scholarly journals Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2631
Author(s):  
Xinchi Chen ◽  
Xiaohong Chen ◽  
Dong Huang ◽  
Huamei Liu
Keyword(s):  
Time Scales ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Efficiency Coefficient ◽  
Forecast System ◽  
Factors Affecting ◽  
Hydrological Forecasting ◽  
Better Than

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.

scholarly journals Comparisons of QPFs Derived from Single- and Multicore Convection-Allowing Ensembles

2019 ◽  
Vol 34 (6) ◽  
pp. 1955-1964
Author(s):  
Adam J. Clark
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Grid Spacing ◽  
Forecast System ◽  
Relative Operating Characteristic ◽  
Core System ◽  
Testing And Evaluation ◽  
Better Than

Abstract This study compares ensemble precipitation forecasts from 10-member, 3-km grid-spacing, CONUS domain single- and multicore ensembles that were a part of the 2016 Community Leveraged Unified Ensemble (CLUE) that was run for the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The main results are that a 10-member ARW ensemble was significantly more skillful than a 10-member NMMB ensemble, and a 10-member MIX ensemble (5 ARW and 5 NMMB members) performed about the same as the 10-member ARW ensemble. Skill was measured by area under the relative operating characteristic curve (AUC) and fractions skill score (FSS). Rank histograms in the ARW ensemble were flatter than the NMMB ensemble indicating that the envelope of ensemble members better encompassed observations (i.e., better reliability) in the ARW. Rank histograms in the MIX ensemble were similar to the ARW ensemble. In the context of NOAA’s plans for a Unified Forecast System featuring a CAM ensemble with a single core, the results are positive and indicate that it should be possible to develop a single-core system that performs as well as or better than the current operational CAM ensemble, which is known as the High-Resolution Ensemble Forecast System (HREF). However, as new modeling applications are developed and incremental changes that move HREF toward a single-core system are made possible, more thorough testing and evaluation should be conducted.

scholarly journals Numerical Guidance Methods for Decision Support in Aviation Meteorological Forecasting

2005 ◽  
Vol 20 (1) ◽  
pp. 82-100 ◽  
Author(s):  
A. J. M. Jacobs ◽  
N. Maat
Keyword(s):  
Weather Prediction ◽  
Forecast Accuracy ◽  
Transformation Method ◽  
Skill Score ◽  
Cloud Base ◽  
Model Data ◽  
Operational Environment ◽  
Automatic Production ◽  
Statistical Measures ◽  
Nwp Model

Abstract Numerical guidance methods for decision making support of aviation meteorological forecasters are presented. The methods have been developed to enhance the usefulness of numerical weather prediction (NWP) model data and local and upstream observations in the production of terminal aerodrome forecasts (TAFs) and trend-type forecasts (TRENDs) for airports. In this paper two newly developed methods are described and it is shown how they are used to derive numerical guidance products for aviation. The first is a combination of statistical and physical postprocessing of NWP model data and in situ observations. This method is used to derive forecasts for all aviation-related meteorological parameters at the airport. The second is a high-resolution wind transformation method, a technique used to derive local wind at airports from grid-box-averaged NWP model winds. For operational use of the numerical guidance products encoding software is provided for automatic production of an alphanumeric TAF and TREND code. A graphical user interface with an integrated code editor enables the forecaster to modify the suggested automatic codes. For aviation, the most important parameters in the numerical guidance are visibility and cloud-base height. Both have been subjected to a statistical verification analysis, together with their automatically produced codes. The results in terms of skill score are compared to the skill of the forecasters’ TAF and TREND code. The statistical measures suggest that the guidance has the best skill at lead times of +4 h and more. For the short term, mainly trend-type forecasts, the persistence forecast based on recent observations is difficult to beat. Verification has also shown that the wind transformation method, which has been applied to generate 10-m winds at Amsterdam Airport Schiphol, reduces the mean error in the (grid box averaged) NWP model wind significantly. Among the potential benefits of these numerical guidance methods is increasing forecast accuracy. As a result, the related numerical guidance products and encoding software have been integrated in the operational environment for the production of TAFs and TRENDs.

Very Short Time Range Forecasting Using CReSS-3DVAR for a Meso-γ-Scale, Localized, Extremely Heavy Rainfall Event: Comparison with an Extrapolation-Based Nowcast

2017 ◽  
Vol 12 (5) ◽  
pp. 967-979 ◽  
Author(s):  
Ryohei Kato ◽  
◽  
Shingo Shimizu ◽  
Ken-ichi Shimose ◽  
Koyuru Iwanami
Keyword(s):  
Heavy Rainfall ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Radar Reflectivity ◽  
Heavy Rainfall Event ◽  
Time Range ◽  
Nwp Model ◽  
Short Time

The forecast accuracy of a numerical weather prediction (NWP) model for a very short time range (≤1 h) for a meso-γ-scale (2–20 km) extremely heavy rainfall (MγExHR) event that caused flooding at the Shibuya railway station in Tokyo, Japan on 24 July 2015 was compared with that of an extrapolation-based nowcast (EXT). The NWP model used CReSS with 0.7 km horizontal grid spacing, and storm-scale data from dense observation networks (radars, lidars, and microwave radiometers) were assimilated using CReSS-3DVAR. The forecast accuracy of the heavy rainfall area (≥20 mm h-1), as a function of forecast time (FT), was investigated for the NWP model and EXT predictions using the fractions skill score (FSS) for various spatial scales of displacement error (L). These predictions were started 30 minutes before the onset of extremely heavy rainfall at Shibuya station. The FSS for L=1 km, i.e., grid-scale verification, showed NWP accuracy was lower than that of EXT before FT=40 min; however, NWP accuracy surpassed that of EXT from FT=45 to 60 min. This suggests the possibility of seamless, high-accuracy forecasts of heavy rainfall (≥20 mm h-1) associated with MγExHR events within a very short time range (≤1 h) by blending EXT and NWP outputs. The factors behind the fact that the NWP model predicted heavy rainfall area within the very short time range of ≤1 h more correctly than did EXT are also discussed. To enable this discussion of the factors, additional sensitivity experiments with a different assimilation method of radar reflectivity were performed. It was found that a moisture adjustment above the lifting condensation level using radar reflectivity was critical to the forecasting of heavy rainfall near Shibuya station after 25 min.

scholarly journals Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

2010 ◽  
Vol 138 (9) ◽  
pp. 3418-3433 ◽  
Author(s):  
Tanja Weusthoff ◽  
Felix Ament ◽  
Marco Arpagaus ◽  
Mathias W. Rotach
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Grid Point ◽  
Skill Score ◽  
Verification Methods ◽  
Driving Model ◽  
High Resolution Models ◽  
Precipitation Events ◽  
Better Than

Abstract High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods—upscaling (UP) and fractions skill score (FSS)—for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lower-resolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.

scholarly journals A Stratified Sampling Approach for Improved Sampling from a Calibrated Ensemble Forecast Distribution

2016 ◽  
Vol 17 (9) ◽  
pp. 2405-2417 ◽  
Author(s):  
Yiming Hu ◽  
Maurice J. Schmeits ◽  
Schalk Jan van Andel ◽  
Jan S. Verkade ◽  
Min Xu ◽  
...  
Keyword(s):  
Sampling Methods ◽  
Skill Score ◽  
Predictive Distribution ◽  
Ensemble Forecast ◽  
Stratified Sampling ◽  
Dependence Structure ◽  
Ensemble Forecasts ◽  
Empirical Copula ◽  
Space Dependence ◽  
Better Than

Abstract Before using the Schaake shuffle or empirical copula coupling (ECC) to reconstruct the dependence structure for postprocessed ensemble meteorological forecasts, a necessary step is to sample discrete samples from each postprocessed continuous probability density function (pdf), which is the focus of this paper. In addition to the equidistance quantiles (EQ) and independent random (IR) sampling methods commonly used at present, the stratified sampling (SS) method is proposed. The performance of the three sampling methods is compared using calibrated GFS ensemble precipitation reforecasts over the Xixian basin in China. The ensemble reforecasts are first calibrated using heteroscedastic extended logistic regression (HELR), and then the three sampling methods are used to sample calibrated pdfs with a varying number of discrete samples. Finally, the effect of the sampling method on the reconstruction of ensemble members with preserved space dependence structure is analyzed by using EQ, IR, and SS in ECC for reconstructing postprocessed ensemble members for four stations in the Xixian basin. There are three main results. 1) The HELR model has a significant improvement over the raw ensemble forecast. It clearly improves the mean and dispersion of the predictive distribution. 2) Compared to EQ and IR, SS can better cover the tails of the calibrated pdfs and a better dispersion of calibrated ensemble forecasts is obtained. In terms of probabilistic verification metrics like the ranked probability skill score (RPSS), SS is slightly better than EQ and clearly better than IR, while in terms of the deterministic verification metric, root-mean-square error, EQ is slightly better than SS. 3) ECC-SS, ECC-EQ, and ECC-IR all calibrate the raw ensemble forecast, but ECC-SS shows a better dispersion than ECC-EQ and ECC-IR in this study.

scholarly journals A Comparison of Perturbations from an Ensemble Transform and an Ensemble Kalman Filter for the NCEP Global Ensemble Forecast System

2016 ◽  
Vol 31 (6) ◽  
pp. 2057-2074 ◽  
Author(s):  
Xiaqiong Zhou ◽  
Yuejian Zhu ◽  
Dingchen Hou ◽  
Daryl Kleist
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Forecast Model ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Boreal Summer ◽  
Lead Times ◽  
Operational Environment ◽  
Forecast System ◽  
Ensemble Mean

Abstract Two perturbation generation schemes, the ensemble transformation with rescaling (ETR) and the ensemble Kalman filter (EnKF), are compared for the NCEP operational environment for the Global Ensemble Forecast System (GEFS). Experiments that utilize each of the two schemes are carried out and evaluated for two boreal summer seasons. It is found that these two schemes generally have comparable performance. Experiments utilizing both perturbation methods fail to generate sufficient spread at medium-range lead times beyond day 8. In general, the EnKF-based experiment outperforms the ETR in terms of the continuous ranked probability skill score (CRPSS) in the Northern Hemisphere (NH) for the first week. In the SH, the ensemble mean forecast is more skillful from the ETR perturbations. Additional experiments are performed with the stochastic total tendency perturbation (STTP) scheme, in which the total tendencies of all model variables are perturbed to represent the uncertainty in the forecast model. An improved spread–error relationship is found for the ETR-based experiments, but the STTP increases the ensemble spread for the EnKF-based experiment that is already overdispersive at early lead times, especially in the SH. With STTP employed, an increase in the EnKF-based CRPSS in the NH is reduced with a larger degradation in both the probability and ensemble-mean forecast skills in the SH. The results indicate that a rescaling of the EnKF initial perturbations and/or tuning of the STTP scheme is required when STTP is applied using the EnKF-based perturbations. This study provided guidance for the replacement of ETR with EnKF perturbations as part of the 2015 GEFS implementation.

scholarly journals Data assimilation of radar reflectivity volumes in a LETKF scheme

2018 ◽  
Vol 25 (4) ◽  
pp. 747-764 ◽  
Author(s):  
Thomas Gastaldo ◽  
Virginia Poli ◽  
Chiara Marsigli ◽  
Pier Paolo Alberoni ◽  
Tiziana Paccagnella
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Observational Error ◽  
The Impact

Abstract. Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

scholarly journals On the predictability of outliers in ensemble forecasts

2012 ◽  
Vol 8 (1) ◽  
pp. 53-57
Author(s):  
S. Siegert ◽  
J. Bröcker ◽  
H. Kantz
Keyword(s):  
Logistic Regression ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Base Rate ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Interpretation

Abstract. In numerical weather prediction, ensembles are used to retrieve probabilistic forecasts of future weather conditions. We consider events where the verification is smaller than the smallest, or larger than the largest ensemble member of a scalar ensemble forecast. These events are called outliers. In a statistically consistent K-member ensemble, outliers should occur with a base rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We study the predictability of outlier events in terms of the Brier Skill Score and find that forecast probabilities can be calculated which are more skillful than the unconditional base rate. This is shown analytically for statistically consistent ensembles. Using logistic regression, forecast probabilities for outlier events in an operational ensemble are calculated. These probabilities exhibit positive skill which is quantitatively similar to the analytical results. Possible causes of these results as well as their consequences for ensemble interpretation are discussed.

scholarly journals Evaluation of Probabilistic Medium-Range Temperature Forecasts from the North American Ensemble Forecast System

2009 ◽  
Vol 24 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Doug McCollor ◽  
Roland Stull
Keyword(s):  
North American ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Value Assessment ◽  
Operating Characteristics ◽  
Forecast System ◽  
Forecast Period ◽  
The North ◽  
Medium Range ◽  
The Future

Abstract Ensemble temperature forecasts from the North American Ensemble Forecast System were assessed for quality against observations for 10 cities in western North America, for a 7-month period beginning in February 2007. Medium-range probabilistic temperature forecasts can provide information for those economic sectors exposed to temperature-related business risk, such as agriculture, energy, transportation, and retail sales. The raw ensemble forecasts were postprocessed, incorporating a 14-day moving-average forecast–observation difference, for each ensemble member. This postprocessing reduced the mean error in the sample to 0.6°C or less. It is important to note that the North American Ensemble Forecast System available to the public provides bias-corrected maximum and minimum temperature forecasts. Root-mean-square-error and Pearson correlation skill scores, applied to the ensemble average forecast, indicate positive, but diminishing, forecast skill (compared to climatology) from 1 to 9 days into the future. The probabilistic forecasts were evaluated using the continuous ranked probability skill score, the relative operating characteristics skill score, and a value assessment incorporating cost–loss determination. The full suite of ensemble members provided skillful forecasts 10–12 days into the future. A rank histogram analysis was performed to test ensemble spread relative to the observations. Forecasts are underdispersive early in the forecast period, for forecast days 1 and 2. Dispersion improves rapidly but remains somewhat underdispersive through forecast day 6. The forecasts show little or no dispersion beyond forecast day 6. A new skill versus spread diagram is presented that shows the trade-off between higher skill but low spread early in the forecast period and lower skill but better spread later in the forecast period.

scholarly journals Global Ensemble Predictions of 2009’s Tropical Cyclones Initialized with an Ensemble Kalman Filter

2011 ◽  
Vol 139 (2) ◽  
pp. 668-688 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Michael Fiorino ◽  
Stanley G. Benjamin
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclones ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Central Pressure ◽  
Ensemble Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Track Error

Abstract Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a high-resolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The second model was a 30-km version of the experimental NOAA/Earth System Research Laboratory’s Flow-following finite-volume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60-member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus human-synthesized “observations” of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO). The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent in magnitude with the track error. Both experimental EPSs had much lower errors than the operational NCEP, UKMO, and CMC EPSs, but the FIM–EnKF tracks were somewhat less accurate than the GFS–EnKF. The ensemble forecasts were often stretched in particular directions, and not necessarily along or across track. The better-performing EPSs provided useful information on potential track error anisotropy. While the GFS–EnKF initialized relatively deep vortices by assimilating the TC central pressure estimate, the model storms filled during the subsequent 24 h. Other forecast models also systematically underestimated TC intensity (e.g., maximum forecast surface wind speed). The higher-resolution models generally had less bias. Analyses were conducted to try to understand whether the additional central pressure observation, the EnKF, or the extra resolution was most responsible for the decrease in track error of the experimental Global Ensemble Forecast System (GEFS)–EnKF over the operational NCEP. The assimilation of the additional TC observations produced only a small change in deterministic track forecasts initialized with the GSI. The T382L64 GFS–EnKF ensemble was used to initialize a T126L28 ensemble forecast to facilitate a comparison with the operational NCEP system. The T126L28 GFS–EnKF EPS track forecasts were dramatically better than the NCEP operational, suggesting the positive impact of the EnKF, perhaps through improved steering flow.

Sign in / Sign up

Export Citation Format

Share Document