Shayamila N

Bio: Shayamila N is an academic researcher. The author has contributed to research in topics: Backscatter. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Development of a 1-Dimensional Data Assimilation to Determine Temperature and Relative Humidity Combining Raman Lidar Backscatter Measurements And a Reanalysis Model

Abstract: Water vapor is the most dominant greenhouse gas in Earth’s atmosphere. It is highly variable and its variations strongly depend on changes in temperature. Atmospheric water vapor can be expressed as relative humidity (RH), the ratio of the partial pressure of water vapor in the mixture to the equilibrium vapor pressure of water over a flat surface of pure water at a given temperature. Liquid water can exist as super-cooled water for temperatures between 0◦C to −38◦C. Thus, RH can be measured either relative to water (RHw) or to ice (RHi). RHi measurements are important in the upper tropospheric region, where the temperature is always less than 0◦C, to study ice supersaturation (ISS) and its relation to the formation of cirrus clouds. I present three studies all using a mathematical scheme called the optimal estimation method (OEM). The OEM is an inverse method that determines the most probable state consistent with the measurements and a priori knowledge. These studies use parts of a large set of existing measurements from the Raman Lidar for Meteorological Observations (RALMO) instrument located at the meteorological observatory in Payerne, Switzerland. I first develop an OEM retrieval for temperature using RALMO’s two pure rotational Raman (PRR) channel measurements. Retrieved temperatures show excellent agreement with coincident balloon-borne radiosonde measurements. A second OEM scheme is introduced to retrieve RHw directly from RALMO measurements of back-scatter due to water vapor and nitrogen. I validate the OEM retrievals developed for temperature and RHw. I then combine the OEM-retrieved temperature and RHw with data from the European Centre for MediumRange Weather Forecasts Re-analysis (ERA5) to compute a new and improved temperature and relative humidity product. The retrieval is enhanced by assimilating it with the ERA5 data. The quality of the RHw retrievals from the assimilated OEM scheme greatly improves over retrievals which use less accurate a priori information. Thirdly, I retrieve RHi to detect ISS layers. I find the frequency of ISS layers in the free troposphere over Payerne to be about 27% using 82.5 hours of measurements.

Elastic Lidar Theory Practice And Analysis Methods

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Retrieval of Temperature From a Multiple Channel Rayleigh-Scatter Lidar Using an Optimal Estimation Method

Abstract: The measurement of temperature in the middle atmosphere with Rayleigh-scatter lidars is an important technique for assessing atmospheric change. Current retrieval schemes for this temperature have several shortcomings, which can be overcome by using an optimal estimation method (OEM). Forward models are presented that completely characterize the measurement and allow the simultaneous retrieval of temperature, dead time, and background. The method allows a full uncertainty budget to be obtained on a per profile basis that includes, in addition to the statistical uncertainties, the smoothing error and uncertainties due to Rayleigh extinction, ozone absorption, lidar constant, nonlinearity in the counting system, variation of the Rayleigh-scatter cross section with altitude, pressure, acceleration due to gravity, and the variation of mean molecular mass with altitude. The vertical resolution of the temperature profile is found at each height, and a quantitative determination is made of the maximum height to which the retrieval is valid. A single temperature profile can be retrieved from measurements with multiple channels that cover different height ranges, vertical resolutions, and even different detection methods. The OEM employed is shown to give robust estimates of temperature, which are consistent with previous methods, while requiring minimal computational time. This demonstrated success of lidar temperature retrievals using an OEM opens new possibilities in atmospheric science for measurement integration between active and passive remote sensing instruments.