Introduction to Ensemble Forecast Applications and Showcases

2019 ◽  
pp. 1181-1185
Author(s):  
Massimiliano Zappa ◽  
S. J. van Andel ◽  
Hannah L. Cloke
Keyword(s):  
Ensemble Forecast

scholarly journals Accelerating assimilation development for new observing systems using EFSO

2018 ◽  
Vol 25 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Daisuke Hotta ◽  
Eugenia Kalnay ◽  
Takemasa Miyoshi ◽  
Tse-Chun Chen
Keyword(s):  
Selection Criteria ◽  
Ensemble Forecast ◽  
Data Selection ◽  
Development Work ◽  
Observation Data ◽  
Precipitation Data ◽  
Trial And Error ◽  
Satellite Precipitation ◽  
Selection Strategies ◽  
Observing Systems

Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

scholarly journals A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

2016 ◽  
Vol 20 (7) ◽  
pp. 2649-2667 ◽  
Author(s):  
Firas Saleh ◽  
Venkatsundar Ramaswamy ◽  
Nickitas Georgas ◽  
Alan F. Blumberg ◽  
Julie Pullen
Keyword(s):  
United States ◽  
River Basin ◽  
Hydrological Model ◽  
Hudson River ◽  
The United States ◽  
Ensemble Forecast ◽  
Short Term ◽  
Hurricane Irene ◽  
Hydrologic Event ◽  
Extreme Hydrological Event

Abstract. This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ∼  36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

Tornado Pathlength Forecasts from 2010 to 2011 Using Ensemble Updraft Helicity

2013 ◽  
Vol 28 (2) ◽  
pp. 387-407 ◽  
Author(s):  
Adam J. Clark ◽  
Jidong Gao ◽  
Patrick T. Marsh ◽  
Travis Smith ◽  
John S. Kain ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Strong Relationship ◽  
Object Identification ◽  
Ensemble Forecast ◽  
Identification Algorithm ◽  
Grid Spacing ◽  
Dimensional Object ◽  
Tornado Outbreaks ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Examining forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment, recent research diagnosed a strong relationship between the cumulative pathlengths of simulated rotating storms (measured using a three-dimensional object identification algorithm applied to forecast updraft helicity) and the cumulative pathlengths of tornadoes. This paper updates those results by including data from the 2011 SSEF system, and illustrates forecast examples from three major 2011 tornado outbreaks—16 and 27 April, and 24 May—as well as two forecast failure cases from June 2010. Finally, analysis updraft helicity (UH) from 27 April 2011 is computed using a three-dimensional variational data assimilation system to obtain 1.25-km grid-spacing analyses at 5-min intervals and compared to forecast UH from individual SSEF members.

scholarly journals Probabilistic Precipitation Forecast Postprocessing Using Quantile Mapping and Rank-Weighted Best-Member Dressing

2018 ◽  
Vol 146 (12) ◽  
pp. 4079-4098 ◽  
Author(s):  
Thomas M. Hamill ◽  
Michael Scheuerer
Keyword(s):  
Probability Distributions ◽  
The United States ◽  
Ensemble Forecast ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Precipitation Forecast ◽  
Prediction System ◽  
Quantile Mapping ◽  
State Dependent ◽  
Grid Points

Abstract Hamill et al. described a multimodel ensemble precipitation postprocessing algorithm that is used operationally by the U.S. National Weather Service (NWS). This article describes further changes that produce improved, reliable, and skillful probabilistic quantitative precipitation forecasts (PQPFs) for single or multimodel prediction systems. For multimodel systems, final probabilities are produced through the linear combination of PQPFs from the constituent models. The new methodology is applied to each prediction system. Prior to adjustment of the forecasts, parametric cumulative distribution functions (CDFs) of model and analyzed climatologies are generated using the previous 60 days’ forecasts and analyses and supplemental locations. The CDFs, which can be stored with minimal disk space, are then used for quantile mapping to correct state-dependent bias for each member. In this stage, the ensemble is also enlarged using a stencil of forecast values from the 5 × 5 surrounding grid points. Different weights and dressing distributions are assigned to the sorted, quantile-mapped members, with generally larger weights for outlying members and broader dressing distributions for members with heavier precipitation. Probability distributions are generated from the weighted sum of the dressing distributions. The NWS Global Ensemble Forecast System (GEFS), the Canadian Meteorological Centre (CMC) global ensemble, and the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast data are postprocessed for April–June 2016. Single prediction system postprocessed forecasts are generally reliable and skillful. Multimodel PQPFs are roughly as skillful as the ECMWF system alone. Postprocessed guidance was generally more skillful than guidance using the Gamma distribution approach of Scheuerer and Hamill, with coefficients generated from data pooled across the United States.

scholarly journals Initial perturbations based on Ensemble Transfrom Kalman Filter with rescaling method for ensemble forecast

2021 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Hanbin Zhang ◽  
Hua Tian ◽  
Yining Shi
Keyword(s):  
Growth Rate ◽  
Kalman Filter ◽  
Weather Forecasting ◽  
Initial Perturbation ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Model Uncertainties ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts

AbstractEnsemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

scholarly journals An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

2021 ◽  
Author(s):  
Eva–Maria Walz ◽  
Marlon Maranan ◽  
Roderick van der Linden ◽  
Andreas H. Fink ◽  
Peter Knippertz
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Predictive Ability ◽  
Window Size ◽  
Predictive Performance ◽  
Ensemble Forecast ◽  
High Mountain ◽  
Rainfall Occurrence ◽  
Precipitation Measurement ◽  
Global Precipitation

AbstractCurrent numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

scholarly journals Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions

2021 ◽  
Author(s):  
Sashikumaar Ganesan ◽  
Deepak Subramani ◽  
Thivin Anandh ◽  
Divij Ghose ◽  
Giridhara R Babu
Keyword(s):  
Immune Escape ◽  
Ensemble Forecast ◽  
Disease Spread ◽  
Critical Parameters ◽  
New Wave ◽  
Public Health Response ◽  
Causal Factors ◽  
Appropriate Behavior ◽  
Policy Interventions ◽  
New Variant

We present an ensemble forecast for Wave-3 of COVID-19 in the state of Karnataka, India, using the IISc Population Balance Model for infectious disease spread. The reported data of confirmed, recovered, and deceased cases in Karnataka from 1 July 2020 to 4 July 2021 is utilized to tune the model's parameters, and an ensemble forecast is done from 5 July 2021 to 30 June 2022. The ensemble is built with 972 members by varying seven critical parameters that quantify the uncertainty in the spread dynamics (antibody waning, viral mutation) and interventions (pharmaceutical, non-pharmaceutical). The probability of Wave-3, the peak date distribution, and the peak caseload distribution are estimated from the ensemble forecast. Our analysis shows that the most significant causal factors are compliance to Covid-appropriate behavior, daily vaccination rate, and the immune escape new variant emergence-time. These causal factors determine when and how severe the Wave-3 of COVID-19 would be in Karnataka. We observe that when compliance to Covid-Appropriate Behavior is good (i.e., lockdown-like compliance), the emergence of new immune-escape variants beyond Sep '21 is unlikely to induce a new wave. A new wave is inevitable when compliance to Covid-Appropriate Behavior is only partial. Increasing the daily vaccination rates reduces the peak active caseload at Wave-3. Consequently, the hospitalization, ICU, and Oxygen requirements also decrease. Compared to Wave-2, the ensemble forecast indicates that the number of daily confirmed cases of children (0-17 years) at Wave-3's peak could be seven times more on average. Our results provide insights to plan science-informed policy interventions and public health response.

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