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COMET Hydrometeorology 00-1Matt Kelsch

Tuesday, 19 October 1999kelsch@comet.ucar.edu

Radar-Derived Precipitation Part 4

I. Radar Representation of Precipitation

II. WSR-88D, PPS

III. PPS Adjustment, Limitations

IV. Effective Use

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• Numerous quality control steps to minimize limitations both in the radar estimate of precipitation, and the rain gauge representation of precipitation.

• Spatial and temporal resolution are excellent for the mesoscale detail of precipitation systems.

– Spatial detail over a large area– Monitor evolution of events between gauge sites– Real time information

V. Effective UseStage I PPS

Strengths

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Stage 1 PPS: Strengths (cont.)

• Opportunity for important rainfall information in remote, poorly instrumented areas.

• Adaptation parameters provide some flexibility for different locations and climate regimes.

• Has the versatility to evolve into a better algorithm that can effectively account for variability on a geographic, seasonal, and even hourly basis.

• Offers important input for a comprehensive, multi-sensor system.

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• Range degradation, overshooting low-levels– Problem associated with propagation of beam, not Z-R.

• Snowfall– More complexity than liquid hydrometeors.– Phase changes and mixed phases exist over small

space/time scales.– Range degradation often co-exists.

• Phase change: hail, melting snow– Radical storm-scale changes in Z to R relationship.– Minimal proof that hail correction can be done with Z-R.– Inconsistent relationship between Z-R and hail occurrence.

Radar-Derived Precip:When changing Z-R coefficients

is not the real solution:

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• Consistently different average DSD (climate)– Tropical versus mid-latitude (warm vs. cold process)– Maritime versus continental

• Consistently different average DSD (season)– Convective versus stratiform

• Precip System character– Identify Convective versus Stratiform signature– Identify warm versus cold rain signature– Identify maritime versus continental

Radar-Derived Precip:When changing Z-R may help:

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Why can’t the adaptation parameters and bias adjustment procedure solve

all the limitations?

Radar bias adjustment is only one uniform adjustment. It depends on adequate representation of precip by the local gauge network.

• Adaptation parameters can greatly help the algorithm performance for a given site and/or season. The parameters “tune” the algorithm for the typical scenario. Atypical events, such as unusually high rainfall rates, may not be diagnosed well.

• The most effective use of PPS is to make it a function of meteorology, not the “normal” climatology.

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Can we account for the important atypical events without degrading the guidance for the more common

typical events?

• Meteorological information from soundings, profilers, and surface reports are a few examples of data sources that can assist with real-time adjustment of adaptation parameters.

• Information from other NEXRAD algorithms, such as HAIL or VIL, may provide some guidance.

• The most effective use of PPS is to make it a function of meteorology, not the “normal” climatology.

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DATA: Soundings and Rainfall RatesWhat are reasonable maximum rainfall rates expected?

* 4.70 in/h IA

4.88 in/h MN

5.43 in/h TX

* 5.70 in/h NM

5.91 in/h Malaysia

6.25 in/h OH

* 6.70 in/h IL (10 in/h 100-yr. event atORD)

6.93 in/h PA

7.00 in/h MD

7.44 in/h China (record)

* 7.87 in/h AL

* 8.00 in/h CO

* 9.50 in/h NE

*16.00 in/h FL

How do these compare with sounding characteristics:Sounding and Rainfall Data* 29 days in Colorado, 13 detailed case studies* 60 days in Alabama* 8 days in TX, LA, MN, PA, KS, MA

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Z = 300R1.4

dBZ

20 0.5 (0.02)

25 1.0 (0.04)

30 2.4 (0.09)

35 5.4 (0.21)

40 12.3 (0.48)

45 27.9 (1.10)

50 63.4 (2.50)

55 144.2 (5.68)

60 328.1 (12.92)

65 748.2 (29.46)

70 1,702.0 (67.01

Z = 300R1.4dBZ50.0 63.4 (2.50)50.5 68.8 (2.71)51.0 74.7 (2.94)51.5 81.1 (3.19)52.0 88.1 (3.47)52.5 95.6 (3.77)53.0 103.8 (4.09)53.5 112.7 (4.44)54.0 122.4 (4.82)54.5 132.5 (5.23)55.0 144.2 (5.68)55.5 156.6 (6.17)56.0 170.1 (6.70)56.5 184.6 (7.27)57.0 200.5 (7.89)57.5 217.7 (8.57)58.0 236.4 (9.31)58.5 256.6 (10.10)59.0 278.6 (10.97)59.5 302.4 (11.91)60. 328.4 (12.92)

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Radar-derived Precipitation:A Summary Of Major Points

• Radar provides one of several useful methods for sampling precipitation

• Quantitative reliability issues are related to the fact that radar is sampling some volume at some elevation to estimate precipitation at the ground

• Radar-derived precipitation is most reliably modeled for liquid hydrometeors; hail and snow add complexity

• The above two points are not effectively corrected by changing Z-R coefficients; Z-R changes should be related to Drop Size Distribution knowledge.

• Radars and rain gauges do not measure equal samples

• Rain gauges do not provide a good representation of precipitation distribution, especially convective precip.

• Radar provides excellent information about the spatial and temporal evolution of precipitation systems.