scholarly journals Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation

2021 ◽  
Author(s):  
Meriem Krouma ◽  
Pascal Yiou ◽  
Céline Déandreis ◽  
Soulivanh Thao
Keyword(s):  
Western Europe ◽  
Weather Forecast ◽  
Skill Score ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Proof Of Concept ◽  
Ensemble Forecasts ◽  
Stochastic Weather Generator ◽  
Skill Scores ◽  
Daily Time

Abstract. In this study, we aim to assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities of Western Europe. The SWG is based on random sampling of analogs of the geopotential height at 500 hPa. The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill scores (continuous rank probability skill score and correlation) for lead times of 5 and 10 days for different areas in Europe. We found that the low predictability of our model is related to specific weather regimes, depending on the European region. Comparing SWG forecasts to ECMWF forecasts, we found that the SWG shows a good performance for 5 days.  This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g. region for analogs) must be tuned for each region in order to optimize its performance.

Ensemble weather forecast of precipitation with a stochastic weather generator based on analogues circulation

2020 ◽  
Author(s):  
Meriem Krouma ◽  
Pascal Yiou ◽  
Céline Déandréis ◽  
Soulivanh Thao
Keyword(s):  
Model Development ◽  
Weather Generator ◽  
Lead Times ◽  
The European Union ◽  
Ensemble Forecasts ◽  
Analogue Model ◽  
Data Set ◽  
Stochastic Weather Generator ◽  
Horizon 2020 ◽  
Skill Scores

<p><strong>Abstract</strong></p><p>The aim of this study is to assess the skills of a stochastic weather generator (SWG) to forecast precipitation in Europe. The SWG is based on the random sampling of circulation analogues, which is a simple form of machine learning simulation. The SWG was developed and tested by Yiou and Déandréis (2019) to forecast daily average temperature and the NAO index. Ensemble forecasts with lead times from 5 to 80 days were evaluated with CRPSS scores against climatology and persistence forecasts. Reasonable scores were obtained up to 20 days.  In this study, we adapt the parameters of the analogue SWG to optimize the simulation of European precipitations. We then analyze the performance of this SWG for lead times of 2 to 20 days, with the forecast skill scores used by Yiou and Déandréis (2019). To achieve this objective, the SWG will use ECA&D precipitation data (Haylock. 2002), and the analogues of circulation will be computed from sea-level pressure (SLP) or geopotential heights (Z500) from the NCEP reanalysis. This provides 100-member ensemble forecasts on a daily time increment. We will evaluate the seasonal dependence of the forecast skills of precipitation and the conditional dependence to weather regimes. Comparisons with “real” medium range forecasts from the ECMWF will be performed.</p><p><strong>References</strong></p><p>Yiou, P., and Céline D.. Stochastic ensemble climate forecast with an analogue model. Geoscientific Model Development 12, 2 (2019): 723‑34.</p><p>Haylock, M. R. et al.. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. - Atmospheres 113, D20 (2008): doi:10.1029/2008JD010201.</p><p> </p><p><strong>A</strong><strong>cknowledge</strong></p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p>

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals Assessing the Skill of Yes/No Forecasts for Markov Observations

2006 ◽  
Vol 134 (9) ◽  
pp. 2601-2611
Author(s):  
William Briggs ◽  
David Ruppert
Keyword(s):  
Hypothesis Testing ◽  
Skill Score ◽  
Sampling Distribution ◽  
Loss Functions ◽  
Precipitation Forecast ◽  
Present Value ◽  
First Order ◽  
Precipitation Occurrence ◽  
Skill Scores ◽  
Wide Range

Abstract Briggs and Ruppert recently introduced a new, easy-to-calculate economic skill/value score for use in yes/no forecast decisions, of which precipitation forecast decisions are an example. The advantage of this new skill/value score is that the sampling distribution is known, which allows one to perform hypothesis tests on collections of forecasts and to say whether a given skill/value score is significant or not. Here, the climate skill/value score is taken and extended to the case where the predicted series is first-order Markov in nature, of which, again, precipitation occurrence series can be an example. It is shown that, in general, Markov skill/value is different and more demanding than is persistence skill. Persistence skill is defined as improvement over forecasts that state that the next value in a series will equal the present value. It is also shown that any naive forecasts based solely on the Markov parameters is always at least as skillful/valuable as are persistence forecasts; in general, persistence forecasts should not be used. The distribution for the Markov skill score is presented, and examples of hypothesis testing for precipitation forecasts are given. These skill scores are graphed for a wide range of forecast/user loss functions, a process that makes their interpretation simple.

scholarly journals A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

2009 ◽  
Vol 24 (4) ◽  
pp. 1121-1140 ◽  
Author(s):  
Adam J. Clark ◽  
William A. Gallus ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Wrf Model ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Central United States ◽  
Statistical Consistency

Abstract An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 h. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ∼ 4 km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

scholarly journals Eastern U.S. Verification of Ensemble Precipitation Forecasts

2017 ◽  
Vol 32 (1) ◽  
pp. 117-139 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Nicholas Balderas ◽  
Jose D. Fuentes ◽  
Seann Reed ◽  
...  
Keyword(s):  
United States ◽  
Lead Time ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Forecast ◽  
Spatial Aggregation ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Eastern United States ◽  
Skill Scores

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.

scholarly journals A Debiased Ranked Probability Skill Score to Evaluate Probabilistic Ensemble Forecasts with Small Ensemble Sizes

2005 ◽  
Vol 18 (10) ◽  
pp. 1513-1523 ◽  
Author(s):  
W. A. Müller ◽  
C. Appenzeller ◽  
F. J. Doblas-Reyes ◽  
M. A. Liniger
Keyword(s):  
Signal To Noise Ratio ◽  
Skill Score ◽  
Negative Bias ◽  
Ensemble Size ◽  
Signal To Noise ◽  
Ensemble Forecasts ◽  
Near Surface ◽  
Skill Scores ◽  
Small Ensemble ◽  
Noise Ratio

Abstract The ranked probability skill score (RPSS) is a widely used measure to quantify the skill of ensemble forecasts. The underlying score is defined by the quadratic norm and is comparable to the mean squared error (mse) but it is applied in probability space. It is sensitive to the shape and the shift of the predicted probability distributions. However, the RPSS shows a negative bias for ensemble systems with small ensemble size, as recently shown. Here, two strategies are explored to tackle this flaw of the RPSS. First, the RPSS is examined for different norms L (RPSSL). It is shown that the RPSSL=1 based on the absolute rather than the squared difference between forecasted and observed cumulative probability distribution is unbiased; RPSSL defined with higher-order norms show a negative bias. However, the RPSSL=1 is not strictly proper in a statistical sense. A second approach is then investigated, which is based on the quadratic norm but with sampling errors in climatological probabilities considered in the reference forecasts. This technique is based on strictly proper scores and results in an unbiased skill score, which is denoted as the debiased ranked probability skill score (RPSSD) hereafter. Both newly defined skill scores are independent of the ensemble size, whereas the associated confidence intervals are a function of the ensemble size and the number of forecasts. The RPSSL=1 and the RPSSD are then applied to the winter mean [December–January–February (DJF)] near-surface temperature predictions of the ECMWF Seasonal Forecast System 2. The overall structures of the RPSSL=1 and the RPSSD are more consistent and largely independent of the ensemble size, unlike the RPSSL=2. Furthermore, the minimum ensemble size required to predict a climate anomaly given a known signal-to-noise ratio is determined by employing the new skill scores. For a hypothetical setup comparable to the ECMWF hindcast system (40 members and 15 hindcast years), statistically significant skill scores were only found for a signal-to-noise ratio larger than ∼0.3.

scholarly journals Cloud-Resolving Typhoon Rainfall Ensemble Forecasts for Taiwan with Large Domain and Extended Range through Time-Lagged Approach

2016 ◽  
Vol 31 (1) ◽  
pp. 151-172 ◽  
Author(s):  
Chung-Chieh Wang ◽  
Shin-Yi Huang ◽  
Shin-Hau Chen ◽  
Chih-Sheng Chang ◽  
Kazuhisa Tsuboki
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Worst Case ◽  
Large Domain ◽  
Wide Range ◽  
Extended Range ◽  
Time Lagged ◽  
The Government

Abstract In this study, the performance of a new ensemble quantitative precipitation forecast (QPF) system for Taiwan, with a cloud-resolving grid spacing of 2.5 km, a large domain of 1860 km × 1360 km, and an extended range of 8 days, is evaluated for six typhoons during 2012–13. Obtaining the probability (ensemble) information through a time-lagged approach, this system combines the strengths of high resolution (for QPF) and longer lead time (for hazard preparation) in an innovative way. For the six typhoons, in addition to short ranges (≤3 days), the system produced a decent QPF at a longest range up to days 8, 4, 6, 3, 6, and 7, providing greatly extended lead times, especially for slow-moving storms that pose higher threats. Moreover, since forecast uncertainty (reflected in the spread) is reduced with lead time, this system can provide a wide range of rainfall scenarios across Taiwan with longer lead times, each highly realistic for the associated track, allowing for advanced preparation for worst-case scenarios. Then, as the typhoon approaches and the predicted tracks converge, the government agencies can make adjustments toward the scenario of increasing likelihood. This strategy fits well with the conventional wisdom of “hoping for the best, but preparing for the worst” when facing natural hazards. Overall, the system presented herein compares favorably in usefulness to a typical 24-member ensemble (5-km grid size, 750 km × 900 km, 3-day forecasts) currently in operation using similar computational resources. Requiring about 1500 cores to execute four 8-day runs per day, it is not only powerful but also affordable and feasible.

Performance of the HWRF Rapid Intensification Analog Ensemble (HWRF RI-AnEn) during the 2017 and 2018 HFIP Real-Time Demonstrations

2020 ◽  
Vol 35 (3) ◽  
pp. 841-856
Author(s):  
William E. Lewis ◽  
Christopher Rozoff ◽  
Stefano Alessandrini ◽  
Luca Delle Monache
Keyword(s):  
Real Time ◽  
Roc Curves ◽  
Skill Score ◽  
High Impact ◽  
Assessment Tools ◽  
Eastern Pacific ◽  
Lead Times ◽  
Rapid Intensification ◽  
Improvement Program ◽  
Skill Scores

Abstract The performance of the Hurricane Weather Research and Forecasting (HWRF) Model Rapid Intensification Analog Ensemble (RI-AnEn) is evaluated for real-time forecasts made during the National Oceanic and Atmospheric Administration (NOAA)’s 2018 Hurricane Forecast Improvement Program (HFIP) demonstration. Using a variety of assessment tools (Brier skill score, reliability diagrams, ROC curves, ROC skill scores), RI-AnEn is shown to perform competitively compared to both the deterministic HWRF and current operational probabilistic RI forecast aids. The assessment is extended to include forecasts from the 2017 HFIP demonstration and shows that RI-AnEn is the only model with significant RI forecast skill at all lead times in the Atlantic and eastern Pacific basins. Though RI-AnEn is overconfident in its RI forecasts, it is generally well calibrated for all lead times. Furthermore, significance testing indicates that for the 2017–18 Atlantic and eastern Pacific sample, RI-AnEn is more skillful than HWRF at all lead times and better than most of the other probabilistic guidance at 48 and 72 h. ROC curves reveal that RI-AnEn offers a good combination of sensitivity and specificity, performing comparably to SHIPS-RII at all lead times in both basins. With respect to specific high-impact cases from the 2018 Atlantic season, performance of RI-AnEn ranges from excellent (Hurricane Michael) to poor (Hurricane Florence). The multiyear assessment and results for two high-impact case studies from 2018 indicate that, while promising, RI-AnEn requires further work to refine its performance as well as to accurately situate its effectiveness relative to other RI forecasts aids.

scholarly journals Predictive skill for atmospheric rivers in the western Iberian Peninsula

2020 ◽  
Vol 20 (3) ◽  
pp. 877-888 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Pedro M. Sousa ◽  
Emanuel Dutra ◽  
Ricardo M. Trigo
Keyword(s):  
Iberian Peninsula ◽  
Extreme Precipitation ◽  
Western Europe ◽  
Large Fraction ◽  
Water Vapor Transport ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Atmospheric Rivers ◽  
Predictive Skill ◽  
Probabilistic Evaluation

Abstract. A large fraction of extreme precipitation and flood events across western Europe are triggered by atmospheric rivers (ARs). The association between ARs and extreme precipitation days over the Iberian Peninsula has been well documented for western river basins. Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 d for events where ARs made landfall in the western Iberian Peninsula during the winters spanning between 2012–2013 and 2015–2016. Vertically integrated horizontal water vapor transport (IVT) and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and the contiguous North Atlantic Ocean. Metrics concerning AR location, intensity, and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation. We considered several regional boxes over western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ensemble forecasts have skill in detecting upcoming AR events, which can be particularly useful to better predict potential hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. The probabilistic evaluation, using receiver operating characteristic (ROC) analysis, shows that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this reverses (∼10 d). Furthermore, we show that this reversal occurs for shorter lead times in areas where the AR contribution is more relevant for winter precipitation totals (e.g., northwestern Iberia).

scholarly journals A Comparative Verification of Raw and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia)

2019 ◽  
Vol 58 (8) ◽  
pp. 1709-1723 ◽  
Author(s):  
Dian Nur Ratri ◽  
Kirien Whan ◽  
Maurice Schmeits
Keyword(s):  
Lead Time ◽  
Economic Value ◽  
Daily Rainfall ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Good Reliability ◽  
Loss Analysis ◽  
Skill Scores ◽  
Accumulated Rainfall

AbstractDynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months; if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.

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