TY - JOUR
T1 - Systematic evaluation of the impact of assimilating a network of ground-based remote sensing profilers for forecasts of nocturnal convection initiation during PECAN
AU - Degelia, Samuel K.
AU - Wang, Xuguang
AU - Stensrud, David J.
AU - Turner, David D.
N1 - Funding Information:
This project is primarily supported by the National Science Foundation (NSF) Award AGS-1359703 and Awards NA16OAR3420115 and NA11OAR4320072. We would also like to acknowledge the high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research and sponsored by the NSF. We also thank Stephen Weygandt, Tammy Weckwerth, and two anonymous reviewers for providing thoughtful comments for this manuscript. Finally, the authors also wish to acknowledge the contributions from all the data providers involved in the PECAN project, as well as the members of the Multi-scale data Assimilation and Predictability Laboratory (MAP; http://weather.ou.edu/;map/index.html) at OU, especially Hristo Chipilski and Aaron Johnson, for many thoughtful discussions related to this work.
Publisher Copyright:
© 2020 American Meteorological Society.
PY - 2020/12
Y1 - 2020/12
N2 - Nocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers.
AB - Nocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers.
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U2 - 10.1175/MWR-D-20-0118.1
DO - 10.1175/MWR-D-20-0118.1
M3 - Article
AN - SCOPUS:85097502064
VL - 148
SP - 4703
EP - 4728
JO - Monthly Weather Review
JF - Monthly Weather Review
SN - 0027-0644
IS - 12
ER -