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

Applying the Optimal Estimation Method for Retrieving Rayleigh-Scatter Lidar Temperatures in the Mesosphere

The Rayleigh-scatter lidar (RSL) system at the Atmospheric Lidar Observatory at Utah State University (ALO-USU) provided a rich database of absolute temperatures throughout the mesosphere from 45 km to above 90 km between 1993 and 2004. Recently, a new method for retrieving absolute temperatures from RSL observations has been developed by a group at the University of Western Ontario (UWO), Canada. The Optimal Estimation Method (OEM) uses machine learning to minimize a cost function by optimizing the temperature parameter in a forward model, in our case the lidar equation, to RSL data. This optimization provides some benefits over the existing method through a robust uncertainty budget and a quantitative determination of the cut-off altitude, or the topmost altitude in the temperature profile. Using this method also provides a slight increase in the top observable altitude and does not have a large dependence on the initial temperature. The OEM procedure was converted from MATLAB, which is used by the UWO group, into Python, which is used at ALO-USU. The temperatures were then reduced using the OEM from observations made between 1993 and 2004. Initial results obtained using the Python version of OEM were compared with those using MATLAB showing good agreement. More observations from ALO-USU were then reduced using OEM and compared with the original reduction method. The results show good agreement between the two methods until higher altitudes. These differences can be attributed to dependence on initial conditions in the original method or over-constraining from overestimating the altitude range to be used in the OEM retrieval. At higher altitudes, however, the temperatures tend to agree within the given uncertainties. Further work with this method is being done to generate a temperature climatology using ALO-USU observations and developing a method to retrieve absolute neutral densities using a modification of the forward model in the OEM

Classification of lidar measurements using supervised and unsupervised machine learning methods

While it is relatively straightforward to automate the processing of lidar signals, it is more difficult to choose periods of good measurements to process. Groups use various ad hoc procedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures (e.g. Wing et al. 2018) to perform a task that is easy to train humans to perform but is time-consuming. Here, we use machine learning techniques to train the machine to sort the measurements before processing. The presented method is generic and can be applied to most lidars. We test the techniques using measurements from the Purple Crow Lidar (PCL) system located in London, Canada. The PCL has over 200 000 raw profiles in Rayleigh and Raman channels available for classification. We classify raw (level-0) lidar measurements as clear sky profiles with strong lidar returns, bad profiles, and profiles which are significantly influenced by clouds or aerosol loads. We examined different supervised machine learning algorithms including the random forest, the support vector machine, and the gradient boosting trees, all of which can successfully classify profiles. The algorithms were trained using about 1500 profiles for each PCL channel, selected randomly from different nights of measurements in different years. The success rate of identification for all the channels is above 95 %. We also used the t-SNE) method, which is an unsupervised algorithm, to cluster our lidar profiles. Because the t-SNE is a data-driven method in which no labelling of the training set is needed, it is an attractive algorithm to find anomalies in lidar profiles. The method has been tested on several nights of measurements from the PCL measurements. The t-SNE can successfully cluster the PCL data profiles into meaningful categories. To demonstrate the use of the technique, we have used the algorithm to identify stratospheric aerosol layers due to wildfires

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

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%

P07. Characterizing the Purple Crow Lidar to investigate potential sources of wet bias

The Purple Crow Lidar is a large aperture lidar, capable of retrieving water vapor profiles into the stratosphere. Water vapor in the upper Troposphere-Lower Stratosphere (UTLS) region is of particular importance in understanding Earth\u27s radiative budget and atmospheric dynamics, making accurate UTLS measurements crucial. A comparison campaign with the NASA/GSFC ALVICE mobile lidar in the spring of 2012 showed PCL water vapor measurements were consistently larger than those of ALVICE in the lower stratosphere, prompting an investigation to characterize the system. The investigation looks into how changes to the data processing approach, as well as applying additional instrumental corrections, would affect the water vapor mixing ratio. We also look into a retrieval of the mixing ratio using optimal estimation method (OEM), which should provide greater insight into the associated data processing parameters and uncertainties

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

The flow of plasma in the solar terrestrial environment

The overall goal of our NASA Theory Program was to study the coupling, time delays, and feedback mechanisms between the various regions of the solar-terrestrial system in a self-consistent, quantitative manner. To accomplish this goal, it will eventually be necessary to have time-dependent macroscopic models of the different regions of the solar-terrestrial system and we are continually working toward this goal. However, with the funding from this NASA program, we concentrated on the near-earth plasma environment, including the ionosphere, the plasmasphere, and the polar wind. In this area, we developed unique global models that allowed us to study the coupling between the different regions. These results are highlighted in the next section. Another important aspect of our NASA Theory Program concerned the effect that localized 'structure' had on the macroscopic flow in the ionosphere, plasmasphere, thermosphere, and polar wind. The localized structure can be created by structured magnetospheric inputs (i.e., structured plasma convection, particle precipitation or Birkland current patterns) or time variations in these input due to storms and substorms. Also, some of the plasma flows that we predicted with our macroscopic models could be unstable, and another one of our goals was to examine the stability of our predicted flows. Because time-dependent, three-dimensional numerical models of the solar-terrestrial environment generally require extensive computer resources, they are usually based on relatively simple mathematical formulations (i.e., simple MHD or hydrodynamic formulations). Therefore, another goal of our NASA Theory Program was to study the conditions under which various mathematical formulations can be applied to specific solar-terrestrial regions. This could involve a detailed comparison of kinetic, semi-kinetic, and hydrodynamic predictions for a given polar wind scenario or it could involve the comparison of a small-scale particle-in-cell (PIC) simulation of a plasma expansion event with a similar macroscopic expansion event. The different mathematical formulations have different strengths and weaknesses and a careful comparison of model predictions for similar geophysical situations provides insight into when the various models can be used with confidence

A interação universidade-empresa na indústria de petróleo brasileira: o caso da Petrobras

This paper examines research collaboration between the Brazilian state-controlled oil company, Petrobras, and universities from 1980 to 2014. Despite the importance of universityindustry research collaboration in Brazilian oil industry, there are few comprehensive and long-time spam studies on this topic. This paper helps to fill a gap in the academic literature by providing comparative historical data on research collaboration between Petrobras and Brazilian universities. Based on the co-authored publications by Petrobras we analyze changes in intensity of this collaboration and its geographical orientation, inter-organizational level and scientific knowledge base. Furthermore, we address the issue of whether changes in Brazilian R&D funding policy have affected trends in collaboration. Our findings show an increasing collaboration between Petrobras and Brazilian universities, resulting in an enlargement of the company’s network collaboration and reinforcing its knowledge base162325350CAPES - COORDENAÇÃO DO APERFEIÇOAMENTO DE PESSOAL DE NIVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOBEX 10715/14-2sem informaçãoEste artigo analisa a colaboração em pesquisa entre a empresa estatal petrolífera brasileira, Petrobras, e universidades no período de 1980 a 2014. Apesar da importância da interação universidade-empresa na indústria de petróleo brasileira, há poucos estudos temporalmente abrangentes sobre o tema. Este trabalho ajuda a preencher uma lacuna na literatura, provendo dados comparativos de longo prazo sobre a colaboração em pesquisa entre a Petrobras e universidades. Baseando-se nas publicações da Petrobras em coautoria com universidades, são analisadas as mudanças na intensidade e orientação geográfica da colaboração, no nível de relação interorganizacional e na base de conhecimentos da empresa. Além disso, o trabalho também aborda os efeitos da recente política de financiamento à pesquisa e desenvolvimento na interação. Os resultados mostram uma crescente interação entre a Petrobras e as universidades brasileiras, levando a um alargamento da rede de colaborações científicas da empresa e reforçando sua base de conhecimento

Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards

Myeloid-derived suppressor cells (MDSCs) have emerged as major regulators of immune responses in cancer and other pathological conditions. In recent years, ample evidence supports key contributions of MDSC to tumour progression through both immune-mediated mechanisms and those not directly associated with immune suppression. MDSC are the subject of intensive research with >500 papers published in 2015 alone. However, the phenotypic, morphological and functional heterogeneity of these cells generates confusion in investigation and analysis of their roles in inflammatory responses. The purpose of this communication is to suggest characterization standards in the burgeoning field of MDSC research