A meteorological–hydrological regional ensemble forecast for an early-warning system over small Apennine catchments in Central Italy

Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows for the use of both a higher spatial resolution and the parameterization schemes specifically developed for representing sub-grid scale physical processes at high resolution. However, precipitation estimation is still affected by errors that can impact the response of hydrological models. To the aim of improving the hydrological forecast and the characterization of related uncertainties, a regional-scale meteorological–hydrological ensemble is presented. The uncertainties in the precipitation forecast and how they propagate in the hydrological model are also investigated. A meteorological–hydrological offline coupled ensemble is built to forecast events in a complex-orography terrain where catchments of different sizes are present. The Best Discharge-based Drainage (BDD; both deterministic and probabilistic) index, is defined with the aim of forecasting hydrological-stress conditions and related uncertainty. In this context, the meteorological–hydrological ensemble forecast is implemented and tested for a severe hydrological event which occurred over Central Italy on 15 November 2017, when a flood hit the Abruzzo region with precipitation reaching 200 mm (24 h)−1 and producing damages with a high impact on social and economic activities. The newly developed meteorological–hydrological ensemble is compared with a high-resolution deterministic forecast and with the observations (rain gauges and radar data) over the same area. The receiver operating characteristic (ROC) statistical indicator shows how skilful the ensemble precipitation forecast is with respect to both rain-gauge- and radar-retrieved precipitation. Moreover, both the deterministic and probabilistic configurations of the BDD index are compared with the alert map issued by Civil Protection Department for the event showing a very good agreement. Finally, the meteorological–hydrological ensemble allows for an estimation of both the predictability of the event a few days in advance and the uncertainty of the flood. Although the modelling framework is implemented on the basins of the Abruzzo region, it is portable and applicable to other areas.

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Regional ensemble forecast for early warning system over small Apennine catchments on Central Italy

10.5194/hess-2019-223 ◽

2019 ◽

Cited By ~ 2

Author(s):

Rossella Ferretti ◽

Annalina Lombardi ◽

Barbara Tomassetti ◽

Lorenzo Sangelantoni ◽

Valentina Colaiuda ◽

...

Keyword(s):

Flash Flood ◽

Central Italy ◽

Warning System ◽

Precipitation Forecast ◽

Hydrological Models ◽

Physical Processes ◽

Weather Forecasts ◽

Modelling Framework ◽

Abruzzo Region ◽

Precipitation Estimation

Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows to use both a higher spatial resolutions and the newly developed parameterization schemes for representing sub-grid scale physical processes. However, precipitation estimation is still affected by errors that can impact on the response of hydrological models. To the aim of considering the uncertainties in the precipitation forecast and how they propagate in the hydrological model, an ensemble approach is investigated. A meteo-hydro ensemble system is built to forecast events in a complex orography terrain where catchments of different size are present. In this context, the meteo-hydrological forecast system is implemented and tested for a severe hydrological event occurred over Central Italy on November 15, 2017. During this period, a flash flood hit the Abruzzo region causing precipitation up to 200 mm/24 hours and producing damages with a high impact on social and economic activities.The newly developed meteo-hydro ensemble system is compared with a high resolution deterministic forecast and with the observations over the same area, showing a very good response. In addition, the ensemble allows for an estimation of the predictability of the event a few days in advance and of the uncertainty of this flood. Although the modelling framework is implemented on the basins of Abruzzo region, it is portable and applicable to other areas.

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Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-3135-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 3135-3149 ◽

Cited By ~ 23

Author(s):

G. Panegrossi ◽

R. Ferretti ◽

L. Pulvirenti ◽

N. Pierdicca

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Ground Truth ◽

Rain Gauge ◽

Precipitation Forecast ◽

Flood Event ◽

Strong Impact ◽

Moisture Field ◽

The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

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A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan

Journal of Disaster Research ◽

10.20965/jdr.2021.p0786 ◽

2021 ◽

Vol 16 (4) ◽

pp. 786-793

Author(s):

Yoshiaki Hayashi ◽

Taichi Tebakari ◽

Akihiro Hashimoto ◽

◽

Keyword(s):

High Resolution ◽

Heavy Rainfall ◽

Daily Rainfall ◽

Radar Data ◽

Rain Gauge ◽

Hourly Rainfall ◽

The Difference ◽

Microwave Imager ◽

Satellite Mapping

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

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Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-10-05038.1 ◽

2011 ◽

Vol 12 (6) ◽

pp. 1414-1431 ◽

Cited By ~ 31

Author(s):

David Kitzmiller ◽

Suzanne Van Cooten ◽

Feng Ding ◽

Kenneth Howard ◽

Carrie Langston ◽

...

Keyword(s):

High Resolution ◽

Positive Impact ◽

Radar Data ◽

Rain Gauge ◽

Hydrologic Model ◽

Estimation Methods ◽

Freezing Level ◽

Distributed Hydrologic Model ◽

Precipitation Estimation ◽

Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

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Prediction of the 14 June 2010 Oklahoma City Extreme Precipitation and Flooding Event in a Multiphysics Multi-Initial-Conditions Storm-Scale Ensemble Forecasting System

Weather and Forecasting ◽

10.1175/waf-d-15-0116.1 ◽

2016 ◽

Vol 31 (4) ◽

pp. 1215-1246 ◽

Cited By ~ 6

Author(s):

Nathan Dahl ◽

Ming Xue

Keyword(s):

Initial Conditions ◽

Stage Iv ◽

Radar Data ◽

Ensemble Forecast ◽

Forecast Skill ◽

Oklahoma City ◽

Precipitation Forecast ◽

Cold Pool ◽

Statistical Correlations ◽

Outflow Boundary

Abstract Prolonged heavy rainfall produced widespread flooding in the Oklahoma City area early on 14 June 2010. This event was poorly predicted by operational models; however, it was skillfully predicted by the Storm-Scale Ensemble Forecast produced by the Center for Analysis and Prediction of Storms as part of the Hazardous Weather Testbed 2010 Spring Experiment. In this study, the quantitative precipitation forecast skill of ensemble members is assessed and ranked using a neighborhood-based threat score calculated against the stage IV precipitation data, and Oklahoma Mesonet observations are used to evaluate the forecast skill for surface conditions. Statistical correlations between skill metrics and qualitative comparisons of relevant features for higher- and lower-ranked members are used to identify important processes. The results demonstrate that the development of a cold pool from previous convection and the movement and orientation of the associated outflow boundary played dominant roles in the event. Without assimilated radar data from this earlier convection, the modeled cold pool was too weak and too slow to develop. Furthermore, forecast skill was sensitive to the choice of microphysics parameterization; members that used the Thompson scheme produced initial cold pools that propagated too slowly, substantially increasing errors in the timing and placement of later precipitation. The results also suggest important roles played by finescale, transient features in the period of outflow boundary stalling and reorientation associated with the heaviest rainfall. The unlikelihood of a deterministic forecast reliably predicting these features highlights the benefit of using convection-allowing/convection-resolving ensemble forecast methods for events of this kind.

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Evaluation of the use of radar-derived precipitation in water balance simulations: A case study in small-medium catchments in Saxony, Germany

10.5194/ems2021-148 ◽

2021 ◽

Author(s):

Thanh Thi Luong ◽

Ivan Vorobevskii ◽

Judith Pöschmann ◽

Rico Kronenberg

Keyword(s):

Water Balance ◽

Spatial Information ◽

Regional Scale ◽

Model Performance ◽

Radar Data ◽

Rain Gauge ◽

Hydrologic Model ◽

Water Balance Components ◽

Precipitation Estimation ◽

Radar Imagery

Water balance estimation/modeling is highly dependent on good-quality precipitation data and often lacks enough spatial information about it. Quantitative precipitation estimation (QPE) using radar data is recognized to have a good potential to significantly enhance the spatial depiction of precipitation compared to conventional rain gauge-based methods. However, precipitation measurements are often underestimated by wind drift or funnel evaporation, so that a correction such as Richter&#8217;s method is required before the data can be applied in the model. In this study, the Richter correction is applied for the first time to a radar-based QPE, namely RADKLIM-RW, to model water balance in ten selected catchments in Saxony, Germany. The modelled water balance components for the period 2001-2017 were evaluated by means of comparison of radar- and gauge-based precipitation inputs. The results showed that RADKLIM-RW was able to produce reliable simulations of discharge and water balance (KGE = 0.56 and 0.71 on the daily and monthly scales respectively). Application of the Richter correction improved the model performance by 5.5% and 8.9 % (for rain gauge-based and RADKLIM precipitation respectively). The study concluded that radar data as precipitation input to (pseudo)distributed hydrologic model shows immense potential to improve water balance simulations.Hightlights:<ul><li>Comparison of precipitation derived from sensor networks and radar imagery for small catchments</li> <li>Evaluation of potential application of radar precipitation in water balance simulation at regional scale</li> <li>Effect of wind correction (&#8220;Richter&#8221; correction) on radar precipitation products</li> <li>Evaluating corrected precipitation on water balance processes</li> </ul>Keywords: HRU, radar climatology, RADKLIM RW (RADOLAN), Richter correction, Open sensor network, water balance simulation, BROOK90

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Assessing Dual-Polarization Radar Estimates of Extreme Rainfall during Hurricane Harvey

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0081.1 ◽

2019 ◽

Vol 36 (12) ◽

pp. 2501-2520 ◽

Cited By ~ 7

Author(s):

David B. Wolff ◽

Walter A. Petersen ◽

Ali Tokay ◽

David A. Marks ◽

Jason L. Pippitt

Keyword(s):

Gulf Coast ◽

Warning System ◽

Extreme Rainfall ◽

Radar Data ◽

Rain Gauge ◽

Harris County ◽

Flood Warning ◽

Precipitation Estimates ◽

Flood Warning System ◽

Automated Technique

Abstract Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in its wake was historic flooding, with some locations measuring more than 60 in. (150 cm) of rain over a 5-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25–29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α = 0.015 and β = 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.

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Generating Calibrated Ensembles of Physically Realistic, High-Resolution Precipitation Forecast Fields Based on GEFS Model Output

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0067.1 ◽

2018 ◽

Vol 19 (10) ◽

pp. 1651-1670 ◽

Cited By ~ 10

Author(s):

Michael Scheuerer ◽

Thomas M. Hamill

Keyword(s):

High Resolution ◽

Mapping Function ◽

Ensemble Forecast ◽

Precipitation Forecast ◽

Coarse Scale ◽

Marginal Distributions ◽

Gamma Distributions ◽

New Variant ◽

Minimum Divergence ◽

Forecast Field

Abstract Enhancements of multivariate postprocessing approaches are presented that generate statistically calibrated ensembles of high-resolution precipitation forecast fields with physically realistic spatial and temporal structures based on precipitation forecasts from the Global Ensemble Forecast System (GEFS). Calibrated marginal distributions are obtained with a heteroscedastic regression approach using censored, shifted gamma distributions. To generate spatiotemporal forecast fields, a new variant of the recently proposed minimum divergence Schaake shuffle technique, which selects a set of historic dates in such a way that the associated analysis fields have marginal distributions that resemble the calibrated forecast distributions, is proposed. This variant performs univariate postprocessing at the forecast grid scale and disaggregates these coarse-scale precipitation amounts to the analysis grid by deriving a multiplicative adjustment function and using it to modify the historic analysis fields such that they match the calibrated coarse-scale precipitation forecasts. In addition, an extension of the ensemble copula coupling (ECC) technique is proposed. A mapping function is constructed that maps each raw ensemble forecast field to a high-resolution forecast field such that the resulting downscaled ensemble has the prescribed marginal distributions. A case study over an area that covers the Russian River watershed in California is presented, which shows that the forecast fields generated by the two new techniques have a physically realistic spatial structure. Quantitative verification shows that they also represent the distribution of subgrid-scale precipitation amounts better than the forecast fields generated by the standard Schaake shuffle or the ECC-Q reordering approaches.

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Snowpack modelling in central Italy: analysis and comparison of high-resolution WRF-driven Noah LSM and Alpine3D simulations

10.5194/egusphere-egu2020-19408 ◽

2020 ◽

Author(s):

Edoardo Raparelli ◽

Paolo Tuccella ◽

Rossella Ferretti ◽

Frank S. Marzano

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Negative Impact ◽

Regional Scale ◽

Central Italy ◽

Surface Model ◽

Negative Bias ◽

Densification Rate ◽

Abruzzo Region

Italy is a territory characterized by complex orography. Its main mountain chains are the Alps, which identify the northern Italian border, and the Apennines, which cross the entire Italian peninsula ranging from north-west to south-east. The major Apennines peaks reach almost 3000 meters and are located in central Italy, in the Abruzzo region. The near Mediterranean sea is an important source of moisture, which permits to this region to experience a substantial snow cover during winter. Thanks to the orientation of the Apennines chain and the height of its peaks the Abruzzo region is characterized by different climate types. This affects the precipitation patterns and the snowpack evolution, resulting in high regional variability of the snow cover. The goal of this study is to investigate the snow cover evolution in the Abruzzo region, using and comparing different snowpack models. To this end we have used the Weather Research and Forecasting (WRF) model to drive the Noah Land Surface Model (LSM) and the sophisticated three-dimensional snow cover model Alpine3D to simulate the snow cover evolution at regional scale. Noah LSM is already on-line coupled with WRF, but this is not the case for Alpine3D. Thus we have modified and used the interfacing library MeteoIO to force Alpine3D with the meteorological data simulated with WRF, off-line coupling the two models. We have validated the WRF simulation using a dense network of automatic weather stations (AWS), obtaining good agreement between simulated and observed data. We have found that the snow depth simulated with Noah LSM presents a negative bias, caused by the inability of the model to reproduce correctly the snow densification rate. Instead, Alpine3D is capable to better reproduce the observed densification rate, thanks to its more detailed description of the snow metamorphism processes. However, the snow depth simulated with Alpine3D presents a negative bias, caused by an underestimation of the new snow depth, which has a negative impact on the entire simulation.

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The Selection of Rain Gauges and Rainfall Parameters in Estimating Intensity-Duration Thresholds for Landslide Occurrence: Case Study from Wayanad (India)

Water ◽

10.3390/w12041000 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1000 ◽

Cited By ~ 6

Author(s):

Minu Treesa Abraham ◽

Neelima Satyam ◽

Ascanio Rosi ◽

Biswajeet Pradhan ◽

Samuele Segoni

Keyword(s):

Early Warning ◽

Early Warning System ◽

Regional Scale ◽

Warning System ◽

Extreme Rainfall ◽

Rain Gauge ◽

Rainfall Event ◽

Future Application ◽

Extreme Rainfall Event ◽

Selection Of

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity–Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.

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