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

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.

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

2021 ◽  
Vol 14 (12) ◽  
pp. 7525-7544
Author(s):  
Julien Totems ◽  
Patrick Chazette ◽  
Alexandre Baron
Keyword(s):  
Water Vapor ◽  
Measurement Errors ◽  
Weather Prediction ◽  
Atmospheric Temperature ◽  
Temperature Profiles ◽  
Optical Elements ◽  
Common Path ◽  
Calibration Uncertainty ◽  
Mean Differences ◽  
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 simultaneously deriving 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 and 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, in particular are investigated, using horizontal shots in a homogeneous 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 (up to 6 ∘C bias below 300 m range). Measurement errors are quantified using simulations and a number of radiosoundings launched close to the laboratory. After de-biasing, the remaining mean differences are below 0.1 g kg−1 on water vapor and 1 ∘C on temperature, and rms differences are consistent with the expected error from lidar noise, calibration uncertainty, and horizontal inhomogeneities of the atmosphere between the lidar and radiosondes.

Significant sources of bias in temperature measurements by rotational Raman lidar, and mitigation in the French mobile system WALI

2021 ◽  
Author(s):  
Julien Totems ◽  
Patrick Chazette ◽  
Alexandre Baron
Keyword(s):  
Measurement Errors ◽  
Weather Prediction ◽  
Absolute Accuracy ◽  
Raman Lidar ◽  
Mobile System ◽  
Optical Elements ◽  
Interference Filters ◽  
Common Path ◽  
Weather Model ◽  
The World

<p>Lidars using rotational Raman backscattering to monitor the temperature profile in the low troposphere offer enticing perspectives for applications in weather prediction, as well as studies of aerosol and water vapor interactions, when deriving simultaneously relative humidity and aerosol optical properties. We describe the technical choices made during the design and calibration of the new temperature Raman channels for the mobile Weather and Aerosol Lidar (WALI), going over the sources of bias and uncertainty stemming from the different optical elements of the instrument. The impacts of interference filters and non-common-path differences between Raman channels, and their mitigation, are particularly investigated; without countermeasures, we find the theoretical magnitude of the highlighted biases can be much larger than the targeted absolute accuracy of 1°C defined by the World Meteorological Organization (WMO). Effective measurement errors are quantified using numerical end-to-end simulations and numerous radiosoundings launched close to the lidar location. Our aim is to fully discuss design choices and sources of bias which have been little reported in the literature. An application of the WALI measurements during heat wave conditions in the summer of 2020 will also be presented, and compared to ERA5 weather model reanalyses.</p>

Measurement of Atmospheric Temperature Profiles Using Raman Lidar

1979 ◽  
Vol 18 (2) ◽  
pp. 225-227 ◽  
Author(s):  
R. Gill ◽  
K. Geller ◽  
J. Farina ◽  
J. Cooney ◽  
A. Cohen
Keyword(s):  
Atmospheric Temperature ◽  
Temperature Profiles ◽  
Raman Lidar

scholarly journals A new method to derive middle atmospheric temperature profiles using a combination of Rayleigh lidar and O<sub>2</sub> airglow temperatures measurements

2012 ◽  
Vol 30 (1) ◽  
pp. 27-32 ◽  
Author(s):  
A. Taori ◽  
A. Jayaraman ◽  
K. Raghunath ◽  
V. Kamalakar
Keyword(s):  
Temperature Profile ◽  
Atmospheric Temperature ◽  
New Method ◽  
Temperature Profiles ◽  
Vertical Temperature ◽  
Lidar System ◽  
Mesospheric Temperature ◽  
Simultaneous Measurements ◽  
Rayleigh Lidar ◽  
First Time

Abstract. The vertical temperature profiles in a typical Rayleigh lidar system depends on the backscatter photon counts and the CIRA-86 model inputs. For the first time, we show that, by making simultaneous measurements of Rayleigh lidar and upper mesospheric O2 temperatures, the lidar capability can be enhanced to obtain mesospheric temperature profile up to about 95 km altitudes. The obtained results are compared with instantaneous space-borne SABER measurements for a validation.

scholarly journals Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

2010 ◽  
Vol 23 (7) ◽  
pp. 1675-1695 ◽  
Author(s):  
Sibylle Vey ◽  
Reinhard Dietrich ◽  
Axel Rülke ◽  
Mathias Fritsche ◽  
Peter Steigenberger ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Seasonal Cycle ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Atmospheric Temperature ◽  
Interannual Variations ◽  
Seasonal Signals ◽  
The Tropics

Abstract In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.

scholarly journals Determining the temporal variability in atmospheric temperature profiles measured using radiosondes and assessment of correction factors for different launch schedules

2014 ◽  
Vol 7 (8) ◽  
pp. 8339-8357
Author(s):  
D. Butterfield ◽  
T. Gardiner
Keyword(s):  
Weather Prediction ◽  
Atmospheric Temperature ◽  
Time Of Day ◽  
Temperature Data ◽  
Rate Of Change ◽  
Temperature Profiles ◽  
Correction Factors ◽  
Temporal Separation ◽  
General Observation ◽  
Diurnal Variability

Abstract. Radiosondes provide one of the primary sources of upper atmosphere temperature data for numerical weather prediction, the assessment of long-term trends in atmospheric temperature, the study atmospheric processes and provide a source of intercomparison data for other temperature sensors e.g. satellites. When intercomparing different temperature profiles it is important to include the effect of temporal mis-match between the measurements. To help quantify this uncertainty the atmospheric temperature variation through the day needs to be assessed, so that a correction and uncertainty for time difference can be calculated. Temperature data from an intensive radiosonde campaign were analysed to calculate the hourly rate of change in temperature at different altitudes and provide recommendations and correction factors for different launch schedules. Using these results, three additional longer term data sets were analysed to assess the diurnal variability temperature as a function of altitude, time of day and season of the year. This provides data on the appropriate correction factors to use for a given temporal separation and the uncertainty associated with them. A general observation was that 10 or more repeat measurements would be required to get a standard uncertainty of less than 0.1 K h−1 of temporal mis-match.

scholarly journals Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

2017 ◽  
Vol 10 (11) ◽  
pp. 4303-4316 ◽  
Author(s):  
Maria Filioglou ◽  
Anna Nikandrova ◽  
Sami Niemelä ◽  
Holger Baars ◽  
Tero Mielonen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Limited Area ◽  
Mixing Ratio ◽  
Atmospheric Conditions ◽  
Raman Lidar ◽  
Mixing Ratios ◽  
Seasonal Analysis ◽  
Relative Errors ◽  
Water Vapor Mixing Ratio

Abstract. We present tropospheric water vapor profiles measured with a Raman lidar during three field campaigns held in Finland. Co-located radio soundings are available throughout the period for the calibration of the lidar signals. We investigate the possibility of calibrating the lidar water vapor profiles in the absence of co-existing on-site soundings using water vapor profiles from the combined Advanced InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) satellite product; the Aire Limitée Adaptation dynamique Développement INternational and High Resolution Limited Area Model (ALADIN/HIRLAM) numerical weather prediction (NWP) system, and the nearest radio sounding station located 100 km away from the lidar site (only for the permanent location of the lidar). The uncertainties of the calibration factor derived from the soundings, the satellite and the model data are  < 2.8, 7.4 and 3.9 %, respectively. We also include water vapor mixing ratio intercomparisons between the radio soundings and the various instruments/model for the period of the campaigns. A good agreement is observed for all comparisons with relative errors that do not exceed 50 % up to 8 km altitude in most cases. A 4-year seasonal analysis of vertical water vapor is also presented for the Kuopio site in Finland. During winter months, the air in Kuopio is dry (1.15±0.40 g kg−1); during summer it is wet (5.54±1.02 g kg−1); and at other times, the air is in an intermediate state. These are averaged values over the lowest 2 km in the atmosphere. Above that height a quick decrease in water vapor mixing ratios is observed, except during summer months where favorable atmospheric conditions enable higher mixing ratio values at higher altitudes. Lastly, the seasonal change in disagreement between the lidar and the model has been studied. The analysis showed that, on average, the model underestimates water vapor mixing ratios at high altitudes during spring and summer.

Combined Raman lidar for the measurement of atmospheric temperature, water vapor, particle extinction coefficient, and particle backscatter coefficient

2002 ◽  
Vol 41 (36) ◽  
pp. 7657 ◽  
Author(s):  
Andreas Behrendt ◽  
Takuji Nakamura ◽  
Michitaka Onishi ◽  
Rudolf Baumgart ◽  
Toshitaka Tsuda
Keyword(s):  
Water Vapor ◽  
Extinction Coefficient ◽  
Atmospheric Temperature ◽  
Raman Lidar ◽  
Backscatter Coefficient ◽  
Temperature Water

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

2021 ◽  
Vol 14 (2) ◽  
pp. 1333-1353
Author(s):  
Giovanni Martucci ◽  
Francisco Navas-Guzmán ◽  
Ludovic Renaud ◽  
Gonzague Romanens ◽  
S. Mahagammulla Gamage ◽  
...  
Keyword(s):  
Dead Time ◽  
Signal To Noise Ratio ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Correction Function ◽  
Small Scale ◽  
Temperature Profiles ◽  
Raman Lidar ◽  
Zero 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 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.

scholarly journals Multiparameter Raman Lidar Measurements for the Characterization of a Dry Stratospheric Intrusion Event

2009 ◽  
Vol 26 (9) ◽  
pp. 1742-1762 ◽  
Author(s):  
Paolo Di Girolamo ◽  
Donato Summa ◽  
Rossella Ferretti
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Relative Humidity ◽  
Potential Temperature ◽  
Atmospheric Temperature ◽  
Deep Penetration ◽  
Raman Lidar ◽  
Dry Air ◽  
Stratospheric Intrusion ◽  
Lidar Measurements

Abstract The University of Basilicata Raman lidar system (BASIL) is operational in Potenza, Italy, and it is capable of performing high-resolution and accurate measurements of atmospheric temperature and water vapor based on the application of the rotational and vibrational Raman lidar techniques in the ultraviolet region. BASIL was recently involved in the 2005 International Lindenberg campaign for Assessment of Humidity and Cloud Profiling Systems and Its Impact on High-Resolution Modeling (LAUNCH 2005) experiment held from 12 September to 31 October 2005. A thorough description of the technical characteristics, measurement capabilities, and performances of BASIL is given in this paper. Measurements were continuously run between 1 and 3 October 2005, covering a dry stratospheric intrusion episode associated with a tropopause folding event. The measurements in this paper represent the first simultaneous Raman lidar measurements of atmospheric temperature, water vapor mixing ratio, and thus relative humidity reported for an extensive observation period (32 h). The use of water vapor to trace intruded stratospheric air allows the clear identification of a dry structure (∼1 km thick) originating in the stratosphere and descending in the free troposphere down to ∼3 km. A similar feature is present in the temperature field, with lower temperature values detected within the dry-air tongue. Relative humidity measurements reveal values as small as 0.5%–1% within the intruded air. The stratospheric origin of the observed dry layer has been verified by the application of a Lagrangian trajectory model. The subsidence of the intruding heavy dry air may be responsible for the gravity wave activity observed beneath the dry layer. Lidar measurements have been compared with the output of both the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and the European Centre for Medium-Range Weather Forecasts (ECMWF) global model. Comparisons in terms of water vapor reveal the capability of MM5 to reproduce the dynamical structures associated with the stratospheric intrusion episode and to simulate the deep penetration into the troposphere of the dry intruded layer. Moreover, lidar measurements of potential temperature are compared with MM5 output, whereas potential vorticities from both the ECMWF model and MM5 are compared with estimates obtained combining MM5 model vorticity and lidar measurements of potential temperature.

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