Can the standard radiosonde system meet special atmospheric research needs?
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Statistical examination of instantaneous dual‐measurements made with the standard U.S. radiosonde indicates rms temperature differences of only 0.3‐0.4°C, but rms pressure differences of up to 2 mb. The imprecision of the aneroid cell is seen to cause large displacements of estimated altitude from the true (instantaneous) altitude of the radiosonde. Researchers in need of absolute height information (radiosonde height as a function of time) will be at a loss if they rely on a single station, unsupported radiosonde measurement. It is also shown, however, that the radiosonde does provide an adequate pressure‐height relationship and thus fulfills the role for which it was intended, that is, estimating the height of a given pressure surface (i.e., synoptic use). The aneroid cell imprecision and its consequences can be avoided by radar tracking of the radiosonde. This method provides a precise measure of absolute height and can be used to calculate precise pressures. Additionally, there is little alteration of the synoptically used pressure‐height relation.Cite
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The radiosonde is one of the most basic but still most popular atmospheric sounding systems. Several sensors which measure pressure, temperature, relative humidity, and wind speed/directions respectively are packed in one compact package and is launched with balloon filled with helium gases. Despite their popularity, recent studies have revealed that radiosonde data often contains serious errors especially for humidity. In addition, it is well known that surface meteorological observation on the ship should be carefully treated to avoid ship body structural influences. In the present paper, the procedure of radiosonde observation from the research vessel MIRAI is described to show how we obtain the accurate data. Besides, surface data correction scheme that has been developed based on the experimental results is also presented.
Atmospheric sounding
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Sets of upper-air data obtained from a number of new and different radiosonde systems are analyzed and intercompared. The temperature, pressure, relative humidity, and wind sensing capabilities of the radiosonde systems are presented.
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A radiosonde is one of the high technology tools and useful for aeronautical. In radiosonde have several sensors like air temperature, air pressure, air damp, vapor point, direction, and airspeed. Data from measure result will be received by supervising station in earth surface. Information will be collected by subarea air upstairs in Meteorology Station Klas I Depati Amir Pangkalpinang. An observation was done at 00.00 UTC (07.00 am) and at 12.00 UTC (07.00 pm). A transmitter in this observation is GPS Radiosonde iMS-100. The result shows that the radiosonde can work well at air pressure of 20 to 1000 hPa.
Airspeed
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Meteorological Radiosonde in the past used to apply navigation system to rout to determine the upper air pressure, temperature, humidity and the wind data through Radars. In this paper GPS Radiosonde test has been recently introduced in IMD is studied. The observations taken from M2K2 Radiosonde have been discussed. GPS Radiosonde obtains wind data as well.
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본 연구에서는 상층기상자료, 자동 기상 관측망 자료 및 신경망기법을 사용하여 단시간 강우 예측 모형을 개발하였다. 호우를 동반한 이송 기상 시스템의 이동 경로가 라디오존데로부터 획득할 수 있는 상층기상 자료 즉 상층 풍향자료와 동일한 방향으로 이동한다는 가정 하에 원거리에서 발생하는 기상현상의 발달과정을 판단 할 수 있는 알고리즘을 개발하고, 이러한 원거리 입력 자료와 예측하고자 하는 값 사이의 비선형 상관관계를 연결하는 기법으로 인공 신경망 기법을 도입하였다. 개발된 모형을 2002년 태풍 루사로 인하여 큰 피해를 입은 감천지역에 적용하였다. 포항과 오산의 라디오존데에서 획득한 700mb에서의 풍향자료와 5년의 자료기간을 가지는 350개의 자동 기상 관측망 자료를 입력 자료로 사용하였으며 결과는 상층 풍향자료를 사용한 경우에 상관계수가 0.41에서 0.73으로 개선되었으며 숙련도도 35%향상되었다. 모형의 개선도를 나타내는 통계치의 개선을 통해 상층기상자료를 활용한 강우예측 모형이 단지 지상 강우계 자료만 사용한 예측보다 개선된 결과를 보여줌을 알 수 있다. In this study, we developed a rainfall forecasting model using data from radiosonde and rain gauge network and neural networks. The primary hypothesis is that if we can consider the moving direction of the rain generating weather system in forecasting rainfall, we can get more accurate results. We assume that the moving direction of the rain generating weather system is same as the wind direction at 700mb which is measured at radiosonde networks. Neural networks are consisted of 8 different modules according to 8 different wind directions. The model was verified using 350 AWS data and Pohang radiosonde data. Correlation coefficient is improved from 0.41 to 0.73 and skill score is 0.35. Statistical performance measures of the Quantitative Precipitation Forecast (QPF) model show improved output compared to that of rainfall forecasting model using only AWS data.
Quantitative precipitation forecast
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Atmospheric sounding
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Abstract : Radiosonde information is extensively used in the analysis and forecast of meteorological phenomena and the accuracy of both analyses and forecasts is dependent primarily upon the accuracy of the meteorological parameters determined from radiosonde flights. To evaluate the accuracy obtainable, 50 radiosonde flights were launched from the U. S. Naval Postgraduate School, Monterey, California. Thirty-five flights carried aloft the AN/AMT-4B model transmitter alongside the prototype AN/AMT-11DX transmitter and 15 flights carried the AN/AMT-11C model along with the AN/AMT-4B. All data obtained were reduced by the Geophysics Division, Pacific Missile Range, Point Mugu, California, on a CDC-3100 computer and graphically by the experimenter on the WBAN-31 series adiabatic charts. Values of temperature, relative humidity, and pressure as determined by each instrument were compared at each 3-minute interval of each flight and values of temperature, pressure-altitude, relative humidity and dewpoint were compared at standard pressure levels. The results obtained afforded a realistic evaluation of the various sensing elements under field conditions and indicate an urgent requirement for the development of a more accurate water vapor sensing device and replacement of the radiosonde baroswitching circuit by a hypsometer for precise determination of pressure values.
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