Dependence of skill and spread of the ensemble forecasts on the type of perturbation and its relationship with long-term norms of precipitation and temperature (original) (raw)
Related papers
Meteorology Hydrology and Water Management, 2017
The usage of Ensemble Prediction System (EPS)-based weather forecasts is nowadays becoming very popular and widespread, because ensemble means better represent weather-related risks than a single (deterministic) forecast. Perturbations of the lower boundary state (i.e., layers of soil and the boundary between soil and the lower atmosphere) applied to the governing system are also believed to play an important role at any resolution. As a part of the research project of the Consortium for Small-scale Modelling (COSMO) at the Institute of Meteorology and Water Management-National Research Institute (IMWM-NRI), a simple and efficient method was proposed to produce a reasonable number of valid ensemble members, taking into consideration predefined soil-related model parameters. Tests, case studies and long-term evaluations confirmed that small perturbations of a selected parameter(s) were sufficient to induce significant changes in the forecast of the state of the atmosphere and to provide qualitative selection of a valid member of the ensemble members. Another important factor that added a significant increment to ensemble spread was the time-lagged approach. All these aspects resulted in the preparation of a well-defined ensemble based on the perturbation of soil-related parameters, and introduced in the COSMO model operational setup at the IMWM-NRI. This system is intended for the use in forecasters' routine work.
On evaluation of ensemble precipitation forecasts with observation-based ensembles
Advances in Geosciences, 2007
Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limitedarea ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km 2 ) of Switzerland with a mean rain gauge distance as good as 7 km: the one-to three-day precipitation forecasts have skill decreasing with forecast lead time but the one-and two-day forecast performances differ not significantly.
Impact of including moisture perturbations on short-range ensemble forecasts
Journal of Geophysical Research, 2009
We are developing a Short-Range Ensemble Prediction (SREP) system based on the Eta Model for use over South America. The Eta Model SREP system uses the CPTEC global model Ensemble Prediction System (EPS) forecasts as initial and lateral boundary conditions. The objectives of this work are to verify the impacts of including moisture perturbations in the global EPS on the SREP and to evaluate the forecast quality from the resulting SREP. We compare the SREP constructed with and without moisture perturbations. We chose four cases of South Atlantic Convergence Zone events that produced heavy rainfall for the tests and evaluation. The Eta Model was set with a horizontal resolution of 10 km and integrated for 6 days. The mean errors of the forecasts based on the two perturbation methodologies are similar, which indicates that including moisture did not increase the forecast error. Precipitation forecasts showed major improvement when moisture perturbation was included. The root mean square error (RMSE) of the SREP ensemble mean forecast from both initial condition perturbations is smaller than the RMSE of the control run. The constructed SREP system exhibits forecast RMSE growth rate larger than the ensemble forecast spread, on the other hand, this difference is reduced compared to the driver global model ensemble forecast system.
2010
Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990’s in numerical centers around the world due to the increase of computation ability. One of the main purposes of numerical ensemble forecast tends to assimilate initial uncertainty (both observation and analysis errors) and forecast uncertainty (model errors) by applying either initial perturbation method, ensemble assimilation, or multimodel/multi-physics method, and stochastic physics. In fact, the mean of ensemble forecasts is offering better forecast than deterministic (or control) forecast after a short lead-time (1-3 days) for the global model application. There is about a 1-2 day improvement in the forecast skill when using ensemble mean instead of a single forecast for longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extended to 8-10 days (or longer) by present state-of-the-art analysis and ensemble forecast system. It is most important tha...
Summer Rainfall Forecast Spread in an Ensemble Initialized with Different Soil Moisture Analyses
Weather and Forecasting, 2007
The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This resul...
University of Tehran, College of Aburaihan, 2023
The lead-time and accuracy of the precipitation forecasts have a substantial influence on the flood forecast and warning systems. The application of Ensemble Precipitation Forecasts (EPFs) derived from numerical precipitation models has been developed due to their impact on increasing flood lead-time. This research aims to improve the skill of numerical precipitation models using post-processing techniques. In this regard, EPFs of three meteorological models, e.g., NCEP, UKMO, and KMA, were extracted for sex precipitation events leading to flood in the Dez river basin during 2013-2019. The statistical approaches and data-driven model were applied to post-process the EPFs. For this purpose, the raw forecast of every single model was corrected using linear and power regression models. Then, the corrected output of single models was combined using the proposed model of Group Method of Data Handling (GMDH). The results indicated that Power Regression Model (PRM) outperformed the linear models to correct raw forecasts. After correction of models' output, more accurate results were obtained by NCEP and UKMO models. Moreover, the Multi-Model Ensemble (MME) system constructed by the GMDH model (MME_GMDH) had a great effect on the skill of numerical precipitation models, so that the Nash–Sutcliffe and normalized error (NRMSE) efficiency criteria for MME_GMDH respectively were improved on average 23% and 11% in comparison with the PRM. A comparative assessment of the discrimination capability of MME with single ensemble models using ROC curve at the thresholds of 2.5 and 10 mm represented a higher discrimination ability by MME_GMDH for both thresholds. Post-processed EPFs exert as a reliable input to the hydrological models for extreme events forecast.
A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems
Monthly Weather Review, 2005
The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; andfor all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources o...
Ensemble forecasting: status and perspectives
2011
One of the main challenges for Numerical Weather Prediction is the Quantitative Precipitation Forecasting (QPF). The accurate forecast of high-impact weather still remains difficult beyond day 2 and many limited-area ensemble prediction systems have been recently developed so as to provide more reliable forecasts than achievable with a single deterministic forecast. As a consequence the calibration of ensemble precipitation forecasts has become a very demanding task, for improving the QPF, especially as an input to hydrological models. Different calibration techniques are compared: cumulative distribution function, linear regression and analogues method.
Predicting weather and climate: Uncertainty, ensembles and probability
Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 2010
Simulation-based weather and climate prediction now involves the use of methods that reflect a deep concern with uncertainty. These methods, known as ensemble prediction methods, produce multiple simulations for predictive periods of interest, using different initial conditions, parameter values and/or model structures. This paper provides a non-technical overview of current ensemble methods and considers how the results of studies employing these methods should be interpreted, paying special attention to probabilistic interpretations. A key conclusion is that, while complicated inductive arguments might be given for the trustworthiness of probabilistic weather forecasts obtained from ensemble studies, analogous arguments are out of reach in the case of long-term climate prediction. In light of this, the paper considers how predictive uncertainty should be conveyed to decision makers.
Ensemble long-term soil moisture forecast using hydrological modeling
RBRH
Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that lon...