Impact of Assimilating Preconvective Upsonde Observations on Short-Term Forecasts of Convection Observed during MPEX

Abstract

This study examines the impact of assimilating preconvective radiosonde observations obtained by mobile sounding systems on short-term forecasts of convection. Ensemble data assimilation is performed on a mesoscale (15 km) grid and the resulting analyses are downscaled to produce forecasts on a convection-permitting grid (3 km). The ensembles of forecasts are evaluated through their depiction of radar reflectivity compared to observed radar reflectivity. Examination of fractions skill scores over eight cases shows that, for four of the cases, assimilation of radiosonde observations nearby to subsequent convection has a positive impact on the initiation and early evolution during the first 3–4 h of the forecasts, even for the smallest resolvable scales of the 3-km grid. For the four cases in which positive impacts near the smallest resolvable scales of the grid are not seen, analysis of the changes to the preconvective environment suggests that suboptimal locations of the soundings compared to the location of convective initiation are to blame. The aggregate positive impacts on forecasts of convection is more clearly seen when spatial scales larger than individual thunderstorms are examined.

Origins of Vorticity in a Simulated Tornadic Mesovortex Observed during PECAN on 6 July 2015

“…These hourly assimilation cycles included conventional observations from the Meteorological Assimilation Data Ingest System (MADIS; Miller et al 2007) and PECAN radiosondes. The MADIS observations include (i) mandatory and significant levels from NWS radiosondes; (ii) surface data from routine aviation weather reports (METARs), marine reports (from both ships and buoys), the Oklahoma Mesonet, and mesonet observations from a variety of national networks; (iii) Aircraft Meteorological Data Relay (AMDAR) reports for wind and temperature; and (iv) atmospheric motion vectors derived from satellite observations (as in Coniglio et al 2016). At 2300 UTC 5 July, the 3-km grid was initialized from the 15-km analysis and was integrated for 1 h. Assimilation on the 3-km grid was performed from 0000 to 0600 UTC 6 July every 15 min and included radiosonde observations, Doppler radial velocity (e.g., Snyder and Zhang 2003;Zhang et al 2004), and reflectivity (e.g., Dowell et al 2004;Tong and Xue 2005;Aksoy et al 2009Aksoy et al , 2010Yussouf and Stensrud 2010;Dowell et al 2011;Yussouf et al 2013) from the mobile and WSR-88D radars listed in Fig.…”

Convection‐Permitting Simulation of a Heavy Rainfall Event in Armenia Using the WRF Model

“…However, there is a room for improvement of simulation of convective rainfall in Armenia through further improving the accuracy of initial and boundary conditions in the WRF model. Previous studies showed that assimilating conventional, satellite, and radar observations improves modeling of convective storms by the WRF model (Coniglio et al, ; Stratman et al, ; Sun et al, ). Therefore, further research is needed on application of data assimilation in high‐resolution modeling over Armenia.…”

“…Those models are referred to as convection‐permitting or convection‐allowing models showing better performance compared to coarser‐resolution models employing convective parameterization. In general, there have been climatological studies on modeling of convective precipitation (Bukovsky & Karoly, ; Gao et al, ; Li et al, ; Ratna et al, ; Schwartz et al, ; Sun et al, , ) and studies focusing on individual storms leading to severe weather events, that is, case studies (Barthlotta et al, ; Chan et al, ; Coniglio et al, ; Mcmillen & Steenburgh, ; Pieri et al, ; Stratman et al, ; Wang et al, ). A comprehensive review on convection‐permitting climate modeling was presented by Prein et al ().…”

“…Convection‐permitting simulations, in turn, are very sensitive to microphysics parameterization (Prein et al, ; Sun et al, ). Furthermore, initial and lateral boundary conditions and land‐surface model used in the WRF model significantly impact simulation of deep convection and convective precipitation (Barthlotta et al, ; Bukovsky & Karoly, ; Coniglio et al, ; Li et al, ). Increasing the spatial resolution showed positive effect in simulation of precipitation extremes in Europe (Chan et al, ; Pieri et al, ), while the added value provided by very high resolution regional climate simulations (1 km and smaller grid spacing) remains unclear (Chan et al, ).…”

Trajectory-Enhanced AIRS Observations of Environmental Factors Driving Severe Convective Storms

“…However, the optimal locations and spatial density of soundings, and their proximity to boundaries and thermodynamic gradients, remain highly uncertain. Coniglio et al (2016) performed data assimilation of radiosonde observations launched during the Mesoscale Predictability Experiment (MPEX) to show that improvements in convective forecasts using WRF-ARW depended on optimal positioning of the radiosondes. One implication is that a dense network of satellite soundings may be useful because the most relevant locations for data assimilation can be customized on a case-by-case basis.…”