PRELIMINARY RESULTS FOR THE 0-1 HOUR MULTISENSOR PRECIPITATION NOWCASTER

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PRELIMINARY RESULTS FOR THE 0-1 HOUR MULTISENSOR
PRECIPITATION NOWCASTER
6R.4

• Shucai Guan and Feng Ding
• RS Information Systems/Hydrology Laboratory
• Richard Fulton and David Kitzmiller
• Hydrology Laboratory
• Office of Hydrologic Development
• National Weather Service, NOAA
• Silver Spring, Maryland
• 32nd Conference on Radar Meteorology26 October
  2005, Albuquerque, NM

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Outline

• Introduction
• Description of the Multisensor Precipitation
  Nowcaster (MPN)
• Analysis Method and Results
• Conclusions

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Introduction

• NWS mission includes warning operations for flash
  flooding conditions, currently the greatest
  storm-related threat to life in the United
  States.
• MPN is developed for NWS Weather Forecast Offices
  to provide additional automated forecast guidance
  and lead-time for issuance of flash flood
  warnings.
• The purpose evaluate the accuracy of a
  scaled-down MPN (no gage data and no mosaicking)
  forecasts of rainrate and establish a baseline of
  performance.

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Description of MPN

• 4-km resolution, updated every 5 min forecast
  1-hour accumulated precipitation and 0-1 hour
  rain rates.
• Can use rain gauge data to adjust the radar
  rainrates.
• Mosaics regional radar data before making the
  forecast.
• Uses a standard local pattern-matching scheme to
  estimate storm motion.
• Three options for the smoothing 1) no
  smoothing, 2) adaptable smoothing using the
  Flash Flood Potential method, or 3) a method
  proposed by Bellon and Zawadski (1994) (hereafter
  called BZ94).
• Growth/decay of local rain rates.

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• 7 flash flood cases in the MD-VA-PA region are
  investigated.
• Six statistics (Bias, RMSE, COR, POD, FAR, and
  CSI) are used to evaluate and compare the
  accuracy of the parameter tests.
• There are13 algorithm configurations for each
  case 2(growth/decay N or G) X 3 smoothing
  (none, FFP method, BZ94 method N or F or B) X 2
  (local vs. area-averaged storm motion L or A)
  persistence (PRS). For example, NFL is test with
  turning off growth/decay, using FFP smoothing and
  local storm motion.

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13 algorithm configurations and their test names
Growth/decay No No No No No No No Yes Yes Yes Yes Yes Yes
Smoothing No No No FFP FFP BZ94 BZ94 No No FFP FFP BZ94 BZ94
Motion No Avg Loc Avg Loc Avg Loc Avg Loc Avg Loc Avg Loc
Test name PRS NNA NNL NFA NFL NBA NBL GNA GNL GFA GFL GBA GBL
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• Example of observed and forecasted 60-minute rain
  rate and one-hour accumulation images for June
  13, 2003
• (MPN with option NFL)

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The bias is ?(forecasted rain rate)/ ?(observed
rain rate).
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(No Transcript)
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(No Transcript)
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Growth/decay option as implemented causes
positive bias in forecasts
Smoothing option reduces bias in forecasts
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Turning off growth/decay option results a
perceptible improvement on RMSE after the 30
minute forecast
Smoothing option reduces RMSE
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The smoothing option increases correlation
Turning on the growth/decay option has
negligible improvement on correlation
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Turning on the growth/decay option and smoothing
option improve POD
The effect of the growth/decay option is much
larger than that of the smoothing option at 60
minutes into the forecast
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Turning on the growth/decay option increases FAR
The smoothing produces notable improvement on FAR
after the 30 minute forecast
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The smoothing improves CSI
The growth/decay option has small mixed effect on
CSI
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-10
-10
-11
71
67
32
32
54
54
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Conclusions

• MPN substantially improves all six statistics
  relative to persistence method. The progressive
  spatial smoothing creates major improvement for
  all six statistics.
• Comparing with persistence, MPN
• Reduces RMSE by 24.
• Raises POD by 71 for rainrate gt 5 mm/h.
• Raises POD by 32 for rainrate gt15 mm/h.
• Decreases FAR by about 10.
• Increases CSI by 67 for rainrate gt 5 mm/h.
• Increases CSI by 54 for rainrate gt 15 mm/h.