Systematic Evaluation of the Impact of Assimilating a Network of Ground-Based Remote Sensing Profilers for Forecasts of Nocturnal Convection Initiation during PECAN

Abstract

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 mid-level 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.

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

“…Assimilating observations from single instruments was shown to have neutral impacts due to (1) forecast sensitivity to the initial moisture and wind fields, (2) deficiencies in the EnKF algorithm for nonlinear processes and (3) insufficient temporal frequency of radiosonde data. Overall, the promising findings from these experiments are in agreement with earlier work (e.g., Chipilski et al, 2020; Degelia et al, 2020) and pave the way for the integration of these instruments in operational convective‐scale NWP systems.…”

A method for generating a quasi-linear convective system suitable for observing system simulation experiments

“…Future work using this OSSE configuration will assimilate different simulated boundary layer profiling instruments to understand their impact on convective initiation and QLCS evolution. Assimilating thermodynamic and kinematic profiles of the boundary layer can improve representation of the lower troposphere and consequently increase convective initiation forecast skill (e.g., Coniglio et al, 2019;Degelia et al, 2019Degelia et al, , 2020Chipilski et al, 2020). These OSSEs will also help determine an optimal assimilation strategy including: sampling frequency and spatial density.…”

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“…As discussed in Section 3, a unique feature of the proposed tracking technique is its ability to identify distinct CIs, the initial object of each identified OT. Object-based CI identification is an active research area with many CAM-based applications [15,22,23]. However, the accuracy of existing methodologies was rarely evaluated.…”