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)?

• 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 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)

• 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

Precipitation SensorsWSR-88D Radar

• 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!

• Good temporal and spatial resolution (6 minute, 1 km range x 1 degree azimuth)

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

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

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

3 Create maps of effective radar coverage areas.– Minimize defective areas

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:

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

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

3 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

VolumeRadar Coverage Map

Result: Mosaicked Radar Estimates (RMOSAIC)

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

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Remove Mean Field Bias (BMOSAIC)

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

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

544332211 WBMOSAICWGWGWGWGMMOSAIC ijij

RMOSAIC MMOSAIC

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 Correlograms2 gauge pairs separated by an inter-gauge distance

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Inter-Gauge Distance (km)

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Statistical Results – 1996 through 1999

METHOD r RMSD MAD BIAS BIAS %

PMOSAIC 0.367 0.207 0.080 0.060 6.027

GMOSAIC 0.419 0.179 0.076 0.050 5.000

RMOSAIC 0.698 0.136 0.054 -0.121 -12.061

BMOSAIC 0.734 0.133 0.052 -0.069 -6.888

MMOSAIC 0.733 0.130 0.051 -0.028 -2.779

• 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

• 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 StudyTropical Storm Gabrielle

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

• Geneva basin: large area/slow response• Wekiva basin: small area/faster response

September 12-16, 2001 Derived PrecipitationTheissen

SPARSE

DENSE

RMOSAIC

MMOSAIC

Geneva MAP and Streamflow

Wekiva MAP and Streamflow

Check out SERFC’s MPE online at

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