A new technique for estimating outgoing longwave radiation using infrared window and water vapor radiances from Kalpana very high resolution radiometer

Singh, Randhir; Thapliyal, P. K.; Kishtawal, C. M.; Pal, P. K.; Joshi, P. C.

doi:10.1029/2007gl031715

Cited by 15 publications

(11 citation statements)

References 19 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the two following examples, we use model results from simulations with the Weather Research and Forecasting (WRF) model (Skamarock et al, 2005). These simulations use the Morrison microphysics scheme (Morrison et al, 2005(Morrison et al, , 2009 and the Rapid Radiative Transfer Model (RRTMG) shortwave and longwave radiation scheme (Iacono et al, 2008).…”

Section: Advantages Of the Implementation In Pythonmentioning

confidence: 99%

tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

Heikenfeld¹,

Marinescu²,

Christensen³

et al. 2019

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as Iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. The tobac framework provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data.

show abstract

Section: Advantages Of the Implementation In Pythonmentioning

confidence: 99%

tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

Heikenfeld¹,

Marinescu²,

Christensen³

et al. 2019

Geosci. Model Dev.

View full text Add to dashboard Cite

show abstract

“…OLR retrievals also have the benefit that they do not depend on other aspects of a complicated radiative transfer model, which require, amongst other assumptions, that pixels are assigned as either cloud or cloud-free for the radiative retrieval of several optical (effective radius and optical depth) and thermal (cloud top temperature and height) cloud properties (McGarragh et al, 2018). For the satellite data, we use an empirical conversion derived in Singh et al (2007) to convert the radiances L from two channels in the GOES-13 measurements, the water vapour channel (WV, 5.8 to 7.30 µm) and a channel in the infrared window (WIN, 10.2 to 11.2 µm), to OLR. Singh et al (2007) report an uncertainty from these conversions within 2.5 W m −2…”

Section: Time Resolution Requirements For Cloud Trackingmentioning

confidence: 99%

“…For the satellite data, we use an empirical conversion derived in Singh et al (2007) to convert the radiances L from two channels in the GOES-13 measurements, the water vapour channel (WV, 5.8 to 7.30 µm) and a channel in the infrared window (WIN, 10.2 to 11.2 µm), to OLR. Singh et al (2007) report an uncertainty from these conversions within 2.5 W m −2…”

Section: Time Resolution Requirements For Cloud Trackingmentioning

confidence: 99%

See 1 more Smart Citation

tobac v1.0: towards a flexible framework for tracking and analysis of clouds in diverse datasets

Heikenfeld¹,

Marinescu²,

Christensen³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. tobac provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data.

show abstract

“…Based on radiative transfer simulations for a set of diverse atmospheric profiles, the accuracy of the Meteosat OLR product is shown to be ∼3 W m −2 [ Schmetz and Liu , 1988]. Singh et al [2007] also found that the accuracy of OLR products derived for Kalpana satellite using a narrowband‐to‐broadband conversion method is ∼2.5 W m −2 . The validation of the OLR product derived from the GOES with broadband OLR observations from the Clouds and the Earth's Radiant Energy System (CERES) onboard polar orbiting satellites Tropical Rainfall Measuring Mission (TRMM) and EOS‐Terra reveal that the accuracy of the narrowband‐to‐broadband flux computation is ∼7 W m −2 on a daily time scale [ Ba et al , 2003].…”

Section: Datamentioning

confidence: 99%

Estimation of net surface shortwave radiation over the tropical Indian Ocean using geostationary satellite observations: Algorithm and validation

et al. 2011

View full text Add to dashboard Cite

This paper presents the development of a methodology to estimate the net surface shortwave radiation (SWR) over tropical oceans using half‐hourly geostationary satellite estimates of outgoing longwave radiation (OLR). The collocated data set of SWR measured at 13 buoy locations over the Indian Ocean and a Meteosat‐derived OLR for the period of 2002–2009 have been used to derive an empirical relationship. The information from the solar zenith angle that determines the amount of solar radiation received at a particular location is used to normalize the SWR to nadir observation in order to make the empirical relationship location independent. As the relationship between SWR and OLR is valid mostly over the warm‐pool regions, the present study restricts SWR estimation in the tropical Indian Ocean domain (30°E–110°E, 30°S–30°N). The SWR estimates are validated with an independent collocated data set and subsequently compared with the SWR estimates from the Global Energy and Water Cycle Experiment‐Surface Radiation Budget V3.0 (GEWEX‐SRB), International Satellite Cloud Climatology Project‐Flux Data (ISCCP‐FD), and National Centers for Environmental Prediction (NCEP) reanalysis for the year 2007. The present algorithm provides significantly better accuracy of SWR estimates, with a root‐mean‐square error of 27.3 W m−2 as compared with the values of 32.7, 37.5, and 59.6 W m−2 obtained from GEWEX‐SRB, ISCCP‐FD, and NCEP, respectively. The present algorithm also provides consistently better SWR compared with other available products under different sky conditions and seasons over Indian Ocean warm‐pool regions.

show abstract

A new technique for estimating outgoing longwave radiation using infrared window and water vapor radiances from Kalpana very high resolution radiometer

Cited by 15 publications

References 19 publications

tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

tobac v1.0: towards a flexible framework for tracking and analysis of clouds in diverse datasets

Estimation of net surface shortwave radiation over the tropical Indian Ocean using geostationary satellite observations: Algorithm and validation

Contact Info

Product

Resources

About