bill lawrence – doh. “hydrologic model output statistics” current short term ensembles have...

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Southern Region Tech Transfer WGRFC Aug6-10, 2007 Bill Lawrence – DOH

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Page 1: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

Southern Region Tech Transfer

WGRFC Aug6-10, 2007

Bill Lawrence – DOH

Page 2: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC HMOS EXPERIENCES

“Hydrologic Model Output Statistics” Current short term ensembles have

proven unreliable, mainly because no hydrologic uncertainty included.

Mary Mullusky suggested using previous forecasts at XEFS workshop in 12/06

Includes both meteorological and hydrologic uncertainty.

Temporary gap-filler till better hydro uncertainty process developed

Page 3: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC HMOS EXPERIENCES

Basic premise is to compare historical forecasts with observed, then infer how a operational forecast might vary considering current observation and deterministic forecast.

Requires long archive of forecasts and observations.

Likely only usable at daily forecast points. ABRFC provided data (forecasts and

observed) for 12 points for 10 year period.

DJ Seo and Satish Regonda primary players.

Page 4: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

HMOS Ensemble Processor

Reflect meteorological & hydrological uncertainties

Deterministic forecasts of precipitation, temperature

Deterministic forecasts of streamflow

Ensemble traces of streamflow

Weather forecasts

Correct bias, account for all uncertainties

Deterministic Preprocessor

Hydrologic Models

HMOS

State Updating/ Data

Assimilation

Page 5: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

HMOS Results: Illinois River near Watts [WTTO2]

Page 6: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

Red River near Arthur City, TX [ARCT2]

ObservationSingle value ensemble

mean forecast

Oper. Forecast

Ensem. mean

Number of ensembles 2000, Regime moves from low to high

12

34

56

7

8

9

Line numbers Percentiles1 – 0th (minimum), 2 – 1th, 3 – 5th, 4- 25th, 5 – 50th

6 – 75th, 7 – 95th, 8 – 99th, 9 – 100th (maximum)

Page 7: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC BACKUP UPDATE

Fully functional new system in place across Southern Region.Pros: Much faster and capable than previous system Allows for creation and distribution to web page of graphics. Allows for >90% functionality (No FOP, no ESRI, etc)

Page 8: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC BACKUP UPDATE

Cons: Region/NOAA required security patches/updates are killing the effectiveness of the system. Requires tremendous resources. Future of the system is very much up in the air with AWIPS2 Where does SR system fit in with “new” national project???

Page 9: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

ABRFC started using P1 in Oct 1996Previous years/months were created using Stage 3

DMIP1 showed different biases pre/post Oct 1996

ABRFC wants consistent bias in gridded data sets

ABRFC also wants to leverage current knowledge of precipitation processing for early years; eliminate obvious bad data

Hope is to rerun bigger events (pre Oct 10\1996) using P3

Page 10: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

Initial team meetings showed project to be complex and time consuming

Needed new software and data restoration

In early 2000s, ABRFC deleted all but 6 hourly PC and HG data from older archive db due to space limitations, wrote to tape

Need to restore hourly data into new archive db on ax

Restore data off old tapes Write shef encoding software Process thru shefdecoder – “clean entry”

Page 11: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

James Paul has already written application to access ax and create gage files needed for RetroP3

Next step is to write software to create mosaic of old DPAs

Likely pick an event or two to test, using human qc and intelligence

If testing is successful, final step is human processing of any/all hours

Very labor intensive, but outcome worth it

Page 12: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

James Paul has also written a PostP3 application…needs additional testing and coding

ABRFC eventually plans to develop a gridded version of local bias for WFOS

Latest hour Average of Last 6 hours Average of Last 24 hours

Page 13: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

Page 14: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

Page 15: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC RETRO P3

Page 16: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC Q2 EXPERIENCES

ABRFC has been collaborating with NSSL for several years now regarding Q2

Goal of Q2 is to create an automated QPE for the entire lower 48 every 5 minutes; main goal is to aid in the flash flood arena.

NSSL is thinking beyond the box, ie different z-r relationships for different areas depending on soundings, etc.

ABRFC is downloading xmrgs of data to compare to our QPEs

Page 17: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC Q2 EXPERIENCES

ABRFC will start sending “bad gage” list soon for automtic inclusion into analysis.

NSSL in no way intends to compete with RFC produced QPE

ABRFC’s initial impressions are that the QPE is very good, esp for an automated process; AP removal is excellent.

Web site : http://www.nmq.nssl.noaa.gov

Page 18: Bill Lawrence – DOH. “Hydrologic Model Output Statistics”  Current short term ensembles have proven unreliable, mainly because no hydrologic uncertainty

ABRFC Q2 EXPERIENCES

Website is useful operationally, as one can “review” echos from “yesterday”.

Excellent archive of both qpe and radar echos.

Since this method will have real-time gages/biases input, along with varying ZRs, and will be updated every 5 minutes, it shows real promise as a precipitation source for the Flash Flood program.