Seasonal ensemble forecasts for soil moisture, evapotranspiration and runoff across Australia

Satellite soil moisture data assimilation for improved operational continental water balance prediction

“…8). Model physics limits the strength of coupling between an analysed state and resulting fluxes (Kumar et al, 2009;Walker et al, 2001). Thus, a small level of improvement in performance in AWRA-L streamflow in response to soil moisture state updating is not unexpected due to a weak coupling between the states and fluxes.…”

“…Tian et al: Satellite soil moisture data assimilation Accurate knowledge of initial soil moisture states gained from data assimilation contributes significantly to the skill of flood forecasting, drought monitoring and weather forecasts (Bolten et al, 2009;Carrera et al, 2019;Wanders et al, 2014b;Yan et al, 2018;Alvarez-Garreton et al, 2015). Wanders et al (2014a) found that the assimilation of remotely sensed soil moisture in combination with discharge observation can improve the quality of the operational flood alerts, both in terms of timing and in the exact height of the flood peak.…”

Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model

“…The operational implementation of the AWRA‐L by the Australian Bureau of Meteorology runs at a daily time step and outputs are spatially and temporally aggregated to provide daily, monthly and annual gridded estimates of streamflow, evapotranspiration, soil moisture, and deep drainage at regional and continental scale seamlessly from the past to the present for over 100 yr (Hafeez et al., 2015). The outputs from the operational AWRA‐L have been widely used in various agricultural applications and natural resources risk assessment and planning, including commodity forecasting, irrigation scheduling, flood and drought risk analysis, as well as flood forecasting (Frost et al., 2018; Hafeez et al., 2015; Nguyen et al., 2019; Van Dijk, 2010; Van Dijk & Renzullo, 2011; Vogel et al., 2021). The version of the AWRA‐L model used in the study was obtained from the Community Modeling system (AWRA‐CMS).…”

Supporting regional and international cooperation in research on extremes in climate prediction and projection ensembles: Workshop summary

“…Presentations in this theme highlighted the current state of prediction of heatwaves, hydrological and hydrometeorological extremes, tropical cyclones, low-pressure monsoon systems, and lightning utilizing subseasonal to seasonal (S2S) and other subseasonal ensemble prediction systems, including applications of machine learning for post-processing to enhance the skill. One perspective common to several of the presentations is that predictability of specific extreme events can be conditional; for example, dry Australian hydrological extremes are predicted more skillfully than wet extremes (Vogel et al, 2021), and Indian heat waves are predicted skillfully beyond week two for certain regions and probability ranges (Mandal et al, 2019). Caveats that were raised include that predicted magnitudes of extreme events are often underestimated by the ensemble mean even when other aspects of an event are predicted accurately (Domeisen et al, 2022); some events may not be predicted as well as expected from the accuracy of historical predictions (Tsai, Lu, Sui, & Cho, 2021); and multi-model ensembles do not always outperform the best-performing individual model (Deoras, Hunt, & Turner, 2021).…”