Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.

Spatial and temporal classification of synthetic satellite imagery:land cover mapping and accuracy validation

This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data.These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing.Furthermore,in order to improve the quality of the land cover mapping,this research employed the spatial and temporal Markov random field classification approach.Test results show that overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification.This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.

The Utility of 1000 - 500 mb Thickness and Weather Type as a Rain-Snow Divide: A 30-Year Study at Albany, NY

Winter synoptic conditions that produce snowfall with bitterly cold temperatures create both social and economic hazards in the capital city of Albany, NY. Sometimes these systems are forecasted in error to produce rain or mixed precipitation. It is beneficial for meteorologists to better understand the commonly used 5400 and 1300 GPM line to better forecast rain versus snow events. Other studies have looked into the use of the 5400 GPM (540 dm) line but none have assessed the validity of this boundary with respect to weather type characterization at Albany. This study aims to determine the reliability of the widely referenced guides for depicting the rain-snow line, and improve forecast aids for the vertical atmosphere during winter precipitation events. The mean daily 500, 850, 925 and 1000 mb heights and weather type frequency of the Spatial Synoptic Classification between November and March of 1980 - 2012 are analyzed. Results indicate that the standard vertical boundaries are inaccurate indicators of a rain versus snow event in Albany. More reasonable rain-snow cut offs for the 1000 - 500 and 1000 - 850 mb thicknesses are 5222 and 1262 GPM. For the 1000 - 925 mb level, 606 GPM is a helpful aid of identifying the rain-snow boundary. Further scrutinizing by weather type indicates that the rain-snow boundary also varies depending on what air mass/weather type is present on a given day. For instance, when the most prominent weather type is observed over Albany (Dry Polar), at the 1000 - 850 mb and 1000 - 500 mb layers, a boundary of 1242 GPM and 5152 GPM is found to be most representative. Results indicate only for the rarest of winter weather types observed over Albany, Moist Tropical, are the standard cut offs useful. Determining the reliability of this precipitation indicator at a specific station, like Albany, could enable meteorologists in other regions of the country to draw parallels between weather type, precipitation, and thickness in their forecast zones.