Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm

Abstract

A one‐dimensional variational (1DVAR) algorithm was developed, which combines measurements taken by the ground‐based microwave radiometer profiler (MWRP) with the Rapid Refresh (RAP) model to retrieve a more accurate temperature and humidity profiles under clear sky. The passive and active microwave‐vector radiative transfer model was used to simulate brightness temperatures and calculate weighting functions for 22 channels of the MWRP. As MWRP measurements are mainly weighted in the lower atmosphere, a measurement adjustment is performed to reduce the contribution from above 10 km. The results of the 1DVAR algorithm have been compared with the MWRP built‐in neural network (NN) retrievals and radiosonde observations, which show that the 1DVAR method outperforms the NN retrieval both in temperature and water vapor. Our statistical study further shows that the water vapor profiles from RAP are biased higher than the radiosonde observations, while the 1DVAR‐retrieved humidity values show significant improvements throughout the entire 10‐km range. The improvements in temperature profiles occur mainly within the lowest 4‐km atmosphere, while upper level retrievals are mostly influenced by initial values. This is consistent with the characteristics of the Jacobian matrix and the background error covariance matrix. The outcomes of 44 clear‐sky cases show that the maximum mean retrieval error of the 1DVAR algorithm is less than 0.2 K for temperature below 4 km and less than 0.15 g/m3 in water vapor density. These results are shown to be significantly better than the NN retrieval (3.0 K and 1.25 g/m3) and also superior to the RAP reanalysis (0.3 K and 0.5 g/m3).

Ground-based Temperature and Humidity Profiling: Combining Active and Passive Remote Sensors

“…Passive ground-based remote sensors are relatively common, as these technologies are more mature, have been commercially available for several decades, and have been operated in networks (e.g., Caumont et al 2016;Geerts et al 2017;Yang and Min 2018). The recent advances in water vapor DIAL (e.g., Spuler et al 2015;Newsom et al 2020) are leading to the possibility that the two DIALs used in this study could be commercially available in the next several years, which is why they formed the focus of this study.…”

Mobile Laboratory Measurements of High Surface Ozone Levels and Spatial Heterogeneity During LISTOS 2018: Evidence for Sea Breeze Influence

“…To complete the analysis, this study also used (1) New York State Mesonet data for the lidar wind vertical profiles (Yang & Min, 2018, http://www.nysmesonet.org/data/), (2) New York State DEC air quality data for O 3 concentration and PM 2.5 mass concentration (http://www.nyaqinow.net/), and (3) TROPOMI satellite data for the tropospheric NO 2 column concentration (Van Geffen et al, 2019, http://www.temis.nl/airpollution/no2.html). The locations of Mesonet sites and DEC O 3 sites are shown in Figure S1.…”

Moving towards a Network of Autonomous UAS Atmospheric Profiling Stations for Observations in the Earth’s Lower Atmosphere: The 3D Mesonet Concept

“…Typically, mesonets include sensors mounted on or below a 10 m tower to sample air temperature, relative humidity, winds, solar radiation, precipitation, pressure, soil temperature, and soil moisture. The New York Mesonet additionally has profiling capability at 17 sites [25]. This is achieved using scanning Doppler lidars (3D wind) and microwave radiometers (temperature and humidity).…”