Editorial Type:
Article
Article Type:
Research Article

Kalman Filter–Based CMORPH

Robert J. Joyce NOAA/Climate Prediction Center, Camp Springs, Maryland, and Wyle, Inc., McLean, Virginia

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Pingping Xie NOAA/Climate Prediction Center, Camp Springs, Maryland

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Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/JHM-D-11-022.1
Page(s):
1547–1563
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Abstract

A Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH) algorithm is developed to integrate the passive microwave (PMW) precipitation estimates from low-Earth-orbit (LEO) satellites and infrared (IR) observations from geostationary (GEO) platforms. With the new algorithm, the precipitation analysis at a grid box of 8 × 8 km2 is defined in three steps. First, PMW estimates of instantaneous rain rates closest to the target analysis time in both the forward and backward directions are propagated from their observation times to the analysis time using the cloud system advection vectors (CSAVs) computed from the GEO–IR images. The “prediction” of the precipitation analysis is then defined by averaging the forward- and backward-propagated PMW estimates with weights inversely proportional to their error variance. The IR-based precipitation estimates are incorporated if the gap between the two PMW observations is longer than 90 min. Validation tests showed substantial improvements of the KF-based CMORPH against the original version in both the pattern correlation and fidelity of probability density function (PDF) of the precipitation intensity. In general, performance of the original CMORPH degrades sharply with poor pattern correlation and substantially elevated (damped) frequency for light (heavy) precipitation events when PMW precipitation estimates are available from fewer LEO satellites. The KF-based CMORPH is capable of producing high-resolution precipitation analysis with much more stable performance with various levels of availability for the PMW observations.

Corresponding author address: Dr. Pingping Xie, NOAA/Climate Prediction Center, 5200 Auth Road, Suite 605, Camp Springs, MD 20746. E-mail: Pingping.Xie@noaa.gov

Abstract

A Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH) algorithm is developed to integrate the passive microwave (PMW) precipitation estimates from low-Earth-orbit (LEO) satellites and infrared (IR) observations from geostationary (GEO) platforms. With the new algorithm, the precipitation analysis at a grid box of 8 × 8 km2 is defined in three steps. First, PMW estimates of instantaneous rain rates closest to the target analysis time in both the forward and backward directions are propagated from their observation times to the analysis time using the cloud system advection vectors (CSAVs) computed from the GEO–IR images. The “prediction” of the precipitation analysis is then defined by averaging the forward- and backward-propagated PMW estimates with weights inversely proportional to their error variance. The IR-based precipitation estimates are incorporated if the gap between the two PMW observations is longer than 90 min. Validation tests showed substantial improvements of the KF-based CMORPH against the original version in both the pattern correlation and fidelity of probability density function (PDF) of the precipitation intensity. In general, performance of the original CMORPH degrades sharply with poor pattern correlation and substantially elevated (damped) frequency for light (heavy) precipitation events when PMW precipitation estimates are available from fewer LEO satellites. The KF-based CMORPH is capable of producing high-resolution precipitation analysis with much more stable performance with various levels of availability for the PMW observations.

Corresponding author address: Dr. Pingping Xie, NOAA/Climate Prediction Center, 5200 Auth Road, Suite 605, Camp Springs, MD 20746. E-mail: Pingping.Xie@noaa.gov
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