• Journal of the Korean earth science society
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• v.34 no.7
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• pp.626-632
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• 2013

In this study the GPS Precipitable Water Vapor (PWV) was derived and evaluated by a radiosode measure during the winter intensive observation in Gangneung site from January 5 till February 29 in 2012. Bernise 5.0 software was used to derive the GPS data. GPS-derived PWV from Zero difference (GANG) and Single difference (GANG and DAEJ) was high variance in time and about 5 times the PWV of radiosonde. GPS post-processing has been performed from two additional IGS site (Xian Dao, Ibaraki-ken) in order to correct the absolute troposphere errors. As a result, the mean bias error (MBE) and root mean square error (RMSE) and correlation compared with radiosonde measure were 0.67 mm, 6.40 mm, and 0.93, respectively. In order to correct the relative troposphere errors from the altitudinal difference between the two GPS receivers, we calculated the GPS-derived PWV by adding the data of GPS that was installed in Gangneung-Wonju University near the Gangwon Regional Meteorological Administration. In the end, the improved result showed that MBE, RMSE and correlation in comparison with radiosonde measures were 0.61 mm, 5.79 mm, and 0.93, respectively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.2
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• pp.195-204
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• 2016

In this paper, the applicability and usefulness of windprofiler input data were investigated to generate three dimensional wind field. A logical diagnostic model CALMET with windprofiler data at ten sites and with weather forecasting model WRF output was evaluated by statistically comparing with the radiosonde data at eight sites. The horizontal wind speed from CALMET simulated with hourly windprofiler data is in good agreement with radiosonde observations within 1.5 m/s of the root mean square error, especially local circulation of wind such as sea breeze over the coastal region. The root mean square error of wind direction ranged $50^{\circ}{\sim}70^{\circ}$ is due to the wind direction error from the windprofiler polluted by ground clutters. Since the exact wind can be produced quickly and accurately in most of the altitude with windprofiler data on CALMET, we expect the method presented in this study to be useful for the monitoring of safe environment as well as weather in the coastal zone.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.265-276
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• 2018

Observation and data analysis techniques have been developed for observational blind areas in the lower atmosphere that are difficult to be monitored with fixed equipment on the ground. The vertical data of temperature and relative humidity are remotely collected by the UHF radiosonde installed on UAV and compared with the data measured in the 10 m weather tower. From the validated vertical profile, extrapolated surface temperature and the bulk transfer method were used to estimate the sensible heat flux depending on the atmospheric stability. Compared with the sensible heat flux measured by the 3-dimensional ultrasonic anemometer on the ground, the error of the sensible heat flux estimated was 23% that is less than the range of 30% allowed in the remote sensing. Estimated atmospheric boundary layer height from UAV sensible heat fluxes can provide useful data for air pollution diffusion models in real time and economically.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.135-139
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• 1999

Wind velocity is one of the primary variables for describing atmospheric state from GMS-5. And its accurate depiction is essential for operational weather forecasting and for initialization of NWP(Numerical Weather Prediction) models. The aim of this research is to incorporate imagery from other available spectral channels and examine the error characteristics of winds derived from these images. Multi spectral imagery from GMS-5 was used for this purpose and applied to Korean region with together BoM(Bureau of Meteorology). The derivation of wind velocity estimates from low and high resolution visible, split window infrared, and water vapor images, resulted in improvements in the amount and quality of wind data available for forecasting.

• Journal of Positioning, Navigation, and Timing
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• v.3 no.4
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• pp.143-147
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• 2014

The mean temperature is one of the key parameters in computing Precipitable Water Vapor (PWV) from Global Positioning System (GPS) measurements and is usually derived as a function of surface temperature through the use of a mean temperature equation (MTE). In this study, two new types of MTEs were developed as functions solely of the observation time so that the mean temperature can be obtained without surface temperature measurements. To validate the new models, we created one-year time series of GPS-derived PWV using the new MTEs and compared them with the radiosonde-observed PWV. The bias and root-mean-square error were on the other of ~1 mm and ~2 mm, respectively.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.49-54
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• 2000

Conventional methods for measuring winds provide wind velocity observations over limited area and time period. The use of satellite imagery for measuring wind velocity overcomes some of these limitations by providing wide area and near condinuous coverage. And its accurate depiction is essential for operational weather forecasting and for initialization of NWP models. GMS-5 provides full disk images at hourly intervals. At four times each day - 0500, 1100, 1700, 2300 hours UTC-a series of three images is received, separated by thirty minutes, centered at the four times. The current wind system generates winds from sets of 3 infrared(IR) images, separated by an hour, four times a day. It also produces visible(VIS) and water vapor(WV) image-based winds from half-hourly imagery four times a day. The derivation of wind from satellite imagery involves the identification of suitable cloud targets. tracking the targets on sequential images, associating a pressure height with the derived wind vector, and quality control. The aim of this research is to incorporate imagery from other available spectral channels and examine the error characteristics of winds derived from these images.

• Atmosphere
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• v.30 no.4
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• pp.319-333
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• 2020

Aircraft observations constitute one of the major sources of temperature observations which provide three-dimensional information. But it is well known that the aircraft temperature data have warm bias against sonde observation data, and therefore, the correction of aircraft temperature bias is important to improve the model performance. In this study, the algorithm of the bias correction modified from operational KMA (Korea Meteorological Administration) global model is adopted in the preprocessing of aircraft observations, and the effect of the bias correction of aircraft temperature is investigated by conducting the two experiments. The assimilation with the bias correction showed better consistency in the analysis-forecast cycle in terms of the differences between observations (radiosonde and GPSRO (Global Positioning System Radio Occultation)) and 6h forecast. This resulted in an improved forecasting skill level of the mid-level temperature and geopotential height in terms of the root-mean-square error. It was noted that the benefits of the correction of aircraft temperature bias was the upper-level temperature in the midlatitudes, and this affected various parameters (winds, geopotential height) via the model dynamics.

• Atmosphere
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• v.27 no.1
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• pp.93-104
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• 2017

The effect of Advanced Microwave Sounding Unit-A (AMSU-A) observations on the short-range forecast in East Asia (EA) was investigated for the Northern Hemispheric (NH) summer and winter months, using the Forecast Sensitivity to Observations (FSO) method. For both periods, the contribution of radiosonde (TEMP) to the EA forecast was largest, followed by AIRCRAFT, AMSU-A, Infrared Atmospheric Sounding Interferometer (IASI), and the atmospheric motion vector of Communication, Ocean and Meteorological Satellite (COMS) or Multi-functional Transport Satellite (MTSAT). The contribution of AMSU-A sensor was largely originated from the NOAA 19, NOAA 18, and MetOp-A (NOAA 19 and 18) satellites in the NH summer (winter). The contribution of AMSU-A sensor on the MetOp-A (NOAA 18 and 19) satellites was large at 00 and 12 UTC (06 and 18 UTC) analysis times, which was associated with the scanning track of four satellites. The MetOp-A provided the radiance data over the Korea Peninsula in the morning (08:00~11:30 LST), which was important to the morning forecast. In the NH summer, the channel 5 observations on MetOp-A, NOAA 18, 19 along the seaside (along the ridge of the subtropical high) increased (decreased) the forecast error slightly (largely). In the NH winter, the channel 8 observations on NOAA 18 (NOAA 15 and MetOp-A) over the Eastern China (Tibetan Plateau) decreased (increased) the forecast error. The FSO provides useful information on the effect of each AMSU-A sensor on the EA forecasts, which leads guidance to better use of AMSU-A observations for EA regional numerical weather prediction.

• Current Optics and Photonics
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• v.2 no.1
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• pp.15-22
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• 2018

To measure atmospheric temperature, water vapor, and aerosol simultaneously, an efficient multi-function Raman lidar using an ultraviolet-wavelength laser has been developed. A high-performance spectroscopic box that utilizes multicavity interference filters, mounted sequentially at small angles of incidence, is used to separate the lidar return signals at different wavelengths, and to extract the signals with high efficiency. The external experiments are carried out for simultaneous detection of atmospheric temperature, water vapor, and aerosol extinction coefficient in Beijing, under clear and hazy weather conditions. The vertical profiles of temperature, water vapor, and aerosol extinction coefficient are analyzed. The results show that for an integration time of 5 min and laser energy of 200 mJ, the mean deviation between measurements obtained by lidar and radiosonde is small, and the overall trend is similar. The statistical temperature error for nighttime is below 1 K up to a height of 6.2 km under clear weather conditions, and up to a height of 2.5 km under slightly hazy weather conditions, with 5 min of observation time. An effective range for simultaneous detection of temperature and water vapor of up to 10 km is achieved. The temperature-inversion layer is found in the low troposphere. Continuous observations verify the reliability of Raman lidar to achieve real-time measurement of atmospheric parameters in the troposphere.

• Spatial Information Research
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• v.18 no.4
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• pp.33-41
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• 2010

Approximately 100 permanent GPS stations are currently operational in Korea. However, only 10 sites have their own weather sensors connected directly to the GPS receiver. Thus. calculation of meteorological data through interpolation of AWS data are needed to determine precipitable water vapors at a specific GPS station without a meteorological sensor. This study analyzed the accuracy of two meteorological data interpolation methods called reverse sea level correction and kriging. As a result, the root-mean square-error of reverse sea level correction were seven times more accurate in pressure and twice more accurate in temperature than the kriging method. For the analysis of PWV accuracy, we calculated GPS PWV during the summer season in :2008 by using GPS observation data and interpolated meteorological data by reverse sea level correction. And, we compared GPS PWV s based on interpolated meteorological data with those from radiosonde observations and GPS PWV s based on onsite GPS meteorological sensor measurements. As a result, the accuracy of GPS PWV s from our interpolated meteorological data was within the required operational accuracy of 3mm.