Transcript Multi-Sources Precipitation Estimation

MULTI-SOURCES PRECIPITATION
ESTIMATION
K. Tesfagiorgis, S. E. Mahani, R. Khanbilvardi
(NOAA-CREST, CCNY, CUNY, NY-10031)
David Kitzmiller (NOAA-NWS Collaborator)
(NOAA NWS/HL, Silver Spring, MD-20910)
NOAA-NESDIS CoRP 7th Annual Symposium
Fort Collins, CO., August, 2010
1
Outline
2




Objectives
Data sets
Comparison of the different precipitation estimation
algorithms
The different bias correction techniques
 Spatial
corrections
 Results for study cases


Conclusion
Future work
Objectives
3


To improve Satellite Precipitation Estimation (SPE)
by selecting appropriate bias correction technique.
To develop a Multi-Sources Rainfall Estimation
algorithm to help optimal rainfall estimations.
 Be
capable of extending radar like outputs inside
radar gap regions using satellite and the surrounding
radar rainfall estimations.
Data Sets
4

Hourly 4kmx4km resolution for the Oklahoma region
bounded by

94.50-100o W Longitude
34.50-37.0o N Latitude
Satellite Rainfall Estimations selected from the following
NESDIS models






Radar Rainfall Estimation


AE (Auto-Estimator)
GMSRA (GOES Multispectral Rainfall Algorithm)
HE (Hydro-Estimator)
SCaMPR-(Self Calibrating Multivariate Precipitation Retrieval)
Blend-(IR/Microwave Blended Algorithm)
Radar Stage IV (ST-IV)
Rain-gauge Measurements
Comparison of the Different Rainfall
Estimations
5
Radar Rainfall Estimation
Satellite Rainfall
Estimations
Bias Score 
Yes
No
Yes
Hits
False Alarms
No
Misses
Correct negatives
Observed yes
Observed no
Hits  False Alar ms
Hits  Misses
Satellite Rainfall
Estimation
False Alarm Ratio 
Bias Score
False Alar ms
Hits  False Alar ms
False Alarm Ratio
8677
GMSRA
2.71cases,
0.63
8677x62x137=73,702,438 pixels considered
HE
1.73
0.46
SCaMPR
2.41
0.68
Auto-Estimator
2.08
0.53
Bias Corrections
6
•
Field Bias Correction
Generally it helps for:
–
–
•
Intensity correction
Frequency correction
Methods of bias corrections:
–
–
–
Ratios of Mean, Median, Maximum
Mean of Ratio of the corresponding rainy pixels in both
Satellite and Radar Rainfall Estimations
Bias ratio field using Inverse Distance method
Bias Corrections: continued…
7
Method 1 of Bias correction
We need to calculate the Multiplicative factors (F) for Bias corrections
RRmax
F
SPEmax
RR
F
SPE
RRmedian
F
SPEmedian
RR-Radar Rainfalls and SPE-Satellite Rainfall Estimates



The ratio of Max and Mean gave a better output. However, ratio of max is not
stable and reliable.
Bias Corrections: continued…
8
•
•
•
How about Spatial Errors that might have already existed?
Before working on the Bias Corrections, it is important to
make spatial corrections between the satellite and the radar
rainfall estimations.
Spatial Correction using the Method of Least Squares
(Brogan 1985):
–
–
–
Apply the method of Hill Climbing to cluster rainy pixels; because
the clustered corresponding rainy pixels are easier to pick up
Pick corresponding points (Rainy Pixels)
Write Least Square equations and apply the method of least
squares on these points as shown in equations shown below.
Spatial Correction
9
R-Radar
S-Satellite
N N i
lon ( R)     lon ( S ) lat ( S )
ij
i
i
i0j0
N N i
lat ( R)     lon ( S ) lat ( S )
ij
i
i
i0j0
lon ( R)1  1 lon ( S )1 lat ( S )1 
lon ( R)  1 lon ( S ) lat ( S )   00 
2
2
2 




......
 ......
  10 

 
  01 
lon ( R) z  1 lon ( S ) z lat ( S ) z 
Coefficients
 00
10
 01
Linear form of the
equations with
N=3
lat ( R)1  1 lat ( S )1 lon ( S )1 
lat ( R)  1 lat ( S ) lon ( S )    00 
2
2
2 




10
......
 ......
 

 
   01 
lat ( R) z  1 lat ( S ) z lon ( S ) z 
Coefficients
Interpretations
Shift in longitude
 00
Shift in latitude
Scale in longitude
10
Scale in latitude
Shear in the longitude
 01
Shear in the latitude
Interpretations
Spatial Correction
10
Corresponding Pixel Locations
Bias Corrections: continued…
11
Method 2 of Bias Correction
- Calculate the bias ratio between ST IV
and HE
- Calulate the bias field using Inverse
distance weight technique
- Multiply HE by the mean field bias
Method 2 provides a more radar like output both spatially and
intesity wise.
Bias Correction: continued…
12
The performance of bias field method for a winter case
Bias Correction: continued…
13
•Ensemble generation of bias fields
•Instead of 1, 100 realizations
Ensembles of rainfall for a pixel around
the center of the study area
Bias Correction continued….
14
Uncorrected
Corrected
Case-2006071022
(YYYYMMDDHH)
Pixels used in the
development of the
algorithm are not part
of the CORR analysis
Case-2006122917
(YYYYMMDDHH)
Conclusion
15






Hydro-Estimator has a better detection capability than the others, so
that it is chosen for further studies that will include radar estimations
and rain-gauge measurements.
There are cases where the alignment algorithm faces difficulties.
When rainfall is very cluttered in radar and continuous in satellite
estimations.
In these cases it is difficult to pick up corresponding rainy pixels.
However we can still apply the Bias field generation Algorithm
without doing the alignment.
Ensemble generation helps to account other errors (Eg. physical,
paralax).
Generation of bias fields can potentially be used to correct satellite
estimations in radar gap regions.
Ongoing Works
16




Check the performance of the model in other geographical
locations.
Implement a technique that will give a multi-sources rainfall
algorithm by merging the radar and the satellite estimations.
Produce gridded rain-gauge measuremts using Bayesian
Kriging and/or inverse distance method.
Merge the gridded rain-gauge with the combined radarsatellite rainfall estimation.
Acknowledgements
17


This study was partially supported and monitored
by the National Oceanic and Atmospheric
Administration (NOAA) under grant number
NA06OAR4810162. The statements contained
within this presentation are not the opinions of the
funding agency or the U.S. government, but reflect
the author’s opinions.
I would like to thank Robert Kuligowski (Ph.D.) for
providing all the necessary data.