Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

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Numerical Prediction of an Antarctic Severe Wind Event with the Weather Research and Forecasting (WRF) Model

Monthly Weather Review ◽

10.1175/mwr3459.1 ◽

2007 ◽

Vol 135 (9) ◽

pp. 3134-3157 ◽

Cited By ~ 66

Author(s):

Jordan G. Powers

Keyword(s):

Mesoscale Model ◽

Initial Conditions ◽

Statistical Significance ◽

Resolution Enhancement ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Polar Regions ◽

Weather Event ◽

Pressure System ◽

Weather Research

Abstract This study initiates the application of the maturing Weather Research and Forecasting (WRF) model to the polar regions in the context of the real-time Antarctic Mesoscale Prediction System (AMPS). The behavior of the Advanced Research WRF (ARW) in a high-latitude setting and its ability to capture a significant Antarctic weather event are investigated. Also, in a suite of sensitivity tests, the impacts of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric motion vectors on ARW Antarctic forecasts are explored. The simulation results are analyzed and the statistical significance of error differences is assessed. It is found that with the proper consideration of MODIS data the ARW can accurately simulate a major Antarctic event, the May 2004 McMurdo windstorm. The ARW simulations illuminate an episode of high-momentum flow responding to the complex orography of the vital Ross Island region. While the model captures the synoptic setting and basic trajectory of the cyclone driving the event, there are differences on the mesoscale in the evolution of the low pressure system that significantly affect the forecast results. In general, both the ARW and AMPS’s fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) tend to underforecast the wind magnitudes, reflecting their stalling and filling of the system near Ross Island. It is seen, however, that both targeted data assimilation and grid resolution enhancement can yield improvement in the forecast of the key parameter of wind speed. It is found that the assimilation of MODIS observations can significantly improve the forecast for a high-impact Antarctic weather event. However, the application to the retrievals of a filter accounting for instrument channel, observation height, and surface type is necessary. The results indicate benefits to initial conditions and high-resolution, polar, mesoscale forecasts from the careful assimilation of nontraditional satellite observations over Antarctica and the Southern Ocean.

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Impact of Soil Moisture Initialization on Temperature Extreme Detection in the context of Regional Climate Modeling

10.5194/egusphere-egu2020-17121 ◽

2020 ◽

Author(s):

Matilde García-Valdecasas Ojeda ◽

Juan José Rosa-Cánovas ◽

Emilio Romero-Jiménez ◽

Patricio Yeste ◽

Sonia R. Gámiz-Fortis ◽

...

Keyword(s):

Soil Moisture ◽

Initial Conditions ◽

Climate Modeling ◽

Regional Climate ◽

Wrf Model ◽

Regional Climate Modeling ◽

Weather Research And Forecasting ◽

Temperature Extremes ◽

Temperature Extreme ◽

The Impact

Land surface-related processes play an essential role in the climate conditions at a regional scale. In this study, the impact of soil moisture (SM) initialization on regional climate modeling has been explored by using a dynamical downscaling experiment. To this end, the Weather Research and Forecasting (WRF) model was used to generate a set of high-resolution climate simulations driven by the ERA-Interim reanalysis for a period from 1989 to 2009. As the spatial configuration, two one-way nested domains were used, with the finer domain being centered over the Iberian Peninsula (IP) at a spatial resolution of about 10 km, and nested over a coarser domain that covers the Euro-CORDEX region at 50 km of spatial resolution.The sensitivity experiment consisted of two control runs (CTRL) performed using as SM initial conditions those provided by ERA-Interim, and initialized for two different dates times (January and June). Additionally, another set of runs was completed driven by the same climate data but using as initial conditions prescribed SM under wet and dry scenarios.The study is based on assessing the WRF performance by comparing the CTRL simulations with those performed with the different prescribed SM, and also, comparing them with the observations from the Spanish Temperature At Daily scale (STEAD) dataset. In this sense, we used two temperature extreme indices within the framework of decadal predictions: the warm spell index (WSDI) and the daily temperature range (DTR).These results provide valuable information about the impact of the SM initial conditions on the ability of the WRF model to detect temperature extremes, and how long these affect the regional climate in this region. Additionally, these results may provide a source of knowledge about the mechanisms involved in the occurrence of extreme events such as heatwaves, which are expected to increase in frequency, duration, and magnitude under the context of climate change.Keywords: soil moisture initial conditions, temperature extremes, regional climate, Weather Research and Forecasting modelAcknowledgments: This work has been financed by the project CGL2017-89836-R (MINECO-Spain, FEDER). The WRF simulations were performed in the Picasso Supercomputer at the University of M&#225;laga, a member of the Spanish Supercomputing Network.

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Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR

Atmospheric Measurement Techniques ◽

10.5194/amt-7-2919-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 2919-2935 ◽

Cited By ~ 12

Author(s):

I. Maiello ◽

R. Ferretti ◽

S. Gentile ◽

M. Montopoli ◽

E. Picciotti ◽

...

Keyword(s):

Heavy Rainfall ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Central Italy ◽

Wrf Model ◽

Forecast Accuracy ◽

Radar Data ◽

Heavy Rainfall Event ◽

Dwr Data

Abstract. The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators. In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data.

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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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Sensitivity of 24-h Forecast Dryline Position and Structure to Boundary Layer Parameterizations in Convection-Allowing WRF Model Simulations

Weather and Forecasting ◽

10.1175/waf-d-14-00078.1 ◽

2015 ◽

Vol 30 (3) ◽

pp. 613-638 ◽

Cited By ~ 17

Author(s):

Adam J. Clark ◽

Michael C. Coniglio ◽

Brice E. Coffer ◽

Greg Thompson ◽

Ming Xue ◽

...

Keyword(s):

Boundary Layer ◽

Initial Conditions ◽

Layer Structure ◽

Vertical Mixing ◽

Wrf Model ◽

Weather Research And Forecasting ◽

The North ◽

Boundary Layer Processes ◽

Moisture Profiles ◽

Dry Bias

Abstract Recent NOAA Hazardous Weather Testbed Spring Forecasting Experiments have emphasized the sensitivity of forecast sensible weather fields to how boundary layer processes are represented in the Weather Research and Forecasting (WRF) Model. Thus, since 2010, the Center for Analysis and Prediction of Storms has configured at least three members of their WRF-based Storm-Scale Ensemble Forecast (SSEF) system specifically for examination of sensitivities to parameterizations of turbulent mixing, including the Mellor–Yamada–Janjić (MYJ); quasi-normal scale elimination (QNSE); Asymmetrical Convective Model, version 2 (ACM2); Yonsei University (YSU); and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes (hereafter PBL members). In postexperiment analyses, significant differences in forecast boundary layer structure and evolution have been observed, and for preconvective environments MYNN was found to have a superior depiction of temperature and moisture profiles. This study evaluates the 24-h forecast dryline positions in the SSEF system PBL members during the period April–June 2010–12 and documents sensitivities of the vertical distribution of thermodynamic and kinematic variables in near-dryline environments. Main results include the following. Despite having superior temperature and moisture profiles, as indicated by a previous study, MYNN was one of the worst-performing PBL members, exhibiting large eastward errors in forecast dryline position. During April–June 2010–11, a dry bias in the North American Mesoscale Forecast System (NAM) initial conditions largely contributed to eastward dryline errors in all PBL members. An upgrade to the NAM and assimilation system in October 2011 apparently fixed the dry bias, reducing eastward errors. Large sensitivities of CAPE and low-level shear to the PBL schemes were found, which were largest between 1.0° and 3.0° to the east of drylines. Finally, modifications to YSU to decrease vertical mixing and mitigate its warm and dry bias greatly reduced eastward dryline errors.

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Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia from 1985 to 2009

International Journal of Wildland Fire ◽

10.1071/wf12048 ◽

2013 ◽

Vol 22 (6) ◽

pp. 739 ◽

Cited By ~ 10

Author(s):

Hamish Clarke ◽

Jason P. Evans ◽

Andrew J. Pitman

Keyword(s):

Initial Conditions ◽

Full Range ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Fire Weather ◽

Grid Spacing ◽

Skill Scores ◽

Forest Fire Danger ◽

Environmental Prediction ◽

Weather Research

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.

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A Combined Approach to Improving the Regional Model Forecasts for the Rainy Season in China

Monthly Weather Review ◽

10.1175/mwr-d-17-0037.1 ◽

2017 ◽

Vol 145 (11) ◽

pp. 4593-4603

Author(s):

Yanfeng Zhao ◽

Donghai Wang ◽

Jianjun Xu

Keyword(s):

Rainy Season ◽

Large Scale ◽

Initial Conditions ◽

Wrf Model ◽

Regional Model ◽

Weather Research And Forecasting ◽

Small Scale ◽

Wind Fields ◽

Accumulated Rainfall ◽

Combined Forecasting

A combined forecasting methodology, into which the spectral nudging, lateral boundary filtering, and update initial conditions methods are incorporated, was employed in the regional Weather Research and Forecasting (WRF) Model. The intent was to investigate the potential for improving the prediction capability for the rainy season in China via using as many merits of the global model having better predictability as it does for the large-scale circulation and of the regional model as it does for the small-scale features. The combined methodology was found to be successful in improving the prediction of the regional atmospheric circulation and precipitation. It performed best for the larger magnitude precipitation, the relative humidity above 800 hPa, and wind fields below 300 hPa. Furthermore, the larger the magnitude and the longer the lead time, the more obvious is the improvement in terms of the accumulated rainfall of persistent severe rainfall events.

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Modeling the Impacts of the Large-Scale Atmospheric Environment on Inland Flooding during the Landfall of Hurricane Floyd (1999)

Advances in Meteorology ◽

10.1155/2013/294956 ◽

2013 ◽

Vol 2013 ◽

pp. 1-16 ◽

Cited By ~ 5

Author(s):

Qianhong Tang ◽

Lian Xie ◽

Gary M. Lackmann ◽

Bin Liu

Keyword(s):

Large Scale ◽

Initial Conditions ◽

Wrf Model ◽

Atmospheric Environment ◽

Weather Research And Forecasting ◽

Hydrological Simulation ◽

Hurricane Floyd ◽

Hurricane Dennis ◽

Removal Technique ◽

Tropical Systems

The contribution of the large-scale atmospheric environment to precipitation and flooding during Hurricane Floyd was investigated in this study. Through the vortex removal technique in the Weather Research and Forecasting (WRF) model, the vortex associated with Hurricane Floyd (1999) was mostly removed in the model initial conditions and subsequent integration. Results show that the environment-induced precipitation can account for as much as 22% of total precipitation in the innermost model domain covering North Carolina coastal area and 7% in the focused hydrological study area. The high-resolution precipitation data from the WRF model was then used for input in a hydrological model to simulate river runoff. Hydrological simulation results demonstrate that without the tropical systems and their interactions with the large-scale synoptic environment the synoptic environment would only contribute 10% to the total discharge at the Tarboro gauge station. This suggests that Hurricane Floyd and Hurricane Dennis preceding it, along with the interactions between these tropical systems and the large-scale environment, have contributed to the bulk (90%) of the record amount of flood water in the Tar-Pamlico River Basin.

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Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East

Al-Mustansiriyah Journal of Science ◽

10.23851/mjs.v29i2.227 ◽

2018 ◽

Vol 29 (2) ◽

pp. 26

Author(s):

Thaer Obaid Roomi

Keyword(s):

Middle East ◽

Initial Conditions ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Forecasting Model ◽

Data Sets ◽

Wind Speeds ◽

Computing Skills ◽

Predicted Values ◽

Weather Research

The Weather Research and Forecasting model (WRF) is an atmospheric simulation system designed for both research and operational applications. This worldwide used model requires a sophisticated modeling experience and computing skills. In this study, WRF model was used to predict many atmospheric parameters based on the initial conditions extracted from NOMADS data sets. The study area is basically the region surrounded by the longitudes and latitudes: 15o-75o E and 10.5o-45o N which typically includes the Middle East region. The model was installed on Linux platform with a grid size of 10 km in the X and Y directions. A low pressure trough was tracked in its movement from west to east via the Middle East during the period from 1 to 7 January 2010 as a case study of the WRF model. MATLAB and NCAR Command Language (NCL) were used to display the model output. To evaluate the forecasted parameters and patterns, some comparisons were made between the predicted and actual weather charts. Wind speeds and directions in the prognostic and actual charts of 700 hPa were in agreement. However, the predicted values of geopotential heights in WRF are somewhat overestimate the actual ones. This may be attributed to the differences in the data sources and data analysis methods of the two data agencies, NOMADS and ECMWF.

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EVAPOTRANSPIRAÇÃO DE REFERÊNCIA ESTIMADA PELO MÉTODO DE PENMAN-MONTEITH FAO-56 EM FUNÇÃO DAS SIMULAÇÕES DO MODELO ATMOSFÉRICO DE MESOESCALA WRF - WEATHER RESEARCH AND FORECASTING

Irriga ◽

10.15809/irriga.2015v20n4p762 ◽

2015 ◽

Vol 20 (4) ◽

pp. 762-775

Author(s):

José Leonaldo De Souza ◽

Gustavo Bastos Lyra ◽

Valesca Rodrigues Fernandes ◽

Rosiberto Salustiano Silva-Junior ◽

Guilherme Bastos Lyra ◽

...

Keyword(s):

Mesoscale Model ◽

Meteorological Data ◽

Extreme Temperature ◽

Wrf Model ◽

Simulated Data ◽

Absolute Error ◽

Atmospheric Model ◽

Atmospheric Modeling ◽

Weather Research And Forecasting ◽

Weather Research

EVAPOTRANSPIRAÇÃO DE REFERÊNCIA ESTIMADA PELO MÉTODO DE PENMAN-MONTEITH FAO-56 EM FUNÇÃO DAS SIMULAÇÕES DO MODELO ATMOSFÉRICO DE MESOESCALA WRF - WEATHER RESEARCH AND FORECASTING JOSÉ LEONALDO DE SOUZA1; GUSTAVO BASTOS LYRA2; VALESCA RODRIGUES FERNADES1; ROSEBERTO SALUSTIANO DA SILVA JUNIOR1; GUILHERME BASTOS LYRA3; VINICIUS BANDA SPERLING1; RICARDO ARAUJO FERREIRA JUNIOR3 E IÊDO TEODORO3 1Instituto de Ciências Atmosférica (ICAT), Universidade Federal de Alagoas (UFAL), Campus A.C. Simões, Av. Lourival Melo Mota, s/n, Tabuleiro dos Martins, CEP:57072-900, Maceió - AL, [email protected]/[email protected]/[email protected]/ [email protected] de Florestas, Dep. de Ciências Ambientais, Universidade Federal Rural do Rio de Janeiro, Seropédica - RJ, [email protected] de Ciências Agrarias (CECA), Universidade Federal de Alagoas (UFAL), Rio Largo - AL, [email protected]/[email protected]/[email protected] 1 RESUMO O objetivo do trabalho foi avaliar a estimativa da evapotranspiração de referência (ETo) pelo método de Penman-Monteith parametrizado no boletim FAO-56 (PM-FAO56) utilizando dados meteorológicos observados e os simulados pelo modelo atmosférico Weather Research and Forecasting (WRF). Na estimativa de ETo utilizaram-se dados meteorológicos observados (extremos da temperatura e umidade do ar, radiação solar e velocidade do vento) e simulados pelo WRF no período seco (janeiro a março e de outubro a dezembro de 2008) da região de Rio Largo - AL (9°28’02’’ S, 35º49’44’’ W e 127 m). As estimativas foram avaliadas pelo coeficiente de determinação (r2) entre ETo obtida com os dados observados e simulados, pelo índice de concordância de Willmott (dr) e pelo erro médio absoluto (MAE). O método PM-FAO56 apresentou maior sensibilidade ao saldo de radiação, em relação aos seus termos aerodinâmicos. As estimativas de ETo apresentaram baixa precisão (r2 = 0,41) e acurácia moderada (dr = 0,77 e MAE = 0,79 mm d-1). É necessário melhorar as simulações dos componentes de radiação do WRF para melhor estimar ETo pelo método de PM-FAO56 na região de Rio Largo, AL. Palavras Chave: Dados Meteorológicos, Modelagem Atmosférica, Penman-Monteith DE SOUZA, J. L.; LYRA, G. B.; FERNADES,V. R.; SILVA-JUNIOR, R. S.; LYRA, G. B.; SPERLING, V. B.; FERREIRA JUNIOR, R. A.; TEODORO, I.REFERENCE EVAPOTRANSPIRATION BY PENMAN-MONTEITH METHOD FAO56 USING THE ATMOSPHERIC MESOSCALE MODEL WRF- WEATHER RESEARCH AND FORECASTING 2 ABSTRACT The objective of this study was to assess the Reference evapotranspiration (ETo) by the Penman-Monteith method, described in FAO paper No 56 (PM-FAO56) using observed meteorological data and those simulated by the atmospheric model Weather Research and Forecasting (WRF). For ETo estimate, meteorological data were collected (extreme temperature and air humidity, solar radiation and wind speed) and data were simulated by the WRF in the dry period (January to March and October to December 2008) in Rio Largo region, AL (9°28’02’’ S, 35º49’44’’ W and 127 m). The estimates were evaluated using the determination coefficient (r2) between ETo from observed and simulated data, by the Willmott concordance index (dr) and mean absolute error (MAE). The PM-FAO56 method showed higher sensitivity to net radiation in relation to the aerodynamic terms. Estimates of ETo were of low precision (r2 = 0.41) and moderate accuracy (dr = 0.77 and MAE = 0.79 mm d-1). Simulations of the radiation components of the WRF model have to be improved in order to better estimate ETo by the PM-FAO56 method for the Rio Largo region, AL. Keywords: Meteorological data, atmospheric modeling, Penman-Monteith.

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