Validation of pure rotational Raman temperature data from the Raman Lidar for Meteorological Observations (RALMO) at Payerne

Abstract. The Raman Lidar for Meteorological Observations (RALMO) is operated at the MeteoSwiss station of Payerne (Switzerland) and provides, amongst other products, continuous measurements of temperature since 2010. The temperature profiles are retrieved from the pure rotational Raman (PRR) signals detected around the 355 nm Cabannes line. The transmitter and receiver systems of RALMO are described in detail, and the reception and acquisition units of the PRR channels are thoroughly characterized. The FastCom P7888 card used to acquire the PRR signal, the calculation of the dead time and the desaturation procedure are also presented. The temperature profiles retrieved from RALMO PRR data during the period going from July 2017 to the end of December 2018 have been validated against two reference operational radiosounding systems (ORSs) co-located with RALMO, i.e. the Meteolabor SRS-C50 and the Vaisala RS41. The ORSs have also served to perform the calibration of the RALMO temperature during the validation period. The maximum bias (ΔTmax), mean bias (μ) and mean standard deviation (σ) of RALMO temperature Tral with respect to the reference ORS, Tors, are used to characterize the accuracy and precision of Tral along the troposphere. The daytime statistics provide information essentially about the lower troposphere due to lower signal-to-noise ratio. The ΔTmax, μ and σ of the differences ΔT=Tral-Tors are, respectively, 0.28, 0.02±0.1 and 0.62±0.03 K. The nighttime statistics provide information for the entire troposphere and yield ΔTmax=0.29 K, μ=0.05±0.34 K and σ=0.66±0.06 K. The small ΔTmax, μ and σ values obtained for both daytime and nighttime comparisons indicate the high stability of RALMO that has been calibrated only seven times over 18 months. The retrieval method can correct for the largest sources of correlated and uncorrelated errors, e.g. signal noise, dead time of the acquisition system and solar background. Especially the solar radiation (scattered into the field of view from the zenith angle Φ) affects the quality of PRR signals and represents a source of systematic error for the retrieved temperature. An imperfect subtraction of the background from the daytime PRR profiles induces a bias of up to 2 K at all heights. An empirical correction f(Φ) ranging from 0.99 to 1 has therefore been applied to the mean background of the PRR signals to remove the bias. The correction function f(Φ) has been validated against the numerical weather prediction model COSMO (Consortium for Small-scale Modelling), suggesting that f(Φ) does not introduce any additional source of systematic or random error to Tral. A seasonality study has been performed to help with understanding if the overall daytime and nighttime zero bias hides seasonal non-zero biases that cancel out when combined in the full dataset.

Download Full-text

Validation of temperature data from the RAman Lidar for Meteorological Observations (RALMO) at Payerne. An application to liquid cloud supersaturation

10.5194/amt-2020-289 ◽

2020 ◽

Cited By ~ 1

Author(s):

Giovanni Martucci ◽

Francisco Navas-Guzman ◽

Ludovic Renaud ◽

Gonzague Romanens ◽

S. Mahagammulla Gamage ◽

...

Keyword(s):

Standard Deviation ◽

Dead Time ◽

Weather Prediction ◽

Correction Function ◽

Temperature Profiles ◽

Raman Lidar ◽

Zero Bias ◽

Receiver System ◽

Maximum Bias ◽

Meteorological Observations

Abstract. The RAman Lidar for Meteorological Observations (RALMO) is operated at the MeteoSwiss station of Payerne (Switzerland) and provides, amongst other products, continuous measurements of temperature since 2010. The temperature profiles are retrieved from the pure rotational Raman (PRR) signals detected around the 355-nm Cabannes line. The transmitter-receiver system of RALMO is described in detail and the reception and acquisition units of the PRR channels are thoroughly characterized. The FastCom P7888 card used to acquire the PRR signal, the calculation of the dead-time and the desaturation procedure are also presented. The temperature profiles retrieved from RALMO data during the period going from July 2017 to the end of December 2018 have been validated against two reference operational radiosounding systems (ORS) co-located with RALMO, i.e. the Meteolabor SRS-C50 and the Vaisala RS41. These radiosondes have also been used to perform seven calibrations during the validation period. The maximum bias (ΔTmax), mean bias (μ) and mean standard deviation (σ) of RALMO temperature Tral with respect to the reference ORS Tors are used to characterize the accuracy and precision of Tral in the troposphere. The ΔTmax, μ and σ of the daytime differences ΔT=Tral−Tors in the lower troposphere are 0.28 K, 0.02±0.1 K and 0.62±0.03 K, respectively. The nighttime differences suffer a mean bias of μ = 0.05±0.34 K, a mean standard deviation σ=0.66±0.06 , and a maximum bias ΔTmax=0.29 K over the whole troposphere. The small ΔTmax, μ and σ values obtained for both daytime and nighttime comparisons indicate the high stability of RALMO that has been calibrated only seven times over 18 months. The retrieval method can correct for the largest sources of correlated and uncorrelated errors, e.g. signal noise, dead-time of the acquisition system and solar background. Especially the solar radiation (scattered into the field of view from the Zenith angle Phi affects the quality of PRR signals and represents a source of systematic error for the retrieved temperature. An imperfect subtraction of the background from the daytime PRR profiles induces a bias of up to 2 K at all heights. An empirical correction f(Φ) ranging from 0.99 to 1, has therefore been applied to the mean background of the PRR signals to remove the bias. The correction function f(Φ) has been validated against the numerical weather prediction model COSMO suggesting that f(Φ) does not introduce any additional source of systematic or random error to Tral. A seasonality study has been performed to help understanding if the overall daytime and nighttime zero-bias hides seasonal non-zero biases that cancel out when combined in the full dataset. Finally, the validated RALMO temperature has been used in combination with the humidity profiles retrieved from RALMO to calculate the relative humidity and to perform a qualitative study of supersaturation occurring in liquid stratus clouds.

Download Full-text

Characterization of high temporal resolution prr acquisition by fast comtec card: Deadtime, PRR desaturation, temperature calibration and retrieval.

EPJ Web of Conferences ◽

10.1051/epjconf/201817601017 ◽

2018 ◽

Vol 176 ◽

pp. 01017 ◽

Cited By ~ 1

Author(s):

Giovanni Martucci ◽

Valentin Simeonov ◽

Ludovic Renaud ◽

Alexander Haefele

Keyword(s):

Water Vapor ◽

Temporal Resolution ◽

Dead Time ◽

Temporal Stability ◽

Acquisition System ◽

High Temporal Resolution ◽

Temperature Calibration ◽

Raman Lidar ◽

Meteorological Observations

RAman Lidar for Meteorological Observations (RALMO) is operated at MeteoSwiss and provides continuous measurements of water vapor and temperature since 2010. While the water vapor has been acquired by a Licel acquisition system since 2008, the temperature channels have been migrated to a Fastcom P7888 acquisition system, since August 2015. We present a characterization of this new acquisition system, namely its dead-time, desaturation, temporal stability of the Pure Rotational Raman signals and the retrieval of the PRR-temperature.

Download Full-text

The Helsinki Testbed: A Mesoscale Measurement, Research, and Service Platform

Bulletin of the American Meteorological Society ◽

10.1175/2010bams2878.1 ◽

2011 ◽

Vol 92 (3) ◽

pp. 325-342 ◽

Cited By ~ 40

Author(s):

Jarkko T. Koskinen ◽

Jani Poutiainen ◽

David M. Schultz ◽

Sylvain Joffre ◽

Jarmo Koistinen ◽

...

Keyword(s):

Field Experiments ◽

Weather Prediction ◽

Flux Measurement ◽

Local Analysis ◽

Small Scale ◽

Commercial Development ◽

Finnish Meteorological Institute ◽

Weather Radars ◽

Meteorological Observations ◽

Global Precipitation

Abstract The Finnish Meteorological Institute and Vaisala have established a mesoscale weather observational network in southern Finland. The Helsinki Testbed is an open research and quasi-operational program designed to provide new information on observing systems and strategies, mesoscale weather phenomena, urban and regional modeling, and end-user applications in a high-latitude (~60°N) coastal environment. The Helsinki Testbed and related programs feature several components: observing system design and implementation, small-scale data assimilation, nowcasting and short-range numerical weather prediction, public service, and commercial development of applications. Specifically, the observing instrumentation focuses on meteorological observations of meso-gamma-scale phenomena that are often too small to be detected adequately by traditional observing networks. In particular, more than 40 telecommunication masts (40 that are 120 m high and one that is 300 m high) are instrumented at multiple heights. Other instrumentation includes one operational radio sounding (and occasional supplemental ones), ceilometers, aerosol-particle and trace-gas instrumentation on an urban flux-measurement tower, a wind profiler, and four Doppler weather radars, three of which have dual-polarimetric capability. The Helsinki Testbed supports the development and testing of new observational instruments, systems, and methods during coordinated field experiments, such as the NASA Global Precipitation Measurement (GPM). Currently, the Helsinki Testbed Web site typically receives more than 450,000 weekly visits, and more than 600 users have registered to use historical data records. This article discusses the three different phases of development and associated activities of the Helsinki Testbed from network development and observational campaigns, development of the local analysis and prediction system, and testing of applications for commercial services. Finally, the Helsinki Testbed is evaluated based on previously published criteria, indicating both successes and shortcomings of this approach.

Download Full-text

Mitigation of bias sources for atmospheric temperature and humidity in the mobile Weather & Aerosol Raman Lidar (WALI)

10.5194/amt-2021-132 ◽

2021 ◽

Author(s):

Julien Totems ◽

Patrick Chazette ◽

Alexandre Baron

Keyword(s):

Water Vapor ◽

Measurement Errors ◽

Weather Prediction ◽

Atmospheric Temperature ◽

Temperature Profiles ◽

Raman Lidar ◽

Optical Elements ◽

Interference Filters ◽

Common Path ◽

First Time

Abstract. Lidars using vibrational and rotational Raman scattering to continuously monitor both the water vapor and temperature profiles in the low and middle troposphere offer enticing perspectives for applications in weather prediction and studies of aerosol/cloud/water vapor interactions by deriving simultaneously relative humidity and atmospheric optical properties. Several heavy systems exist in European laboratories but only recently have they been downsized and ruggedized for deployment in the field. In this paper, we describe in detail the technical choices made during the design and calibration of the new Raman channels for the mobile Weather and Aerosol Lidar (WALI), going over the important sources of bias and uncertainty on the water vapor & temperature profiles stemming from the different optical elements of the instrument. For the first time, the impacts of interference filters and non-common-path differences between Raman channels, and their mitigation, are particularly investigated, using horizontal shots in a homogenous atmosphere. For temperature, the magnitude of the highlighted biases can be much larger than the targeted absolute accuracy of 1 °C defined by the WMO. Measurement errors are quantified using simulations and a number of radiosoundings launched close to the laboratory.

Download Full-text

Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar with Turbulence Resolution

10.5194/egusphere-egu21-12283 ◽

2021 ◽

Author(s):

Diego Lange ◽

Andreas Behrendt ◽

Volker Wulfmeyer

Keyword(s):

Boundary Layer ◽

Water Vapor ◽

Data Assimilation ◽

Inversion Layer ◽

Lower Troposphere ◽

Statistical Uncertainty ◽

Temperature Profiles ◽

Free Troposphere ◽

Earth System ◽

Raman Lidar

We present the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS), a new tool for observations in the atmospheric boundary layer and lower free troposphere during daytime and nighttime with very high resolution up to the turbulence scale, high accuracy and precision, and very short latency and illustrate its performance with new measurements examples. ARTHUS measurements resolve the strength of the inversion layer at the planetary boundary layer top, elevated lids in the free troposphere, and turbulent fluctuations in water vapor and temperature, simultaneously (Lange et al., 2019). In addition to thermodynamic variables, ARTHUS provides also independent profiles of the particle backscatter coefficient and the particle extinction coefficient from the rotational Raman signals at 355 nm with much better resolution than a conventional vibrational Raman lidar.The observation of atmospheric moisture and temperature profiles is essential for the understanding and prediction of earth system processes. These are fundamental components of the global and regional energy and water cycles, they determine the radiative transfer through the atmosphere, and are critical for the cloud formation and precipitation (Wulfmeyer, 2015). Also, as confirmed by case studies, the assimilation of high-quality, lower tropospheric WV and T profiles results in a considerable improvement of the skill of weather forecast models particularly with respect to extreme events.Very stable and reliable performance was demonstrated during more than 3000 hours of operation experiencing a huge variety of weather conditions, including seaborne operation during the EUREC4A campaign (Bony et al., 2017, Stevens et al., 2020). ARTHUS provides temperature profiles with resolutions of 10-60 s and 7.5-100 m vertically in the lower free troposphere. During daytime, the statistical uncertainty of the WV mixing ratio is <2 % in the lower troposphere for resolutions of 5 minutes and 100 m. Temperature statistical uncertainty is <0.5 K even up to the middle troposphere. Consequently, ARTHUS fulfills the stringent WMO breakthrough requirements on nowcasting and very short-range forecasting (see www. wmo&#8208;sat.info/oscar/observingrequirements).This performance serves very well the next generation of very fast rapid-update-cycle data assimilation systems. Ground-based stations and networks can be set up or extended for climate monitoring, verification of weather, climate and earth system models, data assimilation for improving weather forecasts.References:Bony et al., 2017, https://doi.org/10.1007/s10712-017-9428-0Lange et al., 2019, https://doi.org/10.1029/2019GL085774Stevens et al. 2020, submitted to ESSDWulfmeyer et al., 2015, doi:10.1002/2014RG000476

Download Full-text

Relevance of a kite-based calibration for a water vapour Raman lidar

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-10577-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 10577-10609

Author(s):

J. Totems ◽

P. Chazette

Keyword(s):

Standard Deviation ◽

Water Vapour ◽

Hydrological Cycle ◽

Weather Prediction ◽

Lower Troposphere ◽

Calibration Method ◽

Numerical Weather Prediction Model ◽

Raman Lidar ◽

Water Vapour Content ◽

The Mediterranean

Abstract. We present a calibration method for a water vapour Raman lidar using a meteorological probe on-board a kite, flown steadily above the lidar site, within the framework of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) and Chemistry-Aerosols Radiative Effect in the Mediterranean (ChArMEx) campaigns. The experiment was carried on in Menorca (Spain) during June 2013, using the mobile Water vapour and Aerosol Lidar WALI. The kite calibration showed a much better degree of co-location with the lidar system than could be achieved with radiosondes, and allowed to calibrate measurements below the full overlap range between the emitter and the receiver. A range-dependent water vapour lidar calibration was determined, with an uncertainty of 2 % in the altitude range 90–8000 m. Water vapour measurements were further compared with radiosondes, showing very good agreement in the lower troposphere (1–5 km) and a relative mean and standard deviation of 5 and 9 %, respectively. Moreover, a reasonable agreement with MODIS integrated water vapour content is found, with a relative mean and standard deviation of 3 and 16 %. However, a discrepancy was found with AERONET retrievals, showing the latter to be underestimated by 28 %. Reanalyses from the ECMWF/IFS numerical weather prediction model were also in agreement with the temporal evolution highlighted with the lidar, with no measurable drift in integrated content over the period.

Download Full-text

Calibration of a water vapour Raman lidar with a kite-based humidity sensor

Atmospheric Measurement Techniques ◽

10.5194/amt-9-1083-2016 ◽

2016 ◽

Vol 9 (3) ◽

pp. 1083-1094 ◽

Cited By ~ 4

Author(s):

Julien Totems ◽

Patrick Chazette

Keyword(s):

Standard Deviation ◽

Water Vapour ◽

Hydrological Cycle ◽

Weather Prediction ◽

Lower Troposphere ◽

Calibration Method ◽

Relative Difference ◽

Raman Lidar ◽

Water Vapour Content ◽

Integrated Water Vapour

Abstract. We present a calibration method for a water vapour Raman lidar using a meteorological probe lifted by a kite, flown steadily above the lidar site, within the framework of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) and Chemistry-Aerosol Mediterranean Experiment (ChArMEx) campaigns. The experiment was carried out in Menorca (Spain) during June 2013, using the mobile water vapour and aerosol lidar WALI. Calibration using a kite demonstrated a much better degree of co-location with the lidar system than that which could be achieved with radiosondes, and it allowed us to determine the overlap function and calibration factor simultaneously. The range-dependent water vapour lidar calibration was thus determined with an uncertainty of 2 % in the 90–8000 m altitude range. Lidar water vapour measurements are further compared with radiosondes, showing very good agreement in the lower troposphere (1–5 km) and a relative difference and standard deviation of 5 and 9 % respectively. Moreover, a reasonable agreement with MODIS-integrated water vapour content is found, with a relative mean and standard deviation of 3 and 16 % respectively. However, a discrepancy is found with AERONET retrievals, showing the latter to be underestimated by 28 %. Reanalyses by the ECMWF/IFS numerical weather prediction model also agree with the temporal evolution highlighted with the lidar, with no measurable drift in integrated water vapour content over the period.

Download Full-text

Forecasting of Tornadoes and Downbursts

The Oxford Handbook of Non-Synoptic Wind Storms ◽

10.1093/oxfordhb/9780190670252.013.3 ◽

2020 ◽

Author(s):

Djordje Romanic

Keyword(s):

Chaos Theory ◽

Prediction Models ◽

Weather Prediction ◽

Small Scale ◽

Weather Forecasts ◽

Wind Speeds ◽

The Past ◽

Extreme Wind ◽

Extreme Wind Speeds ◽

Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

Download Full-text

Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

Remote Sensing ◽

10.3390/rs13010001 ◽

2020 ◽

Vol 13 (1) ◽

pp. 1

Author(s):

Xu Xu ◽

Xiaolei Zou

Keyword(s):

Radio Occultation ◽

Weather Prediction ◽

Lower Troposphere ◽

Error Variance ◽

Specific Humidity ◽

Temperature Error ◽

Observation Error ◽

Bending Angle ◽

Gps Radio Occultation ◽

Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

Download Full-text