Probability and deterministic amount of precipitation on the multi-model ensemble

2020 ◽  
Author(s):  
Juwon Kim ◽  
Hae-Jin Kong ◽  
Hyuncheol Shin
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Ensemble ◽  
Post Processing ◽  
Weather Forecasts ◽  
The Impact

<p>Multi-model ensemble using statistical post-processing is one of the methods to provide the impact of uncertainties of the Numerical Weather Prediction (NWP) models, with low cost and better accuracy for extreme weather forecasts. Extreme weather events such as heat/cold waves, windstorms, and heavy rainfall result in severe damage in human life and properties. However, the performance of the NWP models, particularly, heavy rain forecast is still low due to the intermittent and non-Gaussian properties. The light rain tends to be overestimated and the strong rain tends to be underestimated averagely on the NWP models. Thus the multi-model ensemble using statistical post-processing is activated to correct the discrepancies between the observation and the model intensity of precipitation.<br>The aim of this study is to provide the improvement of precipitation forecasts in probabilistic and deterministic aspects using a multi-model ensemble method with more weights on the less error and without any bias correction. Six types of models, namely, Local Data assimilation and Prediction System (LDPS), Local ENsemble System (LENS), Global Data assimilation and Prediction System (GDPS), Ensemble Prediction System-Global (EPSG) of Korea Meteorological Administration (KMA), the single and ensemble models of European Centre for Medium-Range Weather Forecasts (ECMWF), are used to blend. The preliminary results of the multi-model ensemble show similar results to the ECMWF ensemble mean in deterministic for 3-hourly accumulated precipitation over the East Asia and the middle of the performance among individual models in probabilistic over the South Korea. More details of the methodology, results, and improvements will be discussed in the presentation.</p>

scholarly journals Deep learning for post-processing ensemble weather forecasts

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200092 ◽  
Author(s):  
Peter Grönquist ◽  
Chengyuan Yao ◽  
Tal Ben-Nun ◽  
Nikoli Dryden ◽  
Peter Dueben ◽  
...  
Keyword(s):  
Numerical Models ◽  
Computational Cost ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Ensemble Prediction ◽  
Climate Modelling ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Weather Events

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to the global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore

2019 ◽  
Vol 36 (3) ◽  
Author(s):  
Xiang-Yu Huang ◽  
Dale Barker ◽  
Stuart Webster ◽  
Anurag Dipankar ◽  
Adrian Lock ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Prediction System ◽  
Local Data ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
The United Kingdom ◽  
Convective Scale ◽  
The Impact

Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.

scholarly journals A New View of Heat Wave Dynamics and Predictability over the Eastern Mediterranean

2020 ◽  
Author(s):  
Assaf Hochman ◽  
Sebastian Scher ◽  
Julian Quinting ◽  
Joaquim G. Pinto ◽  
Gabriele Messori
Keyword(s):  
Dynamical Systems ◽  
Heat Wave ◽  
Heat Waves ◽  
Eastern Mediterranean ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Stress Index ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

Abstract. Skillful forecasts of extreme weather events have a major socio-economic relevance. Here, we compare two complementary approaches to diagnose the predictability of extreme weather: recent developments in dynamical systems theory and numerical ensemble weather forecasts. The former allows us to define atmospheric configurations in terms of their persistence and local dimension, which inform on how the atmosphere evolves to and from a given state of interest. These metrics may be used as proxies for the intrinsic predictability of the atmosphere, which depends exclusively on the atmosphere’s properties. Ensemble weather forecasts inform on the practical predictability of the atmosphere, which primarily depends on the performance of the numerical model used. We focus on heat waves affecting the Eastern Mediterranean. These are identified using the Climatic Stress Index (CSI), which was explicitly developed for the summer weather conditions in this region and differentiates between heat waves (upper decile) and cool days (lower decile). Significant differences are found between the two groups from both the dynamical systems and the numerical weather prediction perspectives. Specifically, heat waves show relatively stable flow characteristics (high intrinsic predictability), but comparatively low practical predictability (large model spread/error). For 500 hPa geopotential height fields, the intrinsic predictability of heat waves is lowest at the event’s onset and decay. We relate these results to the physical processes governing Eastern Mediterranean summer heat waves: adiabatic descent of the air parcels over the region and the geographical origin of the air parcels over land prior to the onset of a heat wave. A detailed analysis of the mid-August 2010 record-breaking heat wave provides further insights into the range of different regional atmospheric configurations conducive to heat waves. We conclude that the dynamical systems approach can be a useful complement to conventional numerical forecasts for understanding the dynamics of Eastern Mediterranean heat waves.

scholarly journals Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

2013 ◽  
Vol 141 (5) ◽  
pp. 1506-1526 ◽  
Author(s):  
Christophe Lavaysse ◽  
Marco Carrera ◽  
Stéphane Bélair ◽  
Normand Gagnon ◽  
Ronald Frenette ◽  
...  
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Initial Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Surface Parameter ◽  
Ensemble Prediction System ◽  
Surface Parameters ◽  
Screen Level ◽  
The Impact

Abstract The aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme has been coupled to Environment Canada’s numerical weather prediction model within the REPS. For 20 summer periods in 2009, stochastic perturbations of surface parameters have been generated in several experiments. Each experiment corresponds to 20 simulations differing by the perturbations at the initial time of one or several surface parameters or prognostic variables. The sensitivity to these perturbations is quantified especially for 2-m temperature, 10-m wind speed, cloud fraction, and precipitation up to 48-h lead time. Spatial variability of these sensitivities over the North American continent shows that soil moisture, albedo, leaf area index, and SST have the largest impacts on the screen-level variables. The temporal evolution of these sensitivities appears to be closely linked to the diurnal cycle of the boundary layer. The surface perturbations are shown to increase the ensemble spread of the REPS for all screen-level variables especially for 2-m temperature and 10-m wind speed during daytime. A preliminary study of the impact on the ensemble forecast has shown that the inclusion of the surface perturbations tends to significantly increase the 2-m temperature and 10-m wind speed skill.

scholarly journals Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation

2017 ◽  
Vol 32 (5) ◽  
pp. 1727-1744 ◽  
Author(s):  
Seth Saslo ◽  
Steven J. Greybush
Keyword(s):  
Data Assimilation ◽  
Great Lakes ◽  
Forecast Error ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Prediction ◽  
Great Lakes Region ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Lake Effect

Abstract Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.

scholarly journals Assimilation of Data Derived from Optimal-member Products of TREPS for Convection-Permitting TC Forecasting over Southern China

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 84
Author(s):  
Xubin Zhang ◽  
Meiling Chen
Keyword(s):  
Data Assimilation ◽  
Southern China ◽  
Surface Wind ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The Impact ◽  
Partial Cycle ◽  
Derived Data ◽  
Positive Impacts

To improve the landfalling tropical cyclone (TC) forecasting, the pseudo inner-core observations derived from the optimal-member forecast (OPT) and its probability-matched mean (OPTPM) of a mesoscale ensemble prediction system, namely TREPS, were assimilated in a partial-cycle data assimilation (DA) system based on the three-dimensional variational method. The impact of assimilating the derived data on the 12-h TC forecasting was evaluated over 17 TCs making landfall on Southern China during 2014–2016, based on the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model with the horizontal resolution of 0.03°. The positive impacts of assimilating the OPT-derived data were found in predicting some variables, such as the TC intensity, lighter rainfall, and stronger surface wind, with statistically significant impacts at partial lead times. Compared with assimilation of the OPT-derived data, assimilation of the OPTPM-derived data generally brought improvements in the forecasts of TC track, intensity, lighter rainfall, and weaker surface wind. When the data with higher accuracy was assimilated, the positive impacts of assimilating the OPTPM-derived data on the forecasts of heavier rainfall and stronger surface wind were more evident. The improved representation of initial TC circulation due to assimilating the derived data improved the TC forecasting, which was intuitively illustrated in the case study of Mujigae.

scholarly journals Experiments with EOF-Based Perturbation Methods and Their Impact on the CPTEC/INPE Ensemble Prediction System

2009 ◽  
Vol 137 (4) ◽  
pp. 1438-1459 ◽  
Author(s):  
Antônio Marcos Mendonça ◽  
JoséPaulo Bonatti
Keyword(s):  
South America ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Simultaneous Application ◽  
Ensemble Prediction System ◽  
Perturbation Versus ◽  
The Impact

Abstract The impact of modifications of the perturbation method based on empirical orthogonal functions (EOFs) used operationally upon the ensemble prediction system (EPS) at the Center for Weather Prediction and Climate Studies/National Institute for Space Research (CPTEC/INPE) is evaluated. The main changes proposed in this study are to apply the EOF method to perturb the midlatitudes, apply additional perturbations to the surface pressure (P) and specific humidity (Q) fields, and compute regional perturbations over South America. The impact of these modifications in the characteristics of the initial perturbations and in the quality of the EPS forecasts is investigated. The EPS forecasts are evaluated through average statistical scores over the period 15 December 2004–15 February 2005. The statistical scores used in the evaluation are pattern anomaly correlation, root-mean-square error, ensemble spread, Brier skill score, and perturbation versus error correlation analysis (PECA). Results indicate that with the inclusion of perturbations on P and Q, EOF-based perturbations acquire a more baroclinic structure. It is also observed that the simultaneous application of additional perturbations both in the extratropics and to the P and Q fields improves the performance of CPTEC EPS and enhances the quality of forecast perturbations. Moreover, regional EOF-based perturbations computed over South America have positive impact on the ensemble forecasts over the target region.

scholarly journals A new view of heat wave dynamics and predictability over the eastern Mediterranean

2021 ◽  
Vol 12 (1) ◽  
pp. 133-149
Author(s):  
Assaf Hochman ◽  
Sebastian Scher ◽  
Julian Quinting ◽  
Joaquim G. Pinto ◽  
Gabriele Messori
Keyword(s):  
Dynamical Systems ◽  
Heat Wave ◽  
Heat Waves ◽  
Eastern Mediterranean ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Stress Index ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

Abstract. Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two complementary approaches to diagnose the predictability of extreme weather: recent developments in dynamical systems theory and numerical ensemble weather forecasts. The former allows us to define atmospheric configurations in terms of their persistence and local dimension, which provides information on how the atmosphere evolves to and from a given state of interest. These metrics may be used as proxies for the intrinsic predictability of the atmosphere, which only depends on the atmosphere's properties. Ensemble weather forecasts provide information on the practical predictability of the atmosphere, which partly depends on the performance of the numerical model used. We focus on heat waves affecting the eastern Mediterranean. These are identified using the climatic stress index (CSI), which was explicitly developed for the summer weather conditions in this region and differentiates between heat waves (upper decile) and cool days (lower decile). Significant differences are found between the two groups from both the dynamical systems and the numerical weather prediction perspectives. Specifically, heat waves show relatively stable flow characteristics (high intrinsic predictability) but comparatively low practical predictability (large model spread and error). For 500 hPa geopotential height fields, the intrinsic predictability of heat waves is lowest at the event's onset and decay. We relate these results to the physical processes governing eastern Mediterranean summer heat waves: adiabatic descent of the air parcels over the region and the geographical origin of the air parcels over land prior to the onset of a heat wave. A detailed analysis of the mid-August 2010 record-breaking heat wave provides further insights into the range of different regional atmospheric configurations conducive to heat waves. We conclude that the dynamical systems approach can be a useful complement to conventional numerical forecasts for understanding the dynamics and predictability of eastern Mediterranean heat waves.

scholarly journals Impact of Extreme Weather on Healthcare Utilization by People with HIV in Metropolitan Miami

2021 ◽  
Vol 18 (5) ◽  
pp. 2442
Author(s):  
Daniel Samano ◽  
Shubhayu Saha ◽  
Taylor Corbin Kot ◽  
JoNell E. Potter ◽  
Lunthita M. Duthely
Keyword(s):  
Relative Risk ◽  
The United States ◽  
Extreme Weather ◽  
South Florida ◽  
Heat Index ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Steady Increase ◽  
Daily Weather Data ◽  
The Impact

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.

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