scholarly journals Toward Real-Time Daily PQPF by an Analog Sorting Approach: Application to Flash-Flood Catchments

2012 ◽  
Vol 51 (3) ◽  
pp. 505-520 ◽  
Author(s):  
Renaud Marty ◽  
Isabella Zin ◽  
Charles Obled ◽  
Guillaume Bontron ◽  
Abdelatif Djerboua
Keyword(s):  
Heavy Rainfall ◽  
Prediction Models ◽  
Flash Flood ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Rainfall Events ◽  
Sorting Technique ◽  
Heavy Rainfall Events

AbstractHeavy-rainfall events are common in southern France and frequently result in devastating flash floods. Thus, an appropriate anticipation of future rainfall is required: for early flood warning, at least 12–24 h in advance; for alerting operational services, at least 2–3 days ahead. Precipitation forecasts are generally provided by numerical weather prediction models (NWP), and their associated uncertainty is generally estimated through an ensemble approach. Precipitation forecasts also have to be adapted to hydrological scales. This study describes an alternative approach to commonly used limited-area models. Probabilistic quantitative precipitation forecasts (PQPFs) are provided through an analog sorting technique, which directly links synoptic-scale NWP output to catchment-scale rainfall probability distributions. One issue concerns the latest developments in implementing a daily version of this technique into operational conditions. It is shown that the obtained PQPFs depend on the meteorological forecasts used for selecting analogous days and that the method has to be reoptimized when changing the source of synoptic forecasts, because of the NWP output uncertainties. Second, an evaluation of the PQPFs demonstrates that the analog technique performs well for early warning of heavy-rainfall events and provides useful information as potential input to a hydrological ensemble prediction system. It is shown that the obtained daily rainfall distributions can be unreliable. A statistical correction of the observed bias is proposed as a function of the no-rain frequency values, leading to a significant improvement in PQPF sharpness.

scholarly journals Limited-area short-range ensemble predictions targeted for heavy rain in Europe

2005 ◽  
Vol 9 (4) ◽  
pp. 300-312 ◽  
Author(s):  
K. Sattler ◽  
H. Feddersen
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Heavy Rain ◽  
River Basins ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Target Area ◽  
Extreme Rainfall Events ◽  
Ensemble Prediction System ◽  
Area Of Interest

Abstract. Inherent uncertainties in short-range quantitative precipitation forecasts (QPF) from the high-resolution, limited-area numerical weather prediction model DMI-HIRLAM (LAM) are addressed using two different approaches to creating a small ensemble of LAM simulations, with focus on prediction of extreme rainfall events over European river basins. The first ensemble type is designed to represent uncertainty in the atmospheric state of the initial condition and at the lateral LAM boundaries. The global ensemble prediction system (EPS) from ECMWF serves as host model to the LAM and provides the state perturbations, from which a small set of significant members is selected. The significance is estimated on the basis of accumulated precipitation over a target area of interest, which contains the river basin(s) under consideration. The selected members provide the initial and boundary data for the ensemble integration in the LAM. A second ensemble approach tries to address a portion of the model-inherent uncertainty responsible for errors in the forecasted precipitation field by utilising different parameterisation schemes for condensation and convection in the LAM. Three periods around historical heavy rain events that caused or contributed to disastrous river flooding in Europe are used to study the performance of the LAM ensemble designs. The three cases exhibit different dynamic and synoptic characteristics and provide an indication of the ensemble qualities in different weather situations. Precipitation analyses from the Deutsche Wetterdienst (DWD) are used as the verifying reference and a comparison of daily rainfall amounts is referred to the respective river basins of the historical cases.

scholarly journals The Southern China Monsoon Rainfall Experiment (SCMREX)

2017 ◽  
Vol 98 (5) ◽  
pp. 999-1013 ◽  
Author(s):  
Yali Luo ◽  
Renhe Zhang ◽  
Qilin Wan ◽  
Bin Wang ◽  
Wai Kin Wong ◽  
...  
Keyword(s):  
Rainy Season ◽  
Heavy Rainfall ◽  
Monsoon Rainfall ◽  
Southern China ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Rainfall Events ◽  
The World ◽  
Field Campaigns ◽  
Heavy Rainfall Events

Abstract During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

scholarly journals Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130284 ◽  
Author(s):  
Glenn Shutts ◽  
Alfons Callado Pallarès
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Model Error ◽  
Horizontal Resolution ◽  
Phase Changes ◽  
Ensemble Prediction ◽  
Condensation Temperature ◽  
Ensemble Prediction System ◽  
The Mean ◽  
Temperature Tendency

The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System with respect to horizontal resolution deficiency. High-resolution truth forecasts are compared with matching target forecasts at much lower resolution after coarse-graining to a common spatial and temporal resolution. In this way, model error is defined and its probability distribution function is examined as a function of tendency magnitude. It is found that the temperature tendency error associated with convection parametrization and explicit water phase changes behaves like a Poisson process for which the variance grows in proportion to the mean, which suggests that the assumptions underpinning the Craig and Cohen statistical model of convection might also apply to parametrized convection. By contrast, radiation temperature tendency errors have a very different relationship to their mean value. These findings suggest that the ECMWF stochastic perturbed parametrization tendency scheme could be improved since it assumes that the standard deviation of the tendency error is proportional to the mean. Using our finding that the variance error is proportional to the mean, a prototype stochastic parametrization scheme is devised for convective and large-scale condensation temperature tendencies and tested within the ECMWF Ensemble Prediction System. Significant impact on forecast skill is shown, implying its potential for further development.

A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System

2009 ◽  
Vol 66 (3) ◽  
pp. 603-626 ◽  
Author(s):  
J. Berner ◽  
G. J. Shutts ◽  
M. Leutbecher ◽  
T. N. Palmer
Keyword(s):  
Kinetic Energy ◽  
Prediction Models ◽  
Positive Impact ◽  
Weather Prediction ◽  
Coarse Grained ◽  
Ensemble Prediction ◽  
Spectral Space ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Integration Schemes

Abstract Understanding model error in state-of-the-art numerical weather prediction models and representing its impact on flow-dependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscale-propagating errors caused by unresolved subgrid-scale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarse-grained high-resolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread–error relationship, more realistic kinetic-energy spectra, a better representation of forecast-error growth, improved flow-dependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for large-anomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flow-dependent formulations of the unresolved processes.

scholarly journals Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gamal El Afandi ◽  
Mostafa Morsy ◽  
Fathy El Hussieny
Keyword(s):  
Heavy Rainfall ◽  
Flash Flood ◽  
Wrf Model ◽  
Rain Gauge ◽  
Rainfall Simulation ◽  
Weather Research And Forecasting ◽  
Sinai Peninsula ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Weather Research

Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.

Evaluation of Stochastic Perturbed Parameterization Tendencies on Convective-Permitting Ensemble Forecasts of Heavy Rainfall Events in New York and Taiwan

2019 ◽  
Vol 35 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Kevin M. Lupo ◽  
Ryan D. Torn ◽  
Shu-Chih Yang
Keyword(s):  
Standard Deviation ◽  
Time Scale ◽  
Heavy Rainfall ◽  
Stochastic Perturbation ◽  
The United States ◽  
Model Error ◽  
Ensemble Prediction ◽  
Rainfall Events ◽  
Parameterization Schemes ◽  
Heavy Rainfall Events

Abstract The representation of model error in ensemble prediction systems (EPSs) can be limited by the assumptions within parameterization schemes. Stochastic perturbed parameterization tendencies (SPPT) is one representation of model error that randomly perturbs parameterized physical tendencies using a spatially and temporally correlated red-noise field. This research investigates the sensitivity of ensemble rainfall forecasts produced by the Weather Research and Forecasting (WRF) Model to the configuration of SPPT and independent SPPT (iSPPT) for three meso–synoptic-scale heavy rainfall events over the United States and Taiwan, primarily focusing on the ensemble mean and standard deviation as well as forecast skill. Thirty-two 20-member ensembles, which represent a combination of eight configurations of the stochastic perturbation time scale, length scale, and amplitude scale, and four perturbed parameterization schemes, as well as an unperturbed control simulation, are examined for each event. In each case, rainfall standard deviation is most sensitive to the perturbation time scale and amplitude scale. Moreover, microphysics tendency perturbations are associated with the largest standard deviation in two of the three events, followed by perturbations to the total (nonmicrophysics), turbulent mixing, and radiation parameterized tendencies. Additionally, microphysics tendency perturbations are associated with an increase in the areal coverage of heavy rainfall compared to the control forecast, regardless of whether the control forecast over or underrepresents the observed rainfall distribution.

scholarly journals Benefit of microwave radiometer and cloud radar observations for data assimilation and fog process studies during the SOFOG3D experiment

2021 ◽  
Author(s):  
Pauline Martinet ◽  
Frédéric Burnet ◽  
Alistair Bell ◽  
Arthur Kremer ◽  
Matthias Letillois ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Initial State ◽  
Ensemble Prediction System ◽  
Radar Observations ◽  
Stratus Clouds ◽  
Cloud Radar ◽  
Physical Parameterizations

<p>Fog forecasts still remain quite inaccurate due to the complexity, non linearities and fine scale of the main physical processes driving the fog lifecycle. Additionally to the complex modelling of fog processes, current numerical weather prediction models are known to suffer from a lack of operational observations in the atmospheric boundary layer and more generally during cloudy-sky conditions. Continuous observations of both thermodynamics and microphysics during the fog lifecycle are thus essential to develop future operational networks with the aim of validating current physical parameterizations and improving the model initial state through data assimilation techniques. In this context, an international network of 8 ground-based microwave radiometers (MWRs) has been deployed at a regional-scale on a 300 x 300 km domain during the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) that has been conducted from October 2019 to April 2020. The MWR network has been extended with ceilometers at all MWR sites and additional microphysical observations from the 95 GHz cloud radar BASTA at two major sites as well as wind measurements from a Doppler lidar deployed at the super-site. After an overview of the SOFOG3D objectives and experimental set-up, preliminary results exploiting mainly the MWR network and cloud radar observations will be presented. Firstly, the capability of MWRs to provide temperature and humidity retrievals within fog and stratus clouds will be evaluated and discussed against radiosoundings launched during intensive observation periods (IOPs). Secondly, first retrievals of liquid water content profiles within fog and stratus clouds derived from the synergy between MWRs and the BASTA cloud radar will be presented. To that end, a one dimensional variational approach (1D-Var) directly assimilating MWR brightness temperatures and cloud-radar reflectivities has been developed. 1D-Var retrievals will be validated through a dataset of simulated observations and real fog cases of the SOFOG3D experiment. The capability of MWR and cloud radar observations to improve the initial state of the AROME model during fog conditions will be discussed with a focus on selected case studies. Finally, the usefulness of ground-based remote sensing networks to improve our understanding of fog processes and to validate physical parameterizations will be illustrated using the operational AROME model and the AROME Ensemble Prediction System</p>

scholarly journals Evaluating the Forecast Performance of the Meiyu Front Rainbelt Position: A Case Study of the 30 June to 4 July 2016 Extreme Rainfall Event

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 648 ◽  
Author(s):  
Jie Ma ◽  
Kevin A. Bowley ◽  
Fuqing Zhang
Keyword(s):  
Heavy Rainfall ◽  
Weather Prediction ◽  
Eastern China ◽  
Rainfall Event ◽  
Ensemble Prediction ◽  
Extreme Rainfall Event ◽  
Forecast Performance ◽  
Ensemble Prediction System ◽  
Meiyu Front ◽  
Rain Band

An impactful and poorly forecasted heavy rainfall event was observed in association with the Meiyu front over the Yangtze River valley of China from 30 June–4 July 2016. Operational global numerical weather prediction models for almost all forecast lead times beyond 24 h incorrectly forecasted the location and intensity of the precipitation associated with this event. This study presents the first examination of this poleward bias in the operational models for the Meiyu front, which has been frequently noted by meteorologists at the Chinese Meteorological Administration, and explores areas of forecast error and uncertainty in the prediction of the position of the primary frontal rainbelt that is crucial to the placement and intensity of the heavy rainfall. A new zonal mean maximum accumulated precipitation index is introduced and utilized to identify members in the European Centre for Medium-Range Forecasts (ECMWF) Ensemble Prediction System (EPS) that either perform well or perform poorly in forecasting the location of the Meiyu front. Using this new precipitation metric, five-member subgroups representing the EPS members that were most accurate and those that incorrectly displace the Meiyu front the furthest north were identified. An analysis of composite mean fields for the EPS subgroups and the correlation between the rain band placement and the 500 hPa heights was performed for several EPS model runs. We showed that a successful prediction of the location of the Meiyu front rainbelt position by the EPS is most sensitive to the intensity of the 500 hPa trough located over eastern China for the event. The ensemble members that had the largest northward error in the location of the rain band were found to have a more intense 500 hPa trough than the members that more accurately predicted the rainbelt. The more intense upper level trough was found to have enhanced the lower tropospheric southerly flow equatorward of the front and led to a less zonal-oriented Meiyu front, resulting in a northward displacement of both the rainbelt and the regions of more intense precipitation rates. Finally, an examination of the evolution of the differences between the subgroups shows that the primary differences in 500 hPa intensity propagate in-phase with the 500 hPa trough. We show that it is the intensity of the trough, rather than the rate of propagation, that is the most important source of forecast dissimilarities between the successful and failed forecasts.

scholarly journals Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Yang Yang ◽  
Phillip Andrews ◽  
Trevor Carey-Smith ◽  
Michael Uddstrom ◽  
Mike Revell
Keyword(s):  
New Zealand ◽  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Model Verification ◽  
Ensemble Prediction ◽  
Limited Area ◽  
The North ◽  
Rainfall Occurrence

This numerical weather prediction study investigates the effects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 12 different experiments have been conducted for the period from 13 September to 18 October 2010 using the New Zealand limited area model. Verification has shown that an ensemble based on these experiments outperforms all of the individual members using a variety of metrics. In addition, the rainfall occurrence probability derived from the ensemble is a good predictor of heavy rainfall. Mountains significantly affect the performance of this ensemble which provides better forecasts of heavy rainfall over the South Island than over the North Island. Analysis suggests that underestimation of orographic lifting due to the relatively low resolution of the model (~12 km) is a factor leading to this variability in heavy rainfall forecast skill. This study indicates that regional ensemble prediction with a suitably fine model resolution (≤5 km) would be a useful tool for forecasting heavy rainfall over New Zealand.

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