The Status of the NOAANESDIS Operational AMSUMHS Precipitation Algorithm

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The Status of the NOAA/NESDIS Operational
AMSU/MHS Precipitation Algorithm

• Ralph Ferraro
• NOAA/NESDIS
• College Park, MD USA
• Wanchun Chen, Cezar Kongoli, Huan Meng, Paul
  Pellegrino, Daniel Vila,
• Nai-Yu Wang, Fuzhong Weng, Limin Zhao

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Outline

• Review of AMSU and operational algorithm
• Recent changes
• NOAA-18
• Coastlines
• Upcoming Improvements
• Ocean/emission rainfall
• LZA bias removal
• Snowfall rates
• Future
• METOP
• NPP
• MIRS

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NOAA AMSU Sensor

• AMSU is a cross-track scanning radiometer (unlike
  SSM/I, AMSR-E, TMI)
• AMSU-A (45 km nadir FOV)
• 15 channels
• 23, 31, 50 57 (13), 89 GHz
• AMSU-B (15 km nadir FOV)
• 89, 150, 1831,3,7 GHz
• MHS replaces AMSU-B on N-18
• 89, 157, 1831,3, 190.3 GHz

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NOAA Produces Operational Products from AMSU
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Characteristics of the NOAA AMSU Rain Rate
Algorithm

• Physical retrieval of IWP and De 89 150 GHz
• Use of other window and sounding channels
• Derive needed parameters for retrieval
• Filters for possible ambiguous surfaces
• Use of ancillary data other AMSU derived
  products
• IWP to RR based on limited CRM data and RTM
• RR A0 A1IWP A2IWP2
• 183 GHz bands used to identify deep convection
• Use another set of A coefficients
• 50 GHz bands used to identify snowfall over land

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Example of Global Real Time Data
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Example of Regional Retrievals
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Example of Monthly Data
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Continuous monitoring of algorithm performance
via IPWG validation sites
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Performance vs. GPCC (8/03 12/05)
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AMSU Summary/Limitations

• Land
• In general, performs well
• Too high in convective situations
• Regional biases (of course!), esp. too high in
  drier regimes
• Better sensitivity to lighter rain rates
• Falling snow detection (but not rates)
• Ocean
• Restricted to convective precipitation
• Overall, low due to missing precipitation without
  ice (generally lighter rain intensities)
• Rain coverage less than other sensors
• Conditional rain rates too high
• LZA bias in IWP
• Coastlines
• Not adequately handled
• View angle dependencies
• Larger FOV on scan edges results in varying rain
  rate distributions
• Unrealistic PDFs

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NOAA-18 MHS replaces AMSU-B

• NOAA, DMSP and METOP will operate POES
  constellation
• Changes include
• Microwave Humidity Sounder (MHS) instead of
  AMSU-B
• 157 GHz vs. 150 190.3 GHz vs. 1837
• MHS will fly on NOAA-N (18), -N and METOP
  (Successful launch 10/19)
• Synthetic NOAA-N 150 and 1837 GHz based on
  coincident measurements with NOAA-16
• Operational 29 Sep 2005

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Coastline Precipitation

• Most passive MW algorithms fail miserably in
  coastal regions
• Emissivity contrast between land and ocean
• Different physics package
• Imager approaches
• Extend land algorithm to coasts
• Bring in scattering rain types
• Correct TBs based on of FOV filled with land
• Computer expensive used in regional approaches
• Sounders
• Utilize channels that are mainly insensitive to
  surface

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Improved AMSU Coastline Algorithm

• Utilizes AMSU 53.6, 150, 1831, 3 and 7 GHz to
  identify potential rain and a synthetic IWP along
  coastlines
• Compute rain rate in same manner via IWP
• Also updated land/sea/coast tag
• Implemented into operations 7/31/06
• Substantially better retrievals with minimal
  false alarms

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OLD
NEW
RADAR
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Improvement of Oceanic Rain Rates and Removal of
Angular BiasesDaniel Vila (details at his poster)

• High oceanic bias attributed to unrealistic PDFs
• Function of LZA, problem traced to IWP retrieval
• Corrected via adjustment with SSM/I PDFs
• Oceanic rain coverage low
• Non-convergence of IWP/De algorithm in mostly
  light rain rates
• Corrected by adding in emission component
• Low bias at edge of scan due to large FOV

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Results April 2005
Current AMSU
Warm/shallow rain
Reduced ocean rainfall
Slight reduction over land
Corrected AMSU
SSM/I GPROF V6
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Zonal Mean Rain Frequency

• Significant improvement due to CLW

• Little change
• AMSUgtSSMI due to 150 GHz

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Zonal Mean Rain Amount

• Reduction in rainfall amounts,
• mostly in convective zones

• Corrected AMSU much closer to SSM/I

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Snowfall Rate Algorithm DevelopmentHuan Meng

• Use RTM to retrieve IWP under snow condition
• 1 layer two-streams
• Derive empirical equation connecting IWP with
  NEXRAD reflectivity
• Adopt an existing reflectivity-snowfall rate
  equation
• Derive snowfall rate from IWP

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Matching AMSU-B with Radar Data

• WSR-88D radar reflectivity (Z)
• Z is Quality controlled by QCNN (Quality Control
  Neural Network) algorithm
• Use radar data from the lowest elevation (0.4º)
  and within 100 km.
• Assume AMSU-B spatial sensitivity follows
  Gaussian function within an FOV when matching
  with radar data

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Retrieved AMSU IWP
Corresponding NEXRAD Reflectivity
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Regression between IWP and Z

• Data from 10 snow cases with 227 matching points
• 3rd order fit between IWP and Z
• ? 0.48

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Choice of Z-R Relationship?
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Choice of Z-R Relationship (2)

• Sekhon Srivastava (1970) has the smallest RMS
  with the 1-hr-lag observation
• Z 398 R2.21

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Example of Snow Rate Retrieval
Pro Catch basic snowfall patterns Con Miss snow
or underestimate high snowfall rate
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Next Steps

• Use more realistic snow characteristics in the
  RTM
• Explore approaches to classify snowfall type and
  utilize in snow rate retrieval.
• Add more snow cases to improve the accuracy of
  IWP-Z regression.
• Implement experimental retrievals for CONUS
  winter 2006-07

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Future for AMSU/MHS

• Implement near term algorithm improvements
• FOV biases
• CLW component for ocean rain
• Experimental snowfall rates over CONUS
• Longer term
• Improved AMSU physics package
• MIRS Microwave Integrated Retrieval System
• Temperature and moisture sounding
• Bayesian precipitation retrieval using O2 and H2O
  channels
• Reprocessing of entire AMSU time series
• Snowfall rates
• Transition to operations
• METOP-1 (Oct07)
• Jan/Feb 2007?
• Pipeline processing
• Prepare for NPP/ATMS

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Backup Slides
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http//www.orbit.nesdis.noaa.gov/corp/scsb/mspps/
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AMSU Climate Products shown in NatureMichael
Evans, April 2006
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Falling Snow over Land from AMSU

• Use of AMSU-B 183 GHz bands along with AMSU-A
  53.6 GHz allows for expansion of current
  algorithm to over cold and snow covered surfaces
• AMSU-B channels allow for detection of scattering
  associated with precipitation, but surface blind
  when sufficient moisture exists
• AMSU-A channel 5 allows for discrimination
  between rain and snow
• Feature added in 11/03, snowfall detection only
  (assigned arbitrary rate of 0.1 mm/hr)
• Validation over CONUS winter 2003-04

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Summary/Limitations

• Algorithm Performance
• Can detect snowfall associated with synoptic
  scale systems
• Low false alarms
• Can increase region of application by lowering
  TB54L threshold up to 5 K
• Some increase in false alarms
• Limitations
• Relative moist atmospheres - -5 to 0 C
• Southern extent of snow pack/temperate latitudes
• Precip layer needs to extend to 4-5 km or higher
• No signal in extreme cold climate regimes and
  shallow snow

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Retrieving Ice Water Path Using Radiative
Transfer Model

• One-layer two-stream RTM (Yan Weng, 2006, to be
  submitted to JGR)

I ice water path V total precipitable water De
cloud particle effective diameter Ts surface
temperature A derivatives of TBi over I, V, De,
Ts E error matrix TBi brightness
temperatures at 23.8, 31.4, 89, 150, and 1807 GHz

• Use iteration scheme. Iteration stops if ?TBi
  di (i 1, 5).
• Adopt fast one-layer RTM to meet near real-time
  operational requirement.

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Choice of Z-R Empirical Eq Contd

• Observation Data hourly snowfall observations
  from 12 weather stations
• Highest correlation coefficient between retrieved
  R and snowfall observations with 1-hr lag

Retrieved R represents snow in the atmosphere
because channel 1837 GHz is sensitive to 600
700 mb. The time lag represents the time it takes
the snow to fall to the ground.
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Application to AMSU Measurements
AMSU Precipitation Rates 2004 Nov 24
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The position of the peak in the histograms
(systematic bias), is corrected performing a
Gaussian PDF with µ (peak histogram position) and
? (standard deviation of observed distribution)
depending on LZA and latitude. For high rain
rates, a linear correction scheme with the slope
depending on SSM/I and AMSU-B footprint ratio is
proposed to normalize AMSU-B derived rain rates.
Unrealistic peak location
Inability to retrieve high rain rates for large
LZA
0-10 mm
10-30mm
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Adding Emission Component to Oceanic Rain
RR vs. CLW Proposed correction scheme
Mean AMSU-B derived rain rate for different CLW
values. CI is the convective index computed by
using the three 183 GHz channels. Satellite NOAA
15 - 60N-60S Year 2005.
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Results April 2005
Monthly mean absolute bias (upper panel) and RMSE
(bottom panel) for UNCORR and CORR algorithm
compared with SSM/I GPROF V6.0 estimates for
2005.
Mean rain rate of AMSU-B NOAA-15 operational
derived rain rates (mm/day) (upper panel),
corrected values (middle panel) and mean derived
rain rates (mm/day) for SSM/I F-13 GPROF v6.0 for
April 2005.