Agrometeorological Observation Environment and Periodic Report of Korea Meteorological Administration: Current Status and Suggestions

Abstract

Since the relocation project of equipment in 2011, the overall circumstances of KMA's agrometeorological observation have been significantly improved. Some concerns, however, emerged as a result of the evaluation of observational circumstances in terms of quality assurance after the field surveys on all stations. In order to improve the situation, we suggest: (1) establishment of clear management responsibilities, (2) enhancement of mutual cooperation system between relevant organizations, (3) detailed records of the changes in the observational circumstances, (4) standardization of equipment and sensors, (5) installation of unified information boards, (6) transfer of inappropriate facilities to an adjacent cropland and (7) setup of automated evaporation pan. In order to effectively utilize the high-quality data obtained through improvement of observational circumstances and an elaborate quality control, it is recommended to publish and disseminate regular reports on agrometeorological observations. To produce such a report on a trial basis, we have investigated different types of regular reports issued by domestic and foreign organizations, publication periods, geographical scope, main contents and amount. Based on our current situation, it would be beneficial to learn from the cases of Germany and Canada, which summarize mainly the distinctive agrometeorological phenomena occurred over the past years across the country.

Geostatistical assessment of warm-season precipitation observations in Korea based on the composite precipitation and satellite water vapor data

“…Heavy rainfall causes one of the most serious meteorological disasters in Korea. Most precipitation systems in Korea are highly influenced by the East Asian summer monsoon, called Changma in Korean (Riyu et al, 2001;Kim et al, 2002;Lee et al, 2017b), and hence have high seasonal variation; they also show high spatial variation due to complex topographic features (e.g., Ko et al, 2005;Chang and Kwon, 2007;Park and Lee, 2007;. Since heavy rainfalls including typhoon cause serious damages to both human life and property, improving the their forecast accuracy is of utmost importance.…”

“…The observation density is about 67 km for ASOS and approximately 13 km by including AWS. The agrometeorological observation network consists of 11 AAOS stations (Choi et al, 2015). Based on these precipitation observations, the mesoscale structures of precipitation as well as the hydrologic budgets in Korea have been extensively studied (e.g., Kim and Lee, 2006;Cassardo et al, 2009;Jeong et al, 2012Jeong et al, , 2016Jung and Lee, 2013;Lee et al, 2017a).…”

“…Given the limited resources, it is desirable to optimally design the observation networks, which should be efficient in using least amount of available instruments but sufficient in capturing the precipitation characteristics -both amount and spatiotemporal variability. In other words, understanding temporal and spatial characteristics of precipitation is fundamental and essentially required for the establishment of the optimal observation networks (see Ciach and Krajewski, 2006;Volkmann et al, 2010). Moreover, the appropriateness of such observation networks, in analyzing and/or forecasting the precipitation systems, has seldom been assessed.…”

“…In this study, we investigate the spatial and temporal characteristics of precipitation in Korea using the geostatistical analysis methods -autocorrelogram, Moran's I , and general G (e.g., Ciach and Krajewski, 2006;Schiemann et al, 2011;Emmanuel et al, 2012;Fu et al, 2014). Ciach and Krajewski (2006) estimated the spatial correlation functions in small-scale rainfall, based on rain gauges covering an area of about 3 km by 3 km in central Oklahoma. They showed a dependence of the correlogram parameters on the averaging timescale, large differences of the correlograms in the individual storms, and the ambiguities in correlation estimates based on rainfall intensities.…”

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

“…In this study, SM data at 10 cm of depth were collected at 9 sites (the Seogwipo and Andong sites did not provide good SM data and were not used. The technical specifications of the KMA data were previously reported [37]. Stations No.…”