On the heavy aerosol pollution and its meteorological dependence in Shandong province, China
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Synoptic scale meteorology
Тонкая структура вертикального профиля влажности, влияющая на распространение радиоволн в тропосфере
The objective of the present study is to investigate reliability of testing the remote sensing methods of precipitable water vapour content determination by using comparison with the standard radiosonde observations in troposphere. Possibility Тонкая структура вертикального профиля влажности... 39 of reaching the acceptable reliability during such comparison in condition of using specially prepared and tested radiosondes has shown. This paper shows results of analysis of data from weather radio sounding network on subject of searching troposphere for thin layers with essentially increased or decreased water vapour content. The similar layers' existence was shown during unique experiments with free aerostats, when precise measuring instruments and standard radiosonde's sensors were lifted jointly united. Modern radiosondes have more perfect humidity sensors which able to detect the more fine humidity profile's structure then one that has been detected before by radiosondes with film-based humidity sensors. Based on large amount of statistical data, the existence of thin layers with sudden humidity changes in troposphere has shown.
Precipitable water
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This paper describes a technique for determining probability distributions of parameters necessary for the design of tropospheric scatter communication links. The method, resulting from earlier studies of scatter propagation and the structure of the troposphere, utilizes information which is easily obtained from routine conventional radiosonde observations. Examples of probability distributions, based on radiosonde measurements from southwestern Norway, are presented and compared with existing experimental distributions.
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Abstract. Lidars are well-suited for trend measurements in the upper troposphere and lower stratosphere, particularly for species such as water vapour. Trend determinations require frequent, accurate and well-characterized measurements. However, water vapour Raman lidars produce a relative measurement and require calibration in order to transform the measurement into physical units. Typically, the calibration is done using a reference instrument such as a radiosonde. We present an improved trajectory technique to calibrate water vapour Raman lidars based on the previous work of Whiteman et al. (2006), Leblanc and Mcdermid (2008), and Adam et al. (2010) who used radiosondes as an external calibration source, and matched the lidar measurements to the corresponding radiosonde measurement. However, they did not consider the movement of the radiosonde. As calibrations can be affected by a lack of co-location with the reference instrument, we have attempted to improve their technique by tracking the air parcels measured by the radiosonde relative to the field-of-view of the lidar. This study uses GCOS Reference Upper Air Network (GRUAN) Vaisala RS92 radiosonde measurements and lidar measurements from the MeteoSwiss RAman Lidar for Meteorological Observation (RALMO), located in Payerne, Switzerland to demonstrate this improved calibration technique. We compare this technique to traditional radiosonde-lidar calibration techniques which do not involve tracking the radiosonde. Both traditional and our trajectory methods produce similar profiles when the water vapour field is homogeneous over the 30 min calibration period. We show that the trajectory method more accurately reproduces the radiosonde profile when the water vapour field is not homogeneous over a 30 min calibration period. We also calculate a calibration uncertainty budget that can be performed on a nightly basis. We include the contribution of the radiosonde measurement uncertainties to the total calibration uncertainty, and show that on average the uncertainty contribution from the radiosonde is 4 %. We also calculate the uncertainty in the calibration due to the uncertainty in the lidar's counting system, caused by phototube paralyzation, and found it to be an average of 0.3 % for our system. This trajectory method allows a more accurate calibration of a lidar, even when non-co-located radiosondes are the only available calibration source, and also allows additional nights to be used for calibration that would otherwise be discarded due to variability in the water vapour profile.
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Abstract In this study, we first performed a comprehensive structural analysis of four models of radiosondes (devices intended for use as the meteorological probe of a sounding balloon) manufactured by three different companies – Graw, Vaisala and Meteomodem. The radiosondes were disassembled for visual inspection and manual measurement, three-dimensional computed tomography images were taken of their inner structure, and the outer shapes of the radiosondes were scanned with a structured-light three-dimensional scanner. The structural properties of the radiosondes thus identified were then compared to one other, based on which the Meteomodem M10 was ranked as the least harmful in a potential collision. Next, the Meteomodem M10 radiosonde was used in collision tests with a heavy target and with a pumpkin model, in order to evaluate the possible damage caused by and to the radiosonde in different types of collisions.
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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.
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Based on the ensemble spread, a methodology of measuring uncertainty in weather forecasts, the temperature trend and spread have been estimated using five radiosonde data sets and seven reanalysis products beginning in 1989. The results show that the magnitude of warming or cooling depends on the data sources, atmospheric heights, and geophysical latitudes. Over low‐middle latitudes, the cooling varies from −2.6 K/decade in NCEP‐DOE to −0.8 K/decade in HADAT2 in the lower stratosphere. The warming weakly changes from 0.2 through 0.4 K/decade in the middle troposphere. Over Antarctica, there is a pronounced warming in the low‐middle troposphere in the three NCEP reanalyses and the RATPAC radiosonde data sets, and cooling in the other eight products. Over the Arctic, the warming is observed from the lower troposphere to the lower stratosphere in all twelve data sets. Significant cooling is identified over the middle stratosphere (above 50 hPa) in all five radiosondes. For global mean temperature, the trend is approximately 0.2 K/decade in the troposphere and −0.8 K/decade in the stratosphere. The spread increases significantly with atmospheric height from approximately 0.1 K/decade at 850hPa to 0.8 K/decade at 30hPa. The spread in the reanalysis data sets is much larger than in the radiosondes in the stratosphere. In contrast, the spread in both the reanalysis and radiosondes data sets is very small and shows the trend in better agreement with each other in the troposphere.
Middle latitudes
Atmospheric temperature
Sudden stratospheric warming
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Although there are a number of sources of radiosonde data for validation of observations from other atmospheric sensors, routine operational sondes remain the main source for a large volume of data. In this study radiosonde moisture profiles are renormalized using Global Positioning System (GPS) Integrated Precipitable Water (IPW) vapor. The GPS‐adjusted radiosonde humidity profiles are then compared to the Atmospheric Infrared Sounder (AIRS) measurements. As a check, AIRS measurements are also compared with unadjusted radiosonde moisture profiles. It is shown that the GPS‐adjusted values are in better agreement with the AIRS measurements. On the basis of this result, the GPS‐adjusted radiosondes are used to assess the AIRS potential accuracy. This is valid because the errors in the AIRS measurements and the adjustments are independent. The GPS‐based renormalization of radiosonde humidity measurements produced a significant improvement in the agreement between AIRS and Vaisala RS 57 H type radiosondes in the lower troposphere, where much of the atmospheric water vapor resides. The adjustment also resulted in improved agreement between AIRS and radiosonde IPW estimates. The results showed a day/night bias in the radiosonde values as compared to the GPS and the AIRS values, demonstrating the potential use of the technique for evaluating and correcting this bias. Established corrections for humidity errors also have been applied to some operational radiosonde observations, specifically the published temperature correction developed for the Vaisala RS80 H type radiosonde. This correction produced a much smaller effect than the GPS adjustment.
Atmospheric Infrared Sounder
Precipitable water
Hygrometer
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Abstract The usage of radiosonde humidity data in climate studies and atmospheric reanalysis has been greatly hampered by the inhomogeneity issue mainly caused by radiosonde sensor changes. In this work, high‐quality precipitable water (PW) products derived from a national Global Positioning System (GPS) network in China covering the period from 1999 to 2015 were used to quantify errors in radiosonde‐derived PW products for different radiosonde types. Correlations between PW biases and factors including the station location, mean PW, weighted mean temperature (T m ), and solar elevation angle were carefully analyzed. Biases in PW products derived from the mechanical radiosondes (GZZ2, used at most stations before 2001) show strong correlations with the station elevation and T m , and a PW correction model was then developed. For GTS1 and GTS1‐1 (widely used in current operational system), due to the unobvious correlations between PW biases and these factors, corrections were estimated as mean values of PW biases at all collocated stations. For GTS1‐2, GTS2(U)‐1, and TD2‐A (used at a few stations), subject to insufficient GPS‐radiosonde collocated stations, they are left uncorrected. The corrected radiosonde‐derived PW products (referred as Corr) were compared with the uncorrected products (referred as Raw) as well as PW products derived from the radiosonde data homogenized using the method proposed by Dai et al. (2011; referred as Dai, https://doi.org/10.1175/2010JCLI3816.1 ). Corr products show better agreements with GPS‐derived PW than Raw and Dai, and artificial significant decreasing PW trends in the recent two decades in Raw products were greatly reduced after applying the proposed corrections.
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The quality of humidity measurements from global operational radiosonde sensors in upper, middle, and lower troposphere for the period 2000–2011 were investigated using satellite observations from three microwave water vapor channels operating at 183.31±1, 183.31±3, and 183.31±7 GHz. The radiosonde data were partitioned based on sensor type into 19 classes. The satellite brightness temperatures (Tb) were simulated using radiosonde profiles and a radiative transfer model, then the radiosonde simulated Tb's were compared with the observed Tb's from the satellites. The surface affected Tb's were excluded from the comparison due to the lack of reliable surface emissivity data at the microwave frequencies. Daytime and nighttime data were examined separately to see the possible effect of daytime radiation bias on the sonde data. The error characteristics among different radiosondes vary significantly, which largely reflects the differences in sensor type. These differences are more evident in the mid‐upper troposphere than in the lower troposphere, mainly because some of the sensors stop responding to tropospheric humidity somewhere in the upper or even in the middle troposphere. In the upper troposphere, most sensors have a dry bias but Russian sensors and a few other sensors including GZZ2, VZB2, and RS80H have a wet bias. In middle troposphere, Russian sensors still have a wet bias but all other sensors have a dry bias. All sensors, including Russian sensors, have a dry bias in lower troposphere. The systematic and random errors generally decrease from upper to lower troposphere. Sensors from China, India, Russia, and the U.S. have a large random error in upper troposphere, which indicates that these sensors are not suitable for upper tropospheric studies as they fail to respond to humidity changes in the upper and even middle troposphere. Overall, Vaisala sensors perform better than other sensors throughout the troposphere exhibiting the smallest systematic and random errors. Because of the large differences between different radiosonde humidity sensors, it is important for long‐term trend studies to only use data measured using a single type of sensor at any given station. If multiple sensor types are used then it is necessary to consider the bias between sensor types and its possible dependence on humidity and temperature.
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