Preprints
https://doi.org/10.5194/amt-2022-85
https://doi.org/10.5194/amt-2022-85
 
11 Apr 2022
11 Apr 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

Evaluation of the New York State Mesonet Profiler Network Data

Bhupal Shrestha1, Jerald A. Brotzge1, and Junhong Wang1,2 Bhupal Shrestha et al.
  • 1New York State Mesonet, SUNY University at Albany, Albany, New York
  • 2Department of Atmospheric and Environmental Sciences, SUNY University at Albany, Albany, New York

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 fish-eye 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 sites are compared to nearby in-situ measurements from the Aerosol Robotic Network (AERONET). The overall comparison results show agreement between the DL/MWR and RS data with a correlation of R2 ≥ 0.89 and between AERONET and eSIR AOD data with 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 ºC 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 slightly varying performance, with T errors higher during clear sky days while WVD errors 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 with mostly R2 ≥ 0.70 and biases mostly statistically insignificant. A case study is presented to demonstrate the applicability of DL/MWR for nowcasting a severe weather event. Overall, this study demonstrates the robustness, reliability, and value of the Profiler Network for real-time weather operations.

Bhupal Shrestha et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-85', Anonymous Referee #1, 29 Apr 2022
    • AC1: 'Reply on RC1', Bhupal Shrestha, 16 Jun 2022
  • RC2: 'Comment on amt-2022-85', Anonymous Referee #2, 30 May 2022
    • AC2: 'Reply on RC2', Bhupal Shrestha, 16 Jun 2022

Bhupal Shrestha et al.

Bhupal Shrestha et al.

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Short summary
The NYS Mesonet Profiler Network is comprised of 17 profiler sites, each equipped with a Doppler lidar, microwave radiometer and environmental sky imaging radiometer. This study presents a multi-year, multi-station evaluation based on well-defined reference measurements. Results demonstrate accurate and robust technology that can aid weather operations, and a network testbed that can be used for further expansion, evaluation, and integration of this technology at a large-scale.