Estimation of Rainfall Areal Reduction Factors Using NEXRAD Data

Francisco Olivera, Janghwoan Choi and Dongkyun Kim

Texas A&M University – Department of Civil Engineering

Funded by the Texas Department of Transportation

2006 AWRA GIS-Water Resources IV8-10 May 2006 – Houston, Texas

Purpose of the Project

Characterize: rainfall intensity distribution, storm shape, and storm orientation

Areal Precipitation

Rainfall intensity is not uniformly distributed.

Rain gauges measure precipitation at points, but precipitation over areas has to be estimated (Weather Bureau 1957, 1958, 1958, 1959, 1960, 1964; Rodriguez-Iturbe and Mejia 1974; Frederick et al. 1977; NWS 1980; Omolayo 1993; Srikathan 1995; Bacchi and Ranzi 1996; Siriwardena and Weinmann 1996; Sivapalan and Bloschl 1998; Asquith and Famiglietti 2000; De Michele et al. 2001; Durrans et al. 2002; among others).

ARFs are used to convert point precipitation (PP) into areal average precipitation (PA).

A A PP ARF P

Areal Reduction Factors (ARF)

According to TP-29 (Weather Bureau 1957, 1958, 1958, 1959, 1960, 1964 )

ARF Estimation Methods

Geographically-fixed (used in TP-29)

Concurrent Areal Annual Maxima

Station Annual Maxima

Geo

grap

hica

lly-f

ixed

ra

in g

age

netw

ork

2528

3531

3541

3541

37

38

33.2ARF 0.92

36.0

ARF Estimation Methods

Average precipitationwithin the area

Maximum precipitationwithin the area

ARF =

Spatially-distributed precipitation data allows us to implement other methods for estimating ARFs that capture the storm anisotropy.

Storm-centered ARF

For each storm:

Data

Precipitation data

Type: NEXRAD Multisensor Precipitation Estimator (MPE)

Period: Years 2003 and 2004

Area: West Gulf River Forecasting Center

Time resolution: 1 hour

Spatial resolution: 4km x 4km (approx.)

Study Area

The WGRFC mesh has 165,750 cells. After clipping out Texas (including a 50 km buffer),

the mesh had 56,420 cells.

More than 2.9 billion precipitation values

NEXRAD: Sources of error

Vertical Profile Reflectivity (VPR) effect Related to how well the radar can see the precipitation near the surface. x 2 of overestimation and x 10 of underestimation. It is a major source of error.

Microphysical parameters Related to the different Z-R relationship for different types of storms

(convective, tropical, stratiform) Radar calibration

Site specific (corrected for all radar sites in the USA now) Sampling errors

Arbitrary/random errors (cancels out for lumped model) Truncation error

Related to the numerical processing of the values (magnitude of 0.1mm, important for long low intensity events)

Reliability of NEXRAD Data

When comparing NEXRAD precipitation data to gauged precipitation data: NEXRAD adjustments are not site specific. 16-km2 areal precipitation is not the same as point precipitation. Rain gauges are not perfect.

Methodology

Precipitation Annual Maxima

Storm durations of 1, 3, 6, 12 and 24 hours.

NEXRAD data Annual maxima Concurrent rainfall

Storm Identification

• 21 x 21 cell window (i.e., 84 km × 84 km) around the “center cell”.

• Calculations proceed only if the “center cell” has the maximum concurrent precipitation depth.

• 18,531 storms were identified.

Optimum Ellipse

• For a given area, the ellipse that comprises the maximum precipitation volume was selected.

• For determining the “optimum ellipse”, the shape aspect and orientation were changed systematically.

• After determining the optimum ellipse, the same procedure was applied for a new area value.

Optimum Ellipse Values

Location

Duration (hours)

Rainfall depth (mm) (center)

Area (km2)

Rainfall depth (mm) (ellipse)

ARF

Aspect

Orientation

For each optimum ellipse:

Results

Climate Regions

TWDB (1967), The Climate and Physiography of Texas,

Texas Water Development Board, Report 53, 27 p.

Points represent 1-hour storms.

Region / Season / Depth

Probability distribution:

0

1

2

3

4

0.0 0.2 0.4 0.6 0.8 1.0

All storms (7,479 storms)

Summer storms (6,486storms)

Summer storms in regions #4and #5 (938 storms)

Summer storms in regions #4and #5 of depth between 53and 69 mm (100 storms)

Storm duration: 1 hour

Storm area: 737 km2

ARF

Region

0.0

0.2

0.4

0.6

0.8

1.0

0 100 200 300 400 500 600 700 800

Area (km2)

AR

F

Region #1

Region #2

Region #3

Regions #4 and #5

Region #6

Duration = 1 hour

Area = 737 km2

Depth = 50 mmSummer storms

Season

0.0

0.2

0.4

0.6

0.8

1.0

0 100 200 300 400 500 600 700 800

Area (km2)

AR

F

Winter

Summer

Duration = 1 hourArea = 737 km2Regions #4 and #5Depth = 50 mm

Depth

0.0

0.2

0.4

0.6

0.8

1.0

0 100 200 300 400 500 600 700 800

Area (km2)

AR

F

25 mm

50 mm

75 mm

Duration = 1 hour

Area = 737 km2

Regions #4 and #5Summer storms

Storm Aspect

SummerWinter

Storm Orientation

SummerWinter

Conclusions

Conclusions

Season made a difference in all cases except for a few in regions 1 and 6.

Region made a difference in all cases except for 4 and 5 in summer.

Depth made a stronger difference in summer than in winter storms. However, because the database was very limited in the depth range (i.e., only low return period values were considered), conclusions cannot be definite.

Storm aspect values of around 2 were the most frequent for both seasons and all regions.

SW-NE storm orientations are predominant in winter storms, and less pronounced in summer storms.

ARF vary within a wide range, because it depends on each storm characteristics

Questions?