Evaluation of the New York State Mesonet Profiler Network data

Abstract

Abstract. The New York State Mesonet (NYSM) Profiler Network consists of 17 stations statewide. Each station operates a ground-based Doppler lidar (DL), a microwave radiometer (MWR), and an environmental Sky Imaging Radiometer (eSIR) that collectively provide profiles of wind speed and direction, aerosol, temperature, and humidity along with solar radiance, optical depth parameters, and fisheye sky images. This study presents a multi-year, multi-station evaluation of Profiler Network data to determine the robustness and accuracies of the instruments deployed with respect to well-defined measurements. The wind speed (WS) measured by the DL and temperature (T) and water vapor density (WVD) measured by the MWR at three NYSM Profiler Network sites are compared to nearby National Weather Service radiosonde (RS) data, while the aerosol optical depth (AOD) measured by the eSIR at two Profiler Network sites are compared to nearby in situ measurements from the Aerosol Robotic Network (AERONET). The overall comparison results show agreement between the DL or MWR and RS data with a correlation of R2≥0.89 and a correlation between AERONET and eSIR AOD data of R2 ≥ 0.78. The WS biases are statistically insignificant and equal to 0 (p > 0.05) within 3 km, whereas T and WVD biases are statistically significant and are below 5.5 ∘C and 1.0 g m−3, within 10 km. The AOD biases are also found to be statistically significant and are within 0.02. The performance of the DL, MWR, and eSIR are consistent across sites with similar error statistics. When compared during three different weather conditions, the MWR is found to have varying performance, with T errors higher during clear-sky days, while WVD errors are higher during cloudy and precipitation days. To correct such observed biases, a linear regression method was developed and applied to the MWR data. In addition, wind shear from the DL and 14 common thermodynamic parameters derived from the MWR show an agreement with RS values where correlation is mostly R2 ≥ 0.70 and biases are mostly statistically insignificant. A case study is presented to demonstrate the applicability of DL and MWR for nowcasting a severe weather event. Overall, this study demonstrates the robustness and value of the Profiler Network for real-time weather operations.