Assimilation of all-sky seviri infrared brightness temperatures in a regional-scale ensemble data assimilation system

Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher order nonlinear BC terms were used. Overall, experiments employing the observed cloud top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background

Nonlinear bias correction for satellite data assimilation using Taylor series polynomials

Output from a high-resolution ensemble data assimilation system is used to assess the ability of an innovative nonlinear bias correction (BC) method that uses a Taylor series polynomial expansion of the observation-minus background departures to remove linear and nonlinear conditional biases from all-sky satellite infrared brightness temperatures. Univariate and multivariate experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the BC predictors. The results showed that even though the bias of the entire observation departure distribution is equal to zero regardless of the order of the Taylor series expansion, there are often large conditional biases that vary as a nonlinear function of the BC predictor. The linear 1st order term had the largest impact on the entire distribution as measured by reductions in variance; however, large conditional biases often remained in the distribution when plotted as a function of the predictor. These conditional biases were typically reduced to near zero when the nonlinear 2nd and 3rd order terms were used. The univariate results showed that variables sensitive to the cloud top height are effective BC predictors especially when higher order Taylor series terms are used. Comparison of the statistics for clear-sky and cloudy-sky observations revealed that nonlinear departures are more important for cloudy-sky observations as signified by the much larger impact of the 2nd and 3rd order terms on the conditional biases. Together, these results indicate that the nonlinear BC method is able to effectively remove the bias from all-sky infrared observation departures

Using Temporal Changes in Drought Indices to Generate Probabilistic Drought Intensification Forecasts

In this study, the potential utility of using rapid temporal changes in drought indices to provide early warning of an elevated risk for drought development over subseasonal time scales is assessed. Standardized change anomalies were computed each week during the 2000–13 growing seasons for drought indices depicting anomalies in evapotranspiration, precipitation, and soil moisture. A rapid change index (RCI) that encapsulates the accumulated magnitude of rapid changes in the weekly anomalies was computed each week for each drought index, and then a simple statistical method was used to convert the RCI values into drought intensification probabilities depicting the likelihood that drought severity as analyzed by the U.S. Drought Monitor (USDM) would worsen in subsequent weeks. Local and regional case study analyses revealed that elevated drought intensification probabilities often occur several weeks prior to changes in the USDM and in topsoil moisture and crop condition datasets compiled by the National Agricultural Statistics Service. Statistical analyses showed that the RCI-derived probabilities are most reliable and skillful over the central and eastern United States in regions most susceptible to rapid drought development. Taken together, these results suggest that tools used to identify areas experiencing rapid changes in drought indices may be useful components of future drought early warning systems

Drought Early Warning and the Timing of Range Managers’ Drought Response

\u27e connection between drought early warning information and the timing of rangeland managers’ response actions is not well understood. \u27is study investigates U.S. Northern Plains range and livestock managers’ decision-making in response to the 2016 flash drought, by means of a postdrought survey of agricultural landowners and using the Protective Action Decision Model theoretical framework. \u27e study found that managers acted in response to environmental cues, but that their responses were significantly delayed compared to when drought conditions emerged. External warnings did not influence the timing of their decisions, though on-farm monitoring and assessment of conditions did. \u27ough this case focused only on a one-year flash drought characterized by rapid drought intensification, waiting to destock pastures was associated with greater losses to range productivity and health and diversity. \u27is study finds evidence of unrealized potential for drought early warning information to support proactive response and improved outcomes for rangeland management

Climatology and composite evolution of flash drought over Australia and its vegetation impacts

This study describes flash drought (FD) inferred from the Evaporative Stress Index (ESI) over Australia and its relationship to vegetation. During 1975-2020, FD occurrence ranges from less than one per decade in the central arid regions to 10 per decade toward the coasts. Although FD can occur in any season, its occurrence is more frequent in summer in the north, winter in the southern interior and southwest, and across a range of months in the far southeast and Tasmania. With a view towards real-time monitoring, FD “declaration” is defined as the date when the ESI declines to at least -1, i.e., drought conditions, after at least 2 weeks of rapid decline. Composite analysis shows that evaporative demand begins to increase about 5-6 weeks before declaration with an increase in solar radiation, while evapotranspiration initially increases with evaporative demand but then decreases in response to the soil moisture depletion. Solar radiation increases simultaneously with precipitation deficit, both reaching their peak around declaration. FD intensity peaks with soil moisture depletion, 2-3 weeks after declaration. The composite wind speed only shows a modest increase around declaration. The composite FD ends 4 weeks after rapid decreases in solar radiation and increases in precipitation. Satellite-derived vegetation health composites show pronounced decline in the non-forested regions, peaking about 4-8 weeks after FD declaration, followed by a recovery period lasting about 12 weeks after flash drought ends. The forest-dominated regions, however, are little impacted. Modelled pasture growth data shows reduced values for up to 3 months after the declaration month covering the main agricultural areas of Australia

Wet season rainfall onset and flash drought: The case of the northern Australian wet season

In this paper, we report on the frequency of false onsets of wet season rainfall in the case of the Northern Australian wet season and investigate the role of large-scale tropical climate processes such as the El Nino–Southern Oscillation, Indian Ocean Dipole (IOD) and Madden–Julian Oscillation. A false onset occurs when a wet season rainfall onset criterion is met, but follow-up rainfall is not received for weeks or months later. Our analysis of wet season rainfall data from 1950 through 2020 shows a false onset occurs, on average, between 20 and 30% of wet seasons across all of northern Australia. This increases at a regional and local level such as at Darwin, the Northern Territory (NT), and parts of Queensland's north coast to over 50%. Seasonal climate influences, such as a La Niña pattern and a negative IOD that typically expedite the wet season rainfall onset, also increase the likelihood of a false onset over northern Australia. Our analysis also finds that periods of false onsets can sometimes, but not always, coincide with periods of rapid soil moisture depletion. The false rainfall onsets that develop into flash drought can be potentially disruptive and costly and are of potential significance for agriculture and fire management in northern Australia, and in other monsoonal climates that also typically experience a slow build-up to the seasonal monsoon. In conclusion, effective rainfall indicates that many seasons experience ‘false onsets’ with dry conditions after early rainfall. We propose that false onsets are a physical characteristic of the climate of northern Australia which occurs with relatively high frequency. In addition, these false onsets may sometimes co-occur with a flash drought

Atmospheric motion vectors from model simulations. Part I: Methods and characterization as single-level estimates of wind

The objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the “truth” with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60–75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s−1). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data.The study was funded by EUMETSAT Contract EUM/ CO/10/46000000785/RB

Nonlinear conditional model bias estimation for data assimilation

In this study, we develop model bias estimators based on an asymptotic expansion of the model dynamics for small time scales and small perturbations in a model parameter, and then use the estimators to improve the performance of a data assimilation system. We employ the well-known Lorenz (1963) model so that we can study all aspects of the dynamical system and model bias estimators in a detailed way that would not be possible with a full physics numerical weather prediction model. In particular, we first work out the asymptotics of the Lorenz model for small changes in one of its parameters and then use statistics from cycled data assimilation experiments to demonstrate that the asymptotics accurately represent the behavior of the model and that the coefficients of the nonlinear asymptotical expansion can be reasonably estimated by solving a least squares minimization problem. In data assimilation, the background error covariance matrix usually estimates the uncertainty of the model background, which is then used along with the observation error covariance matrix to produce an updated analysis. If the uncertainty of the model background is strongly influenced by time-dependent model biases, then the development of nonlinear bias estimators that also vary with time could improve the performance of the assimilation system and the accuracy of the updated analysis. We demonstrate this improvement through the combination of a constant background error covariance matrix with a dynamically-varying matrix computed using the model bias estimators. Numerical tests using the Lorenz (1963) model illustrate the feasibility of the approach and show that it leads to clear improvements in the analysis and forecast accuracy

Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index

Reliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate evapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Standardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overall, the results demonstrate that ESI change anomalies can provide early warning of incipient drought impacts on agricultural systems, as indicated in crop condition reports collected by the National Agricultural Statistics Service. In each case examined, large negative change anomalies indicative of rapidly drying conditions were either coincident with the introduction of drought in the USDM or lead the USDM drought depiction by several weeks, depending on which ESI composite and time-differencing interval was used. Incorporation of the ESI as a data layer used in the construction of the USDM may improve timely depictions of moisture conditions and vegetation stress associated with flash drought events