scholarly journals Ensemble-Based Forecast Uncertainty Analysis of Diverse Heavy Rainfall Events

2010 ◽  
Vol 25 (4) ◽  
pp. 1103-1122 ◽  
Author(s):  
Russ S. Schumacher ◽  
Christopher A. Davis
Keyword(s):  
United States ◽  
Tropical Cyclones ◽  
Heavy Rainfall ◽  
Warm Season ◽  
The United States ◽  
Forecast Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Cool Season ◽  
Ensemble Prediction System

Abstract This study examines widespread heavy rainfall over 5-day periods in the central and eastern United States. First, a climatology is presented that identifies events in which more than 100 mm of precipitation fell over more than 800 000 km2 in 5 days. This climatology shows that such events are most common in the cool season near the Gulf of Mexico coast and are rare in the warm season. Then, the focus turns to the years 2007 and 2008, when nine such events occurred in the United States, all of them leading to flooding. Three of these were associated with warm-season convection, three took place in the cool season, and three were caused by landfalling tropical cyclones. Global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System are used to assess forecast skill and uncertainty for these nine events, and to identify the types of weather systems associated with their relative levels of skill and uncertainty. Objective verification metrics and subjective examination are used to determine how far in advance the ensemble identified the threat of widespread heavy rains. Specific conclusions depend on the rainfall threshold and the metric chosen, but, in general, predictive skill was highest for rainfall associated with tropical cyclones and lowest for the warm-season cases. In almost all cases, the ensemble provides very skillful 5-day forecasts when initialized at the beginning of the event. In some of the events—particularly the tropical cyclones and strong baroclinic cyclones—the ensemble still shows considerable skill in 96–216-h precipitation forecasts. In other cases, however, the skill drops off much more rapidly as lead time increases. In particular, forecast skill at long lead times was the lowest and spread was the largest in the two cases associated with meso-α-scale to synoptic-scale vortices that were cut off from the primary upper-level jet. In these cases, it appears that when the vortex is present in the initial conditions, the resulting precipitation forecasts are quite accurate and certain, but at longer lead times when the model is required to both develop and correctly evolve the vortex, forecast quality is low and uncertainty is large. These results motivate further investigation of the events that were poorly predicted.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

scholarly journals Verification of TIGGE Multimodel and ECMWF Reforecast-Calibrated Probabilistic Precipitation Forecasts over the Contiguous United States*

2012 ◽  
Vol 140 (7) ◽  
pp. 2232-2252 ◽  
Author(s):  
Thomas M. Hamill
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Analysis Data ◽  
Radar Data ◽  
The United States ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Time Sharing ◽  
Ensemble Prediction System ◽  
Precipitation Events

Abstract Probabilistic quantitative precipitation forecasts (PQPFs) were generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database from July to October 2010 using data from Europe (ECMWF), the United Kingdom [Met Office (UKMO)], the United States (NCEP), and Canada [Canadian Meteorological Centre (CMC)]. Forecasts of 24-h accumulated precipitation were evaluated at 1° grid spacing within the contiguous United States against analysis data based on gauges and bias-corrected radar data. PQPFs from ECMWF’s ensembles generally had the highest skill of the raw ensemble forecasts, followed by CMC. Those of UKMO and NCEP were less skillful. PQPFs from CMC forecasts were the most reliable but the least sharp, and PQPFs from NCEP and UKMO ensembles were the least reliable but sharper. Multimodel PQPFs were more reliable and skillful than individual ensemble prediction system forecasts. The improvement was larger for heavier precipitation events [e.g., >10 mm (24 h)−1] than for smaller events [e.g., >1 mm (24 h)−1]. ECMWF ensembles were statistically postprocessed using extended logistic regression and the five-member weekly reforecasts for the June–November period of 2002–09, the period where precipitation analyses were also available. Multimodel ensembles were also postprocessed using logistic regression and the last 30 days of prior forecasts and analyses. The reforecast-calibrated ECMWF PQPFs were much more skillful and reliable for the heavier precipitation events than ECMWF raw forecasts but much less sharp. Raw multimodel PQPFs were generally more skillful than reforecast-calibrated ECMWF PQPFs for the light precipitation events but had about the same skill for the higher-precipitation events; also, they were sharper but somewhat less reliable than ECMWF reforecast-based PQPFs. Postprocessed multimodel PQPFs did not provide as much improvement to the raw multimodel PQPF as the reforecast-based processing did to the ECMWF forecast. The evidence presented here suggests that all operational centers, even ECMWF, would benefit from the open, real-time sharing of precipitation forecast data and the use of reforecasts.

scholarly journals Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

2017 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Seann Reed ◽  
Peter Ahnert ◽  
Pablo Mendoza ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Warm Season ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Near Surface

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised by the following components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); iii) NOAA’s Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the U.S. middle Atlantic region, ranging in size from 381 to 12,362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble flood forecasts but QR outperforms ARX(1,1). Preprocessing alone has little effect on improving the skill of the ensemble flood forecasts. Indeed, postprocessing alone performs similar, in terms of the relative mean error, skill, and reliability, to the more involved scenario that includes both preprocessing and postprocessing. We conclude that statistical preprocessing may not always be a necessary component of the ensemble flood forecasting chain.

scholarly journals Composites of Heavy Rain Producing Elevated Thunderstorms in the Central United States

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Laurel P. McCoy ◽  
Patrick S. Market ◽  
Chad M. Gravelle ◽  
Charles E. Graves ◽  
Neil I. Fox ◽  
...  
Keyword(s):  
United States ◽  
Heavy Rainfall ◽  
Warm Season ◽  
Heavy Rain ◽  
The United States ◽  
Large Variability ◽  
Null Case ◽  
Central United States ◽  
Mean Fields ◽  
Event Occurrence

Composite analyses of the atmosphere over the central United States during elevated thunderstorms producing heavy rainfall are presented. Composites were created for five National Weather Service County Warning Areas (CWAs) in the region. Events studied occurred during the warm season (April–September) during 1979–2012. These CWAs encompass the region determined previously to experience the greatest frequency of elevated thunderstorms in the United States. Composited events produced rainfall of >50 mm 24 hr−1 within the selected CWA. Composites were generated for the 0–3 hr period prior to the heaviest rainfall, 6–9 hours prior to it, and 12–15 hours prior to it. This paper focuses on the Pleasant Hill, Missouri (EAX) composites, as all CWA results were similar; also these analyses focus on the period 0–3 hours prior to event occurrence. These findings corroborate the findings of previous authors. What is offered here that is unique is (1) a measure of the interquartile range within the composite mean fields, allowing for discrimination between variable fields that provided a strong reliable signal, from those that may appear strong but possess large variability, and (2) composite soundings of two subclasses of elevated thunderstorms. Also, a null case (one that fits the composite but failed to produce significant rainfall) is also examined for comparison.

scholarly journals A Diagnostic Verification of the Precipitation Forecasts Produced by the Canadian Ensemble Prediction System

2008 ◽  
Vol 23 (4) ◽  
pp. 596-616 ◽  
Author(s):  
Syd Peel ◽  
Laurence J. Wilson
Keyword(s):  
Warm Season ◽  
Ensemble Member ◽  
Ensemble Prediction ◽  
Cool Season ◽  
Forecast System ◽  
Ensemble Prediction System ◽  
Receiver Operating Characteristic Curves ◽  
Skill Scores ◽  
Station Location ◽  
Observational Record

Abstract A comparatively long period of relative stability in the evolution of the Canadian Ensemble Forecast System was exploited to compile a large homogeneous set of precipitation forecasts. The probability of exceedance of a given threshold was computed as the fraction of ensemble member forecasts surpassing that threshold, and verified directly against observations from 36 stations across the country. These forecasts were stratified into warm and cool seasons and assessed against the observations through attributes diagrams, Brier skill scores, and areas under receiver operating characteristic curves. These measures were deemed sufficient to illuminate the salient features of a forecast system. Particular attention was paid to forecasts of 24-h accumulation, especially the exceedance of thresholds in the upper decile of station climates. The ability of the system to forecast extended dry periods was also explored. Warm season forecasts for the 90th percentile threshold were found to be competitive with, even superior to, those for the cool season when verifying across the sample lumping together all of the stations. The relative skill of the forecasts in the two seasons depends strongly on station location, however. Moreover, the skill of the warm season forecasts rapidly drops below cool season values as the thresholds become more extreme. The verification, particularly of the cool season, is sensitive to the calibration of the gauge reports, which is complicated by the inclusion of snow events in the observational record.

scholarly journals Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

2018 ◽  
Vol 22 (3) ◽  
pp. 1831-1849 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Seann Reed ◽  
Peter Ahnert ◽  
Pablo Mendoza ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Warm Season ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Streamflow Forecasts

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.

Convective Storms and the Insurance Industry: Challenges and Opportunities

2020 ◽  
Author(s):  
Kirsten D. Orwig
Keyword(s):  
United States ◽  
Tropical Cyclones ◽  
Insurance Industry ◽  
Property Values ◽  
Construction Materials ◽  
The United States ◽  
Building Code ◽  
Convective Storms ◽  
Challenges And Opportunities ◽  
Insured Loss

Convective storms affect countries worldwide, with billions in losses and dozens of fatalities every year. They are now the key insured loss driver in the United States, even after considering the losses sustained by tropical cyclones in 2017. Since 2008, total insured losses from convective storms have exceeded $10 billion per year. Additionally, these losses continue to increase year over year. Key loss drivers include increased population, buildings, vehicles, and property values. However, other loss drivers relate to construction materials and practices, as well as building code adoption and enforcement. The increasing loss trends pose a number of challenges for the insurance industry and broader society. These challenges are discussed, and some recommendations are presented.

Sign in / Sign up

Export Citation Format

Share Document