Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval

Andrew Heidinger, NOAA/NESDIS/ORAWashington D.C, USA

Outline

Physical basis for cloud base information in hyperspectral data

Efficient radiative transfer modelling

Simulated results for MODIS 0.94 m water vapour bands Preliminary results from MODIS

Conclusions and future work

Why attempt to measure cloud base?

Current passive sensors microwave, visible, infrared sensors offer little direct sensitivity of cloud base.Active sensor satellite programs like Cloudsat and Calipso, will offer detailed information but poor spatial and temporal sampling.Cloud base determines the downwelling longwave radiation at the surface and the magnitude of in-cloud radiative heatingSupport of aviation cloudiness requirements

Current capabilities: Non-overlapped Cloud amount, cloud-top position, optical thickness

Physical Basis for Cloud Base Retrieval from Solar Reflectance in Absorption Lines

This remote sensing technique has a long heritage in astrophysics where the depth of absorption lines was used to estimate how much gas is in an atmosphere (co2 on mars)

Outside of a molecular absorption band, two clouds (with same optical thickness) would appear identical.

In an absorption line, the greater the pressure thickness of a cloud, the more absorbing gas the more absorption will occu and the lower the reflectance.

Sun Satellite

Cloud

Using MODIS as a Test bed for Hyperspectral Cloud Base Retrieval

MODIS resolves the 0.94 m water vapour line(ch 17,18,19) – a relatively strong and broad absorption band as well as one channel centred in the 1.4 m band.(ch26)

Ideally, a hyperspectral sensor would provide information for othergases such as co2 and o2. This would remove the need to retrieve thewater vapour profile as well.

Allows for testing of approach on globally available satellite data

Sample MODIS Ch2,Ch17,CH18,Ch26 Reflectances for Stratus near CA

MODIS channels offer the following vertical profile of cloud base sensitivity

The 1.38 m channel offers information for cirrus and will be available on VIIRS

Information in MODIS concerning Cloud Base

Efficient Radiative Transfer Modeling

To model reflectance in an absorption band, you need to account forthe interaction of the gas with the scatters in the cloud

Using the equivalence theorem of Irvine(1964), if you know the photonpathlengths without absorption, you can include absorption easily.

So the forward model is based on a lookup table of the form

R = f(, re,)

Use of the photon pathlength (two more dimensions in a lookup table) allows for rapid estimation of reflectances for many channels of differing absorption strength (ideal for hyperspectral modelling).

Pathlength Dist.

Limits of Forward ModelModel assumes no vertical heterogeneity in cloud and gas(cloud is assumed saturated)

For thick clouds, this limits accuracy

Approximation included to estimate using pathlengths howscatterings are distributed through multiple layers in cloud

Retrieval Algorithm

Sensitivity analyses showed that to retrieve cloud pressure thickness,Cloud optical depth and above cloud precipitable water

Cloud top pressure is assumed known to 50 mb.(from MODIS IR/CO2)

Results indicate, cloud base pressure is more difficult to retrieve thanabove cloud precipitable water.

Noise in Ch26 of MODIS is prohibits it use for cloud base retrievalfor some scenarios (preliminary).

Forward model is used in 1d-Var retrieval approach using reflectancesand reflectance ratios (ch18/ch2).

High clouds of moderate optical thickness are easiest, low thin cloudsin a moist atmosphere are most challenging.

Above cloud water vapour is retrievable for clouds below 500 mb.

Hyperspectral spectra should allow for retrieval of smallerwater vapour amounts over higher clouds

Above Cloud Water Vapor

Simulated ResultsSimulations with 2% calibration errors on reflectances show 20 mb accuracy for many scenariosAs shown, there is a need for cloud top pressure and information on water vapour profile helps

Sample Retrieval Results for Stratus off California viewed by MODIS

Ch31 (11 m) Temperature Reflectance Ratio (Ch18/Ch2)

Reflectance Ratio clearly shows above cloud water vapour andcloud height – cloud pressure thickness signal not clear in this scene

Sample Results for the nadir slice through this scene

May not be properly separating cloud base signal from above cloud tpw

Increase in apparent cloud pressure thickness due to multi-layers clouds

Conclusions and Future Work

The MODIS channels in the 0.94 m water vapour absorption offer achance to test some hyperspectral cloud retrieval concepts

A fast radiative transfer model suitable for modelling reflectancesin absorption bands has been developed

Simulation indicates cloud base should be retrievable for many single layer cloudiness scenarios.

Application to MODIS of cloud base retrieval has been demonstratedbut not yet validated.

This retrieval will be validated over a range of cloudiness using MODIS

Similar approach to estimating aerosol height will be explored.