Improving Satellite-based Precipitation Estimate by using - - PowerPoint PPT Presentation

Improving Satellite-based Precipitation Estimate by using Integration of Ground Radar and GOES Observations

Zhe Feng, Xiquan Dong, Baike Xi

University of North Dakota Patrick Minnis NASA Langley Amir AghaKouchak University of California, Irvine

2010 Fall AGU. Dec 14, 2010

Motivation

• Importance of satellite precipitation estimate

▫ Global coverage, high resolution

• Issues

▫ Uncertainties in fine resolution products from

• ptical sensor estimates

▫ Needs to be evaluated and continually improved

UCI PERSIANN CCS

• IR Tb based retrieval

▫ Cloud patch identification ▫ Retrieval based on patch Tb features ▫ Calibrated with radar + gauges

• Advantage

▫ Not rely on local pixel Tb ▫ High resolution (0.04°, hourly)

• Issues

▫ Assumes raining over entire identified cloud patch

Hong et al. (2004)

Approach

• Combine radar and GOES Observations
• Classify precipitating clouds and anvil clouds
• Compare CCS rainfall against surface gauges
• Find insights from GOES retrieved

microphysics for different cloud types

• A work in progress …

+

Hybrid

Ecd Ccu

CT Ece Ece Ese

Esd Csu

Ese

Hybrid Classification

NEXRAD

Low-Level

Weak/No Rain Rain

Thick Anvil

Convective Stratiform Dee p

GOES GOES

Thin Anvil Thin Anvil

Method

Hybrid Classification CCS Rainfall

• Match hybrid classification with CCS rainfall
• Then the matched CCS rainfall is classified into different cloud

types (Conv, SF, Deep, thick and thin anvils)

• Finally they are compared with Mesonet gauges in Oklahoma

Mesonet Gauges

Use Mesonet to Find Rain Cases

• Compare significant rain events in Oklahoma
• Jun – Aug 2009 (37 cases)
• Case must last more than 2 hours

Case Average Comparison

• Rainfall percentage

▫ Mesonet: almost all from conv/sf ▫ CCS: significant amount from anvil

• Simplify cloud types

▫ [Conv/Sf/DeepCld]  Deep Tower: 74% ▫ Thick Anvil: 15% ▫ Thin Anvil: 8%

Mesonet Case Rain % CCS Case Rain % Mesonet Case Rain % CCS Case Rain %

Average Rainfall Comparison

Hourly Case Average

CCS underestimate deep tower rain, overestimate anvil rain

GOES Cloud Microphysics

Classification CCS Rainfall Optical Depth Ice Effective Diameter

GOES Microphysics

Daytime Nighttime

• Optical depth shows significant difference between thin anvil

and other two types

• Difference exist for both daytime/nighttime
• Therefore the optical depths in thin anvil region may be used to

improve the CCS precipitation retrieval (filter out non raining areas)

• The optical depths in thick anvil and deep tower are difficult to

distinguish (on pixel level)

Thin Anvil Thin Anvil

Idealized Cloud Patch τ Gradient

Conceptual Cloud Patch

τ

Distance Core

Conclusion & Future Work

1) CCS underestimated rain over deep convective tower and overestimated rain

• ver anvil regions

2) The optical depths in thin anvil region can be used to improve CCS precipitation retrieval (8%) 3) GOES derived τ patch parameters will help further improve CCS cloud classification

Questions?

14

Idealized Cloud Patch τ Gradient τ

Distance Core

GOES Microphysics

Ice Effective Diameter IR Temperature Daytime Nighttime

Optical Depth vs. TIR

CCS Rain vs. TIR