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 Warm, liquid phase, frequently occur, i.e. nimbostratus and stratocumulus Large spatial coverage, important for radiation budget Large spatial coverage, important for radiation budget Warm rain, without ice process Warm rain, without ice process

3. Introduction: Satellite Observation of Cloud and Precipitation - VIS/NIR/IR Solar reflectance at visible, NIR - Tau, re, LWP Solar reflectance at visible, NIR - Tau, re, LWP Cloud emission at IR window – Top Temperature Cloud emission at IR window – Top Temperature Top Temperature – Precipitation Top Temperature – Precipitation Pros: high resolution, small surface impact, works over both land and ocean 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 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 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 Ice scattering at high frequency (i.e. 85GHz) – Rain Rate over land Pros: day & night, observe the whole profile over ocean Pros: day & night, observe the whole profile over ocean Cons: low resolution, big surface impact, no LWP over land 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) 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) Relationship between vertical re variation and rain process (re profile & rain) Potential of cloud microphysical parameter on warm rain estimation (warm rain estimation) Potential of cloud microphysical parameter on warm rain estimation (warm rain estimation)

6. 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!

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

8. Part I: re profile & LWP estimation Previous Studies of LWP estimation Problem : Assume vertically constant r e. r e 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

9. Part I: re profile & LWP estimation Data & Methods Aqua MODIS – T, tau, re 3.7, LWP 3.7, re 2.1, LWP2 2.1, re 1.6, LWP 1.6, re profile (re1, re2), LWP rep, Aqua MODIS – T, tau, re 3.7, LWP 3.7, re 2.1, LWP2 2.1, re 1.6, LWP 1.6, re profile (re1, re2), LWP rep, Aqua AMSR-E – LWP AMSR-E Aqua AMSR-E – LWP AMSR-E MODIS 1X1km, AMSR-E 13X7 km, Compare LWP 3.7 and LWP rep with LWP AMSR-E MODIS 1X1km, AMSR-E 13X7 km, Compare LWP 3.7 and LWP rep with LWP AMSR-E Latitude -40 0 ~40 0, Tc>273K, solar zenith angle 273K, solar zenith angle < 50 0, satellite view angle < 30 0

10. Part I: re profile & LWP estimation LWP comparison between MODIS/AMSR-E(Cont.) Bias caused by the vertically constant re ~ 10% Bias caused by the vertically constant re ~ 10% re profile corrects the bias re profile corrects the bias re(h) N re P cloud LWP 3.7 +2.6% LWP rep +2.9% re(h) I re P cloud LWP 3.7 +12.6% LWP rep +5.2% D re P cloud LWP 3.7 -11.2% LWP rep +0.1%

11. Part I: re profile & LWP estimation LWP comparison between MODIS/AMSR-E re profile improves the comparison with AMSR-E re profile improves the comparison with AMSR-E Constant re assumption has opposite impact on IreP/DreP cloud Constant re assumption has opposite impact on IreP/DreP cloud LWP 3.7 LWP rep

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

13. Part II: Warm Rain Estimation data CloudSat CPR rain rate product, 1.7X1.3km, nadir over ocean only CloudSat CPR rain rate product, 1.7X1.3km, nadir over ocean only Aqua AMSR-E rain rate product, 5X5 km Aqua AMSR-E rain rate product, 5X5 km Aqua MODIS cloud estimates, 1X1 km Aqua MODIS cloud estimates, 1X1 km Ship-borne radar. 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 > 0 0 C. The low-level liquid clouds over ocean in Jan 2008. Color represents optical depth. At the nadir position of A-Train track. Top T > 0 0 C.

15. Part II: Warm Rain Estimation Rain contribution by clouds with top T>0 °C AMSR-E for deep rain, CPR for shallow rain AMSR-E for deep rain, CPR for shallow rain Warm cloud (top T > 0 0 C) contributes 28.8% of raining occurrence (R>0.05mm/hr), and 17.6% of rain amount Warm cloud (top T > 0 0 C) 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 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% AMSR-E underestimates warm rain by nearly 50% Most underestimation happens for low cloud (top<3.5km) 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 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 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 Terra MODIS, re profile, tau, LWP, 1X1 km Average within 5X5 km boxes, overcast samples Average within 5X5 km boxes, overcast samples

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

20. Conclusion Low-level liquid clouds contributes significantly to global precipitation Low-level liquid clouds contributes significantly to global precipitation Satellite passive microwave observation underestimates shallow warm rain 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 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 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!