the status of noaa/nesdis precipitation algorithms...

11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 1

The Status of NOAA/NESDIS Precipitation Algorithms and Products

Ralph FerraroNOAA/NESDIS

College Park, MD USA

S. Boukabara, E. Ebert, K. Gopalan, J. Janowiak, S. Kidder, R. Kuligowski, H. Meng, M. Sapiano, H. Semunegus, T. Smith, A. Sudradjat, D. Vila,

N‐Y. Wang, F. Weng, L. Zhao


Outline•

Operational products–

GOES‐based products

–

POES‐based products

–

Blended products

•

Validation efforts

•

Non‐NOAA products

•

Climate products

•

Summary and Future


NESDIS Operational Precipitation Products

Applications Current Capability Future CapabilityMSPPS AMSU Rain Rate, TPW, CLW, etc.

(NOAA‐15*, ‐16, ‐18, ‐19 and Metop‐A)Extended to include Metop‐BMIRS will be the upgrade of MSPPS as NOAA enters

to NPP, JPSS, and GPM era.

MIRS AMSU Rain Rate, TPW, CLW, etc.(NOAA‐18, ‐19 , DMSP F16, Metop‐A)

AMSU/MHS Rain Rate (DMSP F18, F19, Metop‐B) ATMS Rain Rate (NPP, JPSS)GPM Rain Rate (M‐T, GMI)

Hydro‐

Estimator

Instantaneous, 1‐hr, 3‐hr, 6‐hr and 24‐hr rainfall

estimate over CONUS (GOES‐11 and ‐13)Extended to include multi‐day rainfall estimate.Extended coverage from CONUS to globalScaMPR

will be the upgrade of HE (GOES‐R)

SCaMPR Under development 1‐hour, 6‐hour and 24‐hour rainfall total over

CONUS (GOES‐R)

Blended

Hydro

Blended TPW and TPW percentage of normal products(NOAA‐15, ‐16, ‐17, ‐18, ‐19, Metop‐A, GPS, GOES,

DMSP F13* , F14*, F15*) Blended RR products(NOAA‐15, ‐16, ‐18, ‐19, Metop‐A, DMSP F16, F17)

Blended TPW and TPW percentage of normal(Extended to include: DMSP F16, F17, F18 NPP, M‐T,

GCOM‐W, JPSS, GOES‐R)Blended RR products(Extended to include: DMSP F18, NPP, M‐T, GCOM‐

W, GMI, JPSS)

eTRAP

Deterministic rain amount QPFs

and probabilistic POP

forecasts for each of four 6h time periods (e.g., 00‐06h,

06‐12h, 12‐18h, 18‐24h) as well as the 24 hour

cumulative time period.(Rain Rate from NOAA‐15, ‐16 , ‐18, ‐19 and Metop‐A,

TRMM TMI, Aqua AMSR‐E)

Extended to include: F16, F17, F18, HE, NPP, M‐T,

GCOM‐W, JPSS, GMI

V14.2 12 Jan 2010

Courtesy of L. Zhao


GOES‐based Short‐Term Rainfall Products•

Current:–

Hydro‐Estimator•

IR‐only plus adjustments using NWP

model data

•

Operational over CONUS; global

experimentally

–

Experimental SCaMPR•

Multi‐spectral IR calibrated against

MW

•

Currently CONUS‐only•

GOES‐R (2016+) Era:–

Rainfall Rate•

Modification of SCaMPR

with

additional spectral bands

–

0‐3 h Rainfall Potential•

Extrapolation‐based nowcast–

0‐3 h Probability of Rainfall•

Conditional probabilities based on

rainfall nowcasts

Hydro‐Estimator

SCaMPR

Rainfall Rate

Rainfall PotentialRainfall

Probability

Courtesy of R. Kuligowski


POES‐based L2 and L3 products•

Current:–

MSPPS•

“Heritage”

AMSU algorithms utilizing

high frequency and H2O channels

–

Snowfall identification over land•

Some “fixes”

for aging sensors•

Other products like TPW, CLW, etc.•

Global•

L2 and L3 products–

MIRS•

1DVAR scheme–

T, RH, hydrometeor profiles, TPW,

CLW, emissivity, etc.•

Portable to variety of sensors•

Global•

Primary POES + DMSP•

JPSS era:–

MIRS for NPP/ATMS, JPSS/ATMS•

MSPPS will be phased out–

GCOM AMSR‐2?

MSPPS Rain Rate

MSPPS TPW

MSPPS Climatology

Courtesy of L. Zhao, S. Boukabara, H. Meng, D. Vila

MIRS Rain Rate MIRS WV Profiles


AMSU/MHS Snowfall Rate Algorithm

•

Retrieve Ice Water Path

from passive microwave

measurements from

AMSU/MHS and RTM

•

Calculate ‘cloud depth’

from NWP T and V profiles

•

Derive snowfall rate

•

Image sequence of the US

Mid‐Atlantic snowstorm

on Feb 5‐6, 2010 (left:

satellite retrieval; right:

NEXRAD reflectivity)

SFR (mm/hr)

Courtesy of H. Meng


Blended Hydrological Products•

To support weather

forecasters (AWIPS), NOAA

moving towards integrated

products–

Better products that are

“transparent”

to forecaster–

Makes forecaster more efficient–

Optimizes computer resources

•

Two primary products–

TPW•

SSMI/SSMIS; AMSU; (AMSR‐E

and TMI)

–

Precipitation Rate•

SSMI/SSMIS and AMSU•

Developing synergy with

CMORPH/QMORPH

–

Data latency is key driver

Courtesy of S. Kidder and L. Zhao


Ensemble Tropical Rainfall Potential (eTRaP)

•

Forecast of 24‐hour rainfall

potential for tropical systems about

to make landfall.•

Based on extrapolation of

microwave‐derived rainfall rates

along predicted storm track.•

Ensembles improve deterministic

forecasts and provide uncertainty

information•

Additional ensemble members

(SSMIS, HE) plus orographic, shear,

storm rotation adjustments planned•

Produced worldwide and available

via the Internet:

http://www.ssd.noaa.gov/PS/TROP/etrap.html

QPFEM P≥50 mmQPFPM

P≥100 mm P≥150 mm P≥200 mm

18 UTC / 23 ‐

00 UTC / 24

00 – 06 UTC / 24

06 – 12 UTC / 24

12 – 18 UTC / 24

Courtesy of R. Kuligowski and E. Ebert

http://www.ssd.noaa.gov/PS/TROP/etrap.html


Courtesy of J. Janowiak and D. VilaNESDIS Satellite Product “Swath”

Validation over U. S.

•

Based on IPWG heritage, NESDIS

providing funding to sustain/enhance

this activity and gear it towards

supporting operational and emerging

algorithms–

MSPPS, MIRS, HE, SCaMPR, etc.–

Evaluated MIT/Staelin

past year•

Validation is performed on

ensembles of swath data

against

NCEP “Stage IV”

radar/gauge data–

Swath products matched to within +30

minutes from ground data

•

Going one step beyond…–

Quarterly reports generated and

delivered to NESDIS Precipitation

Product Oversight Panel


Non‐NOAA Related Products•

DMSP SSMI/SSMIS–

“Vintage”

EDR’s

developed at

NESDIS•

TRMM TMI–

Leading the GPROF‐land

efforts–

New V7 algorithm developed•

Reduces warm season bias

compare to PR V6

•

AMSR‐E–

Same role as in TRMM–

Also prototyping new surface

classification methodology•

Through ancillary data,

eliminates Grody‐Ferraro ‘era’

static screening methods

Courtesy of N-Y. Wang, K. Gopalan, A. Sudradjat

Longitude (deg)

TMI v6 - PR bias (mm/month)

-150 -100 -50 0 50 100 150-50

0

50

-150

-100

-50

0

50

100

150

Longitude (deg)

TMI regression - PR bias (mm/month)

-150 -100 -50 0 50 100 150-50

0

50

-150

-100

-50

0

50

100

150


Climate Products

•

SSM/I–

Legacy products for GPCP

–

Transitioning to NCDC•

SSMIS extension

•

New QC scheme

•

NOAA/NCDC CDR program–

SSMI FCDR’s (CSU lead)

–

AMSU FCDR’s & TCDR’s (NESDIS)

•

Other time series–

CHOMPS

–

Reconstructions

Courtesy of D. Vila, T. Smith, H. Semunegus, M. Sapiano


1. Normalized anomaly (z score) or deviations from climatology calculated to determine data quality

2. Temperature and geolocation threshold checks for each footprint antenna temperature calculated

3. Complete 1987-present record and embedded quality flags in netCDF-CF

4. All SSM/I antenna pattern correction coefficients detailed in paper (previously not publicly available)

5. Channel time series analysis for each platform6. Pre-cursor to NOAA FCDR dataset for

customers

Extended and Improved SSM/I Period of Record (Semunegus et al., 2010)


SSMIS Continuity

•

SSM/I useful data period

ended in 2009–

Need to extend into SSMIS;

main difference is 91 vs. 85 GHz

•

Colocated

data–

Establish empirical relationship

between 85 and 91 GHz•

2006‐07 timeframe between

F15 & F16

–

Results indicate that method is

adequate for•

both orbital and monthly scale

products

•

Methodology extended to F17

satellite

–

See Vila et al. 2010

SSMI/T/T2 SSMI/S Freq. (Ghz) ./ Polarization.

Footprint (km)

Freq. (GHz) ./ Polarization

Footprint (km)

19.350 / H & V

43 x 69

19.350 / H & V

73 x 47

22.235 / V

40 x 60

22.235 / V

73 x 47

37.000 / H & V

28 x 37

37.000 / H & V

41 x 31

85.500 / H & V

13 x 15

91.655 / H &V

14 x 13 (imager)

Courtesy of D. Vila


High‐Resolution Precipitation Analysis: CHOMPS•

CHOMPS–

Cooperative Institute of

Climate Studies (CICS) High‐

resolution Optimally

interpolated Microwave

Precipitation from Satellites

•

All available passive MW satellites used

–

Minimize time of day biases

with more sampling

–

Reprocess MW radiances

with most up‐to‐data

algorithm

–

Builds product off of hourly

gridded fields for each

sensor type

•

1998 ‐

2008

Courtesy of T. Smith


Develop improved historical precipitation reconstruction (1900-2008), merging recently-developed reconstructions based on satellite-era statistics and historical data (see Smith et al. 2010 J. Clilmate)

Merged Reconstruction• EOF reconstruction (REOF), fits

precipitation anomalies to a set of EOFs• REOF(Blend) historical REOF from

gauges and updates from REOF(GPCP) in recent years

• Less sampling in early 20th century, may make problem with multi- decadal signal

• CCA reconstruction (RCCA) uses SST & SLP historical predictors

• Multi-decadal change consistent with theoretical AR4 estimate

• Merge oceanic low-pass RCCA with oceanic high-pass REOF(Blend)

Precipitation ReconstructionsCourtesy of T. Smith and M. Sapiano


Summary and Future•

NOAA generates several operational precipitation products–

GOES‐based–

POES‐based–

Emerging blended products…

•

NOAA also actively involved in other missions–

DMSP–

NASA –

TRMM, AMSR‐E

•

NOAA has growing Climate Data Record Program•

Future–

GOES – SCaMPR/GOES‐R•

Enhancements to bring in lightning data

–

POES – NPP and JPSS•

MSPPS to MiRS

–

Blended Products!–

CDR’s

the status of noaa/nesdis precipitation algorithms...

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