Assimilating Surface Mesonet Observations with the EnKF to Improve Ensemble Forecasts of Convection Initiation on 29 May 2012

Abstract

Surface data assimilation (DA) has the potential to improve forecasts of convection initiation (CI) and short-term forecasts of convective evolution. Since the processes driving CI occur on scales inadequately observed by conventional observation networks, mesoscale surface networks could be especially beneficial given their higher temporal and spatial resolution. This work aims to assess the impact of high-frequency assimilation of mesonet surface DA on ensemble forecasts of CI initialized with ensemble Kalman filter (EnKF) analyses of the 29 May 2012 convective event over the southern Great Plains.Mesonet and conventional surface observations were assimilated every 5 min for 3 h from 1800 to 2100 UTC and 3-h ensemble forecasts were produced. Forecasts of CI timing and location were improved by assimilating the surface datasets in comparison to experiments where mesonet data were withheld. This primarily occurred due to a more accurate representation of the boundary layer moisture profile across the domain, especially in the vicinity of a dryline and stationary boundary. Ensemble forecasts produced by assimilating surface observations at hourly intervals, instead of every 5 min, showed only minor improvements in CI.The 5-min assimilation of mesonet data improved forecasts of the placement and timing of CI for this particular event due to the ability of mesonet data to capture rapidly evolving mesoscale features and to constrain model biases, particularly surface moisture errors, during the cycling period.

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

“…This blind region to the radar largely lies in the planetary boundary layer (PBL), in which PBL processes such as horizontal convective rolls, drylines, and cold-pools can trigger convection under a favorable large-scale environment (Sobash and Stensrud, 2015). In particular, surface-based cold pools produced by the evaporation of precipitation can influence the initiation, development and propagation of convective systems (e.g., Tompkins, 2001;Lima and Wilson, 2008).…”

An Evaluation of the Impact of Assimilating AERI Retrievals, Kinematic Profilers, Rawinsondes, and Surface Observations on a Forecast of a Nocturnal Convection Initiation Event during the PECAN Field Campaign

“…Hitchcock et al (2016) also show midlevel moisture improvements, but from assimilating special rawinsonde observations collected during a field campaign. Similarly, Sobash and Stensrud (2015) demonstrate improved diurnal CI forecasts through the assimilation of surface mesonet observations that increase the moisture within the boundary layer. Until recently, previous studies that evaluate the impact of assimilating AERI profiles have only assimilated simulated observations (Hartung et al 2011;Otkin et al 2011).…”

Crowd‐sourced observations for short‐range numerical weather prediction: Report from EWGLAM/SRNWP Meeting 2019

“…Some of these sources have great potential to be used in meteorology and NWP models. Useful observations may come from nonconventional sensors such as vehicles (Anderson et al ., 2012), personal weather stations (Madaus and Hakim, 2014; Sobash and Stensrud, 2015; Clark et al ., 2018; Gasperoni et al ., 2019; Nipen et al ., 2020) and smartphones (Madaus and Mass, 2017; McNicholas and Mass, 2018b; Hintz et al ., 2019b). Alternatively, “social sensing” derives weather observations from social media content.…”