Analysis of Integrated Vapor Transport Biases

Abstract

High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line Analysis Correction-based Additive Inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7-day and 14-day IVT bias by 16-17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the south Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveal areas with compensating errors.

Deterministic and stochastic tendency adjustments derived from data assimilation and nudging

“…Through testing multiple nudging configurations, we found that the most effective treatment of model bias is accomplished by adjusting model $$$ U $$$ and $$$ V $$$ alone when generating the nudging increments (validation was performed over the testing period, not shown). Reynolds et al (2022) showed, using DA‐derived bias correction and stochastic forcing, that wind errors dominate the integrated vapor transport errors in the Tropics and subtropics, thus adjustments to these fields alone should show downstream impacts to thermodynamic transport variables and precipitation. Additionally, multiple relaxation timescales were tested (from 2 to 10 hr at 1‐hr intervals), and 6 hr was found to be optimal by quantitative examination of the climatological biases (not shown) when generating the nudging increments for the subset of model years 2011–2019.…”

“…Offline bias correction is incapable of remedying this problem, whereas online bias correction can adjust the actual model attractor and allow the model to access observed modes of variability it would have otherwise bypassed. Averaging and reinserting the increments from nudging or DA during runtime has been implemented previously and can improve the background model state for weather and climate applications dramatically (e.g., Kharin & Scinocca, 2012; Chang et al, 2019; Crawford et al, 2020; Lu et al, 2020; Reynolds et al, 2022). These studies have shown that online corrections result in significant improvements in the skill of weather forecasts, which outcompete results obtained by posteriori corrections.…”

Effect of elevation feedbacks and climate mitigation on future Greenland ice sheet melt

“…normalized with respect to the control simulation (Hurrell, 1995). The integrated vapor transport (IVT) north-and eastward components are computed following Reynolds et al (2022), using:…”

The Impact of Analysis Correction-Based Additive Inflation on Subseasonal Tropical Prediction in the Navy Earth System Prediction Capability

“…Kim et al 2014;Kim 2017;Gonzalez and Jiang 2017;Lim et al 2018;Kim et al 2019;Vannitsem et al 2021;Rushley et al 2022). Forecast errors can arise from systematic errors which are due to errors in parameterizations and model numerical calculations, errors in the boundary conditions and missing processes, and random errors attributable to uncertainty in unresolved subgrid-scale physics, parameterization, and noise (Saha 1992;Buizza et al 1999;Danforth and Kalnay 2008;Klocke and Rodwell 2014;Piccolo et al 2018;Bhargava et al 2018;Chang et al 2019;Crawford et al 2020;Reynolds et al 2022). Forecast skill can be increased through improvements to the data assimilation and modeling systems (including bias reduction), and, in an ensemble system, through improvements to the representation of initial condition and model uncertainty.…”