The multisensor precipitation estimator and evaluations over the florida peninsula
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The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula. Greg Quina SC DHEC, Bureau of Air Quality. Based on graduate research prepared at Florida State University. What is the Multisensor Precipitation Estimator (MPE)?.

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The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula

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The multisensor precipitation estimator and evaluations over the florida peninsula

The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula

Greg Quina

SC DHEC, Bureau of Air Quality

Based on graduate research prepared at Florida State University


What is the multisensor precipitation estimator mpe

What is the Multisensor Precipitation Estimator (MPE)?

  • Objective merging of rain gauge and bias-corrected radar data via optimal estimation

  • Hourly, 4 km resolution

  • Useful for providing accurate high-resolution rainfall for Flash Flood and River Flood Forecast Guidance

  • Implemented at RFC and some NWS offices

  • Final gridded precipitation estimates have less error than either the input radar or the input gauge data alone


Precipitation sensors rain gauges

Precipitation SensorsRain Gauges

  • Accurate 8 inch diameter tipping bucket measurement

  • Limitations

    • High rain rates

    • Wind and evaporative losses

    • Electronic/mechanical issues

    • Clogs

    • Poor spatial resolution

    • High maintenance cost for a meso-network


Gauge data dense network

Gauge Data (Dense Network)

  • 622 gauges from SJRWMD, SWFWMD, and SFWMD

  • 3 HRL gauges used as verification

  • Hourly-accumulated tipping bucket

  • Quality controlled


Gauge only products

PMOSAIC (Thiessen Polygons)

Closest available gauge

GMOSAIC (Gauge-only analysis)

Optimal Estimation

Gauge-only Products


Precipitation sensors wsr 88d radar

Precipitation SensorsWSR-88D Radar

  • Good temporal and spatial resolution

    (6 minute, 1 km range x 1 degree azimuth)

  • Limitations

    • Obstructions and undesired scatterers

    • Improper beam filling and overshooting

    • Evaporative, condensational, and wind effects below radar beam

    • Brightband and hail contamination

    • Determining drop size distribution and appropriate Z-R relationship

    • Truncation errors in the Precipitation Processing System (PPS)

    • Radar calibration problems

    • These limitations all add up to a bias that changes from hour to hour and even over the domain of a radar!


Radar data

Radar Data

  • Hourly Digital Precipitation Data (HDP) produced by PPS at each radar site… now called DPA

  • 4 km resolution

  • 230 km detection range from radar

  • Elevation angle used is based on hybrid scan data


Steps in determining effective radar coverage area

Steps in Determining Effective Radar Coverage Area

  • Compute radar-derived precipitation climatologies for each radar (seasonal/monthly).

  • Define max and min thresholds to place on climatology. Radar estimates are not trusted beyond these thresholds.

  • Create maps of effective radar coverage areas.

    • Minimize defective areas


Mosaicking procedure which radar to use at each grid cell

Mosaicking ProcedureWhich radar to use at each grid cell

  • Each of the following criteria MUST be satisfied for the chosen radar for each grid cell:

  • The radar data MUST be available for the given hour,

  • The specified cell location MUST lie within the effective radar coverage area for that radar, AND

  • The height of the radar beam at the cell location MUST not exceed ANY other radar beam height that satisfies 1 and 2


Height of lowest unobstructed sampling volume

Height of Lowest Unobstructed Sampling Volume

Radar Coverage Map


Result mosaicked radar estimates rmosaic

Result: Mosaicked Radar Estimates (RMOSAIC)


Radar bias correction

Radar Bias Correction

  • Correct radar using “ground truth” data

  • Find non-zero gauge/radar pairs that are within each specific effective radar coverage area

  • A radar bias correction factor is calculated by dividing the total gauge amount by the total radar amount at different time spans

  • Mean field bias for each radar and each hour

For additional details, see Seo et al. (1999)


Remove mean field bias bmosaic

Remove Mean Field Bias (BMOSAIC)

BMOSAIC(I,J)=BIASa(k) x RMOSAIC(I,J)


Mmosaic final mpe product

MMOSAIC (Final MPE Product)

  • Merge gauge and bias corrected radar observations

  • Weight the nearby gauges vs. radar as a function of a gauge’s distance from grid point (i,j)

    • Sum of all weights equal to 1

RMOSAIC

MMOSAIC


Variability within a 4 x 4 km area

Variability within a 4 x 4 km area

  • Much of the difference between the precipitation products and point gauge observations is due to the natural spatial variations of precipitation within the 4 x 4 km cells.

  • We evaluate this spatial variation by making correlograms of hourly gauge data vs. gauge-to-gauge distance:

    • Six years of hourly gauge data (1996-2001)

    • 79 gauges between 26-27 degrees North and 80-81 degrees West.


Hourly correlograms 2 gauge pairs separated by an inter gauge distance

Hourly Correlograms2 gauge pairs separated by an inter-gauge distance


Statistical results 1996 through 1999

Statistical Results – 1996 through 1999

  • MPE products were verified against 3 HRL gauges

  • All MPE products and gauge values must be valid with at least one product or gauge value recording at least 0.01” precipitation

  • Hourly scatterplots (most rigorous test)… results will look better when looking at daily and monthly data.


Seasonal and precipitation type

Seasonal and Precipitation Type

  • Correlations are generally better in stratiform type precipitation and cold season

  • Gauge-only products have poor correlations, especially in convective type and warm season

  • Radar biases are greatest in stratiform/cold season

    • 50% RMOSAIC underestimates in stratiform cases

    • RMOSAIC truncation errors remain in other radar-influenced products

  • BMOSAIC proved “tough to beat” in warm season and convective events.


September 2001 hydro case study tropical storm gabrielle

September 2001 Hydro Case StudyTropical Storm Gabrielle


National weather service river forecast system nwsrfs interactive forecast program ifp

National Weather Service River Forecast System (NWSRFS) Interactive Forecast Program (IFP)

  • NWSRFS simulates streamflow using the Sacramento Soil Moisture Accounting Model (SACSMA)

    • conceptual model of the land phase of the hydrologic cycle

    • applied to lumped basin using 6-hour time steps

    • Sixteen parameters represent basin characteristics such as percentage of impervious areas, vegetation cover, evapotranspiration, and percolation rates

  • NWSRFS is operational at most RFCs, and our configuration resembles that used at SERFC (i.e., same model calibration and unit hydrographs)


Two headwaters chosen for this study

Two headwaters chosen for this study

  • Geneva basin: large area/slow response

  • Wekiva basin: small area/faster response


September 12 16 2001 derived precipitation

September 12-16, 2001 Derived Precipitation

Theissen

MMOSAIC

DENSE

RMOSAIC

SPARSE


Geneva map and streamflow

Geneva MAP and Streamflow


Wekiva map and streamflow

Wekiva MAP and Streamflow


Check out serfc s mpe online at

Check out SERFC’s MPE online at

http://www.srh.noaa.gov/serfc/qpfvsmap.shtml


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