scholarly journals Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

2008 ◽  
Vol 8 (3) ◽  
pp. 445-460 ◽  
Author(s):  
M. P. Mittermaier
Keyword(s):  
Weather Prediction ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Intensity Scale ◽  
Radar Rainfall ◽  
Scale Method ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
The Uk ◽  
Rainfall Estimates

Abstract. A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

scholarly journals Equatorial Waves and the Skill of NCEP and ECMWF Numerical Weather Prediction Systems

2018 ◽  
Vol 146 (6) ◽  
pp. 1763-1784 ◽  
Author(s):  
Juliana Dias ◽  
Maria Gehne ◽  
George N. Kiladis ◽  
Naoko Sakaeda ◽  
Peter Bechtold ◽  
...  
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Lead Times ◽  
Forecast Errors ◽  
Forecast System ◽  
Convective Processes ◽  
Skill Scores ◽  
The Tropics

Despite decades of research on the role of moist convective processes in large-scale tropical dynamics, tropical forecast skill in operational models is still deficient when compared to the extratropics, even at short lead times. Here we compare tropical and Northern Hemisphere (NH) forecast skill for quantitative precipitation forecasts (QPFs) in the NCEP Global Forecast System (GFS) and ECMWF Integrated Forecast System (IFS) during January 2015–March 2016. Results reveal that, in general, initial conditions are reasonably well estimated in both forecast systems, as indicated by relatively good skill scores for the 6–24-h forecasts. However, overall, tropical QPF forecasts in both systems are not considered useful by typical metrics much beyond 4 days. To quantify the relationship between QPF and dynamical skill, space–time spectra and coherence of rainfall and divergence fields are calculated. It is shown that while tropical variability is too weak in both models, the IFS is more skillful in propagating tropical waves for longer lead times. In agreement with past studies demonstrating that extratropical skill is partially drawn from the tropics, a comparison of daily skill in the tropics versus NH suggests that in both models NH forecast skill at lead times beyond day 3 is enhanced by tropical skill in the first couple of days. As shown in previous work, this study indicates that the differences in physics used in each system, in particular, how moist convective processes are coupled to the large-scale flow through these parameterizations, appear as a major source of tropical forecast errors.

scholarly journals Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter Storms

2017 ◽  
Vol 32 (3) ◽  
pp. 1057-1078 ◽  
Author(s):  
Steven J. Greybush ◽  
Seth Saslo ◽  
Richard Grumm
Keyword(s):  
East Coast ◽  
Initial Conditions ◽  
Wave Pattern ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Winter Storms ◽  
Position Errors ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
East Coast Winter Storms

Abstract The ensemble predictability of the January 2015 and 2016 East Coast winter storms is assessed, with model precipitation forecasts verified against observational datasets. Skill scores and reliability diagrams indicate that the large ensemble spread produced by operational forecasts was warranted given the actual forecast errors imposed by practical predictability limits. For the 2015 storm, uncertainties along the western edge’s sharp precipitation gradient are linked to position errors of the coastal low, which are traced to the positioning of the preceding 500-hPa wave pattern using the ensemble sensitivity technique. Predictability horizon diagrams indicate the forecast lead time in terms of initial detection, emergence of a signal, and convergence of solutions for an event. For the 2016 storm, the synoptic setup was detected at least 6 days in advance by global ensembles, whereas the predictability of mesoscale features is limited to hours. Convection-permitting WRF ensemble forecasts downscaled from the GEFS resolve mesoscale snowbands and demonstrate sensitivity to synoptic and mesoscale ensemble perturbations, as evidenced by changes in location and timing. Several perturbation techniques are compared, with stochastic techniques [the stochastic kinetic energy backscatter scheme (SKEBS) and stochastically perturbed parameterization tendency (SPPT)] and multiphysics configurations improving performance of both the ensemble mean and spread over the baseline initial conditions/boundary conditions (IC/BC) perturbation run. This study demonstrates the importance of ensembles and convective-allowing models for forecasting and decision support for east coast winter storms.

scholarly journals Subseasonal Forecasts of Tropical Cyclones in the Southern Hemisphere Using a Dynamical Multimodel Ensemble

2020 ◽  
Vol 35 (5) ◽  
pp. 1817-1829 ◽  
Author(s):  
Paul Gregory ◽  
Frederic Vitart ◽  
Rabi Rivett ◽  
Andrew Brown ◽  
Yuriy Kuleshov
Keyword(s):  
Data Assimilation ◽  
Southern Hemisphere ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Optimum Number ◽  
Ensemble Size ◽  
Multimodel Ensemble ◽  
Combining Forecasts ◽  
Forecast Lead Time ◽  
Forecasting System

AbstractSubseasonal tropical cyclone forecasts from two operational forecast models are verified for the 2017/18 and 2018/19 Southern Hemisphere cyclone seasons. The forecasts are generated using the ECMWF’s Medium- and Extended-Range Ensemble Integrated Forecasting System (IFS), and the Bureau of Meteorology’s seasonal forecasting system ACCESS-S1. Results show the IFS is more skillful than ACCESS-S1, which is attributed to the IFS’s greater ensemble size, increased spatial resolution, and data assimilation schemes. Applying a lagged ensemble with ACCESS-S1 increases forecast reliability, with the optimum number of lagged members being dependent on forecast lead time. To investigate the impacts of atmospheric assimilation at shorter lead times, comparisons were made between the Bureau of Meteorology’s ACCESS-S1 and ACCESS-GE2 systems, the latter a global Numerical Weather Prediction system running with the same resolution and model physics as ACCESS-S1 but using an ensemble Kalman filter for data assimilation. This comparison showed the data assimilation scheme used in the GE2 system gave improvements in forecast skill for days 8–10, despite the smaller ensemble size used in GE2 (24 members per forecast compared to 33). Finally, a multimodel ensemble was created by combining forecasts from both the IFS and ACCESS-S1. Using the multimodel ensemble gave improvements in forecast skill and reliability. This improvement is attributed to complementary spatial errors in both systems occurring across much of the Southern Hemisphere as well as an increase in the ensemble size.

scholarly journals Coupled hydro-meteorological modelling on HPC platform for high resolution extreme weather impact study

2016 ◽  
Author(s):  
Dehua Zhu ◽  
Shirley Echendu ◽  
Yunqing Xuan ◽  
Mike Webster ◽  
Ian Cluckie
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Hydrological Model ◽  
Numerical Solutions ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Weather Impact ◽  
Forecast Lead Time

Abstract. High performance computing (HPC) has long been used in the disciplines of atmospheric and oceanic sciences, and remains the main tool of choice to extract numerical solutions to complex geophysical problems on the global scale, often accompanied with very large numbers of degrees of freedoms. However, with the growing recognition that the spatially distributed feedback from the land surface is important to weather and the climate system, representation of the land surface is established with increasingly complex (and physically complete) models, which often leads to the coupling of heterogeneous models such as numerical weather prediction (NWP) models and hydrological models. As a result, the spatial grids and the temporal resolutions have become finer and thereby computers with far greater computational and storage capacity are in great demand than those used in the past. Additionally, impact-focused studies that require coupling of accurate simulations of weather/climate systems as well as impact-measuring hydrological models that demand larger computer resources in its own right. In this paper, we present a preliminary analysis of an HPC-based hydrological modelling approach, which is aimed at utilising and maximising HPC power resource, to support the study on extreme weather impact due to climate change. Here, two case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) UKV, alongside a Linux-based hydrological model, HYdrological Predictions for the Environment (HYPE). The results of this study suggest that high resolution rainfall estimation produced by the UKV has similar performance to that of NIMROD radar rainfall products as input in a hydrological model, but with the added-value of much extended forecast lead-time.

scholarly journals Error Propagation of Radar Rainfall Nowcasting Fields through a Fully Distributed Flood Forecasting Model

2007 ◽  
Vol 46 (6) ◽  
pp. 932-940 ◽  
Author(s):  
Enrique R. Vivoni ◽  
Dara Entekhabi ◽  
Ross N. Hoffman
Keyword(s):  
Error Propagation ◽  
Lead Time ◽  
Hydrologic Model ◽  
Forecast Errors ◽  
Flood Forecast ◽  
Spatiotemporal Resolution ◽  
Radar Rainfall ◽  
Forecast Lead Time ◽  
Flood Simulations ◽  
Storm Characteristics

Abstract This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locations depending on the forecast lead time and storm characteristics. Interior differences in error propagation are shown to be effectively averaged out for larger catchment scales.

scholarly journals Verification of real-time WRF-ARW forecast in IMD during monsoon 2010

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 333-356
Author(s):  
ANANDA K. DAS ◽  
P. K. KUNDU ◽  
S. K. ROY BHOWMIK ◽  
M. RATHEE
Keyword(s):  
Real Time ◽  
Meteorological Parameters ◽  
Mesoscale Model ◽  
Monsoon Season ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Forecast Errors ◽  
Orographic Rainfall ◽  
Skill Scores ◽  
Model Efficiency

Performance of the mesoscale model WRF-ARW has been evaluated for whole monsoon season of 2011. The real-time model forecasts are generated day to day in India meteorological Department for short-range weather prediction over the Indian region. Verification of rainfall forecasts has been carried out against observed rainfall analysis whereas for all other meteorological parameters verification analysis which was generated using WRFDA assimilation system. Traditional continuous scores and categorical skill scores are computed over seven different zones in India in the verification of rainfall. For other parameters (upper-air as well as surface), continuous scores are evaluated with temporal and spatial features during whole season. The forecast errors of meteorological parameters other than rainfall are analyzed to portray the model efficiency in maintaining monsoon features in large scale along with localized pattern. In the study, time series of errors throughout the season also has been maneuvered to evaluate model forecasts during diverse phases of monsoon. Categorical scores suggest the model forecasts are reliable up to moderate rainfall category for all seven zones.  But, rainfall areas with rainfall above 35.5 mm per day associated with migrated weather system from Indian seas could not be predicted as the model displaces them in the forecast. The verification for a whole monsoon season has shown that the model has capability to predict orographic rainfall for the interactive areas with low level monsoon flow over Western Ghats.  The model efficiency are in general brought out for a single monsoon season and errors characteristics are discussed for further improvement which could not perceived during real-time use of the model. 

The role of arctic forecast errors in the evolution of northern extra-tropical forecast skill

2020 ◽  
Author(s):  
Thomas Haiden
Keyword(s):  
Forecast Error ◽  
Weather Prediction ◽  
Polar Vortex ◽  
Forecast Skill ◽  
The Arctic ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Medium Range Forecast ◽  
Medium Range

<p><br>Increases in extra-tropical numerical weather prediction (NWP) skill over the last decades have been well documented. The role of the Arctic, defined here as the area north of 60N, in driving (or slowing) this improvement has however not been systematically assessed. To investigate this question, spatial patterns of changes in medium-range forecast error of ECMWF’s Integrated Forecast System (IFS) are analysed both for deterministic and ensemble forecasts. The robustness of these patterns is evaluated by comparing results for different parameters and levels, and by comparing them with the respective changes in ERA5 forecasts, which are based on a ‘frozen’ model version. In this way the effect of different atmospheric variability on the estimation of skill improvement can be minimized. It is shown to what extent the strength of the polar vortex as measured by the Arctic and North-Atlantic Oscillation (AO, NAO) influences the magnitude of forecast errors. Results may indicate whether recent and future changes in these indices, possibly driven in part by sea-ice decline, could systematically affect the longer-term evolution of medium-range forecast skill.</p>

scholarly journals Can an ensemble give anything more than Gaussian probabilities?

2003 ◽  
Vol 10 (6) ◽  
pp. 469-475 ◽  
Author(s):  
J. C. W. Denholm-Price
Keyword(s):  
Weather Prediction ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
Gaussian Probability Density Function ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Gaussian Fit ◽  
Probability Forecasts ◽  
The Uk ◽  
Probability Density Function Pdf

Abstract. Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant increases in skill were achieved from bias-corrected, calibrated forecasts and for probability forecasts of thresholds that are located well inside the climatological limits at the examined sites. Forecast probabilities for more climatologically-extreme thresholds, where the verification more often lies within the tails or outside of the PDF, showed little difference in skill between the forecast PDF and the Gaussian forecast.

scholarly journals Evaluating CFSv2 Subseasonal Forecast Skill with an Emphasis on Tropical Convection

2017 ◽  
Vol 145 (9) ◽  
pp. 3795-3815 ◽  
Author(s):  
Nicholas J. Weber ◽  
Clifford F. Mass
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Tropical Convection ◽  
Forecast Skill ◽  
Lead Times ◽  
Forecast Errors ◽  
Predictive Skill ◽  
Forecast Lead Time ◽  
Temporal Averaging ◽  
Total Variability

This study examines the subseasonal predictive skill of CFSv2, focusing on the spatial and temporal distributions of error for large-scale atmospheric variables and the realism of simulated tropical convection. Errors in a 4-member CFSv2 ensemble forecast saturate at lead times of approximately 3 weeks for 500-hPa geopotential height and 5 weeks for 200-hPa velocity potential. Forecast errors exceed those of climatology at lead times beyond 2 weeks. Sea surface temperature, which evolves more slowly than atmospheric fields, maintains skill over climatology through the first month. Spatial patterns of error are robust across lead times and temporal averaging periods, increasing in amplitude as lead time increases and temporal averaging period decreases. Several significant biases were found in the CFSv2 reforecasts, such as too little convection over tropical land and excessive convection over the ocean. The realism of simulated tropical convection and associated teleconnections degrades with forecast lead time. Large-scale tropical convection in CFSv2 is more stationary than observed. Forecast MJOs propagate eastward too slowly and those initiated over the Indian Ocean have trouble traversing beyond the Maritime Continent. The total variability of simulated propagating convection is concentrated at lower frequencies compared to observed convection, and is more fully described by a red spectrum, indicating weak representation of convectively coupled waves. These flaws in simulated tropical convection, which could be tied to problems with convective parameterization and associated mean state biases, affect atmospheric teleconnections and may degrade extended global forecast skill.

scholarly journals The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part I: Methodology and Seasonal Comparison

2010 ◽  
Vol 138 (8) ◽  
pp. 3084-3106 ◽  
Author(s):  
Madalina Surcel ◽  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
United States ◽  
Interannual Variability ◽  
Diurnal Cycle ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Rainfall ◽  
Diurnal Cycles ◽  
Skill Scores ◽  
Diurnal Cycle Of Precipitation ◽  
Measure Of Performance

Abstract The diurnal cycle of precipitation over the continental United States is characterized through the analysis of radar rainfall maps and is used as a measure of performance of the Global Environmental Multiscale (GEM) model during the spring (April–May) and summer (July–August) of 2008. The main interest is to determine the effects of different types of forcing (synoptic versus thermal) on the average daily variability of precipitation and on the model’s representation of it. A secondary objective is to study the interannual variability of the diurnal cycle. The investigation is based on the analysis of time–longitude diagrams of precipitation fields, of average statistics, and of model skill scores. The results show that the main differences between the spring and summer diurnal cycles are the duration of propagating systems, the frequency of convective events in the southeastern United States, and more interannual variability of the spring diurnal cycle. However, most interesting is that the timing of precipitation initiation over the Rockies is in phase with the cycle of solar warming for both seasons, despite the strong synoptic forcing during spring. Also, east of the Rockies, the diurnal cycle is mainly determined by transport mechanism and is consequently out of phase with the solar cycle. While GEM represents fairly well the timing of precipitation initiation along the Rockies during both seasons, it fails to correctly depict the propagation characteristics of these systems. During spring, the simulated systems show more variability in propagation paths than observed, while during summer, the observed propagation is simply not captured by GEM. This is probably a consequence of different propagation mechanisms acting in the model and in the atmosphere, and between spring and summer.

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