Forecasts Of Extreme Precipitation Research Articles

Effects of Sea Spray on Extreme Precipitation Forecasting: A Case Study in Beijing of China

AbstractThis study investigates the effects of sea spray on extreme precipitation forecast in Beijing of China between 28 July and 2 August 2023 as a case test. In this case, fully coupled model increased upward moisture in the Bohai and Yellow Seas and increased accumulated rainfall by 21% in North China. For the extreme precipitation events with the 5‐day accumulated precipitation exceeding 500 mm, the atmosphere‐only model did not forecast the events; the coupled model without sea spray performed well with the 0.29 threat score (TS) and 88 mm root mean square error (RMSE); in the fully coupled model, the effects of sea spray increased atmospheric instability, which increased the precipitation around Beijing and yielded a more accurate forecast with the 0.37 TS and 65 mm RMSE. This paper suggests a scientific clue to improve numerical simulation for extreme rainfall events, however, more cases are still needed for statistical evaluation.

Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model

In the realm of weather forecasting, the implementation of Artificial Intelligence (AI) represents a transformative approach. However, AI weather forecasting method still faces challenges in accurately predicting meso- and smaller-scale processes and failing to directly capture extreme precipitation due to regression algorithm’s nature, coarse resolution, and limitations in key variables like precipitation. Therefore, we propose a state-of-the-art technology which integrates the strengths of the Pangu-weather AI weather forecasting with the traditional regional weather model, focusing specifically on enhancing the prediction of extreme precipitation events, as mainly exemplified by an unprecedented precipitation in North China from 29 July to 1 August 2023, and an additional extraordinary precipitation event as a supplementary validation to further ensure the accuracy of this technology. The results show that the AI-driven approach exhibits superior performance in capturing the spatial and temporal dynamics of extreme precipitation events. Remarkably, with a threshold of 400 mm, the AI-driven model secures a Threat Score (TS) of 0.1 for forecast lead time reaching up to 8.5 d. This performance notably surpasses the performance of traditional GFS-Driven models, which achieve a similar TS only within a limited 3-day forecast lead time. This considerable enhancement in forecast accuracy, especially over extended lead times illustrates the AI-driven model’s potential to advance in long-term forecasts of extreme precipitation, previously considered challenging, emphasizing the potential of AI in augmenting and refining traditional weather prediction.

Comparison of probabilistic forecasts of extreme precipitation for a global and convection‐permitting ensemble and hybrid statistical–dynamical method based on equatorial wave information

AbstractRecent work has demonstrated that skilful hybrid statistical–dynamical forecasts of heavy rainfall events in Southeast Asia can be made by combining model forecasts of the phases and amplitudes of Kelvin, Rossby, and westward‐moving Rossby gravity waves with climatological rainfall statistics conditioned on these waves. This study explores the sensitivity of this hybrid forecast to its parameter choices and compares its skill in forecasting extreme rainfall events in the Philippines, Malaysia, Indonesia, and Vietnam to that of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The hybrid forecast is found to outperform both the global and convection‐permitting ensemble in some regions when forecasting the most extreme events; however, for less extreme events, the ensemble is found more skilful. A weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill of all for almost all definitions of extreme event and in most regions. To quantify the influence of errors in the predicted wave state on the skill of the hybrid forecast, the skill of a hypothetical best‐case forecast was also calculated using reanalysis data to specify the wave amplitudes and phases. This best‐case forecast indicates that errors in the forecasts of all wave types reduce the skill of hybrid forecast; however, the reduction in skill is largest for Kelvin waves. The skill in convection‐permitting models is greater than for global models in the regions where Kelvin waves dominate, but the added value of limited‐area high‐resolution forecasts is hampered by the poor representation of Kelvin waves in the parent global model.

Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results

Weather forecast services in urban areas face an increasingly hard task of alerting the population on extreme weather events. The hardness of the problem is due to the dynamics of the phenomenon, which challenges numerical weather prediction models and opens an opportunity for Machine Learning (ML) based models that may learn complex mappings between input-output from data. In this paper, we present an ongoing research project which aims at building ML predictive models for extreme precipitation forecast in urban areas, in particular in the Rio de Janeiro City. We present the techniques that we have been developing to improve rainfall prediction and extreme rainfall forecast, along with some initial experimental results. Finally, we discuss some challenges that remain to be tackled in this project.

The Extreme Precipitation Forecast Table: improving situational awareness when heavy rain is a threat

A collaborative team of Science and Operations Officers from the National Weather Service (NWS) Weather Forecast Offices (WFOs), hydrologists from the Lower Mississippi River Forecast Center (LMRFC), and management from the Weather Prediction Center (WPC) worked together to develop and transition a tool into NWS operations called the Extreme Precipitation Forecast Table (EPFT). The EPFT was designed to help NWS forecasters improve their situational awareness (SA) when heavy rainfall threatens their county warning area. The EPFT compares Quantitative Precipitation Forecasts (QPF) to Average Recurrence Intervals (ARIs) from the NOAA Atlas-14 to alert forecasters to the potential for climatologically significant and extreme rainfall. A counterpart to the EPFT, called the Extreme Precipitation Assessment Table (EPAT), compares observed precipitation (i.e., Quantitative Precipitation Estimates [QPE]) to inform forecasters as to the climatological significance of impactful rain events. This paper presents cases demonstrating the usefulness of the EPFT and EPAT in helping forecasters improve their SA in real-time operational settings when heavy rain was a threat.

Forecasts of extreme precipitation in the western Balkans in May 2014: model skill and sensitivity to the vertical co‐ordinate

ABSTRACTThis study examines the simulation of the catastrophic precipitating event that occurred on 14–16 May 2014 over the western Balkans, using two regional models, the Eta model and the Weather Research and Forecasting Non‐hydrostatic Mesoscale Model (WRF‐NMM). This case was an extreme precipitation event with very large precipitation totals exceeding 200 mm in 72 h. The Eta model was run in two variants, switched to use the eta co‐ordinate with sloping steps and switched to use the sigma co‐ordinate. The aim of this study was to analyse the ability of the regional model to forecast heavy precipitation successfully as well as the impact of the vertical co‐ordinate on the forecasting of extreme precipitation events based on realistic model simulation. Experiments show the ability of regional models to reproduce extreme accumulations for 2 days of heavy precipitation with bias adjusted equitable threat score ranging from 0.3 to 0.5 for precipitation greater than the analysed threshold values (25–55 mm day−1), whereby the Eta model exhibits a slight advantage over the WRF‐NMM and the run of the Eta model switched to the eta co‐ordinate has the smallest frequency bias. For larger observed accumulations exceeding 50 mm day−1 all forecasts tend to be more biased. The root mean square precipitation difference varies insignificantly between the three forecasts. The results of the experiment indicate that, in the case of the extreme precipitation considered in this study, scores of the forecasts of the Eta model for 10 m wind speed and 2 m temperature show a small dependence on the vertical co‐ordinate and in favour of the run with the eta co‐ordinate.

Development of Verification Methodology for Extreme Weather Forecasts

AbstractIn 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.

Application of recurrent neural networks for drought projections in California

We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015–2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998–1999 El Niño event. RNN training considered PZI data during 1896–2006 that was validated against the 2006–2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1month. This gives strong confidence to the forecasted precipitation indicator. The 2015–2016 El Niño season proved to be relatively weak as compared with the 1997–1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions.

Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece

Extreme daily precipitation events are involved in significant environmental damages, even in life loss, because of causing adverse impacts, such as flash floods, in urban and sometimes in rural areas. Thus, long-term forecast of such events is of great importance for the preparation of local authorities in order to confront and mitigate the adverse consequences. The objective of this study is to estimate the possibility of forecasting the maximum daily precipitation for the next coming year. For this reason, appropriate prognostic models, such as Artificial Neural Networks (ANNs) were developed and applied. The data used for the analysis concern annual maximum daily precipitation totals, which have been recorded at the National Observatory of Athens (NOA), during the long term period 1891–2009. To evaluate the potential of daily extreme precipitation forecast by the applied ANNs, a different period for validation was considered than the one used for the ANNs training. Thus, the datasets of the period 1891–1980 were used as training datasets, while the datasets of the period 1981–2009 as validation datasets. Appropriate statistical indices, such as the coefficient of determination (R2), the index of agreement (IA), the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE), were applied to test the reliability of the models. The findings of the analysis showed that, a quite satisfactory relationship (R2=0.482, IA=0.817, RMSE=16.4mm and MBE=+5.2mm) appears between the forecasted and the respective observed maximum daily precipitation totals one year ahead. The developed ANN seems to overestimate the maximum daily precipitation totals appeared in 1988 while underestimate the maximum in 1999, which could be attributed to the relatively low frequency of occurrence of these extreme events within GAA having impact on the optimum training of ANN.

Observed Impacts of Duration and Seasonality of Atmospheric-River Landfalls on Soil Moisture and Runoff in Coastal Northern California

AbstractThis study is motivated by diverse needs for better forecasts of extreme precipitation and floods. It is enabled by unique hourly observations collected over six years near California’s Russian River and by recent advances in the science of atmospheric rivers (ARs). This study fills key gaps limiting the prediction of ARs and, especially, their impacts by quantifying the duration of AR conditions and the role of duration in modulating hydrometeorological impacts. Precursor soil moisture conditions and their relationship to streamflow are also shown. On the basis of 91 well-observed events during 2004–10, the study shows that the passage of ARs over a coastal site lasted 20 h on average and that 12% of the AR events exceeded 30 h. Differences in storm-total water vapor transport directed up the mountain slope contribute 74% of the variance in storm-total rainfall across the events and 61% of the variance in storm-total runoff volume. ARs with double the composite mean duration produced nearly 6 times greater peak streamflow and more than 7 times the storm-total runoff volume. When precursor soil moisture was less than 20%, even heavy rainfall did not lead to significant streamflow. Predicting which AR events are likely to produce extreme impacts on precipitation and runoff requires accurate prediction of AR duration at landfall and observations of precursor soil moisture conditions.

Study on ensemble-based forecast of extremely heavy rainfalls in China: Experiments for July 2011 cases

According to the Anderson-Darling principle, a method for forecast of extremely heavy rainfall (abbreviated as extreme rainfall/precipitation) was developed based on the ensemble forecast data of the T213 global ensemble prediction system (EPS) of the China Meteorological Administration (CMA). Using the T213 forecast precipitation data during 2007–2010 and the observed rainfall data in June–August of 2001–2010, characteristics of the cumulative distribution functions (CDFs) of the observed and the T213 EPS forecast precipitation were analyzed. Accordingly, in the light of the continuous differences of the CDFs between model climate and EPS forecasts, a mathematical model of Extreme Precipitation Forecast Index (EPFI) was established and applied to forecast experiments of several extreme rainfall events in China during 17–31 July 2011. The results show that the EPFI has taken advantage of the tail information of the model climatic CDF and provided agreeable forecasts of extreme rainfalls. The EPFI based on the T213 EPS is useful for issuing early warnings of extreme rainfalls 3–7 days in advance. With extension of the forecast lead time, the EPFI becomes less skillful. The results also demonstrate that the rationality of the model climate CDF was of vital importance to the skill of EPFI.

The Madden–Julian Oscillation and the Relative Value of Deterministic Forecasts of Extreme Precipitation in the Contiguous United States

Abstract The Madden–Julian oscillation (MJO) is the most prominent form of tropical intraseasonal variability that impacts weather and climate. Forecast skill of extreme precipitation in the contiguous United States (CONUS) during winter is higher when the MJO is active and has enhanced convection over the Western Hemisphere, Africa, and/or the western Indian Ocean. This study applies a simple decision model to examine the relationships between the MJO and the relative value of deterministic forecasts of extreme precipitation. Value in the forecasts is significantly higher and extends to longer leads (2 weeks) during active MJO.

The Madden–Julian Oscillation and the Relative Value of Deterministic Forecasts of Extreme Precipitation in the Contiguous United States

The Madden–Julian oscillation (MJO) is the most prominent form of tropical intraseasonal variability that impacts weather and climate. Forecast skill of extreme precipitation in the contiguous United States (CONUS) during winter is higher when the MJO is active and has enhanced convection over the Western Hemisphere, Africa, and/or the western Indian Ocean. This study applies a simple decision model to examine the relationships between the MJO and the relative value of deterministic forecasts of extreme precipitation. Value in the forecasts is significantly higher and extends to longer leads (2 weeks) during active MJO.

The Madden-Julian Oscillation and the relative value of deterministic forecasts of extreme precipitation in the contiguous United States

The Madden-Julian Oscillation and the relative value of deterministic forecasts of extreme precipitation in the contiguous United States

The Madden-Julian Oscillation and the relative value of deterministic forecasts of extreme precipitation in the contiguous United States

The Madden-Julian Oscillation and the relative value of deterministic forecasts of extreme precipitation in the contiguous United States

Influence of the Madden–Julian Oscillation on Forecasts of Extreme Precipitation in the Contiguous United States

Abstract Extreme precipitation events are among the most devastating weather phenomena since they are frequently accompanied by loss of life and property. This study uses reforecasts of the NCEP Climate Forecast System (CFS.v1) to evaluate the skill of nonprobabilistic and probabilistic forecasts of extreme precipitation in the contiguous United States (CONUS) during boreal winter for lead times up to two weeks. The CFS model realistically simulates the spatial patterns of extreme precipitation events over the CONUS, although the magnitudes of the extremes in the model are much larger than in the observations. Heidke skill scores (HSS) for forecasts of extreme precipitation at the 75th and 90th percentiles showed that the CFS model has good skill at week 1 and modest skill at week 2. Forecast skill is usually higher when the Madden–Julian oscillation (MJO) is active and has enhanced convection occurring over the Western Hemisphere, Africa, and/or the western Indian Ocean than in quiescent periods. HSS greater than 0.1 extends to lead times of up to two weeks in these situations. Approximately 10%–30% of the CONUS has HSS greater than 0.1 at lead times of 1–14 days when the MJO is active. Probabilistic forecasts for extreme precipitation events at the 75th percentile show improvements over climatology of 0%–40% at 1-day lead and 0%–5% at 7-day leads. The CFS has better skill in forecasting severe extremes (i.e., events exceeding the 90th percentile) at longer leads than moderate extremes (75th percentile). Improvements over climatology between 10% and 30% at leads of 3 days are observed over several areas across the CONUS—especially in California and in the Midwest.