Update’on’NOAA Precipita1on ... - gpm.nasa.gov

Update on NOAA Precipita1on Algorithms and Products and their Contribu1on to GPM

Ralph Ferraro* NOAA/NESDIS/STAR College Park, MD

*Includes contribu1ons by other PI’s on the PMM Science Team, other NOAA researchers and those at ESSIC/CICS at the University of Maryland

4-‐7 August 2014 2014 PMM Science Team Mee1ng – Bal1more, MD 1

4-‐7 August 2014 2014 PMM Science Team Mee1ng – Bal1more, MD

NOAA Needs, Ac1vi1es and Status on GPM

•  R2O and O2R •  Advocacy and Educa1on •  Specific user request and early data prepara1on

2


NOAA User Workshops on GPM

•  Three workshops (2010, 2011, 2013) have helped NOAA: –  Provide advocacy and education about GPM’s relevance to NOAA –  Determine specific user needs to support NOAA core mission goals –  Focus potential R2O for PPS and utilization of existing NOAA Proving Grounds and Testbeds to

accelerate use of GPM data at NOAA •  Fourth workshop – TBD (2015?) or do jointly with NASA?


•  At a minimum, con.nuity of opera.ons for TRMM… •  NWP/JSCDA needs include:

–  Extending CRTM to GMI –  Timely access to GMI L1 data and conversion to BUFR (done in house)

–  “All Weather” use into data assimila.on •  NWS top priori.es for GMI data include:

–  GMI imagery in AWIPS for NHC and related centers –  2A12/L2 precipita.on rates for NWSFO, CPC, etc.

•  NESDIS also requires L1/L2 to enhance “NOAA Unique Products” delivered in McIDAS and AWIPS

•  NWS/OHD u.lizes GOES-‐based (MW enhanced) ScAMPR product which requires the L2 GPM data

–  IMERGE for NWS/OHD, CPC, etc.

Ini1al NOAA Needs for GMI Data

2014 PMM Science Team Mee1ng – Bal1more, MD 5 4-‐7 August 2014

•  Ini1al Implementa1on –  Pull GPM GMI L1B/L1C and GMI L2 data from the NASA PPS sever –  Integrate the L1 and L2 products to provide McIDAS tailoring products –  Distribute the GPM GMI L1 and L2 data to users through DDS

•  JCSDA is taking care of the BUFR reforma^ng to meet NWP needs

–  Distribute the GPM GMI L1B, RR and TPW in McIDAS through ADDE sever and/or DDS depending on users’ requests.

•  Future Implementa1on

–  Follow “implementa.on protocol” (annual funding cycle) to fully meet NOAA users’ unique needs on the GPM-‐related data and products

–  Eventually transi.on to NOAA Enterprise PPS through the NESDIS Enterprise Ground System effort (s.ll evolving)

Current NESDIS GPM Data Plan

2014 PMM Science Team Mee1ng – Bal1more, MD 6 4-‐7 August 2014


Update on NOAA Satellites, Partner Satellites and Precipita1on Products

•  POES/JPSS & GOES – satellites and products •  NASA – GPM •  Interna1onal Partners – GCOM-‐W1 AMSR-‐2, M-‐T SAPHIR

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LEO Flyout Schedule

hap://www.nesdis.noaa.gov/FlyoutSchedules.html hap://www.jpss.noaa.gov

GPM-‐era


NESDIS Opera1onal Hydrological Satellite Products

Geosta1onary (Regional, rapid update) Low Earth Orbi1ng (Global, 3-‐6 hourly)

Visible, IR and WV loops Visible, IR and microwave imagery

Rain Rate Rain and Snowfall Rate

Total Precipitable Water – TPW (cloud free) TPW (all weather; ocean only in some cases)

Snow and Ice Cover Snow cover/water equivalent/ice concentra.on

Soil Moisture

Blended Products

Blended TPW (with LEO, GPS Met and GEO data) and Rain Rate (LEO)

Ensemble Tropical Rainfall Poten.al (eTRaP)

Snow and Ice Maps (IMS)

Global Soil Moisture Map (SMOPS)

9 4-7 August 2014 2014 PMM Science Team Meeting – Baltimore, MD

Opera1onal Product Suites


System Products Satellites/Sensors Res Type Formats MSPPS Rainfall rate, Snowfall rate,

TPW, CLW, etc NOAA-‐18&NOAA-‐19&Metop-‐A & Metop-‐B /AMSU-‐A&MHS

16 km Level-‐2, Level-‐3

HDF-‐EOS, McIDAS area, PNG

MiRS Rainfall rate, TPW, CLW, etc NOAA-‐18 & NOAA-‐19 & Metop-‐A & Metop-‐B; DMSP F18; S-‐NPP /AMSU-‐A&MHS; /ATMS; /SSMIS

16 & 45 km Level-‐2, Level-‐3

HDF-‐EOS,netCDF4, McIDAS area, PNGs

GHE Rainfall rate, mul.-‐hours and mul.-‐ days rainfall total

GOES-‐E & GOES-‐W & MTSAT & Meteosat-‐7 & Meteosat-‐10 IR Imager

4 km Level-‐3 netCDF4, McIDAS area, GRIB1/GRIB2, GIFs

bTPW Global Total Precipitable Water Map

NOAA-‐18, NOAA-‐19, Metop-‐A and Metop-‐B /AMSU-‐A&MHS, GOES-‐W/-‐E, GPS-‐Met, DMSP F18/SSMIS

16 km Level-‐4 HDF-‐EOS, McIDAS area, AWIPS, PNGs

bRR Global Rainfall Rate Map NOAA-‐18, NOAA-‐19, Metop-‐A and Metop-‐B /AMSU-‐A&MHS, DMSP F18/SSMIS

16 km Level-‐4 HDF-‐EOS, McIDAS area, AWIPS, PNGs

eTRAP Prob-‐matched QPF, Probability NOAA-‐18, NOAA-‐19, Metop-‐A and Metop-‐B /AMSU-‐A&MHS, GOES-‐W/-‐E, DMSP F17, F18/SSMIS

4 km Level-‐3 ASCII, McIDAS area, GIFs

SMOPS Global Soil Moisture Map Metop-‐A/ASCAT, Coriolis/Windsat, SMOS 0.25 degree

Level-‐4 netCDF4, GRIB2, GIFs

GPDS Rainfall Rate, TPW, CLW, etc. GCOM-‐W AMSR2 5 -‐ 30 km Level-‐2,

Level-‐3

netCDF4, McIDAS

MToPS Rainfall Rate and TPW (MiRS retrieval)

M-‐T SAPHIR 10 km Level-‐2,

Level-‐3

netCDF4, McIDAS

•  NASA –  Formal MOU on GPM signed 9/30/13 –  R20 Transi1on Planning con1nues –  Early adopters for GPM data –  PMM Science Team

•  MOU can help get a “few more things” done on the NOAA side to support the team…

•  Interna1onal: –  Japan – GCOM-‐W1

•  Part of JPSS program, real-‐1me products imminent from NOAA (GPROV2010V2)

–  India/France (EUMETSAT) -‐ M-‐T •  SAPHIR only MiRS (TPW, rain rate) •  Awai1ng finaliza1on of data agreement •  SAPHIR – ATMS cross calibra1on

Partners & Status


Opera1onal Product Examples


Products are available to PMM team: •  Comparison purposes •  Day-‐1 proxies, if

needed (we can provide code)

•  As poten1al input for

IMERGE

ATMS Snowfall Rates – Direct Broadcast

AMSR-‐2 Rainfall Rates

ATMS Rainfall Rates

Courtesy of P. Meyers, T. Islam. H. Meng

Poster 104 Poster 121

Progress on GMI DA at JCSDA JCSDA effort focused on assimila1on of Level 1C-‐R brightness temperatures (or 1B-‐R if available) in NCEP GDAS/GFS –  BUFR encoder wrijen for 1C-‐R HDF5

data –  Ingest into GSI ongoing –  Currently assessing data quality (bias,

standard devia1on, GSI QC/filtering)

Extension of GMI to GSI 1DVAR preprocessor (MIIDAPS)

–  Provide surface/atmospheric characteriza1on for surface sensi1ve/non ocean and cloudy radiances

–  Provide QC, filtering for GMI data –  Focus on resampled data to u1lize all-‐

channels in 1DVAR Figure 2. MIIDAPS applied to GMI Level 1C-‐R showing a) Rain, b) Ice, and c) ChiSq quality control flag over Hurricane Arthur on July 3, 2014.

a)

b)

c)

Figure 1. Comparison of GMI Channel 5 (23.8 GHz) observed versus simulated brightness temperatures using GFS 6-‐hour forecast and CRTM for a) scaaer plot obs vs. sim and b) O-‐F versus GMI scan posi.on with mean bias (red) and standard devia.on (dashed red).

a) b)

Poster 204 Courtesy of K. Garre9, E. Jones, S. Boukabara


MiRS SAPHIR Rain vs. TRMM 2A12 March – August 2013


Courtesy of T. Islam

AMSR-‐2 vs. GMI •  G14 V1.4/2A12 V03B; G10V2

using AMSR-‐2 L1B •  Ini1al assessment

–  Ocean very similar, as expected –  General agreement for spa1al

extent of precip –  G10V2 creates a larger area

with high precipita1on for convec1ve systems

–  G14 has more focused area of convec1on

–  G14 creates lighter precipita1on in stra1form regimes

•  G10V2 typically has minimum rain rate near 1 mm/hr

–  G14 has less low-‐end noise – Possibly due to L1C use


Poster 104

Courtesy of P. Meyers 23 July 2014


Science Team and Related Ac1vi1es

•  PMM Science Team •  GOES-‐R and JPSS PGRR Ac1vi1es •  AMSU/MHS Climate Data Records •  Valida1on Ac1vi1es

16

•  NASA-‐NOAA Algorithm Synergy & NWP Impact Assessment In support of GPM –  S. Boukabara (PI), K. Garre9, V. Tallapragada (co-‐PI), In-‐Hyuk Kwon, Erin Jones

•  NOAA GPM Proving Ground and U1liza1on for HMT-‐SEPS –  R. Cifelli, S. Rudlosky, R. Ferraro, P. Xie

•  Contribu1ons to the MW-‐RE Precipita1on over Land Algorithm –  R. Ferraro, N-‐Y. Wang, Y. You, H.Meng

•  WiMerge: Research and Development of Unified CONUS 3-‐D Mosaics and QPE products –  J. Gourley, Y. Zhang, P. Xie, D. Kitzmiller, B. Kuligowski, P. Kirste9er

•  Characteriza1on of Precipita1on Field in High La1tudes of the Northern Hemisphere for the Future Use with the GPM Mission Products for Hydrological & Climate Change Assessments

–  P. Groisman, D. Easterling, B. Nelson, D. Yang, V. Alexeev, et al.

•  Calibra1on of GMI Sounding Channels and Global Detec1on of Radio Frequency Interference

–  Fuzhong Weng (NESDIS/STAR), Xiaolei Zou (FSU) and Tiger Yang (ESSIC/UMD)

•  Pole-‐to-‐Pole CMORPH and Integrated Regional Precipita1on Analyses –  P.Xie and R.Joyce

•  Analysis and Valida1on of GPM in LAPS Data Assimila1on System –  Y. F. Xie, S. Albers, S. Gutman, D. Birkenheuer, H. L. Jiang, and Z. Toth

•  Contribu1ons to GPM at NOAA NWS/OHD & NESDIS/STAR – Data Fusion and Applica1ons –  Y. Zhang, J. Gourley, R. Kuligowski, D. Kitzmiller, P. Xie

NOAA Projects for PMM

17 2014 PMM Science Team Mee1ng – Bal1more, MD 4-‐7 August 2014

Nine subtasks submi9ed as a single, no-‐cost to NASA, proposal

Poster 115

Poster 118

Poster 204

Posters 213&214

Wed 920 am

Wed 1050 am

Data Fusion

Satellite

Radar Gauge

Best QPE

NOAA GPM Proving Ground and HMT-‐SEPS: QPE Research

2014 PMM Science Team Mee1ng – Bal1more, MD 18

• Purpose: • Test new algorithms and products •  Evaluate product performance •  Facilitate exchange of GPM products within NOAA

•  Infrastructure: • Ground based instrumenta.on • Computer networks •  NOAA personnel

• Strategy for success: • PG will combine resources across NOAA (NWS, NESDIS, and OAR)

•  leverage NOAA’s testbed infrastructure, including HMT-‐SEPS

Quan1ta1ve Precipita1on Es1ma1on (QPE) •  HMT-‐SEPS provides opportunity to test, evaluate, and compare QPE as well as opportunity to improve QPE algorithms

•  Goal: develop best possible QPE forcing for opera.onal users

Courtesy of R. Cifelli

4-‐7 August 2014

Seamless Merging of GOES-‐R, GPM, and Ground Radar Derived QPE for the MRMS System

Distribu.on of VPRs/hydrometeors from GPM and MRMS

Distribu.on of surface rainfall rates from MRMS

ALTITU

DE

GOES-‐R observa.ons near cloud top + GLM

Probability Matching of GOES Cloud Proper.es to Surface Rain Rates

GOES Coverage in Ground Network Gaps

GPM

Weight toward best available sensor for seamless coverage

The Mul.-‐Radar/Mul.-‐Sensor System (MRMS) §  hjp://mrms.ou.edu §  Opera1onal at NCEP: 10/2014 §  Resolu1on over CONUS:

§  0.01° lat x 0.01° long §  2 min update cycle §  33 ver.cal reflec.vity levels

§  Real-‐1me QPE Products: §  Instantaneous precip type/rate §  Radar QPE (1 hr – 10 day accums) §  Gauge QPE §  Local gauge-‐adjusted radar QPE §  Orographic climatology gauge QPE (Mountain

Mapper)

Mo1va1on: §  Ground sensor (radar and gauge) coverage is limited

in Western U.S., especially in cool season. §  Need real-‐.me detec.on of heavy rainfall in data

sparse areas for flash flood forecasts and warnings

§  Ground radar networks, GPM, and GOES-‐R provide complementary observa.ons

Heather Grams and Pierre Kirstetter (CIMMS, University of Oklahoma), "Jonathan J. Gourley and Robert Rabin (NOAA/OAR/NSSL)"

Wed 920 am

A Revised Low-‐Latency, GPM-‐Ready Precipita1on Algorithm: Layout and Preliminary Evalua1on

Robert J. Kuligowski, Yu Zhang, Yaping Li, and Yan Hao

•  NESDIS has developed an IR-‐based, MW-‐calibrated rain rate algorithm with very short latency (minutes)

•  Previous PMM Science Team work demonstrated improvements from adding TRMM data for calibra1on

•  Numerous improvements to the algorithm since then in prepara1on for use on GOES-‐R

•  Currently evalua1ng the impact of these improvements on a mul1-‐year reprocessed data set

•  Longer-‐term: integrate into a mul1-‐sensor (satellite-‐radar-‐gauge) product

Less false alarm rainfall in improved algorithm

Higher skill at all intensi1es in improved algorithm


Pole-‐to-‐Pole CMORPH and GOES-‐enhanced Regional Precipita1on Analyses

P.Xie and R.Joyce §  Pole-‐to-‐pole CMORPH

§  0.05olat/lon over the globe pole-‐to-‐pole with explicit representaRon of snowfall

§  Tests with March 2014 data showed very good results

§  GOES-R Enhanced Regional CMORPH

§  Enhanced by precip estimates from GOES-R IR+ observations

§  Refined space / time resolution (2km / 15-min)

§  Products comprised of different latencies (15?-min to 18 hours)

MWCOMB (top); PMW rain + snow (middle); Stage IV (bottom) 09:00 UTC 3 March 2014 [mm/hr]

Synthe1c GOES-‐R enhanced regional CMORPH precipita1on es1mates for July 1, 2013, produced on a 4km resolu1on using satellite PMW and GOES IR data available at a latency of 30 minutes.


ESRL/GSD/FAB Progress

Dan Birkenheuer, Yuanfu Xie, Kirk Holub

•  GOAL: Assess impact of GPM radar data in analysis and modeling (vLAPS and WRF)

•  Case selected – TRMM proxy test data for GPM model impact (see Figure on right)

•  Obtained TRMM data converted to Cartesian coordinate system for ingest (courtesy Bob Morris NASA).

•  Discovered more favorable op.on to recode Bob’s IDL sotware to populate full vLAPS/WRF domain with TRMM reflec.vi.es. Current work effort.

•  Will run case with and without TRMM data and include ground radar in both. Desire to see posi.ve impact adding satellite radar data.

•  Possibly select a mountain case if funding permits. Since we now have a beaer means to iden.fy cases ater working with Bob Morris.


Case selected for first impact study showing TRMM proxy overpass with perfect storm coverage 26 Jul 13. Convec1ve line progressing across the FL peninsula from NW to SE. Satellite caught radar echoes off shore. Next case planned – mountain convec1on in which there is lijle or no ground radar available.

GOES-‐R & JPSS Risk Reduc1on Programs


•  GOES-‐R (S. Goodman) –  Sensors useful for rapid refresh precipita.on es.ma.on

and storm monitoring •  ABI & GLM

–  Ongoing projects of interest include •  Use of GLM to improve PMW and ABI rainfall •  Fusion of ground radar and IR rain es.mates •  Also funds NOAA PMM PI’s

–  Contribu.ons/interest in GPM GV •  Ground ligh.ng sensors in support of CHUVA

•  JPSS (M. Goldberg) –  Key sensors to precipita.on

•  ATMS, AMSR-‐2, (SSMIS), (GMI) –  Ongoing projects of interest include

•  ATMS snowfall rates •  CMORPH enhancements with SFR

–  Also emphasizes use of direct broadcast data •  Proving Grounds and Testbeds

–  These accelerate the use of new products to NWS –  PG’s at NCWCP (and an emerging one at CICS-‐MD) can help

accelerate GPM product uses/applica.ons •  Willing to work closely with NASA on GPM •  This was one of the key recommenda.ons from the NOAA GPM User

Workshops

Minute Lightning Density with 2 Long-‐track Tornados

DCLMA Applica.ons – Synergy from GOES-‐R ABI & GLM, & JPSS GCOM AMSR2


Lightning Flashes Each Second Overlaid on AMSR2 Precipita1on

P. Meyers, Univ. of MD/CICS S. Rudlosky, NESDIS/STAR N. Wang, CICS, IMSG

Mo1on Vectors from Lightning Density


Lightning-‐Advected Rain Rates from AMSR2


AMSU/MHS Climate Data Records

•  Four year project funded by NOAA/NCDC nearing comple.on –  AMSU-‐A and AMSU-‐B/MHS FCDR’s

(L1C) for window channel –  2000-‐2010, all satellites –  Final delivery by end of 2014

•  Data would be available to NCDC •  Beta data sets now available

–  Will also generate L2 products via MSPPS “legacy” system

•  Rain and snow rate should be of interest to the IMERGE team

•  Would like to conduct impact studies

•  NCDC to con.nue to fund during 2-‐3 year transi.on period –  Should be able to extend data sets

through 2015 •  Looking to extend to NPP/ATMS

–  Would allow for reprocessing of en.re MiRS .me series


H. Meng, T. Smith, NESDIS/STAR W. Yang, I. Moradi, J. Beauchamp, Univ. MD/CICS

Precipita1on Cal/Val Center


hjp://cics.umd.edu/ipwg/index.html

S. Rudlosky, NESDIS/STAR & J.J Wang, UMD/CICS

•  NOAA can provide NASA several useful data sets that can contribute to GPM program –  POES and JPSS L1 data sets –  MiRS, SFR, AMSR-‐2 L2, CDR’s, HMT-‐SEPS, ancillary data, etc.

•  Through our MOU with NASA, NOAA has been –  Developing BUFR formaaed GMI data for tes.ng by JCSDA –  Providing early access to GPM data/products for tes.ng and evalua.on

•  NOAA will con.nue its ac.ve par.cipa.on on PMM Science Team –  Algorithm teams –  GV ac.vi.es –  …wherever we can contribute the most…

•  Several emerging new R&D efforts supported by JPSS and GOES-‐R programs –  Fusing together satellite (including GLM) and ground data

•  NOAA Proving Grounds and Testbeds will be leveraged to –  Accelerate the use of GPM data by NOAA/NWS –  Accelerate the use of NOAA-‐unique products using GPM

•  New R2O opportuni.es emerging from NOAA/NESDIS reconfigured ground segment program which plans to integrate all satellites into common system

Summary/Take Away Points


Documents

Update’on’NOAA Precipita1on ... - gpm.nasa.gov