Operational setup of the soil-perturbed, time-lagged Ensemble Prediction System at the Institute of Meteorology and Water Management – National Research Institute (original) (raw)
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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...
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.
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...
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...
The Met Office has recently introduced a short-range ensemble prediction system known as MOGREPS. This system consists of global and regional ensembles, with the global ensemble providing the boundary conditions and initial condition perturbations for the regional ensemble. Perturbations to the initial conditions are calculated using the Ensemble Transform Kalman Filter (ETKF), which is a computationally efficient version of the Ensemble Kalman Filter. Model uncertainties are represented in the system through a series of schemes designed to tackle the structural and sub-grid-scale sources of model error.
Perturbing Surface Initial Conditions in a Regional Ensemble Prediction System
Monthly Weather Review, 2016
Two techniques for perturbing surface initial conditions in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF) are presented and investigated in this paper. The first technique is the noncycling surface breeding (NCSB), which combines short-range surface forecasts driven by perturbed atmospheric forcing and the breeding method for generating the perturbations on surface initial conditions. The second technique, which is currently used in the ALADIN-LAEF operational version, applies an ensemble of surface data assimilations (ESDA) in which the observations are randomly perturbed. Both techniques are evaluated over a twomonth period from late spring to summer. The results show that the evaluation is more favorable to ESDA. In general, the ensemble forecasts of the observed near-surface meteorological variables (screen-level variables) of ESDA are more skillful than NCSB, in particular for 2-m temperature they are statistically more consistent and reliable. A slightly better statistical reliability for 2-m relative humidity and 10-m wind has been found as well. This could be attributed to the introduction of surface data assimilation in ESDA, which provides more accurate surface initial conditions. Moreover, the observation perturbation in ESDA helps to better estimate the initial condition uncertainties. For the forecast of precipitation and the upper-air variables in the lower troposphere, both ESDA and NCSB perform very similarly, having neutral impact.
Soil and Tillage Research, 2018
Soil moisture (SM) is a key component of the global energy cycle that regulates all domains of the natural environmental and the agricultural system. In this research, the challenge is to develop a low-cost data-intelligent SM forecasting model using climate dynamics (i.e., the climate indices, atmospheric and hydro-meteorological parameters) as the model inputs. A newly designed, multi-model ensemble committee machine learning approach based on the artificial neural network (ANN-CoM) is developed to forecast monthly upper layer (∼0.2 m from the surface) and the lower layer (∼0.2-1.5 m deep) SM at four agricultural sites in Australia's Murray-Darling Basin. ANN-CoM model is validated with respect to non-tuned second-order Volterra, M5 model tree, random forest, and an extreme learning machine (ELM) models. To construct the ANN-CoM model, the input variables comprised of the hydro-meteorological data from the Australian Water Availability Project, large-scale climate indices and atmospheric parameters derived from the Interim ERA European Centre for Medium-Range Weather Forecasting ECMWF reanalysis fields leads to a total of 60 potential predictors used for SM forecasting. To reduce the model input data dimensionality for accurate forecasts, the Neighborhood Component Analysis (NCA) based feature selection algorithm for regression purposes (fsrnca) is applied to determine the relative feature weights related to the targeted variable. The optimal predictor variables are then screened with an ELM model as the fitness function of the fsrnca algorithm to identify the set of most pertinent model variables. Extensive performance evaluation using statistical score metrics with visual and diagnostic plots show that the ensemble committee based, ANN-CoM model is able to effectively capture the nonlinear dynamics involved in the modeling of monthly upper and lower layer SM levels. Therefore, the ANN-CoM multimodel ensemble-based approach can be considered to be a superior SM forecasting tool, portraying as an amicable, integrated (or ensemble) machine learning stratagem that can be explored for soil moisture modeling and applications in agriculture and other hydro-meteorological phenomena.
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...
Proposed strategy for ensemble prediction in the Bureau of Meteorology
2005
Ensembles provide information on forecast uncertainty, enabling better decision making where weather poses a risk or opportunity, and allowing forecasts to be usefully extended beyond the deterministic range. This paper outlines a strategy for how the Bureau can develop and enhance its ensemble prediction systems to provide accurate deterministic and probabilistic forecasts of surface and upper air fields of interest to forecasters and the public, covering Australia and surrounding waters, on time scales relevant to nowcasting, weather, and seasonal climate prediction.
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.