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Climate DownscalingClimate Downscaling
TechniquesTechniques
Marina TimofeyevaMarina TimofeyevaUCAR/NWS/NOAAUCAR/NWS/NOAA
TALK OUTLINETALK OUTLINE
What is DownscalingWhat is Downscaling
NWS NCEP CPC climate outlooks and NWS NCEP CPC climate outlooks and regional climateregional climate
Downscaling methodsDownscaling methods
Application of CPC methods in Developing Application of CPC methods in Developing Local Climate Products Local Climate Products
Messages to take homeMessages to take home
DOWNSCALINGDOWNSCALING
DOWNSCALING is DOWNSCALING is the transformation the transformation from a LARGE from a LARGE SCALE feature to a SCALE feature to a SMALL SCALE one SMALL SCALE one (not necessarily of (not necessarily of the same kind).the same kind).DOWNSCALING DOWNSCALING implies implies increasesincreases resolutionresolution of of output.output.
NWS NCEP CPC weather & climate outlooksNWS NCEP CPC weather & climate outlooks
NWS NCEP CPC climate outlooksNWS NCEP CPC climate outlooksThis is a map of 344 climate divisions currently in use over the U.S. Note the changing size as one goes from east to west, as well as from one state to another.
CPC uses 102 mega or forecast divisions in their forecasts. The divisions in the West closely correspond to NCDC climate divisions.
NWS NCEP CPC climate outlooksNWS NCEP CPC climate outlooks
National precipitation map based on high-resolution PRISM data National precipitation map based on high-resolution PRISM data (left) and on Climate Divisions (right). Note the large gradients and (left) and on Climate Divisions (right). Note the large gradients and fine-scale variability in the Western U.S. that is not reproduced in the fine-scale variability in the Western U.S. that is not reproduced in the right map. right map.
NWS NCEP CPC climate outlooksNWS NCEP CPC climate outlooks
Precipitation Climatology 1971 - 2000Precipitation Climatology 1971 - 2000
Slide courtesy of Klaus Wolter, CDCSlide courtesy of Klaus Wolter, CDC
Official CDs for Colorado (left) Official CDs for Colorado (left) and Experimental CDs (right) and Experimental CDs (right) based on multivariate statistical based on multivariate statistical analysis of climate data that analysis of climate data that also include SNOTEL data. also include SNOTEL data. Such new CDs are being Such new CDs are being derived for the entire U.S.A.derived for the entire U.S.A.
NWS NCEP CPC climate outlooks and NWS NCEP CPC climate outlooks and Regional ClimateRegional Climate
Slide courtesy of Klaus Wolter, CDCSlide courtesy of Klaus Wolter, CDC
NWS NCEP CPC climate outlooks and NWS NCEP CPC climate outlooks and Regional ClimateRegional Climate
# of
sta
tions
tha
t #
of s
tatio
ns t
hat
did
not
reje
ct t
he t
est
Hdi
d no
t re
ject
the
tes
t H
00
If climate at station is the same as at climate division, then mean and variance If climate at station is the same as at climate division, then mean and variance at station and climate divisions should be the same. THEY ARE NOT!at station and climate divisions should be the same. THEY ARE NOT!
0
1
2
3
4
5
6
7
8
9
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
NUMBER OF STATIONS (OUT OF 9) THAT HAVE NUMBER OF STATIONS (OUT OF 9) THAT HAVE SQUARED CORRELATION WITH CD >=0.8SQUARED CORRELATION WITH CD >=0.8
NWS NCEP CPC climate outlooks and NWS NCEP CPC climate outlooks and Regional ClimateRegional Climate
0 20 40 60 80 100 120
01
02
03
04
05
06
0
0
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90 100 110 120
DYNAMICAL DOWNSCALINGDYNAMICAL DOWNSCALING
0 20 40 60 80 100 120
010
2030
4050
60
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
DYNAMICAL DOWNSCALINGDYNAMICAL DOWNSCALING
ETA and AVN are examples on ETA and AVN are examples on meteorological meteorological scalescale Climate applicationsClimate applications::
Regional Spectral Model (RSM) and Seasonal Regional Spectral Model (RSM) and Seasonal Forecast Model (SFM) Forecast Model (SFM) Nested in T62 and T40 NCEP coupled AOGCM Nested in T62 and T40 NCEP coupled AOGCM using 50, 30 and 20 km resolution grids using 50, 30 and 20 km resolution grids tested for 1997 winter El Ninotested for 1997 winter El Nino results: results:
• RSM shows improvement in temperature RSM shows improvement in temperature forecast in comparison with AOGCMforecast in comparison with AOGCM• “ “50-km RSM is 50-km RSM is unableunable to forecast anomalies to forecast anomalies over high mountains” over high mountains” • 20-km RSM provides “realistic distribution of 20-km RSM provides “realistic distribution of precipitation”, but precipitation”, but overestimateoverestimate its maxima its maxima
DYNAMICAL DOWNSCALINGDYNAMICAL DOWNSCALING
STATISTICAL DOWNSCALINGSTATISTICAL DOWNSCALINGWEATHER GENERATORSWEATHER GENERATORS
Climate observations
Statistics
Climate predictions
Global Circulation
Pattern predicted
Statistics
Class
Calibration
Prediction
Frequency analysis
Global Circulation
Patternobserved
STATISTICAL DOWNSCALINGSTATISTICAL DOWNSCALINGCORRELATION MODELSCORRELATION MODELS
Climate Observations
GCM fields
Statistics Statistics
Statistical relationship
Modeled climateStatistics
Prediction
Calibration
EXAMPLE: PARTNERSHIP PROJECTEXAMPLE: PARTNERSHIP PROJECT
Western Region HQ (Andrea Bair)Western Region HQ (Andrea Bair)
NWS OCWWS CSD (Marina Timofeyeva)NWS OCWWS CSD (Marina Timofeyeva)
NWS NCEP CPC (David Unger)NWS NCEP CPC (David Unger)
APPLICATION OF CPC METHODS IN APPLICATION OF CPC METHODS IN DEVELOPING LOCAL CLIMATE DEVELOPING LOCAL CLIMATE
PRODUCTSPRODUCTS
Utilized CPC Methods of DownscalingUtilized CPC Methods of Downscaling
Use of composites in the Use of composites in the forecast of local climate forecast of local climate using climate variability using climate variability modes modes
Translation of probability Translation of probability of exeedance (POE) of exeedance (POE) outlooks from climate outlooks from climate divisions to local station divisions to local station temperature forecaststemperature forecasts
Temperature Outlook Precipitation Outlook Degree Day Outlook
Temperature Outlook Precipitation Outlook Degree Day Outlook
Utilized CPC Methods of DownscalingUtilized CPC Methods of Downscaling
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
PO
F (
%)
PO
F (
%)
Forecasted Temperature (Forecasted Temperature (°F)°F)
Translation of forecast division POE to Translation of forecast division POE to station forecast was developed in station forecast was developed in CPC by Barnston, Unger, and He. CPC by Barnston, Unger, and He. The POE outlooks became available The POE outlooks became available in December 1994, and the in December 1994, and the translations to station temperature in translations to station temperature in 2000.2000.
Observed T
http://www.cpc.ncep.noaa.gov/pacdir/NFORdir/citydir/cpcctytd.dat
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
-85 -80 -75 -70 -65
- Stats[1:14, 5]
35
40
45
Sta
ts[1
:14
, 4
]
AL
AK
AZAR
CA
CO
CT
DE
FL
GAHI
ID
IL IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
Boston
New York City
Rochester
CharlotteGreensboro
Raleigh
Cincinnati
Cleveland
ColumbusDayton PhiladelphiaPittsburgh
Norfolk
Providence
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
Step 1: Defining stations where the station outlooks are usedStep 1: Defining stations where the station outlooks are used
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
y = 1.2658x - 9.5544R2 = 0.9104
y = 1.1707x - 5.1282R2 = 0.9335
15
20
25
30
35
40
45
50
15 20 25 30 35 40 45 50
CD Temperature
Sta
tion
Tem
pera
ture
1458
130
Linear (1458)
Linear (130)
In most cases there is a strong relationship between temperature In most cases there is a strong relationship between temperature at station (y axis) and climate division (x axis). at station (y axis) and climate division (x axis).
Step 2: Developing regression equations for those stationsStep 2: Developing regression equations for those stations
1960 1970 1980 1990 2000SLCtsDiff[1, season, ]
45
67
89
SLC
tsD
iff[3
, se
ason
, ]
season= 9
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOEStep 2: Developing regression equations for those stationsStep 2: Developing regression equations for those stationsRegression coefficients are adjusted for the trendRegression coefficients are adjusted for the trend
years
Dif
fere
nce
Dif
fere
nce
Tst
atio
n –
Tcd
T
stat
ion
– T
cd ((
ºF)
ºF)
D iff D iff D iffyear year year
1
1
0 5 10 0 5
1
0 5 10 0 51 10( . * ) .*
( . * ) .*
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
Step 3: Test of regression equations stability - Cross validation Step 3: Test of regression equations stability - Cross validation
Calibration data set Verification Year
1971 1971
1972 1972
1973 1973
1974 1974
1975 1975
… …
1997 1997
1998 1998
1999 1999
2000 2000
Cross validation allows expansion of the test sample Cross validation allows expansion of the test sample and protects against over fittingand protects against over fitting
Calibration data set Verification Year
1971 1971
1972 1972
1973 1973
1974 1974
1975 1975
… …
1997 1997
1998 1998
1999 1999
2000 2000
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
Step 3: Test of regression equations stability - Cross validation Step 3: Test of regression equations stability - Cross validation
Methods for Climate Forecast VerificationMethods for Climate Forecast VerificationRanked Probability ScoreRanked Probability Score and Continuous Ranked Probability ScoreContinuous Ranked Probability Score
0
1
2
3
4
5
-48%
-45%
-40%
-30%
-20%
-10%
Pf=
Po+
0%
Pf=
Po-
0%
10%
20%
30%
40%
45%
48%
Error in Probability Estimate
RP
S
The Rank Probability Score (RPS), graphically represents the performance of the probability forecast (Wilks, 1995).
RPS F Otile tiletile
tile
% %%
%2
1
13
0
20
40
60
80
100
84 84 85 85 86 86 86 86 87 87 88 88 88
Temp_forecast (F)
PO
E (
%)
Tobs
CRPSSCRPS
CRPSfo recast
c a to y
1lim log
RPS is not sensitive for forecast spread.
0
0.2
0.4
0.6
0.8
1
30 40 50 60 70 80 90 100 110
Temp, mean =70
PO
E (
%)
std=15 std=5
Tobs
CRPS T T F Otile tile tile tiletile
tile
% % % %%
%
1 1
2
1
13
Continuous Rank Probability Score (CRPS) takes into account spread of the forecasted distribution.
CRPS Skill Score (CRPSS) is the final measure of the forecast performance.
Progress Report - CRPSSProgress Report - CRPSSWarm Seasons
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5
Fresno Las Vegas Los Angeles Phoenix Portland
Sacramento Salt Lake City San Francisco Seattle
Cold Seasons
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5
Fresno Las Vegas Los Angeles Phoenix Portland
Sacramento Salt Lake City San Diego San Francisco Seattle
CRPSSCRPS
CRPSfo recast
c a to y
1lim log
Reliability DiagramsReliability Diagrams
Reliability Diagrams exhibit the correspondence between the observed and forecasted percentiles. Reliability Diagrams allow verification of each POE for each station. The analysis is done for a forecast and compared with climatology.
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Forecast Probability
Ob
serv
ed
Re
lati
ve
Fre
qu
en
cy
Under-forecastingUnder-forecasting
Over-forecastingOver-forecasting
Methods for Climate Forecast VerificationMethods for Climate Forecast Verification
Progress Report – Reliability DiagramsProgress Report – Reliability Diagrams
Warm Seasons
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Forecast Probability
Ob
serv
ed F
req
uen
cy
Perfect upper low er
Fre LAS LAX
PHX PDX AVERAGE
Cold Seasons
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Forecast Probability
Ob
serv
ed F
req
uen
cy
Perfect upper low er
Fre LAS LAX
PHX PDX Average
Bias AnalysisBias Analysis
MSE F Oyear yearyear
year
1
8
2
1
8
The bias is computed for CPC forecasts and for climatology using the equation shown on the right for climate divisions and stations for each forecast month and each lead season. The bias shows range and sign of deviations between forecasts and observations. Mean Square Error (MSE) is other common accuracy measure of climate forecast leading to skill score (SS) estimates.
Methods for Climate Forecast VerificationMethods for Climate Forecast Verification
B IAS F Oyear yearyear
year
1
8
SSM SE
M SEF
CLIMO
1
0
0.01
0.02
0.03
-40 -20 0 20 40Forecast-Observation
PD
F
Expected PDF of difference between forecasts and observation is normal distribution with mean that is not significantly different from 0.
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
When this method fails…When this method fails…
y = 1.2658x - 9.5544R2 = 0.9104
y = -0.0711x + 29.528R2 = 0.0009
y = 1.1707x - 5.1282R2 = 0.9335
15
20
25
30
35
40
45
50
15 20 25 30 35 40 45 50
CD Temperature
Sta
tion
Tem
pera
ture
1458
130
9181
Linear (1458)
Linear (9181)
Linear (130)
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
When this method fails…When this method fails…
0.5
0.6
0.7
0.8
0.9
1.0
0.5 0.6 0.7 0.8 0.9 1
0.5
0.7
0.8
0.9
R – Measure of Confidence in Downscaling
ρ
Sta
tio
n F
ore
cast
Sp
read
Sta
tio
n F
ore
cast
Sp
read
Utilized CPC Methods of Downscaling - Utilized CPC Methods of Downscaling - POEPOE
When this method fails…When this method fails…
Mean = 0.30St. Dev.= 0.38Median = 0.19Mode = 0.01Skewness = 3.11Kurtosis = 14.67
Mean = 60.7St.Dev.= 13.6Median = 59.5Mode = 52.0Skewness = 0.225Kurt = -0.526
Temperature is a Temperature is a normally distributed normally distributed variable, therefore the variable, therefore the downscaling method downscaling method based on regression based on regression can provide good can provide good estimatesestimates
Precipitation (right chart) is too Precipitation (right chart) is too skewed for normal distribution. skewed for normal distribution. The regression would require The regression would require a transformation of this a transformation of this variable. Compositing can be variable. Compositing can be used for Precipitation forecasts used for Precipitation forecasts because it does not employ because it does not employ regression analysis.regression analysis.
Levels of sophistication in use of composites:Levels of sophistication in use of composites:
1.1. Composite meansComposite means
2.2. Raw Composite distributionRaw Composite distribution
3.3. Smooth resampled Composite Distribution - boot Smooth resampled Composite Distribution - boot strapping techniquesstrapping techniques
4.4. All of the above with trend and some other mode of All of the above with trend and some other mode of climate variability taken into account using new climate variability taken into account using new approach developed by Higgins, Unger and Kimapproach developed by Higgins, Unger and Kim
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITESCOMPOSITES
Nino 3.4 SST (Nino 3.4 SST (°C)°C)
No
rth
Dak
ota
, D
JF T
emp
N
ort
h D
ako
ta,
DJF
Tem
p
(( °C
)°C
)
198319921987
1973
19581995
1966
1988
1998
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITESCOMPOSITES
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 1COMPOSITES – level 1
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 2COMPOSITES – level 2
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 2COMPOSITES – level 2
B N A
Jan 1 3 5
Feb 1 4 4
Mar 5 3 1
JFM 0 7 2
FORECASTFORECAST:: Given El Nino, Denver Tmean has a shift in Tmean toward above normal for Jan, below normal for Mar, and near normal for JFM
Nino3.4
Term
Warm Neutral Cold
Above 2/9=0.22 9/32=0.28 2/9=0.22
Near 7/9=0.78 12/32=0.38 4/9=0.45
Below 0/9=0 11/32=0.34 3/9=0.33
JFM compositesJFM composites
For each Nino 3.4. event we compute probability of For each Nino 3.4. event we compute probability of the climate variable to be in Below/Near/Above the climate variable to be in Below/Near/Above normal category.normal category.
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 2COMPOSITES – level 2
FORECAST USING CURRENT CPC Nino 3.4:FORECAST USING CURRENT CPC Nino 3.4:
CPC CURRENT ENSO FORECASTCPC CURRENT ENSO FORECAST::http://www.cpc.ncep.noaa.gov/products/precip/CWlink/ENSO/total.html
NINO 3.4 INITIAL TIME 3 2003PROJECTION FRACTION BELOW NORMAL ABOVEAMJ 1 0.039 0.372 0.589MJJ 2 0.066 0.446 0.488JJA 3 0.126 0.494 0.380…JFM JFM 99 0.3430.343 0.4340.434 0.2430.243FMA 10 0.318 0.438 0.244
Nino3.4
Term
Warm Neutral Cold
Above 2/9=0.22 9/32=0.28 2/9=0.22
Near 7/9=0.78 12/32=0.38 4/9=0.45
Below 0/9=0 11/32=0.34 3/9=0.33
P P P P P P Pca tegorysta tion
above even tsta tion
aboveN ino
near even tsta tion
nearN ino
below even tsta tion
belowN ino /
./
./
.* * *3 4 3 4 3 4
Example – ElNino with 8.5 month lead (forecast for JFM 2004):Example – ElNino with 8.5 month lead (forecast for JFM 2004):
P
P
P
abovesta tion
nearsta tion
belowsta tion
0 22 0 243 0 28 0 434 0 22 0 343 0 2523
0 78 0 243 0 38 0 434 0 45 0 343 0 5375
0 0 243 0 34 0 434 0 33 0 343 0 2302
. * . . * . . * . .
. * . . * . . * . .
* . . * . . * . .
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 2COMPOSITES – level 2
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 3COMPOSITES – level 3
El Nino Years
January February March
1958
1966
1969
1973
1983
1987
1992
1995
1998
X
X
XX
X
X
X
X
X
X
X
X
To obtain a smooth To obtain a smooth distribution we can use distribution we can use resampling, which allows resampling, which allows 9933 = 729 different = 729 different combinations of the combinations of the months in each season. months in each season.
To better represent To better represent extremes in the extremes in the distribution we sample distribution we sample with replacement using with replacement using boot strapping technique.boot strapping technique.
Utilized CPC Methods of Downscaling – Utilized CPC Methods of Downscaling – COMPOSITES – level 4COMPOSITES – level 4
DJF
Nor
th D
akot
a T
emp
DJF
Nor
th D
akot
a T
emp
(( °C
) °C
)
The trend should not be removed arbitrarily. Hinge, for example, explains climate The trend should not be removed arbitrarily. Hinge, for example, explains climate changes during the last decades. 11-year Moving Average (MA) explains decadal changes during the last decades. 11-year Moving Average (MA) explains decadal variations in climate.variations in climate.
Research Products Versus Operational ProductsResearch Products Versus Operational Products
Method Research Output Operational Products
POE Regression equation coefficients
Method performance evaluation based on hind cast applications (1995-2003)
Seasonal forecasts issued monthly for 13 seasonal leads using equations and CPC CD forecasts
Verification issued monthly for forecast made in preceding month
Composites All composite statistics: mean, variance, each category probabilities with and without trend, and POE.
Method performance evaluation based on hind cast applications (1982 – 2003)
Seasonal forecasts issued monthly for 16 seasonal leads using station statistics and CPC Nino 3.4. forecasts
Verification issued monthly for forecast made in preceding month
WR NEW LOCAL CLIMATE PRODUCTSWR NEW LOCAL CLIMATE PRODUCTSWhere do we go…Where do we go…
EXPECTED OPERATIONAL FUNCTIONS: REGIONAL EXPECTED OPERATIONAL FUNCTIONS: REGIONAL CSPMCSPM
1. POE downscaling1. POE downscaling 1.1. Developing the translation equations for 87 sites (completed) 1.1. Developing the translation equations for 87 sites (completed) 1.2. Tests of the equations (2003-2004) 1.2. Tests of the equations (2003-2004) 1.3. Monthly coordination of local product release (starting 2005) 1.3. Monthly coordination of local product release (starting 2005) 1.4. Annual update of the equations (starting 2005)1.4. Annual update of the equations (starting 2005)
2. Composites2. Composites 2.1. Coordination of Composites products existing or released in 2.1. Coordination of Composites products existing or released in the local offices the local offices 2.2. Developing Composites for 87 sites (2003-2004) 2.2. Developing Composites for 87 sites (2003-2004) 2.3. Hind cast test of the composites (2003-2004) 2.3. Hind cast test of the composites (2003-2004) 2.4. Monthly coordination of local product release (starting 2005) 2.4. Monthly coordination of local product release (starting 2005) 2.5. Annual update of the composites (starting 2005)2.5. Annual update of the composites (starting 2005)
WR NEW LOCAL CLIMATE PRODUCTSWR NEW LOCAL CLIMATE PRODUCTSWhere do we go…Where do we go…
EXPECTED OPERATIONAL FUNCTIONS: WFO CSFP EXPECTED OPERATIONAL FUNCTIONS: WFO CSFP
1. POE downscaling: 1. POE downscaling: 1.1. Selection of sites within WFO CWA for downscaling (completed)1.1. Selection of sites within WFO CWA for downscaling (completed) 1.2. Delivering on monthly basis seasonal outlooks for selected 1.2. Delivering on monthly basis seasonal outlooks for selected
sites within WFO CWA (starting in FY05) sites within WFO CWA (starting in FY05) 1.3. Verification of the previous month forecasts (starting in FY05)1.3. Verification of the previous month forecasts (starting in FY05)
2. Composites: 2. Composites: 2.1. Keeping self updated on climate variability mode status 2.1. Keeping self updated on climate variability mode status
(starting in October 2003) (starting in October 2003) 2.2. Developing composites local studies (optional) 2.2. Developing composites local studies (optional) 2.3. Release of monthly and seasonal outlooks for selected 2.3. Release of monthly and seasonal outlooks for selected
sites within each WFO CWA (starting in 2005) sites within each WFO CWA (starting in 2005) 2.4. Verification of the Composites forecasts (starting in 2005) 2.4. Verification of the Composites forecasts (starting in 2005)
3. Public outreach on these new products3. Public outreach on these new products
MESSAGES TO TAKE HOMEMESSAGES TO TAKE HOME
THERE ARE NO THERE ARE NO NWS CONSISTENTNWS CONSISTENT LOCALLOCAL CLIMATE CLIMATE
PRODUCTSPRODUCTS AVAILABLE AVAILABLE NOWNOW
DOWSCALING CAN BE USED AS A TOOL FOR LOCAL DOWSCALING CAN BE USED AS A TOOL FOR LOCAL
CLIMATE PRODUCTS CLIMATE PRODUCTS
THE LOCAL CLIMATE PRODUCT SHOULD BE CONSISTENT THE LOCAL CLIMATE PRODUCT SHOULD BE CONSISTENT
WITH THE NATIONAL WEATHER SERVICE PRODUCTS WITH THE NATIONAL WEATHER SERVICE PRODUCTS
(CPC)(CPC)
CPC METHODS COULD BE USED IN DEVELOPING SUCH CPC METHODS COULD BE USED IN DEVELOPING SUCH
LOCAL CLIMATE PRODUCTSLOCAL CLIMATE PRODUCTS
IN DEVELOPING OF DOWNSCALING AT LEAST THREE NWS IN DEVELOPING OF DOWNSCALING AT LEAST THREE NWS
ENTITIES SHOULD BE INVOLVED: REGIONAL OFFICE, ENTITIES SHOULD BE INVOLVED: REGIONAL OFFICE,
CSD, AND CPCCSD, AND CPC