ALBERT

Description: Today, commercial microwave radiometers profilers (MWRP) are robust and unattended instruments providing real time accurate atmospheric observations at ~ 1 min temporal resolution under nearly all-weather conditions. Common commercial units operate in the 20–60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FI) commonly used in operational meteorology. The FI considered here include K index, Total Totals, KO index, Showalter index, T1 Gust, Fog Threat, Lifted Index, S Index (STT), Jefferson Index, MDPI, Thompson Index, TQ Index, and CAPE. Values of FI computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in Central Europe (Lindenberg, Germany), while the second one located at high altitude in North America (Whistler, Canada). It is demonstrated that FI computed from MWRP well correlate with those computed from radiosondes, with the additional advantage of nearly continuous update. The accuracy of MWRP-derived FI is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FI computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions). We conclude that MWRP retrievals can be used to produce meaningful FI, with the advantage (with respect to radiosondes) of nearly continuous update.

Description: The potential for measurement redundancy to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. We evaluated the usefulness of entropy and mutual correlation concepts, as defined in information theory, for quantifying random uncertainty and redundancy in time series of the integrated water vapour (IWV) and water vapour mixing ratio profiles provided by five highly instrumented GRUAN (GCOS, Global Climate Observing System, Reference Upper-Air Network) stations in 2010–2012. Results show that the random uncertainties on the IWV measured with radiosondes, global positioning system, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8%. Comparisons of time series of IWV content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy with the IWV time series measured by radiosondes and therefore the highest potential to reduce the random uncertainty of the radiosondes time series. Moreover, the random uncertainty of a time series from one instrument can be reduced by ~ 60% by constraining the measurements with those from another instrument. The best reduction of random uncertainty is achieved by conditioning Raman lidar measurements with microwave radiometer measurements. Specific instruments are recommended for atmospheric water vapour measurements at GRUAN sites. This approach can be applied to the study of redundant measurements for other climate variables.

Description: In the frame of the project "LuFo iPort VIS" which focuses on the implementation of a site-specific visibility forecast, a field campaign was organised to offer detailed information to a numerical fog model. As part of additional observing activities, a 22-channel microwave radiometer profiler (MWRP) was operating at the Munich Airport site in Germany from October 2011 to February 2012 in order to provide vertical temperature and humidity profiles as well as cloud liquid water information. Independently from the model-related aims of the campaign, the MWRP observations were used to study their capabilities to work in operational meteorological networks. Over the past decade a growing quantity of MWRP has been introduced and a user community (MWRnet) was established to encourage activities directed at the set up of an operational network. On that account, the comparability of observations from different network sites plays a fundamental role for any applications in climatology and numerical weather forecast. In practice, however, systematic temperature and humidity differences (bias) between MWRP retrievals and co-located radiosonde profiles were observed and reported by several authors. This bias can be caused by instrumental offsets and by the absorption model used in the retrieval algorithms as well as by applying a non-representative training data set. At the Lindenberg observatory, besides a neural network provided by the manufacturer, a measurement-based regression method was developed to reduce the bias. These regression operators are calculated on the basis of coincident radiosonde observations and MWRP brightness temperature (TB) measurements. However, MWRP applications in a network require comparable results at just any site, even if no radiosondes are available. The motivation of this work is directed to a verification of the suitability of the operational local forecast model COSMO-EU of the Deutscher Wetterdienst (DWD) for the calculation of model-based regression operators in order to provide unbiased vertical profiles during the campaign at Munich Airport. The results of this algorithm and the retrievals of a neural network, specially developed for the site, are compared with radiosondes from Oberschleißheim located about 10 km apart from the MWRP site. Outstanding deviations for the lowest levels between 50 and 100 m are discussed. Analogously to the airport experiment, a model-based regression operator was calculated for Lindenberg and compared with both radiosondes and operational results of observation-based methods. The bias of the retrievals could be considerably reduced and the accuracy, which has been assessed for the airport site, is quite similar to those of the operational radiometer site at Lindenberg above 1 km height. Additional investigations are made to determine the length of the training period necessary for generating best estimates. Thereby three months have proven to be adequate. The results of the study show that on the basis of numerical weather prediction (NWP) model data, available everywhere at any time, the model-based regression method is capable of providing comparable results at a multitude of sites. Furthermore, the approach offers auspicious conditions for automation and continuous updating.

Description: The potential for measurement redundancy to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. We evaluated the usefulness of entropy and mutual correlation concepts, as defined in information theory, for quantifying random uncertainty and redundancy in time series of atmospheric water vapor provided by five highly instrumented GRUAN (GCOS [Global Climate Observing System] Reference Upper-Air Network) Stations in 2010–2012. Results show that the random uncertainties for radiosonde, frost-point hygrometer, Global Positioning System, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8%. Comparisons of time series of the Integrated Water Vapor (IWV) content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy and therefore the highest potential to reduce random uncertainty of IWV time series estimated by radiosondes. Moreover, the random uncertainty of a time series from one instrument should be reduced of ~ 60% by constraining the measurements with those from another instrument. The best reduction of random uncertainty resulted from conditioning of Raman lidar measurements with microwave radiometer measurements. Specific instruments are recommended for atmospheric water vapor measurements at GRUAN sites. This approach can be applied to the study of redundant measurements for other climate variables.

Description: In the frame of the project "LuFo iPort VIS" which focuses on the implementation of a site specific visibility forecast, a field campaign was organised to offer detailed information to a numerical fog model. As part of additional observing activities a 22-channel microwave radiometer profiler (MWRP) was operating at the Munich Airport site in Germany from October 2011 to February 2012 in order to provide vertical temperature and humidity profiles as well as cloud liquid water information. Independently from the model-related aims of the campaign, the MWRP observations were used to study their capabilities to work in operational meteorological networks. Over the past decade a growing quantity of MWRP has been introduced and a user community (MWRnet) was established to encourage activities directed at the set up of an operational network. On that account, the comparability of observations from different network sites plays a fundamental role for any applications in climatology and numerical weather forecast. In practice, however, systematic temperature and humidity differences (bias) between MWRP retrievals and co-located radiosonde profiles were observed and reported by several authors. This bias can be caused by instrumental offsets as well as by the absorption model used in the retrieval algorithms. At the Lindenberg observatory besides a neural network provided by the manufacturer, a measurement-based regression method was developed to reduce the bias. These regression operators are calculated on the basis of coincident radiosonde observations and MWRP brightness temperature (TB) measurements. However, MWRP applications in a network require comparable results at just any site, even if no radiosondes are available. The motivation of this work is directed to a verification of the suitability of the operational local forecast model COSMO-EU of the Deutscher Wetterdienst (DWD) for the calculation of model-based regression operators in order to provide unbiased vertical profiles during the campaign at Munich Airport. The results of this algorithm and the retrievals of a neural network, specially developed for the site, are compared with radiosondes from Oberschleißheim located about 10 km apart from the MWRP site. The bias of the retrievals could be considerably reduced and the accuracy, which has been assessed for the airport site, is quite similar to those of the operational radiometer site at Lindenberg above 1 km height. Additional investigations are made to determine the length of the training period necessary for generating best estimates. Thereby three months have proven to be adequate. The results of the study show that on the basis of numerical weather prediction (NWP) model data, available everywhere at any time, the model-based regression method is capable to provide comparable results at a multitude of sites. Furthermore, the approach offers auspicious conditions for automation and continuous updating.