Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

Abstract Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

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Application of SpectralWeather to prediction of flood and extreme rain events in the Maritime Continent

10.5194/egusphere-egu21-14574 ◽

2021 ◽

Author(s):

Beata Latos ◽

Thierry Lefort ◽

Maria K. Flatau ◽

Piotr J. Flatau ◽

Dariusz B. Baranowski ◽

...

Keyword(s):

Natural Hazards ◽

Mesoscale Convective System ◽

Heavy Rain ◽

Convective System ◽

Kelvin Wave ◽

Maritime Continent ◽

Equatorial Wave ◽

Extreme Rain ◽

Rain Events ◽

Tropical Weather

Monitoring of equatorial wave activity and understanding their nature is of high priority for scientists, weather forecasters and policy makers because these waves and their interactions can serve as precursors for weather-driven natural hazards, such as extreme rain and flood events. We studied such precursors of the January 2019 heavy rain and deadly flood in the central Maritime Continent region of southwest Sulawesi, Indonesia. It is shown that a convectively coupled Kelvin wave (CCKW) and a convectively coupled equatorial Rossby wave (CCERW) embedded within the larger-scale envelope of the Madden-Julian Oscillation (MJO), contributed to the onset of a mesoscale convective system. The latest developed over the Java Sea and propagated onshore, resulting in extreme rain and devastating flood.&#160;For the analysis of the January 2019 flood, we explored large datasets and detected interesting features to find multivariate relationships through visualization. We used SpectralWeather &#8211; a new tool supporting tropical weather training, research and forecasting, easily accessible at https://www.spectralweather.com. Extending Cameron Beccario's earth.nullschool.net project, SpectralWeather focuses on spectral decomposition of meteorological and oceanic fields into equatorial waves &#8211; CCKW, MJO, CCERW and Mixed Rossby-Gravity waves. SpectralWeather uses ECMWF ERA5 reanalysis at several levels, NASA GPM rainfall datasets, OMI OLR index, NEMO SST, AVISO sea surface height, and OSCAR currents.This new visualization tool can help to quantify and understand factors triggering natural hazards in the global tropics. We will discuss its interface and available features, based on the example of the January 2019 Sulawesi flood and other flood and extreme rain events in the Maritime Continent.&#160;&#160;&#160;

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Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics

Weather and Forecasting ◽

10.1175/waf-d-14-00157.1 ◽

2016 ◽

Vol 31 (1) ◽

pp. 217-236 ◽

Cited By ~ 5

Author(s):

María E. Dillon ◽

Yanina García Skabar ◽

Juan Ruiz ◽

Eugenia Kalnay ◽

Estela A. Collini ◽

...

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Meteorological Data ◽

Weather Prediction ◽

Wrf Model ◽

Convective System ◽

Efficient Manner ◽

Maryland College ◽

Data Assimilation System ◽

Assimilation System

Abstract Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina [Servicio Meteorológico Nacional (SMN)], but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.

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Detecting Water Vapor Variability during Heavy Precipitation Events in Hong Kong Using the GPS Tomographic Technique

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0115.1 ◽

2017 ◽

Vol 34 (5) ◽

pp. 1001-1019 ◽

Cited By ~ 12

Author(s):

Biyan Chen ◽

Zhizhao Liu ◽

Wai-Kin Wong ◽

Wang-Chun Woo

Keyword(s):

Hong Kong ◽

Water Vapor ◽

Heavy Rain ◽

Heavy Precipitation ◽

Radiosonde Data ◽

Weather Forecasts ◽

Moisture Variation ◽

Tomographic Technique ◽

Rain Events ◽

Precipitation Events

AbstractWater vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010–14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000–2000, 2000–3000, 3000–5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%–25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events.

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Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

Remote Sensing ◽

10.3390/rs12060973 ◽

2020 ◽

Vol 12 (6) ◽

pp. 973

Author(s):

Wenqing Xu ◽

Like Ning ◽

Yong Luo

Keyword(s):

Wind Speed ◽

Data Assimilation ◽

Satellite Data ◽

Surface Wind ◽

Wrf Model ◽

Wind Farms ◽

Resource Assessment ◽

Near Surface ◽

Wind Resource Assessment ◽

Wind Resource

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.

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A Diagnostic Study of a Retreating Mei-Yu Front and the Accompanying Low-Level Jet Formation and Intensification

Monthly Weather Review ◽

10.1175/mwr3099.1 ◽

2006 ◽

Vol 134 (3) ◽

pp. 874-896 ◽

Cited By ~ 32

Author(s):

George Tai-Jen Chen ◽

Chung-Chieh Wang ◽

Li-Fen Lin

Keyword(s):

Mesoscale Convective System ◽

Heavy Rain ◽

Meridional Wind ◽

Convective System ◽

Diagnostic Study ◽

Cloud Band ◽

Weather Forecasts ◽

Low Level ◽

Mesoscale Convective ◽

Low Level Jet

Abstract During 7–8 June 1998, an organized mesoscale convective system (MCS) formed within the mei-yu frontal cloud band and moved northeastward to produce heavy rain over the island of Taiwan. During this period, the section of the mei-yu front east of Taiwan moved northward, most significantly for about 300 km over 12 h. Meanwhile, a low-level jet (LLJ) developed within the environmental southwesterly flow to the south of the mei-yu front and the MCS. Observations revealed that the front retreated as low-level meridional wind components over the postfrontal region shifted from northerly to southerly. Using European Centre for Medium-Range Weather Forecasts (ECMWF) analyses with piecewise potential vorticity (PV) inversion technique and other methods, a diagnostic study was carried out to investigate the northward frontal movement and the formation of the LLJ. Results indicated that diabatic latent heating from the MCS, large enough in scale, generated positive PV and height fall at low levels. The enhanced height gradient induced northwestward-directed ageostrophic winds and the LLJ formed southeast of the MCS through Coriolis torque. The southwesterly flow associated with this diabatic PV perturbation led to rapid retreat of the frontal segment east of Taiwan at a speed of about 25 m s−1, while the movement was dominated by horizontal advection in the present case. During this process of readjustment toward geostrophy, a thermally indirect circulation also appeared over and south of the front, and the LLJ formed within its lower branch at 850 hPa. The enhanced southwesterly winds reached LLJ strength because they were superimposed upon a background monsoon flow at the same direction. To the lee of Taiwan, the topography also played the role in enhancing local wind speed at lower levels and contributed toward the frontal retreat at nearby regions.

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Radar data assimilation impact over nowcasting a mesoscale convective system in Catalonia using the WRF model

Tethys Journal of Weather and Climate of the Western Mediterranean ◽

10.3369/tethys.2018.15.01 ◽

2015 ◽

Cited By ~ 1

Author(s):

Cáceres

Keyword(s):

Data Assimilation ◽

Wrf Model ◽

Mesoscale Convective System ◽

Radar Data ◽

Convective System ◽

Mesoscale Convective ◽

Radar Data Assimilation

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Process-Based Evaluation of Stochastic Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events

Monthly Weather Review ◽

10.1175/mwr-d-21-0090.1 ◽

2021 ◽

Author(s):

Shu-Chih Yang

Keyword(s):

Water Vapor ◽

Wrf Model ◽

Heavy Rain ◽

Model Error ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

High Precipitation ◽

Prediction Systems ◽

Rain Events ◽

Low Precipitation

Abstract Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud-level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.

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Utilization of Red-Green-Blue (RGB) modification composite for nighttime convective cloud monitoring over North Sumatra region

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012019 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012019

Author(s):

I J A Saragih ◽

K Tarigan ◽

M Sinambela ◽

M Situmorang ◽

K Sembiring ◽

...

Keyword(s):

Satellite Image ◽

Convective Cloud ◽

Weather Radar ◽

Heavy Rain ◽

Radar Data ◽

Convective System ◽

Rainfall Analysis ◽

The North ◽

Observation Method ◽

North Sumatra

Abstract Located between the Indian Ocean and the Malacca Strait, also the presence of the Bukit Barisan Mountains cause high convective activity in the North Sumatra region. The Himawari-8 satellite has 16 atmospheric observation channels that allow for observations of the convective system growth phase. The Red-Green-Blue (RGB) composite method is used to display a variety of satellite image composite information. The nocturnal convective system that often forms in the coastal areas of Sumatra causes heavy rains. A nocturnal convective system observation method is needed to publish early warning information on extreme weather. This research was conducted to observe the nocturnal convective system during heavy rain events in the North Sumatra region using a modification of RGB composite. This research used the Himawari-8 satellite data, Coloumn Max (CMAX) products of Medan weather radar data, and Global Satellite Mapping of Precipitation (GSMaP) rainfall estimation data. Comparison of RGB modified products with Night Microphysics RGB products and CMAX weather radar products, as well as time-series rainfall analysis. The results showed that the RGB modification product could capture the beginning of the convective system's growth, development, and spatial movement. The convective cloud distribution pattern corresponds to the area of heavy rain. There is a slight difference in cloud growth area between the satellite and radar products indicated the parallax error from the satellite image.

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An Assessment of the Impact of ATMS and CrIS Data Assimilation on Precipitation Prediction over the Tibetan Plateau

10.5194/amt-2017-31 ◽

2017 ◽

Author(s):

Tong Xue ◽

Jianjun Xu ◽

Zhaoyong Guan ◽

Long S. Chiu ◽

Han-Ching Chen ◽

...

Keyword(s):

Tibetan Plateau ◽

Data Assimilation ◽

Satellite Data ◽

Heavy Rainfall ◽

Heavy Rain ◽

Advanced Technology ◽

Data Sets ◽

The Tibetan Plateau ◽

Precipitation Prediction ◽

The Impact

Abstract. Using the National Oceanic and Atmospheric Administration’s Gridpoint Statistical Interpolation data assimilation system and the National Center for Atmospheric Research’s Advanced Research Weather Research and Forecasting (WRF-ARW) regional model, the impact of assimilating advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) satellite data on precipitation prediction over the Tibetan Plateau in July 2015 was evaluated. Four experiments were designed: a control experiment and three data assimilation experiments with different data sets injected: conventional data only, a combination of conventional and ATMS satellite data, and a combination of conventional and CrIS satellite data. The results showed that the monthly mean of precipitation is shifted northward in the simulations and shows an orographic bias described as an overestimation in the upwind of the mountains and an underestimation in the south of the rainbelt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24-hourand last 24-hour accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy rainfall periods of 3–6, 13–16, and 22–25 July. The observed water vapor conveyance from the southeastern Tibetan Plateau was larger than in the model simulations, which induced inaccuracies in the forecast of heavy rain on 3–6 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy rainfall process than the control. Overall, the satellite data assimilation can enhance the WRF-ARW model’s ability to predict the spatial and temporal pattern of precipitation in July 2015 although the model capability exists a significant limitation in the complex terrain area.

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