Mathias Schreier

Radiance Comparisons of MODIS and AIRS Using Spatial Response Information

Abstract The combination of multiple satellite instruments on a pixel-by-pixel basis is a difficult task, even for instruments collocated in space and time, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) on board the Earth Observing System (EOS) Aqua. Toward the goal of an improved collocation methodology, the channel- and scan angle–dependent spatial response functions of AIRS that were obtained from prelaunch measurements and calculated impacts from scan geometry are shown within the context of radiance comparisons. The AIRS spatial response functions are used to improve the averaging of MODIS radiances to the AIRS footprint, and the variability of brightness temperature differences (ΔTb) between MODIS and AIRS are quantified on a channel-by-channel basis. To test possible connections between ΔTb and the derived level 2 (L2) datasets, cloud characteristics derived from MODIS are used to highlight correlations between these quantities and ΔTb, es...

Cloud‐induced uncertainties in AIRS and ECMWF temperature and specific humidity

The uncertainties of the Atmospheric Infrared Sounder (AIRS) Level 2 version 6 specific humidity (q) and temperature (T) retrievals are quantified as functions of cloud types by comparison against Integrated Global Radiosonde Archive radiosonde measurements. The cloud types contained in an AIRS/Advanced Microwave Sounding Unit footprint are identified by collocated Moderate Resolution Imaging Spectroradiometer retrieved cloud optical depth (COD) and cloud top pressure. We also report results of similar validation of q and T from European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts (EC) and retrievals from the AIRS Neural Network (NNW), which are used as the initial state for AIRS V6 physical retrievals. Differences caused by the variation in the measurement locations and times are estimated using EC, and all the comparisons of data sets against radiosonde measurements are corrected by these estimated differences. We report in detail the validation results for AIRS GOOD quality control, which is used for the AIRS Level 3 climate products. AIRS GOOD quality q reduces the dry biases inherited from the NNW in the middle troposphere under thin clouds but enhances dry biases in thick clouds throughout the troposphere (reaching −30% at 850 hPa near deep convective clouds), likely because the information contained in AIRS retrievals is obtained in cloud-cleared areas or above clouds within the field of regard. EC has small moist biases (~5–10%), which are within the uncertainty of radiosonde measurements, in thin and high clouds. Temperature biases of all data are within ±1 K at altitudes above the 700 hPa level but increase with decreasing altitude. Cloud-cleared retrievals lead to large AIRS cold biases (reaching about −2 K) in the lower troposphere for large COD, enhancing the cold biases inherited from the NNW. Consequently, AIRS GOOD quality T root-mean-squared errors (RMSEs) are slightly smaller than the NNW errors in thin clouds (1.5–2.5 K) but slightly larger than the NNW errors for thick COD (reaching 3.5 K near the surface). The AIRS BEST quality control retains retrievals with uncertainties closer to those of the NNW. The AIRS error estimates reported in the L2 product tend to underestimate the precision (RMSE) implied by comparisons to the radiosonde measurements and do not reflect the observed cloud dependency of uncertainties.

Comparing MODIS and AIRS Infrared-Based Cloud Retrievals

AbstractComparisons are described for infrared-derived cloud products retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) using measured spatial response functions obtained from prelaunch AIRS calibration. One full day (1 January 2005) of global collection-5 MODIS and version-5 AIRS retrievals of cloud-top temperature Tc, effective cloud fraction f, and derived effective brightness temperature Tb,e is investigated. Comparisons of Tb,e demonstrate that MODIS and AIRS are essentially radiatively consistent and that MODIS Tb,e is 0.62 K higher than AIRS Tb,e for all scenes, increasing to 1.43 K for cloud described by AIRS as single layer and decreasing to 0.50 K for two-layer clouds. Somewhat larger differences in Tc and f are observed between the two instruments. The magnitudes of differences depend partly on whether MODIS uses a CO2-slicing or 11-μm brightness temperature window retrieval method. Some cloud- and regime-type differences and si...

Observation-Based Longwave Cloud Radiative Kernels Derived from the A-Train

AbstractThe authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu–Liou radiative transfer model are shown. Good agreement between observation- and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clear-sky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs...

On the Quantification of Atmospheric Rivers Precipitation from Space: Composite Assessments and Case Studies over the Eastern North Pacific Ocean and the Western United States

AbstractAtmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003–12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN–Cloud Classification System (CCS)] in capturing ARs’ precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integrated water vapor composite observations. It was found that satellite products are more consistent over ocean than land and often significantly underestimate precipitation rate over land compared to ground observations. Incorrect detection of precipitation from IR-based methods is prevalent over snow and ice surfaces where microwave estimates often show underestimation or missing data. Bias adjustment using ground obser...

Subpixel Characterization of HIRS Spectral Radiances Using Cloud Properties from AVHRR

AbstractThis paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures (Tb) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and Tb statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the Tb histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of Tb biases among spatially coincident satellite intersec...

Fusion of microwave and infrared data for enhancing its spatial resolution

The images acquired by microwave sensors are blurry and of low-resolution. On the other hand, the images obtained using infrared/visible sensors are of sufficiently high-resolution. In this paper, we develop a data fusion methodology and apply it to enhance resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument. We tested our method using precipitation scenes captured with the Advanced Microwave Sounding Unit (AMSU) microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR).

Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data

The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. We tested our method using a precipitation scene captured with the Advanced Microwave Sounding Unit (AMSU-B) microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR) infrared instrument and compared the results to simultaneous radar observations. We show that the data fusion product is better than the original AMSU-B and AVHRR observations across all statistical indicators.