The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation

2014 ◽  
Vol 140 (682) ◽  
pp. 1552-1562 ◽  
Author(s):  
C. Kühnlein ◽  
C. Keil ◽  
G. C. Craig ◽  
C. Gebhardt
Keyword(s):  
Initial Condition ◽  
Ensemble Forecasts ◽  
Convective Scale ◽  
The Impact

scholarly journals Impact of Stochastically Perturbed Terminal Velocities on Convective-Scale Ensemble Forecasts of Precipitation

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shizhang Wang ◽  
Xiaoshi Qiao ◽  
Jinzhong Min
Keyword(s):  
Real Data ◽  
Terminal Velocity ◽  
Precipitation Forecast ◽  
Ensemble Forecasts ◽  
Microphysics Scheme ◽  
Light Rain ◽  
Hourly Precipitation ◽  
Error Characteristics ◽  
Convective Scale ◽  
The Impact

The impact of stochastically perturbing the terminal velocities of hydrometeors on convective-scale ensemble forecasts of precipitation was examined. An idealized supercell storm case was first used to determine the terminal velocity error characteristics for a one-moment microphysics scheme in terms of the terminal velocities from a two-moment scheme. Two real cases were employed to evaluate the forecast skills resulting from perturbing the terminal velocities with real data. The results indicated that the one-moment scheme produced terminal velocities that were approximately three times higher than those of the two-moment scheme for snow and hail, which often resulted in overpredictions of hourly precipitation and areal accumulated precipitation. Therefore, stochastically perturbing the terminal velocities according to their error characteristics matched the observed hourly precipitation and areal accumulated precipitation better than the symmetrical perturbations. For the two-moment scheme, the symmetrical perturbations of the terminal velocities tended to produce lower falling speeds of precipitation hydrometeors; therefore, more light rain was produced. Compared to the unperturbed two-moment scheme, symmetrically perturbing the terminal velocities resulted in smaller precipitation errors when precipitation was overestimated but comparable or slightly larger precipitation errors when precipitation was underestimated. This work demonstrates the sensitivity of precipitation ensemble forecasts to terminal velocity perturbations and the potential benefits of adopting these perturbations; however, whether the perturbations actually result in significant improvements in precipitation forecast skill needs further study.

scholarly journals Assimilating AIRS Temperature and Mixing Ratio Profiles Using an Ensemble Kalman Filter Approach for Convective-Scale Forecasts

2012 ◽  
Vol 27 (3) ◽  
pp. 541-564 ◽  
Author(s):  
Thomas A. Jones ◽  
David J. Stensrud
Keyword(s):  
Kalman Filter ◽  
Weather Prediction ◽  
Forecast Model ◽  
Mixing Ratio ◽  
Ensemble Forecasts ◽  
Southern Plains ◽  
Ensemble Mean ◽  
Convective Scale ◽  
Filter Approach ◽  
The Impact

Abstract One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

scholarly journals The Impact of Mesoscale Environmental Uncertainty on the Prediction of a Tornadic Supercell Storm Using Ensemble Data Assimilation Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Nusrat Yussouf ◽  
Jidong Gao ◽  
David J. Stensrud ◽  
Guoqing Ge
Keyword(s):  
Data Assimilation ◽  
Environmental Variability ◽  
Ensemble Forecasts ◽  
Near Surface ◽  
Model Based ◽  
Supercell Storm ◽  
Convective Scale ◽  
Physics Ensemble ◽  
Background Fields ◽  
The Impact

Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative.

scholarly journals The importance of atmosphere-ocean-wave coupling in ensemble regional convective-scale forecasts of midlatitude cyclones

2021 ◽  
Author(s):  
Emanuele Silvio Gentile ◽  
Suzanne L. Gray ◽  
Janet F. Barlow ◽  
Huw W. Lewis
Keyword(s):  
Wind Speed ◽  
Ocean Wave ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Wave Coupling ◽  
Stochastic Perturbations ◽  
Wind Speeds ◽  
Deterministic Systems ◽  
Convective Scale ◽  
The Impact

<p>Convective-scale ensemble prediction systems (EPS) are critical tools to accurately forecast damaging surface winds in the short range,  capturing the local details of their variability and providing guidance on the associated forecast uncertainty. Due to computational cost, operational convective-scale EPS are atmosphere-only models, which represent ocean and wave effects through sea-state independent parametrizations, and therefore do not account for the impact of an evolving ocean and wave state during the forecast. Benefits of integrating atmosphere, ocean, and wave feedbacks into a single coupled multimodel system have been shown by global-scale deterministic systems and EPS, and convective-scale deterministic systems. These benefits lead to the question of what are the corresponding benefits of coupling in convective-scale EPS. To address this question, we present the first convective-scale regional ensemble coupled system focused on the UK domain and surrounding seas (termed RECS-UK). We demonstrate the robustness of the impact of atmosphere-ocean-wave coupling and stochastic perturbations to model physics parametrizations on forecasts of extratropical cyclone Ciara and quantify the importance of these coupling impacts relative to initial condition error. </p><p>Coupling to the ocean leads to localised reductions in the 10-m wind speeds due to cooling of sea surface temperatures, which increase the stability in the surface layer. However, these localised impacts on coupling to ocean are not apparent when comparing the ensemble strike probabilities of exceeding a storm wind threshold (set to 20 m s<sup>-1</sup><sup>)</sup> for the atmosphere-ocean-coupled and control (atmosphere-only) ensembles. In contrast, coupling additionally to waves leads to substantial reductions in wind strike probability and consistently reduces, by up to 1 m s<sup>-1</sup><sup>,</sup> the ensemble forecast median and mean of Ciara’s wind speeds at all simulation hours during which Ciara is in the model domain. Each atmosphere-ocean-wave coupled ensemble member simulates the dynamical response of wind speeds to the forced young ocean waves, with maximum reductions in high wind speed regions. The largest 10-m wind speed spread from stochastic and initial condition perturbations is found away from the strongest wind speed regions of Ciara, but the impact of coupling to waves is more enhanced in these strongest wind speed regions, and is also comparable in size there with the largest sensitivity to stochastic and initial condition perturbations. The implications of this work are that the impacts on 10-m wind speed of coupling convective-scale atmospheric models to ocean and wave models can be robust across an ensemble and be of comparable size to those of initial condition and stochastic physics perturbations. However, convection-permitting atmosphere-ocean-wave coupled EPS should be assessed in different meteorological conditions and further tested on longer timescales prior to operational implementation. </p>

scholarly journals Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble Forecasts

2012 ◽  
Vol 27 (4) ◽  
pp. 972-987 ◽  
Author(s):  
Yong Wang ◽  
Simona Tascu ◽  
Florian Weidle ◽  
Karin Schmeisser
Keyword(s):  
Horizontal Resolution ◽  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Initial Condition ◽  
Weather Variables ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Model Based ◽  
Surface Weather

Abstract The regional single-model-based Aire Limitée Adaptation Dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble prediction system (EPS) is evaluated and compared with the global ECMWF-EPS to investigate the added value of regional to global EPS models. ALADIN-LAEF consists of 16 perturbed members at 18-km horizontal resolution, while ECMWF-EPS includes 50 perturbed members at 50-km horizontal resolution. In ALADIN-LAEF, the atmospheric initial condition uncertainty is quantified by using blending, which combines large-scale uncertainty generated by the ECMWF-EPS singular-vector approach with small-scale perturbations resolved by the ALADIN breeding technique. The surface initial condition perturbations are generated by use of the noncycling surface breeding (NCSB) technique, and different physics schemes are employed for different forecast members to account for model uncertainties. The verification and comparison have been carried out for a 2-month period during summer 2007 over central Europe. The results show a quite favorable level of performance for ALADIN-LAEF compared to ECMWF-EPS for surface weather variables. ALADIN-LAEF adds more value to precipitation forecasts and has greater skill for 10-m wind and mean sea level pressure results than does ECMWF-EPS. For 2-m temperature, ALADIN-LAEF forecasts have larger spread, are statistically more consistent, but also have less skill than ECMWF-EPS due to the strong cold bias in the ALADIN forecasts. For the upper-air weather parameters, the forecast of ALADIN-LAEF has a larger spread, but the forecast skill of ALADIN-LAEF is from neutral to slightly inferior compared to ECMWF-EPS. It may be concluded that a regional single-model-based EPS with fewer ensemble members could provide more added value in terms of greater skill for near-surface weather variables than the global EPS with larger ensemble size, whereas it may have limitations when applied to upper-air weather variables.

Future changes in heatwaves over Africa at the convection-permitting scale

2021 ◽  
Author(s):  
Cathryn Birch ◽  
Lawrence Jackson ◽  
Declan Finney ◽  
John Marsham ◽  
Rachel Stratton ◽  
...  
Keyword(s):  
Climate Model ◽  
Daily Rainfall ◽  
Shortwave Radiation ◽  
Daily Maximum ◽  
Future Change ◽  
Driving Mechanisms ◽  
The Future ◽  
Convective Scale ◽  
Climate Model Simulations ◽  
The Impact

<p>Mean temperatures and their extremes have increased over Africa since the latter half of the 20th century and this trend is projected to continue, with very frequent, intense and often deadly heatwaves likely to occur very regularly over much of Africa by 2100. It is crucial that we understand the scale of the future increases in extremes and the driving mechanisms. We diagnose daily maximum wet bulb temperature heatwaves, which allows for both the impact of temperature and humidity, both critical for human health and survivability. During wet bulb heatwaves, humidity and cloud cover increase, which limits the surface shortwave radiation flux but increases longwave warming. It is found from observations and ERA5 reanalysis that approximately 30% of wet bulb heatwaves over Africa are associated with daily rainfall accumulations of more than 1 mm/day on the first day of the heatwave. The first ever pan-African convection-permitting climate model simulations of present-day and RCP8.5 future climate are utilised to illustrate the projected future change in heatwaves, their drivers and their sensitivity to the representation of convection. Compared to ERA5, the convection-permitting model better represents the frequency and magnitude of present-day wet bulb heatwaves than a version of the model with more traditional parameterised convection. The future change in heatwave frequency, duration and magnitude is also larger in the convective-scale simulation, suggesting CMIP-style models may underestimate the future change in wet bulb heat extremes over Africa. The main reason for the larger future change appears to be the ability of the model to produce larger anomalies relative to its climatology in precipitation, cloud and the surface energy balance.</p>

scholarly journals A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

2007 ◽  
Vol 135 (4) ◽  
pp. 1424-1438 ◽  
Author(s):  
Andrew R. Lawrence ◽  
James A. Hansen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Ensemble Size ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Chaotic Model ◽  
The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

Role of initial condition and parametric uncertainty in a severe hailstorm forecast

2021 ◽  
Author(s):  
Patrick Kuntze ◽  
Annette Miltenberger ◽  
Corinna Hoose ◽  
Michael Kunz
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Parametric Uncertainty ◽  
Radar Data ◽  
Cloud Microphysics ◽  
Initial Condition ◽  
Relative Contribution ◽  
Cloud Properties ◽  
Set Up ◽  
The Impact

<p>Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.<br>To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.<br>In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.</p>

 The Climate Response to Emissions Reductions due to COVID-19

2021 ◽  
Author(s):  
Chris Jones ◽  
Keyword(s):  
Atmospheric Composition ◽  
Shortwave Radiation ◽  
Initial Condition ◽  
Emissions Reductions ◽  
Near Surface ◽  
Surface Climate ◽  
Climate Signals ◽  
Near Term ◽  
Regional Analyses ◽  
The Impact

<p>Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We perform a coordinated Intercomparison, CovidMIP, of Earth System model simulations to assess the impact on climate of these emissions reductions. Eleven models performed multiple initial-condition ensembles to produce over 280 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over East Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.</p><p>This first-look at results has focussed on surface climate, but future analysis will include attribution of drivers of climate signals; longer term implications of emissions reductions and options for economic recovery; quantifying changes in extremes; influence on atmospheric circulation and the carbon cycle.</p>

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