the status of the noaa/nesdis operational amsu/mhs precipitation algorithm

23-27 October 2006 3rd IPWG Melbourne, Australia

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

Algorithm

Ralph FerraroNOAA/NESDIS

College Park, MD USA

Wanchun Chen, Cezar Kongoli, Huan Meng, Paul Pellegrino, Daniel Vila, Nai-Yu Wang, Fuzhong Weng, Limin Zhao


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


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, 183+1,3,7 GHz

•MHS replaces AMSU-B on N-18•89, 157, 183+1,3, 190.3 GHz

Satellite Launch Date LTAN

NOAA-15 13 May 1998 1735

NOAA-16 21 September 2000 1529

NOAA-17 24 June 2002 2218

NOAA-18 20 May 2005 1342

METOP-1 19 October 2006 2130


NOAA Produces Operational Products from AMSU


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 + A1*IWP + A2*IWP2

• 183 GHz bands used to identify deep convection– Use another set of ‘A’ coefficients

• 50 GHz bands used to identify snowfall over land


Example of Global Real Time Data


Example of Regional Retrievals


Example of Monthly Data


Continuous monitoring of algorithm performance via IPWG validation sites


Performance vs. GPCC (8/03 – 12/05)

Mean Mean Mean Bias Bias R R

SSMI AMSU GPCC SSMI AMSU SSMI AMSU

60S-60N 60.9 66.5 75.6 0.80 0.88 0.64 0.69

40S-40N 72.0 76.4 80.5 0.89 0.95 0.70 0.74

20S-20N 102.1 119.3 129.3 0.72 0.94 0.81 0.81

Zonal Mean Land Rainfall (1/04 - 12/05)

0

20

40

60

80

100

120

140

160

-45 -35 -25 -15 -5 5 15 25 35 45 55

Latitude

Rai

nfa

ll (

mm

)

SSMI

AMSU

GPCC


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 PDF’s


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. 183+7

– MHS will fly on NOAA-N (18), -N’ and METOP (Successful launch 10/19)

• Synthetic NOAA-N 150 and 183+7 GHz based on coincident measurements with NOAA-16

• Operational 29 Sep 2005


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” TB’s based on % of FOV filled with land• Computer expensive; used in regional approaches

• Sounders– Utilize channels that are mainly insensitive to surface


Improved AMSU Coastline Algorithm

• Utilizes AMSU 53.6, 150, 183+1, 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


OLD NEW

RADAR


Improvement of Oceanic Rain Rates and Removal of Angular Biases

Daniel Vila (details at his poster)

• High oceanic bias attributed to unrealistic PDF’s– Function of LZA, problem traced to IWP retrieval

• Corrected via adjustment with SSM/I PDF’s

• 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


Results – April 2005

Current AMSU

Corrected AMSU

SSM/I GPROF V6

Warm/shallow rain

Reduced ocean rainfall

Slight reduction over land


Zonal Mean Rain Frequency

•Little change•AMSU>SSMI due to 150 GHz

•Significant improvement due to CLW


Zonal Mean Rain Amount

•Reduction in rainfall amounts,mostly in convective zones

•Corrected AMSU much closer to SSM/I


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


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


Retrieved AMSU IWP

Corresponding NEXRAD Reflectivity


Regression between IWP and Z

• Data from 10 snow

cases with 227

matching points

• 3rd order fit between

IWP and Z

= 0.48


Choice of Z-R Relationship?IWP-Snowfall Rate Empirical Equations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.5 1 1.5 2 2.5 3

IWP (kg/m2)

R (

mm

/hr)

|

Carlson&Marshall

Vasiloff

Ohtake&Henmi

Imai

Puhakka

Boucher&Weiler

Sekhon&Srivastava

Fujiyoshi


Choice of Z-R Relationship (2)• Sekhon & Srivastava (1970) has the smallest

RMS with the 1-hr-lag observation:

Z = 398 R2.21

C&M VAS OHT IMA PUH B&W S&S FUJ

0.5140 0.4084 0.3722 0.3735 0.3532 0.3522 0.3514 0.3872


Example of Snow Rate Retrieval

Lancaster, OH, Feb 15 - 17, 2003

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1000 2000 3000 4000 5000Time (min)

Sn

ow R

ate

(mm

/hr)

| Obs 1-hr late

Retrieval

Newark, OH, Feb 15 - 17, 2003

0

0.2

0.4

0.6

0.8

1

1.2

0 1000 2000 3000 4000 5000Time (min)

Sn

ow R

ate

(mm

/hr)

|

Obs 1-hr lateRetrieval

Pro: Catch basic snowfall patterns

Con: Miss snow or underestimate high snowfall rate


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


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


Backup Slides


http://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/


AMSU Climate Products shown in NatureMichael Evans, April 2006


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


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


Retrieving Ice Water Path Using Radiative Transfer Model

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

7180

150

89

31

23

1)(

B

B

B

B

B

TT

S

e

T

T

T

T

T

AEAA

T

D

V

II: 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 180±7 GHz

• Use iteration scheme. Iteration stops if ΔTBi ≤ δi (i = 1, 5).

• Adopt fast one-layer RTM to meet near real-time operational requirement.


Choice of Z-R Empirical Eq – Cont’d• Observation Data: hourly snowfall observations from 12 weather stations• Highest correlation coefficient between retrieved R and snowfall

observations with 1-hr lag:

0-hr late 0.5-hr late 1-hr late 2-hr late 3-hr late 4-hr late

0.1281 0.2370 0.3239 0.2898 0.2122 0.1921

Retrieved R represents snow in the atmosphere because channel 183±7 GHz is sensitive to 600 – 700 mb. The time lag represents the time it takes the snow to fall to the ground.


Application to AMSU Measurements

Radar ReflectivityAMSU Precipitation Rates 2004 Nov 24


0-10 mm 10-30mm

Unrealistic peak location Inability to retrieve high rain rates for large LZA

The position of the peak in the histograms (systematic bias), is corrected performing a Gaussian PDF with µ (peak histogram position) and б (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.


Adding Emission Component to Oceanic Rain

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.

RR vs. CLW: Proposed correction scheme


Results – April 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.

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.

the status of the noaa/nesdis operational amsu/mhs precipitation algorithm

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