A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature.
While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts a Type-II conditional bias. As a statistical technique, the MEFP cannot detect, and thus. Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables.
Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures.
The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts.
Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium-Range Weather Forecasts.
This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step.
In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting. Heavy precipitation as a risk factor for shigellosis among homeless persons during an outbreak - Oregon, Shigella species are the third most common cause of bacterial gastroenteritis in the United States.
During a Shigella sonnei outbreak in Oregon from July through June , Shigella cases spread among homeless persons with onset of the wettest rainy season on record. We conducted time series analyses using Poisson regression to determine if a temporal association between precipitation and shigellosis incidence existed.
Models were stratified by housing status. Heavy precipitation likely contributed to shigellosis transmission among homeless persons during this outbreak. When heavy precipitation is forecast , organizations working with homeless persons could consider taking proactive measures to mitigate spread of enteric infections. Published by Elsevier Ltd. Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season.
Evaluation of 5 day forecasts in three seasons demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light-to- heavy rainfall 0. By analyzing the synoptic situations predicted by the identified more skillful ECMWF and less skillful JMA and CMA EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm-moist air into south China by the low-level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast.
The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall.
A slight improvement in the performance is obtained by shortening the forecast lead time from h to h to h for the cases with large-scale forcing, but not for the locally produced cases. Heavy precipitation events in northern Switzerland. Heavy precipitation events in the Alpine region often cause floods, rock-falls and mud slides with severe consequences for population and economy.
Breaking synoptic Rossby waves located over western Europe, play a central role in triggering such heavy rain events in southern Switzerland e. Massacand et al. In contrast, synoptic scale structures triggering heavy precipitation on the north side of the Swiss Alps and orographic effects have so far not been studied comprehensively.
An observation based high resolution precipitation data set for Switzerland and the Alps MeteoSwiss is used to identify heavy precipitation events affecting the north side of the Swiss Alps for the time period For the analysis north side of the Swiss Alps is divided in two investigation areas north-eastern and western Switzerland following the Swiss climate change scenarios Bey et al.
A subjective classification of upper level structures triggering heavy precipitation events in the areas of interest is presented. Four classes are defined based on the orientation and formation of the dynamical tropopause during extreme events in the northern part of Switzerland and its sub-regions.
The analysis is extended by a climatology of breaking waves and cut-offs following the method of Wernli and Sprenger to examine their presence and location during extreme events. References Bey I. Wernli, and H. Davies, Heavy precipitation on the Alpine South side: An upper-level precursor. MeteoSwiss Documentation of Meteoswiss grid-data products. Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: i the extent to which errors in soil moisture initialization degrade streamflow forecasts , and ii the extent to which the downscaling of seasonal precipitation forecasts , if it could be done accurately, would improve streamflow forecasts.
The reduction in streamflow forecast skill with forecasted streamflow measured against observations associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself.
This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized "perfect model" analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: i evaporation variance is significant relative to the precipitation variance, and ii the subgrid spatial variance of precipitation is adequately large.
In the large-scale continental region studied the conterminous United States , these two conditions are met in only a somewhat limited area. This study investigates the stage-dependent rainfall forecast skills and the associated synoptic-scale features in a persistent heavy rainfall event in south China, Guangdong Province, during March , using operational global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts.
This persistent rainfall was divided into two stages with a better precipitation forecast skill in Stage 2 S2 than Stage 1 S1 although S2 had a longer lead time. Using ensemble-based sensitivity analysis, key synoptic-scale factors that affected the rainfall were diagnosed by correlating the accumulated precipitation of each stage to atmospheric state variables in the middle of respective stage.
The precipitation in both stages was found to be significantly correlated with midlevel trough, low-level vortex, and particularly the low-level jet on the southeast flank of the vortex and its associated moisture transport.
The rainfall forecast skill was mainly determined by the forecast accuracy in the location of the low-level jet, which was possibly related to the different juxtapositions between the direction of the movement of the low-level vortex and the orientation of the low-level jet.
The uncertainty in rainfall forecast in S1 was mainly from the location uncertainty of the low-level jet, while the uncertainty in rainfall forecast in S2 was mainly from the width uncertainty of the low-level jet with the relatively accurate location of the low-level jet. Heavy metal immobilization via microbially induced carbonate precipitation and co- precipitation.
Microbially induced CaCO3 precipitation MICP has been successfully used in applications such as porous media consolidation and sealing of leakage pathways in the subsurface, and it has the potential to be used for remediation of metal and radionuclide contaminants in surface and groundwater. In this work, MICP is investigated for removal of dissolved heavy metals from contaminated mine discharge water via co- precipitation in CaCO3 or formation of other metal carbonates.
The bacterially catalyzed hydrolysis of urea produces inorganic carbon and ammonium and increases pH and the saturation index of carbonate minerals to promote precipitation of CaCO3. We performed laboratory batch experiments of MICP in alkaline mine drainage sampled from an abandoned mine site in Montana and containing a mixture of heavy metals at near neutral pH.
Both a model bacterium, Sporosarcina pasteurii, and a ureolytic bacterium isolated from sediments on the mine site were used to promote MICP. Removal of dissolved metals from the aqueous phase was determined via inductively coupled plasma mass spectrometry and resulting precipitates were analyzed via electron microscopy and energy dispersive x-ray spectroscopy EDX.
Both S. MICP by the native bacterium reduced concentrations of the heavy metals zinc, copper, cadmium, nickel and manganese in the water. Analysis of precipitates revealed calcium carbonate and phosphate minerals were likely present. The native isolate is undergoing identification via 16S DNA sequencing. Ongoing work will evaluate biofilm formation and MICP by the isolate in continuous flow, gravel-filled laboratory columns.
This research. Twenty years of regional climate simulated by the Weather Research and Forecasting model for North America has been analyzed to study the influence of the atmospheric rivers and the role of the land surface on heavy precipitation and flooding in the western U. Compared to observations, the simulation realistically captured the 95th percentile extreme precipitation , mean precipitation intensity, as well as the mean precipitation and temperature anomalies of all the atmospheric river events between Contrasting the President Day and New Year Day atmospheric river events, differences in atmospheric stability are found to have an influence on themore » spatial distribution of precipitation in the Coastal Range of northern California.
Although both cases yield similar amounts of heavy precipitation , the case was found to produce more runoff compared to the case. Antecedent soil moisture, the ratio of snowfall to total precipitation which depends on temperature , and existing snowpack all seem to play a role, leading to a higher runoff to precipitation ratio simulated for the case. This study underscores the importance of characterizing or simulating atmospheric rivers and the land surface conditions for predicting floods, and for assessing the potential impacts of climate change on heavy precipitation and flooding in the western U.
Operational h probabilistic quantitative precipitation forecasts : Recent performance and potential enhancements. The NOAA National Weather Service has maintained an automated, centralized h prediction system for probabilistic quantitative precipitation forecasts since This advective-statistical system ADSTAT produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a km grid box.
Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence.
A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the s.
An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations.
The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts.
Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability.
Although significant scientific improvements took place in the global models for weather forecasting , they are still not adequate at a regional scale e. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors.
We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts.
We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning. Skillful forecasts of 3-month total precipitation would be useful for decision making in hydrology, agriculture, public health, and other sectors of society. However, with some exceptions, the skill of seasonal precipitation outlooks is modest, leaving uncertainty in how to best make use of them.
Seasonal precipitation forecast skill is generally lower than the skill of forecasts for temperature or atmospheric circulation patterns for the same location and time. By contrast, associated temperature and circulation patterns are larger scale, in keeping with the anomalous boundary conditions e. Using two atmospheric general circulation models forced by observed sea surface temperature anomalies, the skill of simulations of total seasonal precipitation is examined as a function of the size of the spatial domain over which the precipitation total is averaged.
Results show that spatial aggregation increases skill and, by the skill measures used here, does so to a greater extent for precipitation than for temperature. Corroborative results are presented in an observational framework at smaller spatial scales for gauge rainfalls in northeast Brazil. The findings imply that when seasonal forecasts for precipitation are issued, the accompanying guidance on their expected skills should explicitly specify to which spatial aggregation level the skills apply.
Information about skills expected at other levels of aggregation should be supplied for users who may work at such levels. Recent climate changes and abnormal weather phenomena have resulted in increased occurrences of localized torrential rainfall. Urban areas in Korea have suffered from localized heavy rainfall, including the notable Seoul flood disaster in and The urban hydrological environment has changed in relation to precipitation , such as reduced concentration time, a decreased storage rate, and increased peak discharge.
These changes have altered and accelerated the severity of damage to urban areas. In order to prevent such urban flash flood damages, we have to secure the lead time for evacuation through the improvement of radar-based quantitative precipitation forecasting QPF.
The purpose of this research is to improve the QPF products using spatial-scale decomposition method for considering the life time of storm and to assess the accuracy between traditional QPF method and proposed method in terms of urban flood management. The layout of this research is as below. First, this research applies the image filtering to separate the spatial-scale of rainfall field. Second, the separated small and large-scale rainfall fields are extrapolated by each different forecasting method.
Third, forecasted rainfall fields are combined at each lead time. Finally, results of this method are evaluated and compared with the results of uniform advection model for urban flood modeling. It is expected that urban flood information using improved QPF will help to reduce casualties and property damage caused by urban flooding through this research. Predictability of horizontal water vapor transport relative to precipitation : Enhancing situational awareness for forecasting western U.
The western United States is vulnerable to socioeconomic disruption due to extreme winter precipitation and floods. Traditionally, forecasts of precipitation and river discharge provide the basis for preparations. Herein we show that earlier event awareness may be possible through use of horizontal water vapor transport integrated vapor transport IVT forecasts. Applying the potential predictability concept to the National Centers for Environmental Prediction global ensemble reforecasts, across 31 winters, IVT is found to be more predictable than precipitation.
IVT ensemble forecasts with the smallest spreads least forecast uncertainty are associated with initiation states with anomalously high geopotential heights south of Alaska, a setup conducive for anticyclonic conditions and weak IVT into the western United States. IVT ensemble forecasts with the greatest spreads most forecast uncertainty have initiation states with anomalously low geopotential heights south of Alaska and correspond to atmospheric rivers.
The greater IVT predictability could provide warnings of impending storminess with additional lead times for hydrometeorological applications. Evaluating the extreme precipitation events using a mesoscale atmopshere model.
Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas.
This study examines the performance of the Weather Research and Forecasting WRF model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin.
WRF performance with and without data assimilation at high spatial resolution 4 km is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates MPE.
WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.
Offline simulations over the conterminous United States CONUS with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: i the extent to which errors in soil moisture initialization degrade streamflow forecasts , and ii the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts.
The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance.
Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.
Observed heavy precipitation increase confirms theory and early model. Environmental phenomena are often first observed, and then explained or simulated quantitatively.
The complexity and diversity of processes, the range of scales involved, and the lack of first principles to describe many processes make it challenging to predict conditions beyond the ones observed. Here we use the intensification of heavy precipitation as a counterexample, where seemingly complex and potentially computationally intractable processes to first order manifest themselves in simple ways: the intensification of heavy precipitation is now emerging in the observed record across many regions of the world, confirming both theory and a variety of model predictions made decades ago, before robust evidence arose from observations.
We here compare heavy precipitation changes over Europe and the contiguous United States across station series and gridded observations, theoretical considerations and multi-model ensembles of GCMs and RCMs.
We demonstrate that the observed heavy precipitation intensification aggregated over large areas agrees remarkably well with Clausius-Clapeyron scaling.
The observed changes in heavy precipitation are consistent yet somewhat larger than predicted by very coarse resolution GCMs in the s and simulated by the newest generation of GCMs and RCMs. As the anthropogenic climate signal strengthens, there will be more opportunities to test climate predictions for other variables against observations and across a hierarchy of different models and theoretical concepts. Knutti, , Observed heavy precipitation increase confirms theory and early models, Nature Climate Change, in press.
Prudent assimulation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. Including AIRS profiles in assimilation process enhances the low-level instability and produces stronger updrafts and a better precipitation forecast than the CNTL run. Moore, Angelyn W. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting.
GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV precipitable water vapor using in situ pressure and temperature measurements, the basis for GPS meteorology.
Forecasting of monsoon heavy rains: challenges in NWP. Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction NWP models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution.
Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The recent JJAS Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding.
The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least.
Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts.
Following Stauffer et al. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system Integrated Nowcasting through Comprehensive Analysis , high resolution analyses are used for the computation of the observed climatology and for model training.
The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums.
Investigating NWP initialization sensitivities in heavy precipitation events. This study aims to investigate the effect of different types of model initialization applied to extreme storms simulations. Storms with extreme precipitation can usually produce flash floods that cause several damages to the society. Lives and property are destroyed from the landslides when they could be speared if forecasted a few hours in advance.
The forecasts depend on several factors; among them the initialization fields play an important role. These fields are the starting point for the simulation and therefore it controls the quality of the forecast.
This study evaluates the sensitivities of WRF to the initialization from two perspectives, 1 resolution and 2 initial atmospheric fields. Two storms that lead to flash flood are simulated. These storms present contrasting characteristics, NI was a maritime originated storm enhanced by local orography while GE was a typical summer convection. Three different sources of atmospheric fields defining the initial conditions are applied: a ECMWF operational analysis at resolution of 0.
The rainfall forecasted is compared against in situ ground radar and surface rain gauges observations through a set of quantitative precipitation forecast scores. Predictability of heavy sub-hourly precipitation amounts for a weather radar based nowcasting system. Heavy precipitation events and subsequent flash floods are one of the most dramatic hazards in many regions such as the Mediterranean basin as recently stressed in the HyMeX HYdrological cycle in the Mediterranean EXperiment international programme.
The focus of this study is to assess the quality of very short range below 3 hour lead times precipitation forecasts based on weather radar nowcasting system. Specific nowcasting amounts of 10 and 30 minutes generated with a nowcasting technique Berenguer et al , are compared against raingauge observations and also weather radar precipitation estimates observed over Catalonia NE Spain using data from the Meteorological Service of Catalonia and the Water Catalan Agency.
Results allow to discuss the feasibility of issuing warnings for different precipitation amounts and lead times for a number of case studies, including very intense convective events with 30minute precipitation amounts exceeding 40 mm Bech et al , As indicated by a number of verification scores single based radar precipitation nowcasts decrease their skill quickly with increasing lead times and rainfall thresholds.
Part I: Overview, damage survey and radar analysis. Journal of. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models.
Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. McCarty et al. Reale et al. Singh et al. Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation.
The application of numerical weather prediction NWP products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts.
Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days.
However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons.
The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.
Rainfall observation and forecasting using remote sensing such as RADAR Radio Detection and Ranging and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation forecasted precipitation data as the input variables.
The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data. The six rainfall events during the flood seasons in and were used for the input data.
The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study. Development of successful method of forecast of storm winds, including squalls and tornadoes and heavy rainfalls, that often result in human and material losses, could allow one to take proper measures against destruction of buildings and to protect people.
Well-in-advance successful forecast from 12 hours to 48 hour makes possible to reduce the losses. Prediction of the phenomena involved is a very difficult problem for synoptic till recently. Details about Viewsat Receiver Weather Forecaster.
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