English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors in geostationary orbits, which provide more frequent observations at higher spatial resolutions. Advanced Meteorological Imager (AMI) onboard South Korea's second geostationary satellite (GEO-KOMPSAT-2A), scheduled for launch in early 2018, is an example of these sensors. This paper introduces operational prototype algorithms that attempt to produce QPN products for GEO-KOMPSAT-2A. The AMI QPN products include the potential accumulated rainfall and the probability of rainfall (PoR) for a 3-h lead time. The potential accumulated rainfall algorithm consists of two major procedures: 1) identification of rainfall features on the outputs from the GEO-KOMPSAT-2A rainfall rate algorithm; and 2) tracking of these rainfall features between two consecutive images. The potential accumulated rainfall algorithm extrapolates precipitation fields every 15 min. Rainfall rates at each time step are accumulated to yield the 3-hourly rainfall. In addition, the extrapolated precipitation fields at 15-min intervals are used as inputs for the PoR algorithm, which produces the probability of precipitation during the same 3-h period. The QPN products can be classified as extrapolated features associated with precipitation. The validation results show that the extrapolated features tend to meet the designated accuracy for the prototype development stage. We also confirm a tendency for decreasing accuracy of the QPN products with increasing forecast lead time. Mitigating the dependence on lead time may remain a challenge that can be incorporated into the next generation of QPN algorithms.

Development of Prototype Algorithms for Quantitative Precipitation Nowcasts From AMI Onboard the GEO-KOMPSAT-2A Satellite