Perhaps the most critical aspect of the DLSM is the representation of clouds along the line of sight of each DWL "shot". For any given detection sensitivity of the DWL, clouds will determine how often observations are made throughout the troposphere. The challenge is to accomplish reasonable cloud representation of backscatter, attenuation, porosity, multi-layering and multiple-scattering effects using the grid-point values of idealized 3-D numerical models.

GADS Nature Run Cloud Fields

The DLSM relies upon the cloud algorithms incorporated into the 3-D models. The cloud information (percentage, liquid water content, horizontal location and vertical location) for T213-based GADS and DAO-based GADS is obtained directly from the nature runs. For a T106-based GADS, the AGM cloud model uses a Slingo cloud parameterization scheme to compute cloud percentage and infer cloud type. While these cloud representations may be adequate for many research/operational applications, they are not adequate for characterizing the LOS environment for a 1-10 m lidar beam.

The DLSM does not claim to be valid on a shot to shot simulated performance. However, the model is designed to minimize the number of fixed cloud properties and to allow the user to vary the cloud properties in a way to test model's sensitivity to the line of sight perspective.

Global Statistics 

We expect that clouds (including subvisual cirrus) will be in the field-of-view (FOV) of a space-based lidar 70-80% of the time. This estimate is based upon the recently reported analysis of two years of HIRS data (Menzel et al., 1992), the cirrus climatology derived from SAGE data by Woodbury and McCormick (1986) and the Nimbus-7 global cloud climatology (Stowe et al., 1989). Much of this cloud coverage is high cloud (above 400-500 mb) and is semi-transparent (~ 30-40%). Very thin or subvisual cirrus (ç <.07) is probably not detected by HIRS or Nimbus-7 but may be occasionally represented in the SAGE observations. Thus, we conclude that the occurrence of very thin cirrus is clearly underestimated in current climatologies.

Of particular interest to a space-based lidar program are the semi-transparent and optically thin clouds since they provide strong returns without full extinction (Emmitt and Wood, 1988). When one considers that the statistics given above are, in most cases, exclusive - i.e., they do not provide a good representation of coincident clouds at different altitudes, it is very likely that there are many occasions when there are multi-layers of thin clouds underlaid by opaque clouds. 

SWA examined the distribution of total cloud coverage as a function of latitude for each nature run. While the total global coverage is reasonable when compared to Nimbus-7 statistics, we find that each model atmosphere underestimates cloud cover. The amount of T106 midlevel cloud forecast for the tropics is considerably less than the 30-40% reported using the satellite data. Low level cloud amounts were smaller than expected in the T213 causing NCEP to create a low level cloud adjustment scheme. The DAO nature run is currently undergoing cloud amount evaluations.

Cloud Vertical Structure Model

The LSM cloud vertical structure model constructs a vertical profile of cloud information from the platform's viewpoint  given a vertical profile of a DWL " shot" locations along the slant path range (i.e. pulse length) and the 4-D interpolated GADS variables at the DWL " shot" locations.

As the model's loops from the top of the atmosphere to the surface of the earth, the following logic is used.

First, there has to be at least a 5 % cloud fraction for a cloud to be present at alevel.

Second, if there is a cloud present, the cloud is considered opaque unless the liquid water content is less than an LSM pre-set liquid water content threshold (currently set as 5%) and the air temperature is less than an LSM pre-set air temperature threshold (currently set as 273K) , then the cloud is considered to be a cirrus cloud. 

If the cloud is cirrus, the fractional cirrus cloud amount is not interpreted literally. Instead, the LSM assumes 100% cirrus coverage and uses the fractional cirrus cloud amount to scale the cloud's optical depth. Thus all DWL "shots" would yield returns from cloud material and pass, attenuated, through to lower levels.

If the cloud is opaque and it is the first level that an opaque cloud is present, the LSM uses the cloud's fractional amount to randomly decide the DWL's opportunity of getting a cloud return or an aerosol/molecular return. For subsequent cloud levels, the opportunity  is dependent upon whether the cloud is contiguous or not. If contiguous, then only the additional amount of cloud in the DWL's view is randomly considered. If the cloud is non-contiguous, then the potential of cloud amount that is in the DWL view is randomly distributed in order to compute the amount of additional cloud present at the level.

Cloud Porosity Model

Based upon recent data returned from NASA's LITE mission (Winkler and Emmitt, 1998), the porosity of clouds appear to be greater than expected. Initial analysis suggest that, while nearly 80% of all DWL single "shots" are effected by water/ice clouds, 50% of those single "shots" are also able to provide a PBL (or ground return). This characterization is sufficiently more optimistic than previous expectations to merit the inclusion of a "porosity" algorithm into the LSM.

Preliminary runs with extreme values for porosity (0-50%) illustrated the non-linear effects that this parameter has on the quality and quantity of wind data products. Since SWA is continuing to evaluate the cloud porosity model before any claims of realism can be defended, we have decided not to provide this option at this time in the DLSM version 4.2.

Cloud Optical Property Model

All opaque cloud backscatter values are preset in the LSM to be 1x10-06 m-1 sr-1 for 9 um and 1.86x10-5 for 2 um. We believe this value is properly conservative, since recent mid-layer cloud backscatter, measured with a lidar in the Antarctic, range from 1x10-06 to 1x10-04 m-1 sr-1 (Del Guasta et al., 1993). 

Opaque cloud absorption coefficients are based upon Stephens (1979) as a function of liquid water content and cloud type as shown below.

For cirrus cloud layers, cirrus backscatter is based on Northeastern University's cirrus model and is a function of cirrus cloud temperature as shown below.

The cirrus cloud attenuation model is a modified version of an analytical AFGL cirrus algorithm found in the AFGL models Kneizys et al., 1996 ;Gallery et al., 1983), where

t = e^{-0.14 * L²}

^where

        t - the cirrus transmittance

        L - the cirrus cloud thickness.

Since the LSM is restricted to the coarse vertical resolution of the ECMWF Nature Run, the LSM uses the cirrus cloud percentage as a surrogate for cloud optical thickness.

The major assumption is that while the models derive a percent cirrus cloud coverage (i.e., 30%) from an average relative humidity within a grid volume, it is just as reasonable to interpret a thickness tendency from the same fields. Instead of using the percent coverage as literally meaning that 30% of the grid has cirrus cloud and 70% is totally cloud-free, the LSM assumes that the whole grid area is covered by a cirrus cloud that has an optical thickness that scales to the percent coverage. The cirrus cloud attenuations is defined as

ci_att = 10^a * (CLD_% * 10)²

where

        ci_att - the cirrus cloud attenuation

        a - the AFGL cirrus attenuation coefficient for a 1 km thick layer

        CLD_% - the cirrus cloud percentage cover.

Until new requests and funding is made,  the DLSM version 4.2 assigns the closest wavelength's cloud optical property value for wavelengths outside of the 2 to 9 um range.  

Last Updated: 02/07/2007