Validation of Precipitation and Lightning Observations

Meredith NicholsSeptember 29th, 2015

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Research GoalsThis 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________________________________________

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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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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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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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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Average Spatial Plots

Daily average rainfall estimates for Summer months (JJA) between

2010-2014

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Maximum Spatial Plots

Daily maximum rainfall estimates for Summer months (JJA) between

2010-2014

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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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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).

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Infrared-based satellite products overestimate precipitation

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All satellite products overestimate precipitation during summer

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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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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

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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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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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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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