This study analyzes the performance of five satellite-derived precipitation estimation products relative to rain-gauge and radar observations over the continental United States from 2010-2014. The products generally overestimate precipitation during the summer and CMORPH underestimates during winter. Infrared-based products tend to overestimate the most, while CMORPH and Hydro-Estimator have higher correlations. Future research will use lightning data to help validate satellite estimates in convective and non-convective regions.

1. Validation of Precipitation and
Lightning Observations
Meredith Nichols
September 29th, 2015

2. Research Goals
This study analyzes the performance of five satellite-derived
precipitation estimation products relative to rain-gauge and
radar observations
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Purpose: Characterize errors in satellite precipitation estimates
to better inform the user community, and will help researchers
improve future versions of their precipitation estimates
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Submitted Manuscript:
Nichols, M. A., D. F. Wheeler, P. C. Meyers, and S. D. Rudlosky,
2015: Seasonal and Annual Validation of Satellite Precipitation
Estimates. J. Operational Meteor., under review.
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3. Current CICS Rainfall Validation:
STAR Rainfall Product Cal/Val Center
http://cics.umd.edu/ipwg/
• Currently validates
about 17 different
satellite algorithms
and models
produced by NASA,
NOAA and other
groups
• Creates statistical
maps and time
series plots
• Created by John
Janowiak, and
updated by J.J. Wang
• Daily validation
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4. Approach & Methods
• Study focuses on Continental
United States
• Five years of daily precipitation
estimates (2010–14) are
composited into meteorological
seasons and annual maps to
characterize performance
• Data are objectively analyzed on a
0.25 degree grid
• Average conditional, maximum,
and sum (accumulation) rain
rates are computed
• Five satellite-derived precipitation
products validated relative to:
• Rain Gauge
– From the Climate Prediction
Center (CPC) Unified
Precipitation project
– Contains quality controlled
information from over 7000
stations across the U.S.
• Radar
– Composite of Stage II NWS WSR
88D radar data (Stage II/IV)
– Uses the radar-only Stage IV
product with no bias correction
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5. Satellite-Derived Precipitation Products
• Infrared (IR)-based:
– SCAMPR, Hydro-Estimator, and GPI
• SCAMPR
– NESDIS Self-Calibrating Multivariate
Precipitation Retrieval
– combines the greater accuracy of PMW
precipitation estimates with more
frequently available and higher spatial
resolution IR observations
• Hydro-Estimator
– The National Environmental Satellite,
Data, and Information Service (NESDIS)
product uses GOES IR data, but corrects
for the evaporation of raindrops to help
improve accuracy
• GPI
– CPC technique uses mainly IR data, and
only uses PMW to fill the spatial IR gaps
• Passive Microwave (PMW)-based:
– 3B42RT, CMORPH
• 3B42RT
– The NASA Tropical Rainfall
Measuring Mission (TRMM) Multi-
Satellite Precipitation Analysis
(TMPA) 3B42RT product that
combines PMW and PMW-
calibrated IR to estimate
precipitation in near real-time
• CMORPH
– CPC Morphing technique blends
PMW and IR observations
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6. Average Spatial Plots
Daily average rainfall
estimates for Summer
months (JJA) between
2010-2014
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7. Maximum Spatial Plots
Daily maximum rainfall
estimates for Summer
months (JJA) between
2010-2014
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8. Observed overestimates during both
summer and winter
CMORPH generally overestimates
precipitation by 3–5 mm day -1 over the U.S.
Great Plains during summer
SCaMPR generally overestimates
precipitation by 5–10 mm day-1 over the
U.S. Great Plains during summer
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9. CMORPH underestimates precipitation
during winter
During winter, CMORPH
underestimates precipitation over
the eastern U.S. (1–3 mm day-1)
as well as along the west coast
and inter-mountain west (2–10
mm day -1). 9

10. Infrared-based satellite products
overestimate precipitation
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11. All satellite products overestimate
precipitation during summer
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12. Average Summer Bias
relative to Gauge
• Satellite precipitation
estimates skew
mostly positive
during summer
• Winter exhibits both
positively and
negatively skewed
biases
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13. Average Conditional
Precipitation Correlations
• Hydro-Estimator (red)
produces the highest
correlations during most
years
• Satellite correlations are
much more consistent
compared to radar than
gauge
• Overall, correlations are
higher for satellite
compared to gauge than
radar 13

14. Understanding Possible Causes of
Spatial Biases
• False Alarm Rate (FAR)
• Probability of Detection
(POD)
• Suggestive of whether
satellite biases are
caused by misclassifying
the frequency or
intensity of precipitation
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15. Understanding Possible Causes of
Spatial Biases
The greatest SCaMPR
overestimates occur over
Iowa, Nebraska, and
Kansas:
a region with relatively
low FAR values, indicating
rain-rate intensity
overestimates
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16. Future Research:
Use Lightning to help Validate Rainfall Estimates
Rain Gauge Radar
NLDN
Lightning data will be used to better characterize the performance of the
satellite estimates in convective and non-convective regions
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3B42