A Regional Climate Model Evaluation System: Facilitating the

A Regional Climate Model Evaluation System: Facilitating the

A Regional Climate Model Evaluation System: Facilitating the Use of Contemporary Satellite and
Other Observations for Evaluating Regional Climate Model Fidelity
D. E. Waliser1,2, J. Kim2, C. Mattmann1,3, C. Goodale1, A. Hart1, P. Zimdars1and P. Lean1
1

Jet Propulsion Laboratory, California Institute of Technology; 2 JIFRESSE, UCLA; 3University of Southern California
For more information, please email [email protected]

Background: Why model evaluation?
Climate model projections play a crucial
role in developing plans to mitigate and
adapt to climate variations and change
for sustainable developments.
Assessing model performance is an
important step in linking climate
simulation quality to projection
uncertainty and then to climate change
impacts assessments.

Uncertainties propagate according to model
hierarchy
Bias correction is based on model
evaluation
Determination of the weights in multi-model
ensemble

Model evaluation is also a fundamental
part of model development and
improvement (Figure 1).

Evaluation of
WRF; Oct
user
choice

RCM data

the Simulated Cold Season Hydrology in California
2008 Mar 2009; NCEP Final Analysis forcing
Seasonal-mean 2-m Air Temperature

URL

Extract OBS
data

Metadata

TRMM

Data Table

MODIS

Regridder

Put the OBS & RCM data on the
same grid for comparison

Data Table

MySQL

AIRS
SWE

Data Table

Soil
moisture

Data Table
Data Table

ETC

Common Format,
Native grid,
Efficient architecture

Raw Data:
Various
Formats,
Resolutions,
Coverage

Data extractor
(Fortran binary)

Data Table

Extractor

WRF T2 (K): 00UTC

Extract RCM
data

Metrics Calculator

Calculate comparison metrics
Data extractor
(Fortran binary)

AIRS T2 (K): Ascending passes (1:30PM)

Users
own
codes
for
ANAL
and
VIS.

Visualizer

Plot the metrics

RCMED

RCMET

(Regional Climate Model Evaluation Database)

(Regional Climate Model Evaluation Toolkit)

A large scalable database to store data in
a common format

A library of codes for extracting data
from RCMED and model and for
calculating evaluation metrics

Season-total Precipitation (mm): Multiple
Reference
Data
TRMM

RCMES overview:
Large database (MySQL + Apache Hadoop):
Multiple reference datasets from:
Satellite remote sensing

Issues:

Remote sensing data:
CPC

Differences between REF
datasets
Reference data
intercomparison
Observational uncertainty

CPC precipitation, CRU precipitation, 2-m air temperatures

Assimilation

SWR (SNODAS; JPL&U. Colorado)

Reanalysis

ERA-Interim (e.g. U(p), V(p), q(p), T(p), SLP)

Extractors:

Process data from various data formats into a common
database schema.
Library of statistical metrics:
Python routines with plug-ins in other languages (Fortran, c,
idl) to calculate and plot standard metrics of model
performance. (e.g. Bias, RMS error, Anomaly Correlation,
Probability Distribution Functions).

Evaluation of the CORDEX-Africa Multi-Model Ensemble
Preliminary 20-year runs; 1989 2008
Spatial Variability of the
Precipitation Climatology using
Taylor diagram

Select data period

Next >

Next >

Select Reference Dataset
TRMM
AIRS level III gridded
ERA-Interim
URD
SNODAS

Daily
Monthly
Seasonal
Annual

Map
Time series
Next >

Reference Data
Model
Next >

Next >

Next
>

Select Plots

Select Spatial Grid

Select model data

Mean bias
RMSE
Pattern correlation
PDF Similarity score
Coeff. of Efficiency

Select Data Timestep

Next >

ENS

Select Metrics

2-m temperature
Precipitation
OLR (TOA)
Cloud fraction
10m wind speed

Process >

Precipitation Annual Cycle in 6 Regions using
Portrait diagram
RMSE
Correlation

Overland mean (mm/day)

Sample Graphical User
interface
Select Variable

Bias (mm): WRF-CPC

Satellite fly-over timing
Sensor footprints

Multiple REF
data:

Analysis

Regional Climate Model Evaluation System
(RCMES):
Provide a fast, flexible, comprehensive
system to allow easy comparison of
climate models with observations.
Enable researchers to handle a large
volume of data and reduce time taken for
model evaluation studies from weeks to
hours.
Help model developers with cutting-edge
observations and diagnostics to evaluate
and improve their models.
Help end-users understand the
uncertainties in climate projections for
the regions of interest.
Efficient: Fast access to reference data
and
Nationaltoolkit
Aeronautics and Space Administration
Jet Propulsion
Laboratory
User
Friendly:
Intuitive and
California Institute of Technology
Pasadena, California
transferrable
GUI
www.nasa.gov
Flexible:
Cloud-based architecture
Copyright 2010. All rights reserved.
Expandable:

Bias (mm): WRF-TRMM

WRF

TRMM (1998-2010)
AIRS (2002-2010)
MODIS Cloudiness

Figure 1. The role of model evaluation in the model
development process and uncertainty estimations.

Bias (K): WRF-AIRS

Intuitive presentation schema can facilitate
intercomparison of multiple models

RMSE (mm/day)

Future works:
1.Add additional reference datasets (e.g., other reanalysis, satellite data,
in-situ)
2.Examine remote sensing data for evaluating fine-scale (<10km) regional climate data. 3.Additional metrics calculations and visualizations 4.Improve GUI 5.Use the system to evaluate regional/global climate models associated with National Climate Assessment (NCA), NARCCAP, CMIP5 and CORDEX (Africa and Asia). Reference

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