Integrated Raman Lidar and Microwave Radiometer Retrieval of Atmospheric Water Vapor

Water vapor plays a critically important role in many atmospheric processes. However, it is poorly characterized throughout much of the atmosphere, particularly in the UTLS (Upper Troposphere Lower Stratosphere) region, due to lack of accurate measurements. Raman lidar boasts the capacity for excellent spatial and temporal resolution, but requires an external calibration. Microwave radiometers can be calibrated in absolute terms, but have poor height resolution. In this study, we introduce an integrated water vapor retrieval using an optimal estimation method, where the measurements from the Raman Lidar for Meteorological Observation (RALMO) and a RPG-HATPRO radiometer, both located at the MeteoSwiss station in Payerne, Switzerland. We consider two radiometer forward models for characterizing the radiometer: ARTS2 (Eriksson et al. 2011) and a “lightweight” radiative model (Schroeder & Westwater 1991), comparing and analyzing their performance. The radiometer forward model is combined with a lidar forward model (Sica & Haefele 2016) to yield a forward model capable of retrieval of a calibrated lidar water vapor profile

P16. RALMO Rotational Raman Temperature Retrieval: First Steps Towards The Application of Optimal Estimation Method (OEM)

Background: Temperature is an important atmospheric parameter that plays an extensive role in the fields of atmospheric dynamics, climatology, meteorology, and chemistry. Light detection and ranging (lidar), is a remote sensing technology that can be used for atmospheric temperature profiling. A lidar transmits short laser pulses into the atmosphere and the light scattered by the particles in the atmosphere is collected and measured using a telescope. The atmospheric temperatures can be retrieved by analysing the Pure Rotational Raman (PRR) scatter measurements from the nitrogen and oxygen molecules in the atmosphere. Methods: In this study use the Optimal Estimation Method (OEM) to retrieve lower atmospheric temperatures from the PRR measurements obtained by the Raman Lidar for Meteorological Observations (RALMO) located in Payerne, Switzerland. The OEM is an inverse method requires specification of a forward model (FM) capable of reproducing measurements using the relevant physics and mathematical description of the instrument. It also can retrieve a full uncertainty budget on a profile-by-profile basis. Results: We propose a forward model to retrieve temperature from PRR measurements using the OEM and the model was tested using the synthetic measurements. Discussion & Conclusion: The results showed that the proposed forward model can be used to retrieve temperatures and few other parameters in the forward model such as lidar constants and background terms. As the next step of my PhD project this method will be used for measurements from the RALMO to retrieve temperature profiles. Interdisciplinary Reflection: The OEM can be applied can be used to solve nonlinear inverse problem in any research area

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From a high school biology class to a small research facility, this machine will cheaply (relative to its competitors) automatically pour a layer of agar into a large number of Petri dishes in order to grow bacteria micro-cultures. Designed to be powered within a fume hood, the user simply needs to open up the containment facility, insert stacks of Petri dishes, and pour in a batch of premade agar. Within the hour, approximately 120 Petri dishes should be layered and ready for further experimentation

Application of the optimal estimation method (OEM) to retrieve relative humidity from Raman Lidar backscatter measurements.

Accurate measurements of relative humidity (RH) vertical profiles in the atmosphere is important for understanding the earth\u27s weather and the climate system. RH represent the current state of the water vapor in the atmosphere with respect to the ambient air related to saturation. Even minor changes of the RH in the lower atmosphere has a large impact of the global circulation and cloud formation. Due to its high variability RH measurements in the lower atmosphere is significantly challenging. Raman lidar is one of the potential tools that can provide vertical profiles of RH. Typically, temperature and water vapor mixing ratios need to be estimated separately from the Raman lidar measurements to calculate RH. We have successfully implemented the optimal estimation method (OEM) to retrieve not only vertical profiles of RH but also vertical profiles of temperature, particle extinction and other instrumental parameters from the Raman backscatter measurements obtained by the Raman Lidar for Meteorological Observations (RALMO) located in Payerne, Switzerland. Unlike the traditional method the OEM provides a full uncertainty budget with both random and systematic uncertainties on profile by profile basis. The OEM is also capable of retrieving RH from the Raman lidar measurements in different sky conditions and the OEM retrieved RH agree the radiosonde measured RH within 10-15%

Harmonized retrieval of middle atmospheric ozone from two microwave radiometers in Switzerland

We present new harmonized ozone time series from two ground-based microwave radiometers in Switzerland: GROMOS and SOMORA. Both instruments have measured hourly ozone profiles in the middle atmosphere (20–75 km) for more than 2 decades. As inconsistencies in long-term trends derived from these two instruments were detected, a harmonization project was initiated in 2019. The goal was to fully harmonize the data processing of GROMOS and SOMORA to better understand and possibly reduce the discrepancies between the two data records. The harmonization has been completed for the data from 2009 until 2022 and has been successful at reducing the differences observed between the two time series. It also explains the remaining differences between the two instruments and flags their respective anomalous measurement periods in order to adapt their consideration for future trend computations. We describe the harmonization and the resulting time series in detail. We also highlight the improvements in the ozone retrievals with respect to the previous data processing. In the stratosphere and lower mesosphere, the seasonal ozone relative differences between the two instruments are now within 10 % and show good correlation (R > 0.7) (except during summertime). We also perform a comparison of these new data series against measurements from the Microwave Limb Sounder (MLS) and Solar Backscatter Ultraviolet Radiometer (SBUV) satellite instruments over Switzerland. Seasonal mean differences with MLS and SBUV are within 10 % in the stratosphere and lower mesosphere up to 60 km and increase rapidly above that point

A Bayesian Neural Network Approach for Tropospheric Temperature Retrievals from a Lidar Instrument

We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it to measurements taken by the RAman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland. We give a detailed description of using a Bayesian method to retrieve temperature profiles including estimates of the uncertainty due to the network weights and the statistical uncertainty of the measurements. We trained our model using lidar measurements under different atmospheric conditions, and we tested our model using measurements not used for training the network. The computed temperature profiles extend over the altitude range of 0.7 km to 6 km. The mean bias estimate of our temperatures relative to the MeteoSwiss standard processing algorithm does not exceed 0.05 K at altitudes below 4.5 km, and does not exceed 0.08 K in an altitude range of 4.5 km to 6 km. This agreement shows that the neural network estimated temperature profiles are in excellent agreement with the standard algorithm. The method is robust and is able to estimate the temperature profiles with high accuracy for both clear and cloudy conditions. Moreover, the trained model can provide the statistical and model uncertainties of the estimated temperature profiles. Thus, the present study is a proof of concept that the trained NNs are able to generate temperature profiles along with a full-budget uncertainty. We present case studies showcasing the Bayesian neural network estimations for day and night measurements, as well as in clear and cloudy conditions. We have concluded that the proposed Bayesian neural network is an appropriate method for the statistical retrieval of temperature profiles

Integrated Water Vapor during Rain and Rain-Free Conditions above the Swiss Plateau

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods

An Indoor Microwave Radiometer for Measurement of Tropospheric Water

This article presents the first detailed description of the innovative measurement setup of an indoor tropospheric microwave radiometer [TROpospheric WAter RAdiometer (TROWARA)] that avoids water films on radome. We discuss the performance of a commercial outdoor microwave radiometer [Humidity And Temperature PROfiler radiometer (HATPRO)] for measuring tropospheric water parameters in Bern, Switzerland. The HATPRO is less than 20 m from the TROWARA and has different instrument characteristics. Brightness temperatures measured by HATPRO are analyzed by comparing them with coincident measurements from TROWARA and Radiative Transfer Simulations based on the [European Centre for Medium-Range Weather Forecasts (ECMWF)] operational analysis data (denoted as RTSE). To find the source of brightness temperature bias, a gradient boosting decision tree is used to analyze the sensitivity of eight feature factors to bias. Data processing routines of the two radiometers use different algorithms to retrieve integrated water vapor (IWV) and integrated cloud liquid water (ILW), whereas the same physical algorithms based on the radiative transfer equation are applied to obtain the opacity and rain rate. Using 62 days of data with varied weather conditions, it was found that TROWARA brightness temperatures are in good agreement with RTSE. HATPRO brightness temperatures are significantly overestimated by about 5 K at 22 GHz, compared to TROWARA and RTSE. HATPRO brightness temperatures at 31 GHz agree well with TROWARA and RTSE (within about ±1 K). The overestimated brightness temperatures in the K-band and the HATPRO retrieval algorithm lead to an overestimation of IWV and ILW by HATPRO. The opacities at 31 GHz match very well for TROWARA and HATPRO during no rain with a verified R2of 0.96. However, liquid water floating or remaining water films on the radome of the outdoor HATPRO radiometer induce an overestimation of the rain rate. The physical reason for the overestimated 22-GHz brightness temperatures of the HATPRO is mainly the result of the combined effect of instrument calibration, the surrounding environment of the instrument, and the Sun elevation angle. This can be a problem with the Generation 2 HATPRO radiometer and this problem was resolved in the Generation 5 HATPRO radiometer

Characterization of aerosol hygroscopicity using Raman lidar measurements at the EARLINET station of Payerne

This study focuses on the analysis of aerosol hygroscopicity using remote sensing techniques. Continuous observations of aerosol backscatter coefficient (ßaer), temperature (T) and water vapor mixing ratio (r) have been performed by means of a Raman lidar system at the aerological station of MeteoSwiss at Payerne (Switzerland) since 2008. These measurements allow us to monitor in a continuous way any change in aerosol properties as a function of the relative humidity (RH). These changes can be observed either in time at a constant altitude or in altitude at a constant time. The accuracy and precision of RH measurements from the lidar have been evaluated using the radiosonde (RS) technique as a reference. A total of 172 RS profiles were used in this intercomparison, which revealed a bias smaller than 4¿%¿RH and a standard deviation smaller than 10¿%¿RH between both techniques in the whole (in lower) troposphere at nighttime (at daytime), indicating the good performance of the lidar for characterizing RH. A methodology to identify situations favorable to studying aerosol hygroscopicity has been established, and the aerosol hygroscopicity has been characterized by means of the backscatter enhancement factor (fß). Two case studies, corresponding to different types of aerosol, are used to illustrate the potential of this methodology. The first case corresponds to a mixture of rural aerosol and smoke particles (smoke mixture), which showed a higher hygroscopicity (f355ß=2.8 and f1064ß=1.8 in the RH range 73¿%–97¿%) than the second case, in which mineral dust was present (f355ß=1.2 and f1064ß=1.1in the RH range 68¿%–84¿%). The higher sensitivity of the shortest wavelength to hygroscopic growth was qualitatively reproduced using Mie simulations. In addition, a good agreement was found between the hygroscopic analysis done in the vertical and in time for Case I, where the latter also allowed us to observe the hydration and dehydration of the smoke mixture. Finally, the impact of aerosol hygroscopicity on the Earth's radiative balance has been evaluated using the GAME (Global Atmospheric Model) radiative transfer model. The model showed an impact with an increase in absolute value of 2.4¿W¿m-2 at the surface with respect to the dry conditions for the hygroscopic layer of Case I (smoke mixture).Peer ReviewedPostprint (published version