1. Lidia Cucurull, NCEP/JCSDA
Preliminary impact studies with GPS-RO observations in low resolution WRF-NMM at NCEP
2009/10/29
Yen-Chih Shen, CWB
Lidia Cucurull, NCEP/JCSDA
Boulder, Colorado

2. Outline Background Experiments setup Results Conclusions
Characteristics of the regional model
Forward operator, QC, observation error
Experiments setup
Results
Statistics
Impact on RMSE and ME
Conclusions

3. WRF-NMM Model type: grid point, non-hydrostatic
Vertical coordinate system: sigma-pressure hybrid
Horizontal resolution: 12km (full)
Vertical layers: 60 layers
Domain: North American area

4. The domain in the regional model

5. Forward operator, QC, obs. error
QC: Cutoff (threshold) for rejecting observations depends on latitude, altitude and/or temperature
Observation error characterization is slightly modified for regional model.
Very similar configuration to the NCEP global model
Cucurull, 2009 (in review)
Explain the temperature, pressure and humidity in forward operator,
Forward operator is 3 terms forward operator, we use pressure, water vapor pressure and temperature to get the refractivity and compare with observations.
QC algorithm depends on latitude, altitude and/or temperature to decide reject the observations or not.
About the observation error characterization, we modified it for regional model based on few weeks of stats.
So, in general, these are the same as for the global model.

6. Modifications for the regional model
In global model:
If one obs. fails QC in a profile, the obs. below 5 km (or lower) would be tossed.
In regional model:
Profiles can cross the boundary, so we might not always have obs. > 5 km.
Boundary issue
As regards modifications, in global model, If one obs. fails QC in a profile, the obs. below 5 km would be tossed. Or lower means if this failed observation is lower than 5 km, for example, it is at 4 km, so the observations below 4 km would be tossed.
But in regional model, profiles can cross the boundary, so we might not always have obs. above 5 km.
thus, we have boundary issue.

7. The observations near boundary
Example:
Horizontal shift
~300 km
The highest obs.
< 5 km, toss the obs. below 5 km
> 5 km, the same QC as in GFS
profile
< 5 km
> 5 km
Here is an example. Usually GPS RO would have horizontal shift about 300 km. so, this green dash line means a GPS RO profile. Left is outside of model, right is inside the model domain.
At first, the observations outside would be tossed. Then, if the highest observation in this profile is below 5 km, we would toss the rest of observations. Because we have no observations above 5 km to compare with.
but if the highest observation in this profile is above 5 km, then the same QC as in GFS would be applied.
boundary
Model domain
outside

8. Experiments Control and GPS
GSI version: the operational version with updated GPS RO algorithms
Low resolution WRF-NMM
Full: 1211 x 1067  Low: 303 x 267
~ , every 12 hours
Partial cycling
GPS RO data ingested every 3 hours
Here are the experiments. We have two experiments, one is control run without GPS RO data and another is GPS run.
The GSI version is Q1FY09 plus new GPS codes setup by Lidia this early year.
The model is low resolution WRF-NMM setup by Yoshi. We use low resolution model is because of computer resources.
The experiment is cold start, no cycling.
And the period of experiment is from ~ , every 12 hours we would do 84 hours forecast.
And the GPS RO data are from Dennis, every 3 hours.

9. The regional partial cycling
Initial condition from GFS
hrs
Tm12
guess
Tm12
analysis
Tm09
guess
Tm09
analysis
GSI
GSI
3hrs fcst
GSI
GSI
3hrs fcst
Tm06
guess
Tm06
analysis
Tm03
guess
Tm03
analysis
GSI
GSI
3hrs fcst
GSI
GSI
3hrs fcst
Observation files from Dennis, 3-hr a pack
This slide is the flow chart in the regional cycling. The initial conditions are from GFS, then do interpolation to get the guess at tm12 hours, and do GSI assimilation to get the tm12 hours analysis then do 3 hours forecast to get the guess of tm09 hours.
After these cycling we will get the analysis at tm00 hours then do 84 hours forecast.
So we have 5 assimilation with GPS RO data in GPS run.
Hereafter, I will show some difference between control and GPS run at tm00 hour analysis.
Tm00
guess
Tm00
analysis
Free
forecast
GSI
GSI
hrs

10. Stats between GPS and background~
Tm12
ratios: (O-B)/obs_err ~
Tm00
ratios : (O-B)/obs_err
Troposphere
These two figures are the stats of GPS and background. The left is tm12 and the right is tm00. these lines indicates the ratios. The ratios are O-B divided by obs. error.
It is good if the ratios are from 1 to 2.
So you can see the ratios at these two date are good.
And the bias at this level could be because of the troposphere.

11. Percentage of obs. assimilating
Stats in percentage ~
Percentage of obs. assimilating
percentage
accepted
rejected
tm12
69.87%
30.13%
tm09
69.41%
30.59%
tm06
70.77%
29.23%
tm03
70.40%
29.60%
tm00
72.11%
27.89%
These two tables are the percentages and ratios from fort.212 by GSI standard output.
The percentage of assimilating is about 70% in the regional model. We have about 75 to 80% assimilating percentage in global model.
The average ratios from fort.212 are from 1 to 2. so the ratios are good.
After we see these stats, we can make sure everything in these two experiments is good.

12. RMSE and ME of temperature~
RMSE at FT84 hours ~
Mean Error at FT84 hours
Against observation
Here is the RMSE and ME of temperature against sonde observations.
The left is RMSE and the right is ME at 84 hours.
Except 200mb, we can see we have more improvement at higher levels. GPS RO reduce 1.5% of RMSE at these levels. At lower levels, GPS RO reduce about 0.5% of RMSE.
As regards ME, we have no improvement at lower levels but higher levels. But the improvements are small.

13. RMSE and ME of temperature~
RMSE at 300 mb ~
Mean Error at 300 mb
These are RMSE and ME at 300 mb.
You can see that GPS RO reduce RMSE and ME consistently at every forecast time although the improvements are small.

14. RMSE and ME of wind 2009062100~2009072100 RMSE at FT84 hours
Mean Error at FT84 hours
Here is the RMSE and ME of wind against sonde observations.
The left is RMSE and the right is ME at 84 hours.
The improvements are smaller. It is because GPS RO wont directly effect wind. We have no information in forward operator.
GPS RO reduce about 0.5% of RMSE and dont change ME too much,

15. RMSE and ME of wind 2009062100~2009072100 RMSE at 300 mb
Mean Error at 300 mb
These are RMSE and ME at 300 mb.
after forecast time 60 hours, GPS RO reduce RMSE a little bit.
As regards ME, GPS RO reduce a little bit ME at some forecast time.

16. RMSE and ME of humidity 2009062100~2009072100 RMSE at 850 mb
Mean Error at 850 mb
Finally, these are the RMSE and ME of humidity at 850mb.
GPS RO almost dont have impact on it.

17. Conclusions
The impact in low resolution regional model is statistically neutral/positive. Mainly due to:
Lower numbers of profiles
80~90 profiles in regional domain
Impact of COSMIC is already in the initial conditions
Only partial cycling
Future work will address other seasons and case-studies with the higher-resolution model
Here are conclusions.
The impact in low resolution regional model of GPS RO is neutral/positive.
It is mainly due to: low numbers of profiles in regional domain. We usually have 600 ~ 700 profiles in global model, but we only have 80~90 profiles in regional model domain.
Second, we already have impact of COSMIC data in the initial condition because the initial conditions are from GFS.
And the experiments are no cycling.
I have already prepared the GPS RO codes for regional model. So the GPS RO data is ready for monitoring in regional model.

18. The End

19. Difference of the model top
temperature
wind
These figures includes 5 times of assimilations of gps and 4 times of 3-hr forecasts.
First, lets see the differences of temperature and wind at the model top. The left is temperature and the right is wind.
I choose one date to see the difference, it is
You can see the differences of temperature at model top. The largest increment is about 1.5 degrees. It makes sense.
And the differences of wind at model top is about 0.5 to 1.5 meter per second. It makes sense too. 19

20. Differences of temperature (K)
~500 mb
~300 mb
Now, it is the difference of temperature at about 500 mb and 300 mb.
The increments are reasonable too. And you can find out we have more changes at higher level. 20

21. Differences of humidity (g/kg)
~850 mb
~500 mb
This is the difference of humidity.
Although the figures are busy, the intervals are small.
So these increments are very small, actually GPS RO don't change too much on humidity. 21

22. Differences of wind (m/s)
~500 mb
~300 mb
And this is the differences of wind.
Because we have no wind terms in forward operator, the increments are small. 22