scholarly journals A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

2011 ◽  
Vol 12 (5) ◽  
pp. 713-728 ◽  
Author(s):  
Lan Cuo ◽  
Thomas C. Pagano ◽  
Q. J. Wang
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Research Field ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts ◽  
Active Research ◽  
Nwp Model

Abstract Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.

scholarly journals Evaluations of Upper-Level Turbulence Diagnostics Performance Using the Graphical Turbulence Guidance (GTG) System and Pilot Reports (PIREPs) over East Asia

2011 ◽  
Vol 50 (9) ◽  
pp. 1936-1951 ◽  
Author(s):  
Jung-Hoon Kim ◽  
Hye-Yeong Chun ◽  
Robert D. Sharman ◽  
Teddie L. Keller
Keyword(s):  
East Asia ◽  
Lead Time ◽  
Large Scale ◽  
Weather Prediction ◽  
Analysis Data ◽  
Atmospheric Conditions ◽  
Jet Streams ◽  
Forecast Lead Time ◽  
Upper Level ◽  
Nwp Model

AbstractThe forecast skill of upper-level turbulence diagnostics is evaluated using available turbulence observations [viz., pilot reports (PIREPs)] over East Asia. The six years (2003–08) of PIREPs used in this study include null, light, and moderate-or-greater intensity categories. The turbulence diagnostics used are a subset of indices in the Graphical Turbulence Guidance (GTG) system. To investigate the optimal performance of the component GTG diagnostics and GTG combinations over East Asia, various statistical evaluations and sensitivity tests are performed. To examine the dependency of the GTG system on the operational numerical weather prediction (NWP) model, the GTG system is applied to both the Regional Data Assimilation and Prediction System (RDAPS) analysis data and Global Forecasting System (GFS) analysis and forecast data with 30-km and 0.3125° (T382) horizontal grid spacings. The dependency of the temporal variation in the PIREP and GFS data and the forecast lead time of the GFS-based GTG combination are also investigated. It is found that the forecasting performance of the GTG system varies with year and season according to the annual and seasonal variations in the large-scale atmospheric conditions over the East Asia region. The wintertime GTG skill is the highest, because most GTG component diagnostics are related to jet streams and upper-level fronts. The GTG skill improves as the number of PIREP samples and the vertical resolution of the underlying NWP analysis data increase, and the GTG performance decreases as the forecast lead time increases from 0 to 12 h.

scholarly journals Evaluation and Correction of Quantitative Precipitation Forecast by Storm-Scale NWP Model in Jiangsu, China

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Gaili Wang ◽  
Dan Wang ◽  
Ji Yang ◽  
Liping Liu
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Primary Objective ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Structure Amplitude ◽  
Verification Method ◽  
Amplitude Correction ◽  
Regional Prediction ◽  
Nwp Model

With the development of high-performance computer systems and data assimilation techniques, storm-scale numerical weather prediction (NWP) models are gradually used for short-term deterministic forecasts. The primary objective of this study is to evaluate and correct precipitation forecasts of a storm-scale NWP model called the advanced regional prediction system (ARPS). The evaluation and correction consider five heavy precipitation events that occurred in the summer of 2015 in Jiangsu, China. The performances of the original and corrected ARPS precipitation forecasts are evaluated as a function of lead time using standard measurements and a spatial verification method called Structure-Amplitude-Location (SAL). In general, the ARPS could not produce optimal forecasts for very short lead times, and the forecast accuracy improves with increasing lead time. The ARPS overestimates precipitation for all lead times, which is confirmed by large bias in many forecasts in the first and second quadrant of the diagram of SAL, especially at the 1 h lead time. The amplitude correction is performed by matching percentile values of the ARPS precipitation forecasts and observations for each lead time. Amplitude correction significantly improved the ARPS precipitation forecasts in terms of the considered performance indices of standard measures and A-component and S-component of SAL.

scholarly journals Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

2013 ◽  
Vol 17 (5) ◽  
pp. 1913-1931 ◽  
Author(s):  
D. L. Shrestha ◽  
D. E. Robertson ◽  
Q. J. Wang ◽  
T. C. Pagano ◽  
H. A. P. Hapuarachchi
Keyword(s):  
Numerical Weather Prediction ◽  
Diurnal Cycle ◽  
Lead Time ◽  
Weather Prediction ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Sampling Uncertainty ◽  
Numerical Weather ◽  
Skill Scores ◽  
Forecast Lead Time

Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

scholarly journals Confidence Interval Estimation for Quantitative Precipitation Forecasts (QPF) Using Short-Range Ensemble Forecasts (SREF)

2006 ◽  
Vol 21 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Jung-Sun Im ◽  
Keith Brill ◽  
Edwin Danaher
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Lead Time ◽  
Short Range ◽  
Grid Point ◽  
The United States ◽  
Regression Equations ◽  
Ensemble Forecasts ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts

Abstract The Hydrometeorological Prediction Center (HPC) at the NCEP has produced a suite of deterministic quantitative precipitation forecasts (QPFs) for over 40 yr. While the operational forecasts have proven to be useful in their present form, they offer no information concerning the uncertainties of individual forecasts. The purpose of this study is to develop a methodology to quantify the uncertainty in manually produced 6-h HPC QPFs (HQPFs) using NCEP short-range ensemble forecasts (SREFs). Results presented herein show the SREFs can predict the uncertainty of HQPFs. The correlation between HQPF absolute error (AE) and ensemble QPF spread (SP) is greater than 0.5 at 90.5% of grid points in the continental United States, exceeding 0.8 at 10% of these, for the 6-h forecast in winter. On the basis of the high correlation, the linear regression equations of AE on SP are derived at each point on a grid covering the United States. In addition, the regression equations for data categorized according to the observed and forecasted precipitation amounts are obtained and evaluated. Using the regression model equation parameters for 15 categorized ranges of HQPF at each horizontal grid point for each season and individual forecast lead time, an AE associated with an individual SP is predicted, as is the 95% confidence interval (CI) of the AE. Based on the AE CI forecast and the HQPF itself, the 95% CI of the HQPF is predicted as well. This study introduces an efficient and advanced method, providing an estimate of the uncertainty in the deterministic HQPF. Verification demonstrates the usefulness of the CI forecasts for a variety of classifications, such as season, CI range, HQPF, and forecast lead time.

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

2019 ◽  
Vol 23 (9) ◽  
pp. 3823-3841 ◽  
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.

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction

2009 ◽  
Vol 4 (4) ◽  
pp. 600-605 ◽  
Author(s):  
Hadi Kardhana ◽  
◽  
Akira Mano ◽  
Keyword(s):  
Numerical Weather Prediction ◽  
Lead Time ◽  
Weather Prediction ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Flood Prediction ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Rainfall Runoff Model ◽  
Runoff Model

Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2017 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only. However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

scholarly journals Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

scholarly journals Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

2019 ◽  
Vol 23 (1) ◽  
pp. 493-513 ◽  
Author(s):  
Samuel Monhart ◽  
Massimiliano Zappa ◽  
Christoph Spirig ◽  
Christoph Schär ◽  
Konrad Bogner
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Added Value ◽  
Ensemble Prediction ◽  
Late Winter ◽  
Streamflow Prediction ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Temperature And Precipitation ◽  
Nwp Model

Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.

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