Estimation of Cloud and Estimation of Cloud and Precipitation From Warm Clouds in Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Support of the ABI: A Pre-launch

Study with A-Train Study with A-Train

Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro, F. WengZhanqing Li, R. Chen, R. Kuligowski, R. Ferraro, F. Weng

CICS/ESSIC, University of MarylandCICS/ESSIC, University of MarylandSTAR/NESDIS/NOAA, Camps Spring, MDSTAR/NESDIS/NOAA, Camps Spring, MD

Introduction: Introduction: Low-level liquid cloud Low-level liquid cloud

• Warm, liquid phase, Warm, liquid phase, frequently occur, i.e. frequently occur, i.e. nimbostratus and nimbostratus and stratocumulusstratocumulus

• Large spatial coverage, Large spatial coverage, important for radiation important for radiation budget budget

• Warm rain, without ice Warm rain, without ice processprocess

Introduction: Satellite Observation of Introduction: Satellite Observation of Cloud and Precipitation - VIS/NIR/IRCloud and Precipitation - VIS/NIR/IR

• Solar reflectance at visible, NIR - Tau, re, Solar reflectance at visible, NIR - Tau, re, LWPLWP

• Cloud emission at IR window – Top Cloud emission at IR window – Top TemperatureTemperature

• Top Temperature – Precipitation Top Temperature – Precipitation

• Pros: high resolution, small surface Pros: high resolution, small surface impact, works over both land and oceanimpact, works over both land and ocean

• Cons: no VIS/NIR for night, NIR/IR mainly Cons: no VIS/NIR for night, NIR/IR mainly observe cloud top, misses shallow rainobserve cloud top, misses shallow rain

Introduction: Satellite Observation Introduction: Satellite Observation of Cloud and Precipitation - of Cloud and Precipitation -

MicrowaveMicrowave • Emission at low frequency (i.e. Emission at low frequency (i.e.

37GHz, 19GHz) – LWP, Rain Rate over 37GHz, 19GHz) – LWP, Rain Rate over oceanocean

• Ice scattering at high frequency (i.e. Ice scattering at high frequency (i.e. 85GHz) – Rain Rate over land85GHz) – Rain Rate over land

• Pros: day & night, observe the whole Pros: day & night, observe the whole profile over oceanprofile over ocean

• Cons: low resolution, big surface Cons: low resolution, big surface impact, no LWP over landimpact, no LWP over land

ObjectiveObjective

• Impact of vertical re variation on cloud Impact of vertical re variation on cloud liquid water estimation (re profile & liquid water estimation (re profile & LWP estimation)LWP estimation)

• Relationship between vertical re Relationship between vertical re variation and rain process (re profile & variation and rain process (re profile & rain)rain)

• Potential of cloud microphysical Potential of cloud microphysical parameter on warm rain estimation parameter on warm rain estimation (warm rain estimation)(warm rain estimation)

After Miles et al. (JAS, 2000 JAN)

Vertical variations of cloud droplet sizes and liquid water density for low-level stratiform clouds compiled from various in-situ measurements.

Note the general linear increasing trends!

Chang and Li (JGR, 2002, 2003)

Part I: re profile & LWP estimation Part I: re profile & LWP estimation

Previous Studies of LWP estimationPrevious Studies of LWP estimation

Problem : Assume vertically constant re. re is retrieved from single NIR channel and weighted toward cloud top.

re(h) re

• Overestimate LWP when re increased with height (IreP)

• Underestimate LWP when re decreased with height (DreP)

• Chang and Li’s linear Re profile (re1-top, re2-base) retrieval using 1.6µm, 2.1µm, and 3.7µm, and LWP estimation with re profile

re(h)re

erLWP 3

2

Part I: re profile & LWP estimation Part I: re profile & LWP estimation

Data & MethodsData & Methods• Aqua MODIS Aqua MODIS –– T, tau, re T, tau, re3.73.7, LWP, LWP3.73.7 , re , re2.12.1, LWP2, LWP22.12.1 , ,

rere1.61.6, LWP, LWP1.61.6 , re profile (re1, re2), LWP , re profile (re1, re2), LWPreprep, ,

• Aqua AMSR-E Aqua AMSR-E –– LWP LWPAMSR-EAMSR-E

• MODIS 1X1km, AMSR-E 13X7 km, Compare LWPMODIS 1X1km, AMSR-E 13X7 km, Compare LWP3.73.7 and LWPand LWPrep rep with LWPwith LWPAMSR-EAMSR-E

• Latitude -40Latitude -4000~40~4000, Tc>273K, solar zenith angle < , Tc>273K, solar zenith angle < 505000, satellite view angle < 30, satellite view angle < 3000

Part I: Part I: re profile & LWP re profile & LWP

estimationestimation LWP comparison between MODIS/AMSR-LWP comparison between MODIS/AMSR-

E(Cont.)E(Cont.)

• Bias caused by the vertically Bias caused by the vertically constant re ~ 10%constant re ~ 10%

• re profile corrects the biasre profile corrects the bias

re(h)

N re P cloud

LWP3.7 +2.6%

LWPrep +2.9%

re(h) re(h)

I re P cloud

LWP3.7 +12.6%

LWPrep +5.2%

D re P cloud

LWP3.7 -11.2%

LWPrep +0.1%

Part I: Part I: re profile & LWP re profile & LWP estimationestimation

LWP comparison between MODIS/AMSR-ELWP comparison between MODIS/AMSR-E

• re profile improves the comparison with AMSR-Ere profile improves the comparison with AMSR-E• Constant re assumption has opposite impact on Constant re assumption has opposite impact on

IreP/DreP cloudIreP/DreP cloud

LWP3.7

LWPrep

Part II: Warm RainPart II: Warm Rain EstimationEstimation

ObjectiveObjective• How important is warm rain? How important is warm rain?

• How is satellite passive microwave How is satellite passive microwave observation of warm rain over observation of warm rain over ocean? ocean?

• Does the cloud microphysical Does the cloud microphysical parameter has the potential for warm parameter has the potential for warm rain estimation?rain estimation?

Part II: Warm RainPart II: Warm Rain EstimationEstimation

datadata• CloudSat CPR rain rate product, CloudSat CPR rain rate product,

1.7X1.3km, nadir over ocean only 1.7X1.3km, nadir over ocean only

• Aqua AMSR-E rain rate product, 5X5 kmAqua AMSR-E rain rate product, 5X5 km

• Aqua MODIS cloud estimates, 1X1 kmAqua MODIS cloud estimates, 1X1 km

• Ship-borne radar. Ship-borne radar.

Part III: Warm RainPart III: Warm Rain EstimationEstimation

• The low-level liquid clouds over ocean in The low-level liquid clouds over ocean in Jan 2008. Color represents optical depth. Jan 2008. Color represents optical depth. At the nadir position of A-Train track. Top T At the nadir position of A-Train track. Top T > 0> 000C. C.

Part II: Warm Rain EstimationPart II: Warm Rain EstimationRain contribution by clouds with top T>0 °CRain contribution by clouds with top T>0 °C

• AMSR-E for deep rain, CPR for shallow rainAMSR-E for deep rain, CPR for shallow rain

• Warm cloud (top T > 0Warm cloud (top T > 000C) contributes 28.8% of C) contributes 28.8% of raining occurrence (R>0.05mm/hr), and 17.6% of raining occurrence (R>0.05mm/hr), and 17.6% of rain amountrain amount

• Contribution from all ice-free clouds are even Contribution from all ice-free clouds are even largerlarger

Part II: Warm RainPart II: Warm Rain Estimation Estimation AMSR-E’s Warm Rain Estimation over AMSR-E’s Warm Rain Estimation over

OceanOcean

• AMSR-E underestimates warm rain by nearly AMSR-E underestimates warm rain by nearly 50%50%

• Most underestimation happens for low cloud Most underestimation happens for low cloud (top<3.5km)(top<3.5km)

Part II: Warm RainPart II: Warm Rain Estimation Estimation A quick look of A-Train observations A quick look of A-Train observations

• 20:55~23:35 UTC at 01/06/08 over eastern pacific20:55~23:35 UTC at 01/06/08 over eastern pacific

• AMSR-E misses the shallow warm rain, MODIS cloud AMSR-E misses the shallow warm rain, MODIS cloud observation shows correlation with warm rain observation shows correlation with warm rain

Part II: Part II: re profile & rain re profile & rain Data and Methods (Cont.)Data and Methods (Cont.)

• Terra MODIS, re Terra MODIS, re profile, tau, profile, tau, LWP, 1X1 kmLWP, 1X1 km

• Average within Average within 5X5 km boxes, 5X5 km boxes, overcast overcast samplessamples

Part II: Warm RainPart II: Warm Rain Estimation Estimation Potential of cloud parameters on rain Potential of cloud parameters on rain

estimationestimation

• LWPLWPreprep uses most available information uses most available information

• HSS for AMSR-E rain estimates is 0.312HSS for AMSR-E rain estimates is 0.312

ConclusionConclusion• Low-level liquid clouds contributes significantly to Low-level liquid clouds contributes significantly to

global precipitationglobal precipitation

• Satellite passive microwave observation Satellite passive microwave observation underestimates shallow warm rainunderestimates shallow warm rain

• Cloud microphysical parameter shows potential for Cloud microphysical parameter shows potential for warm rain estimation, which is at least comparable with warm rain estimation, which is at least comparable with passive microwave techniquespassive microwave techniques

• Many challenges to be overcome for operation Many challenges to be overcome for operation application application

Related PublicationsRelated PublicationsChen, R. Z. Li, Kuligowski, R. Ferraro, F. Weng, 2010, A Study of Warm Rain Detection using A-Train Satellite Data, submitted

Chen, R., R. Wood, Z. Li, R. Ferraro, F.-L. Chang, 2008, Studying the vertical variation of cloud droplets effective radius using ship and space-borne remote sensing data, J. Geophy. Res., 113, doi: 10.1029/2007/JD009596.

Chen, R., F.L. Chen, Z. Li, R. Ferraro, F. Weng, 2007, The impact of vertical variation of cloud droplet size on estimation of cloud liquid water path and detection of warm raining cloud, J. Atmos. Sci., 64, 3843-3853.

Chang, F.-L., Z. Li, 2003, Retrieving the vertical profiles of water-cloud droplet effective radius: Algorithm modification and preliminary application, J. Geophys. Res., 108, D(24), 4763, 10.1029/2003JD003906.

Chang, F.-L., Z. Li, 2002 Estimating the vertical variation of cloud droplet effective radius using multispectral near-infrared satellite measurements, J. Geophys. Res., 107, 10.1029 /2001JD0007666, pp12.

Thanks!