Systematic errors in simulated Doppler wind lidar observations

This memo addresses the issue of simulating systematic errors in the DWL observations. The current plan for the NCEP OSSE effort is to assess the potential impact of DWL concepts on global analyses and forecasts. The strategy is to define a range of global coverage and observational accuracies that "bracket" conceivable and reasonable DWL technologies/measurement combinations. A set of "bracketing" OSSEs has been developed and endorsed by the oversight committee. The NCEP OSSE strategy is to proceed through three tiers of impact assessment. Those three levels are:

Range of global coverages, clouds permitting

assumes near perfect LOS measurements where clouds permit coverages will range from full tropospheric (0-20km)/2000 km swath to partial profiles along a single ground track

Range of observational accuracies (Gaussian)

Gaussian errors are added to the near perfect LOS observations simulated for the coverage OSSEs errors will vary from <1m/s RMS to ~ 7-10m/s RMS

Range of systematic errors peculiar to DWL technologies

reprocessing of the simulated DWL for the coverage OSSEs is required to simulate systematic errors systematic errors are those which are characterized by correlated errors along a LOS or set of LOSs

Types of systematic errors

For wind observations, we are most interested in the LOS velocity and its height assignment. By systematic errors we mean the component of the total observational error that is common (highly correlated) to many LOS wind estimates in space and/or time. The only systematic errors that we are concerned with here are those that we know can occur but have no way of knowing when they occur or how large they are in any given instance.

Systematic errors fall into four categories:

instrument/technology unique biases
platform caused biases
sampling biases that are atmosphere independent
measurement biases that are related to atmospheric conditions

Instrument unique

Here are a few examples of DWL technology unique systematic error sources

LO offset is incorrect. Can be caused by poor attitude or position (lat/lon) information.(applies to coherent detection only)

Solar backscatter (applies to direct detection only)

Scanner position errors(angular knowledge errors)

Azimuthal dependent point knowledge errors

Temperature induced pointing errors

Platform induced

In most DWL space mission concepts, the attitude and position information are provided by the platform. Some examples of platform induced errors are:

attitude offsets

positional errors (lat, lon, height)

jitter

Atmosphere independent sampling biases

These sources of systematic errors are related more to the scanning strategy than to the instrument velocity detection scheme. Two examples are provided:

a non-scanning system that only measures the cross-track component of the wind

a < 100% duty cycle instrument that is operated in a atmospheric situation de- coupled manner

Atmospheric/earth surface situation biases

Being from optical sensors, there are several ways that DWL observations can be biased in sampling. While sampling biases are not unique to future lidars in space, it is still important to understand the nature of these opportunity-related biases. Some examples of atmospheric sampling biases are:

LOS observations clouds permitting

LOS observations aerosols permitting

LOS propagation effects related to the refractive index

LOS observations in regions of very strong vertical motions (orographic, thermal)

Surface motions which confound using surface returns to calibrate velocity estimators

Ocean surface currents

Ocean waves

River currents

Blowing sand or snow

Swaying canopies

Proposed strategy for investigating the impacts of systematic errors

The options for including systematic errors in the simulated DWL data sets are boundless. I recommend the following initial attempt to bound the issue.

Add a pointing knowledge offset to all observations that will produce a LOS velocity estimator error of 1 m/s.

Add a pointing knowledge error only for those LOSs that do not contain a ground return

Add a height assignment error to those LOSs not containing a ground return

Add an error that is a function of solar exposure