# Presentation: 474764

## Global Numerical Weather Prediction at the Navy Research Laboratory 

**Global Numerical Weather Prediction at the Navy Research Laboratory**** **
**October 22, 2003**
**Tim Hogan**
**Global Modeling  Section**
**Marine Meteorology Division**
**Naval Research Laboratory**

## NOGAPS

***Navy Operational Global Atmospheric***
***Prediction System***

**PREDICTABILITY   AEROSOL        NUM              PHYSICS        REANALYSIS**
**                                   ****TRANSPORT  METHODS   DEVELOPMENT**

**NOGAPS**

**GLOBAL        ENSEMBLE            OCEAN,  MESOSCALE  AIR/SHIP      USAF,**
**WEATHER,                                         ICE,      WEATHER,    ROUTING      DTRA,**
**TROPICAL                                       WAVE         GFDN                                   ARMY,**
**CYCLONES                                                                                                          NOAA**
**                                                                                                          **

**DATA QC, DATA**
**ASSIMILATION, **
**INITIALIZATION**

-                 ***NRL*****                                    *****FNMOC***

**COUPLED MODELING**

**STRATOSPHERE/MESOSPHERE**

**SCALABLE**
**PROGRAMMING**

##                History of NRL Global Model Resolution

**                                       ****Early modeling efforts**1959- 500 mb Quasi-geostrophic barotropic model for thickness and height1970- N. Hemisphere Primitive Equation Model (63x63 grid points/5 levels) 1975- Added S. Hemisphere PE Model (63 x 63 grid points/5 levels) for first global coverage
**Implementation of global UCLA-based model with a Successive Corrections Analysis scheme**1982- NOGAPS 1.0 (4.0o x 5.0o/L6)1983- NOGAPS 2.0 (2.4o x 3.0o/L6)1983- NOGAPS 2.1 (2.4o x 3.0o/L9)1987- NOGAPS 2.2 (2.4o x 3.0o/L9)**Implementation of global Spectral model with a Multivariate Optimum Interpolation Analysis scheme**1988- NOGAPS 3.0 (T47/L18)    ~2.50o horizontally1989- NOGAPS 3.1 (T47/L18)     Physics upgrade 1989- NOGAPS 3.2 (T79/L18)    ~1.50o horizontally1992- NOGAPS 3.3 (T79/L18)     Physics upgrade 1994- NOGAPS 3.4 (T159/L18)   ~0.75o horizontally1998- NOGAPS 4    (T159/L24)   Increase vertical resolution 2000- NOGAPS 4    (T159/L24)   Emanuel Cumulus Parameterization/MPI
- 2002- NOGAPS 4    (T239/L30)   ~0.50o horizontally
**Implementatation of the NRL Atmospheric Variational Data Assimilation System (NAVDAS)**
- 2004- NOGAPS 4    (T239/L30)

## NRL Global Modeling Section 

**Mr. William Clune****	****(CSC) Synoptic evaluation**
**Dr. Marie Flatau****	****	****(NRL) Coupled Modeling**
**Dr. Francis Giraldo****	****(NRL) Numerical Methods**
**Dr. Tim Hogan****	**** ****	****(NRL) Ops/beta Support, Parameterizations**
**Dr. Young-Joon Kim ****	****(NRL) Stratospheric Modeling, Parameterizations**
**Dr. Rolf Langland****	****	****(NRL) Predictability/Adjoint Methods/Targeting**
**Dr. Paul May****	****	****(CSC) Coupled Modeling**
**Dr. Melinda Peng****	****	****(NRL) Parameterizations**
**Dr. Carolyn Reynolds****	****(NRL) Predictability/Adjoint Methods/Ensembles**
**Dr. Thomas Rosmond****	****(NRL) Scalability, Predictability, Numerical Methods**
**Dr. Joao Teixeira****	****	****(UCAR) Parameterizations**
**                                                    *****FNMOC/Code 400***
**Dr. Randy Pauley****	****	****(FNMOC) Data assimilation**
**Mr. Torsten Duffy****	****	****(FNMOC) Forecast model**
**Dr. Mike Sestak ****	****	****(FNMOC) Ensembles**

##  NOGAPS (FY 03) 

**MVOI analysis of heights and winds, Complex QC/aircraft QC,**
**Cressman moisture analysis,**
**NMI of analysis increments,**
**Spectral  T239 (720 x 360 grid),**
**30 vertical levels/FD,**
**Central FD,**
**12 Minutes/forecast-day, 32-bit prec**
**Model top 1 mb (45 km),**
**Palmer’s et al. GWD,**
**Louis’s vertical mixing ,**
**Shallow cumulus mixing,**
**Emanuel cumulus convection ,**
**Harshvardhan’s  radiation,**
**Teixeira’s cloud scheme,**
**Large-scale precipitation**
**Bucket hydrology/Skin temperature,**
**Fixed SST,**
**Breeder perturb for ensembles.**

**    **

**NAVDAS 3 D var. anal of t and winds,**
**NAVDAS advanced QC,**
**NAVDAS 3-D var. anal. moisture,**
**NMI of analysis increments,**
**Spectral/FE or Reduced grid T479,**
**48-60 vertical levels,**
**Semi-Lagrangian,**
**12 Minutes/forecast-day, 32-bit precision**
**Model top 0.1 mb (65 km),**
**Advanced GWD,**
**Louis vertical mixing,**
**Mixing off strato-cumulus tops,**
**Emanuel cumulus convection ,**
**NASA (Chou’s)  radiation,**
**Explicit cloud scheme,**
**Liquid water prediction,**
**Full Land Surface Model,**
**Coupled SST/Ice**
**Errico/Baumhefner  perturb for ensembles.**

***NOGAPS (FY 04+)***

## Global Model Annual Mean Forecast StatisticsN. Hemisphere 500 mb HeightsAnomaly Correlation

**Global Model Annual Mean Forecast Statistics****N. Hemisphere 500 mb Heights****Anomaly Correlation**

## Global Model Annual Mean Forecast StatisticsS. Hemisphere 500 mb HeightsAnomaly Correlation

**Global Model Annual Mean Forecast Statistics****S. Hemisphere 500 mb Heights****Anomaly Correlation**

## Porting Numerical Weather Prediction Systems to Linux Clusters

**T79L24 NOGAPS SINGLE PROCESSOR PERFORMANCE**

- FCSTDAYS/WALLHR

**Porting Numerical Weather Prediction Systems to Linux Clusters**

## NOGAPS scalability: SP4/Linux Cluster

## Predictability Research at NRL

-  _Improve the model_
-  _Improve the initial state_
-  Better data assimilation
-  Better observing network
-  _Ensemble Forecasts_

_**Predictability Research at NRL**_

***Weather and weather forecasts have enormous  military impact.***

_**How can we make better forecasts?**_

## Ensemble Perturbation Formulation

- Ensemble perturbations (top) reflect the daily analysis error estimates, i.e, observations of the day, produced by NAVDAS (bottom).
- Can produce large number of independent ensemble members

- 500-hPa Temperature standard deviations for 80-member ensemble perturbations (top) and the NAVDAS analysis error estimate(bottom).  Contour interval of 0.2 K

**Ensemble Perturbation Formulation**

**Notes:**
The standard deviation of the 80-member ensemble perturbations (at initial time), top panel, looks similar to the standard deviation of the analysis error estimate produced by the data assimilation system (NAVDAS), bottom panel.  This estimate changes with each new analysis depending on the observations used to produce that analysis.

Bottom line is 1) the method gives us big ensemble perturbations where we think our analysis errors are also likely to be big (bigger over polar regions, and over the North Pacific, for example, and smaller of well observed land areas like N. America and Europe).

2) This method is capable of producing a large number of independent ensemble members cheaply. We have plans to compare this method to ETKF, and also try some hybrid methods.

## Ensemble Perturbation Formulation

**10-member ensemble spread (500 hPa Height). ****Green: SH extratropics****, ****Pink: NH extratropics****, ****Blue: Global****. Solid curves: same time step, dashed curves: different time steps.**

**Running ensemble introduces plausible variability in the model physics. When ensemble members have different time-steps, the ensemble spread increases.**

**Ensemble Perturbation Formulation**

**Notes:**
Ensembles need to have sufficient “spread” in order to capture truth. Typically, ensembles are “underdispersive”, that is, they don’t spread out enough. One reason an ensemble may be under-dispersive is that model uncertainty is not represented (or not adequately represented). Results here show that ensemble spread in the Northern Hemisphere extratropics (measured in terms of the RMS differences between the ensemble members and the control 500-mb height field) is larger when time-step is varied between different ensemble members. This is a simple way to introduce plausible variability in the ensembles due to uncertainties in the model physics.

## NOGAPS ENSEMBLE

- Products on web:  Gale Probability (Pac, Atl, W Asia, SW Asia,
-                                                          Europe, California);
-                               Precipitation Probability (N Amer, N Pac, N Atl,
-                                                                        W Asia, SW Asia, Europe);
-                               500 mb height spread (NH, SH, IO, Europe);
-                               Upper Level Winds (W Asia).
- Future Plans:         New perturbation technique,
-                               More members,
-                               Increased resolution,
-                               Remote ops at NAVO’s HPCC site.

***NOGAPS ENSEMBLE***

## NAVDAS_AR ...

**NAVDAS_AR ****...**

**We have formulated and begun construction of a natural four dimensional follow-on to NAVDAS, NAVDAS-AR, which was initiated by Roger Daley. **
**The minimization component (inner loop) has been developed and the results of initial testing are encouraging.**
**NAVDAS-AR parallels the 4DVAR system implemented at ECWMF except that it is cast in observation-space and doesn’t require a perfect model assumption; but we are using it as a special case.**
**It is based on the accelerated representer (****AR****) algorithm (Xu and Daley, 2002) using the existing NAVDAS infrastructure and the non-linear, tangent-linear and adjoint models of NOGAPS.**

## NAVDAS_AR (cont)

**NAVDAS_AR (cont)**

**An effort is currently underway to examine a hybrid ensemble technique (NAVDAS + Ensemble Transform Kalman Filter) to estimate flow dependent initial background error covariance    .**
**Two different procedures dealing with the nonlinearity (the outer loop) have been tested for a simple nonlinear problem.  We intended to compare the two algorithms in NAVDAS-AR.**

## Horizontal Temperature Analysis Increments

**Comparison of NAVDAS-AR and NAVDAS based on T47L30 NOGAPS**

**Purpose of this experiment:** Examine the capability of NAVDAS-AR in assimilating real observations.
**Data assimilation window:** 09Z – 15Z 26DEC2002.  The analysis increments are compared at 12Z.
**Observations:** Same innovation vector (~400,000 obs) generated from NAVDAS is used in both NAVDAS and NAVDAS-AR except that observations are binned in half hour bins in NAVDAS-AR.
**Dynamic model and background:** A T47L30 NOGAPS was used as the forecast model to produce the background fields and for the NAVDAS-AR TLM and adjoint sweeps. A perfect model assumption (It is a special case) was used.
**NAVDAS-AR results:** Left panels.
**NAVDAS results:** Right panels

## Fully Coupled NOGAPS/MVOI/Ocean-Ice

- NOGAPS

- Ocean OI

- Ocean/Ice

*** ******Data/Assimilation Forecast Cycle***

- T,S,v

- NAVDAS

- Fluxes Stresses

- SST
-  Ice

- T,q,v

**Notes:**
Future fully coupled data assimilation system diagram.

## GLOBAL COUPLED MODELING

- Surface temperature correction using only Ocean MVOI as the background field.

- Surface temperature correction using NOGAPS/POP/ as the background field to Ocean MVOI.

**Notes:**
24hr RMS surface temperature analysis error for August 2000
Surface temperatures are estimated with the MVOI analysis using the previous day’s analysis
as the background field.  Daily surface temperature errors are calculated from the analysis
“increments” (roughly, the difference between the new observations and the background field)
Also referred to as the “persistence forecast error”.

## Slide 19

## Slide 20

## Slide 21

## Direct Assimilation of Height

**Direct Assimilation of Height**

**No Assimilation of Heights**

**A month-long test of direct SSH assimilation method for Sept 2002. The Gulf Stream is further   offshore (good), has a more coherent downstream structure (good), and doesn't have a strong recirculation structure off Cape Hatteras (good).  The direct assimilation data triples  the data counts and pushes the MVOI, thus requiring MPI conversion. **

## Direct Assimilation of Height

- No Assimilation of Heights

## Figures showing statistical comparisons of T159L24 with T239L30, taken from NRL’s transition report for the T239L30.

## Research Area 1

**Fundamental entities for moist convective transport are sub-cloud drafts of length ~ O(100m)**
**Detrainment can potentially occur at any level **

- Emanuel scheme implemented in NOGAPS in 1999 for testing

**The Emanuel scheme (Emanuel 1991, Emanuel and Zivkovic-Rothman 1999 ) tries to address the following characteristics of convective clouds:**

***Research Area 1***
**New Convective Parameterization:**
**Emanuel Scheme **

**Notes:**
The Emanuel Scheme: Emanuel 1991, Emanuel and Zivkovic-Rothman 1999

## Research Area 1

**Vorticity at 500 mb **

**Vertical motion near **
**the TC center**

_**Comparison of tropical cyclone predictions**_

**NOGAPS Arakawa- Schubert’s Scheme **

**Emanuel **
**Scheme**

**storm center**

**storm center**

***Research Area 1***
**New Convective Parameterization:**
**Emanuel Scheme **

**Notes:**
RAS scheme: Relaxed Arakawa-Schubert cumulus parameterization scheme
The Emanuel scheme reduced the global systematic errors, eliminated spurious tropical cyclone genesis, and improved tropical cyclone track prediction 
*  On the analyses of a tropical cyclone prediction: top panels are from the RAS scheme and the botton panels are from the Emanuel scheme

## Research Area 1

_**Tropical cyclone track distance errors**_
**(Aug, Sep 1998)**

_**Systematic errors**_

- Transition: Replaced the RAS scheme with the Emanuel
- scheme in the operational NOGAPS in May 2000

- The Emanuel scheme:
- improved global weather predictions
- eliminated spurious tropical cyclone genesis
- Improved tropical cyclone track predictions

**RAS (Control)**

***Research Area 1***
**New Convective Parameterization:**
**Emanuel Scheme **

**Emanuel **

## Research Area 2

***Research Area 2***
**Improving the Emanuel scheme (1)**

**Zonal mean heating rate from  convection (Aug. 1998)**

**Observed vertical heating profile **
**(K/day) (Yanai et al. 1973)**

- Pressure

- _Weaknesses of the Emanuel scheme_:
-  unrealistic upper level heating
-  broad areas of light precipitation
-  under-prediction of intense storms

- 100
- 200
- 300
- 400
- 500
- 600
- 700
- 800
- 900
- 1000

- -2   -1   0   1   2   3   4   5   6   7   8   9  10

**Q****1**

**Q****2**

**Q****R**

**Q****R: ****Radiational  heat source**
**Q****1: ****Convective heat source**
**Q****2:  ****Moisture source**

- 90   70   50   30  10  -10   -30  -50  -70  -90

**Threat score for U.S.**

- 100
- 200
- 300
- 400
- 600
- 800
- 1000

- Pressure

**(mm/hr)**

**Notes:**
Large heating at lower levels from the Emanuel scheme is associated with shallow convection 

## Partition of cloud base mass flux (M) into mixing cloud mass flux (Mi ) at each level 

**Partition of cloud base mass flux (***M***) into mixing cloud mass flux (***M**i ***) at each level**** **

**Original:**

- Modified:

**Zonal mean heating rate from convection August 1999**

**Modified:**

- Original:

- Change the partition profile from buoyancy gradient to buoyancy

*B*: Buoyancy
*** **B*: Buoyancy difference across *** p*

***Research Area 2***
**Improving the Emanuel scheme (1)**

_**Sample of buoyancy profile**_

**Notes:**
M: Undiluted cloud bass mass flux
Mi: Undiluted cloud mass flux at each level i  that will mix with the environment
B: Buoyancy
 B: Buoyancy difference across  p
 p: pressure difference
: mixing coefficient = 0.04

## Research Area 2

**TC Track distance errors**
**1999 Aug-Sep.**

- Transition: Implemented the revised Emanuel
-  scheme in the operational NOGAPS in May 2001

_**Impacts on global forecasts**_

- Convective heating depends crucially on the cloud mass flux
- Distribution of mixing cloud mass flux based on buoyancy produces realistic heating profiles and improves global forecasts

**Control **
** ****New vertical mixing profile **

***Research Area 2***
**Improving the Emanuel scheme (1)**

## Research Area 3

- _Research areas_: Cloud base height and cloud base mass flux
**-  Parcel is lifted from level of maximum static energy to its LCL (cloud base) **
**-  Cloud base mass flux (*****M*****) is computed as follows**

**LCL**

**Cloud Base Height**

**Precipitation**

_**Column model results for a Great Plains simulation (8/1/95)**_

- source
- level

_**Problem:**_** The scheme under-predicts intense convective systems**

***Research Area 3***
**Improving the Emanuel scheme (2)**

- *T**v     **: *temperature difference between level above LCL and the BL
- *T**sub  **: *mean buoyancy below cloud base
- Tmax : temperature parameter for sub-grid scale disturbances
-        : relaxation factor,        :      damping factor

**Notes:**
Tv = temp diff between level above LCL and the average within BL.
 : relaxation factor, 
: damping factor
Tmax: perturbation temperature for sub-grid scale disturbance 

Figures show an ARM case study comparing with cloud model result using single column model  

## Research Area 3

**Do not require positive parcel buoyancy at the first level above the LCL**
**Define the cloud top based on vanishing buoyancy, not vanishing CAPE**
**Include ice effect in the heat capacity**

_**Second major modification to the scheme **_

**Cloud Base Height**

**Precipitation**

**Control **

**New Version**

***Research Area 3***
**Improving the Emanuel scheme (2)**

##  Control 

-  **Control **
**  ****New Emanuel**

**TC track errors for 2001**

- Transition: Implemented the revised Emanuel
- scheme in the operational NOGAPS in May 2002

- _Impacts on global forecasts_

- New version that allows selecting of cloud with higher cloud base enhances precipitation and storm intensities
- Eliminating cloud overshooting reduces upper level warm bias
- New version improves overall prediction scores but introduces higher wind bias slightly

***Research Area 3***
**Improving the Emanuel scheme (2)**

## Research Area 4

**Change ****C****u**** from 0.7 to 0 to have full CMT effect**

***Research Area 4***
**Improving the Emanuel scheme (3)**

- _U-Tendency for 9/1/2002_

**C****u****=  0**

**C****u****=  0.7**

**Notes:**
Implementation of the second major modification of the scheme introduced some spurious tropical storms.
Remedy:  Allow full convective momentum mixing by setting Cu to zero

## Research Area 4

- Transition: Incorporated full convective momentum mixing in the
- Emanuel scheme in the operational NOGAPS in Dec 2002

-  Enhanced CMT reduces the secondary circulation of storms and
-  weakens storm intensities --- reduces the wind bias and RMS

***Research Area 4***
**Improving the Emanuel scheme (3)**

**Control**

**New Em (Full CMT)**

**TC track errors ( Oct 2002)**

**Notes:**
Implementation of the second major modification of the scheme introduced some spurious tropical storms.
Remedy:  Allow full convective momentum mixing by setting Cu to zero

## θl

***θ******l***

- Teixeira and Hogan, J. Climate, 2002

- _Diagnostic scheme to determine the cloud fraction (__a__):_
- (1) Stratocumulus
- (2) Shallow cumulus

***Research Area 5***
**New Cloud Scheme for Boundary Layer Clouds**

**Notes:**
The new cloud scheme to determine the cloud fraction, a, is a combination of two schemes: 
Diagnostic formula for stratocumulus based on LES study
 Diagnostic formula for shallow cumulus based on single-column model
      D is the production term and E is the erosion

##  Control               New cloud scheme

- Transition: New cloud scheme implemented
- in the operational NOGAPS in Dec 2000

-  Cloud coverage plays an important role in global energy distribution:
-  The  new cloud scheme alleviated the long-standing problem of warm
-     bias in the polar region
-  Forcing from NOGAPS to the Ice Model is improved

-  **Control          ****     ****New cloud scheme**

***Research Area 5***
**New Cloud Scheme for Boundary Layer Clouds**

- _Impact on global forecast:_
**Temperature error (K) in the Northern  Polar Region**

## Data Assimilation Runs for January 2003

**Data Assimilation Runs for January 2003**
**OPS/MVOI vs. Mean Orography/New Physics/NAVDAS**

## Data Assimilation Runs for Dec 2002- Jan 2003

**Data Assimilation Runs for Dec 2002- Jan 2003**
**No GWDRAG vs. UKMO GWDRAG**

## Data Assimilation Runs for Dec 2002- Jan 2003

**Data Assimilation Runs for Dec 2002- Jan 2003**
**Troen/Mahrt vs. Louis**

## Data Assimilation Runs for July 2003

**Data Assimilation Runs for July 2003**
**NOGAPS/NCEP vs. Mean Orography/New Physics/NAVDAS**

## Slide 42

## Eddy Kinetic Energy Comparison

##      195   125     80    53    

-      195   125     80    53
-        Number of forecasts

- NOGAPS vs. GFS
-  TC TRACK ERROR (nm)
- July 1, 2003 – Oct 12, 2003