The Atmospheric Library is made up of an extensive set of integrated atmospheric data bases created by the Atmospheric Generator Model (AGM). The AGM, which is not part of the current DLSM, provides meteorological inputs from control fields, generated correlated fields, mesoscale fields to global meteorological fields. The AGM can create three types of atmospheric files: Global Atmospheric Data Set (GADS), Mesoscale Atmospheric Data Set (MADS)* and Pure/Spectral Atmospheric Data Set (PADS)*. The library screen includes options that allows the user to customize the effects of opaque clouds, cirrus clouds, molecular and aerosol backscatter, molecular and aerosol attenuation, atmospheric turbulence for the simulation. 

Atmosphere

In the DLSM version 4.2, only DAO GADS are supported.  A GADS is a FORTRAN 90 direct access file created by using a nature run's data in liaison with the AGM optical property models, cloud models, and terrain data set. The Lidar Simulation Model (LSM) retrieves atmospheric profiles from a GADS as a function of latitude and longitude for each laser shot. Each profile contains an atmospheric profile, aerosol and molecular optical properties, cloud information and terrain.

The equatorial spatial resolution of a DAO based data set is 69.5 km by 55.5 km with a temporal resolution of 6 hours. It contains 207,936 grid area profiles (records) per time period. The atmospheric library contains 90 days of DAO based GADS. Only Sept. 11th - Oct. 12 1999 is provided in the DLSM version 4.2. 

Line of Sight Variance

Variance along the line of sight (pulse length) is not included in the DLSM version 4.2.

Subgrid Scale Variance  

The LSM has three options for estimating the wind variance on the sub-grid scale of the model. The first method uses pre-computed wind variance over the 3-dimensiona' grid volume for sub-grid scale uncertainties and then scales the variance to smaller (pulse) scales by the Von Karman relationship. This “reasonable” approach has been used for many years but has not been fully verified by real data. The second method represents the uncertainties by scaling them to 20 % of the mean model wind speed. Comparisons of the uncertainties with the NMC rawinsonde profiles suggest that the simulated variances using the 20 % rule are not unreasonable. Last, the user can choose no sub-grid scale variance option.  

Cloud Porosity

The LSM uses the total condensate, temperature and liquid water content (LWC) to parameterize between the presence of opaque and cirrus clouds within a model grid. While the resulting cloud amounts and distributions are plausible and consistent with the model physics, we have little validation based upon real data taken with lidars for various cloud conditions. Cloud optical properties and Cloud porosity parameterizations are based upon extensive data bases from available literature, the cloud types and the atmospheric model inputs. The LSM has a cloud porosity function that was derived from some preliminary analyses of LITE data.  Given the porosity found (> 50% of all lidar shots that intercept cloud also exhibit an earth’s surface return), the exclusion of this effect in a simulation will tend to under represent the data coverage of the simulated coherent DWL. More specifically, since coherent systems can make useful wind measurements along a single LOS while current direct detection concepts require multiple LOS samples, the omission of porosity will understate the potential impact of the coherent concepts. In the DLSM version 4.2, cloud porosity functions are not available.

Optical Properties

Previous DLSMs have provided several options for computing aerosol and molecular optical properties. The foremost option couples FASCODE (Gallery et al., 1983) with LSM unique databases and model atmospheres to produce aerosol and molecular  backscatter and attenuation profiles as a function of the DWL’s wavelength. The optical property's natural variability due to altitude, location, seasons, and meteorological conditions are taken into consideration. A simpler option couples the distribution of expected backscatter directly to the model atmosphere’s relative humidity.  However, the distribution of backscatter variability on scales affecting single LOS processing is coarse at best. It is important to consider that the effects of backscatter variability within a shot’s illuminated volume can lead to erroneous height assignments for any detection technique.  Thus, the emphasis in this case is on representing the vertical structure of the aerosol/cloud distributions.

In the DLSM version 4.2, only simple bracketing aerosol and molecule optical property data sets are used by the LSM These data sets were created for Global Tropospheric Wind Studies and are fully documented in the SWA web document, Design Atmospheres for use in GTWS Concept Studies. The DLSM allows the user to choose from using either median profiles or log-normal distributed  profiles for either background source, enhanced source or FASCODE atmospheres.

Last Updated: 02/07/2007