PREPOP Meeting Wednesday, March 23, 2011 10 AM – NOON Room 602 WWB 1.

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PREPOP Meeting

Wednesday, March 23, 201110 AM – NOONRoom 602 WWB

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Agenda1. Operational Product Status Updates (15 min)

a. Hydro-Estimator precipitation products (Kuligowski/Zhao)b. MSPPS/MIRS precipitation products (Zhao/Ferraro/Boukabara) c. Blended TPW products (Zhao/Kidder/Paquette)d. eTRaP (Seybold/Kidder)e. GOES histogram precip product (Schreitz/Xie)

2. Developmental Project Updates (15 min)a. MSPPS snowfall rate (Ferraro/Meng/Zhao)b. MIRS NPP rainfall rate (Boukabara/Iturbide/Zhao)c. eTRaP enhancements (Ma/Kuligowski/Kidder)d. Soil Moisture Products System (Zhan/Zhao)e. POES-GOES-GPS Blended TPW and RR (Zhao/Kidder/Ferraro)f. Satellite Cal/Val efforts for Rainfall Estimates and POES-AMSU Monitoring (Janowiak)g. New satellite data products for TV broadcast market (Ferraro)h. SCaMPR improvements (Kuligowski)

3. Discuss Inactive Projects and Pending PREPOP Projects (All; 5 min)4. Special Discussion (60 min)

a. Impact of MSPPS changes in response to AMSU-B band issues (Zhao/Janowiak; 20 min)b. MIRS Rainfall Rate recalibration (Iturbide-Sanchez; 20 min)c. SMOPS (Zhan; 20 min)

5. New Business (All; 10 min)6. Review of Action Items (All; 5 min) 7. Adjourn

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a. Operational Hydro-Estimator Update(Zhao/Kuligowski)

• Operational Global HE User Request (SPSRB 1010-0019):– SPIWG reviewed NWS user request and requested written assurance of NWS funding

support; waiting on signed letter from NWS

• Multi-Day HE Total Request (SPSRB 1006-0009)– SPIWG tasked Zhao and Kuligowski to determine if this falls under the OSPO Change

Management process

• ESPC CM Repository– Source code for the HE and SPE have been verified and put in the repository.

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b. MSPPS/MIRS Rainfall Products (1/2) (Zhao / Ferraro / Meng / Boukabara)

• Upcoming Operational Products– F18 MIRS DAP was received from STAR in June 2010, but its operational implementation

is pending IT readiness at OSPO – pending for the new Diamond with the capacity to run MIRS with high resolution, which is now targeted in the June 2011 time frame.

– The updated and improved snowfall rate algorithm was received from Huan, but its operational implementation is delayed due to the OSPO IT freeze – the task will be put in the queue to compete for Contractor support resources after the freeze is lifted in April 2011.

• Tailored Products– A netCDF-to-HDF-EOS encoder has been developed and available to users

• Products Anomaly– No changes for N19, N15 anomalies. – The NOAA-16 AMSU-B channel-18, -19 and -20 are gradually getting very noisy as the

instrument is aging. The RR product generation should be evaluated, and might need to be stopped in near future if no alternative works.

– NOAA-18 “reduced gyro test” will be conducted on March 23-24, 2011; a geo-location error of 10-15 km is expected.

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• Impacts of ESPC Contractor Transition and IT Freeze – The IT freeze is delaying the readiness of the new operational machine, which

consequently impacts the progress to upgrade MIRS to run at the high resolution (at MHS FOVs).

– The IT freeze is delaying the implementation of the updated snowfall algorithm.

b. MSPPS/MIRS Rainfall Products (2/2) (Zhao / Ferraro / Meng / Boukabara)

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c. Updates on the Operational bTPW Products (1/3)(Zhao / Kidder / Ferraro)

• Operational Anomalies – No GOES data were being filled in over the outback of Mexico, while no GPS was available. Changes were made in the GOES TPW

reader to allow the GOES TPW data be ingested into the system correctly.

– A bug was discovered in the GPS TPW analysis, which produced problematic TPW gridded analysis, especially while there are only a few GPS receiver stations. The problem has been fixed and implemented in operations, together with a new GPS station file.

Before After

Before After

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• Operational Anomalies (cont)– GPS data dropouts have been observed more frequently during the past couple of months

• Added the option to pick the data file with maximum stations between NOAAPort and FSL ftp site

• Increased re-visit and also reduced the time latency from 30 min to 60 min when no data are available from the latest hour.

– Surface pressure was observed not correct in the GOES West TPW data file – problem reported and fixed.

• Status of Archive – The archive request is still pending for its final approval at NCDC/CLASS.– The backlog data files will have to be deleted due to the space limits at ESPC if the archive

can not be started in two or three months.– Archive assessment for the blended RR product has been provided to NCDC, and also an

archive initiation was send to NCDC following the newly developed SPSRB archive guidance.

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• Impacts of ESPC Contractor Transition and IT Freeze– The ESPC Contractor Transition put marginal impact on the project schedule.– The ESPC IT freeze is expected to be lift as scheduled on April, 2011, which will allow the

transition of the TPW enhancement and blended RR products to start, it will have to compete with all other tasks for Contractor resources.

• Blended TPW products tailored for TV broadcasters – Set-up the routine support to transfer data for WorldWinds.


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d. eTRaP Ma / Seybold / Kuligowski / Kidder

(SPSRB 0101-2)

• Liqun Ma replaced Matt Seybold as OSPO Tropical PAL

• User survey by Mike Turk (SAB); key findings:– Broad awareness of product– Greatest benefit to operations in Eastern / Southern

Hemispheres– Equally divided on point vs. area probabilities

• B. Ebert proposed both (gridded point values overlaid with contourd area values); discussion ongoing

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e. GOES histogram precip product (Schreitz / Xie)

• Quarterly Critical Infrastructure Protection (CIP) testing was successfully completed.

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2. Developmental Project Updates (15 min)a. MSPPS snowfall rate (Ferraro/Meng/Zhao)b. MIRS NPP rainfall rate (Boukabara/Iturbide/Zhao)c. eTRaP enhancements (Ma/Kuligowski/Kidder)d. Soil Moisture Products System (Zhan/Zhao)e. POES-GOES-GPS Blended TPW and RR(Zhao/Kidder/Ferraro)f. Satellite Cal/Val efforts for Rainfall Estimates and POES-AMSU Monitoring (Janowiak)g. New satellite data products for TV broadcast market (Ferraro)h. SCaMPR improvements (Kuligowski)




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Active PREPOP R2O ProjectsSPSRB#

Product End User POC

NESDIS Lead

Current Phase

Status (G/Y/R

)

Issue(s) Funding

9802-4

POES/AMSU Rain Rate (SSMIS and NPP rain rates from MIRS only—not part of SPSRB request, but for supporting continuity of operations). MSPPS snowfall rate – extending AMSU RR to solid precipitation.

Xie, Heil (NWS); Kusselson (SAB); Wang (FNMOC)

MSPPS:Ferraro (STAR), Zhao (OSPO)MIRS:Boukabara (STAR), Zhao (OSPO), Meng (STAR)

Operational (Development for MSPPS snowfall rate / NPP ATMS RR, TPW, CLS)

G

MSPPS snowfall rate submitted but IT freeze delays its operational implementation. The implementation of full-resolution MIRS F18 retrievals is still pending IT capacity.

P-PSDI and OSPO base; NDE (SSMIS and NPP only)

9802-5

POES/AMSU TPW (also SSMIS and NPP TPW from MIRS only)

Operational (Development for NPP RR)

G

9802-6

POES/AMSU CLW (also SSMIS and NPP TPW from MIRS only)

Operational (Development for NPP RR)

G

0101-2

Operational Implementation of an Ensemble Tropical Rainfall Potential (eTRaP) [justification under TRaP for HPC/TPC/CPHC/CPC]

Kusselson (SAB)

Seybold (OSPO), Kuligowski (STAR), Kidder (CIRA)

Operational (eTRaP); Development (upgrades)

Y

OSPO IT freeze delaying operational implementation; development delays.

P-PSDI

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Active PREPOP R2O ProjectsSPSRB#


NESDIS Lead

Current Phase

Status (G/Y/R

)

Issue(s) Funding

0707-17

Soil Moisture Products System

Ek, Xie (NWS)

Zhan (STAR)Zhao (OSPO)

Development

G

OSPO IT freeze might have impact on its final operational implementation.

P-PSDI

0708-231006-0008

POES-GOES-GPS Blended Hydrometeorological Products [TPW and RR]

Schrab (NWS)

Zhao, Paquette , Kidder (CIRA), Ferraro (STAR)

Operational (TPW)Development(RR) Y

OSPO IT freeze delays the project schedule and operational implementation.

G-PSDI

1004-0004/ 0005/ 0006/ 0007

Megha-Tropiques Data and Products

Ferraro (STAR); Zhao (OSPO)

Development

G

Plan modified due to launch delay.

P-PSDI

1006-0009

Multi-day (more than 24hrs) NESDIS Hydro-Estimator Rain Estimates

Eckert (NWS)

Kuligowski (STAR);Zhao (OSPO)

Development

G

After OSPO IT freeze is lifted

OSPO base

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a. MSPPS Snowfall RateMeng / Yan / Ferraro / Zhao

(SPSRB 9802-5/6)

• Project Overview– The project will develop an operational surface

snowfall rate algorithm using passive microwave data from AMSU/MHS.

• Recent Accomplishments– Limited case studies

• Next Steps– Algorithm validation

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MIRS NPP Rain RateBoukabara / Iturbide / Zhao

(SPSRB 9802-5/6)

• Project Overview– Adaptation of MiRS to NPOESS Preparatory Project (NPP) ATMS

and integration within NPOESS Data Exploitation (NDE).• Recent Accomplishments

– Developed a netCDF-to-HDF-EOS encoder– Provided the NDE team the algo description for the MIRS Level-3

mapped products. – Analyzed the impact of a Hydrometeor Background Covariance

Matrix based on WRF simulations• Next Steps

– Preparing the detailed documentation for the MIRS Level-3 products

– Analyze new strategies to improve the quality of the MiRS rainfall rate.

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c. eTRaP EnhancementsMa / Seybold / Kuligowski / Kidder

(SPSRB 0101-2)

• Project Overview– Improve the eTRaP product by

• calibrating probabilities against observations to remove bias• determining the optimal product format (point vs. area probabilities)• adding new ensemble members (H-E, SSMIS, R-CLIPER)• adding enhancements (shear, topography, storm rotation)

• Recent Accomplishments– Delays in getting the project started; agreed to schedule

regular conference calls to track progress• Next Steps

– Finish and implement probability calibration– Incorporate H-E and SSMIS data into ensemble

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d. Soil Moisture Products SystemZhan / Zhao (SPSRB 0707-17)

(To be covered in Special Discussion)

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e. POES-GOES-GPS Blended TPWZhao / Kidder / Ferraro

(SPSRB 0708-0023)• Project Overview

– To develop an enhanced Blended TPW product which• Includes SSMIS TPW and MIRS TPW • Has a higher resolution (8 km vs 16km for the current operational bTPW) • Uses an enhanced blending technique to fully utilize the capabilities of GOES PW, MIRS TPW, and GPS TPW

• Recent Accomplishments– Added MIRS TPW over land and water– Added SSMIS TPW– Improved handling of GPS TPW

– Filtering of “eyeball” problem; Fixed Barnes analysis bug; Added land mask capability; Updated GPS station list– Developed an enhanced blending algorithm to fully utilize the strength of each dataset, including

AMSU, SSMIS, GPS and GOES TPWs– Experimental products have been developed and runs hourly at CIRA:http://cat.cira.colostate.edu - Blended TPW with SSMIS and MIRS TPW over landhttp://amsu.cira.colostate.edu/btpw - Blended TPW with the enhanced merging algorithm

• Next Steps– Continue working on the fine tune of the new merging algorithm– Operational implementation of the enhanced TPW products after the OSPO IT freeze is lift– Investigating some apparently anomalous behavior of over-land MIRS TPW (conference call

scheduled 31 March)– Reworking scripting code to allow script-level control of

• Blending algorithm• Data sources

http://cat.cira.colostate.edu/

http://amsu.cira.colostate.edu/btpw

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e. POES-GOES-GPS Blended RRZhao / Kidder / Ferraro

(SPSRB 0708-0023)

• Project Overview– To develop a blended Rain Rate product for NWS forecasters

• Recent Accomplishments– The blended RR product from MSPPS, MIRS, and FNMOC SSMIS are generated and

made available for evaluation on Internet – Worked with John Janowiak for validation– Upgraded the histogram correction with options to

• allow different corrections over land and ocean• choose any satellite as the reference satellite, including DMSP F13• specify the “strength” of correction as none, light, and strong

– The product has been developed and runs hourly at CIRA (http://cat.cira.colostate.edu)

– Provided archive assessment for the blended RR product to NCDC, and submitted an archive initiation following the newly developed SPSRB archive guidance.

• Next Steps– A delta CDR for the blended RR, which is delayed due the Contractor transition, and

is planning to be completed by April, 2011.– Operational implementation of the blended RR products

http://cat.cira.colostate.edu/

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Active PREPOP Development ProjectsProduct

Key Capabilities

End User POC NESDIS Lead Issue(s) Funding

Satellite Cal / Val Efforts for Rainfall Estimates and POES-AMSU Monitoring

Develop real-time STAR precipitation product validation

Kuligowski and Zhao; PREPOP

John Janowiak (STAR/CORP)

None. STAR Cal/Val funds

New Satellite Data Products for TV Broadcast Market (Phase I)

Port “emerging” NESDIS satellite products to TV broadcast community

Dave Gilhousen (WorldWinds), Dan Gallagher (Baron)

Ralph Ferraro (STAR)

None. NOAA SBIR

SCaMPR Improvements

Add TRMM data, visible data, moisture correction

Kuligowski (STAR)

Contractor support ended; implementation is now “out of hide”. Working to incorporate MWCOMB into real-time version, followed by adaptation of GOES-R version (with current Imager bands) in summer to support the GOES-R Proving Ground.

None

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f. Satellite Cal/Val efforts for Rainfall Estimates and POES-AMSU Monitoring

Janowiak

• Project Overview– Provide routine and (as requested) case-study

validation of MW-based rain rate products• Recent Accomplishments

– Added SON 2010 validation to Web page at http://cics.umd.edu/~johnj/STAR/html/US_page.html

– Evaluated impact of AMSU-B band issues on MSPPS changes (see upcoming presentation)

• Next Steps– Continue routine validation and case study validation

as requested

http://cics.umd.edu/~johnj/STAR/html/US_page.html

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g. New Satellite Data Products for TV Broadcast MarketFerraro

• Project Overview– Funded through NOAA’s FY10 SBIR Program– Develop prototype method to deliver new NOAA satellite

products to TV broadcasters• Recent Accomplishments

– Phase I project completed Dec 31, 2010– Blended TPW delivered through Baron Systems package

• Was used by three west coast markets on the air during December heavy precipitation event!

– Both WorldWinds and Baron great to work with.• Next Steps

– Phase II proposal submitted by WorldWinds Inc. • Enhanced product list

– Reviews due April– They will brief NOAA SBIR in May

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h. SCaMPR ImprovementsKuligowski

• Project Overview– Improve the SCaMPR algorithm by incorporating TRMM data

(short-term) and implementing the GOES-R version (medium-term)• Recent Accomplishments

– Evaluating the impact of the TRMM data and working on a journal article

– Working on a real-time version of SCaMPR that will use MWCOMB as the MW input

• Next Steps– Finalize parallel real-time runs of MWCOMB SCaMPR– Implement real-time version of GOES-R SCaMPR (also driven by

MWCOMB) to support GOES-R Proving Ground beginning in summer

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2. Developmental Project Updates (15 min)a. MSPPS snowfall rate (Ferraro/Meng/Zhao)b. MIRS NPP rainfall rate (Boukabara/Iturbide/Zhao)c. eTRaP enhancements (Ma/Kuligowski/Kidder)d. Soil Moisture Products System (Zhan/Zhao)e. POES-GOES-GPS Blended RR (Zhao/Kidder/Ferraro)f. Satellite Cal/Val efforts for Rainfall Estimates and POES-AMSU Monitoring (Janowiak)g. New satellite data products for TV broadcast market (Ferraro)h. SCaMPR improvements (Kuligowski)




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Inactive PREPOP R2O ProjectsSPSRB#


NESDIS Lead

Current Phase

Issue(s) Funding

0311-6

Operational GPROF-6 [GPROF-2004] Precip Estimates

Dropped from SPSRB User Request List

0410-1

High Temporal Satellite Precipitation Estimates

Dropped from SPSRB User Request List

0705-06

GOES Mesoscale Convective System Index

Eckert (NWS)

Lindsey (STAR), Hanna (OSPO)

Development

Development on hold for the time being.

None.

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Inactive PREPOP Development ProjectsProduct

Key Capabilities

End User POC NESDIS Lead Issue(s) Funding

AMSR-E Products

Rainfall and cloud properties from AMSR-E

Kusselson, Turk (SAB); Heil (NWS)

Ferraro (STAR), Ding (OSPO), Zhao (OSPO)

The products are supported as “it is”, and no resource available for making improvements at NESDIS.

None; however, under OSPO EOS “umbrella”.McIDAS application is on OSPO base

SSMIS Rain Rates from GPROF

Rain rates from DMSP F-16/17 SSMIS using GPROF

Xie (CPC), Huffman (NASA), Kummerow (GEWEX)

Ferraro (STAR), Zhao (OSDPD)

Produced in real time, but no funding available for operational transition. May be difficult to fund since MIRS plans to produce SSMIS rain rates also.

None

Intercomparison of H-E, QMORPH, and SCaMPR

Decision tool for determining how best to operationally support SAB during the pre-GOES-R era

Kusselson (SAB)

Kuligowski (STAR)

Will begin when new version of SCaMPR starts running in real time, which should begin in summer 2011 to support the GOES-R Proving Ground.

None

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Pending PREPOP R2O ProjectsPriority

Key Capabilities

LeadSPSRB

#SPSRB Project Plan

TitleUser(s)

Product Team

Status

1 NPP NOAA-Unique Products (NUP)

Heidinger (STAR); TBD (OSDPD)

TBD POES-consistent VIIRS Cloud Products

NCEP EMC; NWS WFO’s accessing CIMSS feed; NESDIS IASI processing system; climate community

TBD Proposing for funding through JPSS

2 Global (60S-60N) coverage of Hydro-Estimator rain rates

Kuligowski (STAR); Zhao (OSDPD)

1010-0019

Global Hydro-Estimator Satellite Rainfall Estimates

NWS (provide to Hydrologic Research Center as part of MOU)

TBD SPIWG requested written guarantee of NWS funding; still waiting to receive.

3 Real-time GCOM-W products

Ferraro (STAR); Zhao (OSDPD)

Part of JPSS

TBD SAB, NWS field offices and CPC

TBD Project being incorporated within JPSS; efforts underway to get funding.

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Impact of AMSU-B band issues on the MSPPS rain rate product

Limin Zhao3/23/2011

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Status of Sensor Health and Product Availability• N15 AMSU-B

– The 183 GHz moisture channels failed on Sep 20, 2010 (local oscillator failed), all data are flagged missing.– The 89 GHz and 150 GHz window channels are providing good quality data– No MSPPS Rain Rate is produced with the current algorithm

• N16 AMSU-B– The 183 GHz moisture channels are getting much noisy as the sensor is aging out although the sensor is still

operated operationally.– The 89 GHz and 150GHz GHz window channels are providing good quality data– MSPPS rain rate products are been producing with the CI-correction off for light rain over land

• N17 AMSU-B– The 183 GHz moisture channels failed on Dec 16, 2009 (local oscillator failed), all data are flagged missing.– The 89 GHz and 150 GHz window channels are providing good quality data– No MSPPS rain rate is produced with the current algorithm

• N18 MHS– All MHS channels are good– MSPPS rain rate product are available

• N19 MHS– Channel 3 (183±1GHz) and its NDET exceeded spec started from Aug 27, 2009, and stabilized around 3.0 K

(exceeding 1.0 K specification) since Oct 7, 2009– Channel 4 (183±3GHz) and its NDET exceeded spec starting from Aug 27, 2009, and stabilized around 0.61 K

(back within 1.0 K specification) since Oct 7, 2009, which is back within 1.0 K specification– MSPPS are being produced, no quality issue observed/reported so far

• Metop-A MHS– All MHS channels are good– MSPPS rain rate product are available

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Use of AMSU-B 183 GHz Channels in MSPPS

• Ice Water Path Retrieval– Conditions for existence of detectable precipitating cloud– Criteria for adding correction over costal lines to recover ice water path that are missed

due to lack of large precipitating ice particles – 183±7 GHz channel for screening false alarm over desert

• Rain Rate Retrieval– Used in deriving the Convective Index for separating convective cores from stratiform

regimes– Criteria for adding correction over ocean and costal to recover light rain that are missed

due to lack of large precipitating ice particles

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Convective Index (CI)• CI algorithm

The CI, which reflects the vertical convection strength of precipitation systems, is calculated using the MHS moisture channels (1831, 1833 and 190) as follows:

CI = 1 for 2 >-3 and 2 > 1 and 2 > 3

CI = 2 for 2 > 0 and 1 > 0 and 3 > 0 and 1 > 3 and 2 > 3

CI = 3 for 2 > 0 and 1 > 0 and 3 > 0 and 1 > 3 and 2 < 3

where 1 = 1831 - 190 , 2 = 1833 - 190, 3 = 1831 - 1833 and the values of 1, 2 and 3 represent the exist of weak, moderate and strong vertical convection. A different IWP - RR relation is applied for these pixels with CI=3.

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Responses to AMSU-B Moisture Channels Issues

• Without Action– No AMSU rain rate products available from N15, N16 and N17– Users have to live with degraded temporal sampling or global refresh rate, which increases from

2.5 ~ 4.0 hr → 6.0 hr while rain rate products are only available from N18, N19 and Metop-A.

• Alternative– Disable the classification of convective and stratiform to allow the products be generated with

slightly degraded quality– Exploring the possibility of retrieving rain rate without using AMSU-B moisture channels

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First Attempt• Alternative CI

– Made an attempt to use only 89 GHZ and 150 GHz channels over land for defining the strong convective cores

CI=3 for BT150 < 173 and BT89 < 220 and land_stag=1

• Changes in the IWP algo– Conditions for existence of detectable precipitating cloud

• Replace (BT176 < 265) with (BT150 < 270 && (BT89 – BT150) > 3)– Disable the correction over costal lines – Replace BT176 with BT150 for screening dessert, and adjust the threshold value according

• Changes in the RR algo– Use the alternative CI over land– Disable the use of CI over ocean– Disable the correction over costal lines

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With AMSU-B Moisture Channels Without AMSU-B Moisture Channels

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Validation of MSPPS Changes in Response to AMSU-B Issues

John Janowiak3/21/2011

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Evaluation of Changes

• Approaches– Verify that the alternative algorithms can generate reasonable retrievals without using

AMSU-B moisture channels – Satellite retrievals are matched with the closest radar hourly rainfall estimate: – N18 is used as the reference to compare the retrievals with and without using AMSU-B

moisture channel

• Performed with Limited Cases– Two weeks worth of data on November, 2010– Two weeks worth of data on March, 2011

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RADAR (N15 match) “N15_new” “N15_new” - RADAR

RADAR (N18 match) “N18_new” “N18_new” - RADAR

“N18_ops” “N18_ops” - RADARTime-space matched satellite & precipitation during March 3-14, 2011

NOTE: time sampling difference between NOAA-15/18 (radar matched for each satellite, separately) “mm” accumulated over period

Difference Maps

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March 3-14, 2011

Nov 15-30, 2010

Swath Rain rate Histograms (light rain)

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March 3-14, 2011

Nov 15-30, 2010

Swath Rain rate Histograms (moderate-heavy rain)

Note Y-axis range differences

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PDFs over global oceans

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Evaluation of Changes

• Approaches spatially, all 3 satellite estimates exhibit very similar patterns and, in general, they underestimate precipitation (relative to radar) in the eastern 1/3 of the nation and overestimate in much of the West. -- Slide 9

• Histograms of precipitation rates in the 0.5 to 2 mm hr-1 range during the March 2011 case are very similar to the Nov 2010 data, with very good agreement among the satellite estimates and with the radar data in the 1.25 to 2 mm hr-1 range. -- Slide 10

• Histograms of precipitation rates in the 2 to 5 mm hr-1 range during the March 2011 period for the modified NOAA-15 algorithm are closer to the radar data than the NOAA-18 algorithms, and are in better agreement with radar compared to the Nov 2010 case. -- Slide 10

• For precipitation rates of 10 to 20 mm hr-1, the modified NOAA-15 results are in very good agreement with the radar data (particularly for rates in the 10-15 mm hr-1 range), and all 3 satellite estimates perform better during this period compared to the Nov 2010 case. -- Slide 11

• All of the satellite estimates exhibit considerably more events with precipitation rates of 25 to 30 mm hr-1compared to radar – although this may be because the radar data are integrated hourly data while the satellite estimates are ‘snapshots’. Note, that while the radar data indicate 0 to 0.03% of the events with precip. >25 mm hr-1 during both Nov and Mar, the satellite %’s are much higher in March than November. -- Slide 11

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Summary

• No MSPPS rain rate products are available from N15 and N17 due to the fail of AMSU-B moisture channels.

• N16 rain rate product is degraded due to increased noise in AMSU-B moisture channels

• A preliminary attempt is made to recover the MSPPS rain rate product without using AMSU-B moisture channels.

• The comparisons with limited data sets show that the retrievals with a-CI in general look good and agree well with that from the operation. No obvious problems have cropped up during the two evaluation periods.

• More detailed analysis and evaluation are needed to fully understand the impact of these changes.

• Looking for comments/suggestions/recommendations from POP and/or Users– Any requirement or desires to recover the RR products from these aged satellites?– Should we make efforts to improve and implement the changes to operation?

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Present Efforts to Improve and Extend the MiRS Rainfall Rate

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MiRS Atmospheric Background Covariance Matrix based on ECMWF 60 and WRF simulations

Temp. and Water Vapor based on ECMWF 60

Hydrometeors based on WRF simulations

Implementation of a New Hydrometeor Background Covariance Matrix based of WRF Simulations

WRF Simulation over Middle Latitude Land

Surfaces for SON season

CONUS

South America

Australia

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Correlation Probability of Detection

Validation of the New Hydrometeor Covariance Matrix Using Stage IV Rainfall Rate

Dark Line: Based on Current Hydrometeor Covariance MatrixBlue Line: Based on New Hydrometeor Covariance Matrix

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False Alarm Rate Heidke Skill Score

Validation of the New Hydrometeor Covariance Matrix Using Stage IV Rainfall Rate

Dark Line: Based on Current Hydrometeor Covariance MatrixBlue Line: Based on New Hydrometeor Covariance Matrix

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Current Covariance Matrix New Covariance Matrix

Estimation of more high rainfall rate cases

Impact of the New Hydrometeor Covariance Matrix on the Rainfall Rate Distribution

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Current Covariance Matrix New Covariance Matrix

Estimation of more high rainfall rate cases

Impact of the New Hydrometeor Covariance Matrix on the Estimation of Rainfall Rate

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Cumulative Validation and Consolidation of MIRS

MIRS is applied to a number of microwave sensors,each time gaining robustness and improving validation

for Future New Sensors POES

N18 ,N19

DMSPSSMIS F16, F18

AQUAAMSR-E

NPP/NPOESSATMS, MIS

: Applied Daily

: Applied occasionally

: Tested in Simulation

Metop-A

The exact same executable, forward operator, covariance matrix used for all sensors

MiRS: A System that is Being Applied to Multiple Sensors

TRMM-TMI

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Extension of MiRS Rainfall Rate to TRMM-TMI Observations. Comparison to N18 Rainfall Rate

MiRS N18 Rainfall Rate MiRS TRMM-TMI Rainfall Rate

~5.0 km spatial resolution~50.0 km spatial resolution

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Extension of MiRS Rainfall Rate to TRMM-TMI Observations. Comparison to TRMM-2A12 Rainfall Rate 1/2

TRMM-2A12 Rainfall Rate MiRS TRMM-TMI Rainfall Rate

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TRMM-2A12 Rainfall Rate MiRS TRMM-TMI Rainfall Rate

Extension of MiRS Rainfall Rate to TRMM-TMI Observations. Comparison to TRMM-2A12 Rainfall Rate 2/2

Major efforts to the improvement of the MiRS TRMM-TMI rainfall rate are related to the improvement of the background covariance matrix and bias correction.

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NOAA-NESDIS Global Soil Moisture Operational Product System (SMOPS)

Xiwu Zhan, Jicheng Liu, Limin Zhao, Mitch GoldbergNOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD, USA

Ken Jensen Raytheon Technical Service Company, Landover, MD, USA

Acknowledgment: Slides about, USDA-ARS & SCAN networks, NOAA-USCRN, ALEXI and NOAA-OHD are borrowed from Drs. P. Houser, G. Scheffner, T. Jacksonn, B. Baker, M. Anderson and B. Cosgrove

57

OUTLINE

Why Soil Moisture

Current Data Products

NOAA-NESDIS SMOPS

Future Plans

58

24-Hours Ahead Atmospheric Model

Forecasts

Observed Rainfall0000Z to 0400Z 13/7/96(Chen et al., NCAR)

Buffalo CreekBasin

"The strong motivation for this land data assimilation and land-monitoring space missions such as Hydros is that the land states of soil moisture, soil ice, snowpack, and vegetation exert a strong control on ...the heating and moistening of the lower atmosphere…forecast of tomorrow's heat index, precipitation, and severe thunderstorm likelihood."

Louis Uccellini, NCEP

“The experience of the last ten years at ECMWF has shown the importance of soil moisture...Soil moisture is a major player on the quality of weather parameters such as precipitation, screen-level temperature and humidity and low-level clouds."

Anthony Hollingworth, ECMWF

Soil Moisture Data Will Improve Numerical Weather Prediction (NWP) Over the Continents by Accurately Initializing Land Surface States

With Realistic Soil Moisture

Without Realistic Soil Moisture

Observed Rainfall from intense storm in Colorado: Model forecasts with and w/o soil moisture:

Actual storm event is forecasted accurately only if soil moisture information is available.

Soil Moisture Impacts on Weather Forecasting

59

Current NWS Operational 30 km Flash Flood Guidance (FFG) is Based on Model Surface Soil Moisture Deficit

Current NOAA and National Drought Mitigation Center (NDMC) Operational Drought Index is also based on Modeled Soil Moisture Data.

Soil moisture Observational data will replace model or proxy SM

Soil Moisture Data for Flood & Drought Monitoring

60

VUT ESCAT (Wagner et al, 1999)

GSFC SMMR (Owe et al, 2001)

USDA TMI (Bindlish et al, 2003)

Princeton TMI (Gao et al, 2006)

NASA AMSR-E (Njoku et al, 2003)

USDA AMSR-E (Jackson et al, 2007)

VUA AMSR-E (Owe et al, 2008)

USDA WindSat (Jackson et al, 2008)

NRL WindSat (Li et al, 2008)

Current Satellite Soil Moisture Data Products:

61

TB,icmp= Tskin {er,p exp (-i/cos) +

(1 – ) [1 – exp (-i/cos)] [1 + Rr,i exp (-i/cos)]}

i = b *VWCRr,i = Rs exp(h cos2θ)

Rs = f(ε) -- Fresnel Equationε = g(SM) -- Mixing model

TB,iobs= TB06h , TB06v , TB10h , TB10v , TB18h , TB18v

}min{

26

1

,,2

i i

cmpiB

obsiB TT

Multi-channel Inversion Algorithm (MCI):

Soil Moisture Retrieval Algorithms:

62

TB10h = Ts [1 –Rr exp (-2 /cos)]

Rr = Rs exp(h cos2θ)

Rs = f(ε) -- Fresnel Equationε = g(SM) -- Mixing model

Ts = reg1(TB37v) or TsLSM

= b * VWCVWC = reg2(NDVI)

Single Channel Retrieval (SCR) Algorithm:

SCR can be applied to different sensors for a consistent satellite soil moisture data product.

Soil Moisture Retrieval Algorithms:

63

Spatial Map

Soil Moisture Retrieval Comparison:

64

SCR SCR

MCI MCI


65

NASA and USDA AMSR-E Compared with In Situ

Measurements


66

External Input

External Output

SMOPS

SMOPS Daily Product

SMOPS 6 Hour Product

Sand Map

Clay Map

Porosity Map

Land Cover Map

AVHRR NDVI

AMSR-E Level 2A Tb

SMOS Soil Moisture

ASCAT Soil Moisture

Land Cover Parameters

6 Hour Product Status

Daily Product Status

NDVI Climatology

In-Situ Data

Validation Results

Archive Product

Archive Product Status

NOAA-NESDIS SMOPS Structure:

67

BTR

ADR

SMR

SCR

SMM

AMSR-E Tb

AVHRR NDVI

Sand Map

Clay Map

Porosity Map

Land Cover Map

Land Cover Parameters

SMOS Soil Moisture

ASCAT Soil Moisture

Footprint Brightness

Temperature with QA and Meta Data

Global Ancillary

Data Maps

Gridded Soil Moisture Maps with QA and Meta Data

SMOPS 6 Hour Product

SMOPS Daily Product

6 Hour Product Status

Daily Product Status

NDVI Climatology

In-Situ DataSMV

Validation Results

AMSR-E Soil Moisture

Archive Product

ArchivedProduct Status

NOAA-NESDIS SMOPS Structure:

68

NOAA-NESDIS SMOPS Data Processing Steps:

Start Read PCF file

End

new input file

run?

NoYesTB or SM data ?

6 hour / daily or

Archive?

Read ASCAT /

SMOS SM

Read footprint

TBs

End of input file?

SMOPS retrieval

algorithm

Merge gridded SM files

Pack SMOPS output data

products and generate status

report

SM

TB 6 hour /daily

archive

Yes

No

Read ancillary data

Gridded ASCAT/ SMOS

SM files

Gridded AMSR-E SM files

Grid ASCAT /

SMOS SMGridded Merged SM files

Global Gridded 6 hour/Daily Soil

Moisture Data Product

2

1

3

4

Repeat branch 1, 2, 3 for all data

after 2 days *

* All data acquired within the 6 hour or whole day time period arrived in the past 48 hours

69

NOAA Global Soil Moisture Data Portal:

70

NOAA NESDIS SM Data Research Plan

Bayesian or other Merging Method

Cubist, ALEXI, etc.

Low Rez Soil Moisture

Data

High Rez Soil Moisture

Proxy

TMI/AMSR-E/WindSat/SMOS/SMAP/Aquarius/

MIS/ASCAT Obs

TM/AVHRR/MODIS/VIIRS/Radar/

GOES Obs

Soil Moisture Ground Obs

Meteorological Forcing & Land

Surface Obs

LIS/EnKF Data Assimilation

Agriculture DSS

Drought Monitoring/

Forecast

Flood Monitoring/

Forecast

Military Applications

Water Resources

Management

Numerical Weather

Predictions

High Rez/Quality Soil Moisture

Data Products

Retrieval Alg.

X. Zhan, NOAA/NESDIS/STAR 2007/04/16

1-10km, Low accuracy 25-150km, Higher accuracy Ancillary Data

71

Potential Role of Passive Microwave Remote Sensing in Flood Forecasting

R. Bindlish, W.T. Crow & T.J. JacksonUSDA ARS Hydrology and Remote Sensing Lab

(funded by NASA EOS/03-0204-0265)

72

AMSR 6.6 H GHz observations

73

Correlation Coefficients

Parameter Precipitation 6.6H

Bowen Downs 0.80 (4) 0.36 (4)

Longreach 0.64 (6) 0.36 (6)

Stonehenge 0.70 (4) 0.31 (6)

Retreat 0.63 (8) 0.30 (8)

Nappa Merrie 0.11 (15) 0.43 (16)

* Numbers in parenthesis donate lag times in days for maximum correlation coefficient

74

Bowen Downs (Thomson River - 22825 km2)

0.00E+00

5.00E+03

1.00E+04

1.50E+04

2.00E+04

2.50E+04

3.00E+04

3.50E+04

4.00E+04

1-J

an

-04

11-J

an

-04

21

-Ja

n-0

4

31

-Ja

n-0

4

10

-Fe

b-0

4

20

-Fe

b-0

4

1-M

ar-

04

Date

Str

ea

mfl

ow

(m

^3

/da

y)

Observed

Predicted (TB + precip)

Predicted (precip only)

75

SUMMARY

Current satellite soil moisture products may not meet the needs for NWP applications

A one-stop global soil moisture data production and distribution system (SMOPS) is being built at NOAA-NESDIS

Soil moisture signal from MW satellite may have the potential to assist flood forecasting

76

THANKS for Listening !

I’ll listen to you anywhere anytime from now at

[email protected] or

1-301-763-8042 x 148

77







78

Review of October 2010 Action ItemsDescription POC Status

Brief PREPOP on the result of the MIRS Rainfall Rate recalibration

Iturbide-Sanchez

Completed at this meeting.

Provide PREPOP with a systematic evaluation of the impact of the MSPPS changes in response to the AMSU-B band issues

Janowiak Completed at this meeting.

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2. Developmental Project Updates (15 min)a. MSPPS snowfall rate (Ferraro/Meng/Zhao)b. MIRS NPP rainfall rate (Boukabara/Iturbide/Zhao)c. eTRaP enhancements (Ma/Kuligowski/Kidder)d. Soil Moisture Products System (Zhan/Zhao)e. POES-GOES-GPS Blended TPW and RR(Zhao/Kidder/Ferraro)f. New satellite data products for TV broadcast market (Ferraro)g. SCaMPR improvements (Kuligowski)




PREPOP Meeting Wednesday, March 23, 2011 10 AM – NOON Room 602 WWB 1.

Documents

Transcript of PREPOP Meeting Wednesday, March 23, 2011 10 AM – NOON Room 602 WWB 1.