1 / 21

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

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. Introduction: Low-level liquid cloud.

moswen
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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!

More Related