Sentence examples for retrieval algorithms to from inspiring English sources

The fundamental requirement for each ATSR instrument is a design that is capable of delivering absolutely calibrated infrared data; therefore this paper concentrates on how the sensors provide the calibrated radiometric observations required for the SST retrieval algorithms to work.

Instead, a tailored approach that modifies AOD retrieval algorithms to directly derive a proxy for ground-level PM2.5 may improve upon the current algorithms.

Further improvements in accuracy can be expected through advances in retrieval algorithms to infer AOD from measured radiation, in improved calculation of the AOD to PM2.5 ratio, and in new satellite instrumentation.

As there is no measurement in direct space in CDI, one has to have some prior knowledge of the shape of the object and encode it in a compact support constraint, to aid the phase retrieval algorithm to converge.

Furthermore, the retrieval algorithms used to derive SST from top-of-the-atmosphere thermal radiance have also evolved complicating the merging of satellite data into a single homogenous well-calibrated climate data record (CDR).

The rationale behind the first tropospheric ozone retrieval algorithms was to isolate the stratospheric ozone column by means of limb measurements [11, 12] or total ozone retrievals over high-altitude clouds [13, 14], and then subtract it from a co-located or neighboring measurement of the total ozone column.

Second, AOD retrieval algorithms aim to accurately estimate AOD, with comparisons with ground-based observations of AOD from the aerosol robotic network (AERONET).

A red-to-NIR band Chla retrieval algorithm proved to be applicable to Green Bay, but gave mostly negative values for Keweenaw Bay.

A retrieval algorithm used to obtain vertical distributions of NO2 and BrO from SCIAMACHY limb measurements is described.

In order to have machines assist in building a song database queryable by features such as tempo, meter, or genre, intelligent Information Retrieval algorithms are necessary to automatically extract such high-level features from raw music data.

Its experimental simplicity (no specific hardware required) coupled to computationally efficient phase retrieval algorithms and significant contrast-to-noise (and dose) ratio improvement in tomographic volumes [22], makes it a very appealing tool and about 50% of the TOMCAT users take advantage of it.