Channel Radiances Research Articles

A Framework for Estimating Clear-Sky Atmospheric Total Precipitable Water (TPW) from VIIRS/S-NPP

Atmospheric water vapor content or total precipitable water (TPW) is a highly variable atmospheric constituent, yet it remains one of the meteorological parameters that is most difficult to characterize accurately. We develop a framework for estimating atmospheric TPW from Visible Infrared Imaging Radiometer Suite (VIIRS) data in this study. First, TPW is retrieved from VIIRS top-of-atmosphere (TOA) radiance of channels 15 and 16 using the refined split-window covariance-variance ratio (SWCVR) method. Then, the VIIRS TPW is blended with the microwave integrated retrieval system (MIRS) derived TPW via Bayesian model averaging (BMA) to improve the accuracy of VIIRS TPW. Three years (2014–2017) of ground measurements collected from SuomiNet sites over North America are used to validate the VIIRS TPW and blended TPW. The mean bias error (MBE) and root mean square error (RMSE) of the VIIRS TPW are 0.21 g/cm2 and 0.73 g/cm2, respectively, and the accuracy of the VIIRS TPW in daytime is much better than at night time. The MBE and RMSE of BMA integrated TPW are 0.06 g/cm2 and 0.35 g/cm2, and the accuracy difference between daytime and nighttime is also removed. The global radiosonde measurements are also collected to validate the BMA integrated VIIRS TPW. The MBE and RMSE of the BMA integrated TPW are 0.09 g/cm2 and 0.44 g/cm2 compared to the radiosonde measurements. This accuracy is also superior to the VIIRS TPW. Therefore, it is concluded that the developed framework can be used to derive accurate clear-sky TPW for VIIRS. This is the first time that we can obtain high accuracy TPW from VIIRS. This study will certainly benefit the study of atmospheric processes and climate change.

AIRS Deconvolution and the Translation of AIRS-to-CrIS Radiances With Applications for the IR Climate Record

Spectra of the earth’s thermal emission as measured by the Atmospheric InfraRed Sounder (AIRS), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral sounders are becoming a significant part of the long-term climate record. These instruments have broadly similar spatial sampling, spectral resolution, and band spans. However, the spectral response functions differ in detail, leading to significant differences in observed spectra. To address this, we translate channel radiances from one sounder to another, including simulation of the response functions of the translation target. We make regular use of such translations from AIRS to CrIS and IASI to CrIS and have implemented and tested IASI-to-AIRS and CrIS-to-AIRS translations as well. Our translation from AIRS to CrIS has some novel features. AIRS is a grating spectrometer with a distinct response function for each channel, whereas CrIS is a Michaelson interferometer with a sinc response function after calibration and corrections. We use our detailed knowledge of the AIRS spectral response functions to deconvolve AIRS channel radiances to a resolution-enhanced intermediate representation. This is reconvolved to CrIS or other instrument specifications. The resulting translation is shown to be more accurate than interpolation or regression.

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

AbstractA 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.

Inter-Calibration of HY-1B/COCTS Thermal Infrared Channels with MetOp-A/IASI

The Chinese Ocean Color and Temperature Scanner (COCTS) on board the Haiyang-1B (HY-1B) satellite has two thermal infrared channels (9 and 10) centred near 11 μm and 12 μm, respectively, which are intended for sea surface temperature (SST) observations. To improve the accuracy of COCTS SSTs, inter-calibration of COCTS thermal infrared radiance is carried out. The Infrared Atmospheric Sounding Interferometer (IASI) on board MetOp-A satellite is used as inter-calibration reference owing to its hyperspectral nature and high-quality measurements. The inter-calibration of HY-1B COCTS thermal infrared radiances with IASI is undertaken for data from the Period 2009–2011 located in the northwest Pacific. Collocations of COCTS radiance with IASI are identified within a temporal window of 30 min, a spatial window of 0.12° and an atmospheric path tolerance of 3%. Matched IASI spectra are convolved with the COCTS spectral response functions, while COCTS pixels within the footprint of each IASI pixel are spatially averaged, thus creating matched IASI-COCTS radiance pairs that should agree well in the absence of satellite biases. The radiances of COCTS 11 and 12 μm channel are lower than IASI with relatively large biases, and a strong dependence of difference on radiance in the case of 11 μm channel. We used linear robust regression for four different detectors of COCTS separately to obtain the inter-calibration coefficients to correct the COCTS radiance. After correction, the mean values of COCTS 11 and 12 μm channel minus IASI radiance are −0.02 mW m−2 cm sr−1 and −0.01 mW m−2 cm sr−1, respectively, with corresponding standard deviations of 0.51 mW m−2 cm sr−1 and 0.57 mW m−2 cm sr−1. Striped noise is present in COCTS original radiance imagery associated with inconsistency among the four detectors, and inter-calibration is shown to reduce, although not eliminate, the striping. The calibration accuracy of COCTS is improved after inter-calibration, which is potentially useful for improving COCTS SST accuracy in the future.

Ingesting microwave sounder radiances for improvement in track forecast of cyclone Vardah

Ingesting microwave sounder radiances from SAPHIR of Megha–Tropiques has been attempted. A local ensemble transform Kalman filter assimilation algorithm is adopted to ingest radiances directly into the ARW–WRF model. The forward radiative transfer calculations were surrogated with an artificial neural network (ANN) based on the fast radiative transfer model. Raining pixels from the observations were removed using a threshold test on the observed brightness temperatures. Following this, corrections of both scan and air mass biases were accomplished using a predictor-based approach. The bias characteristics of each channel were calculated from ab initio clear-sky profiles from European reanalysis Interim reanalysis data. The vertical localization functions required for the radiance observations were chosen to be similar to the weighting function of the respective channel. The overall performance of the SAPHIR radiance assimilation in terms of the average error over the forecast period showed a positive impact on the cyclone track prediction when compared with the control run and the best track data from the Indian Meteorological Department. The effect of assimilation is observed to improve the minimum sea-level pressure values, whereas the improvements in the maximum sustainable wind speed are not significant. An assimilation experiment was set up to ingest channel-wise radiances independently, and it was concluded that the assimilation of channel 5 radiances results in the least error in the track forecast. The effect of using ensembles generated by initial perturbations in (i) temperature and (ii) both temperature and humidity was studied. The ensembles generated from perturbations in both humidity and temperature resulted in a better 72-h track compared with perturbation of only one of them. The overall performance of the assimilation of all the six channels for both 48- and 72-h forecast lead times showed a considerable improvement against the control run without any assimilation. Furthermore, the results show a degradation of the forecast of cyclone track in the first 24 h. The sensitivity toward channel-wise radiances showed a positive impact on the precipitation forecast when compared with global precipitation mission rainfall estimates. Threat and bias scores were used for quantitative assessment of precipitation, which indicated improvements in skill after assimilating all six channel radiances from SAPHIR. Finally, a sequential assimilation experiment was set up, and the improvements in the analysis fields were computed.

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

Abstract. One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2–4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90 %, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model are used in this paper.

Retrieval of Venus' cloud parameters from VIRTIS nightside spectra in the latitude band 25°-55°N

Two years of data from the M-channel of the Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS), on board the European Space Agency mission Venus Express operating around the planet Venus, are analysed. Nocturnal data from a nadir viewpoint in the latitude band 25°N-55°N are selected for their configuration advantages and maximisation of the scene homogeneity. A reference model, and radiance spectrum, is defined based on average accepted values of the Venus main atmospheric and cloud parameters found in the literature. Extensive radiative transfer simulations are performed to provide a synthetic database of more than 10 000 VIRTIS radiances representing the natural variability of the system parameters (atmospheric temperature profile, cloud H2OH2SO4 solution concentration and vertical distribution, particle size distribution density and modal radius). A simulated-observed fitting algorithm of spectral radiances in window channels, based on a weighting procedure accounting for the latitudinal observed radiance variations, is used to derive the best atmosphere-cloud configuration for each observation.Results show that the reference Venus model does not adequately reproduce the observed VIRTIS spectra. In particular, the model accounting for a constant sulphuric acid concentration along the vertical extent of the clouds is never selected as a best fit. The 75%/96% and 84%/96% concentrations (the first values refer to the upper cloud layers and the second values to the lower ones) are the most commonly retrieved models representing more than 85% of the retrieved cases for any latitudinal band considered. It is shown that the assumption of stratified concentration of aqueous sulphuric acid allows to adequately fit the observed radiance, in particular the peak at 1.74 μm and around 4 μm.The analysis of the results concerning the microphysics suggests larger radii for the upper cloud layers in conjunction with a large reduction of their number density with respect to the reference standard. Considerable variation of the particle concentration in the Venus' atmosphere is retrieved for altitudes between 60 and 70 km. The retrieved models also suggest that lower cloud layers have smaller particle radii and larger number density than expected from the reference model. Latitudinal variations of microphysical and chemical parameters are also analysed.

An efficient hybrid method for estimating clear‐sky surface downward longwave radiation from MODIS data

AbstractThis paper proposes an efficient hybrid method for estimating 1 km instantaneous clear‐sky surface downward longwave radiation (LWDN) from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared observations and the MODIS near‐infrared column water vapor (CWV) data product. The LWDN was formulated as a nonlinear function of surface upwelling longwave radiation estimated from the MODIS top‐of‐atmosphere (TOA) radiance of channels 29, 31, and 32, as well as CWV and the MODIS TOA radiance of channel 29. Ground measurements collected at 62 globally distributed sites from six networks were used to develop and validate the proposed hybrid method. The validation results showed that the bias and root‐mean‐square error (RMSE) were 0.0597 W/m2 and 21.008 W/m2. These results demonstrate that the performance of our method is superior to that of other studies reported in the literature. The drawback of our method is that LWDN is overestimated over high‐elevation areas with extremely low CWV (<0.5 g/cm2) and underestimated over regions with tropical climates that have extremely high CWV. A power function relating LWDN to CWV was derived and used as a complementary method to address these circumstances. The overestimation was overcome, and the bias and RMSE decreased from 9.407 W/m2 and 23.919 W/m2 to −0.924 W/m2 and 19.895 W/m2. The underestimation was also alleviated.

A fast code for channel limb radiances with gas absorption and scattering in a spherical atmosphere

We present a radiative transfer code capable of accurately and rapidly computing channel limb radiances in the presence of gaseous absorption and scattering in a spherical atmosphere. The code has been prototyped for the Mars Climate Sounder measuring limb radiances in the thermal part of the spectrum (200–900cm−1) where absorption by carbon dioxide and water vapor and absorption and scattering by dust and water ice particles are important. The code relies on three main components: 1) The Gauss Seidel Spherical Radiative Transfer Model (GSSRTM) for scattering, 2) The Planetary Line-By-Line Radiative Transfer Model (P-LBLRTM) for gas opacity, and 3) The Optimal Spectral Sampling (OSS) for selecting a limited number of spectral points to simulate channel radiances and thus achieving a substantial increase in speed. The accuracy of the code has been evaluated against brute-force line-by-line calculations performed on the NASA Pleiades supercomputer, with satisfactory results. Additional improvements in both accuracy and speed are attainable through incremental changes to the basic approach presented in this paper, which would further support the use of this code for real-time retrievals and data assimilation. Both newly developed codes, GSSRTM/OSS for MCS and P-LBLRTM, are available for additional testing and user feedback.

Fast and accurate hybrid stream PCRTM-SOLAR radiative transfer model for reflected solar spectrum simulation in the cloudy atmosphere.

A hybrid stream PCRTM-SOLAR model has been proposed for fast and accurate radiative transfer simulation. It calculates the reflected solar (RS) radiances with a fast coarse way and then, with the help of a pre-saved matrix, transforms the results to obtain the desired high accurate RS spectrum. The methodology has been demonstrated with the hybrid stream discrete ordinate (HSDO) radiative transfer (RT) model. The HSDO method calculates the monochromatic radiances using a 4-stream discrete ordinate method, where only a small number of monochromatic radiances are simulated with both 4-stream and a larger N-stream (N ≥ 16)discrete ordinate RT algorithm. The accuracy of the obtained channel radiance is comparable to the result from N-stream moderate resolution atmospheric transmission version 5 (MODTRAN5). The root-mean-square errors are usually less than 5x 10-4 mW/cm2/sr/cm-1. The computational speed is three to four-orders of magnitude faster than the medium speed correlated-k option MODTRAN5. This method is very efficient to simulate thousands of RS spectra under multi-layer clouds/aerosols and solar radiation conditions for climate change study and numerical weather prediction applications.

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

AbstractAssimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

Development of a fast and accurate PCRTM radiative transfer model in the solar spectral region.

A fast and accurate principal component-based radiative transfer model in the solar spectral region (PCRTM-SOLAR) has been developed. The algorithm is capable of simulating reflected solar spectra in both clear sky and cloudy atmospheric conditions. Multiple scattering of the solar beam by the multilayer clouds and aerosols are calculated using a discrete ordinate radiative transfer scheme. The PCRTM-SOLAR model can be trained to simulate top-of-atmosphere radiance or reflectance spectra with spectral resolution ranging from 1 cm-1 resolution to a few nanometers. Broadband radiances or reflectance can also be calculated if desired. The current version of the PCRTM-SOLAR covers a spectral range from 300 to 2500 nm. The model is valid for solar zenith angles ranging from 0 to 80 deg, the instrument view zenith angles ranging from 0 to 70 deg, and the relative azimuthal angles ranging from 0 to 360 deg. Depending on the number of spectral channels, the speed of the current version of PCRTM-SOLAR is a few hundred to over one thousand times faster than the medium speed correlated-k option MODTRAN5. The absolute RMS error in channel radiance is smaller than 10-3 mW/cm2/sr/cm-1 and the relative error is typically less than 0.2%.

Tropical Mean Fluxes: A Tool for Calibration and Validation of CERES Radiometers

The Clouds and the Earth's Radiant Energy System (CERES) instrument requires in-flight calibration and validation to maintain its accuracy during orbit operations over an extended period. An internal calibration system provides calibration for the three channels; however, there is no device for calibration of the shortwave response of the total channel. A three-channel comparison technique has been developed to calibrate the shortwave response of the total channel using the tropical oceans as a vicarious calibration target. The difference between day and night outgoing longwave radiances (OLR) averaged over the tropical oceans is used to validate the day OLR. This paper evaluates the efficacy of the technique. A relation is computed at night between the window channel radiance and the OLR retrieved from the total channel for each month for each instrument. The relation has a standard deviation of 0.28 $\text{W}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}$ . Given 120 months of data, the precision of the curved line faired through these data is better than 0.05 $\text{W}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}$ . A bias is found between FM-1 and FM-3 of 0.3 $\text{W}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}$ , which is taken to be the accuracy with which the total channels can be calibrated with the internal blackbodies. This result includes the differences of longwave spectral responses of the instruments. The tropical mean OLR is between 87.4 and 90.2 $\text{W}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}$ at night, with a standard deviation of 0.44 for FM-1 and 0.47 $\text{W}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}$ for FM-3. The average difference between day and night tropical mean from the four instruments is $0.6\pm 0.09 \text{W}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}$ over their data periods.

Polarimetric remote sensing in oxygen A and B bands: sensitivity study and information content analysis for vertical profile of aerosols

Abstract. Theoretical analysis is conducted to reveal the information content of aerosol vertical profile in space-borne measurements of the backscattered radiance and degree of linear polarization (DOLP) in oxygen (O2) A and B bands. Assuming a quasi-Gaussian shape for aerosol vertical profile characterized by peak height H and half width γ (at half maximum), the Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used to simulate the Stokes four-vector elements of upwelling radiation at the top of atmosphere (TOA) and their Jacobians with respect to H and γ. Calculations for different aerosol types and different combinations of H and γ values show that the wide range of gas absorption optical depth in O2 A and B band enables the sensitivity of backscattered DOLP and radiance at TOA to the aerosol layer at different altitudes. Quantitatively, DOLP in O2 A and B bands is found to be more sensitive to H and γ than radiance, especially over the bright surfaces (with large visible reflectance). In many O2 absorption wavelengths, the degree of freedom of signal (DFS) for retrieving H (or γ) generally increases with H (and γ) and can be close to unity in many cases, assuming that the composite uncertainty from surface and aerosol scattering properties as well as measurements is less than 5 %. Further analysis demonstrates that DFS needed for simultaneous retrieval of H and γ can be obtained from a combined use of DOLP measurements at ∼ 10–100 O2 A and B absorption wavelengths (or channels), depending on the specific values of H. The higher the aerosol layer, the fewer number of channels for DOLP measurements in O2 A and B bands are needed for characterizing H and γ. Future hyperspectral measurements of DOLP in O2 A and B bands are needed to continue studying their potential and their combination with radiance and DOLP in atmospheric window channels for retrieving the vertical profiles of aerosols, especially highly scattering aerosols, over land.

Advances in Geostationary-Derived Longwave Fluxes for the CERES Synoptic (SYN1deg) Product

AbstractThe Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community 15 years of globally observed top-of-the-atmosphere fluxes critical for climate and cloud feedback studies. To accurately monitor the earth’s radiation budget, the CERES instrument footprint fluxes must be spatially and temporally averaged properly. The CERES synoptic 1° (SYN1deg) product incorporates derived fluxes from the geostationary satellites (GEOs) to account for the regional diurnal flux variations in between Terra and Aqua CERES measurements. The Edition 4 CERES reprocessing effort has provided the opportunity to reevaluate the derivation of longwave (LW) fluxes from GEO narrowband radiances by examining the improvements from incorporating 1-hourly versus 3-hourly GEO data, additional GEO infrared (IR) channels, and multichannel GEO cloud properties. The resultant GEO LW fluxes need to be consistent across the 16-satellite climate data record. To that end, the addition of the water vapor channel, available on all GEOs, was more effective than using a reanalysis dataset’s column-weighted relative humidity combined with the window channel radiance. The benefit of the CERES LW angular directional model to derive fluxes was limited by the inconsistency of the GEO cloud retrievals. Greater success was found in the direct conversion of window and water vapor channel radiances into fluxes. Incorporating 1-hourly GEO fluxes had the greatest impact on improving the accuracy of high-temporal-resolution fluxes, and normalizing the GEO LW fluxes with CERES greatly reduced the monthly regional LW flux bias.

CERES Top-of-Atmosphere Earth Radiation Budget Climate Data Record: Accounting for in-Orbit Changes in Instrument Calibration

The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments onboard the Terra, Aqua and Suomi National Polar-orbiting Partnership (S-NPP) satellites. As the objective is to create a long-term climate data record, it is necessary to periodically reprocess the data in order to incorporate the latest calibration changes and algorithm improvements. Here, we focus on the improvements and validation of CERES Terra and Aqua radiances in Edition 4, which are used to generate higher-level climate data products. Onboard sources indicate that the total (TOT) channel response to longwave (LW) radiation has increased relative to the start of the missions by 0.4% to 1%. In the shortwave (SW), the sensor response change ranges from −0.4% to 0.6%. To account for in-orbit changes in SW spectral response function (SRF), direct nadir radiance comparisons between instrument pairs on the same satellite are made and an improved wavelength dependent degradation model is used to adjust the SRF of the instrument operating in a rotating azimuth plane scan mode. After applying SRF corrections independently to CERES Terra and Aqua, monthly variations amongst these instruments are highly correlated and the standard deviation in the difference of monthly anomalies is 0.2 Wm−2 for ocean and 0.3 Wm−2 for land/desert. Additionally, trends in CERES Terra and Aqua monthly anomalies are consistent to 0.21 Wm−2 per decade for ocean and 0.31 Wm−2 per decade for land/desert. In the LW, adjustments to the TOT channel SRF are made to ensure that removal of the contribution from the SW portion of the TOT channel with SW channel radiance measurements during daytime is consistent throughout the mission. Accordingly, anomalies in day–night LW difference in Edition 4 are more consistent compared to Edition 3, particularly for the Aqua land/desert case.

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

The present study concentrates on the assimilation of clear‐sky radiances from the recently launched INSAT‐3D satellite. The imager and sounder are two primary meteorological instruments aboard the INSAT‐3D satellite. Pre‐assimilation monitoring of the radiances has been carried out from April to July 2014. A double‐difference technique using the High Resolution Infrared Sounder (HIRS) on MetOp‐A is employed to remove the effect of deficiencies in the model‐analysed profiles which can contribute to the biases. Observed radiances of both instruments appear to be colder (∼1.5–2 K) with respect to radiative transfer model (RTM) simulated radiances, except water vapour channel radiances, which are warmer (0.5–1 K). The standard deviations of observed minus RTM‐simulated radiances are between 0.5 and 1.5 K. Prior to data assimilation, these biases are corrected using a variational bias correction scheme. The largest impacts on the analyses, when assimilating imager radiances, are found in the mid‐ and upper‐tropospheric moisture, while assimilation of sounder radiances impacted both moisture and temperature throughout the troposphere. The more accurate analyses with the INSAT‐3D radiance assimilation lead to improved moisture, wind, temperature and precipitation forecasts compared to the control case in which only conventional observations were assimilated. The results demonstrate the ability of temperature‐ and water vapour‐sensitive radiances to improve not only the temperature and moisture fields, but also the wind fields, enhancing their importance, particularly over tropical regions where wind observations are more important.

Volcanic Ash Retrieval Using a New Geostationary Satellite

Abstract. The paper presents currently developing method of volcanic ash detection and retrieval for the Geostationary Korea Multi-Purpose Satellite (GK-2A). With the launch of GK-2A, aerosol remote sensing including dust, smoke, will begin a new era of geostationary remote sensing. The Advanced Meteorological Imager (AMI) onboard GK-2A will offer capabilities for volcanic ash remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Based on the physical principles for the current polar and geostationary imagers are modified in the algorithm. Volcanic ash is estimated in detection processing from visible and infrared channel radiances, and the comparison of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every 15 min for volcanic ash for pixel sizes of 2 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously, both over water and land. The algorithm has been tested with proxy data generated from existing satellite observations and forward radiative transfer simulations. Operational assessment of the algorithm will be made after the launch of GK-2A scheduled in 2018.

Multichannel analysis of correlation length of SEVIRI images around ground-based cloud observatories to determine their representativeness

Abstract. Images of measured radiance in different channels of the geostationary Meteosat-9 SEVIRI instrument are analysed with respect to the representativeness of the observations of eight cloud observatories in Europe (e.g. measurements from cloud radars or microwave radiometers). Cloudy situations are selected to get a time series for every pixel in a 300 km × 300 km area centred around each ground station. Then a cross correlation of each time series to the pixel nearest to the corresponding ground site is calculated. In the end a correlation length is calculated to define the representativeness. It is found that measurements in the visible and near infrared channels, which respond to cloud physical properties, are correlated in an area with a 1 to 4 km radius, while the thermal channels, that correspond to cloud top temperature, are correlated to a distance of about 20 km. This also points to a higher variability of the cloud microphysical properties inside a cloud than of the cloud top temperature. The correlation length even increases for the channels at 6.2, 7.3 and 9.7 μm. They respond to radiation from the upper atmospheric layers emitted by atmospheric gases and higher level clouds, which are more homogeneous than low-level clouds. Additionally, correlations at different distances, corresponding to the grid box sizes of forecast models, were compared. The results suggest the possibility of comparisons between instantaneous cloud observations from ground sites and regional forecast models and ground-based measurements. For larger distances typical for global models the correlations decrease, especially for short-wave measurements and corresponding cloud products. By comparing daily means, the correlation length of each station is increased to about 3 to 10 times the value of instantaneous measurements and also the comparability to models grows.

Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region

The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content). In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS) is selected as a test case. The Global Data Assimilation System (GDAS) atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from in situ measured radiosonde profiles and GDAS atmospheric profiles, the in situ measured LSTs, and a pair of daytime and nighttime MOD11A1 products in the study area. The results demonstrate that RM-GA has a good ability to estimate the LSTs directly from the MODIS data without any auxiliary atmospheric parameters. Although this research is for local application in the Heihe River basin, the findings and proposed method can easily be extended to other satellite sensors and regions with arid climates and high elevations.