Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation

Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied whether state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurement time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades-long projections by at least 1 t C ha−1. Our results show the benefits of implementing SDA methods for forecasting SOC as well as highlight implementation aspects that need consideration and further research.

Impact of AERI Temperature and Moisture Retrievals on the Simulation of a Central Plains Severe Convective Weather Event

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors.

Verification of Convection-Allowing Model Ensemble Analyses of Near-Storm Environments Using MPEX Upsonde Observations

The Mesoscale Predictability Experiment (MPEX) conducted during the spring of 2013 included frequent coordinated sampling of near-storm environments via upsondes. These unique observations were taken to better understand the upscale effects of deep convection on the environment, and are used to validate the accuracy of convection-allowing (Δ x = 3 km) model ensemble analyses. A 36-member ensemble was created with physics diversity using the Weather Research and Forecasting Model, and observations were assimilated via the Data Assimilation Research Testbed using an ensemble adjustment Kalman filter. A 4-day sequence of convective events from 28 to 31 May 2013 in the south-central United States was analyzed by assimilating Doppler radar and conventional observations. No MPEX upsonde observations were assimilated. Since the ensemble mean analyses produce an accurate depiction of the storms, the MPEX observations are used to verify the accuracy of the analyses of the near-storm environment. A total of 81 upsondes were released over the 4-day period, sampling different regions of near-storm environments including storm inflow, outflow, and anvil. The MPEX observations reveal modest analysis errors overall when considering all samples, although specific environmental regions reveal larger errors in some state fields. The ensemble analyses underestimate cold pool depth, and storm inflow meridional winds have a pronounced northerly bias that results from an underprediction of inflow wind speed magnitude. Most bias distributions are Gaussian-like, with a few being bimodal owing to systematic biases of certain state fields and environmental regions.

Predictability and Dynamics of Warm-Core Mesoscale Vortex Formation with the 8 May 2009 “Super Derecho” Event

The predictability and dynamics of the warm-core mesovortex associated with the northern flank of the 8 May 2009 “super derecho” event are examined by coupling the Advanced Research Weather Research and Forecasting Model with the ensemble adjustment Kalman filter implementation within the Data Assimilation Research Testbed facility. Cycled analysis started at 1200 UTC 2 May 2009, with observations assimilated every 6 h until 1200 UTC 7 May 2009, at which time a 50-member ensemble of 36-h convection-allowing ensemble forecasts were launched. The ensemble forecasts all simulated a mesoscale convective system, but only 7 out of 50 members produced a warm-core mesovortex-like feature similar in intensity to the observed mesovortex. Ensemble sensitivity and composite analyses were conducted to analyze the environmental differences between ensemble members. A more amplified upstream upper-level trough near the time of observed convection initiation is associated with a stronger simulated mesovortex. The amplification of the trough results in increases in the magnitudes of the low-level jet and thermal gradient. Consequently, more moisture is transported poleward into western Kansas, leading to earlier convection initiation in ensemble members with the strongest mesovortices. A circulation budget is performed on the ensemble members with the strongest (member 10) and weakest (member 5) time-averaged circulations. The ascending front-to-rear flow, descending rear-to-front flow, and divergent low-level flow of an MCS are more prominent in member 10, which is hypothesized to allow for the convergence of more background cyclonic absolute vorticity and, thus, facilitating the development of a stronger mesovortex.