Comparing seasonal streamflow forecast systems for management of a fresh water reservoir in the Netherlands

For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed multiple seasonal forecast data sets derived from EFAS, E-HYPE and HTESSEL, which differ in their underlying hydrological formulation, but are all forced with similar input from the ECMWF SEAS5 meteorological forecasts. We post-processed the streamflow forecasts using quantile matching (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Particularly HTESSEL, a land surface scheme that is not optimized for hydrology, needed the largest correction. Averaged over the reforecast period, forecasts were skillful for the longest lead times in spring and early summer. For this period, E-HYPE showed the highest skill; Later in summer, however, skill deteriorated after 1–2 months. When investigating specific years with either low or high flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skilful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to three months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management.

Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts

Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Communicating Properties of Changes in Lagged Weather Forecasts

Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.

Unfolding the relationship between seasonal forecast skill and value in hydropower production: A global analysis

The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world, but with contrasting conclusions on the expected benefits. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958–2000 with three operating schemes: basic control rules, perfect forecast-informed, and realistic forecast-informed. The realistic forecasts are issued by tailored statistical prediction models—based on lagged global and local hydro-climatic variables—predicting seasonal monthly dam inflows. As expected, results show that most dams (94 %) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time-to-fill and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25 % of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design specifications but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and have the potential to reap benefits from forecast-informed operations, and dams with strong potential to benefit from forecast-informed operations but fall in regions lacking forecast accuracy. Overall, these results represent a first qualitative step towards informing site-specific hydropower studies.

Application of Neural Network Technique to improve the location specific forecast of temperature over Delhi from MM5 model

India Meteorological Department (IMD) has been using direct model output (2 meters height temperature) of MM5 model as numerical guidance for forecasting maximum and minimum temperature of Delhi in short range time scale (up to 72 hours). Performance statistics of the direct model outputs of the model for maximum and minimum temperature show that forecast skill of the model is reasonably good, particularly for the minimum temperature. For further improving the model forecast, Neural Network (NN) as well as regression techniques are applied so that the systematic errors of the direct model output of the model for maximum and minimum temperature could be reduced. The study shows that both Neural Network approach and regression technique are capable to improve the forecast skill of maximum and minimum temperature. Daily modified forecasts are found persistently closer to the observations when the method is tested with the independent sample. The methods are found to be promising for operational application.

Understanding the Forecast Skill of Rapid Arctic Sea Ice Loss on Subseasonal Timescales

Predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) timescales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability—in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas, rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on non-extreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and non-extreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events; and that while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization.

Using SEEPS with a TRMM-derived climatology to assess global NWP precipitation forecast skill

Monitoring precipitation forecast skill in global Numerical Weather Prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products on the other hand provide near-global coverage at a resolution of ~10-25 km, but limitations on data quality (e.g. biases) must be accommodated. In this paper the Stable Equitable Error in Probability Space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measurement Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology, and applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS is explored by comparing the gauge scores, based on extracting the nearest model grid point, to those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ~6000 locations (dictated by the number of gauges available globally) to all land points within the TRMM region of 50°N and 50°S. Finally, the forecast performance over the oceanic areas is compared to performance over land. Whilst the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way.