Calibration of a Water Vapour Lidar using a Radiosonde Trajectory Method
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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.Accurate radiosonde measurements of water vapor in the mid and upper troposphere are important for such applications as evaluating remote-sensor water vapor retrievals, initializing numerical models, and improving parameterizations of radiative and cloud processes. Measurements of relative humidity (RH) from Vaisala radiosondes are subject to several measurement errors, most of which increase in magnitude with decreasing temperature (Miloshevich et al. 2000). Several of these measurement errors have already been corrected in part of the Atmospheric Radiation Measurement (ARM) Program dataset by Barry Lesht and others. This research program addresses two remaining measurement errors that can be substantial in the mid and especially upper troposphere (UT).
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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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Water vapour is one of the most important greenhouse gases, since it causes about two third of the natural greenhouse effect of the Earth's atmosphere. To improve the understanding of the role of the water vapour in the atmosphere, extensive water vapour profiles with high spatio-temporal resolution are therefore necessary. A ground-based Raman lidar system is used to perform water vapour measurements in Athens, Greece (37.9°N, 23.6°E, 200 m asl.). Water vapour mixing ratio measurements are retrieved from simultaneous inelastic H 2 O and N 2 Raman backscatter lidar signals at 387 nm (from atmospheric N 2 ) and 407 nm (from H 2 O). Systematic measurements are performed since September 2006. A new algorithm is used to retrieve water vapour vertical profiles in the lower troposphere (0.5-5 km range height asl.). The lidar observations are complemented with radiosonde measurements. Radiosonde data are obtained daily (at 00:00 UTC and 12:00 UTC) from the Hellenic Meteorological Service (HMS) of Greece which operates a meteorological station at the Hellinikon airport (37. 54° N, 23.44° E, 15m asl) in Athens, Greece. First results of the systematic intercomparison between water vapour profiles derived simultaneously by the Raman lidar and by radiosondes are presented and discussed.
Mixing ratio
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The design principle and structural features of a new type dual-channel ground-based microwave radiometer for measurement of water vapor and liquid water in troposphere are discussed.A method independent of radiosonde data is used for system absolute calibrations.A statistical method for retrieving the total water vapor and cloud liquid water content and a nonlinear iterative algorithm for retrieving the water vapor profiles in the troposphere are discussed.Comparison is conducted between the measurement of the integrated amountsof the water vapor and the tropospheric vapor profiles by the radiometer and the radiosonde data.
Microwave radiometer
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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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Water vapor is a key factor of the climate change. The water vapor in upper troposphere is more important to global radiative balance. Satellite remote sensing provides a useful way to retrieve the upper troposphere water vapor at the low temperature. One of the most widely used channels to retrieval the water vapor is the 6.7 μm channel.Soden (1993) suggested a scheme by using this channel to obtain upper troposphere relative humidity. In this paper, we apply this method to the water vapor channel data provided by the GMS-5, and get the distribution of the clear sky upper troposphere water vapor over East Asia. A month-average distribution is confirming by the relative humidity distribution given by the NCEP data. Based on the GMS-5 data of 1996-2002, we get an average distribution of the upper troposphere water vapor over East Asia. This product is an aid ant data set for the research on water cycle and the responding of the atmospheric water vapor to climate change.
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Abstract The research explores the applicability of the gridded (level 3) monthly tropospheric water vapor (version 5) retrievals from the Atmospheric Infrared Sounder (AIRS) instrument and the Advanced Microwave Sounding Unit (AMSU) on board the NASA Aqua satellite over the Tibetan Plateau by comparing them with carefully processed radiosonde data. Local correlation analyses indicate that below 200 hPa, the AIRS/AMSU monthly water vapor retrievals are highly consistent with radiosondes over the whole plateau region, especially in the southeastern part and between 300 and 600 hPa. Relative deviation analyses further show that the differences between monthly mean AIRS/AMSU water vapor retrieval data and radiosondes are, in general, small below 250 hPa, in particular between 300 and 600 hPa and in high-altitude areas. Combined with a further direct comparison between AIRS/AMSU water vapor vertical retrievals and radiosonde observations averaged over the entire domain, these results suggest that the gridded monthly AIRS/AMSU water vapor retrievals can provide a very good account of spatial patterns and temporal variations in tropospheric water vapor content in the Tibetan Plateau region, in particular below 200 hPa. However, differences between AIRS/AMSU retrievals and radiosondes are seen at various levels, in particular above the level of 250 hPa. Therefore, for detailed quantitative analyses of water budget in the atmosphere and the entire water cycle, AIRS/AMSU retrieval data may need to be corrected or trained using radiosondes. Two fitting functions are derived for warm and cold seasons, although the seasonal difference is generally small.
Atmospheric Infrared Sounder
Atmospheric sounding
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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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In this paper we present an intercomparison between ground based lidar, radiosonde and satellite atmospheric water vapor measurements. Comparisons expressed in terms of water vapor profiles, obtained by Raman lidar and simultaneous ballonborne radiosonde, are reported and discussed. The deviation between the two profiles is smaller than 10 percent up to an altitude of 5 km. Furthermore, the intercomparison between lidar and radiosonde data and between lidar and satellite data is performed also in terms of water vapor columnar content. The agreement between lidar and radiosonde columnar content is better than 4 percent. Water vapor contour map are showed in order to demonstrate the high spatial and temporal variability of water vapor in the lower atmosphere. Difficulties in comparing lidar and satellite water vapor columnar contents associated to H2O spatial and temporal variability are discussed in the paper.
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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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