# Presentation: 928882

## Development of neural network emulations of model physics components for improving the computational performance of the NCEP seasonal climate forecastsP.I.s: M. Fox-Rabinovitz, V. Krasnopolsky, NCEP Co-I.s: S. Lord, Y.-T. Hou, UMD Collaborator: A. Belochitski, CTB contact: H.-L. Pan

**Acknowledgments:** The authors would like to thank
- Drs. H.-L. Pan, S. Saha, S. Moorthi, and M. Iredell
- for their useful consultations and discussions. The research is supported by the NOAA CPO CDEP CTB grant NA06OAR4310047.

***NOAA CDEP CTB SAB Meeting, September 11-12, 2008, ESSIC/UMD***

## Outline

- NN Training for the *coupled* NCEP CFS
- Development of NN emulations
- Validation of the10-Year Climate Simulation and Seasonal Predictions (NN run vs. the control (with the original radiation)
- Short Term Plans (through the end of the current grant, Feb. 2009)
- Outline of the Continuation Proposal
-    (3 years, 08/01/2009 – 07/31/2012)

## Background

- Any parameterization of model physics is a relationship or **MAPPING** (continuous or almost continuous) between two vectors: a vector of input parameters, *X*, and a vector of output parameters, *Y*,
- NN is a generic approximation for **any** continuous or almost continuous mapping given by a set of its input/output records:
- SET = {*X**i*, *Y**i*}*i* *= 1, ...,N*

## Neural Network

***Y = F******NN******(X)***

**Continuous Input to Output Mapping**

**Neuron**

## LWR NN175 – Training 

- NN175 emulates LWR **RRTM2** that is the most time consuming physics component in the coupled NCEP CFS/GFS model
- NN architecture:*** High dimensional*** training set consists of LWR inputs and outputs:
- the training set was selected from 12-year (1995 – 2006) T126L64  run
- half of the data is used for training and another half for validation or NN accuracy estimation vs. the original LWR
- every 2 weeks, one day, i.e. eight 3 hourly global files have been recorded; totally – **2,080** files
**100** events/profiles have been selected randomly from each 3 hourly global file
- Totally – **208,000** events/profiles is used for NN training

## CFS Model: LWR NN emulation 

-         ***NN dimensionality and other parameters***:
- 591 inputs: 12 variables (pressure, T, moisture, cloudiness parameters, surface emissivity, gases (ozone, CO2)
- 69 outputs: 6 variables (heating rates, fluxes)
- Number of neurons for NN versions: 50 to 200
**NN dimensionality** for the complex system: 50,000 to 250,000

## NN Approximation Accuracy (on independent data set) vs. Original Parameterization (all in K/day).

| Parameterization | NN | Bias | RMSE | Mean HR | HR |
| --- | --- | --- | --- | --- | --- |
| LWR
(K/day) | NN75 | 1. 10-3 | 0.40 | -1.88 | 2.28 |
|  | NN95 | 4. 10-3 | 0.38 |  |  |
|  | NN175 | -5. 10-4 | 0.32 |  |  |

_**NN Computational Performance:**_** **LWR NN emulations are *two orders of magnitude faster* than the original LWR** **
_**Overall CFS model computational**__ __**performance:**_** ***~ 20-25% faster* when using LWR NN emulations vs. the original LWR
_**Note:**_** The practically zero bias obtained is crucially important for non-accumulating errors during long model integrations. The obtained mean PRMSE is quite small/limited.**

## Validation of the parallel 10-Year Climate Run  and Seasonal Predictions Using NN emulations vs. the Control Run

**Coupled NCEP CFS**
**With NN RRTM2 LWR**

**Acronyms:**
**CTL** – control run
**NN175** – RRTM2 LWR NN with 175 hidden neurons

## Validation: 10-year Parallel Runs

- Control run with RRTM2 LWR for 1995 – 2005
- NN run with NN175 (175 hidden neurons); 1995 – 2005
- Initialization: Control initialization run started in 1990 form initial conditions then:
- Control run (1995-2005) started from the Jan 1995 restart of the initialization run
- NN run (1995-2005) started from the same Jan 1995 restart
- NN LWR vs. RRTM2 speed up is about 100 times
- Total model speed up is about 20-25%
**All figures are prepared using the validation software provided by Dr. S. Saha**

## Nino 3.4 SST Anomaly 

**Control**

**NN**

## 10 year PRATE (djf)

**Control**

**NN**

## 10 year PRATE (djf)

**NN - Control**

## 10 year Total Clouds cldclm (djf)

**NN - Control**

**Control**

**NN**

## 10 year Total Clouds cldclm (jja)

**NN - Control**

**Control**

**NN**

## Seasonal PRATE (djf)

**Control**

**NN**

## Seasonal PRATE (jja)

**Control**

**NN**

## Robustness of NNs to model horizontal resolution

- NN emulation developed for T126L64 has been used for the 10-year T62L64 and 4-month T382L64 parallel runs
- Differences for the parallel 10-year T62L64 runs are small and similar to those of the parallel 10-year T126 runs; the analysis of the 4-month parallel T382L64 runs are in progress

## Robustness of NNs to model horizontal resolution

## Conclusions 

- Developed NN emulations for the NCEP CFS model show ***high accuracy and computational efficiency***
- Validation of NN emulations through 10-year and seasonal NCEP CFS model runs using the NN emulations vs. the control model run show a ***close similarity*** of the runs, namely the differences are mostly within observational errors or the ***uncertainty of observational data or reanalyses***
- NN emulations are robust: can be used for different horizontal resolutions
- New NN methodology has been developed for:
-    -- Compound Parameterization (CP) with Quality Control (QC)
-       of larger errors,
-    -- NN ensembles,
-    -- NN adaptation to climate change
- Increase of tropical inputs/outputs to be used for NN training has to be considered
- Potential applications to NCEP GFS for NWP & DAS

## Recent Journal and Conference Papers

_***Journal Papers:***_** **
**Krasnopolsky, V. M.,  M.S. Fox-Rabinovitz, H.L. Tolman, and A. A. Belochitski, 2008: **_["Neural network approach for robust and fast calculation of physical processes in numerical environmental models: Compound parameterization with a quality control of larger errors."](http://polar.ncep.noaa.gov/mmab/people/vladimir/publications/NN_CP.pdf)_**, *****Neural Networks*****,***** *****21*****, *****535–543;***** doi:10.1016/j.neunet.2007.12.019  ***** **** **
**V. M. Krasnopolsky, M.S. Fox-Rabinovitz, and A. A. Belochitski, 2008: **_["Decadal Climate Simulations Using Accurate and Fast Neural Network Emulation of Full, Long- and Short Wave, Radiation."](http://polar.ncep.noaa.gov/mmab/people/vladimir/publications/8080.pdf)_**, *****Monthly Weather Review******, *****Vol. 136, No. 10.**
**Krasnopolsky, V. M., M. S. Fox-Rabinovitz, Y. T. Hou, S. J. Lord, and A. A. Belochitski, 2008: Accurate and Fast Neural Network Emulations of Full, Long- and Short- Wave Radiation for the NCEP Coupled Climate Forecast System Model: Decadal Climate Simulation and Seasonal Forecasting” in preparation.**
**V.M. Krasnopolsky, 2007, “Neural Network Emulations for Complex Multidimensional Geophysical Mappings: Applications of Neural Network Techniques to Atmospheric and Oceanic Satellite Retrievals and Numerical Modeling”, *****Reviews of Geophysics*****, 45, RG3009, doi:10.1029/2006RG000200**
**V.M. Krasnopolsky, 2007: “Reducing Uncertainties in Neural Network Jacobians and Improving Accuracy of Neural Network Emulations with NN Ensemble Approaches”, *****Neural Networks*****, 20, pp. 454-46**
**V.M. Krasnopolsky and M.S. Fox-Rabinovitz, 2006: "Complex Hybrid Models Combining Deterministic and Machine Learning Components for Numerical Climate Modeling and Weather Prediction", *****Neural Networks*****, 19, 122-134 **
**V.M. Krasnopolsky and M.S. Fox-Rabinovitz, 2006: "A New Synergetic Paradigm in Environmental Numerical Modeling: Hybrid Models Combining Deterministic and Machine Learning Components", *****Ecological Modelling*****, v. 191, 5-18**
_***Conference Papers;***_
**V.M. Krasnopolsky, M. S. Fox-Rabinovitz, Y.-T. Hou, S. J. Lord, and A. A. Belochitski, 2007,** “**Development of Fast and Accurate Neural Network Emulations of Long Wave Radiation for the NCEP Climate Forecast System Model”, NOAA 32nd Annual Climate Diagnostics and Prediction Workshop**
**V. M. Krasnopolsky, M. S. Fox-Rabinovitz, Y. T. Hou, S. J. Lord, and A. A. Belochitski, 2008: "Accurate and Fast Neural Network Emulations of Long Wave Radiation for the NCEP Climate Forecast System Model", *****Proc.of 20th Conference on Climate Variability and Change,***** 88th AMS Annual Meeting, New Orleans, LA, 20-24 January 2008, CD-ROM, P3.10**
**V. M. Krasnopolsky, M.S. Fox-Rabinovitz, and A. A. Belochitski, 2008:**_[ "Ensembles of Numerical Climate and Weather Prediction Models Using Neural Network Emulations of Model Physics"](http://polar.ncep.noaa.gov/mmab/people/vladimir/publications/IJCNN-08.pdf)_**, Proc. of  the 2008 IEEE World Congress on Computational Intelligence, Hong Kong, June 1-6, 2008, CD-ROM, paper NN0498, pp. 1524-1531**** **
***Krasnopolsky, V. M., M. S. Fox-Rabinovitz, Y. T. Hou, S. J. Lord, and A. A. Belochitski, 2009: Fast Neural Network Emulations of Long Wave Radiation for the NCEP Climate Forecast System Model: Seasonal Prediction and Climate Simulation, 89th AMS Annual Meeting, Phoenix, AZ, 11-15 January, 2009, accepted.***
**M. Fox-Rabinovitz, V. Krasnopolsky, and A. Belochitski, 2006: “Ensemble of Neural Network Emulations for Climate Model Physics: The Impact on Climate Simulations”, *****Proc.,***** *****2006 International Joint Conference on Neural Networks, *****Vancouver, BC, Canada, July 16-21, 2006, pp. 9321-9326, CD-ROM**

## Short Term Plans, through the end of the current grant, Feb. 2009

- NN emulations for the RRTM Full, LWR and SWR, Radiation Block for NCEP CFS (GFS/MOM3)
- Validation of Full NN Radiation (LW & SW) for Seasonal Predictions and Climate Simulation
- Submitting a Journal Paper on Full NN Radiation

## Outline of the Continuation Proposal

_***Title:***_*** *****Transferring the efficient neural network methodology of model physics calculation into the coupled NCEP CFS model for enhancing the computational performance of NCEP monthly-to-seasonal and climate predictions**
**Co-PIs: M. Fox-Rabinovitz and V. Krasnopolsky**
**NCEP Co-Is: S. Lord and Y.-T. Hou**
**NCEP Collaborators/Consultants: S. Saha, **
**    ****S. Moorthi, M. Iredell**
**CTB Contact: H.-L. Pan, **
**UMD Collaborator: A. Belochitski**
**Proposed period: 36 months, 08/01/09 – 07/31/12**

## Outline of the CTB Continuation Proposal

- Development of **NN emulations** for the **full**, **LWR & SWR, radiation block and the moisture physics block** for the new/upcoming operational GFS/MOM4 model (we are working with the current GFS/MOM3 model).
- Transferring of new and/or refined NN methodology suitable for the **NCEP CFS operational framework** (for providing the better accuracy and reduced uncertainty):
**Compound Parameterizations** (CP) with a Quality Control (QC) of outliers and larger errors for the full model radiation block, LWR and SWR, NN emulations (Krasnopolsky, Fox-Rabinovitz, Tolman, and Belochitski, 2008).
**Ensembles with NN perturbed physics** (Krasnopolsky, Fox-Rabinovitz, and Belochitski, 2008).
**NN emulations** **adaptive to climate change **(Krasnopolsky and Fox-Rabinovitz 2006, Krasnopolsky 2007)
**NN emulations** for the NCEP CFS **moisture physics** block
-          --   NN emulations for moisture physics
-         --  CP with QC for moisture physics

## Outline of the CTB Continuation Proposal

**Standard and user-friendly NN emulation software** for NCEP users
**Validation**** **of monthly-to-seasonal and climate predictions will be done following the CTB’s protocols for “**Transition to operations**” and “_[Diagnoses of CFS Retrospective Forecasts](http://www.cpc.ncep.noaa.gov/products/ctb/cfs/CFS_Metrics/cfs_metrics.shtml)_”.
- Using NN emulations for the radiation block only will **enhance the computational performance** by ~ 50-60% or more and for the full model physics by ~ 60-70% or more. The NN approach allows us **to calculate model physics faster and radiation more frequently** (i.e., at every model time step) for **different model resolutions** (i.e. T126T64, and T382L64).
- Other applications of the NCEP operational system, short-to-medium term NWP and DAS, will also benefit from development of NN model physics.
- The investigators consider the continuation proposal as a strategic scientific and methodological study with immediate applications to the NCEP CFS operational framework.

## Outline the Full NN Radiation Paper

**Title: ”Accurate and Fast Neural Network Emulations of Model Radiation for the NCEP Coupled Climate Forecast System: Seasonal and Climate Predictions“**
**Krasnopolsky, V. M., M. S. Fox-Rabinovitz, Y. T. Hou, S. J. Lord, and A. A. Belochitski**
**Introduction**
**NCEP Coupled Climate Forecast System**
**Generating a Training Set for the Coupled NCEP CFS Model**
**NN Training**
**Accuracy of Approximation**
**Development of NN Emulations **
**Validation of parallel runs with NN Emulations vs. the Control for Seasonal and Climate Predictions **
**Discussion and Conclusions**** **

## LWR NN175 – Approximation Errors on Independent Data Set in K/Day

| Bias | RMSE | PRMSE | σPRMSE |
| --- | --- | --- | --- |
| -5. 10-4 | 0.32 | 0.23 | 0.22 |

**RMSE Profiles in K/day**

**RMSE Profiles in ****σ**

**Bias Profiles in ****σ**

**Dotted – 150 hidden neurons**
**Solid – 175 hidden neurons (NN46)**
**Dashed – 200 hidden neurons**