecosystems**
**Disease vectors**
**Agriculture**
**Routine Reanalysis and assessment**

## US GODAE: Global Ocean Prediction with HYCOM

**US GODAE: Global Ocean Prediction with HYCOM**

-   Goal: to develop and demonstrate real-time, operational, high resolution ocean prediction systems for the Global Oceans and Basins
-   NCEP Partners with
-   University of Miami/RSMAS
-   NRL Stennis, NRL Monterey, FNMOC
-   NOAA PMEL, AOML
-   Los Alamos National Laboratory
-   Others (international, commercial)
-   Hybrid isopycnal-sigma-pressure ocean model (called Hybrid Coordinate Ocean Model – HYCOM)
-   Funded FY 2003-2007 by NOPP

**Chesapeake  Bay**

## Schedule

- North Atlantic
-               World Oceans
-                                    North-East Pacific
-                                                                        Hawaii

- 2005                       2006                     2007

- Global atmosphere-ocean Coupling and Hurricane-Ocean Coupling

- Initiate interactions with NOS on bay and estuary model boundary conditions; Initiate wave-current interactions.

## Future Enabling Architectures

**Adding Model Components (to increase useful products)**
**ESMF**
**Complexity vs computational efficiency**
**Product accessibility***
**NOMADS**

## ESMF Architecture

**Components Layer:**
**Gridded Components**
**Coupler Components**

**ESMF Superstructure**

**ESMF Infrastructure**

**User Code**

**BLAS, MPI, NetCDF, ...**

**Low Level Utilities**

**Fields and Grids Layer**

**Model Layer**

**External Libraries**

**ESMF provides an environment for assembling geophysical components into applications.**
**ESMF provides a toolkit that ****components use to**
**increase interoperability**
**improve performance ****portability**
**abstract common services**

## NOMADSNOAA Operational Model Archive and Distribution System

**Distribution of **
_**R**_**eal-**_**T**_**ime and Retrospective NCEP  Model Data Sets**

- Jordan C. Alpert  _[jordan.alpert@noaa.gov](mailto:jordan.alpert@noaa.gov)__        _4/22/03

**NOMADS****NOAA Operational Model Archive and Distribution System**

***On demand access to (x, y, z, t, product) space***
***downloaded in user-defined format***

## What can we do for an encore? (cont)

**Continue to exploit the systems we have built**** **
**Increase the rate of development for possible operational implementation**
**Improvements must occur simultaneously for many more applications (waves, hurricanes, precip, aviation, week-2)* **
**Each improvement gives rise to increasing expectations**
- The problems are getting tougher
- As perfection is approached
- Forecast system output increasingly resembles the atmosphere
- Forecast and delivery deadlines shrinking
- It is more difficult to predict the expected improvement from each proposed change

## 2001 GFS Implementation

- Improved model climate in tropics
- Prognostic liquid water
- Radiation interactive with condensed water & cloudiness
- Full simulation of water transport
- PBL  Convective clouds  Detrainment  Cirrus
- Cumulus momentum transport
- Reduced spurious spinup of tropical systems
- Cyclogenesis mostly confined to growth of systems later observed
- Testing involved
- 835 days of retrospective data assimilation & model forecasts
- Summer (tropical cyclones)
- Spring (severe weather)
- Winter (temperature bias)
- Unable to test GFDL initialization
-     thoroughly

## What can we do for an encore? (cont)

**2. Increase the rate of development for possible operational implementation**
**HOW?**

## What can we do for an encore? (cont)

**HOW?**
**Focus efforts on improving operational systems**
**Improved project management**
**Operations needs to have increased influence on scientific direction of applied research**
**Mutual discussion and execution of highest priority development projects**
**More rapid transition of development to operations**
**Increase computing resources beyond Moore’s Law (constant $)**
**Consolidate software & forecast systems**
**GSI (global and regional analysis system)**
**Continue to exploit Test Bed concept**
- Enhanced Visiting Scientist _**exchange**_ program
- Increase the (potential) workforce capabilities
- Student education and training
**Support for non-operational users of operational systems**
**Examples follow**
**Strong management support at NOAA level & above**

## NASA-NOAA-DOD Joint Center for Satellite Data Assimilation (JCSDA)

**NOAA, NASA, DOD partnership**
**Mission**
**Accelerate and improve**** the quantitative use of ****research and operational**** satellite data in ****weather and climate**** prediction models**
**Current generation data**
**Prepare for next-generation (NPOESS, METOP, research) instruments**
**Supports applied research**
**Partners**
**University, Government and Commercial Labs**

## JCSDA Prioritized Applied Research Areas

**Advanced radiative transfer**
**Clouds and precipitation**
**Assess impacts of current instruments**
**Improve sea surface temperature data and use of altimeter data**
**Enhance land surface data sets (surface emissivity model)**
** ****AIRS data implementation**

## Improve Sea Surface Temperature Data [X. Li & Derber]

- Improve Sea Surface Temperature Data [X. Li & Derber]

- SST Difference 29-28 October 2003 - Experiment

- SST Difference 29-28 October 2003 - Control

** ****New physical retrieval from AVHRR data,**
**   ****cast as variational problem**
** ****OPTRAN RTM & Linear Tangent Model**
**  ****Eventual direct use of AVHRR (and other)**
**   ****radiance data**

**RMS and Bias Fits to Independent Buoy SST Data**

**NOAA-16 AVHRR data only**
**Northern Hemisphere Ex. Tropics**

## Improved Surface Emissivity Model for Snow [Yan, Okamoto and Weng)

**Annual Mean RMS T****B**** Difference (Obs – Simulated)**

**SnowEM**

**Operational**

##        The Path to Operational Implementation

-        The Path to Operational Implementation

- Code or Algorithm Development & Refinement
- Repeated case studies, proof of concept, eliminate bugs

- Interface with Operational Codes & Data Structures
- Connect input/output to BUFR/GRIB, develop backup version,
- Make code robust & efficient to fit time/cpu/memory window

- Preliminary Testing
- Low resolution case studies, static initialization, relevant diagnostics,
- warm & cool cases, assess short-term model climate (30 days)

- Low Resolution Parallel Testing
- Connect to fully cycled data assimilation, run for all seasons,
- accumulate verification statistics, identify&solve problems:e.g. biases,
-  amplification through cycling, spin-up/down etc.

- Pre-Implementation Testing
- Operational resolution fully cycled real-time parallel, more
- comprehensive verification, documentation and user notification,
- real-time forecaster exposure/evaluation

**EMC**

**DTC****/**
**OTC**

**1-12**
**+ 3-12**

**1-12 **
**+ ****3-12**

**3-6**

**1-3**

**3-9**

**3-9**

**1-3**

**13-39**

**1-6**

**1-6**

**2-4**

**4-18**
**2-9**
**2-4**

**EMC** total: 13-39 mo

**DTC****-****OTC****-EMC** total: 8-31 mo

_**WRF DTC**_

## Shortfalls (or what do we need to do better)?

- Resources have not kept pace with the rapidly increasing complexity of today’s forecasts
**Project management and technical support for**
**Maintaining and developing complex operational Data Assimilation & Modeling codes and supporting code infrastructure***
**Interacting with external community (data, ideas, code transition, cultural education)***
**Example Hurricane WRF***
**Basic infrastructure (computing, testing & implementation capability)**
- Timeliness of data delivery to operational centers and efficient product dissemination are marginal
- Recent additions to NOAA’s computing will help but...

## Slide 29

## Maximum Significant Wave Heights: Model vs. JASON

- Direct hits: Altimeter through eye and maximum waves
- WNA (green), NAH (red): Good track of build-up, set-down and maximum
- Storm’s eye (lower panel) well captured by both models
- Early stages missed by WNA (green): weak GFS winds, small hurricanes

## Genesis Summary  NCEP GFS  2003 Atlantic Hurricane Season Selected Storms (Isabel, Juan, Kate, Nicholas, Odette, Peter)

|  | Total or Percent |
| --- | --- |
| Total Genesis Opportunities | 92 |
|                    Early or on time | 42% |
|                    Late | 27% |
|                    Failed to generate | 31% |
| Timing       Within |  |
|                    +- 6 h | 13% |
|                    +- 12 h | 41% |
|                    +- 24 h | 67% |
|                    > 24 h | 33% |
| Location     Within |  |
|                       300 km | 47% |
|                       500 km | 77% |
|                     >500 km | 23% |

- Tim
- Marchok
- Qingfu
- Liu

## URI & U. Miami partnerships

**Hurricane Model**

**NCEP**
**WAVEWATCH III**

- Flux

- Wind &
- Air Temp.

**Atmosphere**

**Ocean**

**Waves**

- Currents,

**Wave Boundary Model**

- SST

-   Wave
-     Spectra

- Flux

**Ocean**
**Model**

-   Wind &
- Air Temp.

- Flux

**GFDL**
**Hurricane Model**

**POM**

-   SST &
- Current

- SST

- Flux

**Atmosphere**

**Ocean**

- Elevations,
- & SST

**Operational GFDL Model**

**Future Coupled Hurricane-Wave-Ocean Model**

## Shortfalls (or what do we need to do better)? (cont)

- A solid scientific strategy and policy are needed to guide an expanded and improved observing system
**Evaluation of today’s system (e.g. current JCSDA assessment)***
**Ways to assess potential impact of new instruments (e.g. OSSEs)***
**Examples follow***
- Recent activities are encouraging
- Ocean observing system for climate is step in right direction
- Support for above projects is helping
- Still a long way to go

## The REAL problem is Day 1

**Tropics**
**850 mb Vector (F-A)**
**RMS**

**Jung and **
**Zapotocny**
**JCSDA**
**Funded by **
**NPOESS IPO**

## Satellite data 

**Satellite data **
**~ 10% impact**

**Jung and **
**Zapotocny**
**JCSDA**
**Funded by **
**NPOESS IPO**

## Temperature Channels

**RMS Brightness Temperature Differences Between Observed Radiances **
**and NCEP 6 Hour Global Forecast and Analysis**

- Moisture & Surface Channels

## Doppler Wind Lidar (DWL) Impact

- Time averaged anomaly correlations between forecast and NR for meridional wind (V) fields at 200 hPa and 850 hPa.  Anomaly correlation are computed for zonal wave number from 10 to 20 components.  Differences from anomaly correlation for the control run (conventional data only) are plotted.

- Forecast hour

**OSSE Results – Masutani et al**

## Opinionated Summary

**(Any opinion can be debated and “proven” wrong)**
- Options for product improvement
- More observations  (needs more focus on anticipated product improvement)
- Field experiments (need more focus on understanding the forecast system and correcting its errors)
- Computational techniques (high priority)
- Scientific development (high priority, more innovative approaches, e.g. “resolvable scale modeling”*)
- Testing, engineering and tuning (always necessary)
- * Unfeasible unless huge increases in computing

## Opinionated Summary (cont)

- Product enhancement
- Increased societal impact
- Broader spectrum of applications
- Research Strategy
- Some course corrections needed
**Focus efforts more on improving operational systems**
**Involve more scientists without detracting from current rate of development**
- Obviously more infrastructure and computing needed
- Strong management support at NOAA level & above

## Opinionated Summary (cont)

**The last word:**
**Everyone still has a role to play**
**Sociology and our past may be greater enemies than the science yet to be conquered***
- *On runway, Birmingham AL airport, 10:15 PM, 12% laptop juice
- “Thunderstorms in Baltimore area have created a traffic jam”