Impact of the assimilation of INSAT‐3D radiances on short‐range weather forecasts

Singh, Randhir; Ojha, Satya P.; Kishtawal, C. M.; Pal, P. K.; Kumar, A. S. Kiran

doi:10.1002/qj.2636

Cited by 21 publications

(22 citation statements)

References 42 publications

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“…The idea to assimilate satellite radiance data arises from the need to have the most accurate initial state of the atmosphere to better predict its future state and hence that of the solar irradiance reaching the surface [21,26,39]. Outgoing radiance is related to the geophysical atmospheric state, providing very useful thermodynamic information both over land and over sea.…”

Section: D-var Data Assimilation Of Seviri Radiancementioning

confidence: 99%

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

et al. 2020

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Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

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Section: D-var Data Assimilation Of Seviri Radiancementioning

confidence: 99%

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

et al. 2020

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“…Singh et al . [] compared the INSAT‐3D observed radiances with the RTM simulated radiances using National Centers for Environmental Prediction (NCEP) analyzed profiles. They found a cold bias as large as 2.5 K in the thermal channels of INSAT‐3D imager.…”

Section: Intercomparison Of Modis and Insat‐3d Productsmentioning

confidence: 99%

Land surface temperature from INSAT‐3D imager data: Retrieval and assimilation in NWP model

Singh

Ojha

et al. 2016

JGR Atmospheres

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A new algorithm is developed for retrieving the land surface temperature (LST) from the imager radiance observations on board geostationary operational Indian National Satellite (INSAT‐3D). The algorithm is developed using the two thermal infrared channels (TIR1 10.3–11.3 µm and TIR2 11.5–12.5 µm) via genetic algorithm (GA). The transfer function that relates LST and thermal radiances is developed using radiative transfer model simulated database. The developed algorithm has been applied on the INSAT‐3D observed radiances, and LST retrieved from the developed algorithm has been validated with Moderate Resolution Imaging Spectroradiometer land surface temperature (LST) product. The developed algorithm demonstrates a good accuracy, without significant bias and standard deviations of 1.78 K and 1.41 K during daytime and nighttime, respectively. The newly proposed algorithm performs better than the operational algorithm used for LST retrieval from INSAT‐3D satellite. Further, a set of data assimilation experiments is conducted with the Weather Research and Forecasting (WRF) model to assess the impact of INSAT‐3D LST on model forecast skill over the Indian region. The assimilation experiments demonstrated a positive impact of the assimilated INSAT‐3D LST, particularly on the lower tropospheric temperature and moisture forecasts. The temperature and moisture forecast errors are reduced (as large as 8–10%) with the assimilation of INSAT‐3D LST, when compared to forecasts that were obtained without the assimilation of INSAT‐3D LST. Results of the additional experiments of comparative performance of two LST products, retrieved from operational and newly proposed algorithms, indicate that the impact of INSAT‐3D LST retrieved using newly proposed algorithm is significantly larger compared to the impact of INSAT‐3D LST retrieved using operational algorithm.

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“…Satellitebased SST retrievals are generally performed using radiance observations in the thermal infrared (TIR) region. TIR-based SST is more accurate than microwave-based SST due to the smaller variation of surface emissivity in the TIR region and the stronger dependence of the radiation on the temperature (Singh et al, 2016a). In the microwave region, the variation in emissivity is much larger and the SST dependence on the radiance is linear, thus leading to larger SST uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…The angular dependency is not incorporated in the OPR algorithm directly, rather the regression coefficients are generated for four sets of satellite viewing angles. The INSAT-3D radiances are reported to have large biases (Singh et al 2016a(Singh et al , 2016b. In this study, we have analysed the OPR SST in terms of its accuracy as compared with in-situ observations.…”

Section: Introductionmentioning

confidence: 99%

Physical retrieval of sea-surface temperature from INSAT-3D imager observations

Ojha

Singh

2019

Tellus A: Dynamic Meteorology and Oceanography

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This paper presents results of physical retrieval of sea-surface temperature (SST) from the INSAT-3D imager observations. Radiance measurements from two thermal infrared imager channels are used for retrieving SST. Prior to their use in the retrieval process, the INSAT-3D radiances are corrected for the biases using a newly developed algorithm. The physical retrieval of SST is performed using an optimal estimation method. The optimisation is performed under two scenarios. In the first scenario, the SST retrieval problem is assumed to be linear while in the second one, the assumption of linearity is relaxed. Further, retrievals are performed by considering the reduced, as well as, the full state vector. Overall, physical retrieval performs much better than the regression-based approach. Moreover, in the physical retrieval, the best performance is obtained in the second scenario with the use of the full state vector. Validation of these retrievals with the iQuam SST resulted in a standard deviation (bias) of $0.65 (0.19) K while those from the regression-based method are found to have standard deviation (bias) of $0.68 (-0.17) K.

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Impact of the assimilation of INSAT‐3D radiances on short‐range weather forecasts

Cited by 21 publications

References 42 publications

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Land surface temperature from INSAT‐3D imager data: Retrieval and assimilation in NWP model

Physical retrieval of sea-surface temperature from INSAT-3D imager observations

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