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![Page 1: The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula Greg Quina SC DHEC, Bureau of Air Quality Based on graduate research.](https://reader035.fdocuments.net/reader035/viewer/2022062618/5514be5855034693478b4646/html5/thumbnails/1.jpg)
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
![Page 2: The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula Greg Quina SC DHEC, Bureau of Air Quality Based on graduate research.](https://reader035.fdocuments.net/reader035/viewer/2022062618/5514be5855034693478b4646/html5/thumbnails/2.jpg)
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
![Page 3: The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula Greg Quina SC DHEC, Bureau of Air Quality Based on graduate research.](https://reader035.fdocuments.net/reader035/viewer/2022062618/5514be5855034693478b4646/html5/thumbnails/3.jpg)
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
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Gauge Data (Dense Network)
• 622 gauges from SJRWMD, SWFWMD, and SFWMD
• 3 HRL gauges used as verification
• Hourly-accumulated tipping bucket
• Quality controlled
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Gauge-only Products
• PMOSAIC (Thiessen Polygons)– Closest available gauge
• GMOSAIC (Gauge-only analysis)– Optimal Estimation
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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)
![Page 7: The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula Greg Quina SC DHEC, Bureau of Air Quality Based on graduate research.](https://reader035.fdocuments.net/reader035/viewer/2022062618/5514be5855034693478b4646/html5/thumbnails/7.jpg)
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
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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
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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
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Height of Lowest Unobstructed Sampling
VolumeRadar Coverage Map
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Result: Mosaicked Radar Estimates (RMOSAIC)
![Page 12: The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula Greg Quina SC DHEC, Bureau of Air Quality Based on graduate research.](https://reader035.fdocuments.net/reader035/viewer/2022062618/5514be5855034693478b4646/html5/thumbnails/12.jpg)
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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kBias )(
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For additional details, see Seo et al. (1999)
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Remove Mean Field Bias (BMOSAIC)
BMOSAIC(I,J)=BIASa(k) x RMOSAIC(I,J)
![Page 14: The Multisensor Precipitation Estimator and Evaluations over the Florida Peninsula Greg Quina SC DHEC, Bureau of Air Quality Based on graduate research.](https://reader035.fdocuments.net/reader035/viewer/2022062618/5514be5855034693478b4646/html5/thumbnails/14.jpg)
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
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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.
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Hourly Correlograms2 gauge pairs separated by an inter-gauge distance
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50
Inter-Gauge Distance (km)
Co
rre
lati
on
(r)
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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.
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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.
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September 2001 Hydro Case StudyTropical Storm Gabrielle
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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)
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Two headwaters chosen for this study
• Geneva basin: large area/slow response• Wekiva basin: small area/faster response
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September 12-16, 2001 Derived PrecipitationTheissen
SPARSE
DENSE
RMOSAIC
MMOSAIC
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Geneva MAP and Streamflow
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Wekiva MAP and Streamflow
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Check out SERFC’s MPE online at
http://www.srh.noaa.gov/serfc/qpfvsmap.shtml