EVALUATION OF UPWELLING INFRARED RADIANCE IN A NONEQUILIBRIUM NONHOMOGENEOUS ATMOSPHERE

1982 ◽  
Author(s):  
S. N. Tiwari ◽  
S. V. Subramanian
Keyword(s):  
Infrared Radiance

scholarly journals Assimilation of All Sky Infrared Radiance from INSAT-3D/3DR Satellite in the WRF Model

2021 ◽  
Author(s):  
Prashant Kumar ◽  
Munn Vinayak Shukla ◽  
Atul K Varma
Keyword(s):  
Wrf Model ◽  
Infrared Radiance

Development of a Multiband near-to-far Infrared Radiance COmparator (MIRCO)

2008 ◽  
Vol 29 (3) ◽  
pp. 1094-1106 ◽  
Author(s):  
J. Joly ◽  
P. Ridoux ◽  
J. Hameury ◽  
M. Lièvre ◽  
J.-R. Filtz
Keyword(s):  
Far Infrared ◽  
Infrared Radiance

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

2021 ◽  
Author(s):  
Niama Boukachaba ◽  
Oreste Reale ◽  
Erica L. McGrath-Spangler ◽  
Manisha Ganeshan ◽  
Will McCarty ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Strong Impact ◽  
Modeling Framework ◽  
Atmospheric Infrared Sounder ◽  
Clear Sky ◽  
Earth Observing System ◽  
Infrared Radiance ◽  
Radiance Assimilation ◽  
The Impact ◽  
Tropical Cyclone Season

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

scholarly journals Measurements of total and tropospheric ozone from IASI: comparison with correlative satellite, ground-based and ozonesonde observations

2009 ◽  
Vol 9 (16) ◽  
pp. 6255-6271 ◽  
Author(s):  
A. Boynard ◽  
C. Clerbaux ◽  
P.-F. Coheur ◽  
D. Hurtmans ◽  
S. Turquety ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Tropospheric Ozone ◽  
Total Ozone ◽  
Correlation Coefficients ◽  
Positive Bias ◽  
Ozone Concentrations ◽  
Infrared Radiance ◽  
Atmospheric Sounding ◽  
Ozone Monitoring ◽  
Good Agreement

Abstract. In this paper, we present measurements of total and tropospheric ozone, retrieved from infrared radiance spectra recorded by the Infrared Atmospheric Sounding Interferometer (IASI), which was launched on board the MetOp-A European satellite in October 2006. We compare IASI total ozone columns to Global Ozone Monitoring Experiment-2 (GOME-2) observations and ground-based measurements from the Dobson and Brewer network for one full year of observations (2008). The IASI total ozone columns are shown to be in good agreement with both GOME-2 and ground-based data, with correlation coefficients of about 0.9 and 0.85, respectively. On average, IASI ozone retrievals exhibit a positive bias of about 9 DU (3.3%) compared to both GOME-2 and ground-based measurements. In addition to total ozone columns, the good spectral resolution of IASI enables the retrieval of tropospheric ozone concentrations. Comparisons of IASI tropospheric columns to 490 collocated ozone soundings available from several stations around the globe have been performed for the period of June 2007–August 2008. IASI tropospheric ozone columns compare well with sonde observations, with correlation coefficients of 0.95 and 0.77 for the [surface–6 km] and [surface–12 km] partial columns, respectively. IASI retrievals tend to overestimate the tropospheric ozone columns in comparison with ozonesonde measurements. Positive average biases of 0.15 DU (1.2%) and 3 DU (11%) are found for the [surface–6 km] and for the [surface–12 km] partial columns respectively.

First retrieval of fire radiative power from COMS data using the mid-infrared radiance method

2016 ◽  
Vol 8 (2) ◽  
pp. 116-125 ◽  
Author(s):  
Daesun Kim ◽  
Jaeil Cho ◽  
Sungwook Hong ◽  
Hanlim Lee ◽  
Myoungsoo Won ◽  
...  
Keyword(s):  
Mid Infrared ◽  
Infrared Radiance ◽  
Radiative Power ◽  
Radiance Method ◽  
Fire Radiative Power

Infrared Radiance Model for the Aurorally Disturbed Atmosphere

1973 ◽  
Author(s):  
Richard H. Bishop ◽  
Ruey Y. Han ◽  
Alan W. Shaw ◽  
Lawrence R. Megill
Keyword(s):  
Infrared Radiance

scholarly journals Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms

2019 ◽  
Vol 147 (12) ◽  
pp. 4389-4409 ◽  
Author(s):  
Yunji Zhang ◽  
David J. Stensrud ◽  
Fuqing Zhang
Keyword(s):  
Doppler Radar ◽  
Three Dimensional ◽  
Lead Times ◽  
Probabilistic Prediction ◽  
Ensemble Forecasts ◽  
Radar Observations ◽  
Weather Radars ◽  
Infrared Radiance ◽  
Severe Thunderstorms ◽  
Ensemble Analysis

Abstract This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when in-storm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones.

Surface Emissivity Impact on Temperature and Moisture Soundings from Hyperspectral Infrared Radiance Measurements

2011 ◽  
Vol 50 (6) ◽  
pp. 1225-1235 ◽  
Author(s):  
Zhigang Yao ◽  
Jun Li ◽  
Jinlong Li ◽  
Hong Zhang
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Temperature ◽  
Retrieval Algorithm ◽  
Surface Emissivity ◽  
Angle Dependence ◽  
Infrared Radiance ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

AbstractAn accurate land surface emissivity (LSE) is critical for the retrieval of atmospheric temperature and moisture profiles along with land surface temperature from hyperspectral infrared (IR) sounder radiances; it is also critical to assimilating IR radiances in numerical weather prediction models over land. To investigate the impact of different LSE datasets on Atmospheric Infrared Sounder (AIRS) sounding retrievals, experiments are conducted by using a one-dimensional variational (1DVAR) retrieval algorithm. Sounding retrievals using constant LSE, the LSE dataset from the Infrared Atmospheric Sounding Interferometer (IASI), and the baseline fit dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) are performed. AIRS observations over northern Africa on 1–7 January and 1–7 July 2007 are used in the experiments. From the limited regional comparisons presented here, it is revealed that the LSE from the IASI obtained the best agreement between the retrieval results and the ECMWF reanalysis, whereas the constant LSE gets the worst results when the emissivities are fixed in the retrieval process. The results also confirm that the simultaneous retrieval of atmospheric profile and surface parameters could reduce the dependence of soundings on the LSE choice and finally improve sounding accuracy when the emissivities are adjusted in the iterative retrieval. In addition, emissivity angle dependence is investigated with AIRS radiance measurements. The retrieved emissivity spectra from AIRS over the ocean reveal weak angle dependence, which is consistent with that from an ocean emissivity model. This result demonstrates the reliability of the 1DVAR simultaneous algorithm for emissivity retrieval from hyperspectral IR radiance measurements.

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