The Impact of Assimilating Satellite-derived Layered Precipitable Water, Cloud Water Path and Radar Data on Short-Range Thunderstorm Forecasts

View More View Less
  • 1 Cooperate Institute for Mesoscale Meteorological Studies and School of Meteorology, University of Oklahoma, Norman, Oklahoma and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
  • 2 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
  • 3 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma and NOAA/National Severe Storms Laboratory, Norman, Oklahoma
  • 4 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • 5 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin – Madison, Madison, Wisconsin
© Get Permissions
Restricted access

Abstract

With the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived Layered Precipitable Water (LPW) and Cloud Water Path (CWP) in addition to NEXRAD radar observations on short-term convective scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components, the GSI-EnKF system, and a deterministic 3DEnVAR system. This study examines deterministic 0-6 h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface, radar and satellite derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.

Corresponding author address: Sijie Pan, Cooperative Institute for Mesoscale Meteorological Studies, the University of Oklahoma, and NOAA National Severe Storms Laboratory, 120 David L Boren Blvd, Norman, OK 73072, USA. E-mail: Sijie.Pan@noaa.gov

Abstract

With the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived Layered Precipitable Water (LPW) and Cloud Water Path (CWP) in addition to NEXRAD radar observations on short-term convective scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components, the GSI-EnKF system, and a deterministic 3DEnVAR system. This study examines deterministic 0-6 h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface, radar and satellite derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.

Corresponding author address: Sijie Pan, Cooperative Institute for Mesoscale Meteorological Studies, the University of Oklahoma, and NOAA National Severe Storms Laboratory, 120 David L Boren Blvd, Norman, OK 73072, USA. E-mail: Sijie.Pan@noaa.gov
Save