Assimilation of INSAT‐3D hydro‐estimator method retrieved rainfall for short‐range weather prediction

Gairola

2019

JGR Atmospheres

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The Indian Space Research Organization launched SCATterometer Satellite-1 (SCATSat1) satellite by PSLV-C35 on 26 September 2016 from Sriharikota. In this study, SCATSat-1 retrieved winds are assimilated in the regional atmospheric model subsequent to preliminary evaluation of SCATSat-1 retrieved winds against National Centers for Environmental Prediction final analyses. The Weather Research and Forecasting (WRF) model and its three-and four-dimensional variational (3D-Var and 4D-Var) data assimilation system are used to demonstrate the importance of SCATSat-1 retrieved winds on short-range weather prediction. In the first set of experiments, two parallel runs are performed with and without assimilation of SCATSat-1 retrieved winds using 6-hourly cycling during entire month of December 2016. Results suggest that assimilation of SCATSat-1 retrieved winds improved the WRF model analyses and subsequent forecasts of winds and moisture. In the second set of experiments, parallel assimilation experiments are performed with SCATSat-1 retrieved winds using 3D-Var and 4D-Var method for the entire lifetime of tropical cyclone Ockhi as a case study. Results suggest that assimilation of SCATSat-1 winds has positive impact on Ockhi tropical cyclone prediction with slightly better forecast skill for 4D-Var technique. Moreover, assimilation of SCATSat-1 retrieved winds improved predictions of surface convergence and accumulated rainfall. In the last set of experiments, forecast sensitivity to observations are performed for SCATSat-1 retrieved different resolution winds using the WRF tangent linear and adjoint models for the entire lifetime of tropical cyclone Ockhi. Results suggest that SCATSat-1 retrieved winds have positive impact on tropical cyclone simulation.

Section: Model Description and Methodologymentioning

confidence: 99%

Impact of SCATSat‐1 Retrieved Wind Vectors on Short‐Range WRF Model Predictions Over the South Asia Region

Gairola

2019

JGR Atmospheres

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“…Terrestial data are taken from the United States geological survey climatology. The details of the selected WRF model configurations are opted from Kumar and Varma (). Initially, total 92 particles are prepared with different combinations of 12 different cumulus parameterization (CP) schemes (viz., new eta Kain‐Fritsch scheme [CP1]; Betts‐Miller‐Janjic scheme [CP2]; Grell‐Freitas ensemble scheme [CP3]; Simplied Arakawa Schubert scheme [CP4]; Grell‐3D ensemble scheme [CP5]; Tiedtke scheme [CP6]; modified Kain‐Fritsch with trigger function [CP10]; Multiscale Kain‐Fritsch scheme [CP11]; new GFS simplified Arakawa‐Schubert scheme [CP14]; new Tiedtke scheme [CP16]; Grell‐Devenyi ensemble scheme [CP93]; old Kain‐Fritsch [CP99] schemes), four different microphysics (MP) schemes (viz., Eta [Ferrier; MP5]; WSM6 [MP6]; Goddard GCE scheme [MP7]; Thompson [MP8]), and two different planetary boundary layer (PBL) schemes (viz., YSU [PBL1]; MYNN2 [PBL5]) available in the WRF model (Skamarock et al, ).…”

Section: Wrf Modelmentioning

confidence: 99%

“…Terrestial data are taken from the United States geological survey climatology. The details of the selected WRF model configurations are opted from Kumar and Varma (2016…”

Section: Wrf Modelmentioning

confidence: 99%

Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone

Shukla

2019

JGR Atmospheres

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A big challenge in the satellite data assimilation is the effective use of InfraRed (IR) window channel radiances in the high‐resolution weather model. A hybrid data‐assimilation method is used in this study for very severe cyclonic storm “Vardah,” in which three‐dimensional variational method is used to assimilate control observations, and particle filter method is used to assimilate Indian geostationary satellite INSAT‐3D data. In the context of imperfect weather model, various particles (or ensembles) are prepared with different combinations of model physics. To implement particle filter, INSAT‐3D thermal IR window channel 1 (TIR‐1; center wavelength 11 μm) measured brightness temperature (BT) and cloud mask product are used to assign appropriate weights to different particles to reduce model uncertainties. This step is followed by resampling step in which new particles are generated from high weight particles using stochastic kinetic‐energy backscatter scheme method and dynamical variables are perturbed into the model physics. Results suggest that simulated TIR‐1 BT analysis and forecasts from WPF (with INSAT‐3D data using particle filter experiments) are closer to INSAT‐3D measured TIR‐1 BT in comparison to WCNT (without INSAT‐3D data using particle filter) experiments. Furthermore, approximately 10% to 50% improvements are found in mean track error forecasts in WPF experiments. An improvement of ~10% is noticed in cyclone center position in analysis. Prediction of storm intensity is also improved after assimilation. Results also suggest that vertical structure of WPF simulated humidity, temperature, wind speed, and surface pressure is improved over WCNT runs.

“…Advanced WRF model simulations have been used to support observational results. The WRF model incorporates diverse physics schemes for the treatment of convection and mesoscale precipitation systems (Kumar & Shukla, 2019; Kumar & Varma, 2017). The WRF model set up, physical, and dynamical schemes used for present study are described in supporting information section 2.…”

Section: Data Sourcesmentioning

confidence: 99%

Can Quasi‐Periodic Gravity Waves Influence the Shape of Ice Crystals in Cirrus Clouds?

Saha

Geophysical Research Letters

Sharma

et al. 2020

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The present work, for the first time, unravels the impact of mesoscale gravity waves on the microphysical changes in cirrus clouds over the subtropical Indian region using Raman lidar, satellite, model simulations, and reanalysis data sets. The cirrus clouds are formed from the convective outflow of large‐scale convergence zone extending from south‐west to north‐east Indian region. These clouds are modulated by the upward propagating gravity waves with time periods ~40 and ~20 min over the Raman lidar observational site. The wave‐induced enhancement of moisture leads to supersaturation thereby controlling the ice crystals' size and shape through depositional freezing. The ice crystals size increases, and they transform to irregular shapes in the presence of wave activity. Therefore, the present work is novel and will have implications toward the uncertainties associated with cirrus clouds in both regional and global climate models.