Title: An Intercomparison of Precipitation Values from the OneRain Corporation Algorithm and the National W
1An Intercomparison of Precipitation Values from
the OneRain Corporation Algorithm and the
National Weather Service Procedure Steven M.
Martinaitis, Henry E. Fuelberg, John L.
Sullivan Jr. , and Chandra S. Pathak Departmen
t of Meteorology, Florida State University
South Florida Water Management District
Introduction
Results Statistics for 2005
Results Independent Gauges
- Scatter plot for 2005 calendar year supports the
spatial maps of OneRain having overall greater
rainfall totals than MPE. - The majority of the points (each point represents
a pixel) lie above the one-to-one line (dashed)
except for the more extreme precipitation values
where MPE gt OneRain in most cases. - Difference histograms shows a bimodal
distribution with 52.1 of pixels showing
OneRain 2.0 in. to 16.0 in. greater than MPE. - Mean Areal Precipitation (MAP) MPE 57.025
in. OneRain 59.317 in. - MAP Difference (MPE OneRain) -2.292
in.Percent Difference of MAP -4.018Standard
Deviation of Differences 6.064 in.Correlation
0.796
- To better determine which algorithm provided
better rainfall estimates, both were placed
against a set of 13 NCDC daily COOP gauges
(Right Map of gauge locations). - Each gauge is compared with the respected HRAP
44 km grid cell for MPE and OneRain from 2
January 2005 to 31 December 2005. All days that
showed the gauge and the algorithm equaled to
zero were removed for statistical analysis. - The table below gives the mean daily rainfall
difference (Gauge Algorithm), the standard
deviation of those differences, and the
correlation for all gauges for the entire time
period, the cool season (November to April), and
the warm season (May to October) versus MPE and
OneRain.
- Accurate, reliable rainfall data are needed for
flood forecasting and regulatory decisions. - To counteract the sparseness of rain gauges,
radars provide continuous high spatial (1 km,
1-degree azimuth) and temporal (5-10 min.)
resolution using the National Weather Service
(NWS) WSR-88D Doppler radar network. - An optimum combination of rain gauge and radar
data maximizes the strengths of each component
while minimizing their inherent limitations. We
compare results from two such algorithms NWS and
OneRain Corp. - We consider the 2005 calendar year over southern
Florida. The datasets provided are compared on a
pixel-by-pixel basis, within selected watersheds,
and against an independent gauge dataset.
Data and Methodology
South Florida Water Management District
- The South Florida Water Management District
(SFWMD) consists of 140 watersheds covering
42,082 km2. Our study area encompasses the
entire SFWMD and extends outward to include an
35 mile buffer. - Left The SFWMD with watersheds selected for
study in dark shading. Those watersheds are I.
Boggy Creek, II. North St. Lucie, III. East
Caloosahatchee, and IV. Tamiami East.
- Below Monthly MAP values for the Boggy Creek and
Tamiami East watersheds.
- Below Scatter plots for all gauges for the
entire time period, the cool season, and the warm
season versus MPE and OneRain. Each point
represents a daily pairing of a gauge and the
respected algorithm. The anomalous points on the
warm season and overall scatter plots occurred
during two land-falling tropical cyclones
Hurricane Katrina and Hurricane Wilma.
NWS/FSU Multi-sensor Precipitation Estimator
- Historical database created at The Florida State
University (FSU) combines rain gauge data
provided by the five water management districts
(WMDs) and the National Climatic Data Center
(NCDC) with hourly digital precipitation arrays
(DPAs) provided by the NWS Southeast River
Forecast Center (SERFC). All gauges undergo
quality control described by Marzen and Fuelberg
(2005). - The final hourly FSU product is created using the
NWS Multi-sensor Precipitation Estimator (MPE)
code placed on the Hydrologic Rainfall Analysis
Project (HRAP) 44 km grid and is used by the
Florida Department of Environmental Protection
(FDEP).
Case Study Hurricane Wilma
- Time Period Under Consideration 23 October 2005
at 1700 UTC to 24 October 2005 at 2000 UTC
(elapsed time is 27 hours) - Right Accumulated precipitation during study
time period for both algorithms - Below Line graphs of hourly MAP for both
algorithms and the difference between the two
(vertical line represents landfall on 24 October
2005 at 1030 UTC)
OneRain Corporation Algorithm
- The OneRain Corporation provides the five Florida
WMDs with a near real-time product and an
end-of-month edited product. This study uses only
the edited product. - The algorithm is proprietary, and its
characteristics are unknown. The final OneRain
product is placed on a 22 km Cartesian grid, and
the data are provided at 15 min. intervals.
Methodology
- Time series plots of annual summation of rainfall
are shown below for the gauges Fort Lauderdale,
Ortana Lock 2, and Perrine 4W. The gauge is
represented by the green line while MPE and
OneRain are represented by the red and blue lines.
- To accurately perform the inter-comparison, both
datasets were placed on a common grid at a common
time interval with minimum loss of data quality. - For a pixel-by-pixel comparison, the OneRain 22
km Cartesian data were mapped onto the HRAP 44
km grid using an area weighted averaging
technique within ArcGIS based on the percentage
of coverage of 22 km cell. The same technique is
used to calculate precipitation within the
selected watersheds.
Results Entire Grid for 2005
- From left to right OneRain precipitation for
2005, NWS/FSU MPE precipitation for 2005, and
annual difference (MPE OneRain Difference). - For annual difference, OneRain gt MPE is
brown/yellow shading, and MPE gt OneRain is
green/blue shading.
Conclusions
- In the comparison of the NWS/FSU MPE product and
the OneRain Algorithm, OneRain shows greater
rainfall totals over the majority of the study
area. NWS/FSU MPE tends to show greater values
with more extreme totals. - Hurricane Wilma case study shows a change in
precipitation estimation upon landfall. It is
inconclusive whether this was due to a Z-R
relationship change or how each algorithm handles
tropical events. - With independent gauges, OneRain has a smaller
overall mean daily difference while NWS/FSU MPE
have a smaller standard deviation of these
differences and better gauge-to-pixel
correlation. Time series plots of accumulating
rainfall display how both algorithms vary versus
individual gauges. - Neither one of the algorithms stand out as a
better performing product. - Future Work Compare over 2004 calendar year and
WAM hydrologic modeling - Reference Marzen, J., and Fuelberg, H.E. (2005).
Developing a high resolution precipitation
dataset for Florida hydrological
studies. 19th Conf. on Hydrology, AMS, San
Diego, CA.
- OneRain produces greater rainfall totals prior to
landfall while MPE yields greater rainfall totals
while the eye is over land. However, we were
unable to prove whether this is due to the
WSR-88Ds being switched over to the tropical Z-R
relationship. - Below Example hourly scatter plots prior to,
during, and after landfall