Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro, F. Weng CICS/ESSIC, University of Maryland

1. Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro, F. Weng CICS/ESSIC, University of Maryland STAR/NESDIS/NOAA, Camps Spring, MD

2. Introduction: Low-level liquid cloud • Warm, liquid phase, frequently occur, i.e. nimbostratus and stratocumulus • Large spatial coverage, important for radiation budget • Warm rain, without ice process

3. Introduction: Satellite Observation of Cloud and Precipitation - VIS/NIR/IR • Solar reflectance at visible, NIR - Tau, re, LWP • Cloud emission at IR window – Top Temperature • Top Temperature – Precipitation • Pros: high resolution, small surface impact, works over both land and ocean • Cons: no VIS/NIR for night, NIR/IR mainly observe cloud top, misses shallow rain

4. Introduction: Satellite Observation of Cloud and Precipitation - Microwave • Emission at low frequency (i.e. 37GHz, 19GHz) – LWP, Rain Rate over ocean • Ice scattering at high frequency (i.e. 85GHz) – Rain Rate over land • Pros: day & night, observe the whole profile over ocean • Cons: low resolution, big surface impact, no LWP over land

5. Objective • Impact of vertical re variation on cloud liquid water estimation (re profile & LWP estimation) • Relationship between vertical re variation and rain process (re profile & rain) • Potential of cloud microphysical parameter on warm rain estimation (warm rain estimation)

6. 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! After Miles et al. (JAS, 2000 JAN)

7. Chang and Li (JGR, 2002, 2003)

8. re re(h) re(h) re Part I: re profile & LWP estimation Previous Studies of LWP estimation Problem : Assume vertically constant re. re is retrieved from single NIR channel and weighted toward cloud top. • 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

9. Part I: re profile & LWP estimation Data & Methods • Aqua MODIS – T, tau, re3.7, LWP3.7 , re2.1, LWP22.1 , re1.6, LWP1.6 , re profile (re1, re2), LWPrep, • Aqua AMSR-E – LWPAMSR-E • MODIS 1X1km, AMSR-E 13X7 km, Compare LWP3.7 and LWPrep with LWPAMSR-E • Latitude -400~400, Tc>273K, solar zenith angle < 500, satellite view angle < 300

10. re(h) Part I: re profile & LWP estimationLWP comparison between MODIS/AMSR-E(Cont.) • Bias caused by the vertically constant re ~ 10% • re profile corrects the bias re(h) re(h) N re P cloud LWP3.7 +2.6% LWPrep +2.9% I re P cloud LWP3.7 +12.6% LWPrep +5.2% D re P cloud LWP3.7 -11.2% LWPrep +0.1%

11. Part I: re profile & LWP estimationLWP comparison between MODIS/AMSR-E LWP3.7 LWPrep • re profile improves the comparison with AMSR-E • Constant re assumption has opposite impact on IreP/DreP cloud

12. Part II: Warm Rain EstimationObjective • How important is warm rain? • How is satellite passive microwave observation of warm rain over ocean? • Does the cloud microphysical parameter has the potential for warm rain estimation?

13. Part II: Warm Rain Estimationdata • CloudSat CPR rain rate product, 1.7X1.3km, nadir over ocean only • Aqua AMSR-E rain rate product, 5X5 km • Aqua MODIS cloud estimates, 1X1 km • Ship-borne radar.

14. Part III: Warm Rain Estimation • The low-level liquid clouds over ocean in Jan 2008. Color represents optical depth. At the nadir position of A-Train track. Top T > 00C.

15. Part II: Warm Rain EstimationRain contribution by clouds with top T>0 °C • AMSR-E for deep rain, CPR for shallow rain • Warm cloud (top T > 00C) contributes 28.8% of raining occurrence (R>0.05mm/hr), and 17.6% of rain amount • Contribution from all ice-free clouds are even larger

16. Part II: Warm Rain Estimation AMSR-E’s Warm Rain Estimation over Ocean • AMSR-E underestimates warm rain by nearly 50% • Most underestimation happens for low cloud (top<3.5km)

17. Part II: Warm Rain Estimation A quick look of A-Train observations • 20:55~23:35 UTC at 01/06/08 over eastern pacific • AMSR-E misses the shallow warm rain, MODIS cloud observation shows correlation with warm rain

18. Part II: re profile & rain Data and Methods (Cont.) • Terra MODIS, re profile, tau, LWP, 1X1 km • Average within 5X5 km boxes, overcast samples

19. Part II: Warm Rain Estimation Potential of cloud parameters on rain estimation • LWPrep uses most available information • HSS for AMSR-E rain estimates is 0.312

20. Conclusion • Low-level liquid clouds contributes significantly to global precipitation • Satellite passive microwave observation underestimates shallow warm rain • Cloud microphysical parameter shows potential for warm rain estimation, which is at least comparable with passive microwave techniques • Many challenges to be overcome for operation application

21. Related Publications Chen, 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!