Assimilation of multi-channel radiances in mesoscale models with an ensemble technique to improve track forecasts of Tropical cyclones

Radhakrishnan, Chandrasekar; Sahu, Reetik Kumar; Balaji, C.

doi:10.1007/s12040-021-01798-6

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…This model's ability to simulate atmospheric dynamics at a variety of horizontal and vertical scales and resolve the large-scale atmospheric features and internal dynamics of the system makes it popular [43][44][45][46]. The WRF model has been used for different applications such as tropical cyclone prediction [46][47][48][49], regional climate downscaling [50], and air quality modeling [51] and nowcasting [52]. The WRF-simulated vertical atmospheric profiles are used as a first guess in satellite retrievals [53][54][55].…”

Section: Model Configurationmentioning

confidence: 99%

Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016

Punde

Nischal

Attada

et al. 2022

Climate

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In the present study, dynamically downscaled Weather Research and Forecasting (WRF) model simulations of winter (DJF) seasonal precipitation were evaluated over the Western Himalayas (WH) at grey zone configurations (at horizontal resolutions of 15 km (D01) and 5 km (D02)) and further validated using satellite-based (IMERG; 0.1°), observational (IMD; 0.25°), and reanalysis (ERA5; 0.25° and IMDAA; 0.108°) gridded datasets during 2001–2016. The findings demonstrate that both model resolutions (D01 and D02) are effective at representing precipitation characteristics over the Himalayan foothills. Precipitation features over the region, on the other hand, are much clearer and more detailed, with a significant improvement in D02, emphasizing the advantages of higher model grid resolution. Strong correlations and the lowest biases and root mean square errors indicate a closer agreement between model simulations and reanalyses IMDAA and ERA5. Vertical structures of various dynamical and thermodynamical features further confirm the improved and more realistic in WRF simulations with D02. Moreover, the seasonal patterns of upper tropospheric circulation, vertically integrated moisture transport, surface temperature and cloud cover show more realistic simulation in D02 compared to coarser domain D01. The categorical statistics reveal the efficiency of both D01 and D02 in simulating moderate and heavy precipitation events. Overall, our study emphasizes the significance of high-resolution data for simulating precipitation features specifically over complex terrains like WH.

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Section: Model Configurationmentioning

confidence: 99%

Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016

Punde

Nischal

Attada

et al. 2022

Climate

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“…These studies have reported improvements in forecast tracking errors and the position of rainfall prediction in shortterm forecasts. Additional assimilations of satellite data and multiplatform observations in numerical weather prediction can be found in [5,7,[16][17][18][19][20][21][22], among others.…”

Section: Introductionmentioning

confidence: 99%

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Thodsan

Torsri³

et al. 2022

Atmosphere

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This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 hours. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 hours lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 hours. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

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“…However, ground weather radars provide observations over land, and a few observations are available near coastal regions, but no ground weather radars provide coverage over the open ocean. The prediction skills of numerical weather and climate models mainly depend on the model's initial conditions [5]- [7] and physics parameterization schemes [8]- [10]. Open ocean observations are essential to generate acceptable initial conditions for numerical weather prediction (NWP) models, especially to predict severe weather events like hurricanes, typhoons and tropical cyclones.…”

Section: Introductionmentioning

confidence: 99%

Cross Validation of TEMPEST-D and RainCube Observations Over Precipitation Systems

Radhakrishnan

Chandrasekar

Reising

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents cross-validation of nearly simultaneous observations between the Temporal Experiment for Storms and Tropical Systems -Demonstration (TEMPEST-D) and RainCube (Radar in a CubeSat) satellite microwave sensors over precipitation systems. RainCube senses the atmosphere using a Ka-band nadir-pointing radar, and TEMPEST-D senses using multifrequency millimeter-wave radiometers. Nine precipitation systems were used in this cross-validation study. Occurring over nearly a two-year period, these storms were scattered over many regions of the world from the Tropics to the midlatitudes. A shift correction algorithm was developed to remove the uncertainty between the two sensors' observations due to the time difference and the storm motion between the two instrument overpasses. Correlation coefficients were calculated between self-normalized, inverted RainCube cumulative reflectivity and selfnormalized TEMPEST-D brightness temperatures. As expected, these correlation coefficients are consistently higher for the four TEMPEST-D high-frequency channels than for the single low-frequency channel. The shift correction algorithm improved the average correlation coefficient by 19% for the four high-frequency channels and by 58% for the single low-frequency channel. The average correlation coefficient after shift correction is 0.76 for TEMPEST-D's four high-frequency channels and 0.54 for the single low-frequency channel. The comparisons demonstrated high consistency between the TEMPEST-D and RainCube observations, even though the two microwave sensors' fundamental physics is different; one is passive, and the other is active.

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Assimilation of multi-channel radiances in mesoscale models with an ensemble technique to improve track forecasts of Tropical cyclones

Cited by 6 publications

References 34 publications

Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016

Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Cross Validation of TEMPEST-D and RainCube Observations Over Precipitation Systems

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