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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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 2
Outline•
Operational products–
GOES‐based products
–
POES‐based products
–
Blended products
•
Validation efforts
•
Non‐NOAA products
•
Climate products
•
Summary and Future
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 3
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 4
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 5
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 6
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 7
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 8
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 9
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 10
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 11
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 12
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)
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 13
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 14
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 15
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
11‐15 October 2010 5th IPWG ‐ Hamburg, Germany 16
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