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?