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Exploring and Enhancement of GOES-R ABI for Rainfall Detection and Estimation

Exploring and Enhancement of GOES-R ABI for Rainfall Detection and Estimation. Zhanqing Li CICS University of Maryland. Project Summary.

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Exploring and Enhancement of GOES-R ABI for Rainfall Detection and Estimation

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  1. Exploring and Enhancement of GOES-R ABI for Rainfall Detection and Estimation Zhanqing Li CICS University of Maryland

  2. Project Summary • The Advanced Baseline Imager (ABI) onboard the planned GOES-R series satellites will have new channels for measurements at visible, NIR, and IR channels. This study investigates how to use the GOES-R channels to enhance estimation of cloud liquid water path and precipitation. MODIS, AMSR and CloudSat data are used as proxy to demonstrate our approach.

  3. Motivation/Justification • Traditional GOES satellite precipitation estimation depends on the IR brightness temperature, which is good for deep convective system, but is problematic for other types of precipitation (e.g. stratiform rain) and suffers the ambiguity caused by cirrus clouds. In this study, a precipitation regimes are classified with the new ABI channels and rain rate is estimated with different parameters for these precipitation regimes. • Traditionally, cloud LWP is calculated with effective radius retrieved from single NIR channels, which is bias toward cloud top. The new multiple NIR channels of ABI can be used to retrieve the profile of droplet effective radius and improve the estimation of cloud LWP calculation

  4. Methodology • MODIS radiance measurements will be used as proxy of ABI data. Precipitation regime will be classified using reflectivity measurements from CloudSat radar and the rain rate estimation is from AMSR-E. A synergetic analysis of these data has illustrated the potential to enhance satellite-based precipitation estimation. • Chang and Li’s algorithm (JGR 2002, 2003) is applied to MODIS data to retrieve effective radius profile and calculate cloud LWP. A comparison between MODIS cloud LWP and AMSR-E cloud LWP shows the potential of new ABI channel to improve cloud LWP estimation.

  5. Comparison of cloud droplet size for raining and non-raining clouds

  6. Results • The comparison of MODIS DER profile retrievals with radar rain detection shows that DER profile is capable of detecting raining and non-raining clouds (Chen et al, 2007 in preparation). • The comparison of MODIS LWP with AMSR-E LWP shows that, the new ABI channels has the potential to improve the accuracy of cloud LWP estimation on order of 10% (Chen et. al, JAS 2007).

  7. Future work • Classify precipitation regimes based on CloudSat radar reflectivity profile • Synergetic analyses of MODIS radiance measurements, AMSR-E rain rate estimations, and CloudSat precipitation regime classification to develop a scheme for enhancement of precipitation estimation with ABI channels.

  8. Funding Profile (K) • Manpower • PI: 10K • Graduate Assistantship: 25K • Tuition Fee & benefits: 20K • Computation • Computer and storage 10K • Software and support: 5K • Travel: 5K • Total direct cost: 75K • Indirect cost: 25K • Total budget: 100K

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