Towards retrieving 3-D cloud fractions using Infrared Radiances from multiple sensors Dongmei Xu JCSDA summer colloquium, July 27 2015- August 7 2015.

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Presentation transcript:

Towards retrieving 3-D cloud fractions using Infrared Radiances from multiple sensors Dongmei Xu JCSDA summer colloquium, July August

2015.1— Postdoc Researcher in MMM Laboratory in NCAR — PhD Candidate of Meteorology, Nanjing University of Information Science & Technology (NUIST) — Master of Meteorology, NUIST — Bachelor of Information and Computation Science (Department of Maths), NUIST

PhD thesis: Data assimilation and synthetically retrieving clouds with satellite infrared radiance observations Data assimilation Retrieving clouds cloud detection clear-sky radiance Data assimilation Verification using multi- sensor IR radiance Multivariate and Minimum Residual ( MMR ) method Multi-sensor Advection- Diffusion nowCast system Implemented channel dependent cloud detection scheme and the Metop-2 IASI radiance DA facility in WRFDA Evaluated the impact of data assimilate Metop-2 IASI In WRFDA on the forecasts on typhoon and hurricane cases In both hybrid and 3dvar frameworks.

Why? Cloud parameters, such as cloud top pressure and effective cloud fraction, are useful for cloud initialization in numerical weather prediction (NWP), to understand their impact to the earth’s climate change, to estimate incoming and outgoing thermal radiation budget … It is crucial to develop a fast and efficient algorithm to estimate real-time global cloud information in NWP studies to achieve fresh cloud analysis products.

MMR CRTM (clear) CRTM (clear) T, Q T s, ε s Cloud fractions obs Clear Simulated Tb k_top ： the cloud top level How? MMR (Multivariate and Minimum Residual Method) WRF WRFDA

:the radiance calculated for overcast black cloud at level k. : the fraction of clear sky :the array of vertical effective cloud fractions for K model levels :the observed radiance :the modeled cloudy radiance :the radiance calculated in clear sky at the wavenumber v (1) (2) The formulations: with

DATA

the nadir AIRS IASI

before after

AIRS after before

Main results Inter-comparisons among cloud retrievals from different sensors  Cloud mask  Cloud top pressure  Cloud profile Cloud retrievals from Multi-sensor Advection- Diffusion nowCast system

Cloud mask AIRS GOES -Sounder IASI MODIS GOES -Imager GOES products as reference ( NASA-Langley cloud and radiation products )

1900 UTC 03 June 2012 IASIMODIS AIRSGOES-Sounder GOES-Imager Cloud mask

0800UTC 0900UTC 1100UTC AIRS MODIS Cloud top

Cloud profile

CloudSatAIRSMODIS 0800UTC 0900UTC 1100UTC Cloud profile

Date/heightAIRS (ets/corr) MODIS (ets/corr) 0800 UTC/9 km 0800 UTC/5.5 km 0800 UTC/3 km 0800 UTC/2.5 km 0.32/ / / / / / / / UTC/9 km 0900 UTC/5.5 km 0900 UTC/3 km 0900 UTC/2.5 km 0.27/ / / / / / / / UTC/9 km 1100 UTC/5.5 km 1100 UTC/3 km 1100 UTC/2.5 km 0.10/ / / / / / / /0.63 Cloud profile

Multi-sensor Advection-Diffusion nowCast system

AIRSIASI MODIS GOES- Sounder GOES- Imager （ age_index ）

AIRS IASI MODISGOES- Sounder GOES- Imager

Investigating a new retrieval prototype based on the Particle Filter (PF) algorithm in the framework of GSI (Gridpoint Statistical Interpolation system) Conducting comparisons between the MMR and PF methods. Ongoing work

….. 100% 90% 10% ……. PF APF

CloudSat MMR AIRS PFAPF

Weights for different particles

Conclusions The MMR method is proved to be robust in retrieving the quantitative cloud mask, using radiances from multiple satellites. MMR produced realistic cloud top pressures, with an accuracy varying with the sensors’ spectral resolutions. The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds. The development of a new prototype of cloud retrieval scheme base on particle filter is underway.

Thanks for your time !

Channels’ weight function peak Channels’ abilities to identify clouds Smaller values: sensitive to clouds close to 1: hard to identify clouds