Downscaling of seasonal ensemble forecasts to the convection‐permitting scale over the Horn of Africa using the WRF model

Abstract An ensemble of 72 Weather Research and Forecasting (WRF) Model simulations is evaluated to examine the relationship between the track of Hurricane Sandy (2012) and its structural evolution. Initial and boundary conditions are obtained from ECMWF and GEFS ensemble forecasts initialized at 0000 UTC 25 October. The 5-day WRF simulations are initialized at 0000 UTC 27 October, 48 h into the global model forecasts. Tracks and cyclone phase space (CPS) paths from the 72 simulations are partitioned into 6 clusters using regression mixture models; results from the 4 most populous track clusters are examined. The four analyzed clusters vary in mean landfall location from southern New Jersey to Maine. Extratropical transition timing is the clearest difference among clusters; more eastward clusters show later Sandy–midlatitude trough interaction, warm seclusion formation, and extratropical transition completion. However, the intercluster variability is much smaller when examined relative to the landfall time of each simulation. In each cluster, a short-lived warm seclusion forms and contracts through landfall while lower-tropospheric potential vorticity concentrates at small radii. Despite the large-scale similarity among the clusters, relevant intercluster differences in landfall-relative extratropical transition are observed. In the easternmost cluster the Sandy–trough interaction is least intense and the warm seclusion decays the most by landfall. In the second most eastward cluster Sandy retains the most intact warm seclusion at landfall because of a slightly later (relative to landfall) and weaker trough interaction compared to the two most westward clusters. Nevertheless, the remarkably similar large-scale evolution of Sandy among the four clusters indicates the high predictability of Sandy’s warm seclusion extratropical transition before landfall.

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Downscaling of a seasonal ensemble forecast at the convection-permitting resolution over the Horn of Africa using the WRF model

10.5194/egusphere-egu21-12040 ◽

2021 ◽

Author(s):

Paolo Mori ◽

Thomas Schwitalla ◽

Markos Ware ◽

Kirsten Warrach-Sagi ◽

Volker Wulfmeyer

Keyword(s):

Boundary Conditions ◽

Wrf Model ◽

Added Value ◽

Limited Area ◽

Horn Of Africa ◽

Ensemble Spread ◽

Rain Belt ◽

Physics Ensemble ◽

June July ◽

The Right

Studies have shown the benefits of convection-permitting downscaling at the seasonal scale using limited-area models. To evaluate the performance with real forecasts as boundary conditions, four members of the SEAS5 global ensemble were dynamically downscaled over Ethiopia during June, July, and August 2018 at a 3-km resolution. We used a multi&#8208;physics ensemble based on the WRF model to compare the effects of boundary conditions and physics parametrization producing 16 ensemble members. With ECMWF analyses as a reference, SEAS5 averaged to a +0.17&#176;C bias over Ethiopia whereas WRF resulted in +1.14&#176;C. With respect to precipitation, the WRF model simulated 264 mm compared to 248 mm for SEAS5 and 236 mm for GPM-IMERG. The maximum northward extension of the tropical rain belt decreased by about 2&#176; in both models. Downscaling enhanced the ensemble spread in precipitation by 60% on average, correcting the SEAS5 underdispersion. The WRF ensemble spread over Ethiopia was mostly generated by the perturbed boundary conditions, as their effect is often 50% larger than the physics&#8208;induced variability. The results indicate that boundary condition perturbations are necessary, although not always sufficient, to generate the right amount of ensemble spread in a limited-area model with complex topography. The next step is to use specific methods to calculate the added value provided by the downscaling.

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Impact of a Stochastic Kinetic Energy Backscatter Scheme on Warm Season Convection-Allowing Ensemble Forecasts

Monthly Weather Review ◽

10.1175/mwr-d-15-0092.1 ◽

2016 ◽

Vol 144 (5) ◽

pp. 1887-1908 ◽

Cited By ~ 9

Author(s):

Jeffrey D. Duda ◽

Xuguang Wang ◽

Fanyou Kong ◽

Ming Xue ◽

Judith Berner

Keyword(s):

Kinetic Energy ◽

Wrf Model ◽

Warm Season ◽

Weather Research And Forecasting ◽

Potential Utility ◽

Ensemble Forecasts ◽

Probabilistic Forecasts ◽

Ensemble Mean ◽

Central United States ◽

Surface Observations

The efficacy of a stochastic kinetic energy backscatter (SKEB) scheme to improve convection-allowing probabilistic forecasts was studied. While SKEB has been explored for coarse, convection-parameterizing models, studies of SKEB for convective scales are limited. Three ensembles were compared. The SKMP ensemble used mixed physics with the SKEB scheme, whereas the MP ensemble was configured identically but without using the SKEB scheme. The SK ensemble used the SKEB scheme with no physics diversity. The experiment covered May 2013 over the central United States on a 4-km Weather Research and Forecasting (WRF) Model domain. The SKEB scheme was successful in increasing the spread in all fields verified, especially mid- and upper-tropospheric fields. Additionally, the rmse of the ensemble mean was maintained or reduced, in some cases significantly. Rank histograms in the SKMP ensemble were flatter than those in the MP ensemble, indicating the SKEB scheme produces a less underdispersive forecast distribution. Some improvement was seen in probabilistic precipitation forecasts, particularly when examining Brier scores. Verification against surface observations agree with verification against Rapid Refresh (RAP) model analyses, showing that probabilistic forecasts for 2-m temperature, 2-m dewpoint, and 10-m winds were also improved using the SKEB scheme. The SK ensemble gave competitive forecasts for some fields. The SK ensemble had reduced spread compared to the MP ensemble at the surface due to the lack of physics diversity. These results suggest the potential utility of mixed physics plus the SKEB scheme in the design of convection-allowing ensemble forecasts.

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Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-2513-2019 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2513-2524 ◽

Cited By ~ 3

Author(s):

Leila Goodarzi ◽

Mohammad E. Banihabib ◽

Abbas Roozbahani ◽

Jörg Dietrich

Keyword(s):

Bayesian Network ◽

Relative Error ◽

Wrf Model ◽

Cumulus Parameterization ◽

Small Data ◽

Ensemble Forecasts ◽

Validation Data ◽

Flood Peak ◽

Absolute Relative Error ◽

Cumulus Parameterization Schemes

Abstract. The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN.

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A Comparison of Model Error Representations in Mesoscale Ensemble Data Assimilation

Monthly Weather Review ◽

10.1175/mwr-d-14-00395.1 ◽

2015 ◽

Vol 143 (10) ◽

pp. 3893-3911 ◽

Cited By ~ 11

Author(s):

Soyoung Ha ◽

Judith Berner ◽

Chris Snyder

Keyword(s):

Data Assimilation ◽

Numerical Models ◽

Wrf Model ◽

Forecast Model ◽

Model Error ◽

Ensemble Forecasts ◽

Ensemble Data Assimilation ◽

Ensemble Data ◽

Independent Observations ◽

Almost All

Abstract Mesoscale forecasts are strongly influenced by physical processes that are either poorly resolved or must be parameterized in numerical models. In part because of errors in these parameterizations, mesoscale ensemble data assimilation systems generally suffer from underdispersiveness, which can limit the quality of analyses. Two explicit representations of model error for mesoscale ensemble data assimilation are explored: a multiphysics ensemble in which each member’s forecast is based on a distinct suite of physical parameterization, and stochastic kinetic energy backscatter in which small noise terms are included in the forecast model equations. These two model error techniques are compared with a baseline experiment that includes spatially and temporally adaptive covariance inflation, in a domain over the continental United States using the Weather Research and Forecasting (WRF) Model for mesoscale ensemble forecasts and the Data Assimilation Research Testbed (DART) for the ensemble Kalman filter. Verification against independent observations and Rapid Update Cycle (RUC) 13-km analyses for the month of June 2008 showed that including the model error representation improved not only the analysis ensemble, but also short-range forecasts initialized from these analyses. Explicitly accounting for model uncertainty led to a better-tuned ensemble spread, a more skillful ensemble mean, and higher probabilistic scores, as well as significantly reducing the need for inflation. In particular, the stochastic backscatter scheme consistently outperformed both the multiphysics approach and the control run with adaptive inflation over almost all levels of the atmosphere both deterministically and probabilistically.

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Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

Monthly Weather Review ◽

10.1175/2009mwr2879.1 ◽

2009 ◽

Vol 137 (10) ◽

pp. 3388-3406 ◽

Cited By ~ 51

Author(s):

Ryan D. Torn ◽

Gregory J. Hakim

Keyword(s):

Sensitivity Analysis ◽

Tropical Cyclone ◽

Initial Conditions ◽

Forecast Error ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Forecast Errors ◽

Initial Condition ◽

Ensemble Forecasts ◽

The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

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A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

Weather and Forecasting ◽

10.1175/2009waf2222222.1 ◽

2009 ◽

Vol 24 (4) ◽

pp. 1121-1140 ◽

Cited By ~ 154

Author(s):

Adam J. Clark ◽

William A. Gallus ◽

Ming Xue ◽

Fanyou Kong

Keyword(s):

Wrf Model ◽

Precipitation Forecast ◽

Convective Precipitation ◽

Weather Research And Forecasting ◽

Lead Times ◽

Grid Spacing ◽

Ensemble Forecasts ◽

Probabilistic Forecasts ◽

Central United States ◽

Statistical Consistency

Abstract An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 h. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ∼ 4 km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

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Ensemble-Based Sensitivity Analysis Applied to African Easterly Waves

Weather and Forecasting ◽

10.1175/2009waf2222255.1 ◽

2010 ◽

Vol 25 (1) ◽

pp. 61-78 ◽

Cited By ~ 27

Author(s):

Ryan D. Torn

Keyword(s):

Sensitivity Analysis ◽

Initial Conditions ◽

Wrf Model ◽

Cumulus Parameterization ◽

Initial Condition ◽

Ensemble Forecasts ◽

Large Variability ◽

Multidisciplinary Analysis ◽

Meridional Winds ◽

Cumulus Parameterization Schemes

Abstract An ensemble Kalman filter (EnKF) coupled to the Advanced Research version of the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts of a strong African easterly wave (AEW) during the African Monsoon Multidisciplinary Analysis field campaign. Ensemble sensitivity analysis is then used to evaluate the impacts of initial condition errors on AEW amplitude and position forecasts at two different initialization times. WRF forecasts initialized at 0000 UTC 8 September 2006, prior to the amplification of the AEW, are characterized by large variability in evolution as compared to forecasts initialized 48 h later when the AEW is within a denser observation network. Short-lead-time amplitude forecasts are most sensitive to the midtropospheric meridional winds, while at longer lead times, midtropospheric θe errors have equal or larger impacts. For AEW longitude forecasts, the largest sensitivities are associated with the θe downstream of the AEW and, to a lesser extent, the meridional winds. Ensemble predictions of how initial condition errors impact the AEW amplitude and position compare qualitatively well with perturbed integrations of the WRF model. Much of the precipitation associated with the AEW is generated by the Kain–Fritsch cumulus parameterization, thus the initial-condition sensitivities are also computed for ensemble forecasts that employ the Betts–Miller–Janjić and Grell cumulus parameterization schemes, and for a high-resolution nested domain with explicit convection, but with the same initial conditions. While the 12-h AEW amplitude forecast is characterized by consistent initial-condition sensitivity among the different schemes, there is greater variability among methods beyond 24 h. In contrast, the AEW longitude forecast is sensitive to the downstream thermodynamic profile with all cumulus schemes.

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Investigation of the Mediterranean Cyclone Cleopatra Using the WRF Model: Sensitivity Analysis and Ensemble Forecasting Approach

10.20944/preprints201810.0196.v1 ◽

2018 ◽

Author(s):

Markos P. Mylonas ◽

Kostas C. Douvis ◽

Iliana D. Polychroni ◽

Nadia Politi ◽

Panagiotis T. Nastos

Keyword(s):

Wrf Model ◽

Extreme Rainfall ◽

Horizontal Resolution ◽

Ensemble Forecasting ◽

Ensemble Forecasts ◽

Reanalysis Dataset ◽

Warm Core ◽

Significant Difference ◽

The Mediterranean ◽

Strong Winds

Towards the investigation and further understanding of the development and propagation of Medicanes, this study explores the forecasting capability of WRF model in case of cyclone “Cleopatra” which affected with extreme rainfall and strong winds Sardinia and Calabria, Italy, in November 2013.  This cyclone was unusual in that it developed a warm core but did not fulfill its transformation into a tropical-like cyclone because its core did not expand high enough in the tropospnere. The ERA5 reanalysis dataset was dynamically downscaled from 31 km spatial horizontal resolution to 9 km using WRF model. The methodology consists of; firstly, an extensive physical parameterization schemes sensitivity test and consequently, a short-range ensemble forecasting implementation based on the highest statistical scored physics configuration. All simulation results were validated against surface observations and remote sensing products. Subsequently, the modeled cyclone trajectories are compared to satellite imagery derived from EUMETSAT-SEVIRI gridded data. The findings of the conducted analysis illustrate that ensemble average displays significant difference in performance compared to any of the deterministic runs individually, suggesting that ensemble forecasts will be beneficial in studies assessing cyclonic events in the Mediterranean region.

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A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts

Monthly Weather Review ◽

10.1175/2009mwr3086.1 ◽

2010 ◽

Vol 138 (4) ◽

pp. 1273-1292 ◽

Cited By ~ 75

Author(s):

Altuğ Aksoy ◽

David C. Dowell ◽

Chris Snyder

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Cloud Model ◽

Wrf Model ◽

Radar Data ◽

Negative Effects ◽

Ensemble Forecasts ◽

Radar Observations

Abstract The quality of convective-scale ensemble forecasts, initialized from analysis ensembles obtained through the assimilation of radar observations using an ensemble Kalman filter (EnKF), is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. This work is the companion to Part I, which focused on the quality of analyses during the 60-min analysis period. Here, the focus is on 30-min ensemble forecasts initialized at the end of that period. As in Part I, the Weather Research and Forecasting (WRF) model is employed as a simplified cloud model at 2-km horizontal grid spacing. Various observation-space and state-space verification metrics, computed both for ensemble means and individual ensemble members, are employed to assess the quality of ensemble forecasts comparatively across cases. While the cases exhibit noticeable differences in predictability, the forecast skill in each case, as measured by various metrics, decays on a time scale of tens of minutes. The ensemble spread also increases rapidly but significant outlier members or clustering among members are not encountered. Forecast quality is seen to be influenced to varying degrees by the respective initial soundings. While radar data assimilation is able to partially mitigate some of the negative effects in some situations, the supercell case, in particular, remains difficult to predict even after 60 min of data assimilation.

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