Heavy Precipitation Forecasts Research Articles

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

AbstractAccurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times.

Spatio-Temporal Characteristics of Heavy Precipitation Forecasts from ECMWF in Eastern China

Spatio-Temporal Characteristics of Heavy Precipitation Forecasts from ECMWF in Eastern China

On the predictability of a heavy rainfall event with dual rainbands

An extremely heavy rainfall with dual rainbands occurred in the Fujian province on 22 May 2014, causing severe disasters in Fujian. In order to investigate the key forecasting factors and the predictability of this case, the evaluation and sensitivity analysis of precipitation forecasts by the ensemble prediction system which is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) global Model are carried out. The result show that the ECMWF-EPS have better ability to capture the intensity and spatial distribution of the northern rainband, but significantly underestimated the precipitation in the warm area. Through ensemble forecast sensitivity analysis and comparison between good and poor members, the main factors causing forecast deviations in the two rainbands and affecting the predictability of heavy rainfall were revealed. The forecast of rainfall distribution and intensity in the northern rainband was highly sensitive to the predictability of the weather-scale shear line. The westward bias in the forecasted position and the weakened intensity of the shear line were the main causes of the westward and weaker forecast of heavy precipitation in the northern region. Additionally, the forecast of 850 hPa low-level jets, especially the forecasted intensity of zonal winds, which were closely related to the shear line, significantly influenced the intensity forecast of precipitation in the northern region. The forecast of precipitation in the southern warm area, which was far from the shear line, was more sensitive to the wind speed and thermodynamic conditions of the southwestern airflow in the boundary layer of the upstream inflow region. Most ensemble forecast members underestimated the intensity of the southwestern airflow in the coastal boundary layer of South China, which was the main reason for the near omission of heavy rainfall in the southern warm area. This study provides a quantitative correlation between low-level southwest jets along the coast of Guangdong and Fujian and non-typhoon heavy rainfall in Fujian, and explores their impact on heavy precipitation forecasts.

A study on loss function against data imbalance in deep learning correction of precipitation forecasts

Sample imbalance has always been an important issue to be solved in heavy precipitation forecasts. Introducing appropriate constraints into the loss function of a machine learning model can mitigate the negative impact of sample imbalance on model training. This study explores the role of Dice loss function in dealing with sample imbalance and verifies its application on the correction of heavy precipitation forecasts using the U-Net neural network. On this basis, the application of ordinal classification in forecasts correction is further verified. The results show that the concept of Dice loss is highly similar to that of threat score, which can suppress the negative impact of sample imbalance on heavy precipitation forecasts by ignoring most of the events that are insensitive to heavy precipitation forecasts. Model correction experiments further indicate that the correction models trained with weighted cross entropy, differentiable threat score loss and Dice loss (WCE, DTS and Dice models) improve the precipitation forecasts of ECMWF (European Centre for Medium-Range Weather Forecasts) model. For the forecasts of all precipitation levels, Dice model effectively suppresses false alarms while improving hits, whereas WCE model greatly promotes false alarms while significantly improving hits. In contrast, DTS model significantly suppresses large precipitation false alarms while improving its hits, but causes substantial small precipitation false alarms while significantly promoting its hits. As a result, for large precipitation forecasts, both Dice and DTS models perform significantly better than WCE model, while for small precipitation forecasts, Dice model performs better than DTS and WCE models. Furthermore, the correction model combining ordinal classification and Dice loss exhibits the best forecasting skill. This model further suppresses the false alarms of large precipitation while improving its hits.

Heavy precipitation forecasts over Switzerland – An evaluation of bias-corrected ECMWF predictions

Heavy precipitation episodes are particularly hazardous in mid-sized alpine catchments where runoff tends to increase rapidly after strong rainfall, leaving limited time for warning. A high-quality, unbiased forecast of heavy precipitation with long enough lead times (2–5 days) and an adequate spatial resolution is thus crucial for decision makers. However, at present, weather forecast models are sometimes still too coarsely resolved for such catchments or limited to short lead times. Here we present a computationally cheap post-processing approach for operational applications to bias-correct deterministic heavy precipitation forecasts at medium-range lead times. We assessed forecast performance of uncorrected ECMWF Integrated Forecasting System (IFS) high-resolution (HRES) and ensemble median run (ENS) forecasts with lead times of 2 and 5 days between 2010 and 2019 and evaluated the improvement after interpolation and bias correction. The study is based on observations from 787 meteorological stations, with a focus on areal precipitation in three medium-sized Swiss catchments (Emme, Simme, and Vispa). We found a significantly higher performance of the HRES forecast compared to ENS, especially for a lead time of 5 days. The post-processing approach that we present in this study removes large biases, and lowers false alarm rates. Nonetheless, improvements may be limited for single heavy precipitation events where the model considerably underestimates daily precipitation. Also, a lower false alarm rate can be at the cost of lower hit rates. Quantile mapping is clearly limited for short term prediction in the cases where the error does not substantially exceed natural variability. Despite these limitations, we conclude that bias-corrected forecasts can help improving forecast credibility, which is a key element for decision makers to take actions.

Perturbing Topography in a Convection‐Allowing Ensemble Prediction System for Heavy Rain Forecasts

AbstractFour methods to perturb model terrain are demonstrated in a convection‐allowing scale (3 km) ensemble prediction system (EPS) for heavy rain forecasts. The impact of methods was examined in terms of ensemble mean, spread‐skill relationship and probabilistic forecast. The study was carried out for four major precipitation events. It is found that: (a) using different combinations of topography smoothing and interpolation is a promising way to perturb model topography. Even perturbing the topography alone can greatly improve heavy rain forecasts. The benefit of ensemble forecasts increases with the increase in rainfall amount. (b) Adding random perturbations to model topography is not ideal and could hurt ensemble performance due to a too noisy structure although it increases ensemble spread. Perturbation structure plays a more important role than perturbation size. (c) Using the terrain difference between a higher and lower resolution model to estimate possible errors in model terrain is explored. By adding such terrain differences to the terrain‐generating‐scheme based perturbation (the control experiment) can not only enlarge perturbation size but also keep a good spatial structure. Such a modified ensemble performed similarly in ensemble mean forecasts but improved the ensemble spread and probabilistic forecast. It is recommended that perturbed model terrain should be included in operational EPSs to improve heavy precipitation forecasts. The simple terrain‐generating‐scheme based perturbation method, as described in this study, is effective and can be implemented in an existing operational EPS without extra computing cost.

Spatial Bias in Medium-Range Forecasts of Heavy Precipitation in the Sacramento River Basin: Implications for Water Management

AbstractForecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.

Оценка прогноза сильных осадков по данным оперативной глобальной модели атмосферы ПЛАВ20

Оценка прогноза сильных осадков по данным оперативной глобальной модели атмосферы ПЛАВ20

A Comparison of Methods to Sample Model Errors for Convection-Allowing Ensemble Forecasts in the Setting of Multiscale Initial Conditions Produced by the GSI-Based EnVar Assimilation System

Abstract A gridpoint statistical interpolation (GSI)-based hybrid ensemble–variational (EnVar) scheme was extended for convective scales—including radar reflectivity assimilation—and implemented in real-time spring forecasting experiments. This study compares methods to address model error during the forecast under the context of multiscale initial condition error sampling provided by the EnVar system. A total of 10 retrospective cases were used to explore the optimal design of convection-allowing ensemble forecasts. In addition to single-model single-physics (SMSP) configurations, ensemble forecast experiments compared multimodel (MM) and multiphysics (MP) approaches. Stochastic physics was also applied to MP for further comparison. Neighborhood-based verification of precipitation and composite reflectivity showed each of these model error techniques to be superior to SMSP configurations. Comparisons of MM and MP approaches had mixed findings. The MM approach had better overall skill in heavy-precipitation forecasts; however, MP ensembles had better skill for light (2.54 mm) precipitation and reduced ensemble mean error of other diagnostic fields, particularly near the surface. The MM experiment had the largest spread in precipitation, and for most hours in other fields; however, rank histograms and spaghetti contours showed significant clustering of the ensemble distribution. MP plus stochastic physics was able to significantly increase spread with time to be competitive with MM by the end of the forecast. The results generally suggest that an MM approach is best for early forecast lead times up to 6–12 h, while a combination of MP and stochastic physics approaches is preferred for forecasts beyond 6–12 h.

Evaluation of global ensemble prediction models for forecasting medium to heavy precipitations

In this study, 24-h forecasts of CMA, ECCC, ECMWF, KMA, NCEP and UKMO models were extracted from the TIGGE database and evaluated over selected stations in Iran within the 2010–2018 period. Daily forecast data were interpolated using the inverse distance method to the location of selected synoptic stations, while the frequency bias were further corrected using quantile mapping. The accumulation interval for precipitation was 24 h. Moreover, both raw and frequency-bias-corrected data were evaluated in deterministic and dichotomous modes at precipitation depth thresholds of 5, 15, 25, 35, and 45 mm. Forecasts were further partitioned and evaluated in different seasons in selected stations located in the margin of Alborz and Zagros Mountains. Best performing precipitation models did well over October–December season along the Alborz Mountains and over April–June along the Zagros Mountains. Results indicated that the forecasts were quite improved especially in high threshold after the frequency bias correction. Compared to other models, the ECMWF model provided best results in most stations. In contrast, NCEP performed poorly and could not provide a reliable medium to heavy precipitation forecasts. In general, none of the forecast models provided accurate estimates for precipitation depths over the 35-mm threshold. In fact, at higher precipitation thresholds, all models underpredicted the number of precipitation events.

Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data

Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.

Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

An application of the LTP_DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018

Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast (LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones (LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

AbstractUnderstanding moisture information ahead of tropical cyclone (TC) convection is very important for predicting TC track, intensity, and precipitation. The advanced Himawari imager onboard the Japanese Himawari‐8/‐9 satellite can provide high spatial and temporal resolution moisture information. Three‐layered precipitable water (LPW) with its three water vapor absorption infrared bands can be assimilated to generate better understanding and prediction of TC evolution. The impacts of LPW assimilation in the Weather Research and Forecasting model with nine combinations of physical parameterization schemes, including three cumulus parameterization (CP) and three microphysics parameterization (MP) schemes on TC prediction, have been comprehensively analyzed using Typhoon Hato as a case study. The results indicate that LPW assimilation reduces the average track error and speed up TC movement by better adjustment of the atmospheric circulation fields via changing the vertical structure of moisture and thermal profile. The track forecasts retain sensitivity to CP schemes after LPW assimilation. Also, LPW assimilation improves TC intensity prediction because the latent heat release process is accurately adjusted. It has been revealed that LPW assimilation can weaken the intensity sensitivity to MP schemes more than to CP schemes. Skill scores were used to evaluate precipitation forecasts after Hato's landfall. The results indicate that heavy precipitation forecasts are more sensitive to the choice of MP schemes. After LPW assimilation, the equitable threat scores among different results become similar and all forecast skills are increased. In addition, group statistic results with different initial time show the same conclusions.

Impact of Moisture Information From Advanced Himawari Imager Measurements on Heavy Precipitation Forecasts in a Regional NWP Model

AbstractInformation about moisture distribution and transportation in the preconvection environment is very important for nowcasting and forecasting severe weather events. The Advanced Himawari Imager (AHI) onboard the Japanese Himawari‐8/‐9 provides high temporal and spatial resolution moisture information useful for weather monitoring and forecasting. Algorithms have been developed for three‐layered precipitable water (LPW: surface to 0.9, 0.9–0.7, and 0.7–0.3 in sigma vertical coordinate) retrievals from AHI infrared band radiances using a Geostationary Operational Environmental Satellite‐R series algorithm working group algorithm. The LPW products from AHI have been validated with in situ measurements. An important application of the AHI LPW product is to improve local severe storm forecasts through assimilating high temporal and spatial resolution moisture information into regional‐ and storm‐scale numerical weather prediction (NWP) models. Assimilation techniques and approaches have been developed; the impact on precipitation forecasts for local severe storm over land from the assimilation of LPWs from AHI shows improvement on heavy precipitation forecasts over those from the assimilation of conventional data. Comparisons between AHI infrared band radiance assimilation and LPW assimilation show overall similar or comparable impact on precipitation forecast. The approaches for assimilating LPW can be applied to the assimilation of data from other advanced imagers such as the Advanced Baseline Imager onboard the U.S. next generation of Geostationary Operational Environmental Satellites‐R series, the Advanced Geosynchronous Radiation Imager onboard the Chinese FengYun‐4 series, and the Flexible Combined Imager onboard the upcoming European Meteosat Third Generation.

From Deterministic to Probabilistic Forecasts: The ‘Shift-Target’ Approach in the Milan Urban Area (Northern Italy)

The number of natural catastrophes that affect people worldwide is increasing; among these, the hydro-meteorological events represent the worst scenario due to the thousands of deaths and huge damages to private and state ownership they can cause. To prevent this, besides various structural measures, many non-structural solutions, such as the implementation of flood warning systems, have been proposed in recent years. In this study, we suggest a low computational cost method to produce a probabilistic flood prediction system using a single forecast precipitation scenario perturbed via a spatial shift. In fact, it is well-known that accurate forecasts of heavy precipitation, especially associated with deep moist convection, are challenging due to uncertainties arising from the numerical weather prediction (NWP), and high sensitivity to misrepresentation of the initial atmospheric state. Inaccuracies in precipitation forecasts are partially due to spatial misplacing. To produce hydro-meteorological simulations and forecasts, we use a flood forecasting system which comprises the physically-based rainfall-runoff hydrological model FEST-WB developed by the Politecnico di Milano, and the MOLOCH meteorological model provided by the Institute of Atmospheric Sciences and Climate (CNR-ISAC). The areas of study are the hydrological basins of the rivers Seveso, Olona, and Lambro located in the northern part of Milan city (northern Italy) where this system works every day in real-time. In this paper, we show the performance of reforecasts carried out between the years 2012 and 2015: in particular, we explore the ‘Shift-Target’ (ST) approach in order to obtain 40 ensemble members, which we assume equally likely, derived from the available deterministic precipitation forecast. Performances are shown through statistical indexes based on exceeding the threshold for different gauge stations over the three hydrological basins. Results highlight how the Shift-Target approach complements the deterministic MOLOCH-based flood forecast for warning purposes.

The Representation of Cumulus Convection in High-Resolution Simulations of the 2013 Colorado Front Range Flood

AbstractModel simulations of the 2013 Colorado Front Range floods are performed using 4-km horizontal grid spacing to evaluate the impact of using explicit convection (EC) versus parameterized convection (CP) in the model convective physics “gray zone.” Significant differences in heavy precipitation forecasts are found across multiple regions in which heavy rain and high-impact flooding occurred. The relative contribution of CP-generated precipitation to total precipitation suggests that greater CP scheme activity in areas upstream of the Front Range flooding may have led to significant downstream model error.Heavy convective precipitation simulated by the Kain–Fritsch CP scheme in particular led to an alteration of the low-level moisture flux and moisture transport fields that ultimately prevented the generation of heavy precipitation in downstream areas as observed. An updated, scale-aware version of the Kain–Fritsch scheme is also tested, and decreased model errors both up- and downstream suggest that scale-aware updates yield improvements in the simulation of this event. Comparisons among multiple CP schemes demonstrate that there are model convective physics gray zone considerations that significantly impact the simulation of extreme rainfall in this event.

Reply to “Comments on ‘Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence’”

AbstractMathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.

Cloud Initialization in the Rapid Update Cycle of HIRLAM

Abstract The Nowcasting Satellite Application Facility (NWC SAF) cloud mask from the Meteosat Second Generation (MSG) satellite is introduced in the initialization step of an hourly Rapid Update Cycle (RUC) of the High Resolution Limited Area Model (HIRLAM). MSG cloud-top temperatures and synoptic cloud-base height information are combined at analysis time. This cloud initialization scheme is applied to an experimental run, which is a copy of the operational Royal Netherlands Meteorological Institute [Koninklijk Nederlands Meteorologisch Instituut (KNMI)] RUC model. The experimental run was employed during the period June–December 2011. The RUC model has a forecast length of 6 h. Cloud cover forecasts are verified against MSG cloud cover information and synoptic observations. Forecasts of precipitation, surface pressure, 2-m temperature, and upper-air temperature are verified against synoptic observations and aircraft temperature observations. It is shown that including MSG cloud information in the RUC considerably improves the forecasts of most of these model fields, when compared to the operational control RUC. Both the bias and standard deviation of the errors of the cloud cover forecast are reduced substantially. Forecasts of light precipitation show a slight negative impact, but forecasts of heavy precipitation become better. The bias in 3D temperature fields disappears nearly completely. The error bias of 2-m temperatures has become larger. Two case studies are presented. The first case study had very good forecast performance with respect to low clouds when compared to the reference run. The second case study shows an ambiguous impact; there are still some deficiencies in the cloud initialization and cloud forecast when focusing on a single location.

Sensitivity to Madden–Julian Oscillation variations on heavy precipitation over the contiguous United States

The Madden–Julian Oscillation (MJO) is the most prominent mode of tropical intraseasonal variability in the climate system and has worldwide influences on the occurrences and forecasts of heavy precipitation. This paper investigates the sensitivity of precipitation over the contiguous United States (CONUS) in a case study (boreal 2004–05 winter). Several major storms affected the western and eastern CONUS producing substantial economic and social impacts including loss of lives. The Weather Research and Forecasting (WRF) model is used to perform experiments to test the significance of the MJO amplitude. The control simulation uses the MJO amplitude observed by reanalysis, whereas the amplitude is modified in perturbation experiments. WRF realistically simulates the precipitation variability over the CONUS, although large biases occur over the Western and Midwest United States. Daily precipitation is aggregated in western, central and eastern sectors and the frequency distribution is analyzed. Increases in MJO amplitude produce moderate increases in the median and interquartile range and large and robust increases in extreme (90th and 95th percentiles) precipitation. The MJO amplitude clearly affects the transport of moisture from the tropical Pacific and Gulf of Mexico into North America providing moist rich air masses and the dynamical forcing that contributes to heavy precipitation.