Predictive Capability of a High-Resolution Hydrometeorological Forecasting Framework Coupling WRF Cycling 3DVAR and Continuum

Abstract The typical complex orography of the Mediterranean coastal areas support the formation of the so-called back-building mesoscale convective systems (MCS) producing torrential rainfall often resulting in flash floods. As these events are usually very small-scaled and localized, they are hardly predictable from a hydrometeorological standpoint, frequently causing a significant amount of fatalities and socioeconomic damage. Liguria, a northwestern Italian region, is characterized by small catchments with very short hydrological response time and is thus extremely prone to the impacts of back-building MCSs. Indeed, Liguria has been hit by three intense back-building MCSs between 2011 and 2014, causing a total death toll of 20 people and several hundred millions of euros of damages. Consequently, it is necessary to use hydrometeorological forecasting frameworks coupling the finescale numerical weather prediction (NWP) outputs with rainfall–runoff models to provide timely and accurate streamflow forecasts. Concerning the aforementioned back-building MCS episodes that recently occurred in Liguria, this work assesses the predictive capability of a hydrometeorological forecasting framework composed by a kilometer-scale cloud-resolving NWP model (WRF), including a 6-h cycling 3DVAR assimilation of radar reflectivity and conventional weather stations data, a rainfall downscaling model [Rainfall Filtered Autoregressive Model (RainFARM)], and a fully distributed hydrological model (Continuum). A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators has been explored to drive the meteorological component of the proposed forecasting framework. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving the quantitative precipitation forecast, and, subsequently, the flash flood prediction in cases of back-building MCS events.

Download Full-text

The Effect of Upstream Convection on Downstream Precipitation

Weather and Forecasting ◽

10.1175/waf986.1 ◽

2007 ◽

Vol 22 (2) ◽

pp. 255-277 ◽

Cited By ~ 15

Author(s):

Kelly M. Mahoney ◽

Gary M. Lackmann

Keyword(s):

Moisture Transport ◽

Weather Prediction ◽

The United States ◽

Cold Season ◽

Precipitation Forecast ◽

Study Region ◽

Low Level ◽

Convective Systems ◽

Eta Model ◽

Nwp Model

Abstract Operational forecasters in the southeast and mid-Atlantic regions of the United States have noted a positive quantitative precipitation forecast (QPF) bias in numerical weather prediction (NWP) model forecasts downstream of some organized, cold-season convective systems. Examination of cold-season cases in which model QPF guidance exhibited large errors allowed identification of two representative cases for detailed analysis. The goals of the case study analyses are to (i) identify physical mechanisms through which the upstream convection (UC) alters downstream precipitation amounts, (ii) determine why operational models are challenged to provide accurate guidance in these situations, and (iii) suggest future research directions that would improve model forecasts in these situations and allow forecasters to better anticipate such events. Two primary scenarios are identified during which downstream precipitation is altered in the presence of UC for the study region: (i) a fast-moving convective (FC) scenario in which organized convective systems oriented parallel to the lower-tropospheric flow are progressive relative to the parent synoptic system, and appear to disrupt poleward moisture transport, and (ii) a situation characterized by slower-moving convection (SC) relative to the parent system. Analysis of a representative FC case indicated that moisture consumption, stabilization via convective overturning, and modification of the low-level flow to a more westerly direction in the postconvective environment all appear to contribute to the reduction of downstream precipitation. In the FC case, operational Eta Model forecasts moved the organized UC too slowly, resulting in an overestimate of downstream moisture transport. A 4-km explicit convection model forecast from the Weather Research and Forecasting model produced a faster-moving upstream convective system and improved downstream QPF. In contrast to the FC event, latent heat release in the primary rainband is shown to enhance the low-level jet ahead of the convection in the SC case, thereby increasing moisture transport into the downstream region. A negative model QPF bias was observed in Eta Model forecasts for the SC event. Suggestions are made for precipitation forecasting in UC situations, and implications for NWP model configuration are discussed.

Download Full-text

Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

Hydrology and Earth System Sciences ◽

10.5194/hess-23-3823-2019 ◽

2019 ◽

Vol 23 (9) ◽

pp. 3823-3841 ◽

Cited By ~ 7

Author(s):

Maria Laura Poletti ◽

Francesco Silvestro ◽

Silvio Davolio ◽

Flavio Pignone ◽

Nicola Rebora

Keyword(s):

Data Assimilation ◽

Hydrological Model ◽

Weather Prediction ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Flood Prediction ◽

Meteorological Model ◽

Distributed Approach ◽

Nwp Model ◽

Small Catchments

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

Download Full-text

A Multiscale, Hydrometeorological Forecast Evaluation of National Water Model Forecasts of the May 2018 Ellicott City, Maryland, Flood

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0125.1 ◽

2020 ◽

Vol 21 (3) ◽

pp. 475-499 ◽

Cited By ~ 6

Author(s):

Francesca Viterbo ◽

Kelly Mahoney ◽

Laura Read ◽

Fernando Salas ◽

Bradford Bates ◽

...

Keyword(s):

Flash Flood ◽

Model Development ◽

Extreme Rainfall ◽

Precipitation Forecast ◽

Water Model ◽

Extreme Rainfall Event ◽

Flood Prediction ◽

Streamflow Forecasts ◽

Atmospheric Forcings ◽

National Water

AbstractThe NOAA National Water Model (NWM) became operational in August 2016, producing the first ever real-time, distributed, continuous set of hydrologic forecasts over the continental United States (CONUS). This project uses integrated hydrometeorological assessment methods to investigate the utility of the NWM to predict catastrophic flooding associated with an extreme rainfall event that occurred in Ellicott City, Maryland, on 27–28 May 2018. Short-range forecasts (0–18-h lead time) from the NWM version 1.2 are explored, focusing on the quantitative precipitation forecast (QPF) forcing from the High-Resolution Rapid Refresh (HRRR) model and the corresponding NWM streamflow forecast. A comprehensive assessment of multiscale hydrometeorological processes are considered using a combination of object-based, grid-based, and hydrologic point-based verification. Results highlight the benefits and risks of using a distributed hydrologic modeling tool such as the NWM to connect operational CONUS-scale atmospheric forcings to local impact predictions. For the Ellicott City event, reasonably skillful QPF in several HRRR model forecast cycles produced NWM streamflow forecasts in the small Ellicott City basin that were suggestive of flash flood potential. In larger surrounding basins, the NWM streamflow response was more complex, and errors were found to be governed by both hydrologic process representation, as well as forcing errors. The integrated, hydrometeorological multiscale analysis method demonstrated here guides both research and ongoing model development efforts, along with providing user education and engagement to ultimately engender improved flash flood prediction.

Download Full-text

Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

10.5194/hess-2019-75 ◽

2019 ◽

Author(s):

Maria Laura Poletti ◽

Francesco Silvestro ◽

Silvio Davolio ◽

Flavio Pignone ◽

Nicola Rebora

Keyword(s):

Data Assimilation ◽

Hydrological Model ◽

Weather Prediction ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Flood Prediction ◽

Meteorological Model ◽

Distributed Approach ◽

Nwp Model ◽

Small Catchments

Abstract. Forecasting flash floods with anticipation of some hours is still a challenge especially in environments made by a collection of small catchments. Hydrometeorological forecasting systems generally allow to predict the possibility of having very intense rainfall events on quite large areas with good performances even with 12–24 hours of anticipation. However, they are not able to predict exactly rainfall location if we consider portions of territory of 10 to 103 km2 as order of magnitude. The scope of this work is to exploit both observations and modeling sources to improve the discharge prediction in small catchments with time horizon of 2–8 hours. The models used to achieve the goal are essentially three i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations; ii) a non hydrostatic high-resolution numerical weather prediction (NWP) model; iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other: observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information in input to a nowcasting technique; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events occurred on Liguria Region (Italy) first to produce a standard analysis on predefined basin control sections, then using a distributed approach that exploit the capabilities of the employed hydrological model. The results obtained for these three analyzed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is rarely observed and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

Download Full-text

A hindcast study of the Piedmont 1994 flood: the CIMA Research Foundation hydro-meteorological forecasting chain

Bulletin of Atmospheric Science and Technology ◽

10.1007/s42865-020-00023-4 ◽

2020 ◽

Author(s):

Antonio Parodi ◽

Martina Lagasio ◽

Agostino N. Meroni ◽

Flavio Pignone ◽

Francesco Silvestro ◽

...

Keyword(s):

High Resolution ◽

Hydrological Model ◽

Weather Prediction ◽

Warning System ◽

Atmospheric Model ◽

Added Value ◽

Distributed Hydrological Model ◽

Nwp Model ◽

North Western ◽

Discharge Curves

AbstractBetween the 4th and the 6th of November 1994, Piedmont and the western part of Liguria (two regions in north-western Italy) were hit by heavy rainfalls that caused the flooding of the Po, the Tanaro rivers and several of their tributaries, causing 70 victims and the displacement of over 2000 people. At the time of the event, no early warning system was in place and the concept of hydro-meteorological forecasting chain was in its infancy, since it was still limited to a reduced number of research applications, strongly constrained by coarse-resolution modelling capabilities both on the meteorological and the hydrological sides. In this study, the skills of the high-resolution CIMA Research Foundation operational hydro-meteorological forecasting chain are tested in the Piedmont 1994 event. The chain includes a cloud-resolving numerical weather prediction (NWP) model, a stochastic rainfall downscaling model, and a continuous distributed hydrological model. This hydro-meteorological chain is tested in a set of operational configurations, meaning that forecast products are used to initialise and force the atmospheric model at the boundaries. The set consists of four experiments with different options of the microphysical scheme, which is known to be a critical parameterisation in this kind of phenomena. Results show that all the configurations produce an adequate and timely forecast (about 2 days ahead) with realistic rainfall fields and, consequently, very good peak flow discharge curves. The added value of the high resolution of the NWP model emerges, in particular, when looking at the location of the convective part of the event, which hit the Liguria region.

Download Full-text

Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0094.1 ◽

2015 ◽

Vol 16 (4) ◽

pp. 1843-1856 ◽

Cited By ~ 26

Author(s):

Silvio Davolio ◽

Francesco Silvestro ◽

Piero Malguzzi

Keyword(s):

High Resolution ◽

Weather Prediction ◽

Flood Forecasting ◽

Horizontal Resolution ◽

Precipitation Forecast ◽

Limited Area ◽

Flood Prediction ◽

Meteorological Model ◽

The Impact ◽

Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

Download Full-text

Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012

MAUSAM ◽

10.54302/mausam.v67i2.1298 ◽

2021 ◽

Vol 67 (2) ◽

pp. 323-332

Author(s):

ASHOK KUMAR DAS ◽

SURINDER KAUR

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

India Meteorological Department ◽

Precipitation Forecast ◽

False Alarms ◽

Model Ensemble ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Arw Model ◽

Nwp Model

The Numerical Weather Prediction models, Multi-model Ensemble (MME) (27 km × 27 km) and WRF (ARW) (9 km × 9 km) operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin wise rainfall forecast. The sub-basin wise operational Quantitative Precipitation Forecast (QPF) have been issued by 10 field offices named Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin wise NWP model rainfall forecast for 122 sub basins under these 10 FMOs for the flood season 2012 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of the MME and WRF (ARW) models rainfall at the sub-basin level have been studied in detail. The performance of WRF (ARW) and MME models is compared in the heavy rainfall case over the river basins (Mahanadi etc.) falls under FMO, Bhubaneswar and it is found that WRF (ARW) model gives better result than MME. It is also found that performance of WRF (ARW) is little better than MME when compared over all the flood prone river sub basins of India. For high rainfall categories (51-100, >100 mm), generally these leads to floods, the success rate of model rainfall forecasts are less and false alarms are more. The NWP models are able to capture the rainfall events but there is difference in magnitudes of sub basin wise rainfall estimates.

Download Full-text

Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale

Advances in Meteorology ◽

10.1155/2016/1384302 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 9

Author(s):

Wansik Yu ◽

Eiichi Nakakita ◽

Sunmin Kim ◽

Kosei Yamaguchi

Keyword(s):

Initial Conditions ◽

Forecast Error ◽

Weather Prediction ◽

Uncertainty Propagation ◽

Precipitation Forecast ◽

Flood Forecast ◽

Catchment Scale ◽

Forecast Uncertainty ◽

Specific Discharge ◽

Nwp Model

The common approach to quantifying the precipitation forecast uncertainty is ensemble simulations where a numerical weather prediction (NWP) model is run for a number of cases with slightly different initial conditions. In practice, the spread of ensemble members in terms of flood discharge is used as a measure of forecast uncertainty due to uncertain precipitation forecasts. This study presents the uncertainty propagation of rainfall forecast into hydrological response with catchment scale through distributed rainfall-runoff modeling based on the forecasted ensemble rainfall of NWP model. At first, forecast rainfall error based on the BIAS is compared with flood forecast error to assess the error propagation. Second, the variability of flood forecast uncertainty according to catchment scale is discussed using ensemble spread. Then we also assess the flood forecast uncertainty with catchment scale using an estimation regression equation between ensemble rainfall BIAS and discharge BIAS. Finally, the flood forecast uncertainty with RMSE using specific discharge in catchment scale is discussed. Our study is carried out and verified using the largest flood event by typhoon “Talas” of 2011 over the 33 subcatchments of Shingu river basin (2,360 km2), which is located in the Kii Peninsula, Japan.

Download Full-text

Improving QPF by blending techniques at the Meteorological Service of Catalonia

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-1443-2010 ◽

2010 ◽

Vol 10 (7) ◽

pp. 1443-1455 ◽

Cited By ~ 28

Author(s):

A. Atencia ◽

T. Rigo ◽

A. Sairouni ◽

J. Moré ◽

J. Bech ◽

...

Keyword(s):

Lead Time ◽

Weather Prediction ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Short Term ◽

Convective Structures ◽

Prediction Horizon ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Nwp Model

Abstract. The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

Download Full-text

Potential Vorticity (PV) Thinking in Operations: The Utility of Nonconservation

Weather and Forecasting ◽

10.1175/2007waf2006044.1 ◽

2008 ◽

Vol 23 (1) ◽

pp. 168-182 ◽

Cited By ~ 27

Author(s):

Michael J. Brennan ◽

Gary M. Lackmann ◽

Kelly M. Mahoney

Keyword(s):

Potential Vorticity ◽

High Frequency ◽

Weather Prediction ◽

Precipitation Amount ◽

Extratropical Cyclones ◽

Model Output ◽

Case Examples ◽

Convective Systems ◽

Low Level Jets ◽

Nwp Model

Abstract The use of the potential vorticity (PV) framework by operational forecasters is advocated through case examples that demonstrate its utility for interpreting and evaluating numerical weather prediction (NWP) model output for weather systems characterized by strong latent heat release (LHR). The interpretation of the dynamical influence of LHR is straightforward in the PV framework; LHR can lead to the generation of lower-tropospheric cyclonic PV anomalies. These anomalies can be related to meteorological phenomena including extratropical cyclones and low-level jets (LLJs), which can impact lower-tropospheric moisture transport. The nonconservation of PV in the presence of LHR results in a modification of the PV distribution that can be identified in NWP model output and evaluated through a comparison with observations and high-frequency gridded analyses. This methodology, along with the application of PV-based interpretation, can help forecasters identify aspects of NWP model solutions that are driven by LHR; such features are often characterized by increased uncertainty due to difficulties in model representation of precipitation amount and latent heating distributions, particularly for convective systems. Misrepresentation of the intensity and/or distribution of LHR in NWP model forecasts can generate errors that propagate through the model solution with time, potentially degrading the representation of cyclones and LLJs in the model forecast. The PV framework provides human forecasters with a means to evaluate NWP model forecasts in a way that facilitates recognition of when and how value may be added by modifying NWP guidance. This utility is demonstrated in case examples of coastal extratropical cyclogenesis and LLJ enhancement. Information is provided regarding tools developed for applying PV-based techniques in an operational setting.

Download Full-text