Probabilistic Forecast of the Extended Range Heatwave Over Eastern China

Several probabilistic forecast methods for heatwave (HW) in extended-range scales over China are constructed using four models (ECMWF, CMA, UKMO, and NCEP) from the Subseasonal-to-Seasonal (S2S) database. The methods include four single-model ensembles (SME; ECMWF, CMA, UKMO, and NCEP), multi-model ensemble (MME), and Bayesian model averaging (BMA). The construction and verification of reforecasts are implemented by a defined heat wave index (HWI) which is not only able to reflect the actual occurrence of heatwaves, but also to facilitate forecast and verification. The performance is measured by traditional verification method at each grid point of the 105°E to 132°E; 20°N to 45°N domain for the July, August, and September (JAS) of 1999–2010. For deterministic evaluations of HWI forecast, BMA shows a better pattern correlation coefficient than SME and MME and comparable equitable threat score (ETS) with ECMWF and MME. The good performance of ECMWF and MME take advantage of setting the percentile thresholds for forecasting HW. For the probabilistic forecast, the Brier score of BMA is comparable (superior) to that of MME and ECMWF at short (long) lead-time. BMA also demonstrates an improvement on the reliability of probabilistic forecast, indicating that BMA method is a useful tool for an extended-range forecast of HW. Meanwhile, in the real-time extended-range probabilistic forecast, the beginning date, end date, and probability of HW event can be predicted by the HWI probabilistic forecast of BMA.

Performance of probabilistic forecast of the onset of the rainy season over Java Island based on the application of Constructed Analogue (CA) method on Climate Forecast System Version 2 (CFSV2) model output

The onset of the rainy season is one of the forecast products that is issued regularly by the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), with deterministic information about the month of which the initial 10-days (dasarian) of the rainy season will occur in each a designated area. On the other hand, state-of-the-art of seasonal forecasting methods suggests that probabilistic forecast products are potentially better for decision making. The probabilistic forecast is also more suitable for Indonesia because of the large rainfall variability that adds up to uncertainty in climate model simulations, besides complex geographical factors. The research aims to determine the onset of rainy season and monsoon over Java Island based on rainfall prediction by Constructed Analogue statistical downscaling of CFSv2 (Climate Forecast System version 2) model output. This research attempted to develop a method to produce a probabilistic forecast of the onset of the rainy season, as well as monsoon onset, by utilizing the freely available seasonal model output of CFSv2 operated by the US National Oceanic and Atmospheric Administration (NOAA). In this case, the output of the global model is dynamically downscaled using the modified Constructed Analogue (CA) method with an observational rainfall database from 26 BMKG stations and TRMM 3B43 gridded dataset. This method was then applied to perform hindcast using CFS-R (re-forecast) for the 2011-2014 period. The results show that downscaled CFS predictions with initial data in September (lead-1) give sufficient accuracy, while that initialized in August (lead-2) have large errors for both onsets of the rainy season and monsoon. Further analysis of forecast skill using the Brier score indicates that the CA scheme used in this study showed good performance in predicting the onset of the rainy season with a skill score in the range of 0.2. The probabilistic skill scores indicate that the prediction for East Java is better than the West- and Central-Java regions. It is also found that the results of CA downscaling can capture year-to-year variations, including delays in the onset of the rainy season.

Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation

Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, have become popular. For a more efficient use of MESs, the optimal operation of these systems needs to be considered. This paper focuses on the day-ahead optimal schedule of an MES, including a combined heat and electricity (CHP) unit, a gas boiler, a PV system, and energy storage devices. Starting from a day-ahead PV point forecast, a non-parametric probabilistic forecast method is proposed to build the predicted interval and represent the uncertainty of PV generation. Afterwards, the MES is modeled as mixed-integer linear programming (MILP), and the scheduling problem is solved by interval optimization. To demonstrate the effectiveness of the proposed method, a case study is performed on a real industrial MES. The simulation results show that, by using only historical PV measurement data, the point forecaster reaches a normalized root-mean square error (NRMSE) of 14.24%, and the calibration of probabilistic forecast is improved by 10% compared to building distributions around point forecast. Moreover, the results of interval optimization show that the uncertainty of the PV system not only has an influence on the electrical part of the MES, but also causes a shift in the behavior of the thermal system.

Evaluation of the subseasonal forecast skill of floods associated with atmospheric rivers in coastal Western U.S. watersheds

Atmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The timescale of subseasonal forecasting (two weeks to one month) is a critical lead time for proactive mitigation of flood disasters. The NOAA/Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3-4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times.

Probabilistic forecast of low-level wind shear over Jeju international airport using non-homogeneous regression model

Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operation and management. However, there have been few studies on the probabilistic forecasts of LLWS obtained from ensemble prediction system. In this study, we analyzed a reliability analysis to verify LLWS ensemble member forecasts and observation based on the limited grid points around Jeju International Airport in Jeju. Homogeneous and non-homogeneous regression models were used to reduce the bias and dispersion existing ensemble prediction system and to provide probabilistic forecast. Prior to applying probabilistic forecast model, reliability analysis was conducted by using rank histogram to identify the statistical consistency of LLWS ensemble forecasts and corresponding observations. Based on the results of our study, we found that LLWS ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed for all seasons. To correct such biases, homogeneous regression and non-homogeneous regressions as EMOS (Ensemble Model Output Statistics) and EMOS exchangeable model by assuming exchangeable ensemble members were applied. The prediction skills of the methods were compared by the mean absolute error and continuous ranked probability score. We found that the prediction skills of probabilistic forecasts of EMOS exchangeable model were superior to the bias-corrected forecasts in terms of deterministic prediction.

Risk strategy analysis for an online rental problem of durable equipment with a transaction cost

This study introduces the second-hand market into the famous ski-rental model, presents an online rental problem of durable equipment with a transaction cost, and designs an optimal deterministic competitive strategy. The traditional competitive analysis is based on the worst-case scenario; hence, its results are too conservative. Even though investors want to manage and control their risks in reality, in some cases, they are willing to undertake higher risk to obtain greater benefits. Considering this situation, this study designs a risk strategy combining the decision makers’ risk tolerance with certain and probabilistic forecasts. Numerical analysis shows that the proposed risk strategy can improve the competitive ratio. This study introduces the idea of risk compensation into traditional competitive analysis and designs strategies for online rental of durable equipment based on forecast. The decision maker selects a strategy according to risk tolerance and forecast. If the forecast is correct, then a reward is obtained; otherwise, the risk is guaranteed to be within the decision maker’s risk tolerance. The optimal restricted ratio, that is, the competitive ratio of a risk strategy, is less than the optimal competitive ratio of a deterministic strategy. Therefore, the performance of the proposed risk strategy is better than a deterministic strategy. At the same time, the risk strategy based on the probabilistic forecast represents an extension of the strategy based on a certain forecast. In other words, the risk strategy based on a certain forecast is a special case of the risk strategy based on the probabilistic forecast.