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Evaluation and Improvement of AMSU Precipitation Retrievals Over Ocean
Daniel
1.
1
Vila ,
Ralph R.
1,2
Ferraro
CICS/ESSIC-NOAA, University of Maryland College Park
2.
NOAA/NESDIS/STAR
Introduction
AMSU Rain-Rate Correction Scheme
Since the first launch of the Advanced Microwave Sounding Unit – B (AMSU-B) on
board NOAA 15 in July 1998, many environmental passive microwave (PMW) products
have been operationally generated by NOAA. New products such as Ice Water Path
(IWP) and Ice Particle Size are also derived, owing to the unique AMSU millimeter
wavelength channels (Weng et al, 2003). The ability of AMSU to derive cloud and
precipitation products using these new products is remarkable for a variety of
applications. The high temporal frequency of rainfall retrievals will also help to
understand better the diurnal cycle for different rainfall regimes around the world.
Nevertheless, several researchers found that rainfall derived from the AMSU-B algorithm
differs in many respects from SSM/I and other PMW estimation techniques. While SSM/I
is equipped with channels that detect both emission and scattering signatures, the AMSUB sensor only has high-frequency channels; thus, only precipitation that is detectable
from a scattering signature can be estimated (Joyce et al, 2004). Although the AMSU-A
sensor has some limited ability to infer emission based signals over the ocean (i.e.,
through the use of 31 GHz channel), it has not been incorporated within the AMSU-based
precipitation algorithm due to its much coarser spatial resolution (e.g., 48 km at nadir)
and its saturation at low rain rates. However, there is potential to improve the current
AMSU-B algorithm in light rain situations using such information.
It is the purpose of this paper to describe an AMSU rain-rate correction scheme over the
ocean and to demonstrate its utility comparing the obtained results with other emission
based PMW rainfall retrievals.
• A normalization process using a Gaussian PDF (with µ and б depending on LZA;
obtained from the previous analysis) has been applied to the whole data set in order to
correct this systematic bias.
• It’s also observed that, for high rain rates, the frequency of AMSU bins is lower than
SSM/I EDRR bins.
• This situation is related with the cross-scan characteristics of the instrument (different
footprints for different local zenithal angles, blue line, Figure 3).
• A linear correction scheme with the slope depending on SSM/I and AMSU-B footprint
ratio (purple line, Figure 3) is proposed to normalize AMSU-B derived high rain rates.
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AMSU corrected value
800
2.5
600
2
slope
Pixel Area (km2)
3
1.5
400
15
10
5
1
200
0.5
0
0
0
0
10
20
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60
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5
10
15
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AMSU rain rate value
LZA angle
Figure 3 (left): AMSU-B footprint area (blue line – left axis) and normalized AMSU-B – SSM/I slope (purple line – right
axis) for different LZA. Figure 4 (right): Correction scheme for AMSU-B rain rates (in mm) for three different LZA. Blue
line represents LZA near nadir; purple, between nadir and limb and yellow near limb.
• Cloud Liquid Water (this parameter is performed with the current suite of AMSU-A
operational products) is proposed as a proxy for retrieving rainfall.
• Convective Index (CI), who represent the environment where the cloud is developing,
is also used in this scheme.
• Figure 5 shows the mean value and the standard deviation for collocated AMSU-B
rain rates and AMSU-A CLW for different values of CI (CI = 0, 1 stratiform
environment; CI = 2 moderate convective and CI = 3 strong convective)
Figure 5: Mean rain rate for different
CLW values. Bars represent one standard
deviation. CI is the convective index
computed by using the three 183 GHz
channels (Qiu et al, 2005). Satellite:
NOAA 15 - 60N-60S: 25 January 2005 –
5 February 2005.
45
40
35
30
25
20
15
10
5
ci=0
ci=1
ci=2
Summary and Conclusions
ci=3
0
5
10
15
Evaluation
20
mm
nadir
limb
lza < 32
ssmi
• Mean position of the peak (blue line) and standard deviation (purple line) for derived
rain rate bins versus LZA is presented in Figure 2.
8
8
7
7
6
6
R2 = 0.9974
R2 = 0.9977
5
5
4
4
R = 0.9954
3
3
2
2
1
1
0
0
0
10
20
30
mean
40
sigma
50
60
70
Mean rain rate of AMSU NOAA 15 uncorrected
rain rates (mmh-1) (upper left panel), corrected
values (upper right panel) and SSM/I estimations
(left panel). April 2005
R2 = 0.9914
2
0
10
20
30
40
mean
sigma
50
60
70
Figure 2: Mean position of the peak (blue line, y axis, in mm) and standard deviation (purple line, in mm) for
derived rain rate bins over the ocean vs. LZA (x axis, in degrees) for descending orbits (left panel) and ascending
orbits (right panel). Satellite: NOAA 15 - 60N -60S - Year 2005.
Hurricane Katrina case study: Upper left panel: AMSU uncorrected values for daily mean rainfall rate values
(ascending and descending orbit) for 28 August 2005 over the Gulf of Mexico. Upper right panel: idem upper left after
LZAC. Bottom right panel: idem upper left after LZAC+CLWC. Bottom left panel: SSM/I EDRR retrieval for the same
date
CLW (mm)
10
0
Indian Ocean case study: Upper left panel: AMSU uncorrected values for daily mean rainfall rate values (ascending and
descending orbit) for 7 June 2005 over the Indian Ocean. Upper right panel: idem upper left after LZAC. Bottom right
panel: idem upper left after LZAC+CLWC. Bottom left panel: SSM/I EDRR retrieval for the same date. Only collocated
data were considered in this case study
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4
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6
2.
8
2
0
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2
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4
1.
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1.
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30
1000
1
40
4
3.5
0
50
20
4.5
1200
Figure 1: Relative histogram (in %) of
AMSU derived rain rate bins over the ocean
for different LZA. Satellite: NOAA 15 60N-60S: 25 January 2005 – 5 February
2005. SSM/I EDRR relative histogram
(green line) is also included for comparison
purpose.
60
%
1400
0.
2
0.
4
0.
6
0.
8
AMSU Scattering Signature Approach :
• Advantage: availability of three NOAA POES satellites spaced approximately 4 h apart
with a spatial resolution of 16 km at nadir and a wider swath than SSM/I sensors.
• Weakness : inability to retrieve rain that has little or no ice; and with the cross-scan
characteristics of the instrument (different footprints for different local zenithal angles).
• A remarkable and unrealistic shift in the peak of histogram can be observed for low
rain rates (Figure 1). Lower LZA have a larger shift; while large LZA (or pixels near the
limb) has a smaller shift (and less dispersion also). This situation is also compared with a
conical radiometer retrieval (SSM/I EDRR, in this case) which shows a more realistic
behavior
25
RR (mmh-1)
AMSU Rain-Rate Correction Scheme
Left panel: Zonal mean for AMSU-NOAA 15 corrected rain rates (green line), AMSU-NOAA 15 uncorrected rain rates (black
line) and SSM/I F-13 EDRR (red line). Left axis units: mm h-1. Right panel: frequency events ratio for AMSU-NOAA 15
corrected rain rates (green line), AMSU-NOAA 15 uncorrected rain rates (black line) and SSM/I F-13 EDRR (red line). 60N60S: Apr 2005
1
Daniel Vila – CICS/ESSIC - 2207 Computer & Space Science – College Park, MD 20742 - E-mail :
[email protected].
• The AMSU rain-rate correction scheme has been shown to have the ability to provide
more reliable rainfall estimates using some geometrical aspects of cross-scan
characteristic of AMSU-B sensor and with the inclusion of CLW.
• As demonstrated by the global comparison, the correction scheme greatly reduces the
previous positive bias over ocean.
• The inclusion of CLW in this correction scheme fills the gaps where the inability of
AMSU techniques to retrieve rain that has little or no ice is present. The area covered by
rainfall is greatly increased with the inclusion of CLW compared with SSM/I EDRR
data.
• For representative cases study, the mean total NOAA 15 AMSU corrected and
uncorrected rain rates on 7 June 2005 over the Indian Ocean and on 28 August 2005 over
the Gulf of Mexico during Katrina episode, were presented to show that corrected values
are closer to other emission based PWM retrieval like SSM/I EDRR estimations.
References:
Ferraro R. and coauthors, 2005: NOAA Operational Hydrological Products Derived From the Advanced Microwave Sounding Unit. IEEE,
Transactions on Geosciences and Remote Sensing, 43, 136-1049
Joyce J. and coauthors, 2004: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data
at High Spatial and Temporal Resolution. J. Hydrometeor., 5, 487–503.
Kummerow C. and coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive
microwave sensors. J. Appl. Meteor., 40, 1801-1820.
Zhao L. and F. Weng. 2002: Retrieval of Ice Cloud Parameters Using the Advanced Microwave Sounding Unit. J. Appl. Meteor., 41, 384–
395.