Sub-seasonal forecast capability for Arabian Peninsula convective extremes using convective-permitting regional climate modeling

2021 ◽  
Author(s):  
Hsin-I Chang ◽  
Christopher L Castro ◽  
Christoforus Bayu Risanto ◽  
Thang Luong ◽  
Ibrahim Hoteit
Keyword(s):  
Extreme Events ◽  
Arabian Peninsula ◽  
Regional Climate ◽  
Seasonal Forecast ◽  
Ensemble Forecast ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Operating Characteristics ◽  
Synoptic Patterns ◽  
Extreme Precipitation Events

<p>Severe weather associated with convective thunderstorms is becoming more intense globally and is also observed in the Arabian Peninsula (AP). AP convective extremes are often observed during winter season (October to March). Improvements in extreme weather forecast for sub-seasonal to seasonal forecast increase the preparedness of convective extremes and related hazards. We designed a series of ensemble forecast downscaling using the Weather Research and Forecasting model (WRF) at convective-permitting spatial scale. The driving global sub-seasonal to seasonal reforecast is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).</p><p>Sub-seasonal WRF simulations are performed on the AP’s top 20 extreme precipitation events reported in the last 20 years, downscaling from the 11 ECMWF hindcast ensemble members. Each of the events recorded at least 20 mm/day rainfall in the Jeddah station. Several aspects of the simulated events are evaluated: (1) Precipitation forecast capability: determine forecast window of opportunity in the regional climate model at 1-week, 2-week and 3-week lead time, identify the value added using convective-permitting type modeling; (2) Teleconnection pattern forecast capability: determine forecast skill for the dominant large scale pattern related to the convective extremes in the driving ECMWF reforecasts and ERA-Interim reanalysis data; (3) Dominant synoptic patterns associated with the AP’s top 20 extreme events: identify forecast capability for different synoptic-driven extreme events. Historic data analysis identified 3 general synoptic patterns that lead to precipitation extreme. The top 20 extreme events are parsed into the 3 synoptic groups. Sub-seasonal forecast evaluations are then performed with statistical analysis tools commonly used in operational forecast evaluation, such as Probability of Detection (POD), False Alarm Rate (FAR) and Relative Operating Characteristics (ROC); (4) Mesoscale convective system (MCS) tracking: objectively tracking the MCS clouds in satellite observation and WRF downscaled reforecasts using cloud top temperature and precipitation. Through the designed analyses, we can collectively show the ensemble forecast skills for the largest convective events in the AP and advancement in forecast capability at sub-seasonal time scale.</p>

scholarly journals Development of Verification Methodology for Extreme Weather Forecasts

2017 ◽  
Vol 32 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Hong Guan ◽  
Yuejian Zhu
Keyword(s):  
Extreme Precipitation ◽  
Weather Forecast ◽  
Extreme Weather ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Forecast System ◽  
Extreme Precipitation Events ◽  
Cold Events ◽  
Extreme Cold ◽  
Verification Methodology

Abstract In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme cold temperature and extreme precipitation forecasts during the winter of 2013/14. A highly correlated relationship between the ANF and EFI allows the determination of two sets of thresholds to identify extreme cold and extreme precipitation events for the two algorithms. An EFI of −0.78 (0.687) is approximately equivalent to a −2σ (0.95) ANF for the extreme cold event (extreme precipitation) forecast. The performances of the two algorithms in forecasting extreme cold events are verified against analysis for different model versions, reference climatology, and forecasts. The verification results during the winter of 2013/14 indicate that ANF forecasts more extreme cold events with a slightly higher skill than EFI. The bias-corrected forecast performs much better than the raw forecast. The current upgrade of the GEFS has a beneficial effect on the extreme cold weather forecast. Using the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) as a climate reference gives a slightly better score than the 40-yr reanalysis. The verification methodology is also extended to an extreme precipitation case, showing a broad potential use in the future.

Simulating extreme precipitation over the Arabian Peninsula using a convective-permitting sub-seasonal reforecast product

2020 ◽  
Author(s):  
Thang M. Luong ◽  
Christoforus Bayu Risanto ◽  
Hsin-I Chang ◽  
Hari Prasad Dasari ◽  
Raju Attada ◽  
...  
Keyword(s):  
Extreme Events ◽  
Extreme Precipitation ◽  
Arabian Peninsula ◽  
Flash Flood ◽  
Value Added ◽  
Rain Gauge ◽  
Potential Predictability ◽  
Extreme Precipitation Events ◽  
Simulated Precipitation ◽  
Precipitation Events

<p>Despite being one of the driest places in the world, the Arabian Peninsula (AP) occasionally experiences extreme precipitation events associated with organized convections. On 25 November 2009, for instance, a cutoff low driven rainfall exceeding 140 mm over a 6-hour period triggered a flash flood event in Jeddah, Saudi Arabia, claiming hundreds of lives and substantially damaging infrastructure. Similar extreme precipitation events have occurred in subsequent years. To assess the potential predictability of extreme precipitation in the Arabian Peninsula, we perform retrospective forecast simulations for several extreme events occurring over the period 2000 to 2018, out to a sub-seasonal timescale (3-4 weeks). Using the Advanced Research version of Weather Research and Forecasting Model (WRF-ARW), we dynamically downscale 11 ensemble members of the European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal reforecasts at convective-permitting resolution (4 km). WRF simulated precipitation is evaluated against various precipitation products, including the Global Precipitation Measurement (GPM) system, Climate Prediction Center morphing technique (CMORPH), and the Saudi Ministry of Water and Electricity(MOWE) and the Presidency of Meteorology and Environment(PME) regional rain gauge measurements. The convective-permitting WRF simulations substantially improve the representation of precipitation relative to the ECMWF reforecast, in terms of spatial distribution and timing. A specific focus in the presentation of the results will be on the potential value added by the use of convective-permitting modeling (CPM) to forecasting extreme events at sub-seasonal timescales. The predictability of the synoptic pattern could be the key for CPM sub-seasonal-type forecast for the AP.</p>

Extreme Quantitative Precipitation Forecast Performance at the Weather Prediction Center from 2001 to 2011

2014 ◽  
Vol 29 (4) ◽  
pp. 894-911 ◽  
Author(s):  
Ellen M. Sukovich ◽  
F. Martin Ralph ◽  
Faye E. Barthold ◽  
David W. Reynolds ◽  
David R. Novak
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Forecast Performance ◽  
Extreme Precipitation Events ◽  
Quantitative Precipitation Forecast

Abstract Extreme quantitative precipitation forecast (QPF) performance is baselined and analyzed by NOAA’s Hydrometeorology Testbed (HMT) using 11 yr of 32-km gridded QPFs from NCEP’s Weather Prediction Center (WPC). The analysis uses regional extreme precipitation thresholds, quantitatively defined as the 99th and 99.9th percentile precipitation values of all wet-site days from 2001 to 2011 for each River Forecast Center (RFC) region, to evaluate QPF performance at multiple lead times. Five verification metrics are used: probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias, and conditional mean absolute error (MAEcond). Results indicate that extreme QPFs have incrementally improved in forecast accuracy over the 11-yr period. Seasonal extreme QPFs show the highest skill during winter and the lowest skill during summer, although an increase in QPF skill is observed during September, most likely due to landfalling tropical systems. Seasonal extreme QPF skill decreases with increased lead time. Extreme QPF skill is higher over the western and northeastern RFCs and is lower over the central and southeastern RFC regions, likely due to the preponderance of convective events in the central and southeastern regions. This study extends the NOAA HMT study of regional extreme QPF performance in the western United States to include the contiguous United States and applies the regional assessment recommended therein. The method and framework applied here are readily applied to any gridded QPF dataset to define and verify extreme precipitation events.

scholarly journals Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

2018 ◽  
Vol 22 (1) ◽  
pp. 673-687 ◽  
Author(s):  
Antoine Colmet-Daage ◽  
Emilia Sanchez-Gomez ◽  
Sophie Ricci ◽  
Cécile Llovel ◽  
Valérie Borrell Estupina ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Historical Period ◽  
Mountainous Regions ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract. The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

The effect of climate change on urban drainage: an evaluation based on regional climate model simulations

2006 ◽  
Vol 54 (6-7) ◽  
pp. 9-15 ◽  
Author(s):  
M. Grum ◽  
A.T. Jørgensen ◽  
R.M. Johansen ◽  
J.J. Linde
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Urban Drainage ◽  
Extreme Precipitation Events ◽  
Rainfall Extremes ◽  
The Future ◽  
Precipitation Events

That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 × 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at time-scales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.

scholarly journals An Improved Weighting Method of Time-Lag-Ensemble Averaging for Hourly Precipitation Forecasts and Its Application in a Typhoon-Induced Heavy Rainfall Event

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 875
Author(s):  
Li Zhou ◽  
Lin Xu ◽  
Mingcai Lan ◽  
Jingjing Chen
Keyword(s):  
Heavy Rainfall ◽  
Time Lag ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ensemble Averaging ◽  
Weighting Method ◽  
Prediction Ability ◽  
Rainfall Events ◽  
Hourly Precipitation ◽  
Heavy Rainfall Events

Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall.

scholarly journals Compound extremes in a changing climate – a Markov chain approach

2016 ◽  
Vol 23 (6) ◽  
pp. 375-390 ◽  
Author(s):  
Katrin Sedlmeier ◽  
Sebastian Mieruch ◽  
Gerd Schädler ◽  
Christoph Kottmeier
Keyword(s):  
Extreme Events ◽  
Climate Models ◽  
Heat Waves ◽  
Dynamic Change ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Northern Germany ◽  
Russian Region ◽  
Climate Simulations ◽  
Potential Impact

Abstract. Studies using climate models and observed trends indicate that extreme weather has changed and may continue to change in the future. The potential impact of extreme events such as heat waves or droughts depends not only on their number of occurrences but also on "how these extremes occur", i.e., the interplay and succession of the events. These quantities are quite unexplored, for past changes as well as for future changes and call for sophisticated methods of analysis. To address this issue, we use Markov chains for the analysis of the dynamics and succession of multivariate or compound extreme events. We apply the method to observational data (1951–2010) and an ensemble of regional climate simulations for central Europe (1971–2000, 2021–2050) for two types of compound extremes, heavy precipitation and cold in winter and hot and dry days in summer. We identify three regions in Europe, which turned out to be likely susceptible to a future change in the succession of heavy precipitation and cold in winter, including a region in southwestern France, northern Germany and in Russia around Moscow. A change in the succession of hot and dry days in summer can be expected for regions in Spain and Bulgaria. The susceptibility to a dynamic change of hot and dry extremes in the Russian region will probably decrease.

scholarly journals Ensemble Projections of Regional Climatic Changes over Ontario, Canada

2015 ◽  
Vol 28 (18) ◽  
pp. 7327-7346 ◽  
Author(s):  
Xiuquan Wang ◽  
Guohe Huang ◽  
Jinliang Liu ◽  
Zhong Li ◽  
Shan Zhao
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Regional Climate ◽  
Climatic Changes ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Local Climate ◽  
Ensemble Simulations ◽  
Extreme Precipitation Events ◽  
Data Portal

Abstract In this study, high-resolution climate projections over Ontario, Canada, are developed through an ensemble modeling approach to provide reliable and ready-to-use climate scenarios for assessing plausible effects of future climatic changes at local scales. The Providing Regional Climates for Impacts Studies (PRECIS) regional modeling system is adopted to conduct ensemble simulations in a continuous run from 1950 to 2099, driven by the boundary conditions from a HadCM3-based perturbed physics ensemble. Simulations of temperature and precipitation for the baseline period are first compared to the observed values to validate the performance of the ensemble in capturing the current climatology over Ontario. Future projections for the 2030s, 2050s, and 2080s are then analyzed to help understand plausible changes in its local climate in response to global warming. The analysis indicates that there is likely to be an obvious warming trend with time over the entire province. The increase in average temperature is likely to be varying within [2.6, 2.7]°C in the 2030s, [4.0, 4.7]°C in the 2050s, and [5.9, 7.4]°C in the 2080s. Likewise, the annual total precipitation is projected to increase by [4.5, 7.1]% in the 2030s, [4.6, 10.2]% in the 2050s, and [3.2, 17.5]% in the 2080s. Furthermore, projections of rainfall intensity–duration–frequency (IDF) curves are developed to help understand the effects of global warming on extreme precipitation events. The results suggest that there is likely to be an overall increase in the intensity of rainfall storms. Finally, a data portal named Ontario Climate Change Data Portal (CCDP) is developed to ensure decision-makers and impact researchers have easy and intuitive access to the refined regional climate change scenarios.

scholarly journals Probabilistic Precipitation Forecast Postprocessing Using Quantile Mapping and Rank-Weighted Best-Member Dressing

2018 ◽  
Vol 146 (12) ◽  
pp. 4079-4098 ◽  
Author(s):  
Thomas M. Hamill ◽  
Michael Scheuerer
Keyword(s):  
Probability Distributions ◽  
The United States ◽  
Ensemble Forecast ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Precipitation Forecast ◽  
Prediction System ◽  
Quantile Mapping ◽  
State Dependent ◽  
Grid Points

Abstract Hamill et al. described a multimodel ensemble precipitation postprocessing algorithm that is used operationally by the U.S. National Weather Service (NWS). This article describes further changes that produce improved, reliable, and skillful probabilistic quantitative precipitation forecasts (PQPFs) for single or multimodel prediction systems. For multimodel systems, final probabilities are produced through the linear combination of PQPFs from the constituent models. The new methodology is applied to each prediction system. Prior to adjustment of the forecasts, parametric cumulative distribution functions (CDFs) of model and analyzed climatologies are generated using the previous 60 days’ forecasts and analyses and supplemental locations. The CDFs, which can be stored with minimal disk space, are then used for quantile mapping to correct state-dependent bias for each member. In this stage, the ensemble is also enlarged using a stencil of forecast values from the 5 × 5 surrounding grid points. Different weights and dressing distributions are assigned to the sorted, quantile-mapped members, with generally larger weights for outlying members and broader dressing distributions for members with heavier precipitation. Probability distributions are generated from the weighted sum of the dressing distributions. The NWS Global Ensemble Forecast System (GEFS), the Canadian Meteorological Centre (CMC) global ensemble, and the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast data are postprocessed for April–June 2016. Single prediction system postprocessed forecasts are generally reliable and skillful. Multimodel PQPFs are roughly as skillful as the ECMWF system alone. Postprocessed guidance was generally more skillful than guidance using the Gamma distribution approach of Scheuerer and Hamill, with coefficients generated from data pooled across the United States.

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