Process-Based Evaluation of Stochastic Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events

2021 ◽  
Author(s):  
Shu-Chih Yang
Keyword(s):  
Water Vapor ◽  
Wrf Model ◽  
Heavy Rain ◽  
Model Error ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
High Precipitation ◽  
Prediction Systems ◽  
Rain Events ◽  
Low Precipitation

Abstract Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud-level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.

scholarly journals Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

2021 ◽  
Vol 893 (1) ◽  
pp. 012040
Author(s):  
Immanuel Jhonson Arizona Saragih ◽  
Huda Abshor Mukhsinin ◽  
Kerista Tarigan ◽  
Marzuki Sinambela ◽  
Marhaposan Situmorang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Initial Data ◽  
Satellite Data ◽  
Wrf Model ◽  
Heavy Rain ◽  
Convective System ◽  
Large Area ◽  
Weather Forecasts ◽  
Rain Events ◽  
North Sumatra

Abstract Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

scholarly journals A Comparison of Model Error Representations in Mesoscale Ensemble Data Assimilation

2015 ◽  
Vol 143 (10) ◽  
pp. 3893-3911 ◽  
Author(s):  
Soyoung Ha ◽  
Judith Berner ◽  
Chris Snyder
Keyword(s):  
Data Assimilation ◽  
Numerical Models ◽  
Wrf Model ◽  
Forecast Model ◽  
Model Error ◽  
Ensemble Forecasts ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Independent Observations ◽  
Almost All

Abstract Mesoscale forecasts are strongly influenced by physical processes that are either poorly resolved or must be parameterized in numerical models. In part because of errors in these parameterizations, mesoscale ensemble data assimilation systems generally suffer from underdispersiveness, which can limit the quality of analyses. Two explicit representations of model error for mesoscale ensemble data assimilation are explored: a multiphysics ensemble in which each member’s forecast is based on a distinct suite of physical parameterization, and stochastic kinetic energy backscatter in which small noise terms are included in the forecast model equations. These two model error techniques are compared with a baseline experiment that includes spatially and temporally adaptive covariance inflation, in a domain over the continental United States using the Weather Research and Forecasting (WRF) Model for mesoscale ensemble forecasts and the Data Assimilation Research Testbed (DART) for the ensemble Kalman filter. Verification against independent observations and Rapid Update Cycle (RUC) 13-km analyses for the month of June 2008 showed that including the model error representation improved not only the analysis ensemble, but also short-range forecasts initialized from these analyses. Explicitly accounting for model uncertainty led to a better-tuned ensemble spread, a more skillful ensemble mean, and higher probabilistic scores, as well as significantly reducing the need for inflation. In particular, the stochastic backscatter scheme consistently outperformed both the multiphysics approach and the control run with adaptive inflation over almost all levels of the atmosphere both deterministically and probabilistically.

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 8 (2) ◽  
pp. 2739-2782 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Criterion Score ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

scholarly journals Postprocessing ensemble forecasts of vertical temperature profiles

2020 ◽  
Vol 6 (1) ◽  
pp. 45-60
Author(s):  
David Schoenach ◽  
Thorsten Simon ◽  
Georg Johann Mayr
Keyword(s):  
Rank Order ◽  
Probability Distributions ◽  
Ensemble Prediction ◽  
Order Structure ◽  
Temperature Profiles ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Atmospheric Observations ◽  
Prediction Systems

Abstract. Weather forecasts from ensemble prediction systems (EPS) are improved by statistical models trained on past EPS forecasts and their atmospheric observations. Recently these corrections have moved from being univariate to multivariate. The focus has been on (quasi-)horizontal atmospheric variables. This paper extends the correction methods to EPS forecasts of vertical profiles in two steps. First univariate distributional regression methods correct the probability distributions separately at each vertical level. In the second step copula coupling re-installs the dependence among neighboring levels by using the rank order structure of the EPS forecasts. The method is applied to EPS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) at model levels interpolated to four locations in Germany, from which radiosondes are released to measure profiles of temperature and other variables four times a day. A winter case study and a summer case study, respectively, exemplify that univariate postprocessing fails to preserve stable layers, which are crucial for many atmospheric processes. Quantile resampling and a resampling that preserves the relative distance between individual EPS members improve the calibration of the raw forecasts of the temperature profiles as shown by rank histograms. They also improve the multivariate metrics of energy score and variogram score and retain the stable layers. Improvements take place over all times of the day and all seasons. They are largest within the atmospheric boundary layer and for shorter lead times.

scholarly journals ESTIMATIVA DA PROBABILIDADE DE OCORRÊNCIA DE PRECIPITAÇÃO, A PARTIR DE TÉCNICAS ESTATÍSTICAS NÃO PARAMÉTRICAS APLICADAS A SIMULAÇÕES NUMÉRICAS DE WRF. UM CASO DE ESTUDO

2016 ◽  
Vol 38 ◽  
pp. 491
Author(s):  
Lissette Guzmán Rodríguez ◽  
Vagner Anabor ◽  
Franciano Scremin Puhales ◽  
Everson Dal Piva
Keyword(s):  
Wrf Model ◽  
Random Variable ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Precipitation Forecast ◽  
Precipitation Event ◽  
Ensemble Prediction ◽  
False Alarms ◽  
Heavy Precipitation Event ◽  
Skill Scores

In this paper was  used the  kernel density estimation (KDE),  a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic  precipitation forecast, from an ensemble prediction with the  WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities  obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For  accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables,  for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.

scholarly journals Triggering Mechanism of an Extreme Rainstorm Process near the Tianshan Mountains in Xinjiang, an Arid Region in China, Based on a Numerical Simulation

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Zeng ◽  
Lianmei Yang
Keyword(s):  
Water Vapor ◽  
Arid Region ◽  
Middle Atmosphere ◽  
Wrf Model ◽  
Heavy Rain ◽  
Cloud Water ◽  
Tianshan Mountains ◽  
Triggering Mechanism ◽  
Cold Air ◽  
Convergence Line

The current study investigated the triggering mechanism of a record-breaking heavy rain process in the area near the Tianshan Mountains in Xinjiang, an arid region in China, from July 31 to August 1, 2016, based on the simulation using the Weather Research and Forecasting (WRF) model. The results illustrated that the rainstorm system was generated in the middle atmosphere of the western Aksu region near the Tianshan Mountains and gradually evolved into a multicell linear echo during system evolution. The cold air transported from the Tianshan Mountains partly reached the low altitudes during the downhill process, and the warm southwest air from Aksu was lifted, forming oblique updraft airflow. The other part of the cold air converged with the southeastern warm air in the middle atmosphere, and the transportation and convergence of the water vapor related to the southwestern, southeastern, and oblique updraft airflows provided good water vapor conditions for the storm system. Meanwhile, the inclined upward air transported cloud water and ice-phase particles to high altitudes, mixing the two and generating a large amount of supercooled cloud water, which was very beneficial for the development and maintenance of the storm system. These conditions were favorable for power, heat, water vapor, and water condensate particles, which enabled the development and maintenance of the rainstorm system on the convergence line, thus triggering this rare rainstorm process during the movement to the northeast.

The Vice and Virtue of Increased Horizontal Resolution in Ensemble Forecasts of Tornadic Thunderstorms in Low-CAPE, High-Shear Environments

2021 ◽  
Vol 149 (4) ◽  
pp. 921-944
Author(s):  
John R. Lawson ◽  
Corey K. Potvin ◽  
Patrick S. Skinner ◽  
Anthony E. Reinhart
Keyword(s):  
Information Gain ◽  
Horizontal Resolution ◽  
Object Identification ◽  
Ensemble Prediction ◽  
Specific Information ◽  
High Shear ◽  
Ensemble Forecasts ◽  
Forecast Lead Time ◽  
Prediction Systems ◽  
The Cost

AbstractTornadoes have Lorenzian predictability horizons O(10) min, and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1)-km length scale of tornadoes and O(1)-km scale of mesocyclones, operational models running at horizontal grid spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeastern United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of 3. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 vs 1 km), for four low-CAPE, high-shear cases. Verification was grouped as 1) deterministic, traditional methods using pointwise evaluation, 2) a scale-aware probabilistic metric, and 3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at the cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

scholarly journals Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean

2011 ◽  
Vol 12 (4) ◽  
pp. 634-649 ◽  
Author(s):  
Sante Laviola ◽  
Agata Moscatello ◽  
Mario Marcello Miglietta ◽  
Elsa Cattani ◽  
Vincenzo Levizzani
Keyword(s):  
Water Vapor ◽  
Numerical Model ◽  
Liquid Water ◽  
Heavy Rain ◽  
Sensor Data ◽  
Retrieval Algorithm ◽  
Central Mediterranean ◽  
Model Simulations ◽  
Wide Range ◽  
Rain Events

Abstract Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.

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